Download Exploring morphology and drug interactions in pancreatic cancer

Exploring morphology and drug
interactions in pancreatic cancer with
3D cell culture models
Todd Benjamin Shelper
BAppSc, MBiotech
Discovery Biology
Eskitis Institute for Drug Discovery
Griffith Sciences
Submitted in fulfilment of the requirements of the degree of
Doctor of Philosophy
June 2014
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Abstract
Pancreatic cancer continues to have one of the poorest prognoses amongst all cancers,
with a 95% mortality rate. Standard of care chemotherapy has failed to provide
significant clinical benefits, which has led to the development of targeted agents against
validated signalling pathways. However, to date the approach of targeted agents alone,
or in combination with traditional chemotherapeutics, has failed to significantly
improve the prognosis for pancreatic cancer patients. The current standard of care
chemotherapy for advanced pancreatic cancer provides only a modest increase in
survival of several months. Models that improve the predictive potential of drug
discovery programs and gain greater insights into the complexity of tumour biology are
therefore urgently required.
To better understand the mechanisms influencing the anti-cancer activities of current
and novel therapies, we have developed a 3D in vitro micro-tumour cell culture model.
Current in vitro models utilising cell monolayer cultures are unable to recapitulate the
biological and physiological complexities of the in vivo pancreatic tumour
microenvironment and may be poor predictors of drug efficacy. Pancreatic
adenocarcinomas are characterised as having a highly dense and poorly vascularised
stroma that is made up of extracellular matrix (ECM) components and host cells. This
complex tumour microenvironment has been implicated in the chemoresistance profiles
observed in pancreatic tumours. An ECM-based three dimensional (3D) cell culture
model was developed in this study which aims to incorporate elements of the
microenvironment and provide a more relevant system for use in drug discovery
programs. For greater acceptance of this micro-tumour model into drug discovery
processes, plate reader and high content imaging-based assays were developed. To
determine drug effects both 384 and 1536-well microtitre plate format culture systems
were developed to provide automation friendly and cost effective options for drug
discovery.
To study drug interactions using a miniaturised 3D model, a panel of genetically and
morphologically different primary and metastatic pancreatic tumour cell lines were
examined. Six cell lines were characterised in both monolayer (2D) and 3D culture,
with cell surface markers and the 3D structure morphology assessed. Three cell lines
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(AsPC-1, BxPC-3, PANC-1) were selected based on their ability to form 3D structures
reproducibly and were then evaluated against a panel of traditional chemotherapeutics.
The drug response profiles obtained in 3D were compared to monolayer (2D) cell
cultures. A trend showing decreased efficacy and potency was observed in the 3D
culture model compared to the 2D cultures. Quantitative image analysis utilising the
live cell dye, Calcein AM, and the metabolic activity indicator, resazurin, were used to
determine cell viability and drug effects on the 3D cellular structures as well as to
determine compound efficacy. A number of drug resistance mechanisms were examined
in the 3D culture model which included, the impact on cell proliferation rates and
diffusion of drugs in the 3D micro-tumour structures.
To validate the 3D pancreatic cancer cell culture assay, a library of clinically relevant
drugs was screened and agents with anti-pancreatic cancer activity investigated further.
Several drugs were identified that had not been previously reported as having anticancer activity in pancreatic cancer cell lines, including the anti-fungal agent, ciclopirox
olamine and the anti-protozoal agent, maduramicin. A number of established
chemotherapeutic drugs, as well as compounds with previously unreported activity,
were examined in combination studies for possible synergistic anti-cancer effects. The
automated and highly miniaturised 1536-well 3D cell culture assay allowed preclinical
drug combination studies to be performed in a high throughput manner. A number of
drug combinations displayed synergistic activity profiles in the 3D model including,
gemcitabine-paclitaxel and ciclopirox olamine-doxorubicin.
In this study we have demonstrated the ability to culture a range of pancreatic cancer
cell lines in an in vitro cell culture model that not only recapitulates many of the
biological and physical elements of in vivo tumours but also remains amenable to HTS
applications. A more physiologically relevant cell model to examine drug interactions
and provide insights into drug penetration and resistance effects of the tumour
microenvironment, may prove invaluable for future development of anti-cancer
compounds effective in the treatment of pancreatic cancer.
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Statement of Originality
This work has not previously been submitted for a degree or diploma in any university.
To the best of my knowledge and belief, the thesis contains no material previously
published or written by another person except where due reference is made in the
thesis itself.
(Signed)_____________________________
Todd Shelper
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Publications
Three publications arising from research undertaken as part of this PhD are listed
below:
1. Lovitt, C.J., Shelper, T.B. and Avery, V.M. Miniaturized Three-Dimensional
Cancer Model for Drug Evaluation." ASSAY and Drug Development
Technologies 11(7): 435-448. (Appendix 2)
2. Lovitt, C.J., Shelper, T.B. and Avery, V.M. (2014). "Advanced Cell Culture
Techniques for Cancer Drug Discovery." Biology 3(2): 345-367. (Appendix 3)
3. Yu M, Price JR, Jensen P, Lovitt CJ, Shelper TB, Duffy S, Windus LC, Avery
VM, Rutledge PJ, Todd MH. (2011). "Copper, nickel, and zinc cyclam-amino
acid and cyclam-peptide complexes may be synthesized with "click" chemistry
and are noncytotoxic." Inorganic Chemistry 50(24): 12823-12835.
Other publication during candidature not directly relating to research from this PhD are
listed below:
1.
Xu M, Davis RA, Feng Y, Sykes ML, Shelper TB, Avery VM, Camp D, Quinn
RJ. (2012) Ianthelliformisamines A-C, antibacterial bromotyrosine-derived
metabolites from the marine sponge Suberea ianthelliformis. J Nat Prod 75(5):
1001-1005.
2. Martin F, Grkovic T, Sykes ML, Shelper TB, Avery VM, Camp D, Quinn RJ,
Davis RA.(2011). "Alkaloids from the Chinese vine Gnetum montanum." J Nat
Prod 74(11): 2425-2430.
3. Yin S, Davis RA, Shelper TB, Sykes ML, Avery VM, Elofsson M, Sundin C,
Quinn RJ.(2011). "Pseudoceramines A-D, new antibacterial bromotyrosine
alkaloids from the marine sponge Pseudoceratina sp." Org Biomol Chem 9(19):
6755-6760.
4. Yin S, Sykes ML, Davis RA, Shelper TB, Avery VM, Camp D, Quinn RJ.
(2010). "New galloylated flavanonols from the Australian plant Glochidion
sumatranum." Planta Med 76(16): 1877-1881.
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Poster Presentations
“Assay Development and Morphology of Pancreatic Cancer in the Third Dimension.”
23rd Lorne Cancer Conference (2011) Lorne, Australia
“Efficacy of Anticancer Drugs in Pancreatic Cancer 3D Cell Culture.” ASMR
Queensland Postgraduate Student Conference (2012) Brisbane, Australia
“Evaluating Anticancer Agents in a 3D Pancreatic Cancer Cell Culture Model.” 24th
Lorne Cancer Conference (2012) Lorne, Australia
Oral Presentations
“Evaluation Pancreatic Cancer Tumour Biology with 3D Cell Culture Models.”
International Student Research Forum UNMC (2012) Omaha, USA
“Spots to Spheres: Parasites to 3D Cancer Models for Drug Discovery.” High-Content
Analysis (2013) San Francisco, USA
“A HCS Approach to Drug Interactions in a 3D Pancreatic Cancer Cell Culture Model.”
Australian High content Screening Group meeting (2013) Melbourne, Australia
“Drug Interactions in a 3D Pancreatic Cancer in vitro Cell Culture Model.” Australian
Pancreatic Club Meeting (2013) Melbourne, Australia
Session chair and organising committee member of the International Student Research
forum GU (2013) Gold Coast, Australia
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Statement of Acknowledgement of Assistance Received in the
Pursuit of the Research
I would like to acknowledge the co-authors of the publications resulting from the
research undertaken of this PhD project. I would also like to acknowledge Professor
Vicky Avery’s assistance in the planning and development of this project. The
contribution of co-authors is detailed below:
I acknowledge my co-authors Carrie Lovitt and Vicky Avery for their contributions to
the publication “Miniaturized Three-Dimensional Cancer Model for Drug
Evaluation.” Collaborative research was undertaken with Carrie Lovitt in the
development and validation of the 384-well 3D cell culture model. The results from this
study are described in thesis Chapters 3, 4, 5 and 6. Selections from this research are
incorporated into the above mentioned publication which is co-first authored with
Carrie Lovitt.
I acknowledge my co-authors Carrie Lovitt and Vicky Avery for their contributions to
the publication “Advanced Cell Culture Techniques for Cancer Drug Discovery.”.
Contributions were made in the literature reviews and development of this review
manuscript first authored by Carrie Lovitt.
I acknowledge the contributions of the co-authors Mingfeng Yu, Jason Price, Paul
Jensen, Sandra Duffy, Louisa Windus, Vicky Avery, Peter Rutledge and Matthew Todd
to the publication "Copper, nickel, and zinc cyclam-amino acid and cyclam-peptide
complexes may be synthesized with "click" chemistry and are noncytotoxic.". The
monolayer assay methodology utilising the Panc-1 cell line developed in this PhD in
Chapters 2, 3 and 4 was published within the above manuscript.
viii
Table of Contents
Abstract .......................................................................................................................... iii
Publications .................................................................................................................... vi
Poster Presentations ..................................................................................................... vii
Oral Presentations ........................................................................................................ vii
Statement of Acknowledgement of Assistance Received in the Pursuit of the
Research ....................................................................................................................... viii
List of Figures ............................................................................................................... xv
List of Tables ................................................................................................................. xx
List of Abbreviations .................................................................................................. xxii
Glossary ...................................................................................................................... xxiii
Acknowledgments ....................................................................................................... xxv
1
Chapter One: Introduction .................................................................................. 27
1.1
Pancreatic cancer research ............................................................................... 27
1.2
Cancer drug discovery ..................................................................................... 29
1.3
Pancreatic cancer ............................................................................................. 33
1.4
Treatment options ............................................................................................ 37
1.5
Cellular Modelling ........................................................................................... 41
1.6
Three dimensional (3D) cell culture ................................................................ 44
1.7
Drug evaluation in 3D cell culture models ...................................................... 48
1.8
Biomarker expression in 3D cell culture ......................................................... 49
1.9
Molecular targets in pancreatic cancer ............................................................ 51
1.10 Pancreatic cancer 3D cell culture ..................................................................... 53
1.11 Three dimensional cell culture high throughput screening (HTS) assay
development ................................................................................................................ 54
1.12 Research question ............................................................................................ 56
1.13 Aims and objectives ......................................................................................... 57
1.14 References ........................................................................................................ 58
2.
Chapter Two: Characterisation of Pancreatic Cancer Cell Line Models ....... 70
2.1
Introduction ...................................................................................................... 70
2.1.1
Tumour microenvironment ....................................................................... 71
2.1.2
Three dimensional (3D) cell culture ......................................................... 72
2.1.3
Pancreatic cancer cell lines ....................................................................... 78
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2.2
Materials & methods ........................................................................................ 82
2.2.1
Materials and reagents .............................................................................. 82
2.2.2
Cell lines ................................................................................................... 82
2.2.3
Two dimensional monolayer cell culture ................................................. 83
2.2.4
Three dimensional cell culture ................................................................. 83
2.2.5
Cytotoxicity assay .................................................................................... 85
2.2.6
Immunofluorescence microscopy ............................................................. 86
2.2.7
Brightfield microscopy ............................................................................. 89
2.2.8
Statistics .................................................................................................... 91
2.3
Results .............................................................................................................. 91
2.3.1
Optimization of cell culture conditions .................................................... 92
2.3.2 Evaluation of three dimensional (3D) culture systems for pancreatic
cancer cells ............................................................................................................. 97
2.3.3 Growth factor reduced Matrigel three dimensional (3D) cell culture
morphology........................................................................................................... 100
2.3.4
Biomarker expression of pancreatic cancer cell lines ............................ 102
2.3.5
Alternative 3D culture systems .............................................................. 109
2.3.6
High throughput screening (HTS) compatibility .................................... 110
2.4
Discussion & conclusions .............................................................................. 112
2.5.
References ...................................................................................................... 114
3
Chapter 3: Three Dimensional Cell Culture Assay Development for
High Throughput Screening (HTS) .......................................................................... 120
3.1
Introduction .................................................................................................... 120
3.1.1
Assay development ................................................................................. 123
3.1.2
Assay design and format ........................................................................ 125
3.1.3
Methods for measuring cell viability ...................................................... 125
3.1.4
Plate reader (cell population) based method........................................... 126
3.1.5
High content (single cell or object) based method ................................. 127
3.1.6
Statistical validation of assay ................................................................. 128
3.2
Materials & Methods ..................................................................................... 129
3.2.1
Reagents & materials .............................................................................. 129
3.2.2
General cell culture conditions ............................................................... 129
3.2.3
Linearity studies with resazurin, Calcein AM and Hoechst dyes ........... 130
x
3.2.4 Comparison of cell viability and staining methods in monolayer
culture with gemcitabine ...................................................................................... 131
3.2.5 Three dimensional (3D) Culture reproducibility in 384 well
microtitre plate format .......................................................................................... 132
3.2.6 3D cell culture sensitivity and linearity determinations with
resazurin as cell viability and metabolic activity indicator .................................. 133
3.2.7
Intraplate variability and assay performance determinations ................. 133
3.2.8
Assay performance measure equations and statistical analysis .............. 134
3.3
Results ............................................................................................................ 134
3.3.1
Cell viability methods for pancreatic cancer cell lines (2D) .................. 134
3.3.2 Sensitivity of cell viability methods with the reference compound
gemcitabine........................................................................................................... 138
3.3.3 Three dimensional cell culture growth characteristics and
reproducibility ...................................................................................................... 140
3.3.4
Cell line selection ................................................................................... 143
3.3.5 Cell viability methods for pancreatic cancer cell lines in three
dimensional (3D) cell culture ............................................................................... 146
3.3.6
Distribution of sizes of 3D structure over time ...................................... 147
3.3.7 Compound / drug screening assay protocol for three dimensional
and two dimensional culture ................................................................................. 152
3.3.8
Microtitre plate reproducibility and intraplate variability ...................... 154
3.4
Discussions & conclusions ............................................................................ 155
3.5
References ...................................................................................................... 158
4. Chapter Four: Two Approaches to Determining Anti-Cancer Activity in
3D Cell Culture ........................................................................................................... 161
4.1.
Introduction .................................................................................................... 161
4.1.1.
4.2.
Anti-cancer reference drugs.................................................................... 162
Materials & methods ...................................................................................... 166
4.2.1.
Materials and reagents ............................................................................ 166
4.2.2.
Preparation of reference drugs for assay dosing ..................................... 167
4.2.3.
Reference drug dosing of 2D and 3D culture assay plates ..................... 167
4.2.4.
Cell culture preparation .......................................................................... 168
4.2.5. Response to anti-cancer drugs using resazurin and Calcein AM
assay end point measurements.............................................................................. 169
4.2.6.
Data analysis from metabolic assays for 3D culture models .................. 170
xi
4.2.7.
Data analysis for three dimensional 3D cell viability assays ................. 170
4.2.8.
Statistical analysis .................................................................................. 171
4.3.
Results ............................................................................................................ 172
4.3.1.
Chemotherapy agents as reference compounds ...................................... 172
4.3.2.
Cell viability (Calcein AM) imaging assay performance ....................... 174
4.3.3. Drug activity as measured by cell viability methods in 3D cell
culture 175
4.4.
Discussions & conclusions ............................................................................ 183
4.5.
References ...................................................................................................... 185
5. Chapter Five: Comparison of Anti-cancer Activity in 2D and 3D Cell
Culture Models ........................................................................................................... 188
5.1.
Introduction .................................................................................................... 188
5.2.
Materials & methods ...................................................................................... 194
5.2.1.
6.
Materials and reagents ............................................................................ 194
5.3.
Results ............................................................................................................ 205
5.4.
Discussions & conclusions ............................................................................ 232
5.5.
References ...................................................................................................... 237
Chapter Six: Pilot Screen of a Panel of Clinically Relevant Drugs ............... 241
6.1
Introduction .................................................................................................... 241
6.1.1
6.2
Pilot and secondary screen evaluations .................................................. 243
Materials & methods ...................................................................................... 244
6.2.1
Materials and reagents ............................................................................ 244
6.2.2
Drug/compound handling ....................................................................... 244
6.2.3
Preparation of drug plates for assay dosing ............................................ 245
6.2.4
Cell Culture preparation ......................................................................... 245
6.2.5
Primary pilot screen of clinically relevant drugs .................................... 245
6.2.6
Secondary screen of selected drugs in dose response............................. 246
6.2.7
Data analysis from metabolic assays for 2D and 3D culture models ..... 247
6.2.8
Statistical analysis .................................................................................. 247
6.3
Results ............................................................................................................ 247
6.3.1
Primary screen of pilot study .................................................................. 247
6.3.2 Secondary screen of selected drugs with anticancer activity against
pancreatic cancer cell lines in monolayer and 3D assays. .................................... 253
6.3.3
Ciclopirox olamine ................................................................................. 256
xii
6.3.4
Mycophenolic acid ................................................................................. 258
6.3.5
Mitoxantrone dihydrochloride ................................................................ 260
6.3.6
Digitoxin ................................................................................................. 262
6.3.7
Maduramicin ammonium ....................................................................... 264
6.3.8
Salinomycin ............................................................................................ 266
6.3.9
Topotecan hydrochloride ........................................................................ 268
6.3.10 Rubitecan ................................................................................................ 270
6.3.11 Cerivastatin ............................................................................................. 272
6.3.12 Simvastatin ............................................................................................. 274
6.3.13 Secondary screen summary .................................................................... 276
6.4
Discussion & conclusions .............................................................................. 278
6.4.1
Ciclopirox olamine ................................................................................. 279
6.4.2
Mycophenolic acid ................................................................................. 281
6.4.3
Mitoxantrone dihydrochloride ................................................................ 282
6.4.4
Digitoxin ................................................................................................. 284
6.4.5
Ionophores (maduramicin ammonium and salinomycin) ....................... 286
6.4.6
Camptothecin derivatives (rubitecan and topotecan) ............................. 287
6.4.7
Statins (cerivastatin and simvastatin) ..................................................... 288
6.4.8
Summary................................................................................................. 290
6.5.
References ...................................................................................................... 292
7. Chapter Seven: Assay Miniaturisation in 1536-well Microtitre Plate
Format for Combination Studies to Assess Anti-Cancer Synergy. ........................ 298
7.1.
Introduction .................................................................................................... 298
7.1.1.
Preclinical drug combination studies ...................................................... 300
7.1.2.
Drug and compound selection ................................................................ 303
7.2.
Materials and methods ................................................................................... 307
7.2.1.
Materials and reagents ............................................................................ 307
7.2.2.
Preparation of drugs for combination assay dosing................................ 307
7.2.3.
Cell culture preparation .......................................................................... 307
7.2.4. Three dimensional cell culture assay conditions optimisation for
1536 well format................................................................................................... 307
7.2.5. Three dimensional (3D) structure reproducibility in a 1536-well
plate format ........................................................................................................... 308
7.2.6.
Intra-plate variability 1536-well plate format ........................................ 310
xiii
7.2.7.
Drug combination study in 1536 well plate format ................................ 311
7.2.8.
Corresponding drug ratios for determining synergistic effects. ............. 312
7.2.9. Determination of combination index (CI) of drug combinations
assessed by metabolic activity measurements ...................................................... 312
7.2.10. Statistics .................................................................................................. 314
7.3.
Results ............................................................................................................ 320
7.3.1. Miniaturisation of the 384-well 3D pancreatic cell culture based
assay into a 1536-well format............................................................................... 320
7.3.3.
7.4.
Drug combination study utilising 1536-well 3D cell culture assay........ 327
Discussion and conclusions ........................................................................... 336
7.4.1.
1536 well assay development and characterisation ................................ 336
7.4.2. Drug combination studies utilising the 1536-well 3D pancreatic
cancer assay .......................................................................................................... 338
7.4.3.
Gemcitabine combinations (concurrent) ................................................ 339
7.4.4.
Paclitaxel and doxorubicin combinations (sequential) ........................... 341
7.4.5. Ciclopirox olamine (CPX) and doxorubicin combinations
(sequential) ........................................................................................................... 341
7.4.6.
7.5.
8.
Dp44mT and doxorubicin combinations (sequential) ............................ 342
References ...................................................................................................... 345
Chapter Eight: Conclusions............................................................................... 347
8.1
Relevance of research in the field of pancreatic cancer ................................. 347
8.2 Development of a 3D pancreatic cell culture model and evaluation of
resistance mechanisms .............................................................................................. 347
8.3
Pilot screen of 741 clinically relevant agents ................................................ 349
8.4
Drug combination studies in miniaturised format ......................................... 350
8.5
Future directions ............................................................................................ 351
8.6
Summary ........................................................................................................ 352
Appendix 1 .................................................................................................................. 353
Appendix 2 .................................................................................................................. 356
Appendix 3 .................................................................................................................. 357
xiv
List of Figures
Figure 1.1 Trends in death rates from most common cancers between 2001 and
2010 in the US (all races both sexes). ............................................................................ 27
Figure 1.2. Pancreatic Cancer incidence and death rates over the last 35 years. ........... 28
Figure 1.3. An example of a simplified drug discovery pipeline diagram. .................... 30
Figure 1.4. A graphical representation of the human pancreas. ..................................... 34
Figure 1.5. Pancreatic ductal adenocarcinoma progression. .......................................... 35
Figure 1.6. The complexity of pancreatic cancer and the signalling pathways
cellular components believed to be involved in the disease. .......................................... 37
Figure 1.7 Gemcitabine structure ................................................................................... 39
Figure 1.8. Paclitaxel structure ....................................................................................... 40
Figure 1.9. Erlotinib structure ........................................................................................ 41
Figure 1.10. Pubmed listed publications by year involving the keywords “3D cell
culture”. .......................................................................................................................... 45
Figure 1.11. Microenvironment cues that may affect cellular phenotype. ..................... 46
Figure 1.12 Illustration of the five classes of cell adhesion molecules. ......................... 50
Figure 1.13 A simple overview of the IGF system components and its main
effector pathways. ........................................................................................................... 52
Figure 2.1. Diagram of the complex tumour composition and surrounding
microenvironment in pancreatic cancer. ......................................................................... 70
Figure 2.2. Representation of the two main 3D tumour models; anchorage
dependant (scaffold) and anchorage independent (liquid or non-adherent). .................. 74
Figure 2.3. Comparison of in vivo tumour complexity and the in vitro ECM based
(3D) cell culture model. .................................................................................................. 78
Figure 2.4. Passage number had no effect on drug sensitivity on pancreatic cancer
cell lines. ......................................................................................................................... 93
Figure 2.5. No significant cellular response to the reference drug or growth rate
differences between cultures with HI FBS and non-HI FBS.......................................... 94
Figure 2.6 Antibiotics had no effect on growth of pancreatic cancer cell lines
whereas DMSO tolerability was cell line dependant. .................................................... 96
Figure 2.7 Procedure for hydrogel-based 3D cell culture in 384-well microtitre
plates. .............................................................................................................................. 98
Figure 2.8 Comparison of 3D structure formation with different commercial
biological hydrogels. ...................................................................................................... 99
xv
Figure 2.9 Pancreatic cancer cell line 3D structure development over 12 days. .......... 102
Figure 2.10 Expression of E-cadherin in pancreatic cancer cell lines in monolayer
and three dimensional (3D) cell culture. ...................................................................... 104
Figure 2.11. Expression of β-catenin in pancreatic cancer cell lines in monolayer
and three dimensional (3D) cell culture. ...................................................................... 106
Figure 2.12 Expression of insulin-like growth factor 1 (IGF-1R) in pancreatic
cancer cell lines in monolayer and three dimensional (3D) cell culture. ..................... 108
Figure 2.13 Determination of 3D culture growth over time with image based
measurements. .............................................................................................................. 111
Figure 3.1. Basic drug discovery pipeline overview, grouped into preclinical and
clinical stages. ............................................................................................................... 121
Figure 3.2. Linearity studies of pancreatic cancer cell lines in monolayer culture
with resazurin, Calcein AM and Hoechst stains. .......................................................... 136
Figure 3.3 Comparison of effects of gemcitabine using three cell viability methods
in monolayer culture. .................................................................................................... 139
Figure 3.4. Representative images of 3D cell culture of the PANC-1 cell line and
automated object segmentation used for size and growth analysis determinations. .... 141
Figure 3.5. Growth characteristics of pancreatic cancer cell line 3D structures over
12 days. ......................................................................................................................... 142
Figure 3.6. Number of 3D structures per well for pancreatic cancer cell lines 9
days after seeding single cell suspensions. ................................................................... 143
Figure 3.7. Pancreatic cell lines selected for 3D culture assay development
(highlighted in red). ...................................................................................................... 145
Figure 3.8. Resazurin optimisation with cell number and incubation time for
pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1 in 3D culture. ................. 147
Figure 3.9. Distribution of 3D structures in a single well of a 384 well microtitre
plate for the AsPC-1 cell line. ...................................................................................... 149
Figure 3.10. Distribution of 3D structures in a single well of a 384 well microtitre
plate for the BxPC-3 cell line. ...................................................................................... 150
Figure 3.11. Distribution of 3D structures in a single well of a 384 well microtitre
plate for the PANC-1 cell line. ..................................................................................... 151
Figure 3.12. Timetable for performing the monolayer and 3D assays. ........................ 153
Figure 4.1. Concentration response curves of a panel of cytotoxic drugs generated
in a monolayer cell model. ........................................................................................... 173
Figure 4.2. Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with gemcitabine. .................................... 176
xvi
Figure 4.3. Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with doxorubicin. .................................... 177
Figure 4.4 Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with epirubicin. ....................................... 178
Figure 4.5 Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with docetaxel. ........................................ 179
Figure 4.6. Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with paclitaxel. ........................................ 180
Figure 4.7. Resazurin reduction and Calcein AM imaging-based drug activity
determination in 3D pancreatic cell culture with vinorelbine. ..................................... 181
Figure 5.1. Dose response parameters used for drug activity evaluations in 2D
versus 3D models. ........................................................................................................ 190
Figure 5.2. Resistance mechanism that may be involved in anti-cancer drug
resistance in vivo. .......................................................................................................... 193
Figure 5.3 Tumour drug resistance mechanisms that may be recapitulated using
ECM based 3D in vitro cell culture. ............................................................................. 194
Figure 5.4. Response to gemcitabine exposure in both 2D and 3D cell culture
models using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. ................. 207
Figure 5.5. Response to doxorubicin exposure in both 2D and 3D cell culture
models using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. ................. 209
Figure 5.6. Response to epirubicin exposure in both 2D and 3D cell culture
models using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. ................. 211
Figure 5.7. Response to vinorelbine exposure in both 2D and 3D cell culture
models using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. ................. 213
Figure 5.8. Response to docetaxel exposure in both 2D and 3D cell culture models
using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. .............................. 215
Figure 5.9. Response to paclitaxel exposure in both 2D and 3D cell culture models
using pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. .............................. 217
Figure 5.10. Pancreatic cancer cell lines grown in 3D culture display resistance to
selected cytotoxic drugs compared to 2D culture. ........................................................ 219
Figure 5.11. Representative field of PANC-1 nuclei stained with Hoechst and
acquired on the Opera with the 10x air objective. ........................................................ 221
Figure 5.12. Representative rendered image of a single field of BxPC-3 3D
structures on Day 9. ...................................................................................................... 221
Figure 5.13. Proliferation rates of pancreatic cancer cell lines PANC-1, BxPC3and AsPC-1 in both monolayer and 3D cell culture formats. ..................................... 223
Figure 5.14. Cellular penetration of doxorubicin in 2D pancreatic cell culture. .......... 225
xvii
Figure 5.15. Cellular penetration of doxorubicin in 3D pancreatic cell culture. .......... 226
Figure 5.16. The diffusion of doxorubicin in 2D and 3D cell culture over time .......... 227
Figure 5.17. 3D matrices from synthetic or cell-derived sources do not affect the
sensitivity of BxPC-3 and PANC-1 3D cultures to paclitaxel and doxorubicin. ......... 229
Figure 5.18. Effects on metabolic activity of Panc-1 cells after extended
incubation times with paclitaxel removed from culture. .............................................. 231
Figure 6.1. Assay reproducibility, activity distribution and sensitivity
measurements. .............................................................................................................. 248
Figure 6.2. Visual representation of drug / compound activity in Heatmap and
scatterplot forms. .......................................................................................................... 250
Figure 6.3. Summary schematic of the primary pilot screen and hit identification
process through to the selection of 10 drugs for dose response studies in the
monolayer and 3D assays. ............................................................................................ 252
Figure 6.4. Responses observed following ciclopirox olamine treatment of
pancreatic cancer cell lines for 6 days. ......................................................................... 257
Figure 6.5. Responses observed following mycophenolic acid treatment of
pancreatic cancer cell lines for 6 days. ......................................................................... 259
Figure 6.6. Responses observed following mitoxantrone dihydrochloride
treatment of pancreatic cancer cell lines for 6 days...................................................... 261
Figure 6.7. Responses observed following digitoxin treatment of pancreatic cancer
cell lines for 6 days. ...................................................................................................... 263
Figure 6.8. Responses observed following maduramicin ammonium treatment of
pancreatic cancer cell lines for 6 days. ......................................................................... 265
Figure 6.9. Responses observed following salinomycin treatment of pancreatic
cancer cell lines for 6 days. .......................................................................................... 267
Figure 6.10. Responses observed following topotecan hydrochloride treatment of
pancreatic cancer cell lines for 6 days. ......................................................................... 269
Figure 6.11. Responses observed following rubitecan treatment of pancreatic
cancer cell lines for 6 days. .......................................................................................... 271
Figure 6.12. Responses observed following cerivastatin treatment of pancreatic
cancer cell lines for 6 days. .......................................................................................... 273
Figure 6.13. Responses observed following simvastatin treatment of pancreatic
cancer cell lines for 6 days. .......................................................................................... 275
Figure 7.1. Miniaturisation of assay format and reduction in total well area for 3D
cultures. ........................................................................................................................ 302
Figure 7.2. Timetable for performing the developed 3D combination assay. .............. 312
xviii
Figure 7.3. Assay development of the 1536-well 3D culture model with the
pancreatic cancer line PANC-1. ................................................................................... 322
Figure 7.4. Evaluation of 1536 well plate and well effects .......................................... 324
Figure 7.5. 1536 well 3D cell culture assay performance and sensitivity
measurements. .............................................................................................................. 326
Figure 7.6. Normalised isobolograms of gemcitabine based concurrent
combination studies. ..................................................................................................... 329
Figure 7.7. Normalised isobolograms of paclitaxel based concurrent and
sequential combination studies. .................................................................................... 331
Figure 7.8. Normalised isobolograms of Doxorubicin based concurrent and
sequential combination studies. .................................................................................... 333
Figure 7.9. Normalised isobolograms of Dp44mT based concurrent and sequential
combination studies. ..................................................................................................... 335
xix
List of Tables
Table 1.1. An overview of 3D cell culture methods. ...................................................... 47
Table 2.1. Common components of the stromal compartment found in pancreatic
ductal adenocarcinomas. ................................................................................................. 72
Table 2.2. Commercially available 3D scaffold and liquid based platforms. ................ 76
Table 2.3. Cell lines selected for 3D model development. ............................................. 81
Table 2.4 Acapella Image analysis protocol for 2D E-cadherin, β-catenin and
IGF1-R intensity determinations. ................................................................................... 88
Table 2.5. Protocol steps required to segment and analyse size and morphology
data from imagej software. ............................................................................................. 90
Table 2.6. Protocol steps used to obtain size and morphology data from the in cell
developer software. ......................................................................................................... 90
Table 3.1. Acapella script for live cell count using Calcein AM stain on an Opera
imaging platform. ......................................................................................................... 131
Table 3.2. Comparison of the cell viability dye resazurin and manual counts for
calculating cell line doubling times grown in monolayer culture. ............................... 137
Table 3.3. Standard assay performance measures for the 3D and 2D assays. .............. 154
Table 4.1. Anti-cancer reference drugs used to validate the 3D pancreatic cancer
model. ........................................................................................................................... 162
Table 4.2. Concentrations of master drug plates, dilution plates and assay plates. ...... 168
Table 4.3. Image analysis protocol for assessing cell viability on the Operetta with
the Calcein AM dye. ..................................................................................................... 171
Table 4.4. Standard assay performance measures for the HTS assays with the
Calcein AM cell viability assay. ................................................................................... 175
Table 5.1. Concentrations of master drug plates, dilution plates and assay plates. ...... 196
Table 5.2. Cell counting protocol in Acapella for 2D cell culture. .............................. 199
Table 5.3. Developed Volocity protocol to count individual nuclei in 3D cell
culture. .......................................................................................................................... 200
Table 5.4. Opera image analysis protocol for determining average doxorubicin
fluorescence per cell in monolayer culture ................................................................... 202
Table 5.5. Opera image analysis protocol for determining average doxorubicin
fluorescence per central z-slice of individual 3D object .............................................. 203
Table 5.6. Summary of drug interactions between the three pancreatic cancer cell
lines AsPC-1, BxPC-3 and PANC-1 in different culture conditions............................ 220
xx
Table 6.1. Successfully repositioned drugs from the original indication to the
current application ........................................................................................................ 242
Table 6.2. Concentrations of drugs / compounds and DMSO for master drug
plates, dilution plates and assay plates ......................................................................... 246
Table 6.3. Drugs selected for further study from primary pilot screen with activity
against the pancreatic cancer cell line PANC-1 in monolayer culture. ........................ 254
Table 6.4. Summary of secondary drug screening of the three pancreatic cancer
cell lines AsPC-1, BxPC-3 and PANC-1 in 2D and 3D culture. .................................. 277
Table 7.1. Drugs selected for combination studies against the pancreatic cancer
cell line PANC-1 in the 1536-well 3D cell culture format. .......................................... 304
Table 7.2 Protocol Steps Required to Segment and Analyse Size and Morphology
Data from ImageJ Software .......................................................................................... 309
Table 7.3. The range of combination index values and the degree of synergism,
additive effect or antagonism denoted. ......................................................................... 314
Table 7.4 An example drug matrix template utilised for all combination studies........ 316
Table 7.5. Drug concentrations and ratios between paclitaxel and doxorubicin and
the doses examined for synergistic activity. ................................................................. 317
Table 7.6. Drug concentrations and ratios between ciclopirox olamine (CPX) and
doxorubicin and the doses examined for synergistic activity. ...................................... 317
Table 7.7 Drug concentrations and ratios between Dp44mT and doxorubicin and
the doses examined for synergistic activity. ................................................................. 318
Table 7.8 Drug concentrations and ratios between paclitaxel and gemcitabine and
the doses examined for synergistic activity. ................................................................. 318
Table 7.9 Drug concentrations and ratios between BMS-754807 and gemcitabine
and the doses examined for synergistic activity. .......................................................... 319
Table 7.10 Drug concentrations and ratios between ciclopirox olamine (CPX) and
gemcitabine and the doses examined for synergistic activity....................................... 319
xxi
List of Abbreviations
2D
3D
ANOVA
ATCC
AUC
BSA
DIC
DMSO
DOX
ECM
EGFR
EMDR
FBS
FIC
FIX
GFR
HCA
HCS
HIF-1
HTS
IC50
IGF-1R
MOA
nM
PanIN
PBS
PDAC
PDGF
PI3K
PSCs
rBM
ROS
RPMI
SD
SEM
SPARC
VEGFR
WT
μM
Two dimensional
Three dimensional
Analysis of variance
American Type Culture Collection
Area under the curve
Bovine serum albumin
Differential interference contrast
Dimethyl sulfoxide
Doxorubicin
Extracellular matrix
Epidermal growth factor receptor
Environment mediated drug resistance
Foetal bovine serum
Fractional inhibitory complex
Fractional inhibitory index
Growth factor reduced
High content analysis
High content screening
Hypoxia-inducible factor 1
High throughput screening
50% inhibitory concentration
Insulin like growth factor 1 receptor
Mechanism of action
Nanomolar
Pancreatic intraepithelial neoplasia
Phosphate buffered saline
Pancreatic ductal adenocarcinoma
Platelet derived growth factor
Phosphatidylinositol 3-kinase
Pancreatic stellate cells
Reconstituted basement membrane
Reactive oxygen species
Roswell Park Memorial Institute (cell culture media)
Standard deviation
Standard error of the mean
Secreted protein acidic and rich in cysteine
Vascular endothelial growth factor receptor
Wild type
Micromolar
xxii
Glossary
Anchorage dependent culture – 3D cell cultures form by cellular attachment to a
substrate (biological or synthetic).
Anchorage independent culture – 3D cellular structures form by aggregation in nonadherent culture conditions.
Basement membrane (BM) – A form of ECM that constituents are unique to specific
tissues or organs. BM consists of laminins, collagens, proteoglycans and other
glycoproteins that separates epithelia from underlying supporting tissues.
Cell adhesion molecules (CAM) – Cell surface proteins that mediate adhesion to other
cells or extracellular matrix components.
Cellular senescence - The state of proliferative arrest in cells, is a potential tumour
suppressor mechanism and senescence bypass represents an important step in tumour
development.
Concentration response curve (CRC) – Are created by evaluation of multiple
concentrations of a given agent, which might then render a logarithmically-derived
activity curve.
Desmoplastic reaction (DR) or desmoplastic stroma – Is the abundant fibrotic host
tissue reaction referred to as the desmoplastic reaction that surrounds and infiltrates
clusters of tumour cells.
Environment mediated drug resistance (EMDR) – A form of de novo drug resistance
arising from the interaction between cancer cells and their surrounding
microenvironment.
High content screening (HCS) – High content screening or high content analysis is a
method of identifying potential effectors of biological targets through phenotypic
changes and often involves automated image analysis and microscopy to analyse
multiple assay parameters simultaneously.
High Throughput Screening (HTS) – The process of assaying potential effectors of
biological activity against biological targets in a bid to identify new chemical starting
points. It is often defined by the number of agents or data points e.g. greater than 10,000
data points a day or 100,000 compounds evaluated per screen.
Hill Coefficient - Derived slope of a three or four parameter logistic curve fit.
IC50 – The 50% inhibitory concentration of a specific biological function.
Pancreatic ductal adenocarcinoma (PDA) – Is the most common type of pancreatic
malignancy and is believed to arise from the accumulation of acquired mutations in
ductal epithelial cells lining the pancreatic ducts.
xxiii
Signal to Noise Ratio (S:N) – Is defined as the mean signal (Max or Min signal)
divided by the standard deviation of that signal.
Signal Window - A measure of separation between maximum and minimum controls in
an assay that accounts for the amount of variability in the assay.
Tumour microenvironment – The environment surrounding the tumour which
includes cellular and non-cellular components such as host cells (immune cells,
fibroblasts), blood vessels and extracellular matrix elements.
Z'-Factor – A statistical measure of assay quality often used to quantify the suitability
of an assay for high throughput screening. It is a measure of the separation between
maximum and minimum controls in an assay that accounts for the amount of variability
in the assay.
xxiv
Acknowledgments
The work presenting here was carried out in Professor Vicky Avery’s Discovery
Biology laboratory of the Eskitis Institute for Drug Discovery, Griffith University.
Firstly I would like to thank Professor Avery for her intellectual guidance, persistent
encouragement and generous financial support throughout my candidature. Without
Prof Avery’s support, the completion of this thesis would not have been possible.
I would like to thank my supervisors Professor Vicky Avery and Dr Graham Stevenson
for the amazing opportunity to work in such an important field of scientific research and
their ongoing guidance in my professional development.
Thank you to all my colleagues and friends in the Discovery Biology labs and at the
Eskitis Institute. In particular, thanks to past and present members of the institute for
your professional support: Melissa, Sandra, Aaron, Barbara, Greg, Clare, Bec, Trudy,
Deb, Grant, Lou, Amy, Tayner, Sas, Sabine, Leo, John, Angela, Marie and Mike. I
would also like to thank Carrie for her contributions to the completion of my thesis and
publications.
Finally, I would like to thank my family, friends and my partner Robin for their support
and encouragement throughout my studies.
xxv
Chapter One: Introduction
1.1
Pancreatic cancer research
The field of cancer research has grown to such a global scale that almost every country
in the world undertakes research into prevention, diagnosis or treatment of the disease at
some level (Drain et al., 2014). Cancer research is a multibillion dollar global network
which involves almost all fields of science and is today funded privately, publically and
philanthropically. Approximately 40% of the world’s population will develop cancer
during their lifetime and the prevention and treatment of all cancers remains one of the
greatest challenges of our time (Howlader, 2013). In the last 40 years huge advances
have been made in the prevention, detection and treatment of a variety of different
cancers. A general trend of decreasing death rates over the last two decades across a
majority of cancers (in developed nations) has been the most prominent indication of
success of these programs (Malvezzi et al., 2014; Siegel et al., 2014). However, despite
this success there are a small number of specific cancers in which there has been
virtually no increase in survival times nor decrease in death rates. Cancer of the
pancreas unfortunately tops this list (Figure 1.1) (Howlader, 2013; Mayor, 2014).
C a n c e r d e a th ra te tre n d s
b e tw e e n 2 0 0 1 -2 0 1 0
O v e r a ll N e t T r e n d
L u n g ; B ro n c h u s
C o lo n ; R e c tu m
F e m a le B r e a s t
A ll O th e r S ite s
P ro s ta te
N o n -H o d g k in L y m p h o m a
S to m a c h
O v a ry
L e u k e m ia
O r a l C a v ity ; P h a r y n x
C e r v ix
B r a in ; O N S
C o rp u s ; U te ru s
U r in a r y B la d d e r
M e la n o m a o f th e S k in
1
0
-1
-2
P a n c re a s
-3
1
C h a n g e in d e a th r a t e (D e a th s /1 0 0 0 0 0 /Y e a r )
Figure 1.1 Trends in death rates from most common cancers between 2001 and 2010 in the US (all
races both sexes). Data source US Mortality Files, National Centre for Health Statistics, Centres for
Disease Control and Prevention.
Pancreatic cancer continues to have one of the poorest prognoses amongst all cancers,
with a 5 year survival rate of less than 6% and an average survival time, once
27
diagnosed, of only 6 months (Siegel et al., 2014). Since the National Cancer Act of
1971 was introduced in the United States (ushering in the modern political ‘war on
cancer’), there has been almost no change in death rates or increase in survival time for
those diagnosed with pancreatic cancer (Figure 1.2) (Howlader, 2013). Pancreatic
cancer is the tenth most common cancer but was the fourth leading cause of cancer
death (Siegel et al., 2013). Despite the high incidence of mortality, pancreatic cancer
research receives less than 2% of the total publically funded academic cancer grants.
Cancer of the pancreas has a global estimated mortality rate of 330 000 people a year
(Ferlay, 2013). As the population ages in most developed countries, this figure is
expected to continue to climb.
Figure 1.2. Pancreatic Cancer incidence and death rates over the last 35 years. Data source: US
Mortality Files, National Centre for Health Statistics, Centres for Disease Control and Prevention. SEER
9 Incidence 1975-2011 & U.S. Mortality 1975-2010, All Races, Both Sexes.
Cancer death rates have continued to trend down for several other cancers, such as
breast and prostate, due to a variety of factors including early detection methods
available, identification of risk factors, increased funding through awareness programs
and improved clinical treatment options (Fisher, 2011; Malvezzi et al., 2014).
However, for pancreatic cancer the causes remain elusive, with no early detection
available, and 80% of diagnosed cases are in the advanced stages with only palliative
treatment options possible (Hidalgo, 2010; Koorstra et al., 2008).
28
One factor of
particular concern is that pancreatic cancer continues to be resistant to all current anticancer regimes including chemotherapy, radiotherapy and immunotherapy. This bleak
outlook for treatment indicates there is an urgent need for novel and innovative
therapeutic strategies to be developed. For real clinical impacts to be achieved in this
challenging field, focussed research needs to be undertaken at identifying and
developing therapies to treat or manage this disease.
1.2
Cancer drug discovery
Identifying and developing novel therapeutics is the task of the modern drug discovery
industry. Drug discovery programs incorporate a number of biological, chemical and
computational fields of science. In this diverse industry, a range of therapeutic areas are
targeted for the development of novel therapeutics including cardiovascular, central
nervous system (CNS), metabolic, infectious disease, and oncology. Anti-cancer drug
discovery continues to have one of the poorest success rates amongst all of the
therapeutic fields however, with only ~5% of anti-cancer agents (in clinical trials)
making it though clinical development and obtaining approval by European or US
regulatory authorities (Hait, 2010; Hutchinson and Kirk, 2011; Kola and Landis, 2004).
There are a number of complex reasons for the high attrition rates of novel anti-cancer
drugs, including pharmacokinetics, unfavourable toxicity profiles or clinical safety,
formulation, commercial aspects and lack of efficacy. However, the primary point of
failure is in phase 3 trials, where efficacy does not translate from early in vitro and in
vivo studies and low or no clinical efficacy is observed (Kola and Landis, 2004; Morgan
et al., 2012). Lack of efficacy has principally been attributed to the limitations of
preclinical models for drug assessment and identification of the correct molecular
therapeutic targets (Hutchinson and Kirk, 2011). The preclinical models (such as in
vitro cancer cell line and in vivo murine models) currently employed to remove
therapeutic candidates with poor efficacy earlier on in the discovery pipelines are failing
to accurately predict anti-cancer activity outcomes in patients.
As a rule, therapeutics under investigation do not enter the clinic without a rationale or
supporting preclinical evidence of efficacy (Kamb, 2005). This has led those in the
oncology drug discovery field to question whether the preclinical strategies and models
29
used to evaluate novel therapeutic agents are sub-optimal (Begley and Ellis, 2012;
Hutchinson and Kirk, 2011). Identifying the correct biological targets, compounds or
biologicals using the most appropriate preclinical model available may help to reduce
spiralling development costs and ultimately increase the chance of clinical success.
The drug discovery pipeline includes all aspects of developing novel therapeutics from
basic research through to final market approvals (Figure 1.3). This discovery process
can be divided into a number of steps with preclinical and clinical components. The lead
discovery and generation phases involve identifying ‘hits’ or active agents through
screening programs. These hits are then passed through the lead generation and lead
optimization phases which can involve a number of processes such as hit expansion,
cellular response studies, phenotypic selectivity, mechanism of action (MOA) studies
and safety and efficacy profiling in preclinical disease models (Berg et al., 2014).
Figure 1.3. An example of a simplified drug discovery pipeline diagram.
30
Most drug discovery programs utilise a set of high throughput screening (HTS) assays
in the initial steps of drug lead discovery. HTS is the process of assaying potential
effectors of biological activity against biological targets in a bid to identify new
chemical starting points (Hingorani, 2010). The goal of HTS is to accelerate the drug
discovery process by screening large libraries of candidate substances (compounds or
biologicals) rapidly and effectively reducing the number of compounds requiring more
comprehensive biological and chemical profiling. A broad range of technologies are
applied in HTS systems such as advanced assay technologies, automation,
miniaturisation and diverse detection platforms. These technologies are implemented in
either cell-based or isolated target (biochemical) formats but they all involve identifying
‘hits’ from these programs which are then used as leads for further drug development.
Isolated target based approaches utilise purified targets and membrane preparations and
have several advantages including aspects such as; they are often more robust assays
with less assay development required when compared to cell based systems,
intracellular targets such as enzymes can be more readily assessed for complex
molecular interactions, hits can often be profiled for their kinetic and chemical
mechanisms within the same assay and a better understanding of mechanism of action
and structure-activity relationships (SAR) can be determined (Moore and Rees, 2001).
Cell based approaches also have their own advantages including; the activity of the
target is often regulated under physiologically relevant conditions, entire pathways can
be evaluated in a single assay, additional pharmacological information can be obtained
such as cytotoxicity and cellular membrane permeability, costly or technically
challenging isolation of target proteins is not required and poorly characterised targets
can also be more easily screened(Johnston and Johnston, 2002). Both systems have their
merits and the decision is often based on a number of factors including stage in the
discovery process, the particular disease state under investigation and target type or
cellular location (Moore and Rees, 2001). Although isolated target (biochemical) based
assays are still used in drug discovery, cell-based or whole organism screening is
considered to be the more physiologically relevant approach for HTS (Frearson and
Collie, 2009).
Evolving HTS technology such high content screening (HCS) has also encouraged cellbased approaches to be utilised earlier on in the discovery process. HCS employs
31
automated microscopy combined with image analysis to evaluate multiple cellular
responses often at the single cell or structure level (Haney et al., 2006). Traditionally
HCS was limited to late stage lead characterisation as it was expensive and relatively
low throughput. However, with the development of sophisticated automated image
capturing platforms and improved image analysis software, it is now finding higher
prominence in early hit discovery stages including target identification and validation,
lead selection and lead optimisation (Gasparri and Galvani, 2010; Gribbon, 2008;
Haney et al., 2006).
The cellular based functional models utilised at this stage of the drug discovery pipeline
have traditionally determined which compounds will go on to optimisation and
preclinical development. Leads or ‘hits’ are unlikely to proceed through to becoming
drug candidates without demonstrating activity in a cell-based model (Johnston and
Johnston, 2002). The goal of high throughput screening assays is to ultimately predict in
vivo efficacy for the desired research target. However, the availability of cell based HTS
assays that provide accurate and reliable efficacy data remains an ongoing challenge for
anticancer drug discovery.
Utilising more physiological predictive cellular models in the early stages of these
programs (lead discovery and lead optimisation phases) may help to reduce attrition
rates, while promoting only those compounds which have the greatest chance of clinical
progression. Developing more relevant and predictive cellular based model that can be
incorporated into the drug discovery workflow offers unique challenges. However,
developing these models may be one avenue to reduce anticancer drug discovery costs
and as well open new opportunities to academic institutions entering more translational
oriented research.
Both public and private sectors have played roles in drug discovery programs for
anticancer therapeutics. Universities and other academic institutions often contribute to
the basic scientific understandings that lead to pharmaceutical companies and other
private entities developing the commercial drugs. Traditionally academia was involved
only at the fundamental level, with a focus on identification of potential therapeutic
targets. However, recently with the costs of entering drug discovery becoming more
viable and an emphasis on translational outcomes, an increasing number of academic
32
institutions are now becoming involved in the drug development process (Clark et al.,
2010; Nelson, 2008).
Academic institutions are expected to play an ever increasing role in industry-academic
partnerships in the future (Frearson and Collie, 2009; Nelson, 2008). Collaborations and
outsourcing of early drug discovery activities by the pharmaceutical industry has
blurred the line between industry and academia’s role in early drug development
(Lengauer et al., 2005). In the field of anti-cancer drug discovery academic institutions
are already actively contributing to the development of new drugs (Cai and Mostov,
2009; Eccles et al., 2008). Recently, a promising K-ras targeted pancreatic cancer drug
was discovered through a high throughput screening campaign at Max Planck Institute
of Molecular Physiology and is showing promising results in preclinical evaluations
(Zimmermann et al., 2013). Furthermore, one academic based institute alone; the
Institute for Cancer Research (London, UK), currently has 13 drugs in clinical
development (Workman, 2010). By embracing new technologies such as more pathophysiologically relevant cellular models (for example three dimensional (3D) in vitro
cell culture), there is the real possibility that academic institutions may play a large role
in delivering therapeutic solutions for a range of diseases including pancreatic cancer.
The work presented here aims to investigate an innovative cell culture approach and its
potential use in overcoming research limitations in the highly challenging endeavour of
pancreatic cancer drug discovery. It is hoped that employing novel strategies such as
more predicative preclinical model systems will improve the chances of success in
identifying and developing novel anticancer drugs against pancreatic cancer. Examining
the utility of these more relevant cellular models may encourage emerging academic
based drug discovery institutes to incorporate these systems into their research
programs.
1.3
Pancreatic cancer
The pancreas is complex gland organ, essential for a range of functions in the body. It is
located in the abdomen, intertwined between the stomach, spleen and duodenum (Figure
1.4). It has dual functions that are involved in both the digestion (exocrine) and
33
endocrine systems, producing hormones crucial to glucose metabolism, as well as
essential digestive enzymes (Kawamoto et al., 2005). The endocrine portion of the
pancreas is composed of cell clusters known as islet of Langerhans. These cell clusters
constitute only 1% of the pancreas volume but are responsible for excreting the
important metabolic hormones including insulin and glycogen. The majority of the
pancreas is composed of the exocrine portion of the gland which secretes digestive
enzymes to the intestines. Many disease states can affect the normal production of
hormones and enzymes from this organ. They range from the manageable such as
diabetes, to the essentially lethal exocrine cancer. Pancreatic cancer is a heterogeneous
group of neoplasms or tumours, and although cancers of the endocrine system do occur,
it is at a very low rate (Cowgill and Muscarella, 2003). The majority of pancreatic
cancers originate from the exocrine portion of the organ (85% – 95%), of which almost
all are believed to originate from the ductal epithelial cells lining the pancreatic ducts.
Pancreatic cancer arising from ductal lineage can be identified by a number of
immunohistochemical staining methods such as staining for MUC1 and cytokeratin 19
(Chen and Baithun, 1985). These ductal adenocarcinomas form primarily in the head
and body of the pancreas. A common feature of pancreatic ductal adenocarcinomas
(PDAC) is the intense desmoplastic stromal reaction present surrounding the cancer
cells (Froeling et al., 2010). An abundant extracellular matrix (ECM) synthesis with
extensive collagen and laminin production characterizes the ductal adenocarcinoma
desmoplastic stroma (Chu et al., 2007). The terms pancreatic ductal adenocarcinoma
and pancreatic cancer will be used interchangeably from here on.
Figure 1.4. A graphical representation of the human pancreas. Located in the abdomen between the
stomach and spleen it responsible for a dual digestion and endocrine secretion function.
34
The causes behind pancreatic cancer remain unknown, although many studies have
associated several risk factors. The factors which are consistently reported are age,
smoking, diet and family history of the disease (Li et al., 2004). Smoking is believed to
be the largest risk factor and may be involved in up to 30% of all cases (Mulder et al.,
2002). The pathology of pancreatic ductal adenocarcinoma is described as the formation
of a firm, highly sclerotic neoplastic epithelium mass, with infiltrating branches from
the main tumour (Maitra and Hruban, 2008). The development of pancreatic cancer is
believed to gradually develop through the formation of precursor legions of the ductal
epithelial cells. The precursor lesions known as pancreatic intraepithelial neoplasia
(PanIN) are formed in the pancreatic ducts and may eventually progress to invasive
carcinoma. The formation of PanIN lesions and the progression to invasive
adenocarcinoma are believed to be driven by activation or mutations of oncogenes,
tumour suppression genes and genomic maintenance genes (Figure 1.5) (Hruban et al.,
2000). As with many cancers, pancreatic cancer results from the accumulation of
acquired mutations (Hidalgo, 2010). Pancreatic ductal carcinogenesis is believed to
follow a predictable time course with pancreatic ductal epithelium progressing from
normal to increasing grades of neoplasia through to invasive cancer (Li et al., 2004).
Figure 1.5. Pancreatic ductal adenocarcinoma progression. Development from normal pancreatic
ductal epithelial cells via PanIN lesions to invasive adenocarcinoma including the functional
microenvironment (desmoplastic reaction). Republished Morris.J.P et al, KRAS, Hedgehog, Wnt and the
twisted developmental biology of pancreatic ductal adenocarcinoma. Nature Reviews Cancer, 2010.
Macmillan Publishers Limited.
35
The most commonly altered oncogene detected in pancreatic adenocarcinoma is K-ras,
with as high as 95% of pancreatic ductal cancers having an activating point mutation in
this gene (Almoguera et al., 1988; Biankin et al., 2012). Mutations in K-ras are also one
of the earliest mutations present in precursor legions and are essential for development
of invasive cancers in murine animal models (Lee and Bar-Sagi, 2010). K-ras acts as a
regulator of growth factor signalling and activates a number of downstream signalling
pathways involved in cellular proliferation (suppression of normal cellular senescence),
survival and differentiation (Campbell et al., 1998). K-ras is a member of the RAS
family of proteins (signal transducing GTPases). The wild type form has the ability to
switch between active (GTP-bound) and non-active forms via cell surface signalling
(Hruban et al., 2000). However, the oncogenic form of K-ras (point mutation of codon
12, 13, 61) leads to the constitutively active form resulting in uncontrolled stimulation
of downstream signalling involved in cell survival and proliferation mechanisms
(Iovanna et al., 2012). With the highest rate of K-ras mutations of any malignancy,
pancreatic cancer treatment via a K-ras targeted agent is an attractive option. However,
despite intensive effort from industry and academia, to date no effective
pharmacological inhibitor of oncogenic K-ras has had clinical success (Baker and Der,
2013). Although k-ras is believed to be involved in tumour formation and early stage
progression, the K-ras dependency for viability of established cancers remains unknown
(di Magliano and Logsdon, 2013). Studies conducted with patient derived cancer cell
lines indicated not all pancreatic cancer sub types may be k-ras dependant for survival
(Singh et al., 2009). Therefore, even if an effective K-ras inhibitor (either directly
targeted or downstream effectors) not all pancreatic cancers may respond.
Many other well established common cancer tumour-suppressors such as p53, p16 and
p19 are also frequently identified in pancreatic cancer cells (Jones et al., 2008). A
summary of the complex molecular biology involved in the initiation and progression of
pancreatic cancer can be seen in Figure 1.6.
36
Figure 1.6. The complexity of pancreatic cancer and the signalling pathways cellular components
believed to be involved in the disease. Reproduced with permission from Hidalgo. M. et al. Pancreatic
cancer, Vol 362. The New England Journal of Medicine, 2010, Copyright Massachusetts Medical
Society.
The dismal prognosis of pancreatic cancer was initially believed to be as a result of the
malignancy’s ability for early dissemination of the metastatic phase of the disease.
However, recently sequencing data from excised tumours and metastatic tissue indicates
there is at least a decade between the initiating mutation and formation of the primary
tumour mass. Metastatic potential is then not acquired for up to 5 years later with
patient death on average two years after that (Yachida et al., 2010). Therefore, if this
proves to be confirmed in the population, there is a significant therapeutic window that
has yet to be exploited if advances in early detection can be achieved.
1.4
Treatment options
Traditional therapy for early stage detection involves surgical resection with adjuvant
chemotherapy. Early detection and surgery resection still offers the greatest chance of
37
survival. However, with few biomarkers available for early detection and difficulty in
diagnosing by non-specific symptoms such as back pain and loss of appetite, up to 85%
of patients are diagnosed in the late metastatic stages of the disease (Li et al., 2004).
For these advanced pancreatic cancer patients, gemcitabine based chemotherapy has
remained the standard treatment of care for decades (Li et al., 2010). These
chemotherapy based treatments offer a statistically relevant increase in median survival
time. However, the increase from 4.5 months to around 6 months is still an extremely
bleak outcome.
Targeting genetic dependencies in cancers represents the most rapidly emerging class of
anticancer therapeutics (Caponigro and Sellers, 2011). There are several examples of
clinically successful molecular targeted agents across a range of cancers in the past
decade, such as the tyrosine kinase inhibitors, Imatinib for treating chronic
myelogenous leukaemia, Erlotinib in some lung cancers and the Braf inhibitors in
melanoma (Fisher, 2011; Workman and de Bono, 2008). Although several targeted
cancer therapies have been successful, a large number have failed clinical trial
progression, particularly in the case of pancreatic cancer. Therapies targeting vascular
endothelial growth factor receptor (VEGFR), insulin like growth factor-1 (IGF-1R),
hedgehog pathway, MEK and src alone or in combination with gemcitabine have all
failed to show clinical efficacy. The EGFR targeted compound erlotinib is currently the
only targeted therapy that has FDA approval for treatment for advanced pancreatic
cancer. Combined with traditional chemotherapy, the treatment provided only a 12 day
median survival increase (5.9 to 6.2 months) (Moore et al., 2007; Saif, 2007).
There are currently numerous clinical trials underway for pancreatic cancer with over 30
large trials having been conducted in the last decade, unfortunately most have provided
little clinical benefit. Of all the trials conducted, gemcitabine monotherapy had
remained the standard treatment with only three combination regimes providing a
statistically significant increase in patient survival times recently (gemcitabine-erlotinib,
FOLFIRINOX and gemcitabine-nab-paclitaxel). These combination therapies have
suggested clinical outcomes of a mean increase in survival time from around 6 up to 11
months compared to monotherapy gemcitabine alone (Peddi et al., 2013). Nanoparticle
albumin-bound paclitaxel (nab-paclitaxel) combined with gemcitabine has now been
approved for treatment of late stage metastatic pancreatic cancer. With success in
38
treatment regimens for pancreatic cancer currently measured in weeks and months,
novel therapeutic options are desperately needed.
The establishment of gemcitabine as the single agent standard of care almost 20 years
ago provided the first evidence of prolonged survival for non-resectable advanced stage
pancreatic cancer (Burris et al., 1997). Gemcitabine is a pyrimidine nucleoside antimetabolite and exerts anti-neoplastic effects on range of cancers including pancreatic
(Figure 1.7). It exhibits cell phase specific activity, blocking the progression through
G1/S phase and preventing DNA synthesis in S phase. Gemcitabine is metabolised into
several active forms by cellular kinases; monophosphate (dFdCMP) diphosphate
(dFdCDP) and triphosphate (dFdCTP) nucleosides. The active phosphorylated forms
are believed to have multiple intracellular targets including ribonucleotide reductase as
well as inhibition of DNA polymerase, ultimately preventing DNA synthesis and
induction of apoptosis in replicating cells. Transport of gemcitabine across the cellular
membrane occurs via a number of active nucleoside transporters (NTs) (Mini et al.,
2006). Gemcitabine is considered to be a small but significant improvement over older
generation platinum based treatments with similar activity but with a reduced toxicity
profile (Toschi et al., 2007). Its lack of cross resistance with other anticancer agents also
make it an important candidate in future combinational therapies.
Figure 1.7 Gemcitabine structure
Paclitaxel is an antimitotic agent that binds tubulin and prevents functional microtubule
development (Figure 1.8). The stabilization of the microtubules prevents disassembly
and correct metaphase spindle configuration which in turn prevents mitosis progression
and eventually activates apoptosis. In a nanoparticle albumin bound form (nab)paclitaxel has recently been incorporated into combinational treatment with
gemcitabine. The incorporation of albumin is believed to not only reduce
hypersensitivity reactions (resulting from the solvents required to dissolve paclitaxel)
39
but improve intra tumour delivery caused by the uptake of albumin by tumour and
surrounding stromal cells (Peddi et al., 2013). The formulation achieves this increase in
tumour accumulation of paclitaxel through the binding of albumin to secreted protein
acid rich in cysteine (SPARC) often found overexpressed in the surrounding stroma
cells (Saif et al., 2010). The combination of paclitaxel with gemcitabine provides a
modest improvement in median survival of approximately 2 months.
Figure 1.8. Paclitaxel structure
Erlotinib is a reversible tyrosine kinase inhibitor (TKI) that targets the epidermal growth
factor receptor (EGFR) to enhance apoptosis (Figure 1.9). This receptor is often
overexpressed in pancreatic cancer cells and has been implicated in the progression of
the cancer. The EGFR receptor family is linked to cell survival, proliferation, migration
and differentiation (Moss and Lee, 2010). The TKI Erlotinib intracellularly binds to the
ATP binding site of the receptor decreasing ERK1/2 phosphorylation and interfering
with downstream signalling (Mahipal et al., 2014). Despite unimpressive clinical
efficacy it remains the only targeted therapy with approval for treatment of advanced
pancreatic cancer.
40
Figure 1.9. Erlotinib structure
Although there has been much focus on characterisation of pancreatic cancer at the
genetic level, the molecular mechanisms which link the genetic changes to the
aggressive nature of the disease are still poorly understood (Fisher, 2011). A better
understanding of the biology and physiology of pancreatic cancer is needed to open new
avenues for treatment and increase the number of targeted agents progressing through
clinical development. Recent recognition that tumour microenvironment is an important
component of pancreatic cancer has led to the development of cellular models which
may provide insights into the complexity of this disease (Miyamoto et al., 2004).
With only small increments in survival times predicted with traditional cytotoxics and
the failure of current targeted therapies, it is important that future development of novel
therapeutics to utilise the most optimal cellular models available that reflects human
tumour biology as closely as possible.
1.5
Cellular Modelling
With such a dismal prognosis, initial basic research into pancreatic cancer focussed
primarily on the biology of the cancer cells themselves. However, attempts at
elucidating the cellular and molecular mechanisms of the cancer cells and exploiting
these therapeutically has failed to improve patient outlook. Although great insights into
pancreatic cancer cell biology have been achieved, a tumour is not simply an aggregate
of cancer cells but a complex almost ‘organ-like’ structure. A number of host elements
such as blood vessels, immune/inflammatory cells, stromal cells and extracellular
matrix (ECM) are all required to support the growth of the tumour. It is this tumour
41
microenvironment interaction that has recently been the focus of not only basic research
but also drug discovery investigation. The tumour microenvironment of pancreatic
cancer is increasingly seen as a major contributor to the chemoresistance observed in
vivo (Grippo, 2012).
Examination of pancreatic cancer tumours in vivo reveals a unique histological profile
that is not seen in other solid tumours, such as breast or prostate cancer. Pancreas
tumours often lie considerable distances from blood vessels and are surrounded by a
dense desmoplastic reaction (DR) (which is a deposition of fibrous connective tissue)
(Kern et al., 2011). The high proportion of tumour stroma (or DR) (which is primarily
composed of fibroblasts, pancreatic stellate cells and ECM containing proteins which
can facilitate proliferation, migration and invasion) has thought to contribute to the
aggressive nature, chemoresistance and tumour survival of the malignancy (Hwang et
al., 2008). In some pancreatic adenocarcinomas over 80% of the mass is composed of
stromal elements and only a minority actual cancer cells (Froeling et al., 2010). This
high volume of tumour stroma is the most prominent histological feature of pancreatic
cancer and has been implicated in a tumour survival via a number of de novo drug
resistance mechanisms such as those involved in environment mediated drug resistance
(EMDR), hypoxia driven resistance as well as physical aspects such as drug penetration
barriers.
Environment meditated drug resistance emerges as cancer cells interact with their
surrounding microenvironment and are transiently protected from cell death. This
survival process may be initiated by signalling events from soluble growth factors (such
as cytokines and chemokines) or cellular adhesion to other cells or components of the
extra cellular matrix (such as laminin and collagen) (Meads et al., 2009). Hypoxia has
also been implicated as a mechanism by which poorly vascularised tumours resist
treatments. Hypoxic conditions trigger hypoxia-specific transcription factors (HIFs) as
well as changes in metabolism and proliferation rates (Rebucci and Michiels, 2013).
The dense stroma and lack of vascularisation may also introduce physical barriers that
can reduce treatment effectiveness. It has been previously demonstrated that the
presence of ECM proteins in culture can diminish the sensitivity of pancreatic cancer
cells to chemotherapeutic agents (Miyamoto et al., 2004). The unique relationship
between pancreatic cancer and its microenvironment, combined with the failure of
42
traditional approaches makes pancreatic cancer a relevant and critical target to explore
with alternative cell models.
The shift from traditional cytotoxic chemotherapeutic agents to translational research of
molecular targeted agents over the past 15 years is primarily as a result of the advances
in our understanding of the genetic and molecular abnormalities underpinning the
various malignancies. From the FDA approval in 2001 of the breakthrough treatment of
chronic myelogenous leukaemia (CML) with Imatinib, there has been a focus of
identifying small molecules or biologicals that target specific aberrant proteins or cancer
pathways in oncology drug discovery programs (Masui et al., 2013). However, much of
the initial drug discovery research from lead discovery to preclinical testing still utilises
information obtained from traditional cell culture models that have remained unchanged
for decades (Kisaalita, 2010). Mechanisms of action, in vitro efficacy, toxicity studies
and initial hit to lead discovery programs are often based upon the limited and
physiologically irrelevant monolayer cell culture model.
Preclinical animal models have evolved over the last two decades from conventional
xenograft models to complex systems such as genetically engineered mouse models
(GEMMS) (Olive and Tuveson, 2006). GEMMS are capable of more faithfully
reproducing aspects of the corresponding human disease such as KRAS mutant driven
responses of pancreatic cancer (Singh et al., 2010). These improved mouse models offer
an alternative to traditional orthotopic xenograft models for preclinical therapeutic
evaluation and provide pancreatic tumours whose pathology and response to therapeutic
agents more closely resembles that of human pancreatic cancer (Hruban et al., 2006).
Clinical trials have also adapted to account for the heterogeneity of tumours in the
populations. Targeted therapies may only be effective on a small subset of the clinical
trial cohort and so biomarker and genetic screening is being used to assign patients to
the appropriate trial. Preclinical models, as well as clinical trial design, have evolved to
better predict oncology based drug development. However, basic research and hit to
lead discovery phases of drug development are utilising whole cell based assays which
have not developed at the same speed.
All models used to study cellular interactions are flawed in some way. The often used
quote from George Box; “Remember that all models are wrong: the practical question
43
is how wrong do they have to be to not be useful” is particularly relevant for not only a
drug discovery program but also basic research that drives the direction of drug
development. Although there is no doubt monolayer cell culture has been useful in the
past, do the questions being asked today in pancreatic cancer research, call for a more
representative model?
The limitation of current in vitro whole cell models is that they do not recapitulate the
complexities of in vivo tumour microenvironment. Tumours are extremely complex
three dimensional (3D) structures comprised of not only cancer cells but numerous
stromal elements (Mueller and Fusenig, 2004). The interactions with host stroma and
basement membrane as well as cell to cell and cell to ECM contacts play important
roles in cancer signalling pathways of cell survival (Santini et al., 2000). For drug
discovery campaigns as well as basic cancer research, 3D cell culture has been
suggested as the evolution to current two dimensional (2D) culture models.
In vitro medical research has relied on plastic-ware based 2D cell culture for much of
the cellular investigations, with a 2D monolayer of cells (adhering to a plastic substrate
such as polystyrene) still the standard cell culture system in most research institutions
(Eisenstein, 2006). However, investigating physiologically processes such as tumour
responses to therapeutics on an artificial substrate devoid of any biological context may
be sub-optimal and a poor predictor of in vivo tumour activity.
1.6
Three dimensional (3D) cell culture
Three dimensional cell culture systems encompass a diverse and growing field in the
biomedical research industry from tissue engineering to drug screening. The importance
of spatial arrangement and intracellular material to tissue structure and development in
vitro was revealed last century in development biology studies (Moscona, 1952). In
cancer research 3D cell culture has been used as a tool to evaluate tumours in some
form for over 30 years (Yuhas et al., 1978). In just over a decade the number of
publications involving 3D cell culture has gone from approximately 20 a year to over
700 in 2013 (Figure 1.10). As well as an increase in interest from academic institutions,
44
there is increasing evidence to indicate that industry has also begun to embrace this
technology (Personal communication, Bayer Healthcare; HCA 2012 presentation, San
Francisco, USA).
3D Cell culture
450
400
350
300
250
200
Publications
150
100
50
0
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
P
u
b
l
i
c
a
t
i
o
n
s
Year
Figure 1.10. Pubmed listed publications by year involving the keywords “3D cell culture”.
Many fields of cell biology are utilising 3D culture models to investigate applications
including stem cell biology, tissue remodelling and the study of normal organ
physiology (Kim, 2005). To demonstrate the importance and utility of 3D culture
systems a body of evidence has been established proving the difference between culture
models. 3D systems have demonstrated to be better at reproducing in vivo like
responses than 2D monolayers (Green and Yamada, 2007; Schmeichel and Bissell,
2003). Cell function and behaviour (Cukierman et al., 2001; Yamada and Cukierman,
2007), signal transduction (Schmeichel and Bissell, 2003), and gene expression
(Birgersdotter et al., 2005; Bissell et al., 1982; Ghosh et al., 2005) are all very different
in 2D monolayers versus 3D models.
However, the scope of this research will
concentrate on 3D cell models in cancer biology with application in drug discovery
programs only.
Three dimensional (3D) organotypic models have been used extensively in cancer
research to help determine mechanisms of tumour invasion and proliferation (Yamada
and Cukierman, 2007), impacts of drug treatment (Hicks et al., 2006) and to compare
45
normal physiological function (Froeling et al., 2009). The majority of publications in
this field cite a more physiological relevant model as the reason for exploration of 3D
culture over 2D monolayer culture. The concept of 3D cell cultures was established to
overcome the shortcomings of 2D based cell culture. Biochemical and mechanical
signals, cell to cell communication and tissue specific architecture are lost under the
simplified 2D conditions (Mazzoleni et al., 2009). Figure 1.11 describes the possible
physical, chemical spatial cues that that can affect the status of a 3D culture.
Figure 1.11. Microenvironment cues that may affect cellular phenotype. Republished with
permission from Lai, et al., Biomarkers for simplifying HTS 3D cell culture platforms for drug discovery.
Drug Discovery Today (2011).
To successfully study the biological responses and molecular mechanisms of human
cancers it is necessary to recreate the 3D architecture of in vivo tumour tissue in culture.
The advantages of 3D cultures are in their in vivo like geometry which enables the
direct relationship between structure and function to be visualised. Morphological
studies of multicellular tumour-like structures can give insights into cell specific
responses as a predicator of tumour phenotypes. Although 3D cell culture has been
explored extensively in cancer research, there is currently no ‘one type fits all’ 3D cell
culture system for drug discovery. There are a range of models currently being
developed in the field of cancer research each with advantages and disadvantages. Table
1.1 gives a brief overview of the technologies available and their amenability to drug
discovery and in particular HTS.
46
Table 1.1. An overview of 3D cell culture methods.
BM = Basement membrane, ECM = Extracellular matrix.
HTS
compatibility
Method
Description
Advantages
Disadvantages
ex vivo model
Slices of patient
tumour are
embedded in a
porous gel or
scaffold in media
Spheroids are
created from
single cells either
by magnetic
levitation or
simulating zero
gravity in media
Tissue architecture
and cellular
interactions
preserved
Technically complex
Cell viability limited
Low throughput
Difficult
Quick production
and large quantities
of cell aggregates
possible
Difficult
A physical 3D
environment is
manufactured
from synthetic
polymers as
inserts or directly
into plasticware
for cell growth
Media conditions
or non-adherent
surfaces induce
cell aggregation
Reproducible
Provide physical
and structural
support
Custom equipment
required
Artificial induction of
3D morphology
without
microenvironmental
anchorage such BM or
ECM
Cumbersome transfer
lacking ECM proteins
of in vivo BM or ECM
components
Imaging difficult
Simple
Synthetic peptides
are used to
recreate fixed
combinations of
various ECM and
BM components
and cells are
grown on or in the
matrix
Accurate recreation
of in vivo BM and
ECM
Facilitates rapid
screening
Consistent
composition
Long shelf life
Artificial induction of
3D morphology
without
microenvironmental
anchorage such BM or
ECM
Expensive
Simple components
may not recreate
complex in vivo like
BM or ECM
Bioreactor or
Rotary cell
culture
Synthetic
Scaffold
support
Multicellular
tumour
spheroids
(MCTS)
Synthetic
Hydrogel
matrix
Technically easy
Relatively cheap
Mass production
possible
47
Intermediate
Simple
Biological
Hydrogel
matrix
Naturally derived
BM or ECM is
layered onto
surfaces by either
ECM producing
cells such as
fibroblasts or
directly collected
from biological
sources
Accurate recreation
of in vivo BM and
ECM
Facilitates rapid
screening
Variable composition
between batches
Expensive
Lacks ability to
customise components
Simple
Using 3D structures or spheroid based cell models is becoming increasingly accepted
for solid tumour based investigations (Pampaloni et al., 2007; Sutherland, 1988). These
3D spheroids simulate micro-metastases or micro-regions of larger tumours
(Sutherland, 1988). These tumour models offer a practical intermediate between in vitro
2D monolayer cultures and tumours in vivo. However, the choice of exactly which 3D
model system and its particular cellular and matrix microenvironment has substantial
impacts on experimental outcomes (Kim, 2005). Deciding on which system is the most
appropriate depends on the particular research being undertaken.
1.7
Drug evaluation in 3D cell culture models
One of the major reasons for adoption of 3D in vitro tumour models has been as a
preclinical tool to evaluate anticancer drug candidates (Burdett et al., 2010). Three
dimensional cell culture is reported to more accurately reflect in vivo drug responses
and mechanisms of chemoresistance than monolayer based culture (Bates et al., 2000;
Matsusaki et al., 2014; Pampaloni et al., 2007; Thoma et al., 2014). The factors
affecting anticancer therapeutics in vivo are believed to be more faithfully recreated in
3D in vitro cell culture (Mehta et al., 2012). A number of studies have demonstrated
cells grown as 3D cultures have a higher resistance to anti-cancer drugs compared to
cells grown in monolayer cultures. This activity profile of higher resistance is often
mirrored in clinical settings (Friedrich et al., 2009; Lin and Chang, 2008; Olive and
Durand, 1994). The increased chemoresistance has been attributed to a number of de
novo (intrinsic) mechanisms such as microenvironment factors including: increased cell
to cell contact, pro survival cell to ECM signalling, general 3D cellular architecture,
48
lowered proliferation rate, oxygen and drug diffusion gradients and impact of site
specific non-cancer cells (Astashkina et al., 2012; Hamilton, 1998).
1.8
Biomarker expression in 3D cell culture
Response profiles of existing drugs is one method used to evaluate the ability of 3D cell
culture models to mimic in vivo conditions. However, there currently is an absence of
validated 3D biomarkers that can be used for evaluating the physiological relevance of
culture models. To evaluate 3D cell culture models and determine if biomarkers could
be used to validate culture conditions suitability, molecules involved in cell to cell
interactions or well characterised targeted therapy pathways may be used. Cell adhesion
molecules (CAM) are cell surface molecules involved in mediating adhesion and
binding of cells to other cells or cells to ECM components. There are five classes of
CAM’s: cadherins, immunoglobulin superfamily, selectins, mucins and integrins
(Figure 1.12). Several members of these classes are believed to play an important role in
tumour development and have been identified as predictive markers for treatment
success in a number of malignancies (Canel et al., 2013). They are transmembrane
proteins that provide bridging between extracellular and intracellular scaffolds. As well
as mediating adhesion of cells they are also involved in numerous cell signalling events
(Santini et al., 2000). Members of the cell adhesion molecule families may be potential
biomarkers for correlating 3D pancreatic cancer cell culture models back to in vivo
tumour physiology.
49
Figure removed
Figure 1.12 Illustration of the five classes of cell adhesion molecules. Which include cadherins,
immunoglobulin superfamily, selectins, mucins and integrins. Republished with permission from Lodish
H et al, Molecular Cell Biology. (2000).
E-cadherin is a CAM that has been identified as often having a reduced expression
profile in pancreatic cancer cells compared to normal ductal cells (Menke et al., 2001).
E-cadherin plays a pivotal role in cell-cell adhesion, cell-cell recognition, the survival of
cells and dissemination and metastatic potential (Li and Ji, 2003; Lowy et al., 2002). Ecadherin is an adhesion molecule that together with β-catenin, forms the major
component of adherens junctions in cellular adhesion. The intracellular domain of Ecadherin attaches directly to β-catenin which links the protein complex to the actin
cytoskeleton. The E-cadherin/β-catenin complex plays an important role in normal
cellular adhesion and signal transduction as well as the maintenance and structural
organisation of epithelial cells (Lodish et al., 2000).
These proteins have been shown to have reduced expression patterns when cells are
grown in 3D versus monolayers cultures in a number of other cancers including prostate
(Windus et al., 2012). Types of ECM proteins present in the culture (collagen I), as well
as the physical contact with other cells, have also been shown to determine the
expression level of E-cadherins in vitro with a reduction in expression linked to
increased proliferation and migration behaviour (Menke et al., 2001). The type of ECM
proteins (collagen I) used in culture has also been shown to effect the expression of
other CAM’s (Grzesiak and Bouvet, 2006; Kisaalita, 2010).
50
1.9
Molecular targets in pancreatic cancer
Markers that can be used to show different expression patterns may be useful in
categorising cell culture models but ultimately biological functional outcomes should be
assessed to validate 3D cultures. A number of molecular targets have been assessed for
efficacy in pancreatic cancer including epithelial growth factor receptor (EGFR),
vascular endothelial growth factor receptor (VEGFR), histone deacetylase (HDAC) and
mammalian target of rapamycin (mTOR). The Insulin-like Growth Factor 1 Receptor
(IGF-1R) pathway is one of the most recent molecular targets to be the subject of drug
discovery efforts and clinical trial evaluations (Scartozzi et al., 2011). The IGF-I
pathway has been implicated in having a major role in survival, proliferation and
resistance to therapy of multiple human cancers (Baserga et al., 1994). Insulin growth
factor 1 is produced by the liver and mediates normal function of growth hormone
(GH). IGF-1R is a transmembrane tyrosine kinase that is present in normal tissue but is
often overexpressed in pancreatic tumour cells (Bergmann et al., 1995; Carboni et al.,
2009). This member of the insulin receptor subclass consists of extracellular tetramer of
two alpha subunits that bind IGF-1 and two intracellular beta subunits which have
tyrosine kinase catalytic domain with an ATP binding site. IGF-1 binds to IGF-1R
which causes a conformational change in the receptor, leading to activation of the
kinase domain (Tao et al., 2007). IGF-1R utilises primarily the insulin receptor substrate
1 (IRS-I) and the collagen homology substrate (SHC) as immediate downstream
effectors which can lead to the activation of multiple signalling cascades including the
IRS/Phosphoinositide 3-kinase (PI3K) / AKT cell survival pathway and the Shc/Ras/
Mitogen-activated protein (MAP) kinase cell proliferation pathway (Mauro and
Surmacz, 2004). The IGF-1R targeted therapeutics aim to prevent proliferation and cell
survival by disrupting these anti-apoptotic intracellular signal transduction networks.
The pathway has also been implicated in modulating cell-to-cell adhesion molecules
(Mauro and Surmacz, 2004).
51
Figure 1.13 A simple overview of the IGF system components and its main effector pathways. It is
composed of three ligands: IGF-I, IGF-II and insulin. Following ligand binding to IGF-1R, its tyrosine
kinase activity is activated, which stimulates signalling through intracellular networks that regulate cell
proliferation and cell survival. Primarily activation of IGF-IR results in phosphorylation of proteins
belonging to either SHC or the IRS family. This leads to subsequent phosphorylation and activation of the
Ras and PI3-kinase signalling pathways. These pathways are implicated in metastasis, resistance and
survival of tumour cells.
A variety of approaches have been used as potential targeted therapies against IGF-1R
signalling including antisense oligonucleotides, antagonistic and neutralising antibodies
and tyrosine kinase inhibitors (Hofmann and Garcia-Echeverria, 2005). Early preclinical
trials with both antibody based and tyrosine kinase small molecule inhibitors targeting
IGF-1R showed promising activity (Adachi et al., 2010; Awasthi et al., 2012; Levitt and
Pollak, 2002). However, despite this promising preclinical data and strong rationale for
targeting IGF-1R clinical trial results with in pancreatic cancer have failed to show
clinical efficacy either alone or in combination with standard of care chemotherapy
(Chen and Sharon, 2013; Guha, 2013). There are number of suggested reasons for the
poor performance of these therapies in the clinic. No accurate biomarker is available to
predict patient response (as the presence of overexpression of IGF-1R alone may not be
predictive of a response) (Guha, 2013). The majority of pancreatic tumours are also
heterogeneous and not dependant on one signalling pathway for proliferation and
survival. The IGF1-R is also structurally similar to insulin receptor (IR) which may
cause toxicity and safety problems in trials. Finally there is numerous cross talk with
52
other signalling pathways such as VEGF, EGFR, IR and hypoxia-inducible factor 1 α
(HIF1α) which may be unregulated following IGF1-R disruption (Tao et al., 2007).
Despite the poor clinical performance of targeted therapies, the well characterised
nature in vitro and in vivo, make the IGF-1R pathway a pertinent choice to study 3D cell
models for closer examination of how targets and associated downstream signalling
pathways are effected in different cell models (Sachdev and Yee, 2007). It has been
documented that certain cancer cell lines in monolayer culture are only partially
sensitive to IGF-1R inhibition, whereas 3D cultures (anchorage independent) and in
vivo explant models exhibit increased apoptosis when IGF-1R is inhibited (Baserga,
1997). Therefore the anti-apoptotic effect of IGF-1R inhibition may be modulated
differently depending on the microenvironment mediated pathways involved (Surmacz,
2003).
1.10 Pancreatic cancer 3D cell culture
There are limited examples of specific 3D cell culture systems of pancreatic cancer cell
lines using a range of different culture methods. One of the first examples of published
data from a MCTS based 3D culture system utilising pancreatic cancer cell lines was
from Mcleod et al in 1997 (McLeod et al., 1997). Over the ensuing 15 years a range of
3D cell culture systems have been investigated with most being monoculture low
throughput based studies. However, there are limited examples of high throughput
compatible systems for inclusion in HTS programs. HTS compatible pancreatic cancer
3D assays have been reported but have been mainly limited to non-adherent coatings
(agarose), in media aggregation additives (Polyhydroxyethylmethacrylate (polyHEMA)) or micro-patterned plastics that prevent adherence to induce 3D structure
formation (Dufau et al., 2012; Matsuda et al., 2010; Wen et al., 2013). Recently a 3D
ECM based in vitro tumour model was reported for a single pancreatic cancer cell line
(Celli et al., 2014). Several of these examples are discussed below.
53
The hanging drop system (a form MCTS 3D cell culture), forms spheroids by
aggregating cells in a droplet of media hanging from a specialised microplate. Although
no other micro-environmental components such as basement membrane or ECM was
used in this system, spheroid morphology was achieved and differences between
chemotherapeutic drugs sensitivities were observed (Tung et al., 2010). Increased
chemoresistance, changes in gene expression and altered metabolism of pancreatic
cancer cell lines were reported in a cellulose-aggregation induced 3D cell culture system
(Longati et al., 2013).
Further published data describes pancreatic cancer cells grown on collagen scaffolds to
produce 3D cultures and the expression pattern differences in monolayer culture of
several cell surface adhesion molecules, such as E-cadherin, β-catenin and α2β1 integrin
(Grzesiak and Bouvet, 2006; Grzesiak et al., 2005). Media treatments causing cells to
aggregate in suspension (Gaviraghi et al., 2010) as well as synthetic Polyethylene
terephthalate (PET) scaffolds have been used to study and compare 2D and 3D systems
(Zhang and Yang, 2011). There are even fewer examples of biological based hydrogel
initiated 3D culture systems that maintain the most physiological in vivo like
microenvironment (Froeling et al., 2009; Gutierrez-Barrera et al., 2007; Sempere et al.,
2011). In particular only one published example of HTS validated 3D pancreatic cell
models utilising a biological basement membrane has recently been published (Celli et
al., 2014). Investigating more complex and possibly predictive 3D cell culture for true
high throughput assays is the current challenge of many academic and private research
institutions in the drug discovery field.
1.11 Three dimensional cell culture high throughput screening
(HTS) assay development
Standard cell culture models, particularly in anti-cancer drug discovery HTS campaigns
have in the past involved adhering cancer cells to a plastic substrate bathed in media.
Compounds or biologicals under investigation are added to the culture and the desired
end points measured after a predetermined incubation period. This well established and
54
validated model had been relatively unchallenged with most cell based assay
development maintaining this basic monolayer biology.
Decades of publications suggest that cancer based 3D tissue culture models offer a more
sophisticated means of micking in vivo microenvironments and includes a number of
systems that will be important in selecting potential drug candidates (such as more in
vivo like responses in concentration gradients of therapeutic agents and signalling
molecules, composition and structure of microenvironments surrounding the cancer
cells (Carvajal Ce et al., 2012; Wen et al., 2013)). However, utilisation of 3D cell
culture systems for primary cellular screens has yet to be fully adopted by industry
(Breslin and O'Driscoll, 2013). It may however, be the complexities of these models or
the associated costs that have resulted in the hesitancy to adopt them for HTS assay
development. Depending on the 3D methodology chosen to recapitulate the in vivo like
state, there may be an equal increase in experimental variability and complexity upon
incorporation of those in vivo factors.
In order for a 3D cell culture system to be effective as anti-cancer HTS assays it should
conform to the same criteria as standard monolayer in vitro anti-cancer screening
methods. Traditional screening assay doctrine insists that several constraints should be
placed upon any in vitro system which might be used to predict in vivo drug sensitivity.
These are, the system must be reproducible, adaptable to different tumour types,
relatively inexpensive, fast to perform and show strong correlation between in vitro and
clinical studies (Eagle and Foley, 1958; Renzis and Aleo, 1978). Adhering to these
constraints requires a pre-study validation of a number of factors related to automation,
scale up, stability and variability studies (Terry et al., 2009). Validation that the 3D cell
culture system is predictive of the in vivo environment is required through comparative
analysis of mouse xenographs and human immunohistological tumour samples.
For 3D cell culture to become standard in HTS, the biology should adhere to the
standards of assay development but not affect the overall quality of the assay (general
robustness and reproducibility). The current challenge is to utilise existing lab
instrumentation and assay technologies to enable implementation of 3D cell models into
current assay workflows. Although the use of 3D models in drug discovery is
increasing, there are still only limited published examples of cancer based 3D cell
55
cultures models that have demonstrated validated assay methodology from design
through to active screening (Lovitt et al., 2014).
Several recently published articles have begun to provide more information on assay
development in 3D. A 3D cell culture based HTS model was recently reported for
prostate cancer were co-culture (stromal and tumour cells) using the biological ECM
based system was effectively setup and validated against a small library of compounds
(Krausz et al., 2013). A lung cancer study using an artificial scaffold for 3D cellular
formation, tested a panel of established chemotherapy agents which identified a stem
cell enriched chemo-resistant culture compared to monolayer models (Godugu et al.,
2013). A small library of 230 compounds were tested in 3D breast cancer culture model
with cell viability and epithelial to mesenchymal transition (EMT) markers determined
(Li et al., 2011). Horman and colleagues recently developed a co-culture 3D assay for
colon cancer cells that was subsequently used to screen a natural product compound
library (1528 purified compounds) using an image based detection system (confocal
microscopy based colony counting system ) (Horman et al., 2013).
There is currently an unmet need for the design and verification of effective, validated
assays useful for 3D cell culture based models in pancreatic cancer. The work presented
aims to build on existing information and provide detailed new insights in attempt to
meet those needs.
1.12 Research question
The aim of this research is to determine whether in vitro 3D cell culture models can be
used to more accurately study cellular and drug interactions in pancreatic cancer and
obtain a greater understanding of the mechanisms underpinning the disease.
56
1.13 Aims and objectives
An expert panel recently convened and produced a report in Annals of Oncology (Van
Laethem et al., 2012) entitled:
“New strategies and designs in pancreatic cancer research: consensus guidelines
report from a European expert panel”.
The panel concluded as a major objective for future pancreatic cancer research that
‘there is an urgent need to:
(i)
(ii)
Develop and apply suitable high-throughput screening strategies to identify
novel functionally relevant targets and,
use appropriate preclinical in vivo models recapitulating the human situation
to validate putative drug targets in vivo’
If the ultimate goal of cancer research is to have a positive outcome on patient survival,
then so far pancreatic cancer research has been largely unsuccessful. Patients diagnosed
with advanced pancreatic cancer today have almost the same dismal chance of survival
as they did 50 years ago. The broad objective of the work presented here is to advance
the field of pancreatic cancer research beyond academic investigation of the disease and
towards tangible clinical outcomes for patients by primarily improving the success of
drug discovery programs.
The initial aim is to establish a 3D in vitro micro-tumour culture model based on a
range of diverse pancreatic cancer cell lines and optimise the growth conditions for use
in further investigational studies. Cell surface markers related to cell to cell and cell to
ECM interactions will be characterised and compared to monolayer models. Drug
activity in the 3D model will then be compared to traditional culture conditions using a
panel of current cytotoxic chemotherapeutic agents and assessed with several assay
technologies. Assay endpoints using high content screening technology as well as
whole-well plate reader systems, will be investigated. The integration of data
acquisition and analysis will also be examined using commercial and open source
solutions.
New insights into pancreatic cancer will be revealed by developing this model as a
streamlined tool for use in drug interaction studies compatible with high throughput
screening. The performance of assays will be assessed and validated in HTS conditions
57
with a small pilot screen of biologically active compounds. Compounds of interest will
be further explored in the 3D model incorporating drug combination studies in a highly
automated miniaturised format. IGF-1R targeted therapies currently under clinical trial
investigation will be assessed using this drug combination format, compared to both
existing monolayer assay systems and reported in vivo responses.
Finally, to provide evidence that the model system developed here enables quantitative
assessment of drug interactions in a standardized highly HTS amenable format that can
be easily adopted by laboratories involved in pancreatic cancer drug candidate research.
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69
2. Chapter Two: Characterisation of Pancreatic Cancer Cell Line
Models
2.1 Introduction
The prominent pathological and molecular characteristics of pancreatic cancer include:
1) An extremely dense desmoplastic reaction comprising of a complex hypovasculated tumour surrounding microenvironment (Whatcott et al., 2013) and,
2) Intra-tumour and intra-patient molecular and cellular heterogeneity (Samuel and
Hudson, 2012).
To develop a more pathologically or biologically relevant cell model based on these
characteristics two main approaches were selected. Firstly to recapitulate the complex
micro-environmental effects observed in vivo, a 3D structure inducing system was
employed. Secondly, a panel of pancreatic cancer cell lines covering the expanse of
common genomic variations was used to incorporate genetic heterogeneity into the
model. This chapter will describe the investigations undertaken to ascertain the critical
properties of a selection of six pancreatic cancer cell lines and the impact that this had
on the 3D culture system ultimately chosen for the drug discovery cell culture model
used throughout the course of this project.
Figure 2.1. Diagram of the complex tumour composition and surrounding microenvironment in
pancreatic cancer. Adapted with permission from Joyce JA, Pollard JW: Microenvironmental regulation
of metastasis. Nat Rev Cancer 2009 and Minchinton Nature reviews 2006.
70
2.1.1
Tumour microenvironment
Pancreatic cancer is uniquely identified as having an extremely dense desmoplastic
reaction surrounding the primary tumour. A desmoplastic reaction or response is the
formation of an abundant fibrous or dense connective tissue in response to neoplasia.
The tumour stroma is an extremely complex compartment and can vary greatly in size
(may contribute up to 90% of total tumour volume) and component distribution,
depending on the location in the body (and type of tumour present). The stromal
compartment of pancreatic ductal adenocarcinomas includes a number of cellular and
extracellular constituents that interact with tissue framework within the body, as well as
the cancer cells themselves, to contribute to the neoplastic phenotype of pancreatic
cancer (Chu et al., 2007). A number of host cell types are associated in the stroma
development and composition include pancreatic stellate cells, fibroblasts, epithelial,
endothelial, inflammatory and nerve cells (illustrated in Figure 2.1) (Rasheed, 2012).
Although these cells have been directly implicated in having active roles in progression
of the malignancy, it is the dense fibrotic nature of the tumour surrounding stroma that
is believed to largely contribute to the resistance to current therapies and the
pathogenesis of pancreatic cancer.
This highly fibrotic reaction is produced primarily from sequestered stellate, epithelial
and myofibroblast-like cells that are involved in secreting large quantities of
extracellular matrix components (ECM) including collagen, fibronectin and laminin
proteins (Whatcott et al., 2013). The ECM is essential for physical scaffolding for the
cellular components and as well as these physical cues, it is also involved in crucial
biochemical signalling cues required for tumourigenesis (Venkatasubramanian, 2012).
A large body of literature has implicated these extracellular components as the likely
reason for the limited efficacy of current therapeutics and is summarised in Table 2.1
(Whatcott et al., 2013). Briefly, in vitro studies indicate the secretion of ECM proteins
is believed to contribute to the intrinsic resistance and malignant phenotype of
pancreatic cancer by a number of biological and physical mechanisms.
The abundance of collagen and glycosaminoglycans contributes to the diminished
efficacy of anti-cancer therapeutics as well as promoting cancer cell proliferation
(Minchinton and Tannock, 2006; Miyamoto et al., 2004). The ECM constituents of the
71
stroma can also stimulate the expression of ATP-binding cassette (ABC) multidrug
resistance transporters in tumour cells and activate the phosphatidylinositide 3-kinase
(PI3-kinase) cell survival pathway (Mitsiades et al., 2004). The sparse vascularisation,
increased interstitial fluid pressure and copious amounts of deposited ECM defined by
the desmoplastic reaction also provides a physical barrier to drug penetration. This is
supported by evidence that collagen has been shown to decrease the penetration of
macro and small molecules in breast cancer (Diop-Frimpong et al., 2011) as well as
attenuate the efficacy of anti-cancer compounds in pancreatic cancer studies (Armstrong
et al., 2004).
Table 2.1. Common components of the stromal compartment found in pancreatic ductal
adenocarcinomas. Adapted from (Whatcott et al., 2013).
Stromal ECM component
Collagen I,II,III
Laminin
Fibronectin
Verisican
Osteonectin
Heparin & Keratin
sulphates
Hyaluronan
2.1.2
Effects on tumourigenesis
Promotes proliferation
Promotes apoptotic resistance
Promotes apoptotic resistance
Promotes apoptotic resistance & proliferation
Promotes proliferation
Promotes tumour progression & poor survival
marker
Increases interstitial fluid pressure, enhances
apoptotic resistance
Three dimensional (3D) cell culture
There is an ever increasing variety of 3D culture models being developed and
commercialised for use in cancer based research (Kimlin et al., 2013). These models
range from cells cultured as multicellular aggregates (such as multicellular tumour
spheroids) to cells cultured on non-biological inserts and cells embedded in biological
or synthetic matrices. There is no one size fits all product, with each model having its
own advantages and disadvantages, as well as level of complexity (Pampaloni et al.,
2007). These 3D culture models can be grouped into two primary categories based on
the method used to induce three dimensional growth:
1) Anchorage independent (liquid / non adherent) or
2) Anchorage dependent (scaffold).
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One of the earliest methods for studying tumour-like structures in vitro was the
multicellular tumour spheroid (MCTS) model (Yuhas et al., 1978). This anchorage
independent method encourages cell aggregation by preventing cellular attachment to
any substrate. This can be achieved through either prefabricated low attachment plates
(Scivax NanoCulture® plate), coating surfaces to prevent attachment (polyhydroxyethyl methacrylate, polydimethylsiloxane, agarose, and agar), gyratory shakers
or confining cells to a drop of liquid (hanging drop system) (Friedrich et al., 2009;
Matsuda et al., 2010; Tung et al., 2010; Yeon et al., 2013). These methods recreate a
number of in vivo tumour-like characteristics that 3D cellular structures promotes
(including heterogeneous proliferation, nutrient, oxygen gradients and increased cellcell contacts) and can be amenable to high throughput microtitre plate formats(Friedrich
et al., 2007; Mueller-Klieser, 2000). However, these systems lack a biological or
synthetic scaffold as an anchorage point and more complex cancer cell-ECM
interactions. They also have a number of challenges for use in drug screening
applications, such as issues involving drug dosing and media exchanges due to the nonadherent nature of the model.
The second category involves the use of scaffolds (either biological or synthetic) to
induce cancer cells to form 3D structures in culture. A number of well-defined synthetic
scaffolds have been developed such as polyethylene glycol (PEG) and poly-εcaprolactone (PCL) which can be also be combined with biological components.
Synthetic scaffolds have a number of advantages such as well-defined components,
important for reproducibility and controllable pore size and density (Raza et al., 2013;
Thoma et al., 2014). However, although highly customisable, the specific tumour
architecture must be validated and sufficiently well characterised for this model to
replicate the in vivo tumour environment accurately. There are also several
disadvantages to synthetic scaffolds, such as lack of biological signalling with relevant
ECM components and difficulty in obtaining drug response measurements (such as
morphological analysis using imaging-based technologies due to incompatibility of
materials).
Biological scaffolds such as the basement membrane extracts (Matrigel ®) provide a
semi-solid matrix that promotes cellular attachment and induction of three dimensional
growth (Kleinman and Martin, 2005). These laminin-rich ECM hydrogels induce
73
similar tumour characteristics as the anchorage independent 3D culture models with
additional ECM interactions. The 3D structures produced with biological hydrogels
(unlike the uniform structures of anchorage independent models) often have distinct
morphology and size ranges. This more biologically relevant substrate (made up of
basement membrane proteins extracted from tumours) enables ECM to cell signalling
that may provide more predictive drug responses in vitro (Grzesiak et al., 2007).
Figure 2.2. Representation of the two main 3D tumour models; anchorage dependant (scaffold) and
anchorage independent (liquid or non-adherent). (a)Anchorage dependent model utilised either a
biological or synthetic scaffold to induce 3D culture development. (b) Anchorage independent models
utilises non-adherent substrates or culture conditions to induce 3D culture growth. Republished with
permission from (Thoma, et al. Advanced 3D cell culture systems modeling tumor growth determinants
in cancer. Target Discovery Drug Delivery Reviews, 2014) Elsevier.
A large number of these technologies have been commercialised and are now available
for purchase in different formats (Table 2.2). The technologies available are either end
products pre-fabricated for immediate use such as the hanging drop plate system or nonadherent microtitre plates. No preparations are required for these liquid (anchorage
independent) based models and they offer a cost effective solution to 3D culture
generation. Alternative products such as Matrigel simply supply the basement
74
membrane extract scaffold and microtitre plates have to be first produced before 3D
cultures can be developed, they also require thorough optimisation. However, with such
a vast array of technologies and products available, there would be insufficient
resources and time to examine each product individually. All technologies available
recapitulate the physical three dimensional (3D) state of in vivo tumours by inducing 3D
structure formation of the cells. The methods for the 3D induction are extremely diverse
from the relatively simple monoculture multicellular tumour spheroid model through to
the complex co-culture microfluidic lab-on-chip products (Drifka et al., 2013). Some of
these technologies are more amenable to high throughput studies, which require robust
reproducible structure growth, few handling steps and compatibility with plate readers
and imaging platforms. Therefore, the technologies were evaluated based on which
system incorporates more of the specific pancreatic cancer microenvironment cues that
are believed to be critical in chemo-resistance and pathogenesis of the cancer as well as
HTS
compatibility.
Due to
the increasingly reported
involvement of the
microenvironment (in particular the ECM components) in many of the biological and
physical resistance mechanisms found in pancreatic tumours, only those 3D systems
that incorporated biological ECM where selected for evaluation (Armstrong et al.,
2004).
75
Table 2.2. Commercially available 3D scaffold and liquid based platforms. Adapted from
(Asthana and Kisaalita, 2012).
Company
Trade name
3DBiomatrix
Perfecta3D plates
Perfecta3D
scaffolds
InSphero
GravityPlus plates
BD
Matrigel
Glycosan Biosystems
Extracel
GlobalCellSollutions/Hamilton GEM
Trevigen
Cultrex 3D Matrix
Sigma
HydroMatrix
MaxGel
QGel
MT 3D Matrix
Kollodis BioSciences
MAPTrix HyGel
Synthecon Inc.
BIOFELT
Biomerix 3D
Scaffold
Life Technologies
Geltrex
AlgiMatrix
ZellWerk
Sponceram
amsbio
alvetex
3DM Inc.
PuraMatrix
Corning
UltraWeb
3DBiotek
3D Insert PCL
3D Insert PS
3D Insert PLGA
b-TCP Disc
MicroTissues Inc.
3D Petri Dish
Advanced
Happy Cell ( Trinity College)
suspension media
3D method
Hanging drops
Hydrogel
Hanging drops
Laminin, Collagen
Hyaluronic acid and Collagen
Magnetic alginate microcarrier
BME, Laminin, Collagen
Synthetic Peptide Hydrogel
Human ECM
Hydrogel
Chemically defined Hydrogel
PGA, PLLA, PLGA, custom
Polycarbonate polyurethaneurea
Laminin, Collagen
Alginate
Ceramic
Polystyrene
Peptide
Polyamide
Polycaprolactone
Polystyrene
Poly(DL-lactide-co-glycolide)
b-Tricalcium phosphate
Agarose
Suspension media
Extracellular matrix (ECM) based 3D cell culture method
A number of commercial cell culture reconstituted basement membrane or matrix based
products are currently available on the market (such as Matrigel, Cultrex or Geltrex).
These products utilise Englebreth-Holm-Swarm tumours in mice as a natural source for
tumour related ECM components. The commercial product is considered to be a
reconstituted basement membrane capable of inducing cells into differentiation and 3D
76
growth similar to that of the tissue of origin (Kleinman and Martin, 2005). These
products contain a complex mixture of extracellular proteins and growth factors. The
main components have been identified as laminins, collagens, enactin, proteoglycans
and a number of growth factors including transforming growth factor β (TGF-β),
epidermal growth factor (EGF), insulin-like growth factor 1(IGF-1), platelet derived
growth factor (PDGF) (Hughes et al., 2010; Vukicevic et al., 1992).
The ECM based culture system was ultimately chosen for evaluation in developing a
monoculture 3D pancreatic cancer cell line model. This system is a compromise
between recapitulating the complex tumour microenvironment found in vivo and current
technical feasibility in a drug development based assay model. With ECM
microenvironment factors implicated in the mechanisms of resistance the malignancy
has evolved to current therapies, a 3D model which encourages interactions with these
components may allow for a more physiologically relevant and predicative model.
Therefore, a model based on cancer cells embedded on top of a layer of tumour derived
ECM proteins was chosen for developing the pancreatic cancer 3D in vitro model
system. As well as ECM to cell interactions, a number of other micro-environmental
conditions are also captured in this model including the spatial and physical cues,
effects on cell proliferation, metabolism changes and nutrient / drug gradients in 3D
structures. The following commercial ECM based products, Matrigel, Cultrex or
Geltrex will be assessed for suitability in a high throughput screening (HTS) amenable
3D model.
77
Figure 2.3. Comparison of in vivo tumour complexity and the in vitro ECM based (3D) cell culture
model. (A) A representation of a pancreatic cancer tumour with numerous microenvironmental
components such as host cells (epithelial cells, blood vessels, fibroblasts) and stromal elements (ECM).
(B) Representation of a single well of a microtitre plate with the artificial environment replicating some
elements of the in vivo tumour (including ECM, 3D structural growth and heterogeneous regions within
these structures). Republished with permission from Lovitt et al. Miniaturized Three-Dimensional
Cancer Model for Drug Evaluation. Assay drug and development Technology. (2013) Mary Ann Liebert
Inc.
2.1.3
Pancreatic cancer cell lines
Pancreatic cancer research, as with the study of many other cancers, has largely been
based on in vivo and in vitro investigations of established cell lines. Experimentation
involving well characterised and commonly available cell lines remains a convenient
starting point for drug discovery studies (Sipos et al., 2003). Investigations of a range of
carcinomas, including pancreatic, indicate that cell lines are capable of partially
recapitulating the biological features and responses seen in clinical samples (Deer et al.,
2010). The genomic and molecular basis of pancreatic ductal adenocarcinomas is
78
reflected in the common mutations mirrored in both cell lines and patient tumour
samples (Deer et al., 2010; Li et al., 2004). Most pancreatic cancers are defined as
multi-genetic in nature, with abnormalities in two broad gene classifications; oncogenes
and tumour suppressor / genomic maintenance genes. K-ras mutations are present in
over 90% pancreatic adenocarcinomas (the highest frequency of all human cancers) (Li
et al., 2004). The loss of tumour suppressor genes CDKN2a/p16, TP53 and
MADH4/SMAD4 are the next most frequently genetic alterations in pancreatic cancers.
Recent genome studies have identified other genetic alterations including genes
involved in chromatin modification (EPC1, ARID2), DNA damage (ATM) and
numerous other mechanism (Biankin et al., 2012).
Pancreatic cancer is classified as a heterogeneous cancer (Biankin et al., 2012) and the
selection of cell lines that cover as much of the phenotypic and genotypic diversity as
possible may be an important consideration in any drug development study. The
heterogeneity in both the primary tumour and metastases found in pancreatic cancer is
often overlooked in early drug discovery studies, where a single cell line may be used as
the representative biology of the carcinoma (Yachida et al., 2010). This study aims to
select a panel of well characterised cell lines, capable of reflecting the diverse genotypic
and phenotypic characteristics of the malignancy. A number of parameters were chosen
to select the six cell lines to be used throughout this project and representative of the
various stages and disease phenotypes. The desired characteristics included genotypic
markers, morphological differentiation, tumour origin and metastatic potential as
selection criteria. The number of cell lines chosen obviously does not represent the full
heterogeneity of the human disease.
A possible limitation of using existing pancreatic cancer cell lines and evaluating their
phenotypic behaviour in a 3D environment is the length of time that the cell lines have
been cultured in monolayer conditions. This long term planar culturing may have
altered the cell lines ability to form 3D tumour like structures. However, examples from
previous pancreatic cancer studies as well as developmental and functional cell biology
studies have shown that even after long term culture as monolayers cell lines are
capable of regaining their physiological form and function when exposed to certain
environmental conditions. For example, in breast cancer studies, malignant cell lines
(maintained as monolayers) once cultured in 3D environments exhibit a disruption of
79
tissue organisation and polarity that correlates with in vivo tumour morphology (Weaver
et al., 1997). Conversely non-malignant mammary gland cells form clusters with central
lumen capable of functional casein expression (Aggeler et al., 1991) once cultured in a
3D environment. A previous study also revealed that immortalised non-malignant
pancreatic ductal epithelial cells (maintained in monolayer conditions) formed well
differentiated organised structures with a polar hollow cavity once cultured in a 3D
environment (Gutierrez-Barrera et al., 2007).
The following cell lines were chosen for the development of the pancreatic cancer 3D
cell culture model (summarised in Table 2.3):
BxPC-3 is a cell line obtained from the primary tumour of a 61 year old woman. The
adenocarcinoma formed in the body of the pancreas with no evidence of metastases.
The tumour did not respond to radiation or chemotherapy and the patient died six
months later. When implanted in nude mice the tumours exhibited characteristics such
as biomarker (E-cadherin, carcinoembryonic antigen, human pancreas cancer-associated
antigen and human pancreas-specific antigen) expression resembling the original
patient’s primary tumour (Tan et al., 1986). The cells have a mutation in TP53 tumour
suppressor gene, wild type (WT) K-ras status, , WT CDKN2/p16 and WT for SMAD4
(Loukopoulos et al., 2004). It has been reported to be sensitive to gemcitabine, erlotinib
5-fluorouracil (5-FU), and cisplatin in vitro (Deer et al., 2010; Pan et al., 2008; Tan et
al., 1986).
MIA PaCa-2 cells were derived from the primary tumour of a 65 year old male. The
cells have poor differentiation and a high rate of proliferation. This cell line possesses
multiple mutations in the common tumour suppressor and oncogenes: CDKN2A/p16,
TP53 and K-ras (Yunis et al., 1977). Both resistance and sensitivity to gemcitabine in
vitro have been reported previously (Fryer et al., 2011; Rathos et al., 2012).
PANC-1 was obtained from a 56 year old man with a primary adenocarcinoma in the
head of the pancreas. Lymph node metastases were identified and the cells exhibited
poor differentiation in culture. PANC-1 cells have a heterozygously expressed K-ras
mutation, as well mutations in CDKN2A/p16, TP53 and SMAD4 genes. Human
80
epidermal growth factor receptor 2 (HER2/neu) is also overexpressed in this cell line
(Deer et al., 2010). PANC-1 has reported to be gemcitabine, 5-fluorouracil (5-FU), and
cisplatin resistant in vitro (>100µM) (Chen et al., 2012; Pan et al., 2008).
SU.86.86 cell line was obtained from a liver metastasis of a 57 year old woman
diagnosed with a ductal adenocarcinoma of the pancreas. The cells have a heterozygous,
overexpressed K-ras mutation, as well as CDKN2A/p16 and TP53 mutations (Deer et
al., 2010). Su.86.86 is reported to be sensitive to gemcitabine, 5-fluorouracil (5-FU),
and cisplatin in vitro (Espey et al., 2011).
AsPC-1 was obtained from a 62 year old female with an adenocarcinoma of the head of
the pancreas from. The cancer had metastasised to several abdominal organs and the
cells are of ascites metastasis origin. AsPC-1 cells have a homozygous K-ras mutation
and TP53 tumour suppressor gene mutation. AsPC-1 has previously been reported to be
gemcitabine, 5-fluorouracil (5-FU), and cisplatin resistant in monolayer in vitro assays
(Arumugam et al., 2009).
Table 2.3. Cell lines selected for 3D model development. Details of cellular origin, metastatic
potential, degree of differentiation in culture and common mutations are listed for six pancreatic cancer
cell lines.
Cell lines
BxPC-3
MIA PaCa-2
PANC-1
Su.86.86
Origin
Primary
(late stage)
Primary
(late stage)
Primary
(late stage)
Metastatic
potential
in vitro
High
Low
Low
Liver metastases
Ascites
metastases
n/a*
Capan-1
Liver metastases
*Not described in the literature
High
AsPC-1
High
Differentiation
Common mutations
Poor
Moderate to
poor
p53,p16, SMAD4
K-ras (homozygous),
p53, p16
K-ras (heterozygous),
p53, p16
K-ras (heterozygous,
p53, p16
K-ras (homozygous),
p53
Poor
Moderate to
poor
Poor
Well-formed
polarised
structures
K-ras (homozygous),
p53, p16, SMAD4
Although small, the selected panel of cell lines represent a broad cross-section of
pancreatic mutations and provides a compromise between technical feasibility for drug
discovery assay development and the diversity observed in patient tumours.
81
2.2 Materials & methods
2.2.1
Materials and reagents
384 well black side-clear bottom tissue culture treated plastic (TC) bottom optical
imaging microplates (#6007550, Perkin Elmer Waltham, MA) and Falcon black sideclear bottom (TC) microplates (#3353962, BD Bioscience, San Jose, CA) were used for
all cell based assays described throughout. RPMI 1640 (Life Technologies, Carlsbad,
CA)
was
supplemented
with
10
mM
HEPES
(4-(2-hydroxyethyl)-1-
piperazineethanesulfonic acid). Foetal bovine serum non-heat inactivated (Non HI-FBS)
and heat inactivated FBS (FBS) were purchased from Life Technologies. Stock FBS
was stored as frozen aliquots at -20°C and added to RPMI media (and HEPES) before
each experiment. To reduce variability throughout the experiments, a single batch of
non-heat-inactivated and heat inactivated serum was purchased and used for all
subsequent studies. All media and media supplements were purchased from Life
Technologies. Accutase was purchased from Life Technologies and stored as frozen
aliquots at -20°C. Phosphate buffered saline (PBS), Dimethyl sulfoxide (DMSO),
Tween 20, Triton x-100 and bovine serum albumin (BSA #A3059) were purchased
from Sigma Aldrich (St. Louis, MO) Matrigel® was purchased from BD scientific,
Cultrex™ from R&D systems (Minneapolis, MN) and Geltrex™ from Life
Technologies.
Primary antibodies used were as follows: goat polyclonal anti-IGF-1R antibody (Santa
Cruz Biotechnology, Dallas. Texas), rabbit polyclonal anti-E-cadherin antibody (Santa
Cruz Biotechnology), rabbit polyclonal anti-beta catenin (Santa Cruz Biotechnology
Secondary antibodies, chicken anti-rabbit Alexa Fluor 594, chicken anti-rabbit Alexa
Fluor 488, were purchased from Life Technologies. Cell stains Hoechst 33342, Texas
Red phalloidin and Alex Fluor 488 phalloidin were purchased from Life Technologies.
2.2.2
Cell lines
82
Human pancreatic adenocarcinoma cell lines AsPC-1 (CRL-1682), BxPC-3 (CRL1687), Capan-1 (HTB-79), MIA PaCa-2 (CRL-1420), PANC-1 (CRL-1469) and
Su.86.86 (CRL-1837) were purchased from the American Type Culture Collection
(ATCC Manassas, USA). The cells were maintained in RPMI (Life Technologies)
supplemented with HEPES (10mM) (Life Technologies) and 10% heat-inactivated fetal
bovine serum (HI-FBS, Life Technologies). All cell lines were maintained in a 5% CO2
humidified incubator at 37°C. Cells were immediately expanded upon arrival and frozen
down as master seed stock in long term liquid nitrogen storage using DMSO (5%) based
freezing medium (RPMI + 20% HI-FBS). Batches of cells from the seed stock where
then expanded to working stocks as required for the duration of the project. Cells
brought up from liquid nitrogen where tested routinely for mycoplasma contamination.
Cell lines in continuous culture where split at ratio of 1:4 once culture flasks reached
70-90% cell confluence. Accutase (Life Technologies) was used to detach cell
monolayers for culture splitting, according to the manufacturer’s recommendations.
2.2.3
Two dimensional monolayer cell culture
Cells were maintained in complete media conditions (RPMI 1640 with 10% HI FBS and
10mM HEPES) until 70-85% confluency was achieved. The cells were then
disassociated from tissue culture flasks with Accutase (15 minutes 37°C) (1ml for
75cm2 flasks and 2ml for 150cm2) (Life Technologies) and resuspended in 5ml of
complete media. Cells were manually counted with a haemocytometer using trypan blue
stain and seeded in 384 well plates at specified densities (as per experimental condition)
using complete media conditions. Cells were maintained in a 5% CO2 humidified
incubator at 37°C.
2.2.4
Three dimensional cell culture
Commercial hydrogel based 3D matrix
Biological basement membrane based hydrogel (Matrigel, Cultrex, Geltrex) was
removed from -20ºC storage and thawed slowly overnight at 4ºC. Pancreatic cancer cell
83
lines were cultured on either standard phenol red Matrigel (#35420), growth factor
reduced phenol red free Matrigel (#356231), Cultrex (#3432-010-01), or Geltrex
(#A1413201) using an adapted technique described elsewhere for 96 well-format 3D
breast and pancreatic cancer models with minor adjustments (Debnath et al., 2003;
Gutierrez-Barrera et al., 2007; Lee et al., 2007b). Fifteen microliters of cold hydrogel
(either 100% Matrigel or 70% mixture in media) was manually added to the wells of a
384 well microtitre plate using a 16 channel Finn pipette. The hydrogel was allowed to
solidify at 37°C for 60 minutes, cells were then harvested with accutase from cell
culture flasks. Cells were resuspended as single cells in complete media at appropriate
cell concentrations. For the 70% matrix cultures 50µl of cell suspension was overlaid on
top of the matrix, while for the 100% cultures a further 50µl of media containing 5%
hydrogel was added on top of the seeded cells (cell concentration dependant on
experiment). Cells were maintained in a 5% CO2 humidified incubator at 37°C for
between 7-21 days, with media replaced every third day either manually with a Finn
pipette or using the automated Bravo™ liquid handling platform (Agilent).
Fibroblast derived 3D matrix
Fibroblast derived 3D matrix production was performed as per methods described by
(Serebriiskii et al., 2008). NIH3T3 fibroblast cells were cultured in DMEM medium
supplemented with 10% FBS for 20 passages (to overcome their normal contact growth
inhibition). Cells were harvested and seeded at 2000 cells per well (50µl) in a 384 well
microtitre plate. Media was changed every 2 days, supplemented with 50 µg/ml cell
culture-tested ascorbic acid. The ascorbic acid treatment was used to induce ECM
production. After 8 to 20 days, fibroblast cells were washed from plates with an alkaline
detergent treatment (0.5% TritonX and 20mM NH4OH). A cell free layer of ECM
remained attached to the plates and could be stored at 4°C in PBS. Pancreatic cancer
cells were seeded onto these ECM deposited plates.
Egg white derived 3D matrix
Chicken eggs were purchased from grocery stores and shells cleaned with 70% ethanol.
Egg whites derived 3D cell culture was performed as per methods previously described
(Kaipparettu et al., 2008). Eggs white were separated with sterile pipettes and aliquoted
84
into falcon tubes. Fifteen microliters of egg white was pipetted into the wells of a 384
well clear bottom falcon microtitre plate. The plate was heated to 60°C using an empty
microtube heating block for 60 minutes until the egg white became semi-solid.
Harvested pancreatic cancer cells suspensions were added directly into each well (a
range of cell concentrations from 500 to 2000 cells per well in 50µl of media)
2.2.5
Cytotoxicity assay
Cytotoxicity was measured using a resazurin (Sigma-Aldrich) dye reduction assay, as
previously described (McMillian et al., 2002). Cells were seeded in 384 microtitre
plates at appropriate cell densities for monolayer studies and allowed to adhere for 24h
before drug treatments or controls were added. Puromycin (Sigma #P8833) was used as
a reference compound for drug sensitivity studies. Serial dilutions of puromycin (12
points; 10nM to 45µM) were prepared fresh in sterile (autoclaved MilliQ) water. After
72h incubation, the number of viable cells was assessed by the addition of resazurin (4
hour incubation at 37°C). Briefly, a working solution of resazurin (60mM) was made up
fresh and diluted to 6mM in culture media before a 5µl addition to each well containing
a total volume of 50µl of media/cells (600µM). The fluorescent signal was quantified
with a Victor II (Perkin Elmer) or Envision (Perkin Elmer) plate reader at 535/595nm.
Raw data was normalised against the control wells which included triplicate wells of a
water addition only (100% growth) or 45µM puromycin concentration (100% growth
inhibition). The half maximal inhibitory concentration (IC50) was determined using a
linear regression equation of the normalised data in Graphpad Prism (using the variable
slope sigmoidal dose response equation).
85
2.2.6
Immunofluorescence microscopy
For immunofluorescence (IF) microscopy studies, cells were grown in either 2D
monolayers or in 3D cultures as described for the commercial hydrogel based 3D matrix
cultures. Medium was removed from each well by either Finn pipette or Bravo
automated liquid handler and cells were rinsed with 50 µl of PBS and fixed and
permeabilised with 25µl of 4% paraformaldehyde and 0.5% triton X100 for 10 minutes
at RT. Wells were rinsed three times with PBS for 5 minutes. Cells were then blocked
overnight at 4°C with 50µl of blocking solution consisting of PBS with 2% BSA, 0.05%
Tween, 0.1% Triton X and 10% goat serum. The following day microplates were
incubated with primary antibody at between 1:50 and 1:200 in 25µl of blocking solution
overnight at 4°C. The following day each well was washed three times with PBS.
Finally, fluorescently conjugated secondary antibodies (1:400), phalloidin (1:100) and
Hoechst 10mM (1:2000) were incubated with cells (25µl of blocking solution) for 2
hour on a plate shaker. Wells were then washed three times in PBS and 50µl of PBS
was added to keep cells hydrated. Confocal IF images were taken on an Opera™
(Perkin Elmer) high content screening system and analysed using the Opera Acapella™
software (Table 2.4).
86
87
Table 2.4 Acapella Image analysis protocol for 2D E-cadherin, β-catenin and IGF1-R intensity
determinations. Initial step using find nuclei building block, followed by find cytoplasm building block.
The whole cell region was then selected for intensity calculations and several properties were recorded
(mean cell intensity, total well intensity, number of objects) Results were then graphed in results section
2.3.4.
Input Parameters for AAS: 2D Ab intensity.aas
GetData
FindNuclei
NucleiDetectionChannel_string:
Exp1Cam1
NucleiDetectionAlgorithm:
B
ThresholdAdjustment:
0.4
MinimumNuclearArea:
30
NuclearSplittingAdjustment:
3
IndividualThresholdAdjustment:
0.4
MinimumNuclearContrast:
0.1
OutputName:
Cell Nuclei
FindCytoplasm
CytoplasmDetectionChannel_string:
Exp3Cam2
Nuclei_string:
Nuclei
Method:
F
MembraneChannel_string:
Exp3Cam2
CytoplasmIndividualThresholdAdjustment:
0.1
SelectRegion
WholeCells_string:
Nuclei
Region:
Cell
Method:
Standard
channel_string:
Exp1Cam1
WeightedMassCenter_ONOFF:
0
OuterBorder_ONOFF:
0
InnerBorder_ONOFF:
0
HolesAreaLimit:
INF
Gaps_ONOFF:
0
SuffixForPropertyNames:
Whole Cell area
CalculateIntensityProperties
channel_string:
Exp3Cam2
WholeCells_string:
Nuclei
Region:
Cell
Method:
Standard
Mean_ONOFF:
1
Stddev_ONOFF:
1
CV_ONOFF:
1
Median_ONOFF:
1
Sum_ONOFF:
1
Max_ONOFF:
1
Min_ONOFF:
1
SuffixForPropertyNames:
Intensity of Ab
ReturnResults
Method1:
List of Outputs
@Nuclei : Number of Objects1_stat1:
1
@Nuclei : Intensity of Antibody Mean1_stat1: Mean
@Nuclei : Intensity of Antibody Mean1_onoff:
1
@Nuclei : Intensity of Antibody Sum1_stat1: Mean
@Nuclei : Intensity of Antibody Sum1_onoff:
1
88
2.2.7
Brightfield microscopy
As standard image analysis protocols with either the commercial microscope software
(IN Cell Developer, PerkinElmer Acapella) or open source alternatives (Image J) had
not been previously established for 3D cell cultures, a number of analysis techniques
were evaluated. There are numerous challenges in adapting image analysis techniques
designed for monolayer evaluation to 3D cell culture. Image acquisition can be of a
single plane (2D) or multiple Z-stacks (3D) and the analysis itself may also be 2D
(compressed maximum projection image) or 3D (a rendered Z-stack). Each technique
has advantages and disadvantages in relation to time, data storage and complexity of
analysis. However, for this initial evaluation of growth reproducibility the 3D cultures
was assessed using a single 2D brightfield or differential interference contrast (DIC)
image and 2D image analysis with object segmentation by simple thresholding
techniques (Figure 15a). The pixel intensity of the 3D objects can be segmented from
the background ECM layer using DIC or brightfield microscopy. Objects are then
measured for a number of features including area, diameter (feret’s) and roundness. The
accuracy of the 3D measurements from conventional microscopy is based upon the
assumption that the 3D structures follow a general elliptical shape when embedded in
the ECM. However, as the 3D structures increase in size they often become oblate
elliptical shapes whose Z-dimension is often less than their x, y dimensions and
therefore care must be taken in analysing these objects. Images were manually
processed with both ImageJ and IN Cell Developer software, using the procedures listed
in Materials methods Table 2.5 & Table 2.6. Size analysis from the ImageJ protocol
correlated with data produced with the IN Cell developer protocol. Growth kinetics
were quantified using average area size and diameter and graphed using ImageJ.
89
Table 2.5. Protocol steps required to segment and analyse size and morphology data from imagej
software. Reproduced with permission from Lovitt, Assay and Drug Development Technology, (2013).
Protocol Step
Program Action
Result
1
Save image file from microscope or
automated imaging platform
Image file of 384-well with 3D
morphology produced
2
Set scale to appropriate value and
make global for current image set
Correct pixel scaling required for
size analysis
3 (optional)
Subtract background if contrast is
poor for brightfield or DIC images
Enhances image for segmentation
action
4
Image > Adjust > Threshold: Auto or
set manually for particular image set
to segment cells from background
Intensity segmentation used to
identify
3D
structures
from
background
Analyse > Set Measurements >
Include parameters to measure such
as area, diameter, shape descriptors
Analyse > Analyse Particles >
Include size exclusions and output
options
Measurements such as area,
diameter, shape descriptors are
selected
5
6
7
Measurements are recorded and
outputted to results table
To automate image analysis a
macro of steps 1-6 is created and
Batch process run on entire image
stack
Process > Batch > Macro
Table 2.6. Protocol steps used to obtain size and morphology data from the in cell developer
software. Reproduced with permission from Lovitt, Assay and Drug Development Technology, (2013).
Protocol step
Program Action
Result
1
Tiff Image or .XDCE image stack
collected from IN Cell imaging
platform or converted using IN Cell
Translator
Image files of 384-well plates with
3D morphology produced
2
New protocol created in IN Cell
Developer 1.9
Unique protocol created
3
Targets filled and single pixels
removed in target box
Open segmented structures filled
and background removed from
segmentation
4
5
Pre-processing:
Intensity
segmentation
chosen
using
automatic threshold or manual
value
Post-processing: Fill holes, Sieve
single cells and Border Object
Removal selected
For brightfield images, intensity is
used for segmentation of cells from
background
Single cells and 3D structures on
the border are excluded
6 (optional)
Post-processing: Watershed clump
breaking
Separate distinct objects which are
in a close proximity to each other
7
Add user defined measures such as
count, diameter, area and form
factor
Define which measurements are to
be evaluated automatically
90
Analyse entire stack and create
output table
8
2.2.8
Defines which wells are to be
analysed and what output format
the results data will be in
Statistics
Statistical significance (p<0.05) was determined using 2-tailed Student t tests. Graphical
representation of data including IC50 curves was produced using Graphpad Prism™
(San Diego, CA) software.
2.3 Results
A number of biological factors can affect the quality of a cell based assay. To develop a
robust model system, factors such as growth conditions of the individual cell lines and
media parameters were evaluated. Conditions were first examined in standard
monolayer cultures before 3D cell culture evaluations began. The parameters examined
included the influence of the passage number on cell behaviour, DMSO sensitivity,
growth serum type and antibiotic effects. As large numbers of cells may be required for
scaling up for HTS campaigns, cell passage effects on growth rates and drug sensitivity
were tested (Esquenet et al., 1997). Cells were cultured to both early (<10) and late
stage (>20) passage numbers. Serum components have also been shown to have
significant effects on growth characteristics (Mortell et al., 1993; Okano et al., 2006).
Heat inactivated and non-heat inactivated FBS were compared across the cell lines. To
ensure consistency, the same batch of FBS was used throughout all the studies
undertaken. DMSO is frequently used as the solvent of choice for compound libraries
due to its impressive solubility properties and limited effects on mammalian cells at low
concentrations. Determining sensitivity to DMSO is therefore an important and essential
parameter in any cell based drug discovery assay development.
All cell culture experiments required to develop and establish the assay were performed
in 384 well microtitre plates to maintain the desired parameters for future drug
screening assays. 2D cell cultures were run simultaneously with 3D cultures to compare
morphology with cells grown in matrices. Media changes were performed every three
days for live cell imaging plates using described media. All experimental data
91
represents the average of three independent experiments measured in triplicate (means
± SD) unless otherwise stated.
2.3.1
Optimization of cell culture conditions
Effect of passage number
Cell banks were produced for each of the six cell lines with expanded stocks catalogued
and deposited in long term liquid nitrogen storage (cell concentration of 4 x 106 per ml),
with cells utilised for this study within four years. To ascertain whether the cell passage
number may influence the outcomes of experiments to be undertaken, a comparison of
the different cell lines from cultures ranging from early (≤10) to late (≥20) stage passage
number was undertaken. Puromycin was selected as the toxic reference compound to
evaluate changes in sensitivity of the individual cell lines. Puromycin is a potent
translational inhibitor of mammalian cells and provides micromolar activity in cancer
cell lines. The results shown in Figure 2.4 indicate that no differences were observed in
growth rates or sensitivity to puromycin for any of the cell lines tested over the period
specified. Through microscopic observation there was also no detectable change in
morphology or attachment ability of all cell lines during this period (data not shown).
With no significant effect observed for late passage numbers when compared to their
early stage counterparts, continuous cultures were subsequently maintained and used in
the screening assays and subsequent experiments.
92
3
P u r o m y c in IC
50
( M )
4
2
1
S u .8 6 .8 6 H I L a te
P A N C -1 H I L a te
S u .8 6 .8 6 H I E a r ly
P A N C -1 H I E a r ly
M IA -P a C a -2 H I L a te
C a p a n -1 H I L a te
M IA -P a C a -2 H I E a r ly
B x P C -3 H I L a te
C a p a n -1 H I E a r ly
A s P C -1 H I L a te
B x P C -3 H I E a r ly
A s P C -1 H I E a r ly
0
E a r ly v s L a te P a s s a g e
Figure 2.4. Passage number had no effect on drug sensitivity on pancreatic cancer cell lines. Low
passage (Early <10) and high passage (Late >20) cultures were treated with a reference compound
(puromycin) and IC50 determined using resazurin-based metabolic activity assay. Data representing mean
values ± standard deviation of three separate experiments.
Growth serum conditions
The effects of the general cytotoxic inhibitor, puromycin, on cell growth was compared
under the varying media conditions with either 10% non-heat inactivated and heatinactivated FBS for all cell lines. No statistical differences in either drug response
(Figure 2.5a) or growth rates (Figure 2.5b) were observed between FBS types, thus the
heat inactivated batch was used for all subsequent experiments. Reduced percentages of
HI FBS were also examined with 1% and 5% levels tested. These serum conditions
reduced growth rates considerably and were not pursued in further studies
93
Figure 2.5. No significant cellular response to the reference drug or growth rate differences
between cultures with HI FBS and non-HI FBS. (a) Puromycin sensitivity of cell lines comparing
heat inactivated and non-heat inactivated FBS types utilising the resazurin cell viability assay.(b)
Representative growth rates of AsPC-1 and BxPC-3 cells comparing heat inactivated (HI) and non-heat
inactivated Non-HI FBS utilising the resazurin cell viability assay.
Effect of antibiotics
Each cell line was examined for sensitivity to antibiotics in the culture media,
specifically the most commonly used penicillin and streptomycin, as whilst it is
preferred that antibiotics are not included in cell based drug screening assays this may
eventuate if necessary to maintain healthy cultures for extended periods. In addition, the
commercial hydrogels utilised for the 3D cultures use antibiotics, in particular
gentamicin, in matrices, thus the impact of gentamicin on cell growth was also
94
evaluated. No significant effect on growth rates was observed for any of the antibiotics
across the cell lines tested (Figure 2.6a).
Dimethyl sulfoxide (DMSO) tolerability
Determination of DMSO sensitivity of the cell lines of interest is essential in drug
discovery assay development. The compound libraries and reference drugs to be used
for future screening trials are primarily solubilised and delivered in 100% DMSO. The
level of DMSO tolerated by the cells also impacts on the maximum concentration of
compound that can be delivered without any detrimental solvent (DMSO) effect. As
demonstrated in Figure 2.6b the different cell lines had variable responses to DMSO,
with several cell lines (MIA PaCa-2 and Capan-1) having an increased sensitivity to
DMSO. A final assay concentration maximum of 0.4% DMSO was selected for all
subsequent studies, as this was the highest concentration tolerated (greater than 90%
viability) for all cell lines.
95
Figure 2.6 Antibiotics had no effect on growth of pancreatic cancer cell lines whereas DMSO
tolerability was cell line dependant. (a) Antibiotic effects (A; Gentamicin, B; Penicillin/streptomycin)
on viability across the six cell lines. Calculated as a percentage of growth normalised against media only
controls determined using resazurin assay. (b) DMSO sensitivity of cancer cell lines in monolayer (2D)
culture. Percentage of growth is normalised against a negative water control and a positive (40µM
puromycin) control. n=3
96
2.3.2
Evaluation of three dimensional (3D) culture systems for pancreatic
cancer cells
To study the effects of known and novel compounds on pancreatic cancer in a more
physiologically relevant context that captures more of the tumour microenvironment of
in vivo tumours, 3D cell culture was selected as the basic model system. Due to the
sheer number of 3D culture models available and associated high costs to evaluate each
technology, a limited selection were assessed for 3D pancreatic cancer cell culture. As
biological hydrogel-based models recapitulate many of the tumour microenvironment
cues observed in ductal adenocarcinomas of the pancreas in vivo, this system was
selected for further investigations. To evaluate suitability for use in a HTS assay, basic
morphology and phenotypic characteristics were initially used to compare models to the
existing literature. As there was a limited amount of information available on 3D cell
culture models for pancreatic cancer, morphological studies from other epithelial
cancers, such as breast and prostate, were also used as a reference for spheroid
morphology (Harma et al., 2010; Lee et al., 2007a). The embedded or 3D ‘on top’
methodology of 3D induction was selected as most suitable for drug development
studies, as well as compatibility with standard microscopes and high content imaging
platforms (Figure 2.7).
97
Figure 2.7 Procedure for hydrogel-based 3D cell culture in 384-well microtitre plates. (a) Monolayer
and 3D culture morphology with BxPC-3 cells cultured either on tissue culture treated polystyrene, on top
of growth factor reduced (GFR) Matrigel or overlayed with GFR Matrigel (10X objective scale bar =
100µM. (b) Basic Matrigel 3D cell culture methodology, with an example well from a 384 well
microtitre plate.
Commercial biological basement membrane
The commercial biological hydrogels GFR Matrigel, Matrigel, Cultrex and Geltrex
were tested for their ability to induce 3D structure growth across the six pancreatic
cancer cell lines. All products induced a profound effect on cellular morphology of the
pancreatic cancer cell lines as illustrated in Figure 2.8 for cell lines BxPC-3 and
MIAPaCa-2. Cell suspensions were initially seeded onto a layer of each of the matrices
and allowed to develop 3D structures over a period of 12 days. Cell aggregates or
tightly packed spheroids were typically formed from single cells or masses of cells
98
within 3 days and each cell line exhibited a distinct 3D morphology. 3D structure
formation fell into two main categories either i.) Well-defined tightly packed ‘round’ or
ii.) Loose cell aggregate ‘mass’ phenotype. The temporal development of the 3D
structure was observed for each cell line over a period of 12 days as shown in Figure
2.9. All three commercial products were capable of inducing and sustaining 3D growth,
with no obvious phenotypic differences between the products (for the six cell lines
tested) as represented by BxPC-3 and MIA PaCa-2 cells in Figure 2.8.
Figure 2.8 Comparison of 3D structure formation with different commercial biological hydrogels.
(a) Representative DIC images of 3D structure formation in commercial hydrogels with BxPC-3 cells. (b)
Representative DIC images of 3D structure formation with the MIA PaCa-2 pancreatic cancer cell line.
Cells were seeded at 2000 cells/well in 100% hydrogel at day 5 and imaged with 10x objective on the
Olympus Cell R microscope. Scalebar = 100µm.
All commercial matrices tested induced similar 3D structure development and
morphology with the cell lines tested over the period of time under investigation.
However, growth factor reduced Matrigel was ultimately used for subsequent assay
development due to the more defined preparation of the product (Donovan et al., 2001).
The undefined biological nature of the extracted basement membrane results in this
component having the potential to be a source of considerable variability in the assay
systems that utilises it. Thus, separate lot numbers of GFR Matrigel were compared for
growth characteristics, with no change in morphology observed between three batches
of GFR Matrigel. In addition to biological relevance, reproducibility is one of the most
important factors of a robust HTS assay, as there is little value in non-reproducible data.
Therefore, utilising a less variable product such as the GFR Matrigel should reduce
99
batch to batch variation that may be encountered. A commercial HTS campaign using
standard Matrigel found significant differences between batches (unpublished data from
pilot 3D HTS. Jannsen, 2011) with such an impact that IC50 values of compounds could
not be reproduced from batch to batch of standard Matrigel.
2.3.3
Growth factor reduced Matrigel three dimensional (3D) cell culture
morphology
Existing protocols for 3D cancer cell culture models have used a dilution (from 3% to
100%) of the Matrigel basement membrane to not only facilitate plating of the viscous
product but also to alter the basement membranes stiffness to promote better embedding
of the 3D structures (Debnath et al., 2003; Harma et al., 2010; Sempere et al., 2011).
Both 100% and a 70% dilution of GFR Matrigel in complete media were compared
across each cell line. No morphological difference or significant change in size was
observed between the 100% and 70% Matrigel on top method and therefore for ease of
integration and cost efficiency in HTS workflows the 70% on top method was used for
further assay development.
The morphology of each cell line was characterised and a number of parameters were
measured and used to quantify growth rates and the reproducibility of the culture
system. AsPC-1 cells formed loose irregular masses by day 12 and, as with all cell lines
examined, there was no obvious formation of a hollow lumen/acinar (Figure 2.9a). No
stellate, invasive-like processes extending out from the 3D structures were observed.
The morphology observed correlated with previously published AsPC-1 3D cultures
using Matrigel (Sempere et al., 2011). In contrast, BxPC-3 cells formed round tightly
packed spheroids from day 2, which showed no signs of branching or projections. They
formed well differentiated spheroid structures (Figure 2.9b) that slowly increased in size
over time. Capan-1 cells formed irregular round mass spheroids by day three that
exhibited poor attachment and failed to embed into the Matrigel even after twelve days
of culture (Figure 2.9c). Growth rate studies showed extremely slow proliferation and
with the lack of attachment to the basement membrane, the cell line proved difficult to
culture and quantify growth curves. This observation is in contrast to published data
from Dafau and colleagues in which only Capan-1 (BxPC3, MIA PaCa-2, PANC-1 and
AsPC-1 were also tested) was capable of 3D development in a poly-HEMA coated 3D
system (Dufau et al., 2012).
100
MiaPac-2 cells formed branching mass type spheroids when grown on GFR Matrigel
after a period of 2 days and often showed formation of spindle like pseudopodia
structures. They appeared to have poor cell to cell contacts and no obvious polarisation,
which correlates to the morphology observed in previous Matrigel studies with this cell
line (Figure 2.9d) (Gutierrez-Barrera et al., 2007). Similarly PANC-1 cells formed 3D
structures with an irregular mass phenotype that did show any obvious polarity but with
some branching projections (Figure 2.9e). A small population of structures from each
well produced invasive-like projections which correlated with morphology from
previous Matrigel-based 3D studies (Gutierrez-Barrera et al., 2007). Unlike the other
cell lines evaluated, Su.86.86 cells formed extremely large irregular round spheroids
that showed signs of polarisation and differentiation (Figure 2.9f).
Five of the six cell lines (AsPC-1 MIAPaCa-2, BxPC-3, PANC-1, Su.86.86) that had
previously been used in earlier 3D spheroids studies all developed similar morphology
to that which is presented in the literature (Gutierrez-Barrera et al., 2007; Sempere et al.,
2011). Morphological analysis provided useful insights into the ability of the cell lines
to form the three dimensional structures in comparison to previous studies. However, no
study to quantify growth rates by structure size had been previously performed (with
any of the selected pancreatic cancer cell lines). Therefore the ability to quantitate 3D
structure changes in size over time was assessed using microscopy and image analysis
techniques.
101
Figure 2.9 Pancreatic cancer cell line 3D structure development over 12 days. Representative images
of the same 3D structures over 12 days. All cell lines developed distinct 3D morphologies when seeded
on top of 70% GFR Matrigel. Images acquired on an IN Cell 2000 using a 10x objective. Scale bar =
100µm.
2.3.4
Biomarker expression of pancreatic cancer cell lines
102
To further characterise the pancreatic cancer cell lines, the expression of several
pancreatic cancer biomarkers was evaluated. The expression levels and localisation of
cell adhesion molecules E-cadherin and β-catenin and the often overexpressed IGF-1
receptor
were
compared
in
traditional
monolayer
and
3D
culture
using
immunofluorescent microscopy. The molecular events controlling cellular selfassembly have previously been studied in vitro using 3D culture models (Achilli et al.,
2012). Cadherins are critical to this process and expression levels of the E-cadherin / βcatenin complex have been shown to be associated with cellular adhesion (Foty and
Steinberg, 2005; Ramis-Conde et al., 2008). Developmental biology studies found a
differential expression of E-cadherin towards the centre of spheroids compared to the
margins (Achilli et al., 2012). Previous pancreatic cancer in vitro 3D studies either with
stromal co-cultures or culturing with ECM components have shown an altered
expression of cell adhesion molecules between monolayer and 3D culture (DangiGarimella et al., 2011; Froeling et al., 2009). In this study we compared the observed
phenotype of tightly packed round 3D structures (BxPC-3, Capan-1 and Su.86.86) and
the loose cell aggregate mass type and compared these observations to cell adhesion
proteins and IGF-1R expression levels.
E-cadherin expression intensity and localisation varied between cell lines and between
2D and 3D cultures. Each cell line exhibited different expression levels per cell in
monolayer culture (Figure 2.10b).
The metastatic AsPC-1 cell line displayed a
cytoplasmic expression profile with a reduced level of expression in both monolayer
and 3D culture (Figure 2.10a). A similar profile was seen in the primary tumour origin
cell lines MIA PaCa-2 and PANC-1. The low level expression and cytoplasmic
localisation of E-cadherin correlates with observed phenotype in the 3D culture of
loosely packed aggregates. The tightly packed round 3D structure morphology of
BxPC-3, Capan-1 and Su.86.86 also correlated with the highest levels of E-cadherin
expression. All three of these cell lines displayed high expression and membrane
localised E-cadherin levels in monolayer and 3D culture. The heterogeneous expression
across different cell lines observed in this study correlates with reported in vivo
expression in tumours. That is, pancreatic adenocarcinomas with less compact growing
tumour cells have demonstrated a decreased expression of E-cadherin. In contrast, Ecadherin expression was only slightly reduced in compact tumour growth (Weinel et al.,
103
1996). Thus, high expression of E-cadherin in pancreatic cancer cell lines correlates
with more tightly packed and well differentiated 3D structure formation.
Figure 2.10 Expression of E-cadherin in pancreatic cancer cell lines in monolayer and three
dimensional (3D) cell culture. (a) Representative immunofluorescent staining of 2D monolayer cell
cultures and 3D cell cultures (70% Matrigel on top model) grown for 3 days. Composite images acquired
on a Perkin Elmer Opera confocal high content screening platform with a 20X water objective.
Monolayer images are a single plane and 3D images are single z-stack slice through the centre of
structures. Hoechst stain (blue) was used to label nuclei and Alexa-594 conjugated E-cadherin antibody
(red). Scale bar = 20µm (b) Monolayer quantification of E-cadherin intensity for each cell line. Intensity
104
units calculated as average intensity per cell. Data represents mean of triplicate wells ± standard deviation
(n=2). Only linear brightness adjustments were performed on images.
Expression levels and localisation varied considerably between cell lines. However,
expression profiles of β-catenin coincided with E-cadherin expression and localisation
for each cell line. The tightly packed, round 3D phenotype of BxPC-3, Capan-1 and
su.86.86 cell lines displayed an increased level and membrane localisation of β-catenin
in both monolayer and 3D culture (Figure 2.11a). The loosely packed mass phenotype
cell lines AsPC-1 and PANC-1 displayed an inconstant localisation in 2D culture, with
a population of cells maintaining membrane localisation and others cytoplasmic (Figure
2.11a). This was mirrored in the 3D cultures in which membrane localisation of βcatenin was observed in a small group of cells. MIA PaCa-2 cells displayed a low level
nuclear and cytoplasmic localisation of β-catenin in monolayer cultures with this profile
also observed in the 3D culture conditions.
105
Figure 2.11. Expression of β-catenin in pancreatic cancer cell lines in monolayer and three
dimensional (3D) cell culture. (a) Representative immunofluorescent staining of 2D monolayer cell
cultures and 3D cell cultures (70% Matrigel on top model) grown for 3 days. Composite images acquired
on a PerkinElmer Opera confocal high content screening platform with a 20X water objective. Monolayer
images are a single plane and 3D images are single z-stack slice through the centre of structures. Hoechst
stain (blue) was used to label nuclei and Alexa-594 conjugated β-catenin antibody (red). Scale bar =
20µm (b) Monolayer quantification of β-catenin intensity for each cell line. Intensity units calculated as
average intensity per cell. Data represents mean of triplicate wells ± standard deviation (n=2). Only linear
brightness adjustments were performed on images.
106
Insulin like growth factor 1 receptor has recently been a recent target for therapeutic
intervention in pancreatic cancer. The overexpression of this receptor in cancer is linked
to proliferation, invasion and metastasis (Kawanami et al., 2012). A number of agents
(both antibody and small molecule tyrosine kinase inhibitors) have recently been in
clinical trials but have failed to show clinical efficacy (Guha, 2013). The pancreatic
cancer cell lines displayed a range of expression levels in monolayer culture (Figure
2.12b), Cytoplasmic localisation was consistent across all cell lines. Although
quantitative analysis between monolayer and 3D culture conditions proved difficult a
trend of decreased expression was observed in the MIA PACa-2 and PANC-1 cell lines
compared to AsPC-1, BxPC-3, Capan-1 and Su.86.86 cultures (Figure 2.12a). No
change in cellular localisation was observed between the 2D and 3D cultures (equal
cytoplasmic localisation at all sections of the 3D structures). The monolayer expression
levels trends correlated with previous RNA studies and flow cytometry measurements
on the IGF-1R levels in the pancreatic cancer cell lines (Ioannou et al., 2013; Kawanami
et al., 2012).
107
Figure 2.12 Expression of insulin-like growth factor 1 (IGF-1R) in pancreatic cancer cell lines in
monolayer and three dimensional (3D) cell culture. (a) Representative immunofluorescent staining of
2D monolayer cell cultures and 3D cell cultures (70% Matrigel on top model) grown for 3 days.
Composite images acquired on a PerkinElmer Opera confocal high content screening platform with a 20X
water objective. Monolayer images are a single plane and 3D images are single z-stack slice through the
centre of structures. Hoechst stain (blue) was used to label nuclei and Alexa-594 conjugated IGF-1R
antibody (red). Scale bar = 20µm (b) Monolayer quantification of IGF-1Rintensity for each cell line.
Intensity units calculated as average intensity per cell. Data represents mean of triplicate wells ± standard
deviation (n=2). Only linear brightness adjustments were performed on images.
108
2.3.5
Alternative 3D culture systems
Although biological basement membrane based 3D culture offers an excellent model to
capture many of the elements found within the in vivo tumour microenvironments (such
as the cell to ECM interactions and induction of the 3D morphology), several other
methodologies were also investigated for their ability to reproduce 3D culture
conditions.
Due to the considerable cost of commercially available matrices
(approximately $400 per 384 well plate), alternative non-matrices based methods for 3D
culture formation were also examined utilising in plate production of ECM. Utilising
fibroblast cells for their ability to produce ECM components as a method for in well 3D
matrix production has previously been described in the literature for a range of cancer
cells (including Breast, lung, ovarian and pancreatic) (Serebriiskii et al., 2008). Treating
a confluent layer of NIH3T3 fibroblast cells with an ascorbic acid treatment over
several days is reported to induce significant ECM production (Beacham et al., 2007).
However, fibroblast produced ECM plates failed to reproducibly initiate 3D spheroid
growth with inconsistent morphology observed (structures remaining on a single polar
plane and not forming consistent multicellular aggregates) from well to well (data not
shown). The deposition of ECM proteins would unlikely provide enough reliability for a
use in HTS or drug discovery applications and so this protocol was not developed any
further.
Another alternative investigated was egg white basement membrane as Kaipparettu and
co-workers have reported some success with this approach for both breast and prostate
cancer 3D cell culture (MCF-7 and LNCaP cells) (Kaipparettu et al., 2008). However,
the process of layering egg white matrix into plates in our hands proved difficult to
produce a consistent matrix across all wells. The viscosity provided problems with plate
production and automating would be extremely difficult. Cell morphology was also
inconsistent will cells aggregating into irregular shaped masses with large variation
from well to well. Without a reproducible spheroid formation, assay development based
on spheroid morphology would likely be difficult. The lack of relevant ECM proteins
present in the egg white (making it the least physiologically relevant matrix tested) and
difficult handling requirements limit the usefulness of this model and so further
development was not continued.
109
2.3.6
High throughput screening (HTS) compatibility
The 70% GFR Matrigel ‘on top’ 3D culture system was selected for HTS assay
compatibility studies to determine reproducibility and possible use for future assay
development. The suitability of 70% GFR Matrigel system was evaluated using average
spheroid size and diameter as indicators of phenotypic reproducibility and cell
proliferation over time. A range of cell number seeding densities ranging between 500
and 4000 cells per well were examined in the 384 well imaging plates. Cell number
suspensions greater than 4000 cells per well caused cells to aggregate into large masses
in the centre of the well, while lower cell numbers (less than 1000) required extremely
long incubation periods (3-4 weeks) to from 3D structures for certain cell lines (BxPC-3
and Capan-1). A cell density of 2000 cells per well provided even distribution of single
cells across the entire well without aggregates forming (data not shown). A seeding
density of 2000 cells per well was thus chosen for all cell lines and incubation periods
for up to 12 days were used to assess 3D spheroid development (illustrated with PANC1 cells in Figure 2.13). Capan-1 cells failed to adhere to the basement membrane and
growth rates were extremely low or static, thus
it was not possible to obtain
reproducible data for comparison hence the results have not been included for
discussion. For all cell lines tested the media was manually replaced every two days and
images from triplicate wells were taken on a CellR Olympus microscope or the IN Cell
High content imaging system on days 1, 3, 5, 6 and 7.
110
Figure 2.13 Determination of 3D culture growth over time with image based measurements. (a)
Representative brightfield images of plate wells of PANC-1 cells in 3D culture, automatic object
segmentation outlines from the Developer software clearly able to identify 3D objects from the
background signal of the ECM. (b) Average spheroid size and diameter analysed with Image J object
count protocol (Table 2.5). 3D structures produced on 70% GFR Matrigel up to Day 7, seeded at 2000
cell per well. Average of triplicate wells, n=2.
Figure 2.13b illustrates that 3D structure or spheroid size is directly related to time in
culture. With each cell line following a distinct growth rate, 3D structure size can be
reproducibly predicted in culture. This data can be used to establish standard growth or
proliferation parameters for each cell line and to calculate effects of compound
treatments over time on morphology and proliferation. Whilst the trend of increasing 3D
structure size over time for each cell line is similar, the actual size differs considerably
between cell lines at different time periods. There is a common initial increase in 3D
structure size as the single cells form aggregates over the first 3 days. However, each
cell line then follows a distinct growth pattern which may be due to differences in
111
proliferation rates or altered regulation of pathways related to proliferation (induced by
increased cell to cell and cell-ECM interactions). Due to the large number of objects
(increasing statistical power) in each well and reproducible plating techniques, this
model displays promising reproducibility for further HTS assay development.
2.4 Discussion & conclusions
Parameters important for robust assay conditions were examined including the influence
of the cell culture passage number, DMSO sensitivity, growth serum type and antibiotic
effects. The capability to grow cell lines through a number of passages is important for
scale up for possible screening of large compound libraries (where continuous cell
stocks may be required). As media conditions can have a large influence on compound
activity effects of serum and other media additives were assessed and standard media
conditions developed for future studies
A number of studies in the literature have
conflicting reports on which pancreatic cancer cell lines are capable of the 3D growth
across different model systems. An anchorage independent 3D system was unable to
culture AsPC-1, BxPC-3, MIA PaC-2 or PANC-1 cells (Dufau et al., 2012). However,
an anchorage dependant model was successful in cultured the same cell lines (GutierrezBarrera et al., 2007). Before more intensive and expensive assay development was
undertaken we confirmed the ability of various cell lines to develop 3D structures in
culture. Five of the six cell lines selected demonstrated the capacity to culture in 3D
using the biological hydrogel ECM based model (AsPC-1, BxPC-3, MIA PaCa-2,
PANC-1 and Su.86.86). These cell lines represent a variety of pancreatic cancer
subtypes including cells derived from primary and metastatic origins. 3D characteristics
and phenotypes for each cell line were identified and standard growth rates measured. A
number of basic culture and media conditions were also successfully verified in the 70%
GFR Matrigel on top 3D culture model.
The cell adhesion E-cadherin / β-catenin complex and the IGF-1R baseline expression
levels were also evaluated between monolayer and the Matrigel 3D model. Expression
levels and localisation of these proteins were found to be cell line dependant. 3D
structure morphology observed correlated with the expression of the cell adhesion
112
molecules E-cadherin and β-catenin. The cell lines that produced the tightly packed
spheroid structures (BxPC-3, Capan-1 and Su.86.86) maintained a highly expressed and
membrane localised E-cadherin profile, while the loosely packed cell lines (AsPC-1,
MIA PaCa-2 and PANC-1) expressed low levels of cytoplasm localised E-cadherin.
Initial cultures was established utilising an ECM system (GFR Matrigel) for induction
of 3D culture across the panel of pancreatic cancer cell lines (primary origins cell lines
BxPC-3, MIA PaCa-2, PANC-1 and the metastatic origin cell lines AsPC-1 and
Su.86.86) indicates an encouraging opportunity to develop drug discovery assays based
on this model. A number of HTS assay parameters will need to be addressed for this
model to be successfully incorporated into a validated HTS drug screening program
including compatible assay chemistries, culture timings, exposure times of compounds,
compatibility with end point measurement technologies and ability to automate
acquisition and analysis of captured data. The culture conditions determined in this
study will be used to develop an assay platform for quantitative drug response
assessment in the 3D tumour model.
113
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119
3 Chapter 3: Three Dimensional Cell Culture Assay Development for
High Throughput Screening (HTS)
3.1 Introduction
The drug discovery process for oncology therapeutics is extremely complex and
involves interactions of a range of disciplines including science, engineering, business,
law, as well as private and publicly funded institutions and numerous diverse
technologies. The process can be divided into two basic stages; preclinical and clinical.
Over the last decade the industry has seen increasing costs and timelines for both the
discovery-development stages of the process and the clinical stages. The widely
discussed issue of reduced returns on investment of pharmaceutical companies in drug
discovery R&D primarily involves the current poor attrition rates for oncology based
therapeutics (Berggren et al., 2012). As many of these institutions scale back funding
for drug discovery programs and competition for limited publically funded research
remains high, utilising the current resources efficiently has never been more important.
In particular, for poorly funded diseases, such as pancreatic cancer, the focus on
developing innovative strategies to improve the productivity for developing clinically
relevant therapeutics, with the limited resources available, should be a high priority.
The
discovery
phase
of
drug
discovery
programs
involves
identifying
pharmacologically active agents for further lead generation and optimisation as
illustrated in Figure 3.1. It is this early preclinical stage of the drug discovery pipeline
that provides an opportunity to develop better strategies in the discovery process.
Although there a number of reasons for the high failure rate of new chemical entities in
drug discovery programs (such as poor pharmacokinetics, toxicity and undesirable
metabolic properties), it is the lack of efficacy in the clinical setting for oncology based
therapeutics that remains a primary concern (Kola and Landis, 2004). Providing more
accurate and physiologically predictive information earlier on in the discovery pipeline,
may lead to a reduction in attrition rates and ultimately better clinical outcomes for
patients.
120
Figure 3.1. Basic drug discovery pipeline overview, grouped into preclinical and clinical stages.
Reproduced with permission from Berg et al, Consideration of the cellular microenvironment. 2014.
Elsevier.
The beginning of the drug discovery process has traditionally relied on two main
discovery approaches: Target and phenotypic driven drug discovery. Briefly, the target
driven approach relies on identifying a molecular target through which modulation will
produce a therapeutic benefit. Phenotypic driven drug discovery, however, is an
empirical system that interrogates a physiologically relevant biological system for a
measured phenotypic response (Berg et al.; Swinney, 2013). Depending on which
hypothesis driven approach is used either (or both) biochemical or cellular based assays
can be used for the target or hit identification phase. Classical drug discovery process
applies high throughput screening (HTS) techniques in either systems to scale up and
accelerate the number of small molecules that can be examined. High throughput
screening is defined by the number of compounds or agents that can be screened per day
and often involves extremely large (100 000’s to 1 000 000’s) compound libraries. An
assay can be defined as a set of reagents that produce a detectable signal that allows a
121
biological process to be quantified (Sittampalam GS, 2004). A HTS assay must follow
unique guidelines in which both the quality and throughput are rigorously evaluated.
Cell based assays have a number of advantages over biochemical approaches that
include; targets are in a physiologically relevant setting, hits may be more ‘drug-like’
and possess more desirable properties, entire pathways and downstream effects can be
evaluated, the ability of agents to penetrate cell membranes can be assessed and agents
with unknown targets can be identified. However, the suitability of cell-based assay
systems for HTS programs is often dependant on the biology being studied and how
accurately the model system recapitulates that target system biology. For oncology cellbased HTS, a monolayer of immortalised cancer cell lines grown on plastic substrates
have been the traditional model used for assays and the foundation for much of the anticancer therapy discovery process (Breslin and O'Driscoll, 2013). Although, as
correlation between in vitro and clinical studies remains low, and our understanding of
the impact of the tumour microenvironment becomes more established, developing a
more physiologically relevant model that better predicts in vivo tumour biology is
urgently required.
Historically, certain challenges have existed for developing more relevant models (and
assay systems). Often the more physiologically relevant the model, the more complex,
variable and low throughput the system. The data being produced from any assay is
only as good as the intrinsic quality of the assay itself. If a more complex cellular model
negatively effects the reproducibly and robustness of the assay, then it adds little benefit
to the drug discovery process. Developing a three dimensional (3D) cell culture model
based assay that incorporates much of the in vivo tumour architecture and
microenvironment, and still remains suitable (in both quality and scalability) in an
industrial HTS setting, is the focus of this chapter.
With the technological advancements in automation, robotics, computing and biological
reagents in the last decade, 3D in vitro tumour models are now moving from the
constraints of scalability (that limited their use to basic research) to true high throughput
applications. These more complex models allow for more physiologically relevant assay
systems to be developed and utilised in large scale HTS. Pancreatic cancer specific HTS
suitable models that provide more physiologically relevant conditions are still in their
122
infancy, with limited published examples of HTS suitable assays for drug discovery.
Three dimensional cell models are often used in either basic research, mechanistic
studies (Gutierrez-Barrera et al., 2007; Sempere et al., 2011) or much lower throughput
secondary assays which often require manipulation of 3D structures and other processes
not compatible for industrial HTS (Longati et al., 2013; Yeon et al., 2013).
The assay development process established in this chapter presents a unique HTS
amenable assay using 3D cellular models for pancreatic cancer drug discovery which
was initiated at the commencement of this project. At that time there was no
miniaturised 3D pancreatic cancer based assay validated for high throughput screening.
The assay development will focus on the three general elements of developing a 3D
cell-based assay for HTS; the cell culture model, assay chemistry and instrument or
technology components.
3.1.1
Assay development
The reliability of the biological responses measured for identified ‘hits’ or lead agents in
HTS campaigns is entirely dependent on the quality or robustness of the assay
employed. The development, validation and analysis of an assay using well established
performance measures are vital steps before large investments in screening campaigns
are made (Sittampalam, 1997). Utilising novel reagents, or biological models, not
previously used in HTS based assays requires considerable investment.
An emerging trend of cell based in vitro screening strategies is to incorporate more
complex in vivo-like models into assays systems that recapitulate the true biological
response. However, a number of challenges exist in developing these systems and
validating their use for HTS applications. Developing an assay which maintains the
biological complexity required for a more predictive system, but still satisfies the
stringent conditions placed on HTS compatible assays is the challenge of this assay
development. Assays employed in HTS based drug discovery must be thoroughly
validated for biological and pharmacological relevance as well as the robustness of
assay performance (Iversen, 2012). There are numerous factors that can influence the
quality of an assay. To reduce variability and increase the robustness aspects such as
optimising protocols (such as liquid handling parameters, cell seeding procedures and
123
assay incubation times), increasing automation, reducing experimental complexity
through fewer handling steps, as well as quality control over equipment, reagents and
biology are utilised.
Assay development typically involves validation through a number of defined steps
which will be discussed in the following sections (Sittampalam GS, 2004). These
include:

Selecting assay design and format.
The design of an assay and its particular format is based primarily upon the biology of
the target disease, the infrastructure and equipment available, the experience of the
personnel and the scale of the proposed screening program (Kisaalita, 2010).

Optimising assay conditions and protocols for sensitivity and reproducibility.
It is vital that whatever the chosen assay format, the assay remains sensitive and
reproducible. The optimisation phase of assay development involves assessing the
technical and biological limits of reagents, equipment and the compatibility with the
biological components. This may include factors such as determining the linear range of
fluorescent readouts (cell number, reagent limits, incubation and signal development
timings) or determining whether well position in a microtitre plate influences assay
endpoint signal (such as plate effects or incubator effects).

Examining effects of automation and scale up
Scaling up from a low throughput setting to higher throughput can often produce
unforeseen complications. Parameters such as cell stock availability and the stability of
assay signal over extended periods need to be evaluated.

Statistical validation of selected assay performance parameters.
Assay quality has typically been determined according to a number of statistical
parameters such as the industry standard Z’-factor. Together with coefficient of
variation (%CV) and signal window these parameters offer statistical cut off values
for the assay development to either proceed to the screening campaign or
continuation of the development of the assay until these criteria are met.
124
The ultimate goal is to demonstrate that the assay is suitable for the intended purpose,
which in this case is determining the biological activity of chemical entities on a
pancreatic cancer in an in vitro tumour model.
Oncology drug discovery assays typically involve cancer cell lines grown in a two
dimensional (2D) manner, with cell viability assessed after exposure to potential
inhibitors. As this system has been utilised for decades, the technologies and end points
in these well-established assays will be used as the basis for adaptation to the 3D model
based assays.
3.1.2
Assay design and format
The assay format selected for the 3D assay was based on existing compatibility with the
most commonly available lab equipment used currently for established 2D or monolayer
assays. A standard 384 well polystyrene microtitre plate configuration provides an
appropriate platform to produce the 3D model system. This format provides excellent
scalability and is available in a number of different plate compositions to suit different
end point technologies, such as whole well plate reader (with fluorescence or
luminescence measurements) and high content image based microscopy. This
configuration also provides compatibility with equipment utilised for automation,
including liquid handling robotics, plate stackers, plate readers and high content
imaging platforms within our lab. Clear bottom black sided microtitre plates were
chosen as these provide the flexibility for assay end point selection, allowing for either
fluorescence plate reader or microscopy based evaluation. The miniaturised and high
density 384 well microtitre plate format also reduced development and screening costs
as reagent and cell volumes are decreased. The cell culture model, cell types, media
conditions and other parameters related to the 3D model are previously discussed in
Chapter 2. The assay design was based upon the ability to measure the drug or
compound responses of in vitro pancreatic tumour-like culture. The assay technology,
drug exposure length and endpoint determination are discussed below.
3.1.3
Methods for measuring cell viability
125
Cell viability was chosen as the first end point for this 3D cell culture assay, as this
would provide information on the definitive biological activity of chemical entities
being screened against the pancreatic cancer cell lines. Cell viability, or cellular health,
can be monitored directly or indirectly by a number of different technologies. The cell
viability method required for the 3D cell culture assay must be HTS compatible, namely
that it is easy to use, sensitive and cost effective, whilst clearly illustrating the true
physiological effect of the compounds being tested. There are a variety of technologies
that are suitable to determine the number of viable cells following treatment. These
include those which enable either the measurement of the signal produced by a
population of cells (plate reader based) or that specific to the
individual cells
(microscopy or flow cytometry based) (Terry et al., 2009). Measuring a population of
cells (total well) has traditionally been used as this provides higher throughput and has
been demonstrated to be efficient both with respect to reagent cost and time.
Microscopy or high content based approaches however, have the potential to produce
greater details on the effects of treatments on individual cells or macro cell structures.
Both approaches were evaluated in the development of the 3D cell culture assay
described here.
3.1.4
Plate reader (cell population) based method
The utilisation of the resazurin reduction system for HTS has become increasingly
popular for cell viability or proliferation studies in drug screening programs (Niles et
al., 2008). Resazurin provides a sensitive and economical approach for measuring the
metabolic potential of viable cells in a population and has been shown to have
comparable performance to existing cell viability methods such as tetrazolium reduction
and ATP detection assays (Hamid et al., 2004). It can be added as a single reagent step
and is highly amenable to high throughput applications.
Resazurin is a cell permeable redox indicator dye that can be used to indirectly measure
cell viability through the metabolic activity of viable cells. The blue resazurin substrate
(with limited intrinsic fluorescence) is converted to the pink fluorescent form resorufin,
via reduction in metabolically active cells (O'Brien et al., 2000). The proportion of
fluorescent resofurin produced is proportional to the number of viable cells and can be
quantitated via a microplate reader equipped with 535 nm excitation and 595 nm
126
emission filters. The resazurin reduction system offers a number of advantages over
other established cell viability methods. There are however, a number of disadvantages
that must be considered when using this system (Niles et al., 2008). The incubation time
(1-4 hours) requires an additional plate handling step and the incubation period may
also introduce artefacts associated with interactions between the assay components and
compounds being screened. Also, compounds or drugs that have direct effects on
mitochondrial function, such as agents that uncouple the mitochondrial electron
transport system may interfere with the sensitivity of the system (Niles et al., 2008).
Particular care should be taken when interpreting results from chemotherapeutic drugs
which may enhance proliferation and increase mass of mitochondria with may result in
an overestimation of cell viability (Kluza et al., 2004; O'Brien et al., 2000). No assay
technology for detecting cytotoxicity in vitro is problem free. However, resazurin-based
viability assays remains a well-established and reliable assay methodology for HTS
applications.
3.1.5
High content (single cell or object) based method
Imaging or microscopy based measurement of compound activity on cell viability offers
a number of advantages over whole well or population based methods. Imaging based
analysis can offer insights into not only overall activity on the entire culture, but suppopulation affects and morphological or structural effects on the macro structures.
While there are numerous high content screening assays that have been developed for
anti-cancer drug discovery, there are fewer published examples of 3D-based systems
(Thoma et al., 2014). The inherent complexity of imaging three dimensional objects
makes quantification of 3D cell culture extremely challenging. Therefore, for this
project a number of different approaches and technologies were evaluated. Imagingbased approaches utilising the live cell stain, Calcein AM, and the nuclear dye, Hoechst,
were selected for measuring 3D structures and the individual cells within the structures.
Calcein AM is a cell permeant dye that has been extensively used to determine cell
viability in monolayer cell culture models (Lichtenfels et al., 1994). Once inside a
viable cell, the ester is cleaved by non-specific cell esterases and produces the
hydrolysed fluorescent form (Neri et al., 2001). The fluorescent signal can be visualised
via a high content imaging platform such as the Perkin Elmer Operetta or a standard
fluorescent microscope with a 490 nm excitation and 520 nm emission filter set.
127
Segmentation algorithms can be adapted for use with either reconstructed 3D rendered
images or maximal projection 2D images for quantification of cell viability.
Morphological changes to the 3D cell culture structures based on the fluorescent viable
cells can also be quantified.
Hoechst 33342 nuclear staining is a commonly used technique to directly count cells
and determine cell proliferation or viability in imaging assays. Hoechst binds directly to
DNA and can be used to determine a number of assay outputs including cell counts,
nuclear fragmentation and cell cycle analysis. For single object or cell quantification in
3D structures, only reconstructed 3D image sets can be used for analysis. Issues with
acquisition time, data storage and voxel based image analysis may render this approach
unsuitable for higher throughput applications such as large scale HTS.
3.1.6
Statistical validation of assay
The final step in the primary assay development phase is to demonstrate the robustness
of the final assay conditions using a number of well-established statistical parameters.
The standard assay performance measures for biological assays include coefficient of
variation (%CV), signal window (SW) and Z’-factor (Iversen, 2012; Zhang et al., 1999).
These statistical measures aim to provide set cut-off values to which the quality of an
assay can be determined. These measures take into account the maximum and minimum
signals across multiple measurements and the variability between these measurements.
Signal to noise (S/N) and signal to background (S/B) ratios are two summary statistics
that are also used to evaluate the separation maximum assay signal and the background
signal. Both S/N ratio (average maximum signal – average background signal)/SD of
background signal and S/B ratio (average maximum signal / average background signal)
provide basic information on the quality of an assay. However, these ratios fail to take
into account the variability of the maximum and background signal controls and the
dynamic range of the assay in the same calculation (Sui and Wu, 2007). A general
accepted criteria for a biological cell based HTS assay as defined in the literature for
HTS assay development is a %CV less than 20%, SW ≥2 and Z’-factor of ≥ 0.4
(formulations for these calculations provided in the methods section 3.2.7) (Iversen,
2012).
128
Although these assay performance measures provide guidelines for acceptable assay
variability for the initial optimisation phase, continued evaluation must be performed as
the assay moves into the final stages before deployment in a HTS campaign. This final
stage is covered in the proceeded chapter and involves validation of the assay
reproducibility by concentration response assays using known compounds (Iversen et
al., 2006).
3.2 Materials & Methods
3.2.1
Reagents & materials
A stock solution of resazurin sodium salt (Sigma Aldrich) (60mM) was prepared in
phosphate buffered saline (PBS) and frozen in 5ml aliquots at -20°C for up to 6 months.
Stocks were thawed before use and diluted 1 in 10 in complete media (detailed in
section 2.2.2) (6mM) at 37°C. A further 1 in 10 dilution was then made in the assay
plates by dispensing 5µl into 45µl of existing media in each well to a final concentration
of 600µM. Calcein AM (Invitrogen) was prepared fresh from frozen aliquots (-20°C) of
powder before each experiment. Twenty five microliters of DMSO was used to dissolve
the Calcein to produce a stock concentration of 2mM. A further dilution in PBS was
made to a final assay concentration of 2µM. Gemcitabine (Sigma-Aldrich) serial
dilutions were prepared from fresh powdered stock at 10mM in DMSO and diluted in
sterile water to a final DMSO concentration of 4%).
Hoechst nuclear stain was
reconstituted in DMSO at 10mM and stored in frozen aliquots (-20°C). 384 well black
side-clear bottom tissue culture treated plastic (TC) bottom optical imaging microplates
(Perkin Elmer Waltham, MA) were used for all cell based assays described throughout.
3.2.2
General cell culture conditions
All cell lines where maintained in complete media (RPMI media supplemented with
10mM HEPES and 10% Heat inactivated foetal bovine serum (FBS)) and incubated in a
humidified incubator at 37°C with 5% CO2. Refer to Chapter 2 materials and methods
(section 2.2.2) for detailed cell culture conditions.
129
3.2.3
Linearity studies with resazurin, Calcein AM and Hoechst dyes
All experiments were performed by dissociating cells from tissue culture flasks
(≤passage 20) with accutase (Life Technologies), then adding a trypan blue single cell
suspension mixture to a haemocytometer and counting cells under an inverted light
microscope. Cells suspension were then diluted to the appropriate number (cell seeding
range indicated below) in complete media (detailed section 2.2.2) before addition to 384
well microtitre plates with a multichannel Finn pipette. Cells were seeded at 20 000, 10
000, 5000, 2500, 1250, 625, 312, 156, 78, 39 in triplicate wells in 45µl of complete
media (see section 2.2.2 for details). Cell were allowed to adhere for 24 hours before
addition of either 5 µl of resazurin (6mM), Calcein AM (2µM) or Hoechst (10µM).
After a 4 hour incubation period in a humidified incubator (37°C, 5% CO2), microtitre
plates were measured on an Envision plate reader (530nm ex / 585nm em filter set) for
the resazurin based assay format, and the Opera high content imaging platform for
imaging the Calcein AM (490/520nm) and Hoechst (405/450nm) stained cells. A
standard cell counting protocol described in Table 2 was used for analysis of the
Calcein AM images.
The raw data was then graphed using the Graphpad Prism
software using the linear regression equation. Experiments were performed in duplicate
wells, n= 2.
130
Table 3.1. Acapella script for live cell count using Calcein AM stain on an Opera imaging platform.
The find cells building block was used (adjusted to detect calcein stained cells) with the number of
objects output recorded. Image analysis based on 9 field analysis with the 10x objective on the Opera
confocal high content screening system.
Cell counting Acapella script for Calcein AM staining
FindCells
ThresholdAdjustment:
0.15
MinimumNuclearArea:
100
CellsDetectionChannel_string:
Exp1Cam1
CellsDetectionAlgorithm:
B
NuclearSplittingAdjustment:
7
ThresholdAdjustment:
0.15
IndividualThresholdAdjustment: 0.25
MinimumNuclearArea:
100
MinimumNuclearContrast:
0.05
NuclearSplittingAdjustment:
7
OutputName:
Cells
ReturnResults
IndividualThresholdAdjustment: 0.25
List of
Method1:
Outputs
MinimumNuclearContrast:
0.05
@Cells : Number of
Objects1_stat1:
1
OutputName:
Cell count
3.2.4
Comparison of cell viability and staining methods in monolayer culture
with gemcitabine
Experiments were performed by dissociating cells cultured as described in section 2.2.3
from culture flasks with accutase (1ml per 75cm2 flask) then adding a trypan blue single
cell suspension mixture to a haemocytometer and counting cells under an inverted light
microscope. Cells suspensions were then diluted to the appropriate number (AsPC-1,
BxPC-3, MIA PaCa-2, PANC-1 and Su.86.86 300 cells per well and Capan-1 1500 cells
per well) in complete media before addition to 384 well microtitre plates with a
multichannel Finn pipette. Gemcitabine (stock powdered gemcitabine was dissolved in
DMSO at 10mM) serial dilutions were prepared fresh in 100% DMSO master plates
and diluted in intermediate plates in sterile water (4% DMSO). Cells were allowed to
131
adhere to the plates for 24 hours before the addition of 5µl of a serial dilution of
gemcitabine was dispensed into the wells (final DMSO concentration 0.4%) or 5µl of
controls. Controls included triplicate wells of 0.4% DMSO negative (100% growth)
controls and 10% DMSO positive controls (100% death). Media and drug were
manually refreshed at 72 and 120 hours with a multichannel Finn pipette. At the 168
hour time point either resazurin, Calcein AM or Hoechst was dispensed into wells and
incubated for 4 hours. Concentration response curves were generated and normalised
against control well measurements. Dose response curves and IC50 values were
calculated using Graphpad Prism software using the variable slope sigmoidal dose
response equation. n=3
3.2.5
Three dimensional (3D) Culture reproducibility in 384 well microtitre
plate format
To examine the reproducibility of 3D structures that develop in culture over time, size
and number of structures or spheroids was analysed over a period of 12 days. Fifteen
microliters of cold growth factor reduced Matrigel (GFR Matrigel) was diluted in RPMI
media to a final protein concentration of 7.5mg/ml (to reduce variability between
commercial batches all Matrigel was diluted to this protein concentration based on
specifications from the manufacturer) and dispensed directly onto the bottom of 384
well Cell Carrier microtitre imaging plates. Matrigel layered plates were incubated
(37°C) for 30-60 minutes to allow for the extracellular matrix to solidify prior to
addition of the cells. Cells were dissociated from culture flasks with accutase and cell
number determined by the trypan blue exclusion method as described previously. All
cell lines to be tested were resuspended to a final concentration of 2000 cells per well in
45µl of complete media and dispensed on top of the Matrigel layer with a multichannel
Finn pipette, with special attention taken not to disturb the thin layer of extracellular
matrix. RPMI media was refreshed every 72 hours using a custom protocol on the
Agilent Bravo™ liquid handler designed to reduce cell disturbance. Dispensing and
aspirating speeds were reduced (and tip height lowered to 3mm from plate base) and
only 50% of media was removed in a two-step process. On days 1, 3, 6, 9 and 12
cultures were imaged using either the Operetta or IN Cell 1000 high content imaging
systems. Image analysis was performed on the brightfield images by either the ImageJ
or IN Cell scripts described in section 2.2.7 of Chapter 2.
132
Both methods used
segmentation based scripting to detect the 3D structures from the background ECM and
record a number of parameters including diameter, area and object number. These
recorded values were graphed using the Graphpad Prism software. n=3
3.2.6
3D cell culture sensitivity and linearity determinations with resazurin as
cell viability and metabolic activity indicator
Fifteen microliters of cold GFR Matrigel was diluted in RPMI media to a final protein
concentration of 7.6mg/ml and dispensed onto the bottom of 384 well cell carrier
microtitre plates. Matrigel layered plates were incubated for 30-60 minutes to allow for
the extracellular matrix to solidify. Cells were dissociating with accutase and cell
number counted by adding a trypan blue single cell suspension mixture to a
haemocytometer and counting cells under an inverted light microscope. Cells
suspension were then diluted to the appropriate number in complete media before
addition to microtitre plates. A serial dilution of cells (20 000, 10 000, 5000, 2500,
1250, 625 cells per well) were seeded in microtitre plates. Cells were suspended in 45µl
of complete RPMI media and plated in triplicate wells of cell carrier microtitre plates.
Microplates were incubated for 12 days (37°C and 5% CO2) with media changes
performed every 72 hours with the Agilent Bravo™ liquid handling platform. Five
microliters of resazurin (600µM final concentration) was dispensed into wells at days 1,
3, 5, 7, 9 and 12. After 2, 4, 6 and 8 hour incubation period the fluorescent values were
measured by a PerkinElmer Envision™ plate reader (using the 530nm ex / 595nm em
filter set). Raw fluorescent values were analysed with Graphpad Prism software using a
linear regression equation. n=3
3.2.7
Intraplate variability and assay performance determinations
To examine the reproducibility of the culture conditions in the final assay format with
automated liquid handling steps, half of a 384 well microtitre plate was used for each
cell line to calculate statistical performance measures. Fifteen microliters of cold GFR
Matrigel was diluted in RPMI media to a final protein concentration of 7.6mg/ml and
dispensed onto the bottom of 384 well Cell Carrier microtitre plates. Matrigel layered
plates were incubated for 30-60 minutes (37°C) to allow for the extracellular matrix to
solidify. Cells were dissociated from culture flasks with accutase and cell number
determined by the trypan blue exclusion method as described previously (section 2.2.3).
133
All cell lines were resuspended to a final concentration of 2000 cells per well in 45µl of
complete media and dispensed on top of the Matrigel layer with the Bravo™ liquid
handler. RPMI media was refreshed at 72 hours after seeding and then every 48 hours
using a custom protocol (as described in section 3.2.5) on the Bravo™ liquid handler
designed to reduce cell disturbance. Positive and negative controls (112 wells of 10%
DMSO and 112 wells of 0.4% DMSO respectively) were dispensed on days 3, 5, 7 (to
replicate drug dosing schedule) for AsPC-1 and PANC-1 Cell lines and days 6, 8, 10 for
BxPC-3 cells. On day 9 (for AsPC-1 and PANC-1) and day 12 (BxPC-3) 5µl of 600µM
resazurin was dispensed by the Bravo and plates were incubated for 2 hours.
Fluorescent signal was measured on the Envision plate reader and statistical
performance measures were calculated in Microsoft Excel using the equations listed in
section 3.2.7 below.
3.2.8
Assay performance measure equations and statistical analysis
AVGmax equals the mean of the maximum control signal (100% growth) and SDmax
equals the standard deviation of the maximum control signal. AVGmin equals the mean
of the minimum control signal (100% death) and SDmin equals the standard deviation of
the minimum control signal.
𝑆𝐷𝑚𝑎𝑥
The coefficient of variation: %𝐶𝑉 = ( 𝐴𝑉𝐺𝑚𝑎𝑥 ) × 100
Signal window:
𝑆𝑊 =
Z’-Factor:
𝑍′ =
𝐴𝑉𝐺𝑚𝑎𝑥 −𝐴𝑉𝐺𝑚𝑖𝑛
− 3(𝑆𝐷𝑚𝑎𝑥 +𝑆𝐷𝑚𝑖𝑛 )
𝑆𝐷𝑚𝑎𝑥
𝐴𝑉𝐺𝑚𝑎𝑥 −𝐴𝑉𝐺𝑚𝑖𝑛 −3(𝑆𝐷𝑚𝑎𝑥 +𝑆𝐷𝑚𝑖𝑛 )
𝐴𝑉𝐺𝑚𝑎𝑥 −𝐴𝑉𝐺𝑚𝑖𝑛
3.3 Results
3.3.1
Cell viability methods for pancreatic cancer cell lines (2D)
134
To evaluate the sensitivity and suitability of the cell viability methods (resazurin,
Calcein AM, Hoechst) linearity studies were performed using the pancreatic cancer cell
lines described in detail in Chapter 2 (section 2.1.3) cultured in a traditional 2D
monolayer setting. These protocols were then adapted to develop the 3D-based assay
cell viability studies. Resazurin sensitivity was evaluated by determining fluorescent
values per number of cells, and the linearity confirmed on all cell lines grown in a
monolayer condition up to specific cell numbers (cell line dependant values detailed in
Figure 3.2). A number of incubation timings and cell densities were also examined for
the most sensitive fluorescent development that could be detected within the linear
range of the PerkinElmer Envision plate reader. The final protocol of a 600µM
concentration of resazurin and 4 hour incubation time was chosen based on
concentration and incubation timing studies (data not shown) and previously reported
values in the literature (O'Brien et al., 2000).
All cell lines displayed a linear
fluorescent signal from 150 cells to 5000 cells as measured by the coefficient of
determination (R2 value) of the simple linear regression (Figure 3.2a). AsPC-1, BxPC-3,
Capan-1 and PANC-1 produced r2 values greater than 0.9 up to 20 000 cells per well
seeded. A previous studies had indicated the BxPC-3 cell line was incompatible with
the CellTiter-blue® assay and unable to metabolise resazurin (Fryer et al., 2011).
However, we did not observe this finding with BxPC-3 cells producing a similar linear
signal to the other cell lines examined. While MIA PaCa-2 cells resazurin readout
remained linear (high R2 value) up to 10 000 cell per well and Su.86.86 up to 5000
cells/well. Fluorescent values over this number (2 000 000FU) exceeded the detection
limit of the fluorescent detector and where therefore outside the linear range of the plate
reader.
135
Figure 3.2. Linearity studies of pancreatic cancer cell lines in monolayer culture with resazurin,
Calcein AM and Hoechst stains. (a) Resazurin linearity of all cell lines in monolayer culture with
corresponding R2 values (with high R2 value indicating a linear relationship between cell number and
fluorescent signal). All cell lines tested resulted in R2 values greater than 0.9 at cell densities ≤ 5000
cells/well. (b) Calcein AM linearity of all cell lines with corresponding R 2 values. All cell lines tested
resulted in R2 values greater than 0.9 at cell densities ≤ 5000 cells/well. Values are taken from 9 field per
well (10x objective) on the Opera high content imaging system. (c) Linearity of all cell lines with
corresponding R2 values using the nuclear Hoechst stain. All cell lines tested resulted in R 2 values
greater than 0.9 at cell densities ≤ 5000 cells/well. Values are taken from a 9 field per well (10x objective)
on the Opera high content imaging system. Image analysis was performed using the Acapella based
Opera analysis software described in section 3.1.2. n=3.
136
Cell doubling times were also compared using the resazurin cell viability method verses
manual cell counts with a haemocytometer and trypan blue. The doubling times
achieved though manual cell counts correlated with the fluorescent values achieved with
resazurin (Table 3.2). The resazurin method was therefore used for comparing cellular
doubling times in the 3D cell culture models.
Table 3.2. Comparison of the cell viability dye resazurin and manual counts for calculating cell line
doubling times grown in monolayer culture.
Doubling Time (hours)
Manual
Fluorescent
count
value
AsPC-1
41
42
BxPC-3
32
35
PANC-1
43
39
To determine the suitability of the Calcein AM live stain with the pancreatic cancer cell
lines chosen for this study, Calcein AM staining was evaluated first in a monolayer
culture setup. Calcein AM staining provided a linear relationship between cell number
seeded and objects counted with the scripting protocols using both the Opera and
Operetta high content imaging platforms and the associated analysis programs
Acapella™ and Harmony™ respectively. All cell lines achieved R2 values greater than
0.9 with cell numbers between 150 and 5000 cells, except for the Su.86.86 cell line
which is considered linear until 5000 cells only (Figure 3.2b).
The final method investigated for measuring cell viability and / or proliferation based
analysis utilised the nuclear stain, Hoechst. Nuclear object counting was examined in
the monolayer 2D cell culture conditions across the six pancreatic cell lines (AsPC-1,
BxPC-3, Capan-1, MIA PaCa-2, PANC-1 and Su.86.86). All cell lines achieved R2
values greater than 0.9 with cell numbers between 150 and 10 000 cells (Figure 3.2c). A
standard cell segmentation and cell counting analysis was performed using the Opera
confocal imaging platform and the Acapella software (protocol detailed in materials and
methods section 3.2.3).
137
3.3.2
Sensitivity of cell viability methods with the reference compound
gemcitabine
A comparison of the three cell viability methods described above was undertaken using
gemcitabine as a known reference compound with activity against the pancreatic cancer
cell lines selected for this study (Figure 3.3). All of the cell lines tested demonstrated
gemcitabine activity profiles with no significant differences between the three different
measurements of drug activity. All of the methods provided sensitive drug activity
information when using the reference chemotherapeutic drug. The IC50 values across all
cell lines ranged between 2nM and 8.3nM. It was difficult to correlate IC50 values to
those described in the literature as a wide range of activities have been reported for
these cell lines with inconstant values ranging from 5nM to 300µM, depending on the
assay used and drug exposure time (Fryer et al., 2011; Gervasoni et al., 2004;
Kawanami et al., 2012). Although no correlation with published data was possible, the
results in this assay format gave consistent low nanomolar IC50 values across all three
drug activity measurement technologies.
138
Figure 3.3 Comparison of effects of gemcitabine using three cell viability methods in monolayer
culture. (a) Dose response curves from the evaluated cell viability methods (Calcein AM live stain,
Hoechst nuclear stain and resazurin metabolic activity indicator) in a 2D monolayer culture. Dose
response curves represent a single experiment in duplicate wells. (b) Representative Calcein AM
monolayer staining of MIA PaCa-2 cell line exposed to a range of gemcitabine (1nM – 1000nM). (c)
Representative Hoechst monolayer staining of MIA PaCa-2 cell line exposed to a range of gemcitabine
(1nM – 1000nM). Images acquired on an Opera confocal imaging system with a 10x air objective and are
representative of a random field from a single well of a 384 well microtitre plate.
139
3.3.3
Three dimensional
reproducibility
cell
culture
growth
characteristics
and
To determine 3D structure growth reproducibility, image analysis of the 3D cultures
was performed. A number of imaging platforms enabled with brightfield acquisition
were utilised for the initial assessment, including the PerkinElmer Operetta, GE IN Cell
1000 and GE IN Cell 2000 high content imaging platforms. Brightfield imaging was
used to capture the entire well of the 384 well plates, and object segmentation was used
to quantify growth characteristics as described in Chapter 2 section 2.3.5. Image
analysis was performed either with the open source ImageJ software or the commercial
IN Cell Developer software. Both analysis methods could easily segment 3D structures
from background extracellular matrix (ECM) under standard growth conditions as
demonstrated in Figure 3.4. However, with addition of toxic agents such as 10µM
puromycin or 10% DMSO concentration, the destruction of cell structures and
production of cell debris made image analysis challenging and unreliable (100% cell
death well in Figure 3.4). The uneven surface of the layered Matrigel (caused by a
meniscus forming from the attraction of the ECM to the microplate wall) as well as lack
of contrast between cells and background also meant bright field imaging had numerous
disadvantages for reliable image analysis. Therefore, bright field imaging would be
unlikely to be compatible for use in a drug screening assay where automated analysis
would be required across entire microtitre plates. It does however, provide low
throughput analysis of the growth characteristics required for the initial assay
development. A number of parameters were examined to determine culture
reproducibility and determine overall assay quality. These included; consistency of 3D
structure size per well, number of structures per well for each assay end point and
sensitivity of cell viability methods in a 384 well microtitre plate format.
140
Figure 3.4. Representative images of 3D cell culture of the PANC-1 cell line and automated object
segmentation used for size and growth analysis determinations. Stitched image of a whole well of
384 well microtitre plate captured on the IN Cell 2000 with 4X objective (overlayed with segmentation
outlines from IN Cell Developer software). PANC-1 cells shown in 3D culture over a period of 12 days
with a 100% cell death control well (10% DMSO) also shown to highlight difficulty in automatic
thresholding of disrupted 3D structures. Scale bar = 100 µm.
141
The pancreatic cancer cell line growth characteristics for all five cell lines selected for
this study were examined over a 12 day period, in three separate experiments, to
establish whether growth parameters were predictable and reproducible. All of the cell
lines evaluated developed 3D structures that increased in diameter and area over time.
The Su.86.86 cell line developed much larger structures than the other cell lines
examined. With fewer objects towards the final day of measurement, large variations
were observed in average size measurements. The remaining four cells lines BcPc-3,
AsPC-1, PANC-1 and MIA PaCa-2 exhibited reproducible growth curves as they
steadily increased 3D structure size over time.
Figure 3.5. Growth characteristics of pancreatic cancer cell line 3D structures over 12 days. (a)
Mean diameter measured via IN Cell developer diameter measurement and ImageJ Ferets diameter
measurements. (b) Mean area measurements recorded semi-automatically via ImageJ batch processing or
automatically via IN Cell developer software. Error bars represent the standard error of the mean of
duplicate wells in three separate experiments. n=3
142
As a further measure of culture reproducibility, the number of 3D structures was
measured at days 3, 6, 9, 12 after single cell suspension seeding. At day 9 a similar
number of 3D structures were detected for each cell line between three separate
experiments as indicated by the error bars in Figure 3.6.
Each cell line, with the
exception of Su.86.86, consistently produced greater than 25 structures at this 9 day
time point. Greater than 25 objects was considered useful for statistical analysis and for
individual activity effects on the different population sizes. The overall number of
spheroid structures formed by the pancreatic cancer cell lines was found to be consistent
and reproducible and therefore the 3D structure number may be used in the assessment
of drug activity on 3D cultures.
O b je c t C o u n t
60
40
20
S u .8 6 .8 6
P A N C -1
M IA P a C a -2
B x P C -3
A s P C -1
0
C e ll lin e s
Figure 3.6. Number of 3D structures per well for pancreatic cancer cell lines 9 days after seeding
single cell suspensions. Values represent the mean average of manual counts of 3D structures in
duplicate wells of three separate experiments. Error bars equal standard deviation of the mean.
3.3.4
Cell line selection
Due to the relatively high costs of 3D cell culture, only 3 cell lines were chosen for this
project to progress through to the stage of developing a 3D HTS assay. The criteria for
choosing the 3 cell lines was based on reproducible size and number of f 3D structures
formed over time, suitability with cell viability technologies as well as the diversity of
the phenotype and genetic profiles. Capan-1 cells failed to adhere strongly to the
143
Matrigel® and their slow proliferation times (≥150 hours) required extended assay
incubation periods for 3D structure development. For sufficiently robust signal to be
achieved with this cell line, culture times would have to be significantly increased and
image analysis methods altered. Therefore, this cell line was excluded from further
assay development for 3D at this time. The Su.86.86 cell line formed a small number of
extremely large 3D structures (up to 500µm) in the extracellular matrix induced culture.
The large size of these cellular structures made image analysis difficult (even with
lower powered objectives such as the 10x). With such large objects, the number of
fields and well replicates would have to be increased to maintain robust assay
conditions and consequently this cell line was excluded from further development. The
remaining four cell lines all exhibited similar 3D structure size and reproducible 3D
object development over time. All four cell lines maintained greater than 25 objects by
day 12 of the assay with the object sizes compatible with the imaging systems available
(10x and 20x). The three cell lines ultimately selected for further 3D assay development
based on appropriate 3D culture criteria (reproducible 3D structure formation) were
AsPC-1, BxPC-3 and PANC-1. The three cell lines selected represent a range of
pancreatic phenotypes and genotypes. Each cell line also has a unique 3D morphology
when cultured in ECM-inducing 3D growth conditions.
AsPC-1 was the only cell line of metastatic origin (K-ras and p53 mutations) with a 3D
morphology encompassing a loosely packed mass of cells adhering to the ECM. BxPC3 was of primary tumour origin and is wild type for K-ras mutations. The cells formed
round tightly packed spheroid structures that appeared to adhere strongly to the ECM
layer. The PANC-1 cell line was of primary tumour origin and carries a heterozygous
K-ras mutation. These cells formed round intermediately packed 3D structures that
showed some branching and stellate like projections. (Figure 3.7)
144
Figure 3.7. Pancreatic cell lines selected for 3D culture assay development (highlighted in red). 3D
structures after 12 days of growth show distinct cell line specific morphology when cultured on top of
Matrigel. All cell lines seeded at 2000 cell per well. Brightfield images from IN Cell 2000 with a 10x
objective. Scale = 100µm.
145
3.3.5
Cell viability methods for pancreatic cancer cell lines in three
dimensional (3D) cell culture
To evaluate the sensitivity and suitability of the resazurin reduction method (using the
Matrigel-based 3D cell culture model detailed in Chapter 2), 3D linearity studies were
performed on the three pancreatic cancer cell lines chosen. A number of incubation
times and cell numbers were examined over a 12 day period. Microtitre plates
containing various cell numbers were incubated with resazurin and fluorescent values
measured after selected incubation periods (Day 9 represented in Figure 3.8). To
maintain a linear signal, cell lines must be seeded equal to or less than 2500 cells per
well and incubation periods no longer than 2 hours as illustrated in Figure 3.8a (higher
non-linear cell densities were removed from graph). Due to the extended assay length
(9 -12 days) and corresponding large number of proliferating cells per well, incubation
periods greater than 2 hours reached the limit of the fluorescent detector on the Envision
plate reader and would reduce assay sensitivity if utilised in a screening format (Figure
3.8b,c,d). Although longer incubation periods produced higher signal windows (and
therefore better Z’-factor scores) the loss in sensitivity would be unsuitable for drug or
compound screening applications.
146
Figure 3.8. Resazurin optimisation with cell number and incubation time for pancreatic cancer cell
lines AsPC-1, BxPC-3 and PANC-1 in 3D culture. (a)The relationship between cell number in 3D
culture and fluorescence value produced from the resazurin assay. A linear relationship between existed
for cell seeding densities up to 2500 cells per well for all cell lines (AsPC-1, BxPC-3and PANC-1) with
the assay length of 9 days. (b), (c), (d) Cell seeding densities greater than 2500 cell per well or incubation
times longer than 2 hours a loss in linearity was observed. Triplicate wells were used in three separate
experiments. n=3
3.3.6
Distribution of sizes of 3D structure over time
The distribution and frequency of different sized 3D structures was examined across a
12 day period, using bright field analysis of whole wells where 4 images were taken
using a 10x objective and stitched together in an automated fashion. All cell lines were
seeded at 2000 cells per well following the 3D culture protocol outlined in the methods
section 3.2.5. Each cell line produced a unique distribution pattern of different sized 3D
structures over time. Although all lines exhibited an almost normal distribution of 3D
structure sizes, with an increasing truncation of the smaller sized objects as the
structures aggregated with smaller cell populations over time, to form larger sized
structures (Figure 3.9, 3.12 and 3.13). One day after initial seeding of the single cell
suspension, a large number of small cell aggregates of between 10 and 50µm were
produced across all cell lines. At day 9, PANC-1 and AsPC-1 produced 3D structures
with mean diameters of approximately 100µm, while BxPC-3 did not reach this average
147
until day 12. The largest 3D structures of the population of the cell lines were
approximately 350µm for the PANC-1 cell line. In comparison, AsPC-1 and BxPC-3
cell lines produced maximum sized structures of approximately 250µm (within the time
frame evaluated). The reproducible size distribution, as well as consistent structure
number over time (Figure 3.6) observed for all three cell lines, indicates with cell
suspensions seeded at a constant rate, predictable 3D structure size frequency and
distribution can be expected in the assay.
148
Figure 3.9. Distribution of 3D structures in a single well of a 384 well microtitre plate for the AsPC1 cell line. (a) Size analysis performed on stitched brightfield images of whole wells (acquired with an IN
Cell 2000 and 10x objective) from days 1, 3, 6, 9 and 12 with IN Cell Developer or ImageJ analysis
protocols. (b) Representative images of wells from a 384 well microtitre plate from the IN Cell 2000
using a 10x objective. Scalebar = 100µm.
149
Figure 3.10. Distribution of 3D structures in a single well of a 384 well microtitre plate for the
BxPC-3 cell line. (a) Size analysis performed on stitched brightfield images of whole wells (acquired
with an IN Cell 2000 and 10x objective) from days 1, 3, 6, 9 and 12 with IN Cell Developer or ImageJ
analysis protocols. (b) Representative images of wells from a 384 well microtitre plate from the IN Cell
2000 using a 10x objective. Scalebar = 100µm.
150
Figure 3.11. Distribution of 3D structures in a single well of a 384 well microtitre plate for the
PANC-1 cell line. (a) Size analysis performed on stitched brightfield images of whole wells (acquired
with an IN Cell 2000 and 10x objective) from days 1, 3, 6, 9 and 12 with IN Cell Developer or ImageJ
analysis protocols. (b) Representative images of wells from a 384 well microtitre plate from the IN Cell
2000 using a 10x objective. Scalebar = 100µm.
151
3.3.7
Compound / drug screening assay protocol for three dimensional and
two dimensional culture
Cell lines and corresponding cell number limits were determined and a final protocol
was established for both a monolayer and the 3D culture settings for evaluation of
compound activity. Experimental timings and scheduling of cell seeding, media changes
and drug / compound dosing are detailed in Figure 3.12 below. Evaluation of the
temporal distribution of 3D culture growth distribution revealed that by day 9, AsPC-1
and PANC-1 cells achieved averages structure / spheroid sizes of approximately
100µm, while BxPC-3 cells, which proliferated at a slower rate, required an extended
growth until day 12 to achieve this same average structure size. To maintain consistent
size at the end point of the assay, the BxPC-3 cell line was therefore seeded 6 days prior
to any drug or compound exposure. Whilst the PANC-1 and AsPC-1 cell lines were
seeded 3 days prior to drug or compound exposure. This cell seeding timing allowed 72
hours (or 144 hours for BxPC-3 cells) for 3D structures to form before drug treatments
began, irrespective of the growth properties of the individual cell lines used.
A six day or 144 hour drug exposure period was chosen as the assay length. The
decision for the selected period was based on a number of parameters. After longer
periods in culture, 3D structures in wells began to exhibit signs of aggregating together
to form large masses, at which point it became difficult to segment individual objects
using the image analysis software. The 3D structures also begin to shed the outer cell
layers and a loss of structural integrity was observed. Due to concerns about drug or
compound activity and penetration through the large cellular structures, shorter
exposure times were also not selected. Finally, at shorter exposure periods (such as 48h)
the signal window between the maximum and minimum signals was also significantly
reduced (data not shown) impacting on the quality of the assay.
Maintaining as many of the 2D assay parameters as possible for the 3D assay would
allow for relevant activity comparisons between the two assays. Cell numbers were
optimised to maintain the same exposure timings and media change schedule. Three
hundred cells per well provided an 80-90% confluence of the well at the 6 day end point
for all cell lines studied and therefore was selected as the seeding density for the 2D
152
assay. Media volumes and addition-dispensing volumes were matched with the 3D
assay (Figure 3.12a, b).
To determine the robustness of these assay conditions in a 384 well microplate format,
the protocol was next examined with a number of standard HTS assay performance
measures, including Z’-factor, signal window and %CV.
Figure 3.12. Timetable for performing the monolayer and 3D assays. (a) Cell seeding prior to 3D
structure formation at day 0 followed by administration of drugs / compounds and media renewal at days
2 and 4. Evaluation of cellular drug response at assay end point following 144 hours of exposure (cell
viability and metabolic activity). (b) Timetable for performing the developed 2D assay. Cell seeding at
day -1 followed by administration of drugs / compounds and media renewal at days 0, 2 and 4.
Evaluation of cellular drug response at assay end point following 144 hours of exposure (cell viability and
metabolic activity).
153
3.3.8
Microtitre plate reproducibility and intraplate variability
The quality of an assay has traditionally been determined by statistical validation using
the coefficient of variation (%CV), signal window (SW) and Z’-factor measurements.
Results from the 2D and 3D culture based assays are summarised in Table 4. By
utilising the established assay conditions, including DMSO concentration, media
renewal timings and incubation periods, plate uniformity was assessed and signal
variability calculated. For the %CV and Z’-factor scores, the max signal (100% growth
control) was established with the addition of 0.4% DMSO in water only and the
minimum signal (100% death control) with 10% DMSO. These concentrations would
also be used as in plate controls for future screening activities. All liquid handling steps,
including dispensing of control agents and media changes, were performed using the
Agilent Bravo liquid handling platform. Automation of these events was required for a
HTS based assay and also to reduce variability of manual handling steps. To limit the
plate edge effects observed after long incubation periods, the perimeter wells of all 384
well plates contained media only with no cells present.
Table 3.3. Standard assay performance measures for the 3D and 2D assays. %CV = coefficient of
variation, SW = Signal Window and Z’-Factor.
%CV
SW
Z'-Factor
3D Assay
AsPC-1
BxPC-3
PANC-1
10.38
13.51
8.05
4.18
3.44
7.71
0.52
0.47
0.70
2D Assay
AsPC-1
BxPC-3
PANC-1
6.89
11.72
6.39
9.5
9.7
10.3
0.76
0.60
0.83
The monolayer and 3D assays passed the literature acceptance criteria generally
recognized for HTS based assays with Z’-Factor ≥ 0.4 a signal window ≥ 2 and %CV ≤
20 (Iversen, 2012; Iversen et al., 2006). The BxPC-3 cell line in 3D culture had a
borderline acceptable Z’-Factor (if the 0.5 cut-off criteria is utilised) due to increased
variation in maximum signal between wells. The S/B ratio remained similar between
monolayer and 3D conditions (approximately 8 to 10 fold). However, the increased
inter-well variability of the maximum signal reduced the signal window considerably.
154
To achieve a %CV of less than 10% for the BxPC-3 cell line, duplicate wells would be
required per data point.
3.4 Discussions & conclusions
The focus of this chapter details the development process for establishing a novel assay
for use in evaluating drug or compound activity encompassing a more physiologically
relevant 3D pancreatic cell culture model. The reliability of the drug discovery process
is founded upon the quality of the cell based assays employed to determine the
biological activity of unknown agents. Rigorous optimisation and validation using
established assay performance measures, particularly in the case of assays developed
with cell culture models not previously established, is an important step in any drug
discovery process. Here, we have established a 3D culture model utilising a range of
pancreatic cancer cell lines (AsPC-1, BxPC-3 and PANC-1) and validated the suitability
of these assays for use as a HTS assay.
There are limited examples of established and well defined pancreatic cancer in vitro
3D cell culture based HTS assays in the literature, with most studies having a low
throughput capacity (no automation or multiple plate evaluations) (Gutierrez-Barrera et
al., 2007; Longati et al., 2013; Sipos et al., 2003). However, there are several nonattachment based multicellular tumour spheroid based studies with higher throughput
applications. One such application is Wen and colleagues development a 3D cell culture
assay system with agarose coated wells to induce 3D growth in a 96 well plate format
using the acid phosphatase method for determining cell viability (Wen et al., 2013).
This attachment free method of 3D structure generation does not include interactions
with external extracellular matrix components and also only produces a single
aggregated structure per well. Although analysis is significantly easier with a single
structure in each well, the artificial aggregation on agarose may not be as
physiologically relevant without additional microenvironmental interactions such as
with external ECM for attachment. The non-adherent polydimethylsiloxane (PDMS)
microwell plate format based assay developed by Yeon and colleagues also produces a
multicellular tumour spheroid model. However similar to the agarose based models no
external cell to ECM interplay is incorporated into the assay (Yeon et al., 2013). The
155
forced aggregation of cells in this model caused a decrease in 3D structure growth over
time rather than the increase in size and proliferation of cells seen in the Matrigel ECM
inducing presented here.
A recently published study utilising similar Matrigel based 3D cell culture was
evaluated by Celli et al. Utilising a high content imaging assay and a single pancreatic
cancer cell line (PANC-1), a number of treatment assessment endpoints (cell viability,
size dependant activity, architectural changes) were validated for use in a high
throughput setting. The 96 well format assay, utilised fluorescence imaging to evaluate
live (calcium AM) / dead (ethidium bromide) cells within the 3D nodules (structures).
This assay examined the effects of
five reference chemotherapeutic agents on
individual structures in the population and provided methodology on assay development
(Celli et al., 2014). However, only a small number of agents against a single pancreatic
cell line were evaluated in this study and only manual liquid handling steps with a small
number of microtitre plates were performed.
Although there are several methods
published for evaluating novel anti-cancer agents utilising 3D pancreatic cell cultures
(Longati et al., 2013) few have undergone the rigorous evaluation required for
adaptation to HTS applications.
This chapter introduced robust methodology for development of the 3D in vitro
pancreatic cancer screening assay. The assay design and format was selected with cell
line, cell seeding density, assay length, incubation times and cell viability detection
methods being evaluated and subsequently selected. The three cell lines selected for
assay development (AsPC-1, BxPC-3 and PANC-1) were shown to develop
reproducible 3D structure growth over time (up to 12 days), with comprehensive growth
characteristics measured, such as size and distribution of individual structures. The
metabolic indicator dye, resazurin, had not previously been used for cell viability
measurements in a pancreatic cancer 3D culture system. However, linearity studies and
experimentation with cell numbers and incubation times revealed that the dye provided
accurate indication of cell number (penetrated 3D structures of all sizes assessed) and
viability and would be suitable for compound activity evaluations in the 3D culture
assays. Other cancer 3D culture-based assays with epithelial carcinoma and non-small
cell lung cancer cell lines have previously utilised the dye for drug activity
measurements (Godugu et al., 2013; Tung et al., 2010).
156
Optimisation of assay conditions for sensitivity and reproducibility and incorporation of
automated liquid handling steps were examined using selected assay performance
measures. The 3D culture assay produced %CV scores below 15%, signal windows
greater than 2 and Z’-factor scores above 0.4% for all cell lines. Although the 3D
culture conditions provided lower Z’-factor scores across all cell lines than there 2D
counterparts, the assays still maintained low variation and high reproducibility and the
statistical validation to be suitable for HTS applications. A high quality HTS assay
should ultimately be able to identify active agents with a high degree of confidence. The
values obtained for the assay performance measures in this study give confidence to
continue investment in developing the assay for HTS. The assay performance indicators
give an impression of robustness of an assay they do not take into account the
reproducibility over a number of plates, over a period of time or the sensitivity of
compound effects which are all critical factors to be considered. The sensitivity of the
assay will be examined in the proceeding chapter comparing a panel of drugs with
known activity in both 2D and 3D culture conditions.
The incorporation of more complex model systems in HTS is not only challenging for
the developing technologies and materials that reproduce optimal in vivo like biological
conditions, but also with the ability to apply these in a drug discovery setting. The 3D
cell culture based assay developed in this chapter aims to provide a more relevant in
vitro oncological disease model to better evaluate the efficacy of novel therapeutic
candidates. The methodology developed here will be examined in the second stage of
assay development in Chapter 4, where known compounds are used to establish
reproducibility of activity determinations and compared with established monolayer
culture methods.
157
3.5 References
Berg, E., Hsu, Y.-C., and Lee, J.A. Consideration of the cellular microenvironment:
Physiologically relevant co-culture systems in drug discovery. Advanced Drug Delivery
Reviews.
Berggren, R., Møller, M., Moss, R., Poda, P., and Smietana, K. (2012). Outlook for the
next 5 years in drug innovation. Nat Rev Drug Discov 11, 435-436.
Breslin, S., and O'Driscoll, L. (2013). Three-dimensional cell culture: the missing link
in drug discovery. Drug Discov Today 18, 240-249.
Celli, J.P., Rizvi, I., Blanden, A.R., Massodi, I., Glidden, M.D., Pogue, B.W., and
Hasan, T. (2014). An imaging-based platform for high-content, quantitative evaluation
of therapeutic response in 3D tumour models. Scientific reports 4, 3751.
Fryer, R.A., Barlett, B., Galustian, C., and Dalgleish, A.G. (2011). Mechanisms
underlying gemcitabine resistance in pancreatic cancer and sensitisation by the iMiD
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4. Chapter Four: Two Approaches to Determining Anti-Cancer
Activity in 3D Cell Culture
4.1. Introduction
High content screening (HCS) or automated microscopy-based screening is now well
established and widely used in basic research and drug discovery (Gasparri and Galvani,
2010) programs. However, the majority of HCS programs utilise monolayer based
models to assess treatments. There are limited examples of 3D cell culture based HCS
assays that are suitable for true high throughput screening (Celli et al., 2014; Wenzel et
al., 2014).
This chapter will focus on assessing the ability of the Calcein AM-based
high content assay to determine anti-cancer drug activity. Cellular responses to a range
of anti-cancer drugs will be compared to the established resazurin assay to determine
sensitivity and reproducibility in the 3D model. The comparison study between the
metabolic indicator (resazurin) and the cell viability (Calcein AM) assays will provide
indications for the amenability to HTS application.
To determine if the developed cell culture model can be adapted to testing potential
therapeutics in a more tumour-microenvironment orientated context, a panel of
established chemotherapy drugs were selected as typical anticancer agents.
The
selection of drugs for use in demonstrating activity in the 3D assay was based upon
existing data for clinically relevant chemotherapeutic agents that have either been
evaluated previously for treatment against pancreatic ductal adenocarcinomas or
currently used clinically for other cancers. From a large panel of chemotherapeutics, six
drugs from a variety of different chemical classes were chosen for evaluation, including
pyrimidine analogues, anthracyclines and mitotic inhibitors.
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4.1.1. Anti-cancer reference drugs
Table 4.1. Anti-cancer reference drugs used to validate the 3D pancreatic cancer model.
The summary table includes drug class, mechanism of action and structure.
Drug
Gemcitabine
Class
Pyrimidine
analogue
MOA
Inhibits DNA synthesis
by a number of
mechanisms, including
S phase blockade and
targeting
ribonucleotide
reductase and DNA
polymerase.
An anti-mitotic agent
that binds tubulin and
prevents functional
microtubule
development.
Paclitaxel
Taxane
Docetaxel
Taxane
An anti-mitotic agent
that binds tubulin and
prevents mitotic cell
division through
inhibition of the mitotic
spindle assembly.
Doxorubicin
Anthracycline
Intercalates into DNA,
preventing
transcription and
replication by inhibiting
topoisomerase II
progression.
Epirubicin
Anthracycline
Intercalates into DNA,
preventing
transcription and
replication by inhibiting
topoisomerase II
progression.
Vinorelbine
Vinca alkaloid
Targets microtubule
polymerisation and
prevents mitotic spindle
formation and cell
division.
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Structure
Gemcitabine is a cytotoxic agent with activity against a number of solid tumours
including pancreatic, bladder, ovarian, lung and breast cancer (Noble and Goa, 1997).
Gemcitabine has been the standard of care treatment for both locally advanced and
metastatic pancreatic cancer since 1997 (Burris et al., 1997). This pyrimidine analogue
inhibits DNA synthesis and has shown clinical benefits in pancreatic cancer over
previous administered anti-cancer therapies, such as 5-fluourouacil and cisplatin (Toschi
et al., 2005). Although gemcitabine provides a statistical increase in survival of patients
with pancreatic cancer, it provides only modest clinical impact with patients only
surviving several weeks longer compared to those receiving no treatment. Initial
preclinical evaluations relied on in vitro monolayer, soft agar based tumour cloning
assays and orthotropic murine models (Von Hoff, 1996). Based on the data from these
models, relevant clinical activity was predicted. Patient response, however, was poor
and preclinical activity has not translated well into the clinic, with limited increases in
overall survival achieved. Over the past 15 years, a number of randomised clinical trials
have examined combination treatments of gemcitabine with a second therapeutic agent
such as taxanes, topoisomerase inhibitors, platinum based agents and targeted therapies
(Gourgou-Bourgade et al., 2013; Philip, 2008; Poplin et al., 2009). However, almost all
combination studies have provided limited clinical benefits, with only a few
combinations providing statically significant outcomes, namely gemcitabine with
Erlotinib and albumin bound (nab)-paclitaxel combinations (Chiorean and Von Hoff,
2014; Von Hoff et al., 2013).
Paclitaxel was isolated in 1967 from the bark of the Pacific yew tree (Taxus brevifolia)
and was finally approved for clinical use in 1995 for the treatment of a range of cancers
including breast, ovarian and non-small cell lung cancer (Jordan and Wilson, 2004).
Due to promising preclinical activity in pancreatic tumour models (murine in vivo),
paclitaxel has been examined in a number of clinical trials as both a single agent and in
combination therapy (Belli et al., 2012). Paclitaxel treatment of pancreatic cancer as a
monotherapy performed poorly in the clinic, with no significant advantage in survival
times achieved. This was believed to be attributed to the intrinsic and acquired
resistance properties (ABC transporter-mediated multidrug resistance, tubulin
mutations, epigenetic silencing of pro-apoptotic genes) of pancreatic cancer tumours in
vivo as well as the inability of paclitaxel formulation to penetrate the poorly
vascularised tumour mass (Fauzee, 2011).
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In addition, combination therapy with
gemcitabine in a number of early stage and advanced pancreatic cancer clinical trials
provided limited improvement in overall survival (median survival of 5 - 7 months)
(Chiorean and Von Hoff, 2014). Recently however, a nanoparticle liposome
encapsulated form (nab-paclitaxel) has been approved for clinical use in the treatment of
patients with pancreatic adenocarcinomas. The improved solubility and toxicity profile
achieved with nab-paclitaxel has the potential to achieve clinically significant increases
in overall survival (Peddi et al., 2013). With the combination therapy of gemcitabine
and nab-paclitaxel, median overall survival rates were increased to 8.5 months from 6.7
months with gemcitabine alone (Von Hoff et al., 2013). Despite this modest increase in
survival time, it is expected that the nab-paclitaxel and gemcitabine combination will
represent the new gold standard in treatment of advanced pancreatic cancer (Chiorean
and Von Hoff, 2014).
Docetaxel is an analogue of paclitaxel that has been used previously as a second-line
treatment for pancreatic cancer (Saif et al., 2010). This taxane is also used as a secondline treatment in a number of other cancer therapies including breast, prostate, ovarian
and non-small cell lung cancers. Docetaxel differs from paclitaxel in molecular
pharmacology (Table 4.1) with docetaxel and due to its ability to better escape efflux
mechanisms, is thought to achieve higher intracellular concentrations than paclitaxel
(Gligorov and Lotz, 2004). Early preclinical studies using in vitro monolayer and
orthotopic murine models (derived from human pancreatic adenocarcinomas) found
encouraging anti-tumour activity, specifically that with low nano-molar potency
docetaxel, which completely reduced growth of explanted tumours in the murine
models (Balcer-Kubiczek et al., 2006; Bissery et al., 1991). Combinations studies to
date with a variety of cytotoxic agents have failed to provide significant clinical benefits
(median overall survival of 6 – 7.4 months) (Belli et al., 2012). The recent increased
efficacy observed with the reformulated nab-paclitaxel treatments has renewed interest
in taxanes with emerging drug delivery technology (Chiorean and Von Hoff, 2014).
Doxorubicin has been used in the treatment against a wide range of malignancies for
over 30 years, including leukaemias, myelomas, bladder, breast, lung, ovarian and some
endocrine pancreatic tumours (Kouvaraki et al., 2004; Tan et al., 1967). Clinical trials
evaluating doxorubicin in combination as a second line treatment after frontline
gemcitabine therapy for advanced pancreatic adenocarcinomas indicated poor clinical
164
outcomes (Lim et al., 2011). The clinical trials with doxorubicin as either a single agent
or in combination provided no survival advantage (with an 8% response rate as a single
agent and in combination with 5-fluorouracil and mitomycin-C a response rate of 40%)
(Schwartz and Casper, 1995).
Epirubicin is a another member of the anthracycline class of chemotherapy agents that
has been previously used to treat a number of cancers, including but not limited to
breast, ovarian and lung cancer (Robustelli Della Cuna et al., 1983). Epirubicin
combination therapies have been assessed in several pancreatic cancer clinical trials
with limited success (average median survival time of 6 months) (Raderer et al., 1997;
Reni et al., 2005). The combination therapies involving epirubicin, cisplatin, 5flourouracil and gemcitabine, although providing statistically increases in median
disease free survival (15.2 months vs 11.7 with gemcitabine alone), produced increased
haematological toxicity with limited clinical benefits (Reni et al., 2012).
Vinorelbine is the final cytotoxic drug selected for evaluation in this study and has been
administered previously as a single agent or in combination therapies against a number
of metastatic solid tumours (Delord et al., 2000). This semi-synthetic vinca alkaloid has
demonstrated clinical anti-tumour activity in ovarian, breast, bladder and non-small cell
lung cancer with a well-tolerated toxicity profile (Conroy, 2002). There is limited
published data of response rates in preclinical or clinical settings with vinorelbine and
pancreatic cancer. A human tumour xenograft in a murine model determined a 30%
reduction in tumour growth (Hill et al., 1999). The anti-tumour activity was reported in
a small combination (gemcitabine and vinorelbine) clinical trial of multiple cancer
types. One late stage pancreatic cancer patient with a cisplatin resistant tumour recorded
a partial response, with no increase in hematologic toxicity (Delord et al., 2000).
However, most studies revealed no major response or increased clinical outcomes with
mono or combination therapies (Conroy, 2002).
The six chemotherapy agents discussed above (gemcitabine, doxorubicin, vinorelbine,
epirubicin, paclitaxel and docetaxel) were selected for examination in the 2D and 3D
HTS assays (described in Chapter 3). A number of factors influenced the decisions on
selecting these drugs. Firstly, the characterisation of a panel of clinically relevant
chemotherapy drugs would be useful in assessing and validating the developed assays
165
and therefore drugs that have shown anti-cancer activity against pancreatic cancer in
vitro and in vivo were assessed. The ability to determine accurate potency (IC50) values
is an important part of evaluating sensitivity and reproducibility of any screening
program. Therefore, agents that produced drug responses that could not accurately fit
dose response curves (as they failed to inhibit metabolic activity at the top doses tested)
would be difficult to utilise in assessing changes in assay sensitivity over time (these
included tamoxifen, estramustine, bicalutamide, carboplatin, 5-flourouracil and
methotrexate). This removed a number of chemotherapy agents from selection process
(discussed in the results section 4.3.1). The next factor was based on clinical relevance,
at the time of this study, gemcitabine was the standard chemotherapy agent for
pancreatic cancer treatment as was therefore included. The taxanes were included as
they had begun to be reassessed, with the improved drug delivery paclitaxel formulation
of nab-paclitaxel displaying promising signs in clinical trials. Another factor of drug
selection for the reference panel was to compare agents from similar drug classes to
evaluate whether drug responses could be clustered based on mechanism of actions or
phenotypic responses in the 3D model. The final factor was to ascertain any differences
in drug activity against cells in monolayer and 3D culture formats. Evaluating a panel
of drugs that covered a range of preclinical and clinical efficacy profiles and
determining if the cellular activity in response to the therapeutics observed in the 3D
culture had more predictive value than traditional monolayer cultures. Thus, providing
evidence that utilising 3D cell culture in drug discovery practices would be
advantageous.
4.2. Materials & methods
4.2.1. Materials and reagents
Black side-clear bottom (384 well) tissue culture (TC) treated plastic bottom optical
imaging microplates (#6007550, PerkinElmer) were used for all cell based assays
described throughout. Polypropylene 384 clear microtitre plates (Falcon 353265, BD
Biosciences) were used for all master concentration response curve (CRC) drug plates.
All drug dilution plates were prepared using sterile, clear 384 well (Falcon 353961, BD
Biosciences) microplates. The biological hydrogel (Growth Factor reduced (GFR)
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Matrigel) and the synthetic hydrogel, PuraMatrix™, were purchased from Corning Life
Sciences. Resazurin (Sigma-Aldrich) and Calcein AM (Invitrogen) dyes were prepared
as per materials method Chapter 3, section 3.2.1.
4.2.2. Preparation of reference drugs for assay dosing
Stock powders of all chemotherapy agents were purchased in 10mg to 1g lots and
master stocks (stock concentration of 50mM) were prepared in 100% DMSO before
creating master serial dilution plates. The reference drugs were purchased from either
Sigma Aldrich or Tocris Biosciences. The reference drugs included for initial evaluation
were gemcitabine, epirubicin, docetaxel, tamoxifen, estramustine, bicalutamide,
carboplatin, 5-flourouracil, methotrexate (Sigma), vinorelbine, doxorubicin and
paclitaxel (Tocris Biosciences). Twenty point serial dilutions (spanning from 200µM to
0.1nM) were prepared in 100% DMSO utilising an automated serial dilution protocol on
the Agilent Bravo liquid handling platform (See Table 5.1. for details). Master drug
microplates were frozen at -20° C and thawed before each dosing schedule (plates were
discarded after 6 freeze thaw cycles and then re-created from powdered stocks). For
preparation of intermediate dilution plates, the master DMSO plates were defrosted at
room temperature on a plate shaker. The Agilent Bravo liquid handling platform was
used to aspirate 4µl of the serial dilutions from the master DMSO plates. This was then
dispensed into the intermediate dilution plates which contained 96µl of sterile Milli-Q
water and mixed. This 1:25 dilution produced a final DMSO concentration of 4% for
the dilution plates. Drug dosing of assay plates was then achieved by aspirating 5µl
from the intermediate dilution plates and dispensing into the assay plates containing cell
cultures and 45µl of media (final DMSO concentration of 0.4%).
4.2.3. Reference drug dosing of 2D and 3D culture assay plates
Freshly prepared drug dilution plates were used to dose both 2D and 3D culture assay
plates in the schedule described in Chapter 3, section 3.3.6. Directly after media
changes, 5µl of either the drug serial dilution or controls (negative control 0.4% DMSO,
positive control 100% DMSO) were dispensed with an Agilent Bravo liquid handler
into 45µl media containing either the 2D or 3D cell cultures in each assay plate. All
aspirations, mixing and dispensing steps were performed using an automated protocol
167
optimised for each plate type (tip levels, liquid velocity) to ensure that the cell layer or
spheroid was not disturbed (liquid transfer height of 3mm from base of plate and
velocity 1.5µl/s). Disposable tips were discarded after each completed experiment to
ensure against potential contamination or cross over. The concentrations used to assess
the drug dose responses are listed below in Table 5.1. Control wells consisted of a final
assay concentration of 10% DMSO (positive controls, 100% inhibition) and 0.4%
DMSO (negative control, 100% growth).
Table 4.2. Concentrations of master drug plates, dilution plates and assay plates.
Plate
DMSO %
Master
(mM)
Dilution
(mM)
Assay
(µM)
2D Assay 3D
(µM)
100%
4%
0.40%
0.40%
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
0.0002
0.0001
0.00005
0.00002
0.00001
0.000005
0.000002
0.000001
200
100
50
20
10
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
0.0002
0.0001
200
100
50
20
10
2
0.5
0.1
0.02
0.005
0.001
0.0002
Drug
50
Concentration 25
12.5
5
2.5
1.25
0.5
0.25
0.125
0.05
0.025
0.0125
0.005
0.0025
0.00125
0.0005
0.00025
0.000125
0.00005
0.000025
4.2.4. Cell culture preparation
General cell culture maintenance was performed as described in detail in Chapter 3,
section 3.2.2.
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4.2.5. Response to anti-cancer drugs using resazurin and Calcein AM assay
end point measurements.
Fifteen microliters of cold Growth Factor Reduced Matrigel™ (GFR Matrigel) was
diluted in RPMI media to a final protein concentration of 7.6mg/ml and dispensed onto
the bottom of 384 well Cell carrier microtitre plates. Matrigel layered plates were
incubated for 30-60 minutes at 37°C to allow for the extracellular matrix to solidify.
Cells were dissociated from culture flasks with accutase and cell number determined by
the trypan blue exclusion method as describe in section 2.2.3. All cell lines were
resuspended to a final concentration of 2000 cells per well in 45µl of complete media
and dispensed on top of the Matrigel layer with the Agilent Bravo liquid handling
platform. The outer wells of each microtitre plate contained media only to reduce edge
evaporation effects. RPMI media was refreshed at 72 hours after seeding and then
every 48 hours using a custom protocol on the Bravo™ liquid handler designed to
reduce cell disturbance. Dispensing and aspirating speeds were reduced and only 50%
of media was removed in a two-step process.
For the 3D assay plates, drug doses (duplicate wells per concentration) and controls
were dispensed on days 3, 5 and 7 for AsPC-1 and PANC-1 cell lines and days 6, 8 and
10 for BxPC-3 cells. On day 9 (for AsPC-1 and PANC-1) and day 12 (BxPC-3) 5µl of
600µM resazurin was dispensed by the Bravo and plates were incubated for 2 hours in a
humidified incubator (37°C, 5% CO2). Fluorescent signal was measured on an Envision
plate reader and recorded. After metabolic activity measurements were recorded, cell
viability and morphologically analysis was performed with the Calcein AM dye. To
remove background fluorescent from the resazurin dye, the assay plates were washed
three times with PBS using an automated wash protocol on the Agilent Bravo. The wash
protocol was optimised to reduce culture disturbance with reduced aspiration and
dispense rates. The effects of the wash steps on drug response and well reproducibility
were evaluated with no change in potency or CV% detected (data not shown). After the
final wash step, 45µl of PBS was dispensed into each well followed by 5µl of 20µM
Calcein AM that was prepared freshly from powdered aliquots immediately before
addition (final assay concentration 2µM). All 3D assay plates were then incubated for 2
hours in a humidified incubator (37°C, 5% CO2). The non-confocal (wide field) setting
169
was used on the PerkinElmer Operetta high content imaging platform to image the assay
plates as described in Chapter 3. Both bright field and the 488nm channel (Ex. 460/30,
Em. 500/50) were recorded for 6 fields per well with a 5 plane z-stack taken 500µm to
1200 µm above the bottom of the plate (cell line dependant) using the 20x objective.
4.2.6. Data analysis from metabolic assays for 3D culture models
Responses to treatment with the chemotherapy agents were determined by normalising
the reduction in fluorescent signal with control treatments (namely, 0.4% and 100%
DMSO). Using Microsoft Excel and the following formula with positive controls (10%
DMSO wells) AVGmin, negative controls (0.4% DMSO wells) AVGmax and drug
response signal from each well (Sample):
100 −
(100 × 𝑆𝑎𝑚𝑝𝑙𝑒 − 𝐴𝑉𝐺𝑚𝑖𝑛 )
(𝐴𝑉𝐺𝑚𝑎𝑥 − 𝐴𝑉𝐺min )
Graphical information such as dose response curves and other statistical drug response
metrics were then calculated in Graphpad Prism. Normalised data was first transformed
to log scale using the standard log function (X=Log(X)). A non-linear regression
equation was then applied to the data (sigmoidal dose response – variable slope 4
parameters):
𝑌=
𝐵𝑜𝑡𝑡𝑜𝑚 + (𝑇𝑜𝑝 − 𝐵𝑜𝑡𝑡𝑜𝑚)
(1 + 10^((𝐿𝑜𝑔𝐸𝐶50 − 𝑋) × 𝐻𝑖𝑙𝑙𝑆𝑙𝑜𝑝𝑒))
4.2.7. Data analysis for three dimensional 3D cell viability assays
Cell viability and morphological effects on 3D structures from the reference drugs
tested were assessed using the Calcein AM live stain and the Operetta imaging system
utilizing an area of intensity protocol. Following acquisition of the images, the in-built
analysis software, Harmony®, was used to determine changes in cell viability in
response to drug exposure. The image analysis protocol below, was used to extract drug
response data which was then exported to Microsoft Excel. First, the multiple Z section
images were focus stacked using the maximum projection selection. The ‘find cell’
170
building block was then used to segment live stained 3D structures from the background
Matrigel. Total area fluorescence and number of objects per well was calculated and
recorded. Values were normalised with control wells as per section 4.2.6 and drug
response calculated.
Table 4.3. Image analysis protocol for assessing cell viability on the Operetta with the Calcein AM
dye.
Protocol step
Program Action
1
An image stack is recorded on the
Operetta imaging platform.
2
New protocol created in Harmony to
detect the 488 fluorescence channel
3
4
5
Result
Image files of 384 well plates with
both bright field and live stain
fluorescence produced
Unique protocol created
In focus sections of an image are
selected from a series of Z slices and
Maximum projection option selected
flattened into a single focused 2D
image
Segmentation method A selected and Fluorescence intensity is used for
cutoff values derived from control
segmentation of cells from
wells
background
The mean area of fluorescence was Measurements of live cells are
calculated per well and divided by the recorded and dose response curves
number of individual objects
are generated and IC50 values
4.2.8. Statistical analysis
Unless otherwise stated, all data points are presented as the mean ± standard deviation
(SD) of three independent experiments. Statistical significance between assays was
assessed using either the student’s t-test for two groups or one-way ANOVA and
Bonferroni post-hoc test for analysis of multiple group comparisons. All statistical and
graphical analysis was performed in Microsoft Excel and Prism Graphpad.
171
4.3. Results
4.3.1. Chemotherapy agents as reference compounds
To assess the ability of the different methods (Resazurin and Calcein AM) of
determining drug activity in the ECM-based 3D culture system, a selection of clinically
relevant cytotoxic drugs was used to establish activity profiles. A panel of commercially
available drugs were assessed first in a traditional 2D monolayer culture metabolic
activity assay. A number of compounds failed to inhibit the growth of pancreatic cancer
cells even at the extremely high doses tested (>100µM). With such low activity of these
drugs, it would be difficult to compare assay systems using response metrics such as
IC50 values (if the values could not be accurately determined). Therefore, a sub-set of
chemotherapy drugs was made, based on the ability to inhibit the pancreatic cancer cell
lines at greater than 50% response at the top doses tested. The dose response curves of
the cytotoxic drugs tested for the three cell lines are below in Figure 4.1.
172
D o x o r u b ic in (µ M )
P a c lita x e l (µ M )
A s P C -1
B x P C -3
100
P A N C -1
50
A s P C -1
0
50
0
-6
-4
-2
0
2
4
-6
-4
-2
Log µ M
A s P C -1
B x P C -3
P A N C -1
A s P C -1
0.005395
0.01572
0.01413
50
%
50
0
0
-6
-4
-2
0
2
4
-6
-4
-2
Log µ M
0
2
4
Log µ M
D o c e ta x e l (µ M )
E p ir u b ic in (µ M )
100
P A N C -1
A s P C -1
0.001212
0.004067
0.008015
B x P C -3
100
P A N C -1
In h ib itio n
B x P C -3
A s P C -1
0.1199
0.1042
0.2450
50
%
50
%
In h ib itio n
4
100
%
In h ib itio n
P A N C -1
0.09345
0.1293
0.1607
In h ib itio n
100
0
0
-6
-4
-2
0
2
4
-6
-4
-2
Log µ M
0
2
4
Log µ M
C a r b o p la tin (µ M )
T a m o x ife n (µ M )
P A N C -1
A s P C -1
5.378
4.041
27.68
B x P C -3
100
P A N C -1
In h ib itio n
B x P C -3
100
A s P C -1
11.73
19.82
19.08
50
%
50
%
In h ib itio n
2
G e m c itib in e (µ M )
B x P C -3
0
0
-6
-4
-2
0
2
4
-6
-4
-2
Log µ M
0
2
4
Log µ M
E s tr a m u s tin e (µ M )
B ic a lu ta m id e (µ M )
100
P A N C -1
A s P C -1
~ 112.6
~ 100.1
~ 233.2
B x P C -3
100
P A N C -1
In h ib itio n
B x P C -3
A s P C -1
~ 515.1
51.43
10.65
50
%
50
%
In h ib itio n
0
Log µ M
V in o r e lb in e (µ M )
0
0
-6
-4
-2
0
2
4
-6
-4
-2
Log µ M
0
2
4
Log µ M
F lu o r o u r a c il (µ M )
M e th o tre x a te (µ M )
B x P c -3
= N D
P a n c -1
= N D
A s P c -1
= N D
B x P c -3
100
100
In h ib itio n
P a n c -1
A s P c -1
0.01882
~ 0.0002153
0.01065
50
%
50
%
In h ib itio n
0.003571
0.01079
0.01319
%
%
In h ib itio n
P A N C -1
0.1545
0.1408
0.3729
In h ib itio n
B x P C -3
100
0
0
-6
-4
-2
0
2
-6
Log µ M
-4
-2
0
2
Log µ M
Figure 4.1. Concentration response curves of a panel of cytotoxic drugs generated in a monolayer
cell model. IC50 values are expressed as the mean ± SD of duplicate wells. ND = not determined.
173
The pancreatic cancer cell lines exhibited high resistance against a number of
chemotherapy agents including 5-fluorouracil, which had previously been reported to
have poor efficacy in vitro against pancreatic cancer cell lines (Shi et al., 2002). Limited
effects on cell viability, even at the top doses tested (200µM) where observed with
tamoxifen, estramustine, bicalutamide, carboplatin, 5-flourouracil and methotrexate.
These compounds were examined for activity against cells cultured in the 3D model and
exhibited a similar lack of activity (data not shown). The following chemotherapeutic
drugs were chosen for further studies, due to their clinical relevance as anti-cancer
therapeutics gemcitabine, doxorubicin, vinorelbine, epirubicin, paclitaxel and docetaxel.
All of these drugs exhibited activity against cell cultured in monolayer conditions in the
sub-micro molar range, enabling accurate IC50 values to be determined against the
pancreatic cancer cell lines investigated.
4.3.2. Cell viability (Calcein AM) imaging assay performance
A number of features were evaluated for use in measuring the response of cells to anticancer therapeutics, utilising the Calcein AM dye. Morphological features of these
cellular structures such as size, shape, texture and intensity could be quantitated using
the image analysis software Harmony. However, the majority of protocols within the
Operetta software are designed for analysis of a monolayer of individual cells. For the
heterogeneous population of cells in large 3D structures cultured in the ECM-based
model, few of these features could be consistently used to determine anti-cancer
activity. Only one feature set was able to discriminate between positive and negative
control wells reproducibly and obtain Z’-factor scores greater than 0.5, the total
fluorescence area and number of individual objects. The number of fields (3, 6 and 9
fields) acquired per well was also evaluated with 6 and 9 field acquisitions displaying
Z’-factor scores ≥ 0.5. The 6 field acquisition setting was chosen to increase throughput
for possible HTS applications. All cell lines passed the criteria generally recognised for
HTS based assays with Z’-factor ≥ 0.4 a signal window ≥ 2 and %CV ≤ 20 (
174
Table 4.4) (Iversen, 2012).
Table 4.4. Standard assay performance measures for the HTS assays with the Calcein AM cell
viability assay. %CV = coefficient of variation, SW = Signal Window and Z’-Factor.
Calcein AM
AsPc-1
BxPc-3
Panc-1
%CV
SW
Z'-factor
11.87
10.67
12.33
3.94
4.03
3.5
0.54
0.53
0.51
4.3.3. Drug activity as measured by cell viability methods in 3D cell culture
A comparison of methods used to ascertain cell viability in 3D which were previously
described in detail in Chapter 3, was undertaken. The metabolic activity (resazurinbased) of cell lines treated with cytotoxic drugs was compared with the cell viability
(Calcein AM) live imaging assay. Using the IC50 values obtained as indicators of drug
activity, both assay systems provided comparable activity profiles as illustrated in
Figure 4.2 - Figure 4.7. Both assay formats recorded reproducible data, based on the
cellular responses to the reference drugs over numerous experiments and replicates and
thus considered suitable for undertaking full scale HTS. Although the resazurin based
assay provides fast reproducible and easy to analyse readouts, no information on drug
effects on the 3D structures or individual population effects can be ascertained. This
assay format provides single parameter readout for an entire well or drug treatment. The
Calcein AM based imaging assay, whilst comparatively slow and cumbersome with
additional handling, data storage and analysis steps required to produce the desired
cellular response metrics, is more informative. The wealth of information available from
an imaging based assay, such as details on cellular / ECM adhesion or structural effects,
diffusion through solid 3D structures of the agents being and effects on different
populations within the cellular structures examined may be invaluable in studies
predicting anti-tumour efficacy.
175
Figure 4.2. Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with gemcitabine. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with gemcitabine dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
176
Figure 4.3. Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with doxorubicin. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with doxorubicin dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
177
Figure 4.4 Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with epirubicin. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with epirubicin dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
178
Figure 4.5 Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with docetaxel. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with docetaxel dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
179
Figure 4.6. Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with paclitaxel. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with pacletaxel dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
180
Figure 4.7. Resazurin reduction and Calcein AM imaging-based drug activity determination in 3D
pancreatic cell culture with vinorelbine. (a) Representative fields from the Calcein AM staining of 3D
cell cultures (AsPC-1, BxPC-3 and PANC-1 cell lines) after treatment with vinorelbine dose response
decreasing down the page (12 doses: 200µM-0.2nM). (b) Example dose response curves generated from
both cell viability and metabolic activity assays (increasing concentration left to right). (c) Cellular
response measured as potency between the two assay systems with data representing mean values ±
standard deviation of two separate experiments for the Calcein assay and three experiments for the
resazurin assay (duplicate wells). Scale bars = 100µm. Centre field from Operetta acquisition at
490/520nm focus stacked maximum projection image from five z slices. Image brightness levels have
been adjusted for publication purposes.
181
Comparable potency measurements were recorded between the two independent
(Calcein AM and resazurin) 3D assay systems evaluated (Figure 4.2c to Figure 4.7c).
The dose response curves produced from the Calcein AM data was more variable than
with the resazurin assay as can be seen from the larger error bars between duplicates
seen in response curves Figure 4.2 – 4.10b. IC50 values from the metabolic activity
(resazurin) assays were not statistically different from the cell viability (Calcein AM)
based assays across all cell lines and reference drugs examined. However, upon closer
inspection of the dose response curves generated, an altered efficacy or maximal effect
of many of the drugs was observed. Exposure of the three cell lines to gemcitabine
produced similar dose response curves from both assays, with a different maximal effect
only observed in the PANC-1 cultures. The Calcein AM assay recorded a slightly larger
population (approximately 40%) of cells viable at the top doses (50µM - 200µM)
compared to the resazurin assay which only detected 20% of cells remaining
metabolically active (Figure 4.2b). The taxanes (docetaxel and paclitaxel) efficacy
appears to be either under-estimated in the cell viability assay (or over-estimated by the
metabolic activity assay) or a greater population of cells remained viable but not
significantly metabolically active, even at the highest drug doses (50µM - 200µM). The
AsPC-1 and BxPC-3 cells exhibited a small (approximate 10%) reduction in metabolic
activity compared to the viability measure for both taxanes. However, PANC-1 cells
exhibited a larger discrepancy in efficacy, with a 25% drop in metabolic activity
compared to Calcein AM measurements (Figure 4.5c and Figure 4.6c).
The anthracyclines (epirubicin and doxorubicin) exhibited similar efficacy against all
three cell lines tested (AsPC-1, BxPC-3 and PANC-1), with only a small
(approximately 10 -20%) population of cells remaining viable but not metabolically
active in the PANC-1 cell line. At the top dose of the anthracyclines (200µM), there was
100% efficacy determined for AsPC-1 and BxPC-3 in both assay systems.
AsPC-1 and PANC-1 cell line demonstrated similar trends in cellular responses to
vinorelbine with less cells remaining metabolic active (resazurin) than viable (Calcein
AM).
Approximately 50-60% of AsPC-1 cells remained viable and metabolically
active even drug doses as high as 200µM. Sixty percent of cells in the PANC-1 3D
culture remained viable at the tops doses of vinorelbine (50µM - 200µM), with only
182
40% recorded as metabolically active. Despite differences observed in maximum cell
death observed for some of the drugs assayed, the IC50s obtained from the resazurin and
Calcein AM ssays were comparable and not statistically different. Thus, both assays
were useful in determining the cellular response to drug variations in 3D culture
conditions. Multiple independent methodologies also further validate the results
obtained using this novel 3D assay format.
4.4. Discussions & conclusions
Evaluation of the cell viability (Calcein AM) and the metabolic activity (resazurin)
based assay systems, revealed both assays could reproducibly determine activity against
tumour cells over multiple replicates and drug classes. The anti-cancer responses of the
reference drugs were not significantly different between the assay formats examined.
However, a slightly altered efficacy response was measured with certain cell line and
drug combinations in the cell viability assay. The cell lines expressed an altered
resistance profile, where a larger population of cells remained viable (intact membranes
for Calcein AM conversion) than metabolically active (able to convert resazurin to the
fluorescent form) with certain reference compounds (namely the taxanes and
vinorelbine). This altered response may be due to the different detection methodologies
between the two assays or cells exposed to chemotherapy drugs, may remain viable but
not metabolically active, depending on the mode of action and cell cycle effects
produced. Cancer cells may enter a dormant state (senescence or quiescence) in which
metabolic activity is reduced but cell viability remains intact (Aguirre-Ghiso, 2007).
Both assay systems were useful in confirming drug activity profiles in the previously
unevaluated cell culture model for pancreatic cancer. Utilising two independent assay
technologies ensures the drug response data is more likely an accurate representation of
the cellular response to drugs and not an artefact of the technology used or the model
itself. This is of particular importance as chemotherapy drugs have previously been
implicated in causing mitochondrial abnormalities (such as increase in size and activity)
which may introduce unwanted artefacts when measuring drug response with dyes such
as resazurin (Kluza et al., 2004).
183
Although the image based viability assay provided equivalent cellular responses to the
panel of drugs tested, the assay had a number of constraints. The process of imaging
several fields and z-sections using the standard microscope objective was extremely
time consuming compared to the plate reader based assay. The image analysis also
required optimisation for each cell line and produced more variable data. To be used in
a true high throughput screening campaign (>10 000 data points a day) it would require
over thirty 384 plates a day to be imaged which is unlikely to be achieved with the
current setup. The imaging assay however, does provide unique insights in drug effects
on the 3D structures, with effects on cell adhesion and structure integrity detectable.
Despite its unsuitability for true high throughput screening the use of an imaging assay
as a secondary or lower throughput option to determine additional parameters from
compounds screening may prove invaluable. For integration as a primary HTS assay
format future imaging assays could investigate microscopy alterations such as utilising
lower powered objectives and fewer Z-stacks to reduce acquisition times.
Here we have shown a high content assay developed with the Calcein AM live stain,
enables the identification and characterisation of anti-cancer drugs in a 3D cell culture
model. The impact of drug exposure on the 3D structures can be assessed and the cell
viability of individual populations identified. Although the Calcein AM assay in its
current form is unlikely be used in early drug discovery programs, future optimisation
to increase throughput and extract greater drug activity information may ultimately see
more widespread adoption. The true value of these information rich, high content based
assays is yet to be fully explored with 3D cell cultures. The integration of an inherently
complex biological model, such as 3D cell culture with automated microscopy and
sophisticated data analysis in a robust-high throughput manner, remains a challenging
endeavour.
184
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187
5. Chapter Five: Comparison of Anti-cancer Activity in 2D and 3D Cell
Culture Models
5.1. Introduction
Traditional cell based assays used in oncology drug discovery programs have relied on
the well- established two dimensional (2D) monolayer based culture method for in vitro
based assessment of new chemical entities (Haycock, 2011). This involves seeding a
suspension of cancer cells into the wells of a microtitre plate and allowing a 2D
monolayer of cells to form on the plastic surface, before dosing with compounds or
other therapeutic agents to be screened. For decades, this model has been used for
establishing the activity potential of compounds in high throughput screening
campaigns.
Increasingly, potential anti-tumour compounds are failing to recapitulate the in vitro
activity identified in preclinical assessments to in vivo studies, and both academia and
industry alike are exploring alternative cell models with increased predictive potential
(Friedrich et al., 2009; Hirschhaeuser et al., 2010). The anti-tumour activity of anticancer agents or compounds is traditionally linked to the ability of the agent to reduce
proliferation or effect cell viability of a cancer cell line grown in the monolayer culture.
This method, however, is not always representative of the in vivo tumour situation.
Three dimensional (3D) cell culture is an emerging field of cell biology that has begun
to be utilised within the drug discovery industry providing highly desirable increased
value to the early discovery phases. Micro-tumour or multicellular spheroid based 3D
cell cultures are reported to more accurately reflect the structural and biochemical in
vivo tumour conditions. The ability of these models to support cell-to-cell and cell-tomatrix interactions, as well as spatial gradients and drug resistance mechanisms
observed in native tumour tissue, is anticipated to allow more predictive data to be
identified from drug discovery campaigns (Elliott and Yuan, 2011; Friedrich et al.,
2009; Kobayashi et al., 1993).
188
There are limited examples in the literature of in vitro 3D pancreatic cancer cell models
that have shown significantly altered responses to therapeutics compared between
monolayer and 3D cultures (Longati et al., 2013; Wen et al., 2013). However, a much
larger body of work has begun to emerge for numerous other solid tumours such as
breast, ovarian and lung (Hongisto et al., 2013; Klein et al., 2012; Nirmalanandhan et
al., 2010). The vast majority of these identify a loss of efficacy in treatments, with most
therapeutics found to be less effective in the 3D models
compared with the 2D
monolayer cultures (Friedrich et al., 2007). However, the cellular responses to drugs are
often model and cell line dependant, with not all studies confirming the same loss of
therapeutic efficacy in 3D systems (Lama et al., 2013; Nirmalanandhan et al., 2010).
In the search for more physiologically relevant models, any new model or assay based
on a novel cell culture system (such as 3D cell culture) must be rigorously assed not
only for its technical amenability to drug discovery, but also its potential value as a
more relevant model. This chapter will incorporate the second stage of the assay
validation process to:
1) Confirm the suitability of the newly developed 3D assay system in a high
throughput screening (HTS) application using a panel of reference
chemotherapeutic drugs.
2) Examine the activity profiles of these drugs in a traditional monolayer culture
method compared to the extra cellular matrix (ECM) based 3D culture system
described and ultimately to explore the potential mechanisms for any differences
observed.
While the previous chapters looked at optimisation of the dynamic range of the signal
response and the intra plate variability, this chapter will focus on using known drugs to
demonstrate activity profiles and confirm that reproducible response metrics
(pharmacodynamic parameters including IC50 values, area under the curve (AUC),
maximum effect (Emax) and hill coefficients) can be determined over multiple replicate
experiments. The differences in activity between the models will also be examined to
determine if any altered response is related to mechanisms such as drug diffusion
through 3D structures or altered growth rates.
189
5.1.1. Cellular response parameters
The measurements used to establish the drug response against the pancreatic cancer cell
lines (AsPC-1, BxPC-3, PANC-1) include a number of parameters generated from dose
response curve analysis. Although drug response data routinely focuses on the variation
of potency alone (using the IC50 parameter) to compare the activity of therapeutics, this
single metric does not accurately depict the heterogeneous response observed in 3D
culture based systems. The variability observed between the cell populations in 3D
cultures requires a multi-parametric approach to fully detect the drug response
differences between cell lines, drugs and culture models. In this study the IC50 value
will be used as the measure of drug potency, Emax as a measure of drug efficacy and area
under the curve (AUC) as a combined metric for potency and efficacy (Fallahi-Sichani
et al., 2013). Changes to the steepness of the Hill slope (Hill coefficient) will also be
observed between the culture models.
Figure 5.1. Dose response parameters used for drug activity evaluations in 2D versus 3D models.
Calculated using Graphpad Prism software with the variable slope sigmoidal dose response equation.
The classical measure of drug potency is the relative IC50 parameter which is defined as
the half-maximum inhibitory concentration (which corresponds to the drug
concentration required to bring the dose response curve down to the half-way point
between the top and bottom plateaus of the curve). As the top plateau may not always
reach 100% inhibition (in the case of cell populations that remain resistant to drug
treatments) a measure of drug efficacy is also required (Figure 5.1). The Emax parameter
190
is a measure of drug efficacy and varies between 0 and 1 (with 0 corresponding to no
cell inhibition and 1 corresponding to 100% cell death). The AUC parameter is a
measure that combines both potency and efficacy, as it takes in to consideration the Hill
slope and maximum effect in the calculation (Fallahi-Sichani et al., 2013). Analysis of
Hill coefficients (Hill slopes) have traditionally been applied to enzyme inhibitors or
receptor-agonists interactions to determine binding properties. However, a shift in Hill
coefficient or change in steepness may provide insights into altered interactions of the
inhibitors being examined and is also included.
5.1.2. Resistance mechanisms in 3D cell culture
Previous research has suggested that 3D cell cultures provide enhanced models for
testing drug responses compared to monolayer systems by incorporating more
physiologically
and
biologically
relevant
aspects
of
the
in
vivo
tumour
microenvironment (Mehta et al., 2012; Thoma et al., 2014). Cancer cells can develop
numerous complex mechanisms to evade drug induced cytotoxicity (briefly summarised
in Figure 5.2). Resistance mechanisms implicated in tumour drug resistance include
both acquired and intrinsic (de novo). These mechanisms may involve alterations in
cellular metabolism, drug transport proteins, and apoptosis pathways, mutations in drug
targets, DNA repair proteins, and activation of oncogenes or inactivation of tumour
suppressor genes (Rebucci and Michiels, 2013). There are also a number of
environment mediated drug resistance mechanisms that have been implicated in
cancers’ ability to evade drug treatments. These include hypoxia mediated resistance
(activation of hypoxia inducible factors (HIFs) that can regulate metabolism, inhibition
of apoptosis, induction of autophagy and drug resistance protein overexpression), cell
adhesion mediated drug resistance (interaction of cancer cells with the stromal
components such as cancer associated fibroblasts, tumour associated macrophages,
vascular endothelial cells and extracellular matrix proteins), cancer stem cells and
finally, physical barriers (such as drug penetration issues through solid cellular masses
with poor vascularisation) (Zahreddine and Borden, 2013).
Cell-to-matrix interactions involving cell-receptors (such as integrins)
with
extracellular matrix components (collagen and laminin) have been previously
implicated in chemoresistance of cancer cell lines in vitro (Astashkina et al., 2012).
191
Abundant production of ECM is a critical morphological feature of pancreatic cancer,
with pancreatic cancer cells as well associated stromal cells, capable of producing ECM
components (Lohr et al., 1994). Interactions with the ECM protein collagen I have been
shown to reduce the response of pancreatic cancer lines to chemotherapeutic agents in
vitro (Dangi-Garimella et al., 2011).
The ability for drugs to penetrate through solid tumours is especially important in
pancreatic cancer where most primary tumours are surrounded by an extremely dense
and poorly vascularised stroma. This physical tumour microenvironment barrier is
believed to contribute to the poor clinical efficacy of anti-cancer drugs in pancreatic
cancer patients (Chu et al., 2007; Kong et al., 2012; Minchinton and Tannock, 2006).
These barriers can result in reduced drug concentration around target cancer cells within
the tumour mass and produce an incomplete response to the therapeutic agents. 3D cell
culture models have been suggested as appropriate systems to study the cellular
accumulation and diffusion of drugs in solid tumours (Yeon et al., 2013). Cells cultured
in an ECM-based 3D environment in vitro may be able to recapitulate a number of these
environment mediated resistance mechanisms.
192
Figure 5.2. Resistance mechanism that may be involved in anti-cancer drug resistance in vivo.
These may include acquired (A) and intrinsic (B). Figure as originally published in Zahreddine & Borden,
Mechanisms and insights into drug resistance in cancer. Frontiers in Pharmacology. (2013).
A number of mechanisms specific within 3D models have been suggested to impact
cellular responses to anti-cancer drugs (Figure 5.3). These include:
1) Altered proliferation rates observed in the heterogeneous population within the
3D structure (central core of slowly proliferating or quiescent cells with an outer
layer of proliferating cells).
2) Closely packed multicellular arrangement and 3D structure enhance cell-to-cell
contacts and survival signalling which has been shown to influence drug
responses.
3) Incorporation of microenvironmental components, such as external ECM or
other host cells. Cell to ECM signalling, has previously been implicated in drug
resistance.
4) Reproduction of the physical and mechanical barriers involved in diffusional
limitations of nutrients, gases and drugs similar to that of in vivo tumours.
193
Figure 5.3 Tumour drug resistance mechanisms that may be recapitulated using ECM based 3D in
vitro cell culture. Reproduced with permission from Lovitt et al, Advanced Cell Culture Techniques for
Cancer Drug Discovery, Biology. (2014)
A number of these mechanisms will be examined for their possible impact on the
cellular responses to the panel of chemotherapy drugs tested in this 3D model. A
physiologically relevant model that incorporates some of the mechanisms of chemoresistance which is suitable for implementation in a high throughput manner may prove
to be a useful tool in pancreatic cancer drug discovery, and is the objective of these
studies.
5.2. Materials & methods
5.2.1. Materials and reagents
Black side-clear bottom (384 well) tissue culture (TC) treated plastic bottom optical
imaging microplates (#6007550, PerkinElmer) were used for all cell based assays
described throughout. Polypropylene 384 clear microtitre plates (Falcon 353265, BD
Biosciences) were used for all master concentration response curve (CRC) drug plates.
All drug dilution plates were prepared using sterile, clear 384 well (Falcon 353961, BD
Biosciences) microplates. The biological hydrogel (Growth Factor reduced (GFR)
194
Matrigel) and the synthetic hydrogel, PuraMatrix™, were purchased from Corning Life
Sciences. Resazurin (Sigma Aldrich) was prepared as per materials method Chapter 3,
section 3.2.1.
5.2.2. Preparation of reference drugs for assay dosing
Stock powders of all chemotherapy agents were purchased in 10mg to 1g lots and
master stocks (stock concentration of 50mM) were prepared in 100% DMSO before
creating master serial dilution plates. The reference drugs were purchased from either
Sigma Aldrich or Tocris Biosciences. The reference drugs included gemcitabine,
epirubicin, docetaxel, vinorelbine, doxorubicin and paclitaxel (Tocris Biosciences).
Twenty point serial dilutions (spanning from 200µM to 0.1nM) were prepared in 100%
DMSO utilising an automated serial dilution protocol on the Agilent Bravo liquid
handling platform (See Table 5.1. for details). Master drug microplates were frozen at 20° C and thawed before each dosing schedule (plates were discarded after 6 freeze
thaw cycles and then re-created from powdered stocks). For preparation of intermediate
dilution plates, the master DMSO plates were defrosted at room temperature on a plate
shaker. The Agilent Bravo liquid handling platform was used to aspirate 4µl of the
serial dilutions from the master DMSO plates. This was then dispensed into the
intermediate dilution plates which contained 96µl of sterile Milli-Q water and mixed.
This 1:25 dilution produced a final DMSO concentration of 4% for the dilution plates.
Drug dosing of assay plates was then achieved by aspirating 5µl from the intermediate
dilution plates and dispensing into the assay plates containing cell cultures and 45µl of
media (final DMSO concentration of 0.4%).
5.2.3. Reference drug dosing of 2D and 3D culture assay plates
Freshly prepared drug dilution plates were used to dose both 2D and 3D culture assay
plates in the schedule described in Chapter 3, section 3.3.6. Directly after media
changes, 5µl of either the drug serial dilution or controls (negative control 0.4% DMSO,
positive control 100% DMSO) were dispensed with an Agilent Bravo liquid handler
into 45µl media containing either the 2D or 3D cell cultures in each assay plate. All
aspirations, mixing and dispensing steps were performed using an automated protocol
optimised for each plate type (tip levels, liquid velocity) to ensure that the cell layer or
spheroid was not disturbed (liquid transfer height of 3mm from base of plate and
195
velocity 1.5µl/s). Disposable tips were discarded after each completed experiment to
ensure against potential contamination or cross over. The concentrations used to assess
the drug dose responses are listed below in Table 5.1. Control wells consisted of a final
assay concentration of 10% DMSO (positive controls, 100% inhibition) and 0.4%
DMSO (negative control, 100% growth).
Table 5.1. Concentrations of master drug plates, dilution plates and assay plates.
Plate
Master
(mM)
Dilution
(mM)
Assay 2D
(µM)
Assay 3D
(µM)
DMSO %
100%
4%
0.40%
0.40%
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
200
100
50
20
10
5
2
1
0.5
0.2
0.1
0.05
200
100
50
20
10
2
0.5
0.1
0.02
0.005
0.001
0.0002
Drug
50
Concentration 25
12.5
5
2.5
1.25
0.5
0.25
0.125
0.05
0.025
0.0125
196
0.005
0.0025
0.00125
0.0005
0.00025
0.000125
0.00005
0.000025
0.0002
0.0001
0.00005
0.00002
0.00001
0.000005
0.000002
0.000001
0.02
0.01
0.005
0.002
0.001
0.0005
0.0002
0.0001
5.2.4. Cell culture preparation
General cell culture maintenance was performed as described in detail in Chapter 3,
section 3.2.2.
5.2.5. Response to anti-cancer drugs using resazurin assay end point
measurements.
Fifteen microliters of cold Growth Factor Reduced Matrigel™ (GFR Matrigel) was
diluted in RPMI media to a final protein concentration of 7.6mg/ml and dispensed onto
the bottom of 384 well Cell carrier microtitre plates. Matrigel layered plates were
incubated for 30-60 minutes at 37°C to allow for the extracellular matrix to solidify.
Cells were dissociated from culture flasks with accutase and cell number determined by
the trypan blue exclusion method as describe in section 2.2.3 For the 3D assay plates,
all cell lines were resuspended to a final concentration of 2000 cells per well in 45µl of
complete media and dispensed on top of the Matrigel layer with the Agilent Bravo
liquid handling platform. For the 2D assay plates, all cell lines were resuspended to 300
cells per well in 45µl of RPMI. The outer wells of each microtitre plate contained media
only to reduce edge evaporation effects. RPMI media was refreshed at 72 hours after
seeding and then every 48 hours using a custom protocol on the Bravo™ liquid handler
designed to reduce cell disturbance. Dispensing and aspirating speeds were reduced and
only 50% of media was removed in a two-step process.
For the 2D assay plates, drug doses (duplicate wells) and controls were dispensed on
days 0, 2 and 4. After 144 hours of drug exposure, 5µl of 600µM resazurin was
dispensed by the Agilent Bravo and plates were incubated for 4 hours in a humidified
incubator (37°C, 5% CO2) and fluorescence values read on the Envision™ plate reader.
197
The resazurin protocol (Ex. 530nm, Em. 595nm) was optimised for liquid heights and
plate type. For the 3D assay plates, drug doses (duplicate wells per concentration) and
controls were dispensed on days 3, 5 and 7 for AsPC-1 and PANC-1 cell lines and days
6, 8 and 10 for BxPC-3 cells. On day 9 (for AsPC-1 and PANC-1) and day 12 (BxPC-3)
5µl of 600µM resazurin was dispensed by the Bravo and plates were incubated for 2
hours in a humidified incubator (37°C, 5% CO2). Fluorescent signal was measured on
the Envision plate reader and recorded.
5.2.6. Data analysis from metabolic assays for 2D and 3D culture models
Data analysis was performed as described in Chapter 4, section 4.2.6.
5.2.7. Proliferation studies and growth kinetics of pancreatic cancer cells in
monolayer and three dimensional (3D) cell culture models.
Alterations in cellular proliferation rates of the pancreatic cancer cell lines (AsPC-1,
BxPC-3 and PANC-1) were examined in both the 2D and 3D models, using nuclear
counting as a direct measure of cell number. Previously, cell number had been evaluated
based on the indirect measurement of metabolic activity using resazurin. To confirm the
cell proliferation results from the whole well, metabolic activity dependant assay, an
automated direct counting method was employed to compare culture conditions. Culture
conditions were established as per section 4.2.5 for the 2D and 3D assays for all three
cell lines, specifically AsPC-1, BxPC-3 and PANC-1. The Hoechst 33342 dye was
dispensed into wells at various time points (day 0, day 3, day 6, day 9, and day 12) to
give a 10µm final assay concentration and the resultant cell number recorded using
automated image analysis.
Image acquisition was performed on the confocal PerkinElmer Opera™ high content
imaging platform using the 10x air objective and the 405nm laser (455nm emission
filter). For 2D monolayer cultures, a single plane was acquired and the entire well was
imaged using multiple fields (15 fields covering approximately 90% of the well area).
For the 3D cultures, 50 Z-stack slices were acquired at 10 µm intervals throughout the
culture using the same well coverage pattern as the 2D culture (15 fields covering
approximately 90% of the well).
198
5.2.8. Automated analysis of cell numbers detected by Hoechst staining using
Acapella® and Volocity® analysis packages.
The in-built PerkinElmer Opera® analysis software, Acapella, was used to quantitate
cell number in the 2D monolayer cultures based on the number of nuclei detected using
the protocol described in Table 5.2. The find cell building block was used with
algorithm C and threshold adjustments to separate touching nuclei. Cell populations
were the output, reflected as the total cell number per well.
Table 5.2. Cell counting protocol in Acapella for 2D cell culture. The find nuclei building block was
selected and threshold adjusted to detect Hoechst stained nuclei. Total cell count was exported as results.
Cell counting script for Hoechst staining in 2D
FindNuclei
NucleiDetectionChannel_string:
NucleiDetectionAlgorithm:
ThresholdAdjustment:
MinimumNuclearArea:
NuclearSplittingAdjustment:
IndividualThresholdAdjustment:
MinimumNuclearContrast:
OutputName:
ReturnResults
Exp1Cam1
C
0.4
30
7
0.4
0.1
cell count
List of
Outputs
Method1:
@cell count : Number of
Objects1_stat1:
1
199
The Opera in-built image analysis software was only able to analysis 2D image sets,
therefore the 3D analysis package, Volocity, was used to determine 3D culture cell
numbers. Three dimensional cell culture image stacks (50 Z-stacks, across 15 fields)
were exported from the Opera and rendered back into 3D objects. Individual nuclei
where then detected by segmenting voxels from background fluorescence using the
protocol in Table 5.3. Correct segmentation of nuclei was verified by manual counts
before automation of whole well counting. Day 12 evaluations were removed from
analysis as cells in 2D had reached confluence in the wells by day 10. Additionally at
day 12, PANC-1 3D structures had become so large, the limit of the Hoechst staining
and the confocal ability of the Opera had been reached. The fluorescent signal could not
be accurately acquired through the extremely dense 3D structures (distinct segmentation
between nuclei was lost in large structures).
Table 5.3. Developed Volocity protocol to count individual nuclei in 3D cell culture.
Volocity cell count protocol
Step
Program Action
1
2
3
4
5
6
7
Acquired image stacks from microscope are exported as .TIFF files
data sets for importing into Volocity
Analysis protocol created based on find object building blocks and
optimised on example rendered fields
Find objects selected with threshold cut-off values based on staining
intensity of nuclei to background (2-5% average intensity)
Fill holes selected for partial nuclei detection
Remove noise block selected to filter background noise from
Matrigel (fine filter)
Separate touching object block selected to watershed touching
nuclei (size 500µm3)
Filter population block selected to remove artefact staining not of
nuclei size (500 µm3 - 10000 µm3)
200
8
Automated measurement selected for whole well (15 fields, 50 zstacks rendered) and output summarised
9
Summary of cell counts exported as .CSV files
5.2.9. Drug diffusion and distribution through monolayer and 3D structures
with doxorubicin
To evaluate the possible drug diffusion physical barriers present in the 3D cell culture
model, doxorubicin and confocal microscopy were used to assess drug diffusion over
time in culture. Both 2D and 3D culture plates were prepared as described in section
4.2.5. At day 3 (PANC-1) or day 6 (BxPC-3) once 3D structures had formed,
doxorubicin was dispensed into cell cultures at either the approximate IC50 dose
(500nM for 3D, 50nM for 2D) or a higher 2µM dose. For the 2D cultures, cells were
allowed to adhere to plates for 24 hours before the addition of doxorubicin. Appropriate
control wells were included to determine background and vehicle fluorescence levels.
Images were acquired on the confocal Opera imaging platform at 6, 24 and 72 hour time
points following exposure to doxorubicin in both 2D and 3D culture systems (incubated
at 37°C 5% CO2). Doxorubicin inherently fluoresces in the 480-650nm spectrum range
and images were acquired with 10x air objective using the 561nm laser and 600/50
filter. A single plane was acquired for the monolayer condition, while for the 3D
culture, approximately fifty 10µm z-stacks were acquired to cover the range of 3D
structures. Triplicate wells for each variable were assessed in two separate experiments.
Hoechst dye (10µM) was used to aid visualisation of cell and 3D structures boundaries
for analysis purposes.
5.2.10. Image analysis of doxorubicin diffusion in two dimensional (2D) and
three dimensional (3D) cell culture models
To assess diffusion (or penetration) of doxorubicin through the 3D structures, z-stacks
covering whole 3D structures were manually reviewed. A single plane from the centre
of each structure was analysed for pixel intensity to determine the level of doxorubicin
fluorescence above background controls. Intensity values were quantitated using
201
protocols developed in Acapella analysis software. For the 2D quantification of
fluorescence over time, the following protocol was performed (Table 5.4). The find cell
building block was used and the intensity per cell of the doxorubicin channel was
calculated over the four time points. Analysis of 9 fields in triplicate wells over 3
separate experiments were performed with data measured as the average intensity per
cell.
Table 5.4. Opera image analysis protocol for determining average doxorubicin fluorescence per cell
in monolayer culture (Acapella scripting parameters). The find cell building block was selected and
threshold adjusted to detect doxorubicin stained cells. The average intensity properties of each cell was
exported as results.
Doxorubicin fluorescent intensity in monolayer culture
Input Parameters for AAS:
doxorucibin accumulation 2D
cells.aas
FindCells
CellsDetectionChannel_string:
CellsDetectionAlgorithm:
ThresholdAdjustment:
MinimumNuclearArea:
NuclearSplittingAdjustment:
IndividualThresholdAdjustment:
MinimumNuclearContrast:
OutputName:
202
Exp2Cam2
A
0.05
100
7
0.05
0.05
Cells
CalculateIntensityProperties
channel_string:
WholeCells_string:
Region:
Method:
Mean_ONOFF:
SuffixForPropertyNames:
Exp2Cam2
Cells
Cell
Standard
1
Intensity
Cells
ReturnResults
Method1:
@Cells : Number of
Objects1_stat1:
@Cells : Intensity Cells
Mean1_stat1:
@Cells : Intensity Cells
Mean1_onoff:
List of
Outputs
1
Mean
1
To assess the accumulation of doxorubicin in the 3D culture the following protocol
(Table 5.5) was used with manual selection of centre z-stack image for analysis.
Common thresholding was used to select the 3D structure of interest and average
intensity of the selected object was recorded with diameter and area of structure. Eight
of the largest structures per time point in duplicate experiments where selected with the
centre z-stack selected based on half total height of the object.
Table 5.5. Opera image analysis protocol for determining average doxorubicin fluorescence per
central z-slice of individual 3D object (Acapella scripting parameters). The find image region building
block was selected and the common threshold adjusted for doxorubicin at the manually selected z-slice.
Several morphology properties were calculated including area, length and width of objects. The average
intensity of doxorubicin in each object was also calculated and exported as results.
Doxorubicin fluorescent intensity in 3D culture
Input Parameters for AAS:
doxorubicin 3D intensity calc.aas
FindImageRegion
Method:
channel_string:
CommonThreshold:
Clustering_ONOFF:
OutputName:
CalculateMorphologyProperties
WholeCells_string:
Region:
203
Common Threshold
Exp2Cam2
0.4
1
Image Region
Image Region
Image Region
Method:
Area_ONOFF:
Area_Unit:
Roundness_ONOFF:
Width_ONOFF:
Width_Unit:
Length_ONOFF:
Length_Unit:
RatioWidthToLength_ONOFF:
SuffixForPropertyNames:
Standard
1
µm2
0
0
µm
1
µm
0
Image Region size
channel_string:
WholeCells_string:
Region:
Method:
Mean_ONOFF:
SuffixForPropertyNames:
Exp2Cam2
Image Region
Image Region
Standard
1
Intensity of objects
Method1:
@Image Region : Number of
Objects1_stat1:
@Image Region : Intensity of
objects Mean1_onoff:
List of Outputs
CalculateIntensityProperties
ReturnResults
1
1
Analysis of the distribution through the 3D structures was performed in ImageJ using
the
radial
profile
plugin
(available
at
http://rsb.info.nih.gov/ij/plugins/radial-
profile.html). The radial profile analysis determines any change in intensity from the
centre of objects to their borders (Sengupta et al., 2009). Images of the centre z-stack of
BxPC-3 cell line 3D structures were exported from the confocal Opera imaging
platform. The images were then opened in ImageJ and the region of interest (3D
structure centre slice) selected with the circular region of interest (ROI) function. Pixel
intensity is recorded from the centre of the object to the border and graphed in Prism
Graphpad. Values are the averages ± SD corresponding to total pixel intensity present in
each concentric circle from the centroid. At least 8 separate objects, per time point in
duplicate experiments were used for radial profile plots.
5.2.11. Evaluation of cellular response to chemotherapy drugs in a fully
synthetic matrix compared to the biological hydrogel Matrigel.
204
To determine the influence of exogenous extra cellular matrix components on the drug
response of pancreatic cancer 3D cell cultures, PANC-1 3D cell cultures were initiated
in the fully synthetic matrix scaffold PuraMatrix® (Corning Life Sciences) and were
compared to Matrigel based 3D cultures. The drug activity effects of doxorubicin and
paclitaxel were examined on PANC-1 cells in 3D culture. PuraMatrix was diluted to a
similar consistency as the Matrigel based assay (7.5mg/ml) in phosphate buffered saline
(PBS) and layered into 384 well Cell Carrier microtitre plates. The PBS was then
replaced with media with three successive media changes and microtitre plates
incubated for 1 hour at 37°C 5% CO2 in a humidified incubator. Media was removed
and cells were cultured as per the Matrigel seeding procedure in section 4.3.5. Cell
numbers, media conditions and refresh rates, drug dosing and activity determinations
using the resazurin metabolic activity assay were all followed directly as described in
section 4.2.5 to allow direct comparison between conditions.
5.2.12. Statistical analysis
Unless otherwise stated, all data points are presented as the mean ± standard deviation
(SD) of three independent experiments. Statistical significance between culture models
or conditions was assessed using either the student’s t-test for two groups or one-way
ANOVA and Bonferroni post-hoc test for analysis of multiple group comparisons. All
statistical and graphical analysis was performed in Microsoft Excel and Prism
Graphpad.
5.3. Results
5.3.1. Cell viability evaluation via metabolic activity (resazurin) assay in 2D
and 3D cultures.
205
To compare drug responses between the established monolayer culture model and the
ECM based 3D cell culture model, a panel of chemotherapeutic agents were assessed.
Drug activity or response was measured by a number of parameters derived from the
dose response curves generated from both culture models. To reliably compare the
response of cells cultures in 2D and 3D models, assay conditions such as drug dosing,
exposure times, incubation conditions and liquid handling steps were maintained
between both the 2D and 3D assay formats. The Calcein AM staining from Chapter 4
was used to assess drug effects on the 3D culture morphology. All comparisons of
graphed data were statistically assessed by one way analysis of variances (ANOVA) and
Bonferroni’s post hoc test.
Gemcitabine
The cellular response to gemcitabine across all three cell lines displayed a minor, but
not statistically different decrease in potency between the 3D cell culture and monolayer
models (AsPC-1 2D IC50 10.09 ± 1.26, 3D IC50 22.30 ± 8.47; BxPC-3 2D IC50 6.64 ±
3.68, 3D IC50 26.42 ± 21.26; PANC-1 2D IC50 20.64 ± 8.05, 3D IC50 69.54 ± 12.91)
(Figure 5.4). However, an assessment of the dose response curves yielded significant
differences in other parameters, such as area under the curve (AUC) and maximal effect
(Emax) (Figure 5.4c). The data from these two efficacy metrics indicate a significant
change in maximum gemcitabine efficacy in the 3D culture model compared to the
monolayer model, with the largest discrepancy observed with PANC-1 cells (AsPC-1
2D Emax 0.91 ± 0.02, 3D Emax 0.74 ± 0.06; BxPC-3 2D Emax 0.99 ± 0.01, 3D Emax 0.87 ±
0.05; PANC-1 2D Emax 0.97 ± 0.01, 3D Emax 0.78 ± 0.04). The metabolic activity dose
response curves (Figure 5.4b) for all three pancreatic cancer cell lines, indicates a
population of cells in the 3D culture model that remain resistant or metabolically active
even at extremely high doses of drug (200µM). Analysis of the Calcein AM images
indicate that gemcitabine had an impact on the physical integrity of the 3D structures,
with single cells and cellular debris visible. There are a small number, however, of 3D
structures of reduced size (approximately 20%) that appeared to remain viable and
intact at the high drug concentrations.
206
Figure 5.4. Response to gemcitabine exposure in both 2D and 3D cell culture models using
pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the Calcein
AM cell viability assay of increasing doses of gemcitabine. (b) Representative dose response curves from
a single experiment with duplicate wells, data points are mean values ± standard deviation. (c) Response
metrics including IC50 values, maximum inhibition, area under the curve and Hill coefficient obtained
from the resazurin based metabolic activity assay. Data points are mean responses ± standard deviation
from three separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
207
Doxorubicin
An altered response to the anthracycline, doxorubicin, was observed between the
monolayer and 3D culture models across all three cell lines. A significant decrease in
potency was detected by cells in the 3D culture model compared to the cellular response
in the monolayer system (AsPC-1 2D IC50 156.27 ± 44.49, 3D IC50 440.53 ± 100.20;
BxPC-3 2D IC50 55.22 ± 19.81, 3D IC50 616.50 ± 311.95; PANC-1 2D IC50 65.57 ±
16.67, 3D IC50 308.77 ± 33.41). The decreased trend in potency was not seen in the
efficacy data, as the maximal effect remained unchanged, irrespective of whether a
monolayer or the 3D culture model system was used (Figure 5.5c). In contrast to the
effect observed in response to gemcitabine, doxorubicin displayed a near 100%
inhibition effect on metabolic activity of cells at doses above 10µM in both 2D and 3D
culture models. The impact of doxorubicin on 3D structure morphology is revealed
from Calcein AM imaging assay (Figure 5.5a). In all three cells lines, doxorubicin
completely disaggregated the physical organisation of the 3D structures, with only a
limited number of single cells still viable at the top dose (200µM).
208
Figure 5.5. Response to doxorubicin exposure in both 2D and 3D cell culture models using
pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the
Calcein AM cell viability assay of increasing doses of doxorubicin. (b) Representative dose response
curves from a single experiment with duplicate wells, data points are mean values ± standard deviation.
(c) Response metrics including IC50 values, maximum inhibition, area under the curve and Hill coefficient
obtained from the resazurin based metabolic activity assay. Data points are mean responses ± standard
deviation from three separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
209
Epirubicin
The impact of the other member of the anthracycline class of cytotoxic agents,
epirubicin, was also investigated and was demonstrated to mirror the response observed
with doxorubicin. Analysis of the dose response curves indicated similar shifts to the
right of the curve, indicating a decrease in potency of the drug in the 3D models when
compared to the 2D (Figure 5.6b). Significant decreases in drug potency were detected
with all three cell lines, with the most significant difference observed with BxPC-3 cells
(a 12 fold decrease) (AsPC-1 2D IC50 137.60 ± 56.57, 3D IC50 310.01 ± 106.83; BxPC3 2D IC50 54.42 ± 33.02, 3D IC50 640.10 ± 33.02; PANC-1 2D IC50 54.24 ± 20.30, 3D
IC50 248.10 ± 77.28). As seen with doxorubicin, no change in maximum effect was
observed, with near total cell death at the top drug doses. The morphological analysis
using the Calcein AM assay reveals only a small number of single cells remaining
viable at the top doses, with 3D structures significantly reduced in size as drug dose
increases (Figure 5.6a).
210
Figure 5.6. Response to epirubicin exposure in both 2D and 3D cell culture models using pancreatic
cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the Calcein AM cell
viability assay of increasing doses of epirubicin. (b) Representative dose response curves from a single
experiment with duplicate wells, data points are mean values ± standard deviation. (c) Response metrics
including IC50 values, maximum inhibition, area under the curve and Hill coefficient obtained from the
resazurin based metabolic activity assay. Data points are mean responses ± standard deviation from three
separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
211
Vinorelbine
When treated with the vinorelbine, the pancreatic cancer cell lines chosen for this study
exhibited an altered response (minor decrease in potency and efficacy) in 3D cell
culture compared to that observed in classical 2D monolayer cultures. The IC 50 values,
however, were not significantly different between the two cell culture formats for any of
the cell lines tested (AsPC-1 2D IC50 27.03 ± 5.89, 3D IC50 52.14 ± 15.43; BxPC-3 2D
IC50 12.73 ± 7.10, 3D IC50 79.96 ± 39.03; PANC-1 2D IC50 26.70 ± 4.87, 3D IC50 69.29
± 15.30). The maximum effect or efficacy of vinorelbine was, however, significantly
reduced by up to 45% as observed in the AsPC-1 cells, in the 3D culture system
compared to that reported for 2D (AsPC-1 2D Emax 0.98 ± 0.04, 3D Emax 0.52 ± 0.07;
BxPC-3 2D Emax 1.00 ± 0.02, 3D Emax 0.67 ± 0.11; PANC-1 2D Emax 0.86 ± 0.11, 3D
Emax 0.73 ± 0.05). Analysis of both Emax values and the area under the curve (Figure
5.7c) confirmed a highly significant change (p≤0.001) in response between cells in each
model. The dose response curves and the Calcein AM cell viability assay confirmed that
a large ( 45% AsPC-1, 33% BxPC-3 and 13% PANC-1) population of cells, in the 3D
culture model remain not only viable but also metabolically active following exposure
of the drug at the top dose ( 200µM) in all three cell lines. Morphological analysis of the
images obtained for the Calcein AM assay indicated that vinorelbine had an impact on
the physical integrity of the 3D structures (Figure 5.7a). They appeared to also lose their
compact configuration, with a number of cells shedding off from the main structures.
Although many of the cells were disassociated from the main 3D cellular structures,
they remained viable and metabolically active. As the dose of vinorelbine increased (0.1
> 200µM) the size of the remaining 3D structures was further reduced but the 3D
cellular structures remained relatively intact through to the top dose (200µM).
212
Figure 5.7. Response to vinorelbine exposure in both 2D and 3D cell culture models using
pancreatic cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the
Calcein AM cell viability assay of increasing doses of vinorelbine. (b) Representative dose response
curves from a single experiment with duplicate wells, data points are mean values ± standard deviation.
(c) Response metrics including IC50 values, maximum inhibition, area under the curve and Hill coefficient
obtained from the resazurin based metabolic activity assay. Data points are the mean responses ± standard
deviation from three separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
213
Docetaxel
The taxane, docetaxel, was also evaluated in the miniaturised in vitro 3D pancreatic
cancer cell culture model. Metabolic activity, as measured by the resazurin assay
described previously (section 4.2.5), of pancreatic 3D cell cultures treated with
docetaxel indicated an altered response for all three pancreatic cancer cell lines at doses
as low as 10nM. Interestingly, the actual calculated IC50 values for docetaxel treatment
under both 2D monolayer and 3D cell culture conditions, did not show significant
differences between the two approaches for any of the cell lines investigated (Figure
5.8c) (AsPC-1 2D IC50 3.31 ± 0.49, 3D IC50 17.15 ± 3.18; BxPC-3 2D IC50 1.15 ± 0.26,
3D IC50 3.91 ± 1.81; PANC-1 2D IC50 2.03 ± 0.30, 3D IC50 13.44 ± 5.69). However,
significant differences (p≤0.001) were observed in the efficacy of the 3D cell culture
model (both Emax and AUC measurements). Almost 100% of metabolic activity was
inhibited (at the end point of the assay) in the monolayer cell culture, compared with the
3D culture where a large population of cells (approximately 30%) remained
metabolically active from 20µM to 200µM (AsPC-1 2D Emax 0.93 ± 0.03, 3D Emax 0.58
± 0.11; BxPC-3 2D Emax 0.99 ± 0.02, 3D Emax 0.47 ± 0.09; PANC-1 2D Emax 0.94 ±
0.02, 3D Emax 0.77 ± 0.04).
The population of cells that remained metabolically active were also confirmed in the
Calcein AM assay to be viable. At these high drug doses, large populations of cells
remained viable, as can be seen in the representative Calcein AM stained images of
AsPC-1, BxPC-3, PANC-1 cell lines (Figure 5.8a). The main morphological change
observed in the 3D cultures was primarily a reduction in the overall size of 3D
structures. Generally, 3D structure size was reduced 50% compared to the untreated
control samples, with the integrity of the structures remained relatively intact suggesting
limited effect on cell adhesion within the structures and possibly cytostatic effects.
214
Figure 5.8. Response to docetaxel exposure in both 2D and 3D cell culture models using pancreatic
cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the Calcein AM cell
viability assay of increasing doses of docetaxel. (b) Representative dose response curves from a single
experiment with duplicate wells, data points are mean values ± standard deviation. (c) Response metrics
including IC50 values, maximum inhibition, area under the curve and Hill coefficient obtained from the
resazurin based metabolic activity assay. Data points are the mean responses ± standard deviation from
three separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
215
Paclitaxel
The other member of the taxane class of cytotoxic chemotherapy agents selected for
evaluation in both the classical monolayer and 3D cell culture systems was paclitaxel.
As observed with docetaxel, an altered response to drug exposure was detected in the
3D cultures for all three cell lines examined. The taxanes were the most potent class of
chemotherapy agents of all drugs within the reference panel examined and a trend of
decreasing sensitivity was observed across all three cell lines. However, no statistically
significant reduction in potency (IC50) was observed for any of the pancreatic cell lines
(AsPC-1 2D IC50 4.66 ± 0.79, 3D IC50 34.52 ± 8.59; BxPC-3 2D IC50 1.13 ± 0.64, 3D
IC50 17.93 ± 17.27; PANC-1 2D IC50 5.62 ± 1.29, 3D IC50 42.35 ± 0.71). There were
significant reductions (BxPC-3 p≤0.001 and PANC-1 p≤0.01) in efficacy for BxPC-3
and PANC-1 cell lines using the maximal effect parameter (Figure 5.9c) (BxPC-3 2D
Emax 0.99 ± 0.02, 3D Emax 0.473± 0.06; PANC-1 2D Emax 0.97 ± 0.02, 3D Emax 0.85 ±
0.02). The area under the curve parameter also indicated statistically significant
differences (AsPC-1 p≤0.01 and BxPC-3 p≤0.0010) in the AsPC-1 and BxPC-3 cell
lines (AsPC-1 2D AUC 19083 ± 122, 3D AUC 15379 ± 1194; BxPC-3 2D AUC 19931
± 458, 3D AUC 14051 ± 827). Exposure of cells to Paclitaxel (doses from 100nM to
200µM) in the monolayer culture model (144 hours exposure time) resulted in almost
100% inhibition of metabolic activity. However, in the 3D cell culture model, a
population of cells (approximately 15 – 25%) remained metabolically active and viable
at high doses. The impact of paclitaxel on the morphology of the 3D cellular structures
was similar across all three cell lines. Specifically, 3D structure size was reduced as the
dose of drug increased, ultimately showing a 30 - 50% reduction at the highest dose
(200µM) tested. Cells, irrespective of the cell line tested, were not significantly
disassociated by the drug exposure, with smaller structures remaining relatively intact
as compact cellular structures, as illustrated in Figure 5.9a
216
Figure 5.9. Response to paclitaxel exposure in both 2D and 3D cell culture models using pancreatic
cancer cell lines AsPC-1, BxPC-3 and PANC-1. (a) Representative images from the Calcein AM cell
viability assay of increasing doses of paclitaxel. (b) Representative dose response curves from a single
experiment with duplicate wells, data points are mean values ± standard deviation. (c) Response metrics
including IC50 values, maximum inhibition, area under the curve and Hill coefficient obtained from the
resazurin based metabolic activity assay. Data points are the mean responses ± standard deviation from
three separate experiments. Scale bar = 100µm. *p≤0.05, **p≤0.01, ***p≤0.001.
217
Overall, a trend of decreasing sensitivity to all of the evaluated chemotherapy agents
was observed across the three pancreatic cancer cell lines in the 3D culture model
compared to the monolayer system (Figure 5.10). Although the degree of loss of
potency (IC50 values) and efficacy (Emax and AUC) was cell line and drug dependant, all
cell lines were more resistant to the drugs selected for investigation, in the 3D culture
model. Altered efficacy, but not potency, was observed with gemcitabine, vinorelbine
and the taxanes across all three pancreatic cancer cell lines (Table 5.6). The
anthracyclines (doxorubicin and epirubicin) exhibited a shift in drug sensitivity, as
measured by the potency but maintained no change in efficacy between the models. A
number of potential chemoresistance mechanisms may be influencing the resistance
profiles observed in the ECM based 3D pancreatic cancer culture model including
altered proliferation rates, drug diffusion effects and survival signalling from ECM
interactions. These will be examined in the following section.
218
Figure 5.10. Pancreatic cancer cell lines grown in 3D culture display resistance to selected cytotoxic
drugs compared to 2D culture. AsPC-1, BxPC-3 and PANC-1 were cultured in either monolayer or 3D
ECM-based cultures and exposed to drugs for 6 days. Drug activity was measured by the metabolic
indicator (resazurin) assay. Data points are the mean responses ± standard deviation from three separate
experiments. *p≤0.05, **p≤0.01, ***p≤0.001.
219
Table 5.6. Summary of drug interactions between the three pancreatic cancer cell lines AsPC-1,
BxPC-3 and PANC-1 in different culture conditions. A significant change in potency (IC50) and
efficacy (Emax) is represented by either a ↑ for increase from 2D to 3D, ↓ for a decrease and ↔ for no
significant change. Disruption in 3D morphology is described by either; high for total disaggregation of
the structures, med for some fragmentation and shedding of cells but with 3D structures still intact and
low for limited impact on structural integrity and cellular aggregation with no cell shedding.
Shift in potency from 2D to 3D Shift in Efficacy from 2D to 3D
Drug
Disruption of 3D morphology
at 200µM dose
AsPc-1
BxPc-3
Panc-1
AsPc-1
BxPc-3
Panc-1
AsPc-1
BxPc-3
Panc-1
Gemcitabine
↔
↔
↔
↓
↔
↓
Med
Med
Med
Doxorubicin
↓
↓
↓
↔
↔
↔
High
High
High
Epirubicin
↓
↓
↓
↔
↔
↔
High
High
High
Vinolbrine
↔
↔
↔
↓
↓
↓
Med
Med
Med
Docetaxel
↔
↔
↔
↓
↓
↓
Med
Med
Med
Paclitaxel
↔
↔
↔
↓
↓
↓
Med
Med
Med
5.3.2. Proliferation rates between 2D and three dimensional 3D cell culture
models
Previously reported research in other solid tumours, such as breast, prostate, liver and
pancreatic has revealed that as cell masses move further away from vascular tissue,
reduced proliferation rates are often observed (Nyga et al., 2011; Pampaloni et al., 2007;
Sutherland, 1988). This is in stark contrast to standard in vitro monolayer culture, where
the homogenous population often has a uniform and rapid doubling time. Initial data
from earlier resazurin (indirect measure of proliferation) based experiments undertaken
as part of this project (Chapter 3) indicated a reduced proliferation trend in the 3D
model. To examine whether the altered drug response observed between pancreatic
cancer cells grown in a monolayer culture model compared with the 3D cultured cells
may have been influenced by changes in cellular proliferation, the proliferation rates
were compared between the two models. In order to measure a direct proliferation rate
(rather than indirect metabolic activity measurement using resazurin) the nuclear dye
Hoechst 33342 was used to count cell numbers over time (Figure 5.11, Figure 5.12).
220
Figure 5.11. Representative field of PANC-1 nuclei stained with Hoechst and acquired on the Opera
with the 10x air objective. Segmentation overlay of individual nuclei in 2D monolayer culture
conditions. Individual nuclei used for total well cell count as per protocol in section 4.2.9.
Figure 5.12. Representative rendered image of a single field of BxPC-3 3D structures on Day 9.
Individual nuclei segmented using cell count protocol listed in materials and methods section 4.2.9. Image
stack taken from the Opera imaging platform with the 10x objective using Hoechst 33342 stain.
The pancreatic cancer cell lines, AsPC-1, PANC-1 and BxPC-3, were examined in both
cell culture formats over a period of 9 days in culture (
221
Figure 5.13). PANC-1 exhibited only a minor change in proliferation, with a decrease in
doubling time from 36 hours in a monolayer to 44 hours in the 3D model. AsPC-1 cells
exhibited an intermediate decrease in growth rates, with a doubling time of 70 hours in
3D culture compared to 48 in a 2D. While BxPC-3 cells displayed a significantly
reduced growth rate, with a doubling time of 38 hours in monolayer and 156 hours in
the 3D model. Alterations in proliferation rates appeared to be cell line dependant in the
3D culture model.
222
Figure 5.13. Proliferation rates of pancreatic cancer cell lines PANC-1, BxPC-3and AsPC-1 in both
monolayer and 3D cell culture formats. Data calculated from nuclear counting protocols described in
methods section. Data points are expressed as mean of triplicate wells ± SD of 3 replicate experiments.
223
5.3.3. Drug diffusion and distribution in 3D cell culture
To evaluate whether penetration or diffusion of drugs may contribute to the altered
response seen in the in vitro 3D culture model, doxorubicin was used to visualise the
spatial diffusion in the 3D structures over time, using confocal microscopy. The unique
properties of doxorubicin makes it a useful tool in drug penetration / diffusion studies.
By utilising the well-established inherent florescence property of the anti-cancer drug,
doxorubicin, and fluorescent microscopy, we are able to visualise the diffusion of drug
through complex 3D structures that mimic micro tumours in vitro (Karukstis et al.,
1998). Analysis of doxorubicin diffusion has been assessed previously in other in vitro
solid tumours models, as well as anchorage independent 3D pancreatic cell culture
models (Fourre et al., 2006; Yeon et al., 2013).
Direct confocal microscopy of doxorubicin allows for an ‘in-well’ evaluation of drug
accumulation within 3D structures with minimal processing steps. Doxorubicin was first
visualised in a BxPC-3 and PANC-1 monolayer culture using a concentration equal to
the IC50 value of drug (50nM) and a higher 2µM
concentration to guarantee
visualisation (at this concentration it demonstrated 100% cell death). Images were taken
over a period of 72 hours following exposure to doxorubicin. At the more clinically
relevant IC50 dose, it was not possible to distinguish background cell fluorescence, from
doxorubicin fluorescence as demonstrated in Figure 5.14a, c. At the higher drug dose
(2uM), a distinct increase in inter-cellular fluorescence could be observed over time, as
the doxorubicin accumulated in the cells. Even at the 6 hour time point, drug
accumulation in cells can be visualised in both BxPC-3 and PANC-1 cells cultured in a
monolayer (Figure 5.14b, d).
224
Figure 5.14. Cellular penetration of doxorubicin in 2D pancreatic cell culture.(Representative fields
from the Opera confocal microscope using the 10x air objective.) (a) BxPC-3 cells exposed at 6h, 24, 72h
after exposure to doxorubicin (50nM). (b) BxPC-3 cells exposed to 2µM doxorubicin at 6h, 24, 72h. (c)
PANC-1 cells exposed at 6h, 24, 72h after exposure to doxorubicin (50nM). (d) PANC-1 cells exposed to
2µM doxorubicin at 6h, 24, 72h. Scale bar = 100µm. Dox = doxorubicin, Hoechst = Hoechst 33342
nuclear dye.
The 3D cell cultures exhibited a similar pattern of doxorubicin accumulation within the
3D structures in comparison to the monolayer culture (Figure 5.15). At the lower IC50
dose (500nM) only low levels of doxorubicin could be detected in the 3D structures of
PANC-1 cells at the 6 hour time point (Figure 5.15c). Limited doxorubicin could be
225
detected in the more tightly packed 3D structures of BxPC-3 cells levels at this time
point, compared to the more loosely packed PANC-1 structures (Figure 5.15a). At 24
hours after exposure, however, a distinct accumulation of doxorubicin could be detected
in the core of the largest 3D cellular structures in both BxPC-3 and PANC-1 cultures.
Figure 5.15. Cellular penetration of doxorubicin in 3D pancreatic cell culture. (Representative fields
from a single z-stack off the Opera confocal microscope using the 10x air objective) (a) BxPC-3 cells in
3D cell culture exposed at 6h, 24, 72h after exposure to doxorubicin (500nM). (b) BxPC-3 cells in 3D cell
culture exposed to 2µM doxorubicin at 6h, 24, 72h. (c) PANC-1 cells in 3D cell culture exposed at 6h, 24,
72h after exposure to doxorubicin (500nM). (d) PANC-1 cells in 3D cell culture exposed to 2µM
doxorubicin at 6h, 24, 72h. Scale bar = 100µm. Dox = doxorubicin, Hoechst = Hoechst 33342 nuclear
dye.
226
The fluorescent intensity from the 2µM doxorubicin exposure was quantitated in both
models with the BxPC-3 cell line and graphed in
Figure 5.16a. Greater accumulation (as indicated by higher fluorescence intensity) was
observed as exposure times increased. Even at the earliest time point of 6 hours,
doxorubicin had already diffused completely throughout the largest 3D structures.
Figure 5.16. The diffusion of doxorubicin in 2D and 3D cell culture over time (a) Fluorescence
intensity measured on the Opera confocal imaging system in both 2D monolayer and 3D culture models.
2D data expressed as the mean average intensity of 9 fields in triplicate in two separate experiments. 3D
data expressed as the mean average intensity of the centre z-stack image of eight 3D structures in
triplicate wells in two separate experiments. Error bars = standard deviation. (b) Radial profile analysis
plots of doxorubicin fluorescent intensity measured in selected 3d structures from BxPC-3 3D cell culture
exposed to drug for 6, 24, 72 hours. Values are the averages ± SD corresponding to total pixel intensity
present in each concentric circle from the centroid. The centre z-stack image of at least 8 separate objects,
per time point in duplicate experiments were used for radial profile plots.
227
To confirm that complete drug diffusion to the centre core cells of the 3D objects had
been achieved, a radial profile analysis was performed on the centre z stack images
from representative structures exposed to 2µM doxorubicin. BxPC-3 3D cultures were
chosen for this analysis as the 3D structures developed by this cell line had the tightest
packed cells and showed the slowest drug diffusion. The radial profile plots in
Figure 5.16b indicate that the fluorescence intensity remains relatively even from the
centre of the structures to the periphery. The absence of a decrease at the start of a plot
indicates that complete diffusion of doxorubicin throughout the 3D cellular structure has
been achieved, even at the earliest time point of 6 hours.
5.3.4. Effect of exogenous ECM components in 3D culture on cellular drug
response
To ascertain if the Matrigel ECM components may be contributing to the
chemoresistance observed, a synthetic hydrogel based 3D scaffold was compared. To
determine whether the chemoresistance effects were due to direct cell to matrix
interactions with external ECM components, the reference chemotherapy agents,
doxorubicin and paclitaxel, were evaluated using the synthetic scaffold PuraMatrix™
with the PANC-1 and BxPC-3 cell line. All assay conditions were maintained between
the biological and synthetic 3D culture systems (except for the Matrigel substitution).
The PANC-1 cell line formed similar numbers and sizes of 3D structures in the
PuraMatrix compared to Matrigel (data not shown). However, 3D structures appeared to
be less strongly embedded into the PuraMatrix than with Matrigel (visual observations
from cell movement following liquid handling steps) (
Figure 5.17c). There was also a large number of single cells present in the cultures
which appeared to be non-adherent to the synthetic matrix. Response to drug exposure
was determined using the metabolic activity assay (resazurin) and potency calculated as
described for the Matrigel based assays in Section 4.3.3. As indicated in
Figure 5.17a, no statistical differences in potency were measured between either matrix
based 3D culture systems (with PANC-1
Figure 5.17a and BxPC-3
228
Figure 5.17b cell lines). Although these results indicate that exogenous ECM may not
be critical in the chemoresistance observed, pancreatic cancer cells produce many of the
ECM components endogenously. Therefore, cell-to-endogenous matrix interactions may
still play a role in chemoresistance mechanisms by formation of 3D structures.
Figure 5.17. 3D matrices from synthetic or cell-derived sources do not affect the sensitivity of
BxPC-3 and PANC-1 3D cultures to paclitaxel and doxorubicin.
(a) Drug response of doxorubicin and paclitaxel using the biological hydrogel Matrigel™ and the
synthetic matrix PuraMatrix™ with PANC-1 pancreatic cancer cells. (b) Drug response of doxorubicin
and paclitaxel using Matrigel and PuraMatrix with BxPC-3 pancreatic cancer cells. No statistical
difference of potency (IC50 value) was observed between the different 3D culture conditions. Data points
are average IC50 values of three separate experiments. Error bars = standard deviation. (C) Representative
brightfield images of PANC-1 3D cultures utilising the synthetic Puramatrix. Images acquired on the
Perkin Elmer Operetta imaging platform with a 20x air objective. Scale bar = 100µm
229
5.3.5. Response of transient chemotherapeutic agent on efficacy in three
dimensional 3D culture
To examine whether the chemoresistant effects observed with the 3D cultures are
maintained after the drug stress is removed paclitaxel was removed from the 3D assay
(PANC-1 cell line) at the end of the standard exposure time (144 hours) and replaced
with complete media only (replenished every 48 hours) for an additional 144 hours. A
slight increase in drug potency was observed with a significant increase in efficacy
(p≤0.001 for Emax and p≤0.01 for the AUC calculation). The drug concentrations that
achieved only a 70% efficacy response (1µm -40µm) at 144 hours exposure became less
metabolically active after the drug stress had been removed (
Figure 5.18a). The Emax value increased from 0.71±3.45 to 0.96±1.69 and the AUC from
1858±651 to 3659±93.2 indicating that although a population of cells remained
metabolically active at the 144 hour time point, further incubation (144h) with media
only considerably reduced this population (
Figure 5.18c). This may indicate that initially approximately 30% of cells where
transiently resistant to the drug treatment or in state of cell cycle arrest yet still
metabolically active. Longer culture times (without paclitaxel exposure) caused
significant reduction in this resistant population (3-4% metabolically active).
230
Figure 5.18. Effects on metabolic activity of Panc-1 cells after extended incubation times with
paclitaxel removed from culture. (a) Dose response curves from a single experiment (data points are
the averages of quadruplicate wells in three separate experiments, Error bars equal standard deviation) of
the standard control assay of 144 hours of drug exposure compared to the drug removed at 144 hours and
media only for an additional 144 hours. (b) Representative brightfield images from the extended assay
with drug removed after 144 hours at 0.4, 4, 40, 400, 4000nM. Images taken on the Cell R microscope
with a 10x objective. Scalebar = 100µM. (c) Response obtained from the resazurin based metabolic
activity assay. Data points are the mean responses ± standard deviation from three separate experiments.
**p≤0.01, ***p≤0.001
231
5.4. Discussions & conclusions
The primary aim of this chapter was to confirm the suitability of the developed
pancreatic cancer in vitro 3D cell culture assay for use in HTS applications. Before a
novel assay can be utilised in any drug discovery program, it is important that it has
been thoroughly validated with agents that have known and well characterised activity.
The 3D cell culture based assays developed within the context of this project were
successfully utilised to characterise the activity of a panel of clinically relevant
chemotherapy agents. Reproducible responses were measured over multiple replicates
using a number of activity parameters.
The various responses observed for the
pancreatic cancer cell lines (AsPC-1, BxPC-3 and PANC-1) following treatment with
the panel of chemotherapy agents in this model, were also compared to the traditional
well established monolayer assay format. Finally, the possible chemoresistance
mechanisms which may be implicated in the altered response observed in the 3D culture
model, were examined.
Characterisation of drug responses by a multi-parametric approach is critical to
accurately measure the drug impact on cancer cell proliferation / inhibition. Utilising a
single activity metric alone, such as IC50 values, may not give a true indication of the
effect of a drug. The potency and efficacy of the six chemotherapy agents; gemcitabine,
doxorubicin, vinorelbine, epirubicin paclitaxel, docetaxel were successfully evaluated
on three pancreatic cancer cell lines (AsPC-1, BxPC-3 and PANC-1). Reproducible
activity profiles of the reference drugs were established over several independent
experiments and assay formats. These profiles will be vital for use in future screening
programs to assess assay performance.
The secondary aim of this study was to compare drug responses of cells in the 3D
culture model with the standard 2D monolayer system routinely used in HTS. Cell lines
established from patient derived tumours frequently become sensitive to chemotherapy
agents in vitro, where previously the agents had shown poor efficacy in vivo (Thoma et
al., 2014). This phenomenon prevents cancer cell lines from having optimal predictive
potential. The introduction of 3D in vitro cell culture tumour models aims to remove
this discrepancy by re-introducing the microenvironmental physical and chemical cues
that contribute to tumour chemoresistance (Friedrich et al., 2007; Sutherland, 1988).
232
Previous studies in other solid tumour research, as well as pancreatic cancer, has shown
cells grown in a 3D culture system exhibit an altered response to drugs compared to
those cultured in monolayer systems (Kimlin et al., 2013; Olive and Durand, 1994). The
majority of studies provide evidence of an increased drug resistant phenotype when
cells are grown in 3D cultures, which may be more predictive of in vivo activity. Results
from the evaluation of a panel of cytotoxic chemotherapy agents tested against the three
pancreatic cancer cell lines reveal a reduced sensitivity in 3D culture, expressed either
as a decrease in potency (increased IC50 values) or decrease in efficacy (reduced Emax
and AUC values). All three cell lines exhibited similar trends that were drug dependant.
Previous comparative studies between monolayer and 3D cultures have primarily
looked at potency only as the principle indicator of drug response. However, the results
obtained in this study indicate that only significant differences against the
anthracyclines would have been detected by the IC50 parameter alone. Although a trend
of decreased potency was seen with gemcitabine, vinorelbine and the taxanes against
the three cell lines, the marked reduction in efficacy revealed a unique chemoresistance
profile. Those drugs (doxorubicin and epirubicin) that were able to interrupt cellular
adhesion and disaggregate 3D structures the most effectively had the highest efficacy, at
least in this model.
The increased level of drug resistance observed in the 3D culture may be a result of
multicellular resistance, which is a phenomenon believed to arise as cancer cells form
3D structures and establish contacts with other cells and their microenvironment
components (Desoize and Jardillier, 2000; Kunz-Schughart et al., 2004). The process of
multicellular acquired resistance has been linked to a number of mechanisms in
previous studies of 3D cultures, including drug penetration effects, changes in
proliferation, modulation of gene expression profiles and increased survival pathways
linked to cell-to-cell and cell-to-matrix interactions (Friedrich et al., 2009; Thoma et al.,
2014).
The final aim of this study was to provide insights into the chemoresistance mechanisms
that have been implicated in solid tumour 3D in vitro cell culture based research.
Altered proliferation rates of cells grown as 3D structures or heterogeneous populations
of cells that divide at different rates throughout the structures may impact the
effectiveness of drugs that rely on rapidly dividing cells for anti-cancer activity.
233
Gemcitabine has been previously linked to the reported reduced proliferation rates of
pancreatic cancer cells (Dufau et al., 2012). As with many of the other cytotoxic agents
used in chemotherapy that rely on DNA damage or prevention of cell replication as
modes of action, cells that are quiescent or slowly proliferating may be less sensitive. In
this study, we have used a direct determination of cell number using nuclear counting
that has not been previously established for 3D in vitro pancreatic cell culture. The
effect of culturing pancreatic cancer cells in 3D culture on cellular proliferation rates is
particularly cell line dependant. The PANC-1 cell line exhibited a minor decrease in
proliferation rate (22%) compared to monolayer cultures. The AsPC-1 cells had
intermediate reduction (46%) and BxPC-3 cell lines demonstrated a significantly slower
proliferation rate (311%) in 3D culture. Although all three cell lines had a unique
change in growth rate, they all displayed the same drug response trends, indicating
proliferation rates alone may not explain the shift in drug responses.
The issue of drug penetration or diffusion through solid tumours is believed to play a
crucial role in the chemo-resistance seen in the clinical setting (Tunggal et al., 1999).
Drug penetration, through physical barriers created from the distance of the tumours
from vasculature as well other aspects of the tumour microenvironment such as
diffusion through ECM are often unable to be modelled effectively in vitro (Griffith and
Swartz, 2006). The 3D model presented here aims to capture some of this physical
complexity that is often overlooked in traditional monolayer culture systems used in
drug evaluations. Previous studies into solid tumours such as breast and pancreatic
cancer have examined drug diffusion in 3D cell culture models using paclitaxel and
doxorubicin (Nicholson et al., 1997; Yeon et al., 2013). These studies used extremely
high doses of compounds (>100µM) and also required multiple complex handling steps
of cultures to be imaged and analysed. The doxorubicin based drug diffusion studies
examined here utilised more relevant drug concentrations of 50nM to 2µM and
simplified plate processing. The results from these studies indicated that following six
hours exposure of cultures to doxorubicin, the drug had fully diffused through to the
core cells of the largest 3D structures. Greater accumulation of drug at the higher doses
and exposure over time was observed. As the assay length was 6 days of continuous
drug exposure, it is unlikely the chemoresistance mechanism (for doxorubicin) is simply
a physical barrier to drug diffusion through the 3D structures. Although the diffusion
profile of doxorubicin cannot be directly extrapolated to the other chemotherapy agents
234
evaluated (due to different molecular weights and different modes of active cellular
transport of certain drugs) it does provide insights into the effect of 3D structures on
drug diffusion rates.
Specific interactions between cancer cells and the surrounding microenvironment ECM,
such as cell-to-matrix adhesion, have been shown to alter the effects of chemotherapy
agents in vitro (Armstrong et al., 2004; Miyamoto et al., 2004). ECM components in
pancreatic cancer cell culture have also been shown to modulate cellular responses to
growth factors such as epidermal growth factor receptor (EGFR) (Sempere et al., 2011).
To determine if the ECM components of the Matrigel (used as the cell scaffold for
initiating 3D cell growth) were influencing the chemoresistance observed, PuraMatrix
synthetic non-protein based hydrogel was also compared. PuraMatrix was selected as it
is a fully synthetic peptide hydrogel that produces similar 3D structures to Matrigel
without the ECM proteins (Abu-Yousif et al., 2009). Drug responses of cells including
potency and efficacy profiles were not significantly different between the two 3D
culture methods. This data provides evidence that the impact of the exogenous
extracellular matrix proteins is not the primary chemoresistance mechanism in this 3D
culture model. Cell-to-matrix interactions, however, may still play a role in reducing the
sensitivity of cells grown as 3D structures. Pancreatic cancer cells have been reported to
produce endogenous ECM components and may be partially responsible for the
significant stromal deposits present in the in vivo tumour microenvironment (Lohr et al.,
1996; Lohr et al., 1994). The resistance profiles revealed in this study may be
influenced by the endogenously produced ECM components of the cancer cell
themselves, although this wasn’t examined. Research has indicated that multicellular
drug resistance may be conferred by intracellular adhesion mechanisms which have
previously been under appreciated in drug discovery programs (Bates et al., 2000). Cell
adhesion mediated drug resistance based on cell-to-matrix and cell-to-cell interactions
within the 3D structures formed, may play a larger role in the chemoresistance observed
in 3D cell culture models. Future studies, focussed on cell-to-cell and cell-to-matrix
interactions that may influence resistance of cells in 3D models may provide insights
into in vivo tumour resistance mechanisms.
The traditional preclinical model used in pancreatic cancer drug discovery involving a
2D monolayer cell culture platform does not realistically represent the complexity or
235
heterogeneity of the tumour microenvironment. The cellular response to anti-cancer
therapeutics observed in this 3D culture system, often does not translate to other
preclinical models or clinical trials. To demonstrate the ability for an ECM induced 3D
cell culture model to be used for testing therapeutics in a more physiologically relevant
system, we tested a panel of clinically relevant chemotherapy agents against three
pancreatic cancer cell lines. Significant drug response differences were observed
between the monolayer and 3D culture platforms for selected but not all drugs assayed,
and insights into possible mechanisms of chemoresistance were examined. The results
obtained here support the continued evaluation of this 3D in vitro tumour model as a
novel platform for drug response studies. Establishing 3D cell culture models which
restore the histomorphological, functional and some of the microenvironmental features
of in vivo pancreatic cancer tumours and remain suitable for early drug discovery
processes may play an important role in future drug discovery programs. In this study
we have examined a novel 3D pancreatic cancer model that replicates some of the
complex environmental features of in vivo tumours. As is the case in vivo, cancer cells
cultured in 3D in vitro conditions did have 100% response to all chemotherapy agents
examined. This model, may therefore provide useful and possibly more relevant drug
response information anti-cancer drug discovery programs for pancreatic cancer.
236
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6. Chapter Six: Pilot Screen of a Panel of Clinically Relevant Drugs
6.1 Introduction
The final stage in assay development before implementation in a primary high
throughput screening (HTS) program is to evaluate the robustness of the assay with a
pilot screen to demonstrate the feasibility of the system to identify compounds with
unknown activity. To validate the 3D in vitro pancreatic cancer assay, a pilot screen of a
clinically relevant drug library was undertaken utilising the assay protocols previously
established in Chapter 3. The collection of 741 drugs was composed of known and wellcharacterised bioactive compounds that have diverse clinical targets (including
oncology, neuropsychiatry, cardiology and anti-ineffectives). The collection included
FDA approved agents and perspective drugs which failed clinical trial progression (See
Appendix 1 for full list). The library offers a unique opportunity to validate the newly
developed assay with drugs that have a broad range of mechanisms of action (MOA),
chemical structure and previously documented bioactivity and safety profiles.
Drug repurposing involves the development of novel uses for existing drugs and has
seen an increasing trend in recent years in parts of the drug discovery industry (Chong
and Sullivan, 2007). The strategy of drug repurposing or repositioning has a history of
high profile success stories (such as minoxidil and Sildenafil citrate) and has the
potential to accelerate drug discovery programs (Mullard, 2012). Repositioned drugs
that already have regulatory approval (at similar or lower clinical doses) would not
require early clinical phase trials and may have expedited development (Oprea and
Mestres, 2012). The current standard of care chemotherapy drug for pancreatic cancer,
gemcitabine, was initially developed as an anti-viral agent before repositioning as an
anti-cancer agent. Several examples of drugs which have been successfully repositioned
are listed in Table 6.1 (Aronson, 2007), some of
which are drugs repurposed as
oncology therapeutics (such as thalidomide for multiple myeloma). The strategy of drug
repurposing relies on the concept that a single drug may interact with multiple targets
(or targets relevant to one specific disease) or may be involved in several biological
processes (Grau, 2007). A number of approaches have been employed in drug
repurposing programs. These include virtual analysis of published data to identify
241
connections between drugs and targets, as well as physical screening that can identify
existing drugs with unanticipated phenotypic effects.
Table 6.1. Successfully repositioned drugs from the original indication to the current application
Adapted from (Aronson 2007 and Ashburn 2004).
Drug
Amphotericin B
Aspirin
Bromocriptine
Duloxetine
Finasteride
Fluoxetine
Gemcitabine
Original Indication
Fungal infections
Inflammation, pain
Parkinson’s disease
Depression
Prostate hyperplasia
Depression
Viral infections
Methotrexate
Cancer
Minoxidil
Hypertension
Breast and prostate
cancer
Morning sickness
Angina
Cancer
Acne
Cancer
Raloxifene
Thalidomide
Sildenafil
Miltefosine
Tretinoin
Zidovudine
New Indication
Leishmaniasis
Antiplatelet
Diabetes mellitus
Stress urinary incontinence
Hair loss
Premenstrual dysphoria
Cancer
Psoriasis, rheumatoid
arthritis
Hair loss
Osteoporosis
Leprosy, multiple myeloma
Erectile dysfunction
Leishmaniasis
Leukaemia
HIV / AIDS
Increasing interest in drug repurposing in recent years has led a number of
biotechnology companies and academic institutes to focus on drug repurposing as a
valid drug discovery strategy (Oprea and Mestres, 2012). There are a number of
published examples of drugs under investigation in novel disease indications from
successful academic screening programs. These include repurposing of the antihistamine astemizole for adjuvant therapy in prostate cancer, as well as an anti-infective
against plasmodium falciparum the causative organism of malaria (Chong et al., 2006;
Oprea et al., 2011). The anti-viral drug, raltegravir, is in clinical trials for the treatment
of head and neck squamous cell carcinoma and similarly, the non-steroidal antiinflammatory, ketorolac, for ovarian cancer (Oprea et al., 2011).
The drug library selected for investigation as part of the studies reported here, not only
provides an opportunity to evaluate the reproducibility of the 3D cell based assay under
automated screening conditions but also takes advantage of the well characterised
nature of the drugs that may be advantageous in the search for new pancreatic cancer
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therapeutics. Like traditional drug discovery programs, drug repurposing has
experienced many of the hurdles in development required to reach regulatory approved
drug, such as clinical development and intellectual property challenges. However,
highly active, previously unreported drugs against pancreatic cancer cell lines that
already have a large volume of safety and mechanisms of actions data behind them, may
have the potential to accelerate such drugs into late stage drug development programs.
6.1.1
Pilot and secondary screen evaluations
The initial primary screening of the drug library was undertaken using a 2D monolayer
based assay in 384-well format, followed by confirmation using the 3D cell culture
model developed here and the traditional 2D monolayer format. The PANC-1 cell line
was selected for use in the primary screen purely as a representative cell line to
undertake the preliminary evaluations. The PANC-1 cell line has been extensively
characterised in previous pancreatic cancer research reported in the literature. The
expanse of information available made this a priority cell line for evaluating drug
impact. The primary screen of the 741 drugs was performed in single point over three
doses, with a final assay concentration of 22µM, 2.2µM, 0.22µM against the PANC-1
cell line. Active drugs or ‘hits’ were selected based on the ability of the drugs to inhibit
metabolic activity / cell viability. A selection of these hits was chosen based on a
number of criteria including activity profile, availability of commercial products and
original disease indication. Traditionally, hits are selected by ranking all compounds or
data points by a single value and applying a threshold cut off. This value is often
determined by either data distribution (standard deviation of the mean) or by a set
capacity such as 100 most active compounds (Durr et al., 2007) . To determine active
agents based on the threshold cut-off approach, a decision for a cut-off value needs to be
chosen. A cut-off threshold too low will result in an excessive number of hits, while in
contrast a cut-off value too high may result in missed potential interesting agents. Thus,
determining an optimal cut-off values is an important decision for efficient and sensitive
screening programs. The cut-off threshold for hits in this screen was based upon the
approach of four standard deviations of the mean of the in-plate negative controls
(100% growth wells), which is based on previous lab experience in screening
campaigns (Tong et al., 2007). The cut-off value was set at 40% inhibition (4 standard
deviations of the mean) normalised against control values.
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The secondary characterisation assay screened the selected hits from the primary pilot
screen in duplicate 10 point dose response assays in both the 3D and monolayer
formats. This additional evaluation expanded the range of drug doses from 0.004µM to
40µM. The three pancreatic cancer cell lines evaluated against the reference drugs in
Chapter 4 were used to establish inhibitory effects on cell viability and morphological
effects on 3D structures of the selected active drugs/compounds. The pancreatic cancer
cell lines evaluated were PANC-1 (primary origin, K-ras, p53, p16 mutations), AsPC-1
(metastatic origin, K-ras, p53 mutations) and BxPC-3 (primary or origin, p53, p16,
SMAD4 mutations). Metabolic activity and morphological effects of the drugs on cells
cultured in 3D and monolayer cell culture were characterised.
6.2 Materials & methods
6.2.1
Materials and reagents
Black side-clear bottom (384-well) tissue culture (TC) treated plastic bottom optical
imaging microplates (Perkin Elmer #6007550) were used for all cell based assays
described throughout. Polypropylene 384-well clear microtitre plates (Falcon #353265,
BD Biosciences) were used for all master stock and concentration response curve
(CRC) drug plates. The biological hydrogel (growth factor reduced Matrigel) was
purchased from Corning Life Sciences. Resazurin (Sigma Aldrich) dye was prepared as
per materials method section 3.2.1 Chapter 3.
6.2.2
Drug/compound handling
Master drug plates containing 741 clinically relent drugs were prepared by Compounds
Australia (Griffith University) in single point dose (384-well microplate format) at
5.5mM concentration in dimethyl sulphoxide (DMSO). The plates were kept frozen at
20°C for short term storage. The secondary 3D and monolayer compound concentration
response curve (CRC) based studies employed stock powdered drugs obtained from
Sigma Aldrich which were reconstituted at 10mM concentration in 100% DMSO before
10 point serial dilutions were made in master drug microplates.
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6.2.3
Preparation of drug plates for assay dosing
The three master drug microtitre plates were thawed at room temperature for the
primary pilot screen on a plate shaker and dispensed into intermediate dilution plates
using the Agilent Bravo Liquid handler. Three sets of intermediate dilution plates were
prepared using sterile MilliQ water; a 220µM, 22µM and 2.2µM (final DMSO
concentration 4%). The master CRC drug plates for use in the secondary 3D and
monolayer studies were also thawed prior to use on a plate shaker and dispensed into
intermediate dilution plates using the Agilent Bravo Liquid handler. Dilution plates
were prepared with sterile MilliQ water to generate dose responses of relevant drugs
between 400µM and 2nM in a final DMSO concentration of 4%.
6.2.4
Cell Culture preparation
General cell culture maintenance was performed as described in Chapter 3 section 3.2.2
with the cell lines AsPC-1, BxPC-3 and PANC-1.
6.2.5
Primary pilot screen of clinically relevant drugs
For the primary pilot screen, a single pancreatic cancer cell line was chosen for
evaluating drug responses in a 72 hour monolayer culture format. The PANC-1 (K-ras,
p53, p16 mutant, primary tumour origin) cell line was seeded at 1000 cells per well
(45µl of complete media) and incubated for 24 hours at 37°C 5% CO2 to allow cells to
adhere. The following day, 5µl of drug dilutions (0.22µM - 22 µM) were dispensed
automatically into the assay plates via the Bravo Liquid handler. The three drug
dilutions resulted in final assay concentrations of 22µM, 2.2µM and 0.22µM for a total
of 10 plates (9 drug plates and 1 control plate). The control plate included a 12 point
dose response of the reference compound (puromycin) and positive (100% cell
inhibition, 10% DMSO) and negative controls (100% growth, 0.4% DMSO). After 72
hours of drug exposure, 5µl of 600µM resazurin was dispensed by the Agilent Bravo
and plates were incubated (37°C 5% CO2) for 4 hours and fluorescence values read on
the Envision™ plate reader using the previously optimised resazurin protocol (Ex.
530nm, Em. 595nm). Responses to the drugs were calculated as described in Chapter 4
section 4.2.5.
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6.2.6
Secondary screen of selected drugs in dose response
Freshly prepared drug dilution plates were used to dose both 2D and 3D culture assay
plates according to the schedule described in Chapter 4 section 4.3.6. Directly after
media changes, 5µl of either the drug serial dilutions or controls were dispensed into the
media above the cells in each assay plate with Agilent Bravo liquid handler. All
aspiration, mixing and dispensing steps were performed using an automated protocol
optimised for plate types and liquid levels (aspirate and dispense heights adjusted to
3.5mm from plate bottom and liquid velocity reduced to 1.5µl/s). Disposable tips (CP70V11, Fluotics) were discarded after each completed experiment. The concentrations
for the drug dose responses are listed below in Table 6.2. Control wells consisted of a
final assay concentration of 10% DMSO (positive controls, 100% inhibition) and 0.4%
DMSO (negative control, 100% growth). Resazurin dye was used to measure drug
responses as described in Chapter 4 section 4.2.5. Additionally, representative 3D
structure morphology was recorded with the Operetta™ high content imaging platform
immediately after resazurin measurements. Bright field images of four fields of each
well from 3D assays were recorded with a 5 plane z-stack taken 500µm to 1200µm
above the bottom of the plate (cell line dependant) using the 20x objective. In-focus
images were manually selected (as automated maximal projection could not be used
with brightfield images) and used to evaluate drug effects on 3D structures.
Table 6.2. Concentrations of drugs / compounds and DMSO for master drug plates, dilution plates
and assay plates
Plate
DMSO %
Drug
Concentration
Master
(mM)
100%
Dilution
(mM)
4%
Assay 2D Assay 3D
(µM)
(µM)
0.40%
0.40%
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
400
200
100
20
4
1
0.2
0.04
0.01
40
20
10
2
0.4
0.1
0.02
0.004
0.001
246
40
20
10
2
0.4
0.1
0.02
0.004
0.001
0.01
0.005
0.0025
0.001
0.0005
0.00025
0.0001
6.2.7
0.002
0.0002
0.0002
Data analysis from metabolic assays for 2D and 3D culture models
Drug response normalisation and data analysis was performed as described in Chapter 4
section 4.2.6.
6.2.8
Statistical analysis
Statistical significance between conditions was assessed using either the student’s t-test
for two groups or one-way ANOVA and Bonferroni post-hoc test for analysis of
multiple group comparisons. All statistical and graphical analysis was performed in
Microsoft Excel and Graphpad Prism.
6.3 Results
6.3.1
Primary screen of pilot study
The library of 741 clinically relevant drugs was screened in a single point 384-well
format against the PANC-1 cell line in a monolayer resazurin based assay. A total of ten
384-well microtitre plates were screened (complete library in three doses 22µm, 2.2µm,
0.22µm and controls) over a period of 4 days. The fluorescent values from the resazurin
metabolic activity assay was normalised against controls and followed a Gaussian
distribution (each of the 741 agents in the 3 doses is plotted) (Figure 6.1a). A trailing
tail to the right reveals a small population of active compounds distributed evenly
towards the 100% inhibition. The reference drug puromycin revealed a drug sensitivity
consistent with previously experimental range for the PANC-1 cell line, with an IC50
value of 1.146µM (Figure 6.1b). The external control plate contained the reference drug
(puromycin) dose response in addition to 240 positive and negative control wells (for
247
assay quality measurements). The statistical parameters for measuring assay
reproducibility revealed a robust assay with a coefficient of variation (%CV) of 6.61%,
a signal window (SW) of 10.1 and a Z’-factor of 0.76 (Figure 6.1c).
Figure 6.1. Assay reproducibility, activity distribution and sensitivity measurements. (a) Frequency
distribution histogram (measurements binned as percentage inhibition) of all data (3 doses) obtained from
the primary pilot screen. (b) Reference drug (puromycin) dose response curve against the PANC-1 cell
line in the 72 hour monolayer assay. (c) External control plate values for the statistical parameters
coefficient of variation (%CV), Signal Window (SW), Z′ Factor (Z'). (d) Reference drug (doxorubicin)
dose response curves of AsPC-1, BxPC-3 and PANC-1 cell lines in the secondary 3D assay. Data
represents average of duplicate wells, error bars represent the standard deviation. (e) Secondary screening
in-plate controls, coefficient of variation (%CV) (●) based on 100% growth controls and Z′ Factor (▪).
248
The heat map and scatterplot representations (Figure 6.2) of the whole microtitre plate
activity, revealed no significant plate effects, with red wells in the heat maps indicating
hits (activity above the cut-off threshold). The intra-plate negative control well had a
standard deviation (SD) across all 9 plates screened of 9.3%. The cut-off threshold was
calculated at four standard deviations of the mean of the control wells and set at ≥40%
inhibitory activity against the PANC-1 cell line. At the top dose (22µM) there were 109
drugs that displayed ≥40% inhibitory activity, at 2.2µM there were 39 drugs and at the
lowest dose (0.22µM) 23 drugs met this threshold. The 2.2µM dose was used for
selection of drugs to be evaluated for the secondary screen.
249
Figure 6.2. Visual representation of drug / compound activity in Heatmap and scatterplot forms.
(a) Heatmap representation of the 3 doses (22µM Top, 2.2µM Middle and 0.22µM bottom) of drug plate
1. (b) Heatmap representation of the 3 doses (22µM Top, 2.2µM Middle and 0.22µM bottom) of drug
plate 2. (c) Heatmap representation of the 3 doses (22µM Top, 2.2µM Middle and 0.22µM bottom) of
drug plate 3. Red colour indicates greater than 40% activity, yellow 1-39% and green up to 0% activity.
(d) Scatterplot of distribution of the primary screen of all drugs at the 22 µM dose. (e) Scatterplot of
Distribution of the primary screen of all drugs at the 2.2µM dose. (f) Scatterplot of Distribution of the
primary screen of all drugs at the 0.22µM dose. Each data point is representing by a single dot with the
cut off threshold (40%) represented as a solid red line.
250
The 39 drugs that displayed ≥40% inhibitory activity against the PANC-1 cell line in the
2D assay were selected for further evaluation. The hit rate of 5.3% whilst generally
considered to be high, is not unexpected, as the library contains a number of approved
oncology agents with anti-proliferative effects (See Appendix 1). From this list of 39
hits, 11 drug candidates were selected based on commercial availability and original
disease indication (Figure 6.3). A large number of clinical anti-cancer drugs (9) that had
previously been used to treat pancreatic cancer were amongst the 39 drugs identified
and were excluded from further studies. Despite being excluded, the presence of these
drugs in the hit list illustrated that the assay was capable of detecting such compounds,
thus providing a further internal control. The activity profiles of the 11 drugs selected
were then characterised and validated using both the 3D pancreatic cancer cell culture
assay and traditional monolayer assay. The panel of drugs (ciclopirox olamine,
mycophenolic acid, mitoxantrone dihydrochloride, digitoxin, maduramicin ammonium,
salinomycin, topotecan hydrochloride, rubitecan, cerivastatin, simvastatin, racecadotril)
was retested in a 10 point dose response (40µM to 0.2nM), utilising both the 2D and 3D
resazurin based metabolic activity assays. Of the 11 drugs, 10 reconfirmed in the
secondary screen, with only a single drug racecadotril (an anti-diarrheal medication)
failing to reconfirm primary screening data, with no metabolic activity inhibition at any
dose measured in both 2D and 3D assays. Thus an exceptional reconfirmation rate of
91% was obtained.
251
Figure 6.3. Summary schematic of the primary pilot screen and hit identification process through
to the selection of 10 drugs for dose response studies in the monolayer and 3D assays.
252
6.3.2
Secondary screen of selected drugs with anticancer activity against
pancreatic cancer cell lines in monolayer and 3D assays.
The resazurin (metabolic activity) assay used to evaluate the reference drugs in Chapter
4 and 5 was utilised to screen the 10 confirmed drugs identified from the primary pilot
screen. A brief description of the drug, its proposed mechanism of action and its
chemical structure are listed in Table 6.3. A number of drug response metrics were used
to compare drug activity between cell lines and culture model. These included the
potency and efficacy measures IC50, Emax (maximum inhibition) and AUC (area under
the curve). Drug effects on the 3D structure morphology was assessed by representative
brightfield imaging of the cultures at each concentration point (at the assay conclusion
144 hours drug exposure). All drugs used for these evaluations were purchased as new
stock powders and reconstituted before screening.
253
Table 6.3. Drugs selected for further study from primary pilot screen with activity against the
pancreatic cancer cell line PANC-1 in monolayer culture.
Description & Mechanism of
Drug
Structure
Action
Ciclopirox
olamine
6-cyclohexyl-1-hydroxy-4-methyl2(1H)-pyridone 2-aminoethanol
salt (Ciclopirox olamine) is an antifungal agent currently used to
treat cutaneous fungal infections.
This alpha hydroxy-pyridone is an
iron chelator that has shown
preclinical anti-cancer activity
against a number of malignancies.
Mycophenolic
acid
Mycophenolic acid is the active
moiety of mycophenolate mofeti
which is currently used as an
immunosuppressant treatment in
patients undergoing solid organ
transplantation. Mycophenolic
acid inhibits inosine mono
phosphate dehydrogenase
(IMPDH) preventing de novo
biosynthesis of guanine
nucleotides and has previously
shown anti-proliferative effects in
numerous cancers in vitro.
Mitoxantrone
dihydrochloride
Mitoxantrone is an
anthracenedione that inhibits
topoisomerase II activity and
disrupts DNA synthesis. It has
previously been used for
treatment of breast cancer,
prostate cancer, acute
nonlymphocytic leukemia and
multiple sclerosis.
Digitoxin
Digitoxin is naturally derived
sodium pump inhibitor. This
cardiac glycoside is currently used
in the treatment of heart related
diseases such as congestive heart
failure and atrial arrhythmia. It has
recently shown in vitro and in vivo
anti-cancer properties. Digitoxin is
believed to activate pro-apoptotic
pathways in several cancer cell
lines. The proposed mechanism
including inhibition of glycolysis,
inhibition of overexpressed
sodium pump subunits and
inhibition of N-glycan expression.
Maduramicin
ammonium
Maduramicin is an ionophoric
monoglycoside polyether that is
currently used as a coccidiostat in
the poultry industry. This antiparasitic drug induces high
intracellular sodium
concentrations which results in
cellular membrane disruption in
the target protozoan. No anticancer studies have been
published.
254
Drug
Salinomycin
Topotecan
hydrochloride
Rubitecan
Description &
Mechanism of Action
Structure
Salinomycin is a
monocarboxylic polyether
antibiotic that has previously
shown antibiotic,
antimicrobial and
anticoccidial activity. It is
primarily used as an antiparasitic ionophore in feed
stocks. This lipophilic
chelating agent is reported to
disrupt potassium gradients
between cellular membranes,
particularly mitochondria
causing cell death. Antiproliferative and migration
effects have been reported in
various cancer cell lines in
vitro.
Topotecan is camptothecin
analogue that is approved for
treatment of a number
cancers including ovarian,
cervical and small cell lung
cancer. Its anti-tumour
activity is believed to be
mediated through the
inhibition of DNA
topoisomerase I.
Rubitecan is an orally
available camptothecin
analogue that has been
investigated for use against a
number of cancers including
pancreatic and ovarian. Its
anti-tumour activity is
mediated through the
inhibition of DNA
topoisomerase I.
Cerivastatin
Cerivastatin is a member of
the statins, which are a class
of inhibitors of hydroxymethyl-glutaryl coenzyme A
(HMG-CoA) reductase. It is
used for treatment of hypercholesterolemia and has
been shown to have anticancer activity against a
number of malignancies.
Simvastatin
Simvastatin is an inhibitor of
hydroxy-methyl-glutaryl
coenzyme A (HMG-CoA)
reductase. It has a number of
effects on human tissue
including inhibition of plaque
formation, platelet
aggregation and the
improvement fibrinolytic
activity. It has also
demonstrated antiproliferative effects in
numerous in vitro and in vivo
studies of cancer cell lines.
255
6.3.3
Ciclopirox olamine
Ciclopirox olamine (CPX) was one of the ten drugs selected for further investigation in
the secondary screen. This alpha hydroxyl pyridine is currently used for the treatment of
cutaneous fungal infections. It is an intracellular iron chelator that has recently
displayed anti-cancer activity at clinically relevant concentrations in a number of
preclinical cancer models. Oral formulations have been developed and examined in
clinical trials for haematological malignancies, such as acute myeloid leukaemia
(AML), acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL),
chronic myeloid leukemia (CML) (Minden et al., 2014). The in vitro anti-cancer activity
has been attributed to chelation of intracellular iron and the subsequent disruption to cell
cycle progression and production of reactive oxygen species (ROS). The Wnt pathway
involves secreted signalling molecules that regulate cell-to-cell interactions and aberrant
Wnt signalling is believed to contribute to tumour progression and proliferation. An iron
independent mechanism in which CPX inhibits Wnt signalling in tumour cells has also
been implicated (Song et al., 2011).
Analysis of the CPX responses in the 2D monolayer assay reveals that all three cell
lines (AsPC-1, BxPC-3 and PANC-1) had similar potency and efficacy profiles with
IC50 values between 1.1 µM and 1.6µM, Emax 0.99 and AUC between 3630 units and
3703 units. The 3D assay, however, revealed a cell line specific shift in drug response
profiles, with a reduction in efficacy in the AsPC-1 and BxPC-3 cell lines. The Emax
(AsPC-1; 0.80 and BxPC-3; 0.83) and AUC (AsPC-1; 2783 and BxPC-3; 2179) values
indicated a 20% reduction in maximal inhibition at the top doses (40µM) (
Figure 6.4a). Conversely, the PANC-1 cell line displayed no change in efficacy between
the two culture models, with 99% maximal inhibition recorded. BxPC-3 was the only
cell line to exhibit a reduced potency in the 3D model, with a ten-fold reduction in IC50
value, which can be seen in the shift to the right of 3D dose response curve (
Figure 6.4b). Morphological analysis using representative brightfield images
demonstrates that at low doses (0.004µM) of CPX, 3D structures have their structural
integrity intact, with no obvious signs of apoptotic shedding cells (
256
Figure 6.4c). Alternatively, at the highest dose (40µM) 3D cultures are distinctly
different, displaying disorganised cellular structures with single rounded cell
populations.
Figure 6.4. Responses observed following ciclopirox olamine treatment of pancreatic cancer cell
lines for 6 days. (a) IC50, Emax and AUC determinations of ciclopirox olamine against AsPC-1, BxPC-3
and PANC-1 pancreatic cancer cell lines in both monolayer and 3D culture conditions. Results are
representative of the mean inhibition of duplicate wells of a single determination. 95% confidence
intervals for IC50 values are given in parentheses. (b) Comparative dose response curves between 2D
monolayer and 3D resazurin based metabolic activity assays for all 3 cell lines. Representative fields from
each cell line in response to the minimum (0.004µM) and maximum (40µM) drug doses screened.
Brightfield images obtained from an Operetta high content imaging platform using the 20x objective.
Scale bar = 100µm.
257
6.3.4
Mycophenolic acid
MPA is the active moiety of mycophenolate mofetil which was synthesised to increase
the bioavailability of MPA for oral administration. Mycophenolic acid inhibits inositol
monophosphate dehydrogenase (IMPDH), preventing de novo biosynthesis of guanine
nucleotides, resulting in decreased synthesis of RNA and DNA. This drug is currently
an approved immunosuppressant treatment for patients undergoing solid organ
transplantation. MPA has recently been shown to inhibit invasion and migration of
gastric cancer cells and a reduction in pancreatic cancer proliferation and vascular
endothelial growth factor (VEGF) expression in vitro and in vivo (Rodriguez-Pascual et
al., 2012).
The drug profile of MPA against the pancreatic cancer cell lines in monolayer and 3D
culture assays is summarised in
Figure 6.5a. There was little difference between potency and efficacy profiles obtained
for AsPC-1, BxPC-3 and PANC-1 cell lines in the 2D monolayer assay. The potency of
all cell lines examined was between 0.483µM and 1.381µM, with the maximal
inhibition 0.88 to 0.97. The 3D assay revealed a decrease in potency for the BxPC-3
(1.382µM to 2.999µM) and PANC-1 cell lines (0.483µM to 1.896µM). There was also
a pronounced reduction in efficacy for MPA exposure, as observed in the Emax and AUC
values from 2D to 3D culture formats. The efficacy at the top dose dropped for AsPC-1
(Emax 0.88 to 0.36, AUC 3197 units to 1499 units), BxPC-3 (Emax 0.96 to 0.76, AUC
3491 units to 2672 units) and PANC-1 (Emax 0.97 to 0.62, AUC 3715 units to 2339
units). The dose response curves highlight the top doses failure to inhibit metabolic
activity to the same extent as in the monolayer assays (
Figure 6.5b). Examination of the effects on 3D morphology in Figure 6c reveal that
much smaller 3D structures are present at the top dose of 40µM compared to the lower
4nM dose. Despite the reduction in size the structural integrity has not been
258
significantly disaggregated, particularly for AsPC-1 and BxPC-3, with few single cell
shedding from the main structures (
Figure 6.5c).
Figure 6.5. Responses observed following mycophenolic acid treatment of pancreatic cancer cell
lines for 6 days.
(a) IC50, Emax and AUC determinations of Mycophenolic acid against AsPC-1, BxPC-3 and PANC-1
pancreatic cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the
mean inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values are
given in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin
based metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to
the minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from
an Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
259
6.3.5
Mitoxantrone dihydrochloride
Mitoxantrone dihydrochloride (MTX) was demonstrated to exhibit potent activity
against the pancreatic cell line PANC-1 in the primary pilot screen. MTX is an
anthracycline derivative developed in 1979 and belongs to the class of agents called
anthracenediones. Previously, anti-viral, anti-bacterial and anti-tumour activity with
demonstrated clinical activity in leukaemia, lymphoma and breast cancer have been
reported (Koeller and Eble, 1988). The anti-neoplastic activity of MTX has been
attributed to topoisomerase II inhibition and production of ROS (Vibet et al., 2007)
which prevents DNA synthesis and delays cell cycle progression. Recently, the
mechanism of action of MTX in pancreatic cancer has been linked to the inhibition of
the Ubiquitin-specific peptidase 11 (USP11) enzyme activity (Burkhart et al., 2013).
The drug response differences recorded between cells cultured in monolayer and 3D
culture after exposure to MTX is summarised in
Figure 6.6a. In the monolayer assay, there was 100% inhibition of metabolic activity at
the top dose treatments for all cell lines. The AsPC-1 cell line had a small decrease in
efficacy between the assays (2D Emax 1.00, AUC 3953 and 3D Emax 0.92, AUC 3173)
but no significant difference in potency recorded (IC50 values of 0.223 to 0.368). The
BxPC-3 cell line had a 6-fold decrease in potency of cells from exposure to MTX in 2D
to 3D assays (according to the IC50 values 0.068 and 0.412 respectively). The PANC-1
cell line had a similar shift in potency (as displayed in the dose response curves in
Figure 6.6b), with a 4-fold decrease in potency observed between 2D and 3D (0.061 and
0.282) respectively. In both BxPC-3 and PANC-1 cell lines, however, no decrease in
efficacy was observed (BxPC-3 2D Emax 1.00 and 3D 1.00, PANC-1 2D Emax 1.00 and
0.98). The 3D structures of BxPC-3 and PANC-1 displayed strong disaggregation into
single cells at the top dose (40µM) compared to no obvious effects at the lower dose
260
(4nM). There was, however, less structural impact on the AsPC-1 3D cultures at the
highest MTX dose compared to the other two cell lines. Although a large number of
single cells with a rounded apoptotic appearance can be seen, the 3D structures are not
completely disorganised and remain partially intact (
Figure 6.6c).
Figure 6.6. Responses observed following mitoxantrone dihydrochloride treatment of pancreatic
cancer cell lines for 6 days.
(a) IC50, Emax and AUC determinations of Mitoxantrone dihydrochloride against AsPC-1, BxPC-3 and
PANC-1 pancreatic cancer cell lines in both monolayer and 3D culture conditions. Results are
representative of the mean inhibition of duplicate wells of a single determination. 95% confidence
intervals for IC50 values are given in parentheses. (b) Comparative dose response curves between 2D
monolayer and 3D resazurin based metabolic activity assays for all 3 cell lines. Representative fields from
each cell line in response to the minimum (0.004µM) and maximum (40µM) drug doses screened.
261
Brightfield images obtained from an Operetta high content imaging platform using the 20x objective.
Scale bar = 100µm.
6.3.6
Digitoxin
Digitoxin is a member of the steroid-like class of cardiac glycosides and contains a
carbohydrate glycosidically bound to the cardenolide moiety. It has previously been
approved for the treatment and management of congestive cardiac insufficiency,
arrhythmias and heart failure. A large body of research has suggested significant anticancer activity of digitoxin against a number of malignancies including lung, breast,
prostate, melanoma cancer and leukaemia (Elbaz et al., 2012). Published in vitro and in
vivo studies indicate that digitoxin had a high potency (3-33nM), with significant anticancer activity at therapeutic concentrations used in cardiac therapies (Lopez-Lazaro et
al., 2005). A number of proposed anti-cancer mechanisms have been suggested for
digitoxin, including inhibition of the Na+/K+-ATPase pumps, with disruption of
downstream signalling pathways such as mitogen-activated protein kinase (MAPK),
phosphatidyl-inositol-3-kinase (PI3K), and Src kinase signalling. Other suggested
mechanisms for cytotoxic action are calcium-dependent activation of caspases and other
hydrolytic enzymes, the generation of ROS, direct topoisomerase inhibition, induction
of the cell-cycle inhibitor p21Cip1 and inhibition of hypoxia-inducible factor1a (HIF1a)
synthesis (Prassas et al., 2011).
The drug responses of cells cultured in both 2D and 3D formats to digitoxin exposure
over a period of six days are highlighted in
Figure 6.7a. All cell lines in the 2D monolayer culture expressed similar drug response
profiles (IC50 values of AsPC-1; 125nM, BxPc3; 102nM, PANC-1; 34nM) and 100%
efficacy at the top 4 doses in the assay. There was a small trend of decreasing potency
from 2D to 3D models, with a 3 to 7 fold increase in IC50 values calculated in the 3D
assay, with PANC-1 cells recording the largest increase (0.034 to 0.255). The loss of
262
efficacy in the 3D assay was cell line dependant, with the AsPC-1 cell line displaying
the largest fall in maximum inhibition (1.00 to 0.75), a smaller reduction in BxPC-3
(1.00 to 0.89) efficacy and no change observed in the PANC-1 cell line (1.00 to 0.99),
despite the loss of potency. Morphological evaluation of the 3D cultures indicated
disruption of 3D structures across all cell lines, with only the AsPC-1 cell line retaining
limited structural integrity, while the BxPC-3 and PANC-1 structures were reduced to
single cells
Figure 6.7c).
Figure 6.7. Responses observed following digitoxin treatment of pancreatic cancer cell lines for 6
days.
(a) IC50, Emax and AUC determinations of Digitoxin against AsPC-1, BxPC-3 and PANC-1 pancreatic
cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the mean
inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values are
given in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin
263
based metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to
the minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from
an Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
6.3.7
Maduramicin ammonium
The naturally derived polyether antibiotic maduramicin ammonium demonstrated
metabolic activity inhibition of the pancreatic cancer cell line PANC-1 in the monolayer
based pilot screen. Members of the polyether ionophore class of agents have exhibited a
broad spectrum of activity ranging from anti-bacterial through to anti-cancer activity
(Huczynski, 2012). The current indication for maduramicin is for use as an antiprotozoal agent in commercial livestock to prevent coccidiosis. The possible
mechanisms for anti-cancer activity for maduramicin have not been well established but
for other ionophores, disruption in the ion gradients between cellular membranes
inducing apoptosis has been implicated.
The response of all pancreatic cancer cell lines to maduramicin in the monolayer assay
displayed a similar potency profile with IC50 values between 0.444µM and 1.131µM (
Figure 6.8a) and 100% metabolic inhibition at the top dose (AsPC-1, BxPC-3 and
PANC-1; Emax 1.00). The PANC-1 cells were the only cell line to display a large shift in
potency between the 2D and 3D assays (0.444µM to 4.212µM), with a 10 fold increase
in IC50 values. Although no change in potency was observed, AsPC-1 and BxPC-3 cells
both exhibited a large decrease in efficacy, with the maximal inhibition down
approximately 30% for each cell line (AsPC-1 Emax 1.00 to 0.73 and BxPC-3 Emax 1.00
to 0.66) confirmed by the AUC results (AsPC-1 3815units to 2452units and BxPC-3
3652units to 2149units). The morphology of the 3D cultures was assessed (
Figure 6.8c), with no obvious structural effects observed in all cell lines tested at the
lower drug dose of 4nM. However, the top dose maduramicin (40µM) had a large
264
impact on the health of the 3D structures. The AsPC-1 and PANC-1 structures were
significantly smaller and cells disaggregated, while the BxPC-3 structures showed a loss
of integrity but still maintained an organised appearance.
Figure 6.8. Responses observed following maduramicin ammonium treatment of pancreatic cancer
cell lines for 6 days.
(a) IC50, Emax and AUC determinations of Maduramicin ammonium against AsPC-1, BxPC-3 and PANC1 pancreatic cancer cell lines in both monolayer and 3D culture conditions. Results are representative of
the mean inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values
are given in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin
based metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to
the minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from
an Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
265
6.3.8
Salinomycin
Salinomycin was identified as having micromolar activity in the 2D monolayer pilot
screen against the pancreatic cancer cell line, PANC-1. Salinomycin is another member
(together with Maduramicin) of the polyether ionophores and it is isolated from
Streptomyces albus bacteria. This antibiotic is currently approved as an agricultural
treatment in poultry to prevent coccidiosis and as a feed additive in ruminants to
improve nutrient absorption (Huczynski, 2012). It has also been shown to exhibit a
broad spectrum of activity, including anti-bacterial anti-fungal, anti-parasitic, anti-viral,
anti-inflammatory and anti-tumour activity. The unique mechanism of action of
salinomycin responsible for the observed anti-cancer activity makes this drug an
attractive agent to be used in the treatment of drug resistant cancers. Salinomycin is
believed to activate an unconventional pathway of apoptosis in cancer cells of different
origins, in particular cancer stem cells (CSCs). Although the exact anti-cancer
mechanisms remains unclear, a large number of pathways have been implicated,
including an apoptotic pathway that has been demonstrated to be independent of the
tumour suppressor p53 status and not accompanied by cell cycle arrest. Other
mechanisms include the down regulation of the oncoprotein Skp2 and up-regulation of
the tumour suppressor protein p27Kip1 (in breast and ovarian cancer cell lines) (Koo et
al., 2013; Naujokat et al., 2010). The caspase-independent cell death mechanism,
autophagy, has also been implicated, as well as the disruption of potassium gradients
between cellular membranes and production of ROS (Huczynski, 2012; Li et al., 2013).
The responses of the three pancreatic cancer cell lines to salinomycin were investigated
and are summarised in
Figure 6.9. No significant drug response differences between any of the cell lines
examined in monolayer culture was observed (IC50 values of AsPC-1 0.842, BxPC-3
0.862 and PANC-1 1.867), with full inhibition at the top dose (Emax 0.99 for all cell
266
lines). There was no observable change in the potency profiles between the 2D and 3D
assays, as can be seen in
Figure 6.9b with near identical dose response curves slopes. AsPC-1 and BxPC-3,
however, demonstrated a reduced efficacy in the 3D model, suggesting a population of
cells (up to almost 50% for BxPC-3) remained metabolically active at the top dose (Emax
0.90 for AsPC-1 and BxPC-3 Emax 0.52). Representative bright-field images (
Figure 6.9c) from the 40µM dose of drug exposure reveal that for AsPC-1 and PANC-1
cultures, a significant disaggregation of 3D structures has occurred, with large
population of single rounded up cells. The BxPC-3 cell line, which exhibited a 48%
drop in drug efficacy in the 3D format, displayed a reduced size of 3D structures, but
they appeared to maintain some structural integrity and cellular adhesion.
267
Figure 6.9. Responses observed following salinomycin treatment of pancreatic cancer cell lines for 6
days.
(a) IC50, Emax and AUC determinations of salinomycin against AsPC-1, BxPC-3 and PANC-1 pancreatic
cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the mean
inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values are given
in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin based
metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to the
minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from an
Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
6.3.9
Topotecan hydrochloride
Topotecan is a chemotherapeutic agent identified with nanomolar activity against the
PANC-1 cell line in the preliminary drug screen. It is a camptothecin derivative that
mediates anti-cancer activity by inhibiting topoisomerase I and preventing DNA
replication. The cytotoxic effects of topotecan are exerted primarily in the S-phase of
268
cellular replication, as the drug forms a topoisomerase-DNA complex and prevents
reformation of single stranded DNA (Cersosimo, 1998). Topotecan currently has FDA
approval for the treatment of ovarian, cervical and small cell lung cancer but has
previously failed small clinical trials for treatment of pancreatic cancer.
The drug response of the pancreatic cancer cell lines treated with topotecan was
characterised in the secondary dose response screen using the monolayer and 3D cell
culture assays ,where there was a trend of decreasing sensitivity from the 2D to 3D
assays formats (
Figure 6.10a). The metastatic cell line AsPC-1 displayed the smallest change in IC50
values (2D; 56nM to 3D; 119nM), while the primary origin cell lines BxPC-3 cells and
PANC-1 cells were approximately five fold less sensitive to the drug in 3D culture over
the time period studied (6 days drug exposure) (
Figure 6.10b). Efficacy values remained similar between the assays, with near 100%
inhibition at the top three doses tested (as indicated by the Emax and AUC values in
Figure 6.10a). Morphological analysis of the high drug exposure (40µM) cultures
indicated that topotecan disrupted the 3D structures of all cell lines, a large amount of
cellular debris and fragmentation was observed with the BxPC-3 and PANC-1 3D
cultures. The AsPC-1 cultures maintained smaller cell aggregates. However, the results
of the resazurin assay indicate that these aggregates did contain metabolically viable
cells.
269
Figure 6.10. Responses observed following topotecan hydrochloride treatment of pancreatic cancer
cell lines for 6 days.
(a) IC50, Emax and AUC determinations of topotecan hydrochloride against AsPC-1, BxPC-3 and PANC-1
pancreatic cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the
mean inhibition of duplicate wells of a single determination. 95% confidence intervals for IC 50 values are
given in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin
based metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to
the minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from
an Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
270
6.3.10 Rubitecan
Rubitecan is the second camptothecin analogue that was detected as inhibiting
metabolic activity of PANC-1 cells in the primary drug screen and was selected for
further analysis. As with other camptothecins, lethality to cells is caused by the
formation of a stabilizing complex (drug-topoismerase I-DNA) that inhibits the religation phase in DNA replication and induces apoptosis (Clark, 2006). Rubitecan has
been previously investigated for treatment in a number of solid malignancies, including
those of pancreatic and ovarian origin. In preclinical studies, either alone or in
combination with existing cytotoxic agents, rubitecan was reported to have significant
anti-tumour efficacy. However, it is currently not approved for any anti-cancer therapy
by regulatory authorities.
Anti-pancreatic cancer activity was observed against three pancreatic cancer cell lines
with exposure to rubitecan. The drug recorded the lowest IC50 values of all 10 drugs
evaluated in both monolayer and 3D assays (2D all cell lines; 11nM, 3D AsPC-1 27nM,
BxPC-3 47nM, PANC-1 35nM). As was observed with the other camptothecin,
topotecan, a trend of decreasing sensitivity was detected between the 2D and 3D assays,
with a 3 to 4 fold increase in IC50 values (
Figure 6.11). Only AsPC-1 exhibited a slight reduction in efficacy from 2D to 3D at the
top doses (AsPC-1 Emax; 2D 0.98, 3D 0.88). Evaluation of the brightfield images of the
3D cultures again substantiated the outcomes with topotecan as an almost complete
disruption and disaggregation of 3D structures to single cells and cell debris was
observed at the top dose of 40µM. At the lower dose of 4nM, no significant cell
shedding or cell unpacking was visible (
Figure 6.11c).
271
Figure 6.11. Responses observed following rubitecan treatment of pancreatic cancer cell lines for 6
days.
(a) IC50, Emax and AUC determinations of rubitecan against AsPC-1, BxPC-3 and PANC-1 pancreatic
cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the mean
inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values are given
in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin based
metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to the
minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from an
Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
272
6.3.11 Cerivastatin
Cerivastatin is a member of the statin family which are synthetic HMG-CoA reductase
inhibitors and are used for the prevention of cardiovascular diseases by lowering lowdensity lipoprotein (LDL) cholesterol levels. However, cerivastatin was removed from
the market in 2001 for toxicity and safety concerns due to drug-related rhabdomyolysis
deaths. Statins have also been reported to have anti-inflammatory and anti-tumour
activity, with in vitro and in vivo studies presenting evidence of anti-tumour effects
against a number of malignancies (Sassano and Platanias, 2008). Statins have displayed
inhibition of tumour growth, inhibition of angiogensis and preventative effects on the
formation of metastasis in preclinical studies. A number of hmg-coa reductase
dependent mechanisms have been proposed for this activity including the inhibition of
mevalonate synthesis which effects downstream cell cycle regulators, such as dolichol,
genranylpyrophosphate (GPP) and farnesyl-pyrophosphate (FPP), involved in cell
proliferation (Hindler et al., 2006). Cerivastatin has also been demonstrated to inhibit
Ras and Rho- mediated cellular proliferation in in vitro breast cancer studies (Denoyelle
et al., 2001).
The activity of cerivastatin against pancreatic cancer cell lines was confirmed in the
secondary screen which is exposed cerivastatin to cells in both monolayer and 3D cell
culture (
Figure 6.12). The metastatic cell line AsPC-1 observed a decrease in both potency and
efficacy of the drug in 3D culture compared to the 2D monolayer assay. A four-fold
decrease in potency (2D IC50 0.365µM, 3D IC50 1.652µM) and an approximate 10%
drop in maximal inhibition was observed in the 3D assay for this cell line. The K-ras
mutant cell line PANC-1 and the K-ras wild type cell line BxPC-3 displayed similar
potency and efficacy profiles in both 2D and 3D culture systems (IC50 values; BxPC-3
2D 0.806µM to 3D 0.837µM, IC50 values; PANC-1 2D 1.679µM to 3D 1.965µM). The
morphological evaluation of the 3D cultures indicates that culture exposed to low levels
(4nM) of cerivastatin had no obvious effects on 3D structure integrity. However, at the
top dose, the structures were almost completely disrupted to rounded single cell
populations, with only PANC-1 3D structures still maintaining limited aggregation (
Figure 6.12c).
273
Figure 6.12. Responses observed following cerivastatin treatment of pancreatic cancer cell lines for
6 days.
(a) IC50, Emax and AUC determinations of cerivastatin against AsPC-1, BxPC-3 and PANC-1 pancreatic
cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the mean
inhibition of duplicate wells of a single determination. 95% confidence intervals for IC50 values are given
in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin based
metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to the
minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from an
Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
274
6.3.12 Simvastatin
The fungal derivative simvastatin was the second member of the statins to be selected
from the primary pilot screen. Simvastain is a pro-drug and is converted into the active
form (-hydroxy acid) in vivo primarily in the liver and unlike some other statins has the
ability to cross the blood brain and placental barriers (Chan et al., 2003). As with other
statins, simvastatin has also been identified for its ability to induce apoptosis in a range
of cancer cell lines in vitro (Sassano and Platanias, 2008).
The secondary screen involving three pancreatic cancer cell lines confirmed the activity
observed in the pilot screen, which is summarised in
Figure 6.13a. Similar potency profiles were observed in the 2D cultures for all cell lines.
AsPC-1 had an IC50 value of 5.948µM, BxPC-3 was 13.291µM and PANC-1
12.001µM. Only the top dose of drug (40µM) inhibited the metabolic activity greater
than 90% in the monolayer assay for all cell lines (
Figure 6.13b). A decrease in sensitivity to simvastatin was observed universally across
all cell lines in the 3D culture based assay. Although IC50 values could not be generated
in 3D (as dose response curves could not be generated), all cell lines exhibited a drug
response of approximately 30% at the top dose and displayed at least a three-fold drop
in drug sensitivity. The morphological evaluation confirmed the lack of efficacy, with
intact 3D structures present at the top dose (40µM) for all cell lines. Some cell shedding
and reduction in overall size of structures is apparent but they remain virtually intact.
275
Figure 6.13. Responses observed following simvastatin treatment of pancreatic cancer cell lines for
6 days.
(a) IC50, Emax and AUC determinations of simvastatin against AsPC-1, BxPC-3 and PANC-1 pancreatic
cancer cell lines in both monolayer and 3D culture conditions. Results are representative of the mean
inhibition of duplicate wells of a single determination. 95% confidence intervals for IC 50 values are given
in parentheses. (b) Comparative dose response curves between 2D monolayer and 3D resazurin based
metabolic activity assays for all 3 cell lines. Representative fields from each cell line in response to the
minimum (0.004µM) and maximum (40µM) drug doses screened. Brightfield images obtained from an
Operetta high content imaging platform using the 20x objective. Scale bar = 100µm.
276
6.3.13 Secondary screen summary
Ten drugs were selected for further evaluation in a dose response study in both 2D
monolayer and 3D culture conditions. Variations in drug potency where detected
between the three pancreatic cancer cell lines investigated (AsPC-1, BxPC-3 and
PANC-1) in the monolayer culture conditions. All drugs produced IC50 values that were
no more than a 4 fold difference between the cell lines under the conditions studied.
However, the results from the 3D culture based assay recorded a much greater variation
in activity (both potency and efficacy profiles) between the individual cell lines. An
eight fold difference in potency was recorded between the cell lines exposed to
maduramicin and a 40% difference in efficacy was observed against mycophenolic acid
at the top dose.
The three pancreatic cancer cell lines displayed both cell line and drug dependant
responses in the efficacy and potency profiles between the monolayer and 3D culture
assays for the drugs screened (summarised in Table 6.4.). An overall trend of decreased
potency and efficacy was observed for all cell lines when comparing activity in 2D to
that obtained in 3D. The majority of drugs demonstrated a reduction in potency against
the metastatic AsPC-1 cell line, with the exception of mycophenolic acid, mitoxantrone
and salinomycin, where no change was observed. Interestingly, with the three drugs
which exhibited no shift in potency, there was a large decrease in efficacy observed.
The majority of drugs, except for maduramicin, salinomycin and cerivastatin displayed
decreased potency against the cell line of primary pancreatic cancer origin, BxPC-3.
Interestingly exposure of the BxPC-3 cell line to maduramicin was the only
experimental setting tested in the study to record a decrease in IC50 value in 3D
(increase in potency). There was also a large decrease in efficacy detected against
salinomycin of almost 50% but with no shift in IC 50 value produced. A reduced potency
profile against the primary origin cell line PANC-1 cells was observed for all drugs,
except for salinomycin and cerivastatin (in which there was no change) in 3D cell
culture when compared to the 2D assay results. The greatest change in IC50 values
between 2D and 3D assay formats was observed with maduramicin, with a 10-fold
reduction in potency in 3D. The efficacy results indicate that there was not an overall
trend of altered maximal inhibition between the culture conditions, with only
277
maduramicin, mycophenolic acid and simvastatin displaying decreased efficacy (Table
6.4).
Drugs from same class, such as with the statins and ionophores, also exhibited distinct
differences between other members of the same classes and between individual cell
lines. Qualitative analysis of the morphological effects of the various drugs on the 3D
structures also provided insights into the ability of each drug to impact on the physical
integrity of the 3D structures generally resulting in disaggregation of the 3D cultures. In
addition, this enabled confirmation of the data obtained using the resazurin-based
metabolic activity assays, which demonstrated overall reductions in cellular health but
not complete cell death, which coincided with the drugs that failed to completely
disaggregate 3D cell masses.
Table 6.4. Summary of secondary drug screening of the three pancreatic cancer cell lines AsPC-1,
BxPC-3 and PANC-1 in 2D and 3D culture.
The change in potency and efficacy is represented by either a ↑ for increase from 2D to 3D, ↓ for a
decrease and ↔ for no change. Disruption in 3D morphology is described by either; high for total
disaggregation of the structures, med for some fragmentation and shedding of cells but with 3D structures
still intact and low for limited impact on structural integrity and cellular aggregation with no cell
shedding.
278
Shift in potency from 2D to 3D Shift in Efficacy from 2D to 3D
Drug
Disruption of 3D morphology
at 40µM dose
AsPC-1
BxPC-3
PANC-1
AsPC-1
BxPC-3
PANC-1
AsPC-1
BxPC-3
PANC-1
Ciclopirox olamine
↓
↓
↓
↓
↓
↔
High
High
High
Mycophenolic acid
↔
↓
↓
↓
↓
↓
Med
Med
Med
Mitoxantrone
dihydrochloride
↔
↓
↓
↓
↔
↔
Med
High
High
Digitoxin
↓
↓
↓
↓
↓
↔
Med
High
High
Maduramicin
ammonium
↓
↑
↓
↓
↓
↓
High
High
High
Salinomycin
↔
↔
↔
↓
↓
↔
Med
High
High
Topotecan
hydrochloride
↓
↓
↓
↔
↔
↔
Med
High
High
Rubitecan
↓
↓
↓
↓
↔
↔
Med
High
High
Cerivistatin
↓
↔
↔
↓
↔
↔
High
High
Med
Simvastatin
↓
↓
↓
↓
↓
↓
Low
Low
Low
6.4 Discussion & conclusions
The primary screen of the clinically relevant drug library provided the opportunity to
validate the 3D culture based assay developed for a panel of pancreatic cell lines in a
high throughput setting. The pancreatic cancer 3D cell culture metabolic activity assay
was successfully validated through the pilot screen of 741 diverse and clinically relevant
drugs / compounds. Reproducibility and the overall robustness of the assay was
assessed with a panel of drugs screened over multiple microtitre plates in a small scale
HTS setting. The assay proved to be robust and sensitive with a Z’-Factor value >0.5
and coefficient of variation (%CV) <15% across all screening microtitre plates.
Reference chemotherapy agents, including doxorubicin, also returned drug responses
within the expected ranges across multiple plates. The 3D culture based assay was able
to identify, confirm and characterise the anti-cancer activity of numerous drugs that
were identified initially in 2D. Several drug response parameters were successfully
recorded, including potency, efficacy and morphological impact. The assay was capable
279
of determining cell line, culture model and drug dependant activity from a panel of
selected drugs. These results indicate that with extensive and careful optimisation, the
3D culture based pancreatic cancer assay is sufficiently robust for future HTS
campaigns in a pancreatic cancer drug discovery programme.
This study was not only useful in determining the HTS ‘assay readiness’ of the 3D cell
culture system but also in assessing the biological responses of cells cultured in 3D
compared to traditional 2D monolayer based assays. The secondary aim of this study
was to utilise a clinically relevant and previously approved drug library to examine
drugs with unknown anti-cancer effects on pancreatic cancer cell lines. The strategy of
drug repurposing of already-approved drugs for novel therapeutic indications has a
history of success, with the current standard of care chemotherapy agent for pancreatic
cancer (gemcitabine) proving it is possible for existing drugs to find new indications for
therapy. This screen identified a number of drugs with pre-existing anti-cancer activity
but also several that had no previous indication of inhibition of pancreatic cancer cells,
including drugs from the statin, camptothecin and anthracenedione classes.
6.4.1
Ciclopirox olamine
Ciclopirox olamine is an alpha hydroxyl pyridone currently used for treatment of fungal
infections (Weir et al., 2011). Recently, ciclopirox has been identified in a number of
screening programs and is under investigation to be repositioned as a novel anti-cancer
agent. Eberherd and colleagues identified ciclopirox olamine in two independent anticancer cell based screens (survivin inhibition and cytotoxic activity against leukaemia
stem cells) (Eberhard et al., 2009). To provide further evidence for the development of
ciclopirox olamine as a possible anti-cancer agent a number of in vitro (monolayer cell
viability based) and in vivo (mouse models) studies have demonstrated anti-tumour
activity in range of cancers including breast, acute myeloid leukaemia (AML), colon
and myeloma (Eberhard et al., 2009; Weir et al., 2011; Zhou et al., 2010). Cell viability
IC50 for leukaemia and myeloma cells lines are reported to be below 2.5µM, although a
narrow therapeutic window of selectivity between normal and malignant hematopoietic
cells was observed. The in vivo studies observed delayed tumour growth in mouse
models of leukaemia and prevented engraftment of primary AML cells.
280
CPX is
believed to induce cancer cell inhibition by chelation of intracellular iron and the
inhibition of the iron-dependant enzyme ribonucleotide reductase (Zhou et al., 2010).
No previous published information on the anti-cancer activity of ciclopirox on
pancreatic cancer cell lines has been identified.
Analysis of pancreatic cancer monolayer drug responses to ciclopirox olamine in this
study revealed a drug with low micromolar activity and high efficacy. Similar IC 50
values (~1µM) and 99% efficacy across all cell lines was recorded utilising the
metabolic activity assay. These drug responses are similar to those achieved in previous
monolayer cell viability studies with breast carcinoma (MDA-MB-231) and colon
cancer (HT-29) cell lines (approximately 5µM).
However, the 3D culture model
revealed a decreased sensitivity to the drug and a cell line specific loss in efficacy that
was not detected in the monolayer system. All cell lines recorded an increased IC 50
value with BxPC-3 cells (primary tumour origin, WT K-ras) displaying the highest loss
of sensitivity with an 11 fold decrease (1.142µM to 12.604µM). This reduction in
potency was paralleled with a drop in maximal inhibition (approximately 15%). The
drop in efficacy was also observed in the metastatic AsPC-1 cell line (metastatic origin,
K-ras mutation), while the PANC-1 cell line (primary origin, K-ras mutant) remained
sensitive to ciclopirox at the highest doses (40µM) in 3D culture. The results suggest
that a population of cells in certain pancreatic cancer cell lines (AsPC-1 and BxPC-3)
may remain metabolically active in more physiologically relevant culture conditions
that is presented by 3D culture. The exact causes of the altered responses observed
between the cell lines is difficult to account for. However, a possible explanation for
this result could be related to the 3D structure itself, as the BxPC-3 cell line forms
distinctly tight spheroid 3D structures compared to the more loosely packed AsPC-1
and PANC-1 structures and therefore the shift in potency may be related to the more
pronounced cell to cell interactions and multicellular mediated drug resistance. The
AsPC-1 and BxPC-3 cell lines also had the largest reduction in cellular proliferation
between monolayer and 3D culture (46% and 311%).
As iron is essential for proliferation and DNA synthesis and CPX’s anti-cancer activity
has been linked to iron chelation, a greater effect may be observed in cells undergoing
more rapid proliferation. There is data to support this theory as cancer cells often have a
higher requirement for iron because of their increased proliferation rate (Cazzola et al.,
281
1990). The results may indicate that while certain cell lineages (more rapid proliferation
rates) remain sensitive to CPX in the more complex 3D model a more resistant profile
may be observed in other cell lines that maintain a slower growth profile. These results
provide evidence that selective tumours in vivo may have a partial response to potential
anti-cancer treatments based on CPX and further research into the exact mechanism of
action will provide greater insights into the cell line dependant effects observed within
this study. Although a reduced efficacy in AsPC-1 and BxPC-3 cell lines was observed
with the 3D culture conditions, the possible unique mode of action for inhibition of
cancer cells implicated with this drug should encourage further evaluation of ciclopirox
olamine for possible utility in the treatment of pancreatic cancer.
6.4.2
Mycophenolic acid
Mycophenolic acid is the active moiety of mycophenolate mofetil, which is approved as
an immunosuppressant treatment in patients undergoing organ transplantation. The drug
inhibits inosine mono phosphate dehydrogenase (IMPDH) that facilitates the conversion
of inosine mono phosphate to xanthosine monophosphate, involved in the de novo
biosynthesis of guanine nucleotides (Lipsky, 1996). An isoform of IMPDH is expressed
in various malignancies and has been associated with cellular proliferation (RodriguezPascual et al., 2012). Few studies have examined mycophenolic acid for activity in
pancreatic cancer (Shah et al., 2007). A recent screen, however, (cell viability assay in
monolayer 96 well format of 3000 previously FDA approved drugs) identified the drug
as a potent inhibitor of pancreatic cancer cell in vitro (Rodriguez-Pascual et al., 2012).
The study found that although potent activity (IC50 values of 100nM for PANC-1) was
observed in traditional monolayer in vitro assays, this did not translate to in vivo and
clinical trials. Whilst the xenograft mouse models demonstrated some statistical tumour
growth reduction, there was a poor tumour response overall. However, the drug did
effect VEGF levels in the mouse model and so a clinical trial was established to assess
the possible use as an anti-angiogenesis drug. A small phase ‘0’ clinical trial also found
a lack of pharmodynamic effects in patients and further clinical development was not
recommended (Rodriguez-Pascual et al., 2012). The lack of efficacy was speculated to
be due to the failure of the drug to diffuse through the dense poorly vascularised stroma.
Additionally, recent clinical trials with other anti-angiogenesis drugs have questioned
the strategy of reducing tumour vascularisation in pancreatic cancer (Kindler et al.,
282
2011). Increasing tumour vascularisation to facilitate drug diffusion may be the
preferred goal for cancer that already survives in a hypo-vascularised state.
Varying degrees of anti-cancer activity was recorded for mycophenolic acid in the 2D
monolayer assay using the three pancreatic cancer cell lines (AsPC-1, BxPC-3 and
PANC-1). PANC-1 cells displayed the highest sensitivity (IC50 of 483nM), whereas the
BxPC-3 cell line had the lowest (1.4µM). This data correlated with the published results
for PANC-1 (~100nM in the literature to 500nM (Figure 6a)) and BxPC-3 (~5 µM in
the literature to 1.4µM (Figure 6a)) (Rodriguez-Pascual et al., 2012). All cell lines
displayed a reduced potency and efficacy in the 3D model. All cell lines displayed a
large reduction in efficacy from 25% to 60% following exposure to mycophenolic acid
at the top doses (40µM). This metabolic activity data correlated with the morphological
analysis, which indicated a reduced size of the 3D structures, but a large population of
shedding cells was not observed. The lack of cellular disaggregation indicated that even
at the top doses of drug, cellular proliferation may have been inhibited but cells
remained metabolically viable. The 3D cell cultures employed in this screen have
provided valuable insights into more physiologically relevant drug responses not
detected in the monolayer assays which correlates to the poor in vivo responses
observed by other groups. Studies in 3D, such as those we have undertaken, may very
well have provided the necessary data not to proceed to in vivo studies, illustrating the
important role that physiologically relevant 3D cell culture models can have in drug
discovery.
6.4.3
Mitoxantrone dihydrochloride
The anthrecenedione, mitroxantrone, was initially evaluated as a cytotoxic agent against
a range of malignancies, as it had an improved toxicity profile compared to doxorubicin
and daunorubicin
(Koeller and Eble, 1988). However, poor efficacy and limited
response as a single agent was recorded amongst several solid tumours (including
prostate, colorectal and pancreatic) in clinical trial (phase II). This drug is currently
approved for the treatment of metastatic breast, advanced prostate, acute myelogenous
leukaemia (AML), Non-hodgkin’s lymphoma and also multiple sclerosis (MS) (van der
Graaf and de Vries, 1990). The anti-cancer activity of the drug has been attributed to
topoisomerase II inhibition and the prevention of DNA replication and DNA-dependant
RNA synthesis (Fox, 2004). There are limited examples of pancreatic cancer in vitro or
283
in vivo studies which have evaluated the activity of mitroxantrone. An early in vitro
study found 500nM of mitoxantrone caused 40% inhibition of MIA PaCa-2 cells in
monolayer studies (Fountzilas et al., 1984). However, several clinical trials provided
poor indication of efficacy either alone or in combination (Link et al., 1997; Meyer et
al., 2004). Recently, interest in mitoxantrone as a therapeutic agent in pancreatic cancer
treatment has been re-established. Ubiquitin-specific peptidase 11 (USP11) was
identified as a unique target involved in the DNA damage repair system in pancreatic
cancer and a HTS program identified mitoxantrone as a potent agent in cell lines
(Hs766T and Capan-1) with increased endogenous USP11 mRNA levels (Burkhart et
al., 2013). Burkhart and colleagues screened 2000 FDA approved or pharmacologically
active drugs and compounds and found that mitoxantrone inhibited the USP11 enzyme
activity and was a potent inhibitory of cell viability of pancreatic cancer cell lines with
an IC50 of 10nM (in monolayer culture). The response to mitoxantrone was cell line
specific, with the most sensitive cell lines displaying increased endogenous USP11
mRNA levels.
The cancer cell lines of primary tumour origin BxPC-3 and PANC-1, displayed a
similar nanomolar potency and efficacy profile in both monolayer and 3D cell culture
assays following treatment with mitroxantrone. Both cell lines displayed a 5-fold
reduction in potency between the 2D and 3D culture models, with no change in efficacy
demonstrated. The metastatic cell line, AsPC-1, revealed a smaller reduction in potency
in 3D but a 10% drop in maximal efficacy, indicating a population of cells remained
metabolically viable, even at the highest drug concentration. The metabolic viability
indications correlated with the morphological characteristics with only AsPC-1
structures displaying signs of resistance to disaggregation by mitroxantrone.
The value of evaluating a number of drug response metrics is highlighted with the
evaluation of the 3D culture assay. If only a single metric, such as potency (IC50), was
used to evaluate drug responses, the reduction in Mitoxantrone efficacy may not have
been detected.
With recent evidence indicating that targeting pancreatic cancers
overexpressing USP11 may provide a novel treatment option for a selection of
pancreatic cancer patients, further investigations with this drug (and other identified
inhibitors of USP11) should be evaluated.
284
6.4.4
Digitoxin
The cardiac glycoside digitoxin displayed anti-cancer activity against pancreatic cancer
cell lines in both monolayer and 3D culture conditions used in this study. Cardiac
glycosides are a large family of mainly plant derived compounds with regulatory
approval for cardiovascular therapy. Although digitoxin is approved for use in atrial
fibrillation, atrial flutter and paroxysmal atrial tachycardia therapies, it has been largely
discontinued in clinical use (Newman et al., 2008). Digitoxin, as well as a number of
other cardiac glycosides, have recently been suggested as viable drugs for use in cancer
therapy (Elbaz et al., 2012). Originally cardiac glycosides were suggested as therapeutic
options for hormone sensitive cancers, such as breast carcinoma as they are
phytoestrogens (and may affect the development of these cancers). The underlying
molecular mechanisms and anti-neoplastic activity have now been evaluated across a
range of cancer types including prostate, lung, colorectal, head and neck, melanoma,
leukaemia, neuroblastoma and pancreatic (Menger et al., 2013). Beyond the primary
target of inhibition of Na+/K+-ATPase, the exact mechanism by which digitoxin exerts
its anti-cancer effects is yet to be determined.
Although the initial identification of an inhibitory effect on cancer cells was over 45
years ago, there have been few clinical trials undertaken (due partly to a lack of animal
model data as standard murine cells express insensitive variants of the targeted Na+/K+ATPase and narrow therapeutic window) (Slingerland et al., 2013). A large volume of
in vitro work has been carried over the past decade into cardiac glycosides. Two
independent cancer screening programs also identified cardiac glycosides as potential
cancer therapeutics. A cell based HTS (using the breast cancer cell line MDA-MB-468)
published by Prassas et al in 2008 identified cardiac glycosides, including digitoxin, as
potent tissue kallikrens (KLK) transcriptional inhibitors implicated in cancer
progression (Prassas et al., 2008). Multiple members of the cardiac glycosides were
found to be active at low nanomolar concentrations (10 -50nM) suppressing the
transcription of KLK’s. A glioblastoma (GBM) based functional screening assay
(luciferase reporter assay using u87 glioma cells) was later used to identify cardiac
glycosides as sensitizers for GBM cells to TRAIL-induced cell death (Badr et al., 2011).
285
The inhibition of cancer cells in both monolayer and 3D culture was observed after
exposure to varying concentrations of digitoxin over a period of 6 days. The potency
obtained for digitoxin against bothBxPC-3 and AsPC-1 cell lines were similar
(approximately 100nM) to the results obtained by Prassas and colleagues (Prassas et
al., 2008). However, PANC-1 cells displayed a slightly more sensitive potency profile
with a 5 fold lower IC50 value than reported previously. However, the viability assay in
the Passas study was performed using only a 48 hour exposure, whereas the study being
described here was for 144 hours and this may have impacted on the outcomes obtained
for the 2D assay.
This is the first report of the impact of digitoxin on pancreatic cancer cell lines grown
under 3D scaffold dependent conditions, thus a comparison with literature values is not
possible. A reduced sensitivity to digitoxin was observed in all pancreatic cell lines
evaluated in 3D culture conditions, with PANC-1 cells displaying the largest increase in
IC50 values (6 fold) and with AsPC-1 and BxPC-3 only having a 3 fold difference
recorded. The reduction in efficacy was cell line dependant, with the primary cell lines
BxPC-3 and PANC-1 displaying only small reduction in maximal effect at the top
doses. However, the metastatic cell line AsPC-1 displayed a 25% reduced maximal
effect. This was confirmed in the morphological evaluation of the 3D cultures, in which
the BxPC-3 and PANC-1 structures were completely disaggregated to rounded up single
cells, in contrast to the AsPC-1 cells which retained some structural integrity while still
displaying a large percentage of shedding cells.
It is difficult to speculate on the
mechanisms behind the overall resistance trend observed across all cell lines when
cultured in 3D conditions. However, the reduced efficacy observed for cell lines may be
a result of the multicellular resistance mechanisms enhanced by culturing cells in 3D
culture conditions. The reduction in efficacy of metastatic AsPC-1 cells cultured in 3D
may be due to a cell line specific altered expression of Na+/K+-ATPase.
The use of 3D cell culture to determine cell line specific drug responses in a more
physiologically relevant system may provide important insights in future digitoxin
evaluations.
Further studies involving pancreatic cancer and digitoxin may prove
valuable as previous medicinal chemistry efforts on digitoxin analogues have displayed
enhanced potency and increased specificity in a range of cancer cell lines (Langenhan et
al., 2005).
286
6.4.5
Ionophores (maduramicin ammonium and salinomycin)
The cellular responses observed following exposure to the ionophore antibiotics,
maduramicin and salinomycin, were evaluated against pancreatic cancer cell lines in
traditional 2D monolayer and 3D cell culture models. Although the primary indication
of these drugs is as anti-coccidial agents in livestock, salinomycin has recently gained
attention for use as an anti-cancer agent. It identified as a specific inhibitor of cancer
stem cells in a number of different malignancies including breast and pancreatic (Gupta
et al., 2009; Zhang et al., 2011). Salinomycin has been shown not only to have specific
activity against cancer stem cell populations (CD133+) but also induces apoptosis in
non-stem cell cancer cell populations. It is reported to induce apoptosis by an
unconventional method that is not accompanied by cell cycle arrest, is p53 status
independent and can overcome multiple mechanism of drug resistance (Naujokat et al.,
2010).
The mechanisms underlying the inhibition of cancer cells remains poorly
understood and may be cell line specific (Naujokat and Steinhart, 2012). No anti-cancer
activity for maduramicin had previously been reported in pancreatic cancer cells.
Similar potency levels were recorded for all cell lines in both monolayer and 3D cell
culture conditions against salinomycin. Previously cytotoxicity studies reported
inhibition of AsPC-1 cells when exposed to salinomycin at similar IC50 values as those
reported here (Zhang et al., 2011). Although no change in potency was observed
between the 2D and 3D culture conditions, a drop in efficacy was observed across the
cell lines. PANC-1 cells displayed almost no change in efficacy, while a small 10%
drop was observed in AsPC-1 cells. The largest decrease in efficacy was seen with the
tightly packed 3D structures of K-ras wild type BxPC-3 cells. It is unclear whether the
significantly reduced sensitivity (or possible chemoresistance) is a result of the more
relevant microenvironment in the 3D model or the result of genotypic differences of the
BxPC-3 cell lines. The morphological evaluation of the 3D cultures confirmed that
while salinomycin was effective in disrupting the structural integrity of the cultures for
AsPC-1 and PANC-1, BxPC-3 cultures remained relativity tightly packed with limited
cell shedding.
A similar profile of reduced efficacy was also observed with maduramicin and BxPC-3
cells in 3D culture. Interestingly, the drug sensitivity to maduramicin increased from the
287
2D to 3D culture for the BxPC-3 cell line. Although the change in IC50 was only an
approximate two fold reduction and further research needs to be undertaken to
determine if it is an artefact of the screening. It is possible that the mode of action of
maduramicin targets a specific pathway or signalling event which has altered expression
in the more complex culture conditions. The PANC-1 cell line demonstrated a more
standard reduced sensitivity to maduramicin in 3D culture compared to 2D conditions.
This reduction in potency may be a result of the chemical properties of the drug with an
inability to penetrate the larger 3D structures of the PANC-1 culture or chemo-resistant
mechanisms enhanced by the micro environmental cues in the 3D ECM based culture.
Further research to elucidate the mechanism by which both salinomycin and
maduramicin influence growth inhibition of pancreatic cancer cell in vitro may be an
interesting avenue to pursue. As identifying possible unique targets for anti- cancer
research in pancreatic cancer, may overcome the tumour resistance seen with current
treatment strategies. Whilst identifying the target or mode of action is of interest, further
medicinal chemistry optimisation of maduramicin is unlikely due to the unfavourable
structure of this natural product.
6.4.6
Camptothecin derivatives (rubitecan and topotecan)
Rubitecan (water insoluble) and Topotecan (water soluble) are camptothecin derivatives
that were evaluated for activity against the three pancreatic cancer cell lines in
monolayer and 3D cell culture conditions. Camptothecins are highly potent (10 - 60nM)
cytotoxic compounds that inhibit the topoisomerase I enzyme, prevent re-ligation of
single strand DNA breaks and induce apoptosis of S-phase cells (Gerrits et al., 1997).
All three cell lines displayed similar potency and efficacy profiles against both rubitecan
and topotecan in monolayer cultures. Rubitecan proved to be the more potent of the two
drugs with 10nM activity (equal to the most potent chemotherapy drug tested,
paclitaxel) across all cell lines. All three cell lines were particularly sensitive to the
drugs indicating no cell line specific resistance in monolayer culture. There was an
overall trend of decreasing sensitivity in 3D culture although efficacy remained at
almost 100% for all cell lines. For both rubitecan and topotecan, no reduction in
efficacy or chemoresistance indications were observed in the 3D culture conditions.
Morphology evaluations confirmed that most 3D structures had been completely
disaggregated against these highly potent cytotoxic agents.
288
Rubitecan and topotecan have been previously evaluated for use in pancreatic cancer
therapy in a number of clinical trials (Fracasso, 2002; Konstadoulakis et al., 2001;
Stehlin et al., 1999; Stevenson et al., 1998). However, although some limited clinical
responses were observed, there was no significant improvement over existing treatment
to gain regulatory approval (Burris et al., 2005). Due to comparably low response rates
and dose-limiting toxicities it is unlikely (despite the high potency of these drugs) that
further clinical combination studies will be explored at this stage.
6.4.7
Statins (cerivastatin and simvastatin)
Statins are a synthetic class of drugs that competitively inhibit 3-hydroxy-3methylglutaryl coenzyme (HMG-CoA) reductase. Although the primary indication for
statins has been the direct suppression of cholesterol biosynthesis they also have been
shown to exhibit anti-inflammatory and anti-tumour activities (Sassano and Platanias,
2008). In vitro and in vivo studies have demonstrated that statins inhibit tumour growth
and induce apoptosis in a number of malignant cell lines including melanoma, gastric,
glioma, neuroblastoma, breast, prostate and pancreatic cancer (Hindler et al., 2006).
There is also conflicting data that statins may play a role in either possible
carcinogenicity or cancer prevention, with a recent study indicating statins may offer
preventative effects against pancreatic cancer in high risk individuals, such as smokers
(Carey et al., 2013).
A number of mechanisms have been proposed for the anti-tumour activity of statins
including prevention of proliferation through the arrest of cell cycle progression,
induction of apoptosis and inhibition of angiogenesis and tumour growth (Jakobisiak
and Golab, 2009). There are at least eight members of the statin class of drugs, which all
have different hydrophobic and target affinity profiles. The majority of the statins have
been previously studied in various in vitro and in vivo animal studies with the first study
in relation to pancreatic cancer published almost 25 years ago (Sumi et al., 1992). A
recent study compared 8 statins against a panel of pancreatic cancer cell lines using in
vitro monolayer and in vivo mouse models. Cerivastatin provided an increase in survival
time compared to simvastatin in the xenograft mouse studies. However, tumour
progression was only delayed but not prevented in these studies (Gbelcova et al., 2008).
289
Cerivastatin is the most hydrophobic statin and has shown to be up to 10 times more
potent in inducing apoptosis than other statins in a number of cancer cell lines including
acute myeloblastic leukaemia (AML) (Wong et al., 2001). Gbelcová and colleagues also
found cerivastatin to be the most potent of all the commercially available statins against
a panel of pancreatic cancer cell lines (Gbelcova et al., 2008). The data we obtained
from this screen of pancreatic cancer cell lines also observed cerivastatin to be
approximately 10 fold more potent than simvastatin correlating with the previously
published results. In the monolayer culture all cell line displayed similar potency and
efficacy profiles with AsPC-1 displaying the lowest IC50 value. Conflicting data has
implicated ras signalling in some statins inhibition activity. One study has indicated the
K-ras wild type cell line BxPC-3 is significantly less sensitive to cerivastatin and
simvastatin compared to other K-ras mutant cell lines (Gbelcova et al., 2008).
However, previous studies have also shown growth inhibitory effect on pancreatic
cancer cells regardless of ras mutation status (Muller et al., 1998; Sumi et al., 1994).
BxPC-3 and PANC-1 cells were not observed to have reduced potency or efficacy from
2D to 3D cultures, indicating there was no chemoresistance or reduced sensitivity
attributed to the 3D cell culture conditions. However, the AsPC-1 cell line displayed a
reduction in both potency and efficacy when comparing the activities obtained for the
monolayer to the 3D culture conditions. This more resistant phenotype indicates a
population of cell cells may be less sensitive to cerivastatin due to a possible
chemoresistance mechanism induced by the 3D culture conditions. Although the exact
resistance mechanism is unknown the altered efficacy profile observed with the
metastatic cell line AsPC-1, highlights the value of using a panel of cell lines to
evaluate drug responses to determine cell line specific effects. Due to the withdrawal of
cerivastatin from the market following its poor safety profile there have been no clinical
studies on the potential use of cerivastatin for pancreatic cancer therapy. Although it has
the highest potency of all statins, its poor safety profile make it unlikely to be further
investigated for clinical use in its current form. However, a non-systemic delivery or
isolated infusion methods may be a possible option for future studies with statins as
anti-cancer therapies for pancreatic cancer. Significant activity for simvastatin has also
been demonstrated in preclinical studies but efficacy in clinical trials has so far been
disappointing. A recent phase II clinical trial of simvastatin and gemcitabine for
treatment of advanced pancreatic cancer found no clinical benefit compared to
290
gemcitabine alone (Hong et al., 2014). The results from the clinical outcomes may
correlate with the screening study being described here in which simvastatin only
provided 100% efficacy in a monolayer models at very high micromolar concentrations,
with the reduction in cell viability effects not seen in the more physiologically relevant
3D model. With high drug exposure only providing approximately 40% efficacy (
Figure 6.13) across the three cell lines in 3D. The morphological evaluation confirms
the response recorded by the metabolic assay and provides additional information on the
lack of efficacy of simvastatin in the 3D cell culture model.
6.4.8
Summary
The secondary screen was successfully utilised to further characterise drug responses
for comparison between monolayer and 3D culture models. This study highlighted the
importance of having multiple parameters for determining drug activity, particularly in
3D culture models. A number of drugs assessed in this study (such as salinomycin)
although displaying limited changes in potency (as measured by IC50 values), had
significant reductions in efficacy (as measured by Emax and AUC values), which could
be important indicators for drug progression through preclinical drug discovery
pipelines. Morphological evaluations (using simple brightfield microscopy), although
not used for quantitative assessment, also provided observations confirming the
metabolic activity data (obtained from the resazurin assay) and provided insights into
drug interactions with tumour-like structures. Drug response profile discrepancies were
observed between traditional monolayer and the more physiologically relevant 3D cell
culture models. Although it has not been elucidated at this stage, if the drug response
profiles in the 3D culture are more relevant or predictive of the in vivo tumour response,
the differences in potency and efficacy data obtained with the 3D cultures may have the
potential to add important insights into anti-cancer drug screening programs.
The drugs selected from the pilot screen included a number of individual drugs from the
same compound class, such as ionophores, statins and camptothecin derivatives. By
evaluating these drugs in both monolayer and 3D culture conditions with a range of
genetically and phenotypically different pancreatic cancer cell lines, class and cell line
291
specific differences were able to be characterised. This study also confirmed the activity
of previously reported drugs on pancreatic cancer cell lines with mycophenolic acid,
digitoxin, salinomycin and the statins all displaying drug responses in monolayer format
that correlated with the published studies. The additional 3D culture assay drug activity
results demonstrated that potential therapeutic agents may not be as effective against in
vivo tumours as observed in monolayer culture conditions currently utilised in
preclinical studies. Therefore, the 3D-based cell culture models may serve as an
additional preclinical step in drug discovery programs, to determine which drugs or
compounds are more likely to provide greater efficacy in vivo and therefore move
through the preclinical pipeline. For example, utilising a 3D pancreatic cancer cell
culture for the evaluation of mycophenolic acid or simvastatin (poor efficacy in 3D)
may have provided rationale not to pursue clinical trials which ultimately failed to show
clinical efficacy in pancreatic cancer patients.
Ciclopirox olamine and maduramicin were identified for the first time (there is currently
no published data available) to have anti-cancer activity on pancreatic cancer cell lines
in vitro. Ciclopirox may have potential for use in future pancreatic cancer treatments,
while maduramicin is unlikely to be used in further preclinical studies due to its
unfavourable chemical structure (may find use as a tool for target orientated studies).
The identification of drugs with previously unreported activity against pancreatic cancer
cell lines supports the idea that repurposing drugs may be an innovative strategy for
identifying novel cancer therapies.
This pilot screen utilised the 3D culture model as a secondary evaluation only for hits
identified from a traditional monolayer assay first. However, with the results obtained
indicating that robust and reproducible drug responses can be achieved, this assay has
the potential to be a valuable tool in both primary and secondary screening of novel
therapeutic agents. The drug responses provided by utilising a more physiologically
relevant 3D culture assay may also prove invaluable in preclinical drug discovery
programs. The automation and ability to use cost effective reagents (resazurin) allows
this validated in vitro 3D pancreatic cancer assay to be readily incorporated into future
high throughput screening protocols.
292
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298
7. Chapter Seven: Assay Miniaturisation in 1536-well Microtitre
Plate Format for Combination Studies to Assess Anti-Cancer
Synergy.
7.1. Introduction
Due to the heterogenetic nature of pancreatic cancer and a history of chemo-resistance
against single agent treatments, it is anticipated that combination therapy will provide
the greatest potential for successful chemotherapeutic intervention with the disease.
Combinational drug therapies are now common place and have been used in numerous
anti-cancer treatments since the mid-20th century, to achieve better clinical results than
obtained with single agent regimes (Chabner and Roberts, 2005). However, to date, the
advantages of combination therapies against advanced pancreatic cancer have been
minor, with only limited benefits compared to the standard of care single agent,
gemcitabine (Burris and Rocha-Lima, 2008; Ying et al., 2012). One of the current
challenges in the field of pancreatic cancer research is to identify agents administered in
combination therapies that provide clinical advantages over single treatment options.
The incorporation of combination studies into early drug discovery screening programs
may accelerate the identification of potential novel therapeutic combinations.
The rationale for selecting anti-cancer drug combinations has typically followed either a
trial and error methodology or a rational hypothesis driven approach. The traditional
trial and error process involved the assessment of combining therapies based on the
effectiveness of each drug as a monotherapy (avoiding overlapping toxicities) and
clinical trials to then establish dose scheduling, toxicity and efficacy (Waterhouse et al.,
2006). The current rational approach driven by biological hypothesis, aims to target
known molecular drivers in cancer associated pathways (such as with the Bcr-Abl
inhibitor imatinib in chronic myeloid leukaemia) and resistance mechanisms (the
development of secondary mutations in the target protein Bcr-Abl e.g. dasatinib) (AlLazikani et al., 2012).
Clinical experience, animal models and preclinical in vitro studies have all been used to
demonstrate additive or synergistic effects of combination therapies based on either
299
methodology (Chabner and Roberts, 2005). Clinical trials provide the ultimate clinical
efficacy information. They are however, expensive, time consuming, unable to assess
combinations without previously established preclinical activity and impractical for
large numbers of drug combinations. In vivo animal models such as mouse xenograft
and genetically modified mouse models (GEMMs) play an important role in drug
combination evaluations, but they also have limitations such as also being expensive,
time consuming and may lack human tumour relevance (Zoli et al., 2001). A variety of
different in vitro cell culture assay formats have been used in the past to evaluate drug
effects on both established cell lines and primary clinical tumours cultures, these
include monolayer based cell viability assays and colony forming assays (Mayer and
Janoff, 2007). Although in vitro cell culture has been used to evaluate drug
combinations, the use of in vitro data to predict combination effects, such as synergy in
vivo, remains challenging in all fields of cancer research. The in vitro cell culture
settings often do not mimic drug pharmacology and pharmacokinetics in a
physiologically relevant model (Waterhouse et al., 2006).
To improve the preclinical development of drug combination strategies (that will
ultimately provide beneficial therapeutic outcomes in patients) more relevant in vitro
assays capable of assessing the therapeutic effects of drug combinations should be
investigated. Although the rational, hypothesis driven approaches have been successful
in developing combination therapies against a number of malignancies (where there are
clearly identified targets and or resistance mechanisms), to date, this has been largely
ineffective against pancreatic cancer. An alternative (or complimentary) approach to
identify effective drug combinations may be a high throughput unbiased screening
strategy. The ability to discover unexpected synergistic interactions as either part of an
early screening program or in extended preclinical studies has the potential to uncover
unlikely combination regimes (Keith et al., 2005). Such an approach has previously
yielded an unlikely synergistic combination (anti-parasitic agent, pentamidine and an
anti-psychotic, chlorpromazine) identified using an in vitro cell culture model against
lung carcinoma cells (Borisy et al., 2003). For evaluating either large numbers of novel
agents identified in routine screening programs or smaller subsets of potential clinical
candidates, higher throughput and more predictive in vitro models may enhance these
combination treatment strategies. This chapter will focus on the use of a highly
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miniaturised in vitro 3D pancreatic cell culture model to assess the synergistic effects of
selected drug combinations.
7.1.1. Preclinical drug combination studies
Combinational chemotherapy strategies involve the concept of developing combinations
selected on the basis of synergistic drug interactions. Synergy can be defined as the
interaction of multiple agents that results in the therapeutic effects that are greater than
what could be expected from either agent alone (Berenbaum, 1989). The study of
interactions between biologically active agents is an extremely complex field of
research, with a number of molecular and pharmacological factors determining
effectiveness of combinations. Interactions can be generally described as either
synergistic, additive or antagonistic, with a number of assay approaches and
mathematical models (such as the combinational index developed by Chou and
colleagues) used to interpret the combinational effects (Chou and Talalay, 1984). The
CI equation (detailed in section Methods section 7.2.6) is a simple and elegant method
for quantifying synergistic or antagonistic drug interactions in a combination study and
has been utilised for several decades (Chou, 2010).
The factors which may influence synergy include drug dose, drug to drug ratio and the
sequence of drug addition (Tardi et al., 2009). To study drug interactions using a
combination of drugs in vitro, taking into account these factors requires a large number
of data points to achieve accurate determinations. For systematic evaluations of drug
combinations, a series of fixed drug ratios diluted over a range of effective biological
concentrations should be examined. One approach to achieving this is to prepare a series
of concentrations that span the dose response curves of each drug to be used in the
combination study. Each dilution (concentration) of drug A is then mixed with each
dilution of drug B to produce a matrix of solutions that contain both drugs in a range of
concentrations and ratios (Mayer and Janoff, 2007).
For a single combination study using a dose response curve of 12 points would require
almost an entire 384 well plate (single data point with controls) to be used per
evaluation. To utilise the Matrigel based 3D cell culture model developed in this
project, each drug combination evaluation would be relatively expensive (at the current
cost of reagents) and extremely time consuming. The standard area of a 384 well
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microtitre plate requires considerable Matrigel™ (5ml per plate) to generate
reproducible 3D cell cultures. To overcome these limitations the 384-well 3D model
developed in Chapter 3 was miniaturised to a 1536-well microtitre plate format with full
liquid handling automation (including Matrigel dispensing). A reduction in cost per data
point of almost 10 fold, and an increase in throughput, positions this model to be more
readily adopted in a drug discovery process (
Figure 7.1).
Many of the existing assay parameters, such as incubation conditions, media and drug
dosing schedules and drug exposure times were maintained for the miniaturisation from
a 384-well to
1536-well format, however, cell numbers, well volumes and liquid
handling steps were optimised for the smaller well format. The pancreatic
adenocarcinoma cell line, PANC-1, was selected for the use in the 1536-well 3D cell
culture assay. PANC-1 cells are of primary tumour origin and have mutations in K-ras
CDKN2A/p16, TP53 and SMAD4 genes (Deer et al., 2010). The cell line has reported
gemcitabine, 5-fluorouracil (5-FU), and cisplatin resistance in vitro (Chen et al., 2012).
In the extracellular matrix (ECM)-based 3D cell culture model, the cell line forms large
poorly differentiated micro tumour-like masses that exhibited strong adhesion to the
basement membrane (Matrigel). This chapter will examine the effects of miniaturisation
of the current model developed within this project and evaluate its use in combination
studies, utilising a range of reference chemotherapy drugs and a screening hit identified
in the pilot screen (Chapter 6).
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Figure 7.1. Miniaturisation of assay format and reduction in total well area for 3D cultures.
(a) The 384 well plate used for previous 3D assay evaluation utilises a large well area (which requires
expensive reagents such as Matrigel) in which less than half of the area is required for assay output
(yellow box indicates approximate area utilised for Calcein AM cell viability assay 6 fields). (b) The 1536
well microtitre plate format utilises approximately a quarter of the well area of the 384 well format.
303
7.1.2. Drug and compound selection
A number of drugs / compounds were evaluated for use in the pilot combination
screening studies. These included several of the chemotherapeutic agents (previously
characterised in Chapter 4) gemcitabine, doxorubicin, vinorelbine, epirubicin,
paclitaxel, docetaxel, as well as several targeted agents including the epidermal growth
factor receptor (EGFR) inhibitor erlotinib and insulin like growth factor 1 receptor
(IGF-1R) inhibitors BMS-754807, OSI-906, GSK1838705A . One of the active ‘hits’
identified from the pilot screen (ciclopirox olamine) and a selected iron chelator
(Dp44mT) were also examined for possible synergistic effects. Initially, the activity
(potency) of each compound was defined as a single agent in the 3D culture based assay
to determine dose response ranges for combination studies. The EGFR inhibitor,
erlotinib and the IGF-1R inhibitors OSI-906 and GSK1838705A failed to produce dose
response curves, even at extremely high top doses (>100µM) and as accurate IC50
values could not be calculated (dose response curves are a prerequisite for synergy
determination) theses agents were excluded from the panel under investigation (Chou,
2010). The final selection of drugs / compounds for the combination studies were
paclitaxel, doxorubicin, gemcitabine, BMS-754807, ciclopirox olamine and di-2pyridylketone-4,4-dimethyl-3-thiosemicarbazone (Dp44mT) (Table 7.1).
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Table 7.1. Drugs selected for combination studies against the pancreatic cancer cell line PANC-1 in
the 1536-well 3D cell culture format.
The drug class, mechanism of action (MOA) and structure are summarised.
Drug
Class
MOA
Gemcitabine
Pyrimidine
analogue
Paclitaxel
Taxane
Inhibits DNA synthesis by
an number of
mechanisms, including S
phase blockade and
targeting ribonucleotide
reductase and DNA
polymerase.
Paclitaxel is an antimitotic
agent that binds tubulin
and prevents functional
microtubule development.
Doxorubicin
Anthracycline
BMS-754807
Pyrrolotriazine
Ciclopirox
olamine
Hydroxypyridone
Dp44mT
Structure
Doxorubicin is an
antracycline that
intecalates DNA,
preventing transcription
and replication by
inhibiting topoismerase II
progression.
A reversible ATPcompetitive antagonist of
IGF-1R, inhibits the
catalytic domain of the
IGF-1R and prevents
actiavation of the
signalling pathways. It
has been shown to block
the activity of both IGF-1R
and IR and cause
apoptosis in numerous
cancer cell lines in vitro
and in vivo .
6-cyclohexyl-1-hydroxy-4methyl-2(1H)-pyridone 2aminoethanol salt
(Ciclopirox olamine) is an
anti-fungal agent currently
used to treat cutaneous
fungal infections. This
alpha hydroxy-pyridone is
an iron chelator that has
shown pre-clinical anticancer activity against a
number of maligancies.
Pyridyl
di-2-pyridylketone 4,4thiosemicarbazone dimethyl-3thiosemicarbazone
(Dp44mT) is a iron
chelating agent and has
shown to have potent anticancer activity against a
number of cancer cell
lines.
305
Gemcitabine is the current standard of care chemotherapy agent for advanced pancreatic
cancer (described previously in Chapter 4.1.3). It is an inhibitor of ribonucleotide
reductase (RR) and DNA synthesis through a number of mechanisms. Gemcitabine has
been previously reported to exhibit synergistic effects with a number of agents in in
preclinical studies (Giovannetti et al., 2004; Symon et al., 2002). Despite these reports,
numerous clinical trials with gemcitabine combination based treatments have failed to
show efficacy over monotherapy alone (Jacobs, 2002; Ying et al., 2012).
An improved formulation of paclitaxel (an albumin-coated formulation of paclitaxel) is
currently one of the few agents with regulatory approval for combination therapy in
advanced pancreatic cancer. This anti-mitotic taxane prevents microtubule development
and cell proliferation (described previously in Chapter 4.1.3). Although increased
efficacy has been reported in clinical trials for other cancers (such as breast and ovarian)
with combination therapy involving paclitaxel, there are limited preclinical publications
reporting synergistic activity for pancreatic cancer (Chiorean and Von Hoff, 2014).
Doxorubicin is a topoisomerase II inhibitor that prevents cellular replication and has
shown clinical efficacy in a number of malignancies including bladder, breast, lung and
endocrine pancreatic tumours. However, no survival advantage over single
monotherapy was observed with gemcitabine combination clinical trials (Lim et al.,
2011). There are limited published in vitro combination studies on doxorubicin in
pancreatic cancer.
Inhibition of IGF-1R has recently been the focus of several pancreatic cancer clinical
trials (Guha, 2013). The IGF-1 receptor has been shown to be over expressed in
numerous cancers including pancreatic adenocarcinomas and has been implicated in
proliferation, survival and metastasis. The potential involvement of IGF-1R in cancer
progression made this tyrosine kinase one of the most promising new targets in recent
years, with over 22 agents in clinical development against a range of malignancies. A
number of in vitro studies with IGF-1R inhibitors observed additive or synergistic
effects in combination with gemcitabine (Cohen et al., 2005; Kawanami et al., 2012).
However, the majority of antibody and small molecule tyrosine kinase inhibitors in
combination with gemcitabine have recently failed clinical progression against
306
pancreatic cancer (including AMG-479, Ganitumab, Cixutumumab) (Chen and Sharon,
2013; Yee, 2012).
The tyrosine kinase inhibitor BMS-754807 has previously been shown to have
synergistic anti-tumour effects when tested in vitro with several cytotoxic and targeted
anti-cancer agents across a range of malignancies, including with cetuximab (colon
cancer), trastuzumab (breast cancer), lapatinib (lung cancer), bicalutamide (prostate
cancer), dasatinib (sarcoma and colon cancer), paclitaxel (breast, lung), docetaxel
(breast), and vincristine (colon, lung) (Carboni et al., 2009). An in vitro (monolayer
assay with pancreatic cancer cell lines PANC-1 and AsPC-1) and in vivo (murine
xenograft) combination study (with BMS-754807) also found an additive effect with
gemcitabine (Awasthi et al., 2012).
Ciclopirox olamine is an anti-fungal agent that was identified from the pilot screen
(Chapter 6) with activity against pancreatic cancer cell lines (AsPC-1, PANC-1 and
BxPC-3). It is an intracellular iron chelator that has been recently identified as having
anti-tumour effects in a number of hematologic malignancies and is currently in clinical
trials (acute myeloid leukaemia (AML), acute lymphocytic leukemia (ALL), chronic
lymphocytic leukemia (CLL) and chronic myeloid leukemia (CML)) (Minden et al.,
2014). No published data on ciclopirox combination studies has previously been
reported.
Iron depletion through chelation has been explored as a possible therapeutic
intervention in a variety of cancers. Numerous iron chelating agents have been
developed since the first iron chelator desferrioxamine (DFO) was successfully used in
clinical trials for neuroblastoma (Kovacevic et al., 2011). Dp44mT is one of the most
active thiosemicarbazones developed and has displayed potent in vitro and in vivo antitumour activity as a monotherapy with pancreatic cancer cell line PANC-1 (Kovacevic
et al., 2011). No published combination therapies with Dp44mT could be identified with
pancreatic cancer cell lines.
307
7.2. Materials and methods
7.2.1. Materials and reagents
Black side-clear bottom (1536-well) tissue culture (TC) treated plastic bottom optical
imaging microplates (6007550, Perkin Elmer) were used for all cell based assays
described throughout. Polypropylene 384-well clear microtitre plates (Falcon 353265,
BD Biosciences) were used for all master concentration response curve (CRC) drug
plates. All drug dilution plates were prepared using sterile, clear 384-well (Falcon
353961, BD Biosciences) microplates. The biological hydrogel (Growth Factor reduced
(GFR) Matrigel) was purchased from Corning Life Sciences.
Resazurin (Sigma
Aldrich) was prepared as per materials method section 3.2.1 Chapter 3.
7.2.2. Preparation of drugs for combination assay dosing
Stock powdered drugs were obtained from Sigma Aldrich (gemcitabine, ciclopirox
olamine, Dp44mT), Tocris Biosciences (doxorubicin), A.G. Scientific (paclitaxel) or
ChemieTek (BMS-754807). Drugs were reconstituted at between 10-50mM
concentrations in 100% Dimethyl sulphoxide (DMSO) before 18 point serial dilutions
were produced in master drug microplates. A 13-point dose response was selected
(dependant on the IC50 value of each of drug) and replicated either vertically or
horizontally, depending on the combination required. Master drug microplates were
frozen at -20°C and thawed before each dosing schedule (plates were discarded after 6
freeze thaw cycles and then re-created from powdered stocks).
7.2.3. Cell culture preparation
General cell culture maintenance was performed with only the PANC-1 cell line as
described in Chapter 3 section 3.2.2.
7.2.4. Three dimensional cell culture assay conditions optimisation for 1536
well format
308
Assay conditions such as length of incubation and drug dosing schedule were
maintained from previous 384-well assay development section 4.2.5. However, cell
number, reagent volumes and liquid handling steps were re-optimised for 1536-well
plate format. Ice cold Growth Factor Reduced Matrigel (GFR Matrigel) was diluted in
RPMI media to a final protein concentration of 7.6mg/ml and dispensed (1.5µl per well)
onto the bottom of the 1536-well Cell Carrier microtitre plates. This process was fully
automated with the Agilent Bravo™ liquid handling platform, utilising a cooling block
and an optimised tip touching protocol (that precisely layered the Matrigel on the
bottom of the wells at a height of 0.2mm from the base of the plate). Matrigel layered
plates were incubated for 30-60 minutes at 37°C to allow for the extracellular matrix to
solidify. Cells were dissociated from culture flasks with accutase and cell number
determined by the trypan blue exclusion method as describe in Chapter 2 section 2.2.3.
The PANC-1 cell line was resuspended to a final concentration of 400 (200, 400, 600
cells were initially evaluated) cells per well in 8µl of complete RPMI media (see section
2.2.3) and dispensed on top of the Matrigel layer (0.5mm from the bottom of the plate)
with the Bravo liquid handling platform. Complete media (7.2µl) was refreshed at 72
hours (day 3) after seeding and then every 48 hours (day 5 and day 7) using a custom
protocol on the Bravo™ liquid handler designed to reduce cell disturbance (tip height
from base of plate set at 1.2mm and velocity of aspiration and dispense steps at 1.5µl/s).
Controls (0.4% DMSO negative control, 40µM puromycin positive control) were added
(0.8µl) at the 72 hour, 120 hour and 168 hour time points. Dispensing and aspirating
speeds were reduced and only 50% of media was removed in a two-step process (4µl
addition and dispensing steps). Resazurin (0.8µl of 600µM) was dispensed into wells at
the assay conclusion (144 hours of drug exposure, 216 hour total culture time) and
microtitre plates were incubated for two hours (a range of incubation times were
initially evaluated 2, 4, 6 and 8 hours). Metabolic viability was then determined by
fluorescence values read on the En Vision™ plate reader using a 1536-well format
resazurin protocol (Ex. 530nm, Em. 595nm). Metabolic activity responses were
calculated as described in Chapter 4 section 4.2.5.
7.2.5. Three dimensional (3D) structure reproducibility in a 1536-well plate
format
309
The 3D cell culture assay conditions were performed as detailing above in section 6.2.4.
A range of PANC-1 cell number densities (200, 400, 600 cells per well) were evaluated
for their ability to form 3D structures in the 1536-well microplate format over time. At
day 3, day 6 and day 9, brightfield or DIC images were acquired automatically with the
IN Cell 1000 high content imaging platform (4 fields, 10x objective) and manually with
an Olympus CellR microscope (single field, 4x objective). A number of parameters
were recorded, including number of objects, area and diameter of 3D structures.
Measurements were recorded automatically with an Image J macro utilising the entire
well (protocol below in Table 7.2). Three separate experiments, each with quadruplicate
wells were used for each day’s measurements.
Table 7.2 Protocol Steps Required to Segment and Analyse Size and Morphology Data from
ImageJ Software
310
Protocol Step
1
2
3 (optional)
4
5
6
7
Program Action
Result
Save image file from microscope Image file of 384-well with 3D morphology
or automated imaging platform
produced
Set scale to appropriate value and Correct pixel scaling required for size
make global for current image set analysis
Subtract background if contrast is
Enhances image for segmentation action
poor for brightifeld or DIC images
Image > Adjust > Threshold: Auto
or set manually for particular
image set to segment cells from
background
Analyse > Set Measurements >
Include parameters to measure
such as area, diameter, shape
descriptors
Analyse > Analyse Particles >
Include size exclusions and output
options
Process > Batch > Macro
Intensity segmentation used to identify 3D
structures from background
Measurements such as area, diameter,
shape descriptors are selected
Measurements are recorded and outputted
to results table
To automate image analysis a macro of
steps 1-6 is created and Batch process
run on entire image stack (below)
selectWindow(getTitle());
setAutoThreshold("Default");
//run("Threshold...");
setAutoThreshold("Default");
run("Convert to Mask");
run("Dilate");
run("Set Scale...", "distance=1.610 known=1 pixel=1 unit=uM global");
run("Analyze Particles...", "size=500-100000 circularity=0.20-1.00 show=Outlines display
exclude clear summarize in_situ");
close()
close()
close()
selectWindow("Results");
run("Close");
7.2.6. Intra-plate variability 1536-well plate format
To determine if any well or plate effects may be enhanced by such a miniaturised and
low volume assay, the assay quality was assessed with two test plates. PANC-1 3D
cultures were prepared and the assay performed as per section 6.2.4 above. The first
1536-well microtitre plate was divided in half, with positive and negative control wells
which were dispensed to simulate drug assay plates. Media (7.2µl additions) and
controls (2 x 0.4µl additions) were dispensed as per the concurrent combination study
protocol Section 6.2.5. The positive controls contained a final concentration of 40µM of
311
the cytotoxic agent puromycin to produce 100% cell death. The negative control (100%
growth) was a DMSO dilution in sterile water (final assay DMSO concentration of
0.4%). The second microtitre plate was utilised to examine any well to well effects that
might be caused by such low volume liquids and plate positions in the 1536-well
format. A standard checkerboard pattern of positive (40 µM puromycin) and negative
control (0.4% DMSO) wells was examined.
7.2.7. Drug combination study in 1536 well plate format
The 3D PANC-1 cell cultures were prepared as per section 4.2.4 and drug combination
studies were performed either concurrently (simultaneous drug exposure of two agents)
or sequentially (initial drug exposure followed by a secondary drug exposure). The
concurrent and sequential combination studies involved a slightly altered media change
and drug dispensing procedure (See Figure 7.2 for schedule):
Concurrent
Three days after cell seeding (as described in section 7.2.4), the media was refreshed by
aspirating 4µl from each well and dispensing 4µl fresh media. A second aspiration of
4µl from each well was performed and a final dispensing step of 3.2µl of fresh media
was dispensed. Total volume of media in each well was 7.2µl. Each drug under
investigation was then dispensed at the appropriate concentration (see Table 7.4 for
drug matrix layout) at 0.4µl per well for a total well volume of 8µl. This media change
and drug dosing procedure was repeated at day 5 and day 7.
Sequential
Three days after cell seeding (as described section 6.2.4) the media was refreshed by
aspirating 4µl from each well and dispensing 4µl fresh media. A second aspiration of
4µl from each well was performed and a final dispensing step of 3.6µl of fresh media
was dispensed. The total well volume was 7.6µl of media. The first drug of the
sequential combination was then dispensed (0.4µl) at the appropriate drug dilution for a
final well volume of 8µl. At day 5 (after 48 hours of the initial drug exposure) media
was refreshed and the first drug of the sequential series removed. The second drug was
312
then dispensed (0.4µl in to 7.6µl of media) with media change and drug dispensing
repeated on day 7 (total of 96 hours exposure of the second drug).
Figure 7.2. Timetable for performing the developed 3D combination assay.
Cell seeding (day -3) prior to 3D structure formation at day 0 followed by administration of drugs /
compounds and media. Drug renewal at days 2 and 4 according to concurrent or sequential dosing
schedule. Evaluation of cellular drug response at assay end point (cell metabolic activity).
7.2.8. Corresponding drug ratios for determining synergistic effects.
To more accurately determine synergistic effects of combination treatments, it is
preferred to have several data points either side of the IC50 value for each drug.
Therefore, drug ratios were determined based on each individual drugs IC 50 value (with
several data points either side of IC50 values). The specific ratios for each drug
combination are detailed in Table 7.5– Table 7.10 of this methods section.
7.2.9. Determination of combination index (CI) of drug combinations assessed
by metabolic activity measurements
The following methods are used to determine the drug interactions within the 3D culture
metabolic activity assay:
313
The fluorescent values obtained from the EnVision plate reader were normalised against
the control wells (0.4% DMSO for 100% growth and 40µM for 100% cell death) and
potency values (IC50) were calculated in Graphpad Prism 6 using the using the variable
slope sigmoidal dose response equation. As constant drug dilution ratios were not used
in this study, several different drug dosage ranges instead were selected for synergistic
determinations (Table 7.4);

Equal high dose of drug A and B

High dose of drug A and medium dose of drug B

High dose of drug A and low dose of drug B

High dose of drug A and very low dose of drug B

High dose of drug B and medium dose of drug A

High dose of drug B and low dose of drug A

High dose of drug B and very low dose of drug A.
Interactions between the different agents (when used at the above mentioned dose
combinations) were assessed using the combination index (CI) method described by
Chou and Talalay (Chou and Talalay, 1984). Each drug was tested alone and in a
combination paring (ratios and drug paring listed in Table 7.5 - Table 7.10). For each
combination, the IC50 values were determined and the CI value was calculated with the
following formula:
𝐶𝐼 =
(𝐷)𝐴
(𝐷)𝐵
+
(𝐷𝑥)𝐴 (𝐷𝑥)𝐵
Where (Dx)A and (Dx)B are the IC50 values determined for drug A and drug B
respectively and (D)A and (D)B are the IC50 values determined in the respective
combination treatments. A simple interpretation of the CI is that a value of <0.5 denotes
a synergistic trend, while a CI value between 0.5 – 1.0 denotes an additive trend. A CI
value between 1 – 2 indicates indifference or a trend towards antagonism and a CI > 2
represents an antagonistic effect (Chou, 2006). However, there is a more precise semiquantitative scale designed by Chou and co to provide the degree of synergy (Table
7.3).
314
Table 7.3. The range of combination index values and the degree of synergism, additive effect or
antagonism denoted. Adapted from Chou and Martin (Chou, 2006).
Range
of
Combination Index
<0.1
0.1–0.3
0.3–0.7
0.7–0.85
0.85–0.90
0.90–1.10
1.10–1.20
1.20–1.45
1.45–3.3
3.3–10
>10
Description
Very strong synergism
Strong synergism
Synergism
Moderate synergism
Slight synergism
Nearly additive
Slight antagonism
Moderate antagonism
Antagonism
Strong antagonism
Very strong antagonism
As a non-constant ratio of drug dilutions was utilised to compare drugs under
investigation, a normalised isobologram (Chou–Chou plot) method is required to graph
combination effects (Chou, 2010). Normalised isobolograms were constructed from the
fractional inhibitory concentration (FIC) values (calculated to obtain CI values)
obtained from each of concentration ratios tested (Table 7.4).
𝐷𝑟𝑢𝑔 𝐴 𝐹𝐼𝐶 =
(𝐷)𝐴
(𝐷𝑥)𝐴
𝐷𝑟𝑢𝑔 𝐵 𝐹𝐼𝐶 =
(𝐷)𝐵
(𝐷𝑥)𝐵
The drug FIC values were the plotted against each other to produce the normalised
isobolograms presented in the results Section 7.1.1.
7.2.10. Statistics
Statistical significance between culture models or conditions was assessed using the
student’s t-test. All potency determinations (IC50) were determined in Graphpad Prism
315
using the variable slope sigmoidal dose response equation of normalised data points.
Combination index values were calculated in Microsoft Excel using the formulas in
Section 6.2.6 and isobolograms were graphed in Graphpad Prism.
316
Table 7.4 An example drug matrix template utilised for all combination studies. This format combines drug A serial dilutions (in duplicate) from left to right and drug B
serial dilution from bottom to top (duplicate wells). The configuration allows for two 13-point dose response combination studies per 1536 well microtitre plate plus control
wells and reference compounds. Dosing combination are colour coded and described in the legend.
317
Table 7.5. Drug concentrations and ratios between paclitaxel and doxorubicin and the doses examined for synergistic activity.
High dose for both drug A and B
High dose for drug A and medium dose for
drug B
High dose for drug A and low dose for
drug B
High dose for drug A and very low dose for High dose for drug B and medium dose for
High dose for drug B and very low dose for
High dose for drug B and low dose for drug A
drug B
drug A
drug A
Paclitaxel (A) Doxorubicin (B) Drug Ratio Paclitaxel (A) Doxorubicin (B) Drug Ratio Paclitaxel (A) Doxorubicin (B) Drug Ratio Paclitaxel (A) Doxorubicin (B) Drug Ratio Paclitaxel (A) Doxorubicin (B) Drug Ratio Paclitaxel (A) Doxorubicin (B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
20
40
1:2
20
20
1:1
20
10
2:1
20
0.4
50:1
10
40
1:4
5
40
10
20
1:2
10
10
1:1
10
4
2.5:1
10
0.2
50:1
5
20
1:4
1
20
5
10
1:2
5
4
1.25:1
5
2
2.5:1
5
0.04
125:1
1
10
1:10
0.5
10
1
4
1:4
1
2
1:2
1
0.4
2.5:1
1
0.02
50:1
0.5
4
1:8
0.2
4
0.5
2
1:4
0.5
0.4
1.25:1
0.5
0.2
2.5:1
0.5
0.01
50:1
0.2
2
1:10
0.05
2
0.2
0.4
1:2
0.2
0.2
1:1
0.2
0.04
5:1
0.2
0.004
50:1
0.05
0.4
1:8
0.02
0.4
0.05
0.2
1:4
0.05
0.04
1.25:1
0.05
0.02
2.5:1
0.05
0.002
25:1
0.02
0.2
1:10
0.01
0.2
0.02
0.04
1:2
0.02
0.02
1:1
0.02
0.01
2:1
0.02
0.001
20:1
0.01
0.04
1:4
0.005
0.04
0.01
0.02
1:2
0.01
0.01
1:1
0.01
0.004
2.5:1
0.005
0.02
1:4
0.002
0.02
0.005
0.01
1:2
0.005
0.004
1.25:1
0.005
0.002
2.5:1
0.002
0.01
1:5
0.001
0.01
0.002
0.004
1:2
0.002
0.002
1:1
0.002
0.001
2:1
0.001
0.004
1:4
0.0005
0.004
0.001
0.002
1:2
0.001
0.001
1:1
0.0005
0.002
1:4
0.0005
0.001
1:2
Drug Ratio
(A:B)
1:8
1:20
1:20
1:20
1:40
1:20
1:20
1:8
1:10
1:10
1:8
Paclitaxel (A) Doxorubicin (B) Drug Ratio
(µM)
(µM)
(A:B)
0.2
40
1:200
0.05
20
1:400
0.02
10
1:500
0.01
4
1:400
0.005
2
1:400
0.002
0.4
1:200
0.001
0.2
1:200
0.0005
0.04
1:80
Table 7.6. Drug concentrations and ratios between ciclopirox olamine (CPX) and doxorubicin and the doses examined for synergistic activity.
High dose for both drug A and B
CPX (A) Doxorubicin (B) Drug Ratio
(µM)
(µM)
(A:B)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
High dose for drug A and medium dose for
drug B
CPX (A)
(µM)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
Doxorubicin (B) Drug Ratio
(µM)
(A:B)
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
2:1
2:1
2.5:1
2:1
5:1
2:1
5:1
2:1
2:1
2.5:1
2:1
2:1
High dose for drug A and low dose for
drug B
CPX (A)
(µM)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
Doxorubicin (B) Drug Ratio
(µM)
(A:B)
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
4:1
5:1
5:1
10:1
10:1
10:1
10:1
4:1
5:1
5:1
4:1
High dose for drug A and very low dose for High dose for drug B and medium dose for
drug B
drug A
CPX (A)
(µM)
Doxorubicin (B) Drug Ratio
(µM)
(A:B)
40
20
10
4
2
0.4
0.2
0.04
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
318
100:1
100:1
250:1
200:1
200:1
100:1
100:1
40:1
CPX (A)
(µM)
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
Doxorubicin (B) Drug Ratio
(µM)
(A:B)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
1:2
1:2
1:2.5
1:2
1:5
1:2
1:5
1:2
1:2
1:2.5
1:2
1:2
High dose for drug B and low
dose for drug A
CPX (A)
(µM)
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
Doxorubi
cin (B)
(µM)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
Drug
Ratio
(A:B)
1:4
1:5
1:5
1:10
1:10
1:10
1:10
1:4
1:5
1:5
1:4
High dose for drug B and very
low dose for drug A
CPX (A)
(µM)
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
Doxorubi
cin (B)
(µM)
40
20
10
4
2
0.4
0.2
0.04
Drug
Ratio
(A:B)
1:100
1:100
1:250
1:200
1:200
1:100
1:100
1:40
Table 7.7 Drug concentrations and ratios between Dp44mT and doxorubicin and the doses examined for synergistic activity.
High dose for both drug A and B
High dose for drug A and medium dose for
drug B
High dose for drug A and low dose for
drug B
High dose for drug A and very low dose for High dose for drug B and medium dose for High dose for drug B and low dose High dose for drug B and very low
drug B
drug A
for drug A
dose for drug A
Dp44mT (A) Doxorubicin (B) Drug Ratio Dp44mT (A) Doxorubicin (B) Drug Ratio Dp44mT (A) Doxorubicin (B) Drug Ratio Dp44mT (A) Doxorubicin (B) Drug Ratio Dp44mT (A) Doxorubicin (B) Drug Ratio Dp44mT (A)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
0.0004
0.0002
0.0001
0.00004
0.00002
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
1:100
1:100
1:250
1:200
1:200
1:100
1:100
1:40
1:50
1:50
1:40
1:50
1:50
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
0.0004
0.0002
0.0001
0.00004
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
1:50
1:50
1:100
1:100
1:40
1:50
1:20
1:20
1:25
1:20
1:20
1:25
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
0.0004
0.0002
0.0001
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
1:25
1:20
1:50
1:20
1:20
1:10
1:10
1:10
1:10
1:10
1:10
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
1:1
1:1
1:1
1:1
1:1
1:1
1:1
1:1
0.2
0.04
0.02
0.01
0.004
0.002
0.001
0.0004
0.0002
0.0001
0.00004
0.00002
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
1:200
1:500
1:500
1:400
1:500
1:200
1:200
1:100
1:100
1:100
1:100
1:100
0.04
0.02
0.01
0.004
0.002
0.001
0.0004
0.0002
0.0001
0.00004
0.00002
Doxorubi
cin (B)
(µM)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
Drug
Ratio
(A:B)
1:1000
1:1000
1:1000
1:1000
1:1000
1:400
1:500
1:200
1:200
1:250
1:200
Dp44mT (A)
(µM)
0.004
0.002
0.001
0.0004
0.0002
0.0001
0.00004
0.00002
Doxorubi
cin (B)
(µM)
40
20
10
4
2
0.4
0.2
0.04
Drug
Ratio
(A:B)
1:10000
1:10000
1:10000
1:10000
1:10000
1:4000
1:5000
1:2000
Table 7.8 Drug concentrations and ratios between paclitaxel and gemcitabine and the doses examined for synergistic activity.
High dose for both drug A and B
High dose for drug A and medium dose for High dose for drug A and low dose for drug High dose for drug A and very low dose for High dose for drug B and medium dose for
High dose for drug B and very low dose for
High dose for drug B and low dose for drug A
drug B
B
drug B
drug A
drug A
Paclitaxel (A) Gemcitabine (B) Drug Ratio Paclitaxel (A) Gemcitabine (B) Drug Ratio Paclitaxel (A) Gemcitabine (B) Drug Ratio Paclitaxel (A) Gemcitabine (B) Drug Ratio Paclitaxel (A) Gemcitabine (B) Drug Ratio Paclitaxel (A) Gemcitabine (B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
20
10
5
1
0.5
0.2
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
2:1
2:1
2:1
1:1
1:1
1:1.25
1:2
1:2.5
1:2.5
1:2
1:2.5
1:2.5
1:2
20
10
5
1
0.5
0.2
0.05
0.02
0.01
0.005
0.002
0.001
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
4:1
4:1
5:1
2:1
2:1
2:1
1:1
1:1.25
1:1
1:1
1:1.25
1:1
20
10
5
1
0.5
0.2
0.05
0.02
0.01
0.005
0.002
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
8:1
10:1
10:1
4:1
5:1
4:1
2:1
2:1
2:1
2:1
2:1
20
10
5
1
0.5
0.2
0.05
0.02
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
319
80:1
100:1
100:1
40:1
50:1
40:1
20:1
20:1
10
5
1
0.5
0.2
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
1:1
1:1
1:2.5
1:2
1:2.5
1:5
1:5
1:5
1:5
1:5
1:5
1:5
5
1
0.5
0.2
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
Drug Ratio
(A:B)
1:2
1:5
1:5
1:5
1:10
1:12.5
1:10
1:10
1:12.5
1:10
1:10
Paclitaxel (A) Gemcitabine (B) Drug Ratio
(µM)
(µM)
(A:B)
0.2
0.05
0.02
0.01
0.005
0.002
0.001
0.0005
10
5
2.5
1
0.5
0.25
0.1
0.05
1:50
1:100
1:125
1:100
1:100
1:125
1:100
1:100
Table 7.9 Drug concentrations and ratios between BMS-754807 and gemcitabine and the doses examined for synergistic activity.
High dose for drug A and medium dose for drug
High dose for drug A and low dose for drug B
B
High dose for both drug A and B
High dose for drug A and very low dose for
drug B
High dose for drug B and medium dose for drug
A
High dose for drug B and low dose for drug A
BMS-754807 (A) Gemcitabine (B) Drug Ratio BMS-754807 (A) Gemcitabine (B) Drug Ratio BMS-754807 (A) Gemcitabine (B) Drug Ratio BMS-754807 (A) Gemcitabine (B) Drug Ratio BMS-754807 (A) Gemcitabine (B) Drug Ratio BMS-754807 (A) Gemcitabine (B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
20
10
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
2:1
20
10
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
4:1
4:1
5:1
4:1
4:1
5:1
4:1
4:1
5:1
4:1
4:1
5:1
20
10
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
8:1
10:1
10:1
8:1
10:1
10:1
8:1
10:1
10:1
8:1
10:1
20
10
5
2
1
0.5
0.2
0.1
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
80:1
100:1
100:1
80:1
100:1
100:1
80:1
100:1
10
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
1:1
1:1
1:1.25
1:1
1:1
1:1.25
1:1
1:1
1:1.25
1:1
1:1
1:1.25
5
2
1
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
Drug Ratio
(A:B)
High dose for drug B and very low dose for
drug A
BMS-754807 (A) Gemcitabine (B) Drug Ratio
(µM)
(µM)
(A:B)
1:2
1:2.5
1:2.5
1:2
1:2.5
1:2.5
1:2
1:2.5
1:2.5
1:2
1:2.5
0.5
0.2
0.1
0.05
0.02
0.01
0.005
0.002
10
5
2.5
1
0.5
0.25
0.1
0.05
1:20
1:25
1:25
1:20
1:25
1:25
1:20
1:25
Table 7.10 Drug concentrations and ratios between ciclopirox olamine (CPX) and gemcitabine and the doses examined for synergistic activity.
High dose for both drug A and B
High dose for drug A and medium
dose for drug B
CPX (A) Gemcitabine (B) Drug Ratio CPX (A)
(µM)
(µM)
(A:B)
(µM)
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
10
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
4:1
4:1
4:1
4:1
4:1
1.6:1
2:1
1:1.25
1:1.25
1:1
1:1.25
1:1.25
1:1
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
Gemcitabine
(B)
(µM)
5
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
High dose for drug A and low dose for
drug B
High dose for drug A and very low
dose for drug B
High dose for drug B and medium
dose for drug A
High dose for drug B and low dose for
drug A
High dose for drug B and very low
dose for drug A
Drug Ratio CPX (A) Gemcitabine (B) Drug Ratio CPX (A) Gemcitabine (B) Drug Ratio CPX (A) Gemcitabine (B) Drug Ratio CPX (A) Gemcitabine (B) Drug Ratio CPX (A) Gemcitabine (B) Drug Ratio
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
(µM)
(µM)
(A:B)
8:1
8:1
10:1
8:1
8:1
4:1
4:1
1.6:1
2:1
2:1
1.6:1
2:1
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
2.5
1
0.5
0.25
0.1
0.05
0.025
0.01
0.005
0.0025
0.001
16:1
20:1
20:1
16:1
20:1
8:1
8:1
4:1
4:1
4:1
4:1
40
20
10
4
2
0.4
0.2
0.04
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
320
100:1
100:1
250:1
200:1
200:1
100:1
100:1
40:1
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
1:2
1:2
1:2.5
1:2
1:5
1:2
1:5
1:2
1:2
1:2.5
1:2
1:2
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
40
20
10
4
2
0.4
0.2
0.04
0.02
0.01
0.004
1:4
1:5
1:5
1:10
1:10
1:10
1:10
1:4
1:5
1:5
1:4
0.4
0.2
0.04
0.02
0.01
0.004
0.002
0.001
40
20
10
4
2
0.4
0.2
0.04
1:100
1:100
1:250
1:200
1:200
1:100
1:100
1:40
7.3. Results
7.3.1. Miniaturisation of the 384-well 3D pancreatic cell culture based assay
into a 1536-well format.
The process of scaling down an assay from the 384-well format to a 1536-well format
required a number of optimisation steps to successfully miniaturise the 3D culture
model. Cell number seeding was the first variable assessed. A single pancreatic cancer
line (PANC-1) was chosen for the proof concept and viability of the study as no
previously published 1536-well pancreatic cancer ECM based 3D cell culture assays are
reported in the literature. A range of cell numbers (200, 400, 600) were dispensed into
the 1536-well plates (using the Bravo™ liquid handling platform) and imaged over time
(days 3, 6, 9) to evaluate 3D structure growth. All three cell seeding densities resulted in
the formation of 3D structures that increased in area and diameter with time. The 400
cell per well seeding density was chosen as this cell number was demonstrated to
provide the most reproducible 3D structure growth. The use of 400 cells per well was
shown to produce less variability than 200 cell per well (Figure 7.3a) and displayed less
central well aggregation than the 600 cells concentration (Figure 7.3d). For the 400 cells
per well cell density, the number of objects per well reduced over the 9 day culture
period investigated from approximately 60 at day 3 to less than 20 by the final day of
the assay (Day 9), indicating smaller structures had aggregated together over time,
forming spheroid-like cell structures (Figure 7.3c). In comparison, the 384 well model
produced approximately 30 structures (with the PANC-1 cell line). The average size of
the 3D structures was also reduced compared to the 384 well assay, with average sizes
in the 1536-well approximately 100µm in diameter compared to 150µm in the 384 well
format.
To determine if this reduced 3D cell structure number and size would affect overall
assay quality and sensitivity, a number of parameters were assessed. Several control
plates were used to assess the statistical parameters of assay quality; coefficient of
variation (%CV), signal window (SW) and Z′-factor (Z') (Figure 7.3b). All assay
conditions and liquid handling parameters of the proposed combination study were
321
replicated and control wells (positive control 40µM puromycin) were dispensed as per
the drug combinations schedule. The %CV of 12.2% remained below the 20% cut off
required, as discussed in Chapter 3 section 3.1.6 (although slightly higher than the
PANC-1 cell line in 384-well assay of 8.05%). The signal window of 5.01 was slightly
reduced compared to the 384-well assay determination of 7.71. However, the signal to
background (S/B) ratio (average maximum signal / average background signal)
remained at a similar level of approximately 8 fold. The Z’-Factor of 0.6 was
maintained above the 0.5 cut off indicating an acceptable assay (Iversen et al., 2006;
Zhang et al., 1999).
322
Figure 7.3. Assay development of the 1536-well 3D culture model with the pancreatic cancer line
PANC-1.
(a) 3D structure growth curves over time (days 3, 6, 9) with average diameter (ferets) and area measured.
Data points represent averages of quadruplicate wells in 3 separate experiments, error bars equal standard
deviation. (b) The robustness of the assay is measured by the statistical parameters coefficient of variation
(%CV), Signal Window (SW), Z′-Factor (Z'). (c) The number of objects per well over time measured
with the ImageJ segmentation protocol in methods section 6.2.5. Data points represent averages of
quadruplicate wells in 3 separate experiments, error bars equal standard deviation. (d) Representative
brightfield images with 200, 400 and 600 initial seeding densities captured over 9 days (day 3, day 6 and
323
day 9). Images were acquired on the Olympus CellR microscope with the 4x objective. Scale bar =
100µm.
Due to limited prior experience in the laboratory with the low volume 1536-well
microtitre plate formats and concerns of the potential for the occurrence of the well
wicking phenomenon, further evaluations were performed. Wicking is the creeping of
liquid up the walls of the wells via capillary action and can be more prominent in ultralow volume systems such as 1536-well plates (Sullivan, 2001). It may cause well-towell contamination, crosstalk and inaccurate assay readings. As this effect is dependent
on the surface tension and viscosity of the media, as well as the plate surface treatment,
the impact of this effect was unknown in this particular assay. To investigate this
further, a checkerboard pattern of controls (positive 40µl puromycin, negative 0.4%
DMSO) was dispensed into half a 1536 well plate (total well volume 8µl) and the
metabolic activity of adjacent wells were assessed for any well to well effects. No
observable effects were seen in adjacent wells (illustrated in Figure 7.4c) and therefore
the final assay volume of 8µl was selected for the combination study.
324
Figure 7.4. Evaluation of 1536 well plate and well effects
(a) Negative controls wells (0.4% DMSO) normalised values with Blue highlighted box excluding 2 most
outer edge wells (b) Positive control wells (40µM puromycin) normalised values, area outside of blue
highlighted box excluded from assay (c) Checkerboard pattern of control wells (40µM puromycin, 0.4%
DMSO) in the 1536 well plate to determine well to well effects. (e) Representative brightfield images of
the positive (40µM puromycin) and negative (0.4% DMSO) control wells acquired with the IN Cell 1000
high content imaging platform (composite image of 4 fields stitched together). Scale bar = 100µm.
325
7.3.2. Validation of 1536-well metabolic activity pancreatic cancer 3D cell
culture assay
To validate the 3D cell culture assay in the miniaturised 1536-well format, the
sensitivity of a number of reference drugs were assessed. The cellular responses for
doxorubicin, paclitaxel and gemcitabine were assessed using the PANC-1 3D cell
culture systems established for both 384-well (established Chapter 3) and 1536-well
formats (
Figure 7.5b). The sensitivity (IC50 values) and efficacy (Emax) profiles of the drug treated
PANC-1 cell line cultured in 3D in the 1536-well format was comparable to the results
obtained previously in the 384-well format, with no statistical differences observed (
Figure 7.5b). Inter-assay variability was investigated with the in-plate controls (positive
100% cell death; 40µM puromycin, negative controls 100 cell growth; 0.4% DMSO)
from each of the combination studies undertaken (discussed in the following section
7.3.3). The %CV remained between 10-15% for the entire pilot combination study and
the Z’-factor was above 0.5, indicating robust assay performance (
Figure 7.5c).
326
Figure 7.5. 1536 well 3D cell culture assay performance and sensitivity measurements.
(a) Representative dose response curves from a single replicate data points represent the average of
quadruplicate wells and error bars equal the standard deviation. (b) A summary table of drug response
parameters of the PANC-1 cell line in 384 and 1536 well 3D cultures based assays. Potency is determined
by IC50 value and efficacy by the maximal inhibition (E max). (c) Intra-assay variability assessment via the
coefficient of variation %CV and Z’-Factor values from in-plate controls.
327
7.3.3. Drug combination study utilising 1536-well 3D cell culture assay
The resazurin based metabolic activity approach was utilised in the 1536-well 3D cell
culture format to assess possible synergistic effects for a range of drug combinations.
However, initially the activity of each compound was defined as a single agent in the
assay system. The compound response curves were then adjusted to the individual
potency of each drug / compound (See Table 7.5 – Table 7.10 for serial dilution range).
Gemcitabine combinations were investigated using a concurrent combination approach.
PANC-1 3D cell cultures were exposed to gemcitabine and another drug / compound
PANC-1 for the same 144 hour period. Agents were selected from a panel of potential
drugs and compounds that had previously been reported to have synergism with
gemcitabine (in vitro) or were novel agents that had previously not been evaluated in
combination studies.
Paclitaxel and gemcitabine (concurrent)
As the improved drug delivery form of paclitaxel (nab-paclitaxel) has recently shown
promising effects in clinical combinations, a range of dose ratios combining
gemcitabine and paclitaxel were evaluated (Figure 7.6a). Interestingly, the CI values for
all the dose combinations tested displayed synergism. The highest synergistic effects
according to the CI values were the dose ratios that included the highest gemcitabine
ratio. This result opposes previously published data from an earlier published
monolayer in vitro study in which paclitaxel and gemcitabine concurrent exposure was
shown to have a non-additive or antagonistic effect (Theodossiou et al., 1998).
BMS-754807 and gemcitabine (concurrent)
The insulin like growth factor 1 receptor (IGF-1R) inhibitor BMS-754807 has
previously been shown (monolayer based cytotoxicity studies) to have in vitro
synergistic activity in combination with a number of cytotoxic chemotherapy agents in
range of cancers including breast, lung, colon and pancreatic (Carboni et al., 2009). The
isobologram data obtained from this study for gemcitabine and BMS-754907 reveals
that two dose ratios have a trend towards slight synergism (high gemcitabine and
medium BMS-754807 dose, high gemcitabine and very low BMS-754807 dose).
However the remaining doses have indifferent to slight antagonistic effects (Figure
328
7.6b). The high BMS-754807 and very low gemcitabine dose data was insufficient to
produce a dose response curve and so was omitted.
Ciclopirox olamine and gemcitabine (concurrent)
Ciclopirox olamine (CPX) was identified from the pilot screen (Chapter 6) as having
activity against the PANC-1 cell line in both monolayer and 3D cell culture formats.
CPX has also been previously reported to demonstrate anti-tumour activity against a
number of cancers including breast, colon and myeloma (Eberhard et al., 2009). No
previous reported activity against the PANC-1 cell line as either a single agent or in
combination was identified in the literature. The CPX-gemcitabine concurrent
combination isobologram (Figure 7.6c) demonstrates only a single dose ratio (high
gemcitabine dose and very low CPX dose) that exhibited a slight additive effect. All
other dose ratios displayed an indifferent or antagonistic effect (Figure 7.6c).
329
Figure 7.6. Normalised isobolograms of gemcitabine based concurrent combination studies.
(a) Paclitaxel (Drug A) and gemcitabine (Drug B) combination (see Table 7.8 for ratios). (b) BMS754807 (Drug A) and gemcitabine (Drug B) combination (see Table 7.9 for ratios). (c) Ciclopirox
olamine (Drug A) and gemcitabine (Drug B) combination (see Table 7.10 for ratios).
330
Concurrent drug combination therapy is often utilised in clinical settings. However,
sequential dosing has also been shown to alter the synergistic effect of combination
treatment. Schedule dependence and synergistic effect of a number of chemotherapy
agents such as the gemcitabine–cisplatin combination on ovarian, colon and lung cancer
cell lines has previously been reported in vitro and in vivo (Peters et al., 1995).
Therefore, a series of sequential combination studies were investigated using novel
agents or combinations of agents not previously evaluated for pancreatic cancer.
Paclitaxel and doxorubicin (concurrent and sequential)
As a form of paclitaxel (nab-paclitaxel) is now being revaluated for clinical use in
pancreatic cancer treatments, a combination with another previously dismissed
cytotoxic agent doxorubicin was performed. Doxorubicin has shown anti-tumour
activity against a wide range of malignancies including bladder, breast, lung and ovarian
and endocrine pancreatic carcinomas (Kouvaraki et al., 2004). However, it has shown
poor efficacy in clinical trials against pancreatic adenocarcinomas, either as a single
agent or in combination. Previous evaluations in Chapter 4 section 4.3.3 revealed
doxorubicin (and epirubicin) demonstrated extremely high efficacy against all cell lines
examined in both monolayer and 3D culture assays. The anthracyclines were the only
chemotherapy agents evaluated that had greater than 95% efficacy in the 3D culture
model and therefore doxorubicin was selected for combination studies.
The concurrent isobologram of paclitaxel and doxorubicin (Figure 7.7a) demonstrates
that all dose ratios evaluated resulted in no synergistic or additive activity detected with
all CI values > 1. The first sequential combination study was of PANC-1 3D cell
cultures exposed to doxorubicin for 48 hours, followed by drug removal and paclitaxel
exposure for a further 96 hours. The isobologram (Figure 7.7b) also shows no
synergistic activity across all the dose ratios examined (CI >1). Interestingly, however,
the sequential combination study involving 3D cell cultures exposed first to paclitaxel
for 48 hours followed by 96 hours of doxorubicin exposure revealed synergistic activity.
Strong synergism was determined with the two highest doxorubicin dose ratios (high
paclitaxel dose and high doxorubicin dose, medium paclitaxel dose and high
doxorubicin dose). The medium doxorubicin dose and high paclitaxel also displayed
moderate synergism with CI value of 0.74.
331
Figure 7.7. Normalised isobolograms of paclitaxel based concurrent and sequential combination
studies.
(a) Paclitaxel (Drug A) and doxorubicin (Drug B) combination concurrently. (b) Doxorubicin (Drug B)
then paclitaxel (Drug A) combination sequentially. (c) Paclitaxel (Drug A) then doxorubicin (Drug B)
combination sequentially (See Table 7.5 for ratios).
332
Ciclopirox olamine and doxorubicin (concurrent and sequential)
Ciclopirox olamine (CPX) was identified from the pilot screen undertaken in Chapter 5,
as an anti-fungal agent with anti-cancer activity against the pancreatic cancer cell line
PANC-1. CPX demonstrated almost 100% efficacy at high doses (10- 40µM) against
PANC-1 cells grown in 3D culture. Combining CPX and doxorubicin in a concurrent
combination study revealed no synergy at any dose ratio examined. However, the
sequential combination studies demonstrated a schedule dependant synergy profile.
PANC-1 3D cultures exposed to doxorubicin for 48 hours and then ciclopirox for a
further 96 hours displayed a slightly synergistic or additive effect with the high CPX
dose and medium doxorubicin dose ratio (Figure 7.8). The alternative drug dosing
schedule of CPX exposure first for 48 hours followed by a 96 hour exposure to
doxorubicin produced several strongly synergistic dose ratios (High doxorubicin and
medium CPX, high doxorubicin and low CPX) as well as a synergistic dose ratio (high
doxorubicin and high CPX) (Figure 7.8c).
333
Figure 7.8. Normalised isobolograms of Doxorubicin based concurrent and sequential combination
studies.
(a) Ciclopirox olamine (Drug A) and doxorubicin (Drug B) combination concurrently. (b) Doxorubicin
(Drug B) then ciclopirox olamine (Drug A) combination sequentially. (c) Ciclopirox olamine (Drug A)
then doxorubicin (Drug B) combination sequentially. (See Table 7.6 for ratios).
334
Dp44mT and doxorubicin (concurrent and sequential)
The mechanism of action responsible for the anti-cancer activity exhibited by ciclopirox
olamine is yet to be fully determined. However, the ability to bind intracellular iron has
been shown to be functionally important for its cytotoxicity (Eberhard et al., 2009).
Although iron chelation has been widely studied as a therapeutic option in a number of
cancers (including breast, leukaemia), the use of such agents as components of
combination therapy has not been extensively evaluated for pancreatic cancer. The iron
chelator, Dp44mT, was examined for synergism with doxorubicin in the pancreatic
cancer 3D cell culture combination study.
Exposure of Dp44mT and doxorubicin for 144 hours concurrently revealed dose ratio
dependant synergism (Figure 7.9a) .The high doxorubicin and medium Dp44mT dose
ratio and the high Dp44mT and low doxorubicin dose ratio both exhibited synergism.
The sequential combination of 48 hours exposure of doxorubicin followed by 96 hours
of Dp44mT exposure demonstrated no synergy with all dose ratios exhibiting
antagonistic activity (Figure 7.9b). The sequential combination with Dp44mT exposure
for 48 hours and then 96 hours of doxorubicin exhibited a dose ratio dependant effects
(Figure 7.9c). Two high dose Dp44mT ratios (high Dp44mT and medium doxorubicin,
high dose Dp44mT and low dose doxorubicin) displayed moderate synergism, while the
remaining dose ratio displayed no synergism.
335
Figure 7.9. Normalised isobolograms of Dp44mT based concurrent and sequential combination
studies.
(a) Dp44mT (Drug A) and doxorubicin (Drug B) combination concurrently. (b) Doxorubicin (Drug B)
then Dp44mT (Drug A) combination sequentially. (c) Dp44mT (Drug A) then doxorubicin (Drug B)
combination sequentially. (See Table 7.7 for ratios).
336
7.4. Discussion and conclusions
The use of a single chemotherapeutic agent has historically been shown to have
limitations in anti-tumour treatment for pancreatic cancer. The only chemotherapy
strategies that have obtained regulatory approval in the last 15 years are the
combinations of gemcitabine / erlotinib and gemcitabine / nab-paclitaxel (Chiorean and
Von Hoff, 2014). Although the overall survival benefit is minimal (< 3 months) with
these combination strategies, they still remain the only success stories for this
challenging disease. Drug combination based approaches remain the most likely
strategy for improved outcomes in the pancreatic cancer drug discovery and
development fields. However, the evaluation of drug combination efficacy and synergy
is not always applied in the preclinical drug discovery process. Here, we have
developed a highly miniaturised, physiologically relevant cell culture model that is
reproducible, cost effective and amenable to the large data points required for in vitro
drug combination determinations. The 1536-well 3D pancreatic cancer assay was
assessed for reproducibility and sensitivity and validated with a selection of known and
novel anti-pancreatic cancer agents.
7.4.1. 1536 well assay development and characterisation
3D cell culture has only recently been incorporated in HTS drug discovery programs
(Breslin and O'Driscoll, 2013). There are a number of hurdles preventing the 3D culture
model from becoming further implemented into HTS applications and
include;
technical limitations with complex biology of 3D systems in, compatibility with
automation equipment (such as liquid handlers and high content imaging platforms),
robustness of 3D culture based assays and increased costs. In this study we developed
an in vitro 3D based metabolic activity assay that overcome some of these hurdles and
provides a model system for use in future pancreatic cancer drug discovery programs.
The pancreatic adenocarcinoma cell line PANC-1 was chosen for assay development
based on a number of its genetic and resistance characteristics. This cancer cell line
presents a primary origin tumour with K-ras CDKN2A/p16, TP53 and SMAD4
mutations and displays a resistance profile to a number of standard chemotherapy
agents. Due to the previous assay development work with the cell line in a 384-well 3D
337
cell culture assay format, a streamlined optimisation process was undertaken for the
miniaturisation to 1536-well format. Assay incubation conditions, media type and drug
exposure times were maintained the same as those optimised for the 384-well assay. A
number of additional assay parameters were evaluated, including cell number per well,
total assay volume and the effects of automation on assay quality.
A range of cell seeding densities (200 to 600 cells per well) were evaluated for
reproducible 3D growth in the reduced total well area of the 1536-well microtitre plates
over the desired duration of the assay (9 days). The primary objective here was to
ascertain the optimal number of cells required per well to ensure that cells did not
overgrow and exceed the nutrient base available or conversely, did not result in
development of sufficient 3d structures. The cell seeding density of 400 cells per well
was selected based on well distribution and reproducible 3D structure growth, relative
to the nine day growth period of the study. A similar trend with respect to the rate of
cell growth and 3D structure formation to the 384-well assay was observed with the
1536-well format. Notably, an increase in structure size over time but reduction in the
overall number of structures (as single cells and smaller structures initially aggregated
together). Compared to the 384-well format, the PANC-1 3D cultures grown in the
1536-well format produced smaller (mean diameter of ~150µm in 384 format and
~100µm in the 1536 format) and fewer total structures (approximately 30 structures in
the 384 well format and 20 in the 1536 format). Previous experience indicates the
altered growth characteristics are not unexpected when miniaturising assays to smaller
plate formats, as media volumes and gas / nutrient exchange rates may be altered in the
reduced well size.
To examine if the 3D cultures had an altered sensitivity profile under these conditions,
several chemotherapy reference drugs were evaluated and compared to the 384-well
assay results. Gemcitabine (384; 69.54 ± 12.91, 1536; 113.92 ± 29.59), Paclitaxel (384;
42.35 ± 0.71, 1536; 34.41 ± 4.70) and Doxorubicin (384; 308.77 ± 33.41, 1536; 642.3 ±
190.10) all produced similar IC50 values between formats. Potency and efficacy
responses for each of the drugs tested were found not to be significantly different
between the models, indicating that although a reduced cell number and 3D structure
size was observed, this did not impact on the drug activity profiles (potency and
efficacy). As a consequence, the same rationale of increased physiological relevance
338
(ECM and cell contacts and heterogeneous cell populations and gradients) compared to
monolayer cell culture was maintained.
Assay quality was determined by evaluating the standard HTS performance parameters
Z’-factor, %CV and signal window using several control plates for the metabolic
activity (resazurin) assay. At the assay conclusion (nine days after seeding and 144
hours of exposure to controls; 40µM puromycin and 0.4% DMSO) the Z’-factor was
calculated (excluding two edge wells) at 0.59, the %CV at 12.2% and the signal window
at 5.01. There was a slight reduction of all parameters compared to the 384-well format
(384 well values; Z-factor 0.70, %CV 8.05% and SW 7.71), which is primarily due to
the slightly lower signal window (reduction in cell numbers) and increased variability
from well to well due to the technical challenges of small volume (0.4µl liquid handling
and incubator based evaporation effects. However, the assay passed all the acceptance
criteria (Z’-factor > 0.4, %CV <20% and SW > 2) and is considered acceptable for HTS
(Iversen et al., 2006; Zhang et al., 1999).
A final assessment of the 1536-well format was to determine if the assay well volume of
8µl and automatic liquid handling protocols impacted well to well contamination or
cross talk (caused by liquid wicking effect in polystyrene plastic). A checkerboard style
control plate (alternating positive and negative controls wells) confirmed that at the
assay volume selected (8µl) no well to well contamination was detectable. The
preliminary development, characterisation and validation of the 1536-well 3D cell
culture assay was completed and subsequently further validated in a small drug
combination screen to asses assay quality over multiple microtitre plates. To the best of
the author’s knowledge this is the first 1536-well ECM based anchorage dependent 3D
assay suitable for HTS application in pancreatic cancer drug discovery.
7.4.2. Drug combination studies utilising the 1536-well 3D pancreatic cancer
assay
Pancreatic cancer is recognised as an extremely heterogeneous cancer with multiple
oncogenic and tumour suppressor pathways involved. Pancreatic tumours also often
display a unique ability to resist current chemotherapy agents and therefore several
therapeutic interventions are likely to be required for future treatment of this disease.
339
The primary purpose of the miniaturised in vitro assay characterised in this study is for
use in drug combination based applications. Determining synergistic or antagonistic
effects of novel compounds or existing drugs at the preclinical stages of the drug
discovery process may help to better predict in vivo or clinical outcomes.
The dose response matrix method of combination drug evaluation was utilised with the
1536-well format 3D assay. By measuring the complete range of doses responses, a
greater volume of information can be extracted (such as multiple dosing ratios between
the two drugs). This matrix of drug interactions was graphically visualised with the
normalised isobologram, using FIC values calculated as part of the combination index
approach of determining synergism (Chou, 2008; Mayer and Janoff, 2007). The dose
response matrix experimental design (which covers all possible combinations between
two drugs in serials dilutions) takes into account drug dose and drug to drug ratio. The
remaining factor which can influence drug synergy is sequencing of exposure. Clinical
observations in a range of cancers treatments have found that anti-cancer combination
therapies are often critically schedule dependant for synergistic anti-tumour activity.
Therefore, a number of sequential and concurrent dosing schedules were also evaluated
in this model.
7.4.3. Gemcitabine combinations (concurrent)
Gemcitabine and paclitaxel
Paclitaxel is an example of a chemotherapy agent that was previously dismissed as
ineffective against pancreatic cancer, however with an improved delivery form (nabpaclitaxel) this drug is now being brought back into combination treatment programs.
Paclitaxel and gemcitabine concurrent exposure displayed synergistic activity at all dose
ratios with the highest ratio of gemcitabine achieving the most synergy. A previous
study reported in the literature utilising monolayer based cytotoxicity assay, found no
synergy with concurrent exposure. Although this study utilised a different pancreatic
cancer cell line (P-SW) and a much shorter exposure time (24 hours) (Theodossiou et
al., 1998) it may indicate that the 3D model tested here reflects a more relevant model
for combination studies. A murine xenograft model using the albumin bound form of
340
paclitaxel (nab-paclitaxel) found synergistic anti-tumour effects in combination
treatment with gemcitabine (Frese et al., 2012).
The underlying mechanisms resulting in the synergistic effect of this combination are
unknown. However, the dosage levels achieved over such a long period of time (144
hours) may be higher than those able to be achieved clinically in pancreatic tumours.
Nab-Paclitaxel has been shown to achieve a 33% higher tumour uptake relative to the
standard solvent based paclitaxel delivery and also a synergistic enhancement with
gemcitabine (Chiorean and Von Hoff, 2014; Yardley, 2013). The mechanism of the
increased tumour delivery of nab-paclitaxel is still under investigation (may involve
binding of albumin to secreted protein acidic and rich in cysteine (SPARC)). The
synergistic results achieved in this model system may be as a result of artificially high
paclitaxel levels exposed to the 3D structures. Future studies comparing the nabpaclitaxel and paclitaxel combinations with gemcitabine may provide insights into
synergistic activity with the 3D cultures and in vivo tumours.
Gemcitabine and BMS-754807
The concurrent exposure of gemcitabine and the IGF-1R inhibitor, BMS-754807,
revealed dose ratio dependant synergistic effects. Only the two doses with high
gemcitabine ratios exhibited slight synergism or nearly additive effects. Previously
published in vivo mouse xenograft (metastatic AsPC-1 cell line) studies found that
concurrent treatment of gemcitabine and BMS-754807 had an additive effect with
median survival significantly (p = 0.01) extended to 41 days (27 days median survival
time with BMS-754807 monotherapy and 28 days median survival time with
gemcitabine monotherapy) (Awasthi et al., 2012). The results from the combination
study undertaken here in the 3D culture model indicate that only a slight synergistic
anti-tumour effect would be expected with the combination of the IGF-1R inhibitor
(BMS-754807) and gemcitabine. The ratio of gemcitabine and the IGF-1R inhibitor
may also impact the synergistic effects of the treatments and evaluating a wider range of
drug combinations as well as sequential combinations, may offer improved synergism.
The data obtained from utilising a 3D culture-based in vitro combination study may
have provided useful insights into the synergistic potential of IGF-1R inhibitors before
expensive clinical trials were undertaken.
341
Gemcitabine and Ciclopirox Olamine
PANC-1 3D cell cultures were exposed to ciclopirox olamine and gemcitabine
concurrently for 144 hours. Only one dose ratio combination (high gemcitabine and low
ciclopirox olamine) displayed additive effects with the remainder of the dose ratios
displaying antagonism. Initial evaluations suggest there is poor synergy with ciclopirox
olamine and gemcitabine and possibly antagonistic effect at high doses of both drugs.
Future studies may incorporate sequential dosing of each drug to identify possible
sequence specific synergistic activities. No previous studies could be identified with
combination of chemotherapeutic agents and ciclopirox olamine with pancreatic cancer
cell lines.
7.4.4. Paclitaxel and doxorubicin combinations (sequential)
Doxorubicin has previously failed a number of small clinical trials against advanced
pancreatic cancer, with no increase in efficacy or overall survival. However, the drug
displayed the highest efficacy of all reference chemotherapy agents tested in the
established 3D cell culture model and was evaluated in a combination setting. Both the
concurrent exposure of drugs and doxorubicin exposure first followed by paclitaxel
exposure reported no synergistic activity. However, the pre-treatment of paclitaxel first,
followed by doxorubicin displayed schedule and ratio (1:2 and 1:4 ratios of paclitaxel to
doxorubicin) dependant synergism according to the Chou-Talalay method for drug
combination assessment. No previous publications on preclinical combinations of
paclitaxel and doxorubicin with pancreatic cancer cell lines could be identified and the
possible mechanism underlying the synergistic activity is unknown.
7.4.5. Ciclopirox olamine (CPX) and doxorubicin combinations (sequential)
Although CPX did not display synergistic activity with gemcitabine, doxorubicin
combination studies were undertaken. Doxorubicin was chosen as it displayed the
highest efficacy in the 3D cell culture model and the drug completely diffused through
the largest 3D structures (as described previously in Chapter 5). Doxorubicin as an anti342
cancer agent is traditionally limited by cardiotoxicity problems. Recently iron chelators
(such as dexrazoxane) have been used to reduce cardiotoxic effects in the clinic
(Ichikawa et al., 2014). The combination of doxorubicin and the novel iron chelator
CPX was evaluated to determine if synergistic anti-cancer activity could be established
(with an added benefit of a combination treatment with possible cardio protective
properties).
The concurrent exposure of both drugs and the doxorubicin pre-treatment followed by
ciclopirox exposure displayed a similar profile to gemcitabine, with primarily
antagonistic activity with only one dose ratio (high ciclopirox olamine and medium
doxorubicin) displaying slight synergism. Interestingly however, a strongly synergistic
effect was observed when 3D cultures were exposed first to ciclopirox olamine and then
to doxorubicin across numerous dose ratios. Although the mechanisms of the proposed
synergy are unknown, these results may provide guidance for future preclinical or
clinical studies with this novel anti-pancreatic cancer agent. CPX is an intracellular iron
chelator with broad anti-cancer activity and results from the sequential dosing of this
study indicate CPX may sensitise cancer cells to chemotherapy. This is not the first time
CPX has displayed synergistic effects, as it has enhanced the efficacy of cytarabine in
leukaemia in vitro studies. Future studies exploring additional combinations of
ciclopirox olamine may be of potential value in pancreatic cancer research, as drugs
with unique mechanism of action may reduce the overlapping toxicity of traditional
cytotoxic therapeutics.
7.4.6. Dp44mT and doxorubicin combinations (sequential)
The sequential exposure of doxorubicin first, followed by the iron chelator Dp44mT
displayed antagonistic effects across all dose ratios examined. In the same manner as
that observed with CPX and doxorubicin however, concurrent and Dp44mT exposure
first followed by doxorubicin displayed several synergistic and additive dose ratios. The
highest synergism was detected with the drug ratios that the highest Dp44mT doses. A
previous study into the anti-proliferative effects of Dp44mT and pancreatic cancer cell
lines (PANC-1, MIA PaCa-2, Capan-2) found that Dp44mT was a more potent (lower
IC50 value) anti-cancer agent than gemcitabine in vitro. Further in vivo studies using
343
PANC-1 tumour xenografts found that monotherapy with Dp44mT reduced tumour
volumes to similar levels as the gemcitabine monotherapy treatments. Another member
of the thiosemicarbazone class of iron chelators, 3-aminopyridine-2-carboxaldehyde
thiosemicarbazone (3-AP) was evaluated in phase II clinical trials for pancreatic cancer.
However, the treatment induced significant toxicity, with little therapeutic benefit and
the trial was stopped (Mackenzie et al., 2007). It has been reported that Dp44mT is a
more potent and less toxic than 3-AP in an in vivo murine model (Whitnall et al., 2006).
Therefore, future pancreatic cancer combination studies examining potential synergy
between Dp44mT and other chemotherapy agents may be warranted.
Summary
The assay system established and evaluated in this study proved to be consistent and
robust at measuring the drug combination responses via metabolic activity (resazurin)
measurements. The in-plate controls revealed consistently high quality Z-factor scores
above 0.5 across multiple screening plates and stable sensitivity of reference
compounds. A number of synergistic effects where observed (according to the CI
method of determining synergism) including the previously unreported ciclopirox
olamine and doxorubicin combination (schedule and dose ratio dependant). The
information from this combination assay may provide the experimental basis for further
development of this combination.
Combination anti-cancer therapies involve a large number of variables (such as dosing
scheduling and concentration ratios) and achieving these clinical relevant conditions in
vitro is extremely challenging. Although a large range of dose combinations were able
to be assessed with this miniaturised assay format, dosing schedules were limited. This
study primarily looked at three basic drug dosing schedules (concurrent exposure of
both drugs for 144 hours, drug A / B singularly for 48 hours followed by drug B / A for
96 hours). For future drug combinations studies, evaluating a range of dosing schedules
may provide greater insights in those drug combinations that exhibited schedule
dependent synergy. Ultimately, the dosing parameters should be adjusted depending on
the goal of the combination therapy, whether that be increased efficacy or reduced
toxicity.
344
The development of the miniaturised ECM based 3D pancreatic cancer cell culture
model here, attempts to provide a robust, physiologically relevant and cost effective in
vitro tumour model to assess therapeutic responses in drug discovery research. 3D
cancer cell culture models produce a wealth of drug response information and we have
only just started to uncover the potential of these systems. Future treatment strategies
for pancreatic cancer patients are unlikely to involve a single agent or therapy.
Therefore, tools which enable high throughput investigations of multiple anti-cancer
agents simultaneously may provide valuable insights into clinical combination
therapies.
345
7.5. References
Awasthi, N., Zhang, C., Ruan, W., Schwarz, M.A., and Schwarz, R.E. (2012). BMS754807, a small-molecule inhibitor of insulin-like growth factor-1 receptor/insulin
receptor, enhances gemcitabine response in pancreatic cancer. Mol Cancer Ther 11,
2644-2653.
Breslin, S., and O'Driscoll, L. (2013). Three-dimensional cell culture: the missing link
in drug discovery. Drug Discov Today 18, 240-249.
Carboni, J.M., Wittman, M., Yang, Z., Lee, F., Greer, A., Hurlburt, W., Hillerman, S.,
Cao, C., Cantor, G.H., Dell-John, J., et al. (2009). BMS-754807, a small molecule
inhibitor of insulin-like growth factor-1R/IR. Mol Cancer Ther 8, 3341-3349.
Chiorean, E.G., and Von Hoff, D.D. (2014). Taxanes: impact on pancreatic cancer.
Anticancer Drugs.
Chou, T.C. (2008). Preclinical versus clinical drug combination studies. Leukemia &
lymphoma 49, 2059-2080.
Eberhard, Y., McDermott, S.P., Wang, X., Gronda, M., Venugopal, A., Wood, T.E.,
Hurren, R., Datti, A., Batey, R.A., Wrana, J., et al. (2009). Chelation of intracellular
iron with the antifungal agent ciclopirox olamine induces cell death in leukemia and
myeloma cells. Blood 114, 3064-3073.
Frese, K.K., Neesse, A., Cook, N., Bapiro, T.E., Lolkema, M.P., Jodrell, D.I., and
Tuveson, D.A. (2012). nab-Paclitaxel Potentiates Gemcitabine Activity by Reducing
Cytidine Deaminase Levels in a Mouse Model of Pancreatic Cancer. Cancer Discovery
2, 260-269.
Ichikawa, Y., Ghanefar, M., Bayeva, M., Wu, R., Khechaduri, A., Prasad, S.V.N.,
Mutharasan, R.K., Naik, T.J., and Ardehali, H. (2014). Cardiotoxicity of doxorubicin is
mediated through mitochondrial iron accumulation. The Journal of clinical investigation
124, 617-630.
Iversen, P.W., Eastwood, B.J., Sittampalam, G.S., and Cox, K.L. (2006). A comparison
of assay performance measures in screening assays: signal window, Z' factor, and assay
variability ratio. J Biomol Screen 11, 247-252.
Kouvaraki, M.A., Ajani, J.A., Hoff, P., Wolff, R., Evans, D.B., Lozano, R., and Yao,
J.C. (2004). Fluorouracil, doxorubicin, and streptozocin in the treatment of patients with
346
locally advanced and metastatic pancreatic endocrine carcinomas. J Clin Oncol 22,
4762-4771.
Mackenzie, M.J., Saltman, D., Hirte, H., Low, J., Johnson, C., Pond, G., and Moore,
M.J. (2007). A Phase II study of 3-aminopyridine-2-carboxaldehyde thiosemicarbazone
(3-AP) and gemcitabine in advanced pancreatic carcinoma. A trial of the Princess
Margaret hospital Phase II consortium. Investigational new drugs 25, 553-558.
Mayer, L.D., and Janoff, A.S. (2007). Optimizing combination chemotherapy by
controlling drug ratios. Molecular interventions 7, 216-223.
Peters, G.J., Bergman, A.M., Ruiz van Haperen, V.W., Veerman, G., Kuiper, C.M., and
Braakhuis, B.J. (1995). Interaction between cisplatin and gemcitabine in vitro and in
vivo. Seminars in oncology 22, 72-79.
Sullivan, B. (2001). Assay Development in High Density MicroWell® Plates: Use of
Well Geometries, Format, Surface Modification and Optical Properties to Achieve
Optimal Assay Performance. Journal of the Association for Laboratory Automation 6,
47-52.
Theodossiou, C., Cook, J.A., Fisher, J., Teague, D., Liebmann, J.E., Russo, A., and
Mitchell, J.B. (1998). Interaction of gemcitabine with paclitaxel and cisplatin in human
tumor cell lines. Int J Oncol 12, 825-832.
Whitnall, M., Howard, J., Ponka, P., and Richardson, D.R. (2006). A class of iron
chelators with a wide spectrum of potent antitumor activity that overcomes resistance to
chemotherapeutics. Proceedings of the National Academy of Sciences 103, 1490114906.
Yardley, D.A. (2013). nab-Paclitaxel mechanisms of action and delivery. Journal of
controlled release : official journal of the Controlled Release Society 170, 365-372.
Zhang, J.H., Chung, T.D., and Oldenburg, K.R. (1999). A Simple Statistical Parameter
for Use in Evaluation and Validation of High Throughput Screening Assays. J Biomol
Screen 4, 67-73.
347
8. Chapter Eight: Conclusions
8.1 Relevance of research in the field of pancreatic cancer
The current strategy for drug discovery research in the field of pancreatic cancer has
failed to deliver clinically relevant outcomes for patients. The drug discovery process
that has achieved clinical success in many other cancer fields is not replicated for this
malignancy. The failure to translate preclinical findings to clinical settings may indicate
that current preclinical models do not fully capture the physiological complexity or
heterogeneity of human pancreatic cancer. The research presented in this study aims to
evaluate an experimental in vitro model that not only simulates the pathophysiology, but
also accounts for the heterogeneity of pancreatic tumours. The model developed aims to
incorporate elements of this biological complexity into an assay format that is
applicable for drug discovery processes and produces more relevant drug response
information.
Recently, multiple novel agents (including targeted therapies such as inhibitors of
angiogenesis and pathways associated with tumour progression) have failed expensive
phase III clinical trials. Many of these agents had strong preclinical evidence against
other malignancies and were rapidly advanced to pancreatic cancer clinical trials.
Reducing late stage attrition by incorporation of more predictive and relevant models in
the discovery and preclinical stages of the drug discovery pipeline may lead to more
efficient use of resources and ultimately greater progress for this disease.
8.2 Development of a 3D pancreatic cell culture model and
evaluation of resistance mechanisms
348
A panel of genetically and morphologically diverse pancreatic cancer cell lines were
evaluated for use in a three dimensional (3D) micro tumour-like in vitro cell culture
model. The ability to develop 3D structures within an ECM based, anchorage dependant
model was assessed and a number of culture and assay conditions were optimised.
Morphology of 3D growth and expression of cell surface markers was characterised and
three cell lines (AsPc-1, BxPc3 and Panc-1) were selected for further assay
development. These cell lines represent a broad spectrum of common mutations
observed in pancreatic cancer, including K-ras, P53 and p16 and SMAD4. A monolayer
and 3D cell culture assay system were developed to investigate drug responses in a 384well microtitre plate format. Two assay endpoint evaluations were established, a
population based metabolic activity indicator and a high content imaging based cell
viability assay. The assays were assessed for reproducibility and sensitivity using a
selection of clinically relevant chemotherapy agents. Six cytotoxic drugs (gemcitabine,
doxorubicin, epirubicin, paclitaxel, docetaxel and vinorelbine) from several drug classes
were evaluated and drug responses compared between the two culture conditions.
Significant drug response differences were observed between 2D monolayer and 3D
culture conditions, with an overall trend of decreasing efficacy and potency in 3D. A
minor population of cells remained resistant to drug treatment and metabolically active
with several of the drugs assayed, even at extremely high drug doses (200µM).
Differences in cellular responses to these drugs were determined to be cell line and drug
dependant. The anthracyclines (doxorubicin and epirubicin) were the only cytotoxic
drugs to achieve 100% efficacy against all pancreatic cells in 3D culture, with all other
drugs displaying reduced efficacy. Information on the drug effects on cellular viability
and the morphological effects on the 3D structures were characterised for each cell line
and drug combination.
The possible mechanisms behind the altered drug responses (decreased efficacy and
potency) observed in the 3D cell culture were investigated. These included drug
resistance mechanisms such as changes in proliferation rates, cell-to-cell and cell-toextracellular matrix interactions and physical or mechanical barriers to drug diffusion.
Proliferation rates in the 3D culture model were found to be significantly altered from
the 2D monolayer culture. However, the altered proliferation was found to be cell line
specific, with PANC-1 cells revealing a 22% decrease, AsPC-1 a 46% decrease and
BxPC-3 a 311% decrease in 3D culture. The degree of change in proliferation did not
349
directly coincide with changes in drug responses, suggesting that proliferation alone is
unlikely to account for the reduced sensitivity observed in 3D. The inability for certain
drugs to diffuse through 3D cellular structures has been proposed as one mechanism by
which drug responses are reduced in 3D cell culture. However, using doxorubicin, it
was established that after 6 hours of exposure, the drug had diffused through to the
centre of the 3D structures and the physical barriers were not the primary mechanism
for the decreased doxorubicin responses observed in 3D. This does not rule out poor
diffusion through 3D structures as a factor in reduced efficacy of other drugs assayed
unfortunately, their lack of inherent fluorescence does not allow visualisation of
diffusion, as was performed with doxorubicin. Future studies involving chemical
modification or fluorescent tagging of drugs may provide insights into which drugs are
more readily able to diffuse through ECM and 3D structures.
Cell-to-cell and cell-to-matrix interactions have been linked to survival pathways in
cancer. To determine if the externally supplied ECM (Matrigel) was contributing to the
resistance profile in 3D cultures, the cell lines were cultured on a synthetic hydrogel
(PuraMatrix) that reduced cell-to-matrix attachment. 3D structures still formed in this
ECM free environment with similar sized 3D structures and cell-to-cell contacts.
Although, cell-to -matrix attachment was reduced under these conditions and no
external ECM or growth factors were present, a similar drug response profile was
observed with PANC-1 cultures in either the biological or synthetic 3D hydrogel
system. These results indicate that exogenous ECM may not play a significant role in
the chemoresistance profile observed. However, as pancreatic cancer cells have been
shown to produce ECM components endogenously, cell-to-matrix interactions within
the 3D structures may play a critical role in the resistance mechanisms. The assay length
was then extended for the paclitaxel treatment and drug removed to determine if the
resistance profile would be maintained once the drug pressure was removed. Further
incubation of cells after exposure to the drug revealed the resistance profile was only
transient, with cell viability decreasing and efficacy of the drug reaching 100% after the
extended time in culture.
8.3 Pilot screen of 741 clinically relevant agents
350
To validate the 3D model and assay system, a pilot screen of a library of clinically
relevant drugs and compounds was assessed for activity against the pancreatic cancer
cell lines. The strategy behind the selection of this library was based on the fact that it
contains well characterised bioactive drugs and compounds that have diverse biological
targets which may not have been previously assessed for anti-cancer activity against
pancreatic cancer cell lines. Several drugs were identified as inhibitors of metabolic
activity in pancreatic cancer cells and were selected for further characterisation. Ten
drugs were assessed in both 2D and 3D culture conditions using the metabolic activity
assay. As observed previously with the cytotoxic chemotherapy agents, such as
gemcitabine and doxorubicin, altered cellular responses were observed between
monolayer and 3D culture conditions. Two drugs were identified with anti-cancer
activity against the pancreatic cancer cell lines that had previously not been reported in
the literature. Ciclopirox olamine (an anti-fungal) and maduramicin (anti-parasitic)
inhibited pancreatic cancer cell lines and disrupted the morphology and cellular
adhesion of the 3D structures. The identification of agents with novel anti-cancer
activity against pancreatic cell lines illustrates the utility of the developed 3D assays.
8.4 Drug combination studies in miniaturised format
A 1536-well microplate format 3D cell culture assay was developed with the PANC-1
cell line and used to identify synergistic drug combinations. This model demonstrates
one of the first miniaturised HTS assay using pancreatic cancer cells in a 3D anchorage
dependant culture system. The 1536-well assay was validated with a panel of
chemotherapy agents and found to be a robust assay suitable for HTS. A range of drug
combinations were successfully evaluated and the synergistic potential determined. In
agreement with that previously observed in vivo, the majority of compounds / drugs
ratios tested revealed no synergistic effects. However, a number of sequential treatment
regimes displayed synergism. A sequential treatment utilising one of the active agents
identified in the pilot screen, ciclopirox olamine and the chemotherapy agent
doxorubicin revealed synergistic activity not previously reported. Paclitaxel followed by
doxorubicin exposure also displayed synergistic activity at specific dose ratios. The
ability to perform combination studies in not only a high throughput manner but also in
a more physiologically relevant culture system is able to provide synergistic drug
information in early preclinical studies.
351
8.5 Future directions
The 3D in vitro system presented in this study offers an intermediate complexity model
and replicates some of the biological complexity found within human pancreatic
tumours, with many of the matrix components of the desmoplastic reaction included in
the model. However, with increased focus on the desmoplastic reaction surrounding
tumours and the evidence indicating that many host cells play a key role in the drug
resistance profile of pancreatic cancer, a co-culture model is likely to be the next
evolution. Developing an in vitro micro tumour-like model that captures and replicates
the in vivo tumour (host stromal, pancreatic stellate cells and primary cancer cells) more
accurately, may offer a model with even greater predictive value than the monoculture
system developed here. Of course, producing an even more complex model, while still
maintaining the high level of reproducibility required for use in robust HTS assays,
remains an extremely challenging endeavour.
Future assays will also likely take advantage of the immense information available with
high content based evaluations. Drug responses will not only be assessed as a single
endpoint readout as may be the case with traditional 2D screening but information on
population effects within complex 3D micro tumour-like structures and functional
effects, such as impacts on invasion and metastatic potential may be assessed. As
technological advances and computer hardware continue to rapidly evolve, the amount
of information that can be extracted from these systems and the speed at which it is
obtained will inevitably result in better models for anti-cancer drug discovery
programmes.
Although the success of future pancreatic cancer research is uncertain, significant
advances have been made in number of areas that will assist greatly in a more rapid
progression towards favourable treatments. The recent completion of the Pancreatic
Cancer Genome Project marked a notable milestone, with valuable information on the
genetic and molecular basis for the disease being uncovered. The identification of new
biomarkers also offers advanced diagnostic capabilities and improved outlook for early
352
detection. Combining these advancements with the use of more biologically relevant
and predictive models in drug discovery programs may offer hope for future pancreatic
cancer patients.
8.6 Summary
The 3D culture model developed in this study can be deployed in pancreatic cancer
research drug discovery immediately. The ability to increase throughput using a more
relevant in vitro model may accelerate the current slow pace of advancements for
pancreatic cancer treatments. The 1536-well format 3D in vitro culture assay is a
validated, automated, cost effective model that can offer valuable insights into the
responses of novel agents with activity against pancreatic cell lines. It could be
deployed as a primary screening tool, as a secondary assay to deprioritise hits that
display poor efficacy in the 3D model, or in preclinical drug combination studies. With
limited funding available for pancreatic cancer research, it has never been more
important to cost effectively translate novel agents to the clinic. Obtaining more rich
and predicative information on anti-cancer agents earlier in the drug discovery pipeline
has the potential to drastically reduce research expenditure. Adding tools such as these
to study tumour drug interactions at the discovery and preclinical stages of the drug
discovery programs, may ultimately reduce the late stage attrition that has become
commonplace in the field of pancreatic cancer research.
353
Appendix 1
NIMUSTINE HCl
TAZOBACTAM Na
IMIPENEM-CILASTATIN Na
MESTANOLONE
DUTASTERIDE
SELEGILINE
TEGAFUR
ZOLEDRONIC ACID
PIDOTIMOD
ETHAMBUTOL
TRANILAST
TRETINOIN
NICARAVEN
DOXOFYLLINE
PRIMAQUINE PHOSPHATE
CLORPRENALINE HCl
DIPHENOXYLATE HCl
FLUMAZENIL
DORIPENEM
17alpha-HYDROXYPREGNENOLONE-3,17-DIACETATE
DIPYRIDAMOLE
DANTROLENE Na
ERGOCALCIFEROL
PROGESTERONE
PERGOLIDE MESYLATE
Bupivacaine HCl
ACRINOL
MIANSERINE HCl
METHYL PAROXETINE
5beta-PREGNAN-3-alpha,17-alpha-DIOL-11,20-DIONE
TERBINAFINE HCl
IODOANTIPYRINE
DEXFENFLURAMINE HCl
NITROFURANTOIN
FLUPHENAZINE HCl
PIPERACILLIN Na
LOFEXIDINE HCl
LAMIVUDINE
PIOGLITAZONE HCl
RIMANTADINE HCl
GANCICLOVIR
LANSOPRAZOLE
HYDROCHLOROTHIAZIDE
ROLIPRAM
IRSOGLADINE MALEATE
ORAZAMIDE
ESTRAMUSTINE Na PHOSPHATE
MESTERELONE
DESLORATADINE
PROBENECID
ROPIVACAINE HCl
LYNESTRENOL
DOTHIEPIN HCl
ECDYSTERONE
LOXOPROFEN Na
TIMOLOL MALEATE
QUINAPRIL HCl
2-METHYL-6-(PHENYLETHYNYL)PYRIDINE HCl
MITOXANTHRONE HCl
4-PREGNEN-20beta-OL-3-ONE
5alpha-ANDROSTAN-17-ONE
MAZINDOLE
ZONISAMIDE
ISEPAMICIN SULFATE
DIRITHROMYCIN
PRAVASTATIN Na
NORFLOXACIN LACTATE
METRONIDAZOLE
METHYL BOLDENONE
6-THIOGUANINE
ANDROSTADIENOLONE ACETATE
ARGATROBAN
SPECTINOMYCIN HCl
ORBIFLOXACIN
LEFLUNOMIDE
EMTRICITABINE
PHENPROBAMATE
LABETALOL HCl
LONIDAMINE
2-AMINO-4-HYDROXY-S-TRIAZINE
OLANZAPINE
11alpha-HYDROXYMETHYLTESTOSTERONE
ALENDRONATE Na
NICORANDIL
MEDROXYPROGESTERONE ACETATE
VARDENAFIL HCl
ELETRIPTAN
BRONOPOL
IMATINIB MESYLATE
FROPENEM
ENOCITABINE
17alpha-ETHYNYLESTRADIOL
PIZOTIFEN MALEATE
OXCARBAZEPINE
PROCARBAZINE HCl
CHLORZOXAZONE
RITODRINE HCl
WARFARIN Na
MARBOFLOXACIN
ALLOPURINOL
ADRENOCHROME SEMICARBAZONE
GEFARNATE
MUPIROCIN
AMINOGLUTETHIMIDE
5alpha-ANDROSTANE-3alpha,17beta-DIOL
4-AMINO-2-METHYL-10H-THIENO[2,3B][1,5]BENZODIAZEPINE
DULOXETINE HCl
OMEPRAZOLE
OXYBUTYNIN HCl
CARVEDILOL
CILASTATIN Na
DECITABINE
DOLASETRON MESYLATE
ZOLMITRIPTAN
NEVIRAPINE
LOMEFLOXACIN HCl
2-DEOXY-5-FLUOROURIDINE
354
GRANISETRON HCl
alpha-HOMOCHELIDONINE
REBOXETINE MESYLATE
TAMSULOSIN HCl
BISACODYL
TICLOPIDINE HCl
PROPAFENONE HCl
MITOMYCIN C
TERAZOSIN HCl
DIFLOXACIN HCl
CHLORPROMAZINE HCl
4-ANDROSTEN-17alpha-METHYL-17beta-OL-3-ONE
IOHEXOL
ASTEMIZOLE
CLOBAZAM
DESMOPRESSIN ACETATE
HARRINGTONINE
CERIVASTATIN Na
GEMCITABINE HCl
LOMERIZINE DiHCl
FLAVOXATE
OFLOXACIN
EFAVIRENZ
AMOXICILLIN
CINNARIZINE
SILDENAFIL CITRATE
ABIDUOER HCl
BUFLOMEDIL HCl
OXFENDAZOLE
TORASEMIDE
ABAMECTIN
LEVOSULPIRIDE
FUROSEMIDE
ONDANSETRON HCl
FAMOTIDINE
TADALAFIL
MADURAMICIN AMMONIUM
ENALAPRIL MALEATE
FLUTICASONE PROPIONATE
ZIDOVUDINE
ALLOTETRAHYDROCORTISOL
PALONOSETRON HCl
ANASTROZOLE
CLEMIZOLE HCl
AMLODIPINE MALEATE
TRANDOLAPRIL
PACLITAXEL
LUMIRACOXIB
FORMESTANE
ADIPIODONE
TIBOLONE
RETINAMIDE
DIFENIDOL HCl
VORICONAZOLE
AMINOPHYLLINE
CLADRIBINE
LINEZOLIDE
FTORAFUR
MESNA
RESCINNAMINE
PENTOXYVERINE CITRATE
LOMUSTINE
BENSERAZIDE
SARPOGRELATE HCl
VALDECOXIB
IMIPRAMINE HCl
RAMOSETRON HCl
NORFLOXACIN
HEXOPRENALINE SULFATE
ATRACURIUM BESYLATE
TERFENADINE
SALMETEROL XINAFOATE
MAPROTILENE HCl
TETRAHYDROCORTICOSTERONE
CILOSTAZOL
EPLERENONE
7-ETHYL-10-HYDROXYCAMPTOTHECIN
ALLOPREGNANOLONE ACETATE
DOXORUBICIN HCl
CLEMASTINE FUMARATE
2-ACETAMIDOFLUORENE
BOSENTAN
16-DEHYDROPREGNENOLONE ACETATE
TELITHROMYCIN
PILSICAINIDE HCl
SERTRALINE HCl
ANDROSTENEDIOL-3-ACETATE
NISOLDIPINE
GALANTHAMINE HBr
L-DICENTRINE
ACECLOFENAC
GEFITINIB
SANTONIN
CHLOROQUINE DIPHOSPHATE
TRIAMCINOLONE ACETATE
SIMVASTATIN Na
FENOLDOPAM MESYLATE
BENPROPERINE PHOSPHATE
RALTITREXED
SUNITINIB MALEATE
SALBUTAMOL SULFATE
CLOMIPHENE CITRATE
PITOFENONE HCl
QUETIAPINE FUMARATE
VALACICLOVIR HCl
FLUVASTATIN Na
BERBAMINE
ESTRIOL Standard for chromatography
LOVASTATIN
PEMETREXED Na
TOBRAMYCIN SULFATE
NATAMYCIN
CHLORAMPHENICOL
N-ACETAMIDORHODANINE Monohydrate
ZALCITABINE
AZITHROMYCIN
CYNARIN
CLIOQUINOL
11-DEOXY-17-HYDROXYCORTICOSTERONE
TENIPOSIDE
CYCLOSPORIN A
TROPISETRON HCl
RAFOXANIDE
MONTELUKAST Na
HARMINE HCl
FENOFIBRATE
ENALAPRILAT
METHOHEXITAL
INDINAVIR SULFATE
OLMESARTAN MEDOXOMIL
THIOCOLCHICOSIDE
TRIMETAZIDINE 2HCl
VOGLIBOSE
MOCLOBEMIDE
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LORATADINE
PIRACETAM
ADAPALENE
ALOSETRON HCl
ZILEUTON
VINORELBINE BITATRATE
TOREMIFENE CITRATE
PEROSPIRONE HCl
ANDROSTANDIOL
EXEMESTANE
11beta-HYDROXYPROGESTERONE
MUPIROCIN Ca
TRIMEBUTINE MALEATE
VINBLASTINE SULFATE
ERLOTINIB MESYLATE
TOLTERODINE TARTRATE
ANDROSTANOLONE
SORAFENIB
BALSALAZIDE
PYRAZINAMIDE
SIBUTRAMINE HCl
TOTAROL
THIAMPHENICOL
FOSCARNET Na
FINGOLIMOD HCl
CEPHARANTHINE
BICALUTAMIDE
PAZUFLOXACIN
THIAMAZOLE
AMISULPRIDE
RUPATADINE FUMARATE
PANTOPRAZOLE Na SALT
ALLOPREGNANDIONE
CERIVASTATIN LACTONE
NITIDINE CHLORIDE
HALOPERIDOL
CEFIXIME TRIHYDRATE
TRITYL CANDESARTAN CILETEXITIL
AMBROXOL HCl
CILOMILAST
BOLDENONE
ANDROSTENEDIOL DIACETATE
FEXOFENADINE HCl
LANOCONAZOLE
DITHIZONE
OSELTAMIVIR PHOSPHATE
RIFAPENTINE
BUPROPION HCl
CONESSINE
ETIDRONATE DINa
ZOFENOPRIL Ca
RASAGILINE MESYLATE
EPINASTINE HCl
FLUDARABINE PHOSPHATE
TULOBUTEROL HCl
VENLAFAXINE HCl
OCTREOTIDE ACETATE
MEGESTROL ACETATE
TERLIPRESSIN ACETATE
DIMENHYDRINATE
TIAGABINE HCl
SECNIDAZOLE
ROXATIDINE ACETATE HCl
ALIBENDOL
NEBIVOLOL HCl
ARTEMETHER
RAMIPRIL
CEFUROXIME Na
ETHYNYL ESTRADIOL
OXAPROZIN
CEFOPERAZONE
CYTARABINE
ETHINYLESTRADIOL
TIAMULIN HYDROGEN FUMARATE
MECILLINAM
BIFONAZOLE
BENDAZAC
VECURONIUM BROMIDE
SARAFLOXACIN HCl
SULBACTAM Na
MIDECAMYCIN
RAMATROBAN
TOBRAMYCIN
PHENTOLAMINE MESYLATE
TIAPRIDE
STAVUDINE
DOMPERIDONE
LIRANAFTATE
CLOXACILLIN Na
AMOXAPINE
BOLDINE
RIFAMPICIN
LETROZOL
TRICLABENDAZOLE
FLUCONAZOLE
OXYTETRACYCLINE HCl
FLUNARIZINE HCl
CITICOLINE Na
CLEBOPRIDE MALEATE
ACTARIT
VINDESINE SULFATE
CICLESONIDE
RIZATRIPTAN BENZOATE
CIMETIDINE
DEHYDROEPIANDROSTERONE-3-ACETATE
BROMPHENIRAMINE MALEATE
AMANTADINE HCl
SILDENAFIL BASE
CEFODIZIME DINa
DIGITOXIN
CITALOPRAM HBr
TESTOSTERONE ACETATE
BUSPIRONE HCl
IFOSFAMIDE
STEPRONIN
19-NORTESTOSTERONE
ABACAVIR SULFATE
RADIPRODIL
TALIBEGRON HYDROCHLORIDE
HYDROXYUREA
ROCURONIUM BROMIDE
ROSUVASTATIN Ca (CRESTOR)
TENOXICAM
DEFLAZACORT
DEXCHLORPHENIRAMINE MALEATE
ATOMOXETINE HCl
BUTENAFINE HCl
ENTACAPONE
ZAFIRLUKAST
HUNNEMANINE
NILUTAMIDE
ROPINIROLE HCl
PRIMIDONE
ACARBOSE
NIMESULIDE
AMIODARONE HCl
CARBIDOPA
CETIRIZINE DIHCl
ARIPIPRAZOLE
MYCOPHENOLATE MOFETIL
DANAZOL
EPIANDROSTERONE
TEMOZOLOMIDE
CLOPERASTINE HCl
LOPINAVIR
AMOROLFINE HCl
APRACLONIDINE HCl
ROFECOXIB
GABEXATE MESYLATE
TROXIPIDE
CYPERMETHRIN
RIMONABANT
RISEDRONATE Na
BUDESONIDE
KANAMYCIN SULFATE
PRAVASTATIN LACTONE
LOPERAMIDE
ATROSCINE HBr
ACETYLSPIRAMYCIN
(+)-ISOCORYDINE HCl
DANOFLOXACIN BASE
17alpha-HYDROXYPREGNENOLONE
CLINAFLOXACIN HCl
IMIQUIMOD
GESTRINONE
ATORVASTATIN Ca
DOCETAXEL
O6-BENZYLGUANINE
RABEPRAZOLE Na
355
TETRAHYDROCORTISONE-3alpha,21-DIACETATE
FLUCYTOSINE
CLOZAPINE
OLIGOMYCIN C
ZOLEDRONATE DINa
DIFLUCORTOLONE VALERATE
10-HYDROXYCAMPTOTHECIN
NITRENDIPINE
TESTOSTERONE
LEVAMISOLE HCl
RALOXIFENE HCl
NICARDIPINE
CEFPODOXIME PROXETIL
VALSARTAN
CEFDINIR
OXALIPLATIN
DEXBROMPHENIRAMINE MALEATE
TEICOPLANIN
GATIFLOXACIN
ETRETINATE
DARIFENACIN HBr
GLIBENCLAMIDE
IBUDILAST
IFENPRODIL TARTRATE
CYCLOBENZAPRINE HCl
AMLODIPINE BASE
VERAPAMIL 2HCl
BISOPROLOL FUMARATE
CLENBUTEROL HCl
CHLORPROPAMIDE
PRAMIPEXOLE HCl
DAPOXETINE HCl
CLOSANTEL Na
AZASETRON HCl
MYCOPHENOLATE Na
DILTIAZEM HCl
CAMPTOTHECIN
HYDROXYCHLOROQUINE SULFATE
CICLOPIROX OLAMINE
ESTRONE
MELPHALAN
ACETYLKITASAMYCIN
FELBINAC
RUFLOXACIN HCl
ROXITHROMYCIN
EPROSARTAN MESYLATE
ADRENOSTERONE
SIMVASTATIN
OLOPATADINE HCl
IMIDOCARB DIPROPIONATE
RISPERIDONE
NIFEKALANT HCl
MAFENAMIDE ACETATE
TOPOTECAN HCL
SALINOMYCIN Na
ETOPOSIDE
SOLANESOL
MICONAZOLE NITRATE
LOFEPRAMINE HCl
EPTIFIBATIDE
ENOXACIN
VINCRISTINE SULFATE
AMILORIDE HCl
BARNIDIPINE HCl
NITAZOXANIDE
FOSINOPRIL Na
NICOTINAMIDE
FEPRAZONE
METOCLOPRAMIDE HCl
PREDNISOLONE
METHOTREXATE
METHIMAZOLE
PRAZIQUANTEL
IMIQUIMOD ACETATE
CEFADROXIL
HALOMETASONE MONOHYDRATE
SULFASALAZINE
PIPETHANATE ETHOBROMIDE
TETRACYCLINE HCl
ENTECAVIR
RUBITECAN
GLIMEPIRIDE
ATENOLOL
RITONAVIR
MILRINONE
ADEFOVIR DIPIVOXIL
ESCITALOPRAM OXALATE
ALBUTEROL SULFATE
CANDESARTAN CILEXITIL
BROMHEXINE HCl
AMPIROXICAM
21-DEOXYCORTISONE
ORMETOPRIM
SITAGLIPTIN PHOSPHATE
BENZOCAINE
FLUOXETINE HCl
NEDAPLATIN
AMLEXANOX
NIFURSOL
CLOFARABINE
SULFACETAMIDE Na
NORETHINDRONE
SULPIRIDE
RIBOSTAMYCIN SULFATE
OXYMETHOLONE
CIPROFLOXACIN HCl
ZANAMIVIR HYDRATE
PERINDOPRIL
CYCLOSPORIN B
ETORICOXIB
STREPTOMYCIN SULFATE
PIZOTIFEN
DROSPIRENONE
FLUTAMIDE
TOPIRAMATE
MEVASTATIN
NIFEDIPINE
REPAGLINIDE
EZETIMIBE
MEMANTINE HCl
LERCANIDIPINE HCl
INDAPAMIDE
ZOPICLONE
CLOPIDOGREL SULFATE
NATEGLINIDE
3-[(2-METHYL-1,3-THIAZOLE-4-YL)ETHYNYL]PYRIDINE
IBANDRONATE Na
LINCOMYCIN HCl
THIABENDAZOLE
CARMOFUR
MONENSIN Na
LEVOSIMENDAN
TENOFOVIR
ANDROSTENEDIONE
CEFTRIAXONE Na
IMATINIB BASE
NOLATREXED
MOXONIDINE HCl
TENOFOVIR DISOPROXIL FUMARATE
ALPROSTADIL
LAFUTIDINE
EPTAPLATIN
CANADINE
LAPATINIB
CHLORPHENIRAMINE MALEATE
ERLOTINIB HCl
BERBERINE SULFATE NEUTRAL
IPRATROPIUM BROMIDE
RIVASTIGMINE TARTRATE
3-(2-CHLOROETHYL)-2-METHYL-6,7,8,9-TETRAHYDRO-4HPYRIDINO(1,2A)PYRIMIDINE-4-ONE
5alpha-ANDROSTANOLONE
ANAGRELIDE HCl
SULFADOXINE
RILUZOLE
TEGASEROD MALEATE
PRULIFLOXACIN
GRISEOFULVIN
ZIPRASIDONE HCl
ALOPERINE
FINASTERIDE
5-DEOXY-5-FLUOROCYTISINE
RIBAVIRIN
BALOFLOXACIN
CARPROFEN
VORINOSTAT
SPARFLOXACIN
TERBUTALINE SULFATE
TAZAROTENE
PROGLUMIDE
Ro25-6981 maleate
NIMODIPINE
METFORMIN HCl
RACECADOTRIL
PROPIVERINE HCl
CEFATRIZINE PROPYLENE GLYCOL
CHLORDIAZEPOXIDE
DOXAZOSIN MESYLATE
CALCIPOTRIOL
LISINOPRIL
DEHYDROEPIANDROSTERONE
ROSIGLITAZONE MALEATE
FLUNISOLIDE
ZOLPIDEM TARTRATE
COLISTIN SULFATE
ZILPATEROL
PENTOXIFYLLINE
ACIPIMOX
LEVOCETIRIZINE diHCl
GREPAFLOXACIN
BENIDIPINE HCl
CETRAXATE HCl
LACIDIPINE
17alpha-METHYL-19-NORTESTOSTERONE
4-AMINOPHENAZONE
PREGABALIN
EXALAMIDE
PYRANTEL PAMOATE
KETOTIFEN FUMARATE
INAMRINONE
ESOMEPRAZOLE magnesium
CAPTOPRIL
GLIPIZIDE
ROSIGLITAZONE BASE
PEMIROLAST K
VANDETANIB
REBAMIPIDE
CISAPRIDE monohydrate
CEFUROXIME AXETIL
ANIRACETAM
DEQUALINIUM CHLORIDE
LEVOFLOXACIN
MIDAZOLAM HCl
ALLOTETRAHYDROCORTICOSTERONE
TINIDAZOLE
BOLDINE DIMETHYLETHER
GELDANAMYCIN
RISTOCETIN A
SUMATRIPTAN SUCCINATE
PAROXETINE HCl
DASATINIB
DAUNORUBICIN HCl
LORNOXICAM
HUPERZINE B
LAMOTRIGIN
AMRINONE
KITASAMYCIN
DONEPEZIL HCl
LOSARTAN K
FAMCICLOVIR
PRANLUKAST
ANDROSTANEDIONE
MIRTAZAPINE
AZELASTINE HCl
BENAZEPRIL HCl
BROMFENAC Na
LARIXOL ACETATE
ROBENIDINE HCl
PHENIRAMINE MALEATE
SATRAPLATIN
CORTISONE
ENOXAPARIN Na
RISTOMYCIN monoSULFATE
CILNIDIPINE
OXICONAZOLE NITRATE
TOLTRAZURIL
FLEROXACIN
LEVETIRACETAM
CLOMIPRAMINE HCl
SAQUINAVIR BASE
ELACRIDAR
LEUPROLIDE ACETATE
NEFAZODONE HCl
ADRIAMYCIN HCl
NAFAMOSTAT MESYLATE
RIFABUTIN
TELMISARTAN
SAQUINAVIR MESYLATE
GENTAMYCIN SULFATE
ANDROSTERONE
SPIRAMYCIN
MIGLITOL
MEROPENEM
TRAMADOL HCl
SORAFENIB TOLSYLATE
ZALTOPROFEN
NALIDIXIC ACID
EPIRUBICIN HCl
CLOTRIMAZOLE HCL
NELFINAVIR MESYLATE
SPIRONOLACTONE
DOXEPIN HCl
FLUDARABINE
CEFOTETAN DINa
MEBENDAZOLE
DIFLUSINAL
GABAPENTINE
RANOLAZINE DiHCl
CYCLOPHOSPHAMIDE
LOMEGUATRIB
FELODIPINE
6-METHOXY-2-NAPHTHYLACETIC ACID
ALFUZOSIN
TACROLIMUS
DIGOXIN
FORMOTEROL FUMARATE
ROFLUMILAST
ORLISTAT
PENCICLOVIR
TROGLITAZONE
(-)-ARCTIGENIN
FULVESTRANT
KETOROLAC TROMETHAMINE
CEFALEXIN HYDRATE
LAPATINIB TOSYLATE
CELECOXIB
CHLORMADINONE ACETATE
RACTOPAMINE HCl
ACYCLOVIR
EPALRESTAT
CLARITHROMYCIN
ALLOPREGNANDIOL
EBASTINE
BRIMONIDINE D-TARTRATE
IRINOTECAN HCl (trihydrate)
RIFAXIMIN
TOSUFLOXACIN TOSYLATE
AMFEBUTAMONE HCl
MOXIFLOXACIN HCl
GLICLAZIDE
ZIPRASIDONE
ARTESUNATE
AMIKACIN SULFATE
OZAGREL HCl
KETOCONAZOLE
NADIFLOXACIN
CARBAMAZEPINE
LOTEPREDNOL ETABONATE
IRBESARTAN
MIFEPRISTONE
FUDOSTEINE
356
Appendix 2
357
Appendix 3
358