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The Bioanalytical Evaluation of
Novel Therapeutic Modalities in Early Discovery
to Support the Development of
Effective Pharmaceutical Therapies
Against Unique Disease Targets.
Timothy Olah, Ph.D.
Group Director, Bioanalytical Research
Bristol-Myers Squibb Company
([email protected])
2015 AAPS National Biotechnology Conference
1
Outline and Topics for Discussion
• Understanding drug, target interaction, MOA

expanding quantitation beyond circulating drug levels
• Expanding diversity of new therapeutic modalities

challenges in detection and in assessing their benefits and liabilities
• Adapting the “Bioanalytical Process”

refining technical steps for novel therapeutic modalities

developing hybrid techniques in addition to LBA and LC-MS platforms
• Examples of Adaptive Bioanalytical Methods
2
Understanding Drug, Target, and MOA
•
Drug Exposure at the Target Site

•
necessary for pharmacological effect
Binding to the Pharmacological Target

target occupancy required
– alter expression of pharmacology
– target modulation
• Expression of Pharmacological Activity

•
functional modulation of target
Morgan and Graaf, Drug Discovery Today, Vol 17, Numbers 9/10,May 2012
3
Understanding Drug, Target, and MOA
•
Drug Exposure at the Target Site

•
necessary for pharmacological effect
Binding to the Pharmacological Target

target occupancy required
– alter expression of pharmacology
– target modulation
• Expression of Pharmacological Activity

•
functional modulation of target
Morgan and Graaf, Drug Discovery Today, Vol 17, Numbers 9/10,May 2012
4
Understanding Drug, Target, and MOA
•
Drug Exposure at the Target Site

•
necessary for pharmacological effect
Binding to the Pharmacological Target

target occupancy required
– alter expression of pharmacology
– target modulation
• Expression of Pharmacological Activity

•
functional modulation of target
Morgan and Graaf, Drug Discovery Today, Vol 17, Numbers 9/10,May 2012
5
Alternate Modalities: Justification
• Better access to targets that are challenging for other modalities

Molecular size and properties provide different pharmacology
(access to multiple target binding sites or ones inaccessible to larger mAbs)

Provide opportunity to improve selectivity and reduce cross-reactivity

Provide access to integral membrane targets that are often difficult for mAb
• Potential to improve Therapeutic Index

Unique shapes may allow more rapid interaction with target to reduce
systemic exposure and both non-target mediated and target-mediated tox

Targeting and internalization prolongs efficacious exposure (e.g. tumors)
• Ease of engineering to produce homogeneous drug products

Purely synthetic modalities offer opportunities to refine molecular structure

Engineering opportunities to optimize PK and multi-specificity
6
Alternate Modalities: Justification
• Better access to targets that are challenging for other modalities

Molecular size and properties provide different pharmacology
(access to multiple target binding sites or ones inaccessible to larger mAbs)

Provide opportunity to improve selectivity and reduce cross-reactivity

Provide access to integral membrane targets that are often difficult for mAb
• Potential to improve Therapeutic Index

Unique shapes may allow more rapid interaction with target to reduce
systemic exposure and both non-target mediated and target-mediated tox

Targeting and internalization prolongs efficacious exposure (e.g. tumors)
• Ease of engineering to produce homogeneous drug products

Purely synthetic modalities offer opportunities to refine molecular structure

Engineering opportunities to optimize PK and multi-specificity
7
Alternate Modalities: Justification
• Better access to targets that are challenging for other modalities

Molecular size and properties provide different pharmacology
(access to multiple target binding sites or ones inaccessible to larger mAbs)

Provide opportunity to improve selectivity and reduce cross-reactivity

Provide access to integral membrane targets that are often difficult for mAb
• Potential to improve Therapeutic Index

Unique shapes may allow more rapid interaction with target to reduce
systemic exposure and both non-target mediated and target-mediated tox

Targeting and internalization prolongs efficacious exposure (e.g. tumors)
• Ease of engineering to produce homogeneous drug products

Purely synthetic modalities offer opportunities to refine molecular structure

Engineering opportunities to optimize PK and multi-specificity
8
Alternate Modalities: Questions
• When to increase “investment” in novel modalities?

How to select the type of modality to evaluate and to develop?
– efficacy, toxicity, complexity of synthesis,

How much additional validation is necessary?

What are the key hurdles/obstacles? How to de-risk?
• How to position alternate modalities compared to SM or Biologics?

New Therapeutic Areas? Same targets? More speculative targets?

Targets with greater competition (faster to clinic, differentiation)?

Targets where traditional modality generation is challenging?
• Is there cross-site alignment to pursue new modalities?
9
Alternate Modalities: Questions
• When to increase “investment” in novel modalities?

How do you select the type of modality to evaluate and to develop?
– efficacy, toxicity, complexity of synthesis,

How much additional validation is necessary?

What are the key hurdles/obstacles? How can we de-risk these?
• How to position alternate modalities compared to SM or Biologics?

New Therapeutic Areas? Same targets? More speculative targets?

Targets with greater competition (faster to clinic, differentiation)?

Targets where traditional modality generation is challenging?
• Is there internal alignment to pursue new modalities?
10
Alternate Modalities: Questions
• Do some targets warrant a multi-modality approach?

Will there be a higher degree of validation required to select?
• Can alternate modalities be used to validate target, regardless of the
intent to develop as a therapeutic?

What are the advantages of one modality over another for validation?
• What are the technical challenges for assessing different modalities?

Are there technical hurdles (e.g. analytical and bioanalytical capabilities)?

What “novel” resources might be required (e.g. FTEs, instrumentation)?

What is the expected capacity or how many will be evaluated at any given time?
• Can you employ parallel processing for selection and optimization?
11
Alternate Modalities: Questions
• Do some targets warrant a multi-modality approach?

Will there be a higher degree of validation required to select?
• Can alternate modalities be used to validate target, regardless of the
intent to develop as a therapeutic?

What are the advantages of one modality over another for validation?
• What are the technical challenges for assessing different modalities?

Are there technical hurdles (e.g. analytical and bioanalytical capabilities)?

What “novel” resources might be required (e.g. FTEs, instrumentation)?

What is the expected capacity or how many will be evaluated at any given time?
• Can you employ parallel processing for selection and optimization?
12
Evolution of Bioanalytical Research
ADC
Antibodies
and Protein
Constructs
Target/
Biomarker/
Protein
Interactions
(RO)
Present
PEG-Adnectins
small molecule
in vitro ADME screening
drug exposure
and metabolites
2000
Millamolecules
2010
13
Modalities Quantified in Biological Matrices
Neurotransmitters
(dopamine)
MW 150 Da.
Peptides and Proteins
MW 8,000-60,000 Da.
Adnectins
MW 12,000 Da.
Bispecific Ab
MW 150,000 Da.
ASO (oligonucleotides)
MW 5,000 Da.
Millamolecules
(cyclic peptides)
MW 2,000 Da.
ADC
(construct of Antibody-Linker-Drug)
MW >150,000 Da.
14
Strategies to Modulate the Pharmacokinetic Properties
of Small Therapeutic Proteins Including Adnectins
Polymers
Conjugation
Fcg
Conjugation
Fusion
Binding
Recombinant
polymer
mimetics
Fusion
Recombinant
Protein
Binding
Carbohydrates
Increased
hydrodynamic
radius
Conjugation
Modification
Conjugation
Fusion
Binding
Albumin
FcRnmediated
recycling
FcRn
Modified from Kontermann, R., Biodrugs 2009; 23: 93-109
15
The Core Bioanalytical Process
A
Sample Receipt/Tracking
B
Method
Development
H
Method of Detection
C
Results
Information Management
Analytical
Standards
G
Data Archiving
Selective Separation and
Analyte Modification
D
Multiple Levels
Of Resolution
Sample Preparation
F
Data Processing
E
Sample Analysis
16
Method Development is an Iterative Process
Refine
Quan
Method
Development
Qual
Confirm
17
LC-MS-based methods has been used to
quantify all types of analytes in biofluids
 LC-MS is an established technology for quantitative analysis
• Firmly entrenched in pharmaceutical industry since early 1990s
 Bioanalytical strategies are in place for quantitative multiple
component assays from small molecules to multiple peptides
 Wide linear dynamic range without dilution is achievable
 Analytical data obtained on multiple analytes adds greater
depth and validity to calculated measurements and provide
details on the integrity of the modality on the molecular level
 Multiple biomarkers/proteins can be simultaneously measure
by detecting unique peptides from each and unique forms
following enrichment by affinity capture techniques
 therapeutic, target, ligand, biomarker, etc.
18
LC-MS-based methods has been used to
quantify all types of analytes in biofluids
 LC-MS is an established technology for quantitative analysis
• Firmly entrenched in pharmaceutical industry since early 1990s
 Bioanalytical strategies are in place for quantitative multiple
component assays from small molecules to multiple peptides
 Wide linear dynamic range without dilution is achievable
 Analytical data obtained on multiple analytes adds greater
depth and validity to calculated measurements and provide
details on the integrity of the modality on the molecular level
 Multiple biomarkers/proteins can be simultaneously measure
by detecting unique peptides from each and unique forms
following enrichment by affinity capture techniques
 therapeutic, target, ligand, biomarker, etc.
19
Bioavailability of Millamolecules
• Advantages of millamolecules / cyclic peptides as drugs:
• greater specificity, potency, and potential ability to reach specific targets
• Major disadvantages of millamolecules / cyclic peptides as drugs:
• poor membrane permeability:
• physical properties (size, polarity, acceptor/donor counts, violate most Lipinski rules)
• often good substrates for efflux pumps (e.g., P-gp)
• poor metabolic stability leading to high clearance
• poor oral bioavailability: digestive enzymes or intestinal wall penetration
Two main strategies to achieve bioavailability
Molecule
Strategy
Cyclosporin
N-methylation/Flexibility
Veber-Hirschmann peptide
N-methylation/Flexibility
α-Amanitin
Rigid structure - OATP
Cyclotides / knottins
Rigid structure
20
Problematic Molecules for SRM
Peptide Hormones
Cyclic Peptides
Other
21
When a Triple Quad is Insufficient
[M+4H]+4 Prec.
CE = 25 eV
CE = 40 eV
22
High Resolution Selection Ion Monitoring:
Fragmentation free alternative to SRM
SIM
(2a) HRAM
Detection
(1a) Precursor Selection
+ESI
B+ B+
B+ B+b+
+
B+ Ab
+
+
+
B B
B+
+ +
b+ AA+b b+
b+ +A+b+
b
m/zcalc[M+5H] +5
533.2817
533.4821
533.0811
533.6824
533.8827
534.0830
666.3503
534.2833
+4
m/zcalc[M+4H]
666.6008
666.0996
666.8512
667.1015
667.3519
667.6022
888.1313
+3
888.4653
m/zcalc[M+3H]
887.7970
888.7991
889.1330
889.4668
m/z
889.8006
23
High Resolution Selection Ion Monitoring:
ANP at low concentration extracted from serum
LC PEAK
Apex Spectra
100
14000
(A)
NL: 2.9e3
772.0895
(C)
12000
10000
serum blank
8000
50
RT: 1.74
AA: 1090
Signal Intensity
4000
2000
0
14000
(B)
12000
RT: 1.75
AA: 10419
2 ng/mL
10000
Relative Abundance (%)
6000
0
z=4
NL: 5.3e3
100
(D)
z=4
z=4
8000
50
z=4
6000
4000
2000
0
1.2
1.4
1.6
1.8
2.0
Time (min)
2.2
0
m/z
24
25
Survivor-SIM:
Taking greater analytical advantage of molecular uniqueness
Survivor-SIM
(1b) Precursor Selection
B+ B+
B+ B+b+
+
B+ Ab
+
+
+
B B
B+
+ +
bx+ AA+bx bx+
bx+b+A+bx+
x
„Normal‟
(3b) HRAM
Detection
+
AA
+
A+
Frag. Res.
100
Relative Abundance (%)
+ESI
(2b) CID
NL: 5.18E5
NCE = 0
0
NL: 4.18E5
100
NCE = 10
0
100
NL: 4.64E5
NCE = 15
0
NL: 3.84E5
100
NCE = 20
0
661
662
663
664
665
m/z
666
667
668
669
26
Survivor-SIM:
ANP at low concentration extracted from serum
LC PEAK
100
14000
(A)
(C)
Apex
Spectra
NL: 0
12000
10000
serum blank
8000
50
6000
Relative Abundance (%)
Signal Intensity
4000
2000
0
14000
(B)
RT: 1.76
AA: 12524
12000
0
z=4
100
(D)
z=4
2 ng/mL
10000
NL: 5.6e3
z=4
8000
50
6000
z=4
4000
z=4
2000
0
1.2
1.4
1.6
1.8
2.0
Time (min)
2.2
0
m/z
27
10x LLOQ Improvement for Peptide Hormone:
ANP Extracted From Serum
HRAM-SurvivorSIM
HRAM-SIM
Equation
Y = 590.771+5745.61*X-0.322046*X^2 R^2 = 0.9991
Area
Expected (ng/mL) Calculated (ng/mL)
5794
0.500
0.906
9317
0.500
1.519
6991
1.000
1.114
10803
1.000
1.778
14262
2.000
2.380
19967
2.000
3.373
28442
5.000
4.849
29106
5.000
4.964
54282
10.000
9.350
58561
10.000
10.095
280932
50.000
48.926
296539
50.000
51.658
1447385
250.000
255.466
1472784
250.000
260.019
2880995
500.000
516.261
2894393
500.000
518.737
5368862
1000.000
989.168
5421686
1000.000
999.516
9488320
2000.000
1841.342
10153359
2000.000
1988.731
16385003
3500.000
3563.336
16791142
3500.000
3682.363
20391658
5000.000
4888.390
20665761
5000.000
4995.353
%Diff
81%
204%
11%
78%
19%
69%
-3%
-1%
-7%
1%
-2%
3%
2%
4%
3%
4%
-1%
0%
-8%
-1%
2%
5%
-2%
0%
%RSD-AMT
NA
NA
NA
NA
NA
NA
1.7%
1.7%
5.4%
5.4%
3.8%
3.8%
1.2%
1.2%
0.3%
0.3%
0.7%
0.7%
5.4%
5.4%
2.3%
2.3%
1.5%
1.5%
Equation
Y = -1511.23+7858.6*X-0.413607*X^2 R^2 = 0.9973
Area
Expected (ng/ mL)
Calculated (ng/ mL)
2200
0.500
0.472
2522
0.500
0.513
5049
1.000
0.835
6644
1.000
1.038
12882
2.000
1.832
13198
2.000
1.872
33072
5.000
4.402
39255
5.000
5.189
82026
10.000
10.636
82353
10.000
10.678
388608
50.000
49.773
428138
50.000
54.831
1956642
250.000
252.529
2157280
250.000
278.795
3933848
500.000
514.714
3950263
500.000
516.923
7391562
1000.000
992.619
7476280
1000.000
995.328
13428333
2000.000
1898.666
15317115
2000.000
2205.227
20787372
3500.000
3176.382
21295280
3500.000
3274.233
29519596
5000.000
5155.333
30059305
5000.000
5308.189
%Diff
-6%
3%
-17%
4%
-8%
-6%
-12%
4%
6%
7%
0%
10%
1%
12%
3%
3%
-1%
-1%
-5%
10%
-9%
-6%
3%
6%
%RSD-AMT
5.9%
5.9%
15.3%
15.3%
1.5%
1.5%
11.6%
11.6%
0.3%
0.3%
6.8%
6.8%
7.0%
7.0%
1.4%
1.4%
0.3%
0.3%
10.6%
10.6%
2.1%
2.1%
2.1%
2.1%
Asoka Ranasinghe
28
Understanding Drug, Target, and MOA
•
Drug Exposure at the Target Site

•
necessary for pharmacological effect
Binding to the Pharmacological Target

target occupancy required
– alter expression of pharmacology
– target modulation
• Expression of Pharmacological Activity

•
functional modulation of target
Morgan and Graaf, Drug Discovery Today, Vol 17, Numbers 9/10,May 2012
29
Towards Measuring Drug and Target
to Infer in vivo Receptor Occupancy
"the right patient with the right drug at the right dose at the right time."
Possible PK/PD
model of a
biotherapeutic
drug and its
target ligand.
Bioanalysis (2012) 4(20), 2513–2523
30
Challenges to Personalizing Medicine:
Bioanalyst Perspective
• Limited Sample

Precious tissues

Small animal bleed for multiple analysis

Microsampling (DBS)
Understanding PK/PD
relationship in animal models
• Low Concentrations

Endogenous biomarkers (1-10 pM range)

Microdosing

Tissue Distribution
• Complex Matrices

Serum/Plasma

Blood and DBS

Tissue Biopsies
…and patients
31
Comparison of Bioanalytical Strategies for
Measuring Therapeutics and Biomarkers
Step in the Bioanalytical
Process
Therapeutic
Measurements
Biomarker
Measurements
Sample: biological matrix
(type and volume)
More easily (?) obtainable:
Blood, urine, CSF, (biopsy)
Target specific
(membrane, soluble, shed)
Method Development
Analyte well-characterized
prior to administration;
Identify, characterize, detect in
its native environment
“synthesized”
“endogenous” do not exist
Remove endogenous
background
Enrich endogenous
background
Sample Analysis
“Spike” and follow
(0 to “levels” to 0)
“Find” and follow
(baseline to “+/-” change
Data Processing
Multiple component analysis
Response, selectivity
Data Reporting
AUC, Cmax, Vd, t1/2
free, bound, total
Data Archiving
Dose Projections,
Therapeutic Index
“Mining” for Cross Correlation
Standards
Sample Preparation
32
The Core Bioanalytical Process
A
Sample Receipt/Tracking
B
Method
Development
H
Method of Detection
C
Results
Information Management
Analytical
Standards
G
Data Archiving
Selective Separation and
Analyte Modification
D
Multiple Levels
Of Resolution
Sample Preparation
F
Data Processing
E
Sample Analysis
33
Affinity Enrichment LC-MS
Immobilized affinity capture reagent (antibody, target protein, aptamer, etc.)
Bio-fluid or
tissue homogenate
Capture
LC-MS/MS detection
of surrogate peptide
2.0e4
1.9e4
Intensity, cps
Target protein
1.8e4
1.7e4
1.6e4
1.5e4
1.4e4
1.3e4
1.2e4
1.1e4
1.0e4
9000.0
8000.0
7000.0
6000.0
5000.0
4000.0
Wash
3000.0
2000.0
1000.0
0.0
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
Time, min
Digest into
surrogate peptides
Quality of capture reagent
determines specificity
requirements for detection
using LC-MS
34
Finding this balance between dimension and flow
in order to gain sensitivity and selectivity
Finding the “Sweet Spot”:
The optimum balance of
robustness, speed of analysis,
and sensitivity gain for routine
analysis
J. Murphy (Waters)
Improving LLOQ through AE-micro-LC-MS
Clean well/off-bead processing
Traditional Flow (600uL/min)
Blank
4 pM
LLOQ = 208.3 pM
Peptide 2
36
Improving LLOQ through AE-micro-LC-MS:
Clean well/off-bead processing
Microflow (5uL/min)
Blank
LLOQ = 2 pM
4 pM
Traditional Flow (600uL/min)
Blank
Peptide 2
Peptide 2
4 pM
LLOQ = 208.3 pM
Peptide 2
37
The Case for LC-MS in Tissue Analysis
• LC-MS offers greater molecular specificity for
analyte detection which results in higher precision
by minimizing interference from matrix
• Rapidly develop methods for feasibility assessment

without reagents, if levels are sufficiently high

with reagents that may have less stringent specificity
– less dependence on reagents‟ properties, more on MS
– quantify drug, target, ligand, isoforms, etc.
• LC-MS is our starting method for tissues
38
LC-MS data can strengthen
discovery program decisions
IMM program progression was dependent upon the outcome of
preclinical studies and patient plasma/colon target levels
Available Assay Platforms and Reagent Specificity Issues

Immunohistochemistry (IHC)
– available antibodies were not successful

Quantitation of protein levels in plasma and colon tissue from
“NHV” and Patient (UC and CD) Samples

ELISA: “free” levels (human and mouse plasma only)

AE-LC-MS: “free” (human and mouse plasma and tissue)
Human
Sample
s
PreClinical
Models
39
ELISA vs. AE-LC-MS data:
Target protein levels in human plasma
ELISA and LC-MS Levels
40
ELISA vs. AE-LC-MS data:
Target protein levels in human plasma
ELISA and LC-MS Levels
Significantly greater variability in ELISA measurements
41
ELISA vs. AE-LC-MS data:
Target protein levels in human plasma
ELISA and LC-MS Levels
Target protein levels not increased in patient vs. “NHV” plasma
42
Target protein levels in colon by AE-LC-MS
do not show significant association with disease
Colon tissue samples available:

10 UC (Ulcerative Colitis)

10 CD (Crohn‟s Disease)

18 NHV (Normal Healthy Volunteers
Target Protein Levels in Tissue
Program terminated:
• no efficacy in preclinical model
• no differentiation in colon levels
43
The Case for LC-MS
• selectivity/resolution
 specific detection of analyte of interest
• multiplexing
 detect multiple analytes of interest in a single sample
• speed in method development
 quickly assess the probability of measurements
• quantitative bioanalysis continuum
 detect, characterize, quantify (relative to validated)
• improved sensitivity with hybrid platforms
 to reach achievable LLOQ for clinical biopsies
44
Understanding Drug, Target, and MOA
•
Drug Exposure at the Target Site

•
necessary for pharmacological effect
Binding to the Pharmacological Target

target occupancy required
– alter expression of pharmacology
– target modulation
• Expression of Pharmacological Activity

•
functional modulation of target
Morgan and Graaf, Drug Discovery Today, Vol 17, Numbers 9/10,May 2012
45
Bile Acid Analysis in Crashed Serum
Deoxycholic acid
Cholic acid
Full Scan
Survivor-Scan
Kimberly Snow
46
Conclusion
• Greater effort focused on drug, target interaction, MOA

expanding quantitation beyond circulating drug levels
• Expanding diversity of new therapeutic modalities

challenges in quantitation and assessment of benefits and liabilities
• Continuum of the “Bioanalytical Process”

refining technical steps for novel therapeutic modalities

developing hybrid techniques in addition to LBA and LC-MS platforms
• Examples of Adaptive Bioanalytical Methods

more innovative and better (?) therapeutic modalities
47
Acknowledgements
•
Omar Barnaby, Yulia Benitex
•
Tracy Mitchell, Petia Shipkova
•
Joseph Cantone, Christian Caporuscio
•
Joelle Onorato, Bob Langish
•
Eugene Ciccimaro, Georgia Cornelius
•
Adrienne Tymiak
•
Celia D‟Arienzo, Lorell Discenza
•
Dieter Drexler, Colleen McNaney
•
BAS-SI, BAS-Biologics, RWC
•
John Mehl, Asoka Ranasinghe
•
Biology, Chemistry, WAL
•
Eric Shields, Bogdan Sleczka
•
Pharmaceutical Development
•
James Smalley, Kim Snow
•
Richard Wong, Baomin Xin
•
Waters Corporation
•
Carrie Xu, Hongwei Zhang
•
Thermo Fisher Scientific
•
Joanna Zheng, Yongxin Zhu
48