Download Applications of PBPK modeling in preclinical drug development

Pharmaceuticals
Presented at the Hamner Institutes for Health Sciences Course:
Physiologically Based Pharmacokinetic (PBPK) Modeling in
Drug Development and Evaluation
April 6–10, 2009
Applications of PBPK Modeling in
Preclinical Drug Development
Micaela Reddy
DMPK Department, Modeling & Simulation Group
Roche Palo Alto
March 30, 2009
1
Pharmaceuticals
Goals
■ Present examples of multiple ways that PBPK modeling
can be used to address key issues for preclinical projects
■ Describe how PBPK models can be used to generate
hypotheses and prioritize additional experiments
■ Provide a method for developing physiologically based oral
absorption models and discuss the factors that might
impact oral absorption for BCS Class 1, 2, 3 and 4
compounds
■ Illustrate how PBPK/PD modeling can be used to assess
the likelihood that a compound will be efficacious in the
clinic and to characterize the therapeutic window
2
Donor
pH Dependence of Solubility
Acceptor
conc.
10
1
time
0.1
Calculated
Measured
Fitted
0.01
0.001
pH
Intestinal
permeability P-g
p
vo P
In v i
formulation
K
A
cti
v
e
p
r
oc
h
es
s
es
Protein binding
sm
So
i
l
o
lu
b
a
t
bi
e
lit
M
y
Conc
0 1 2 3 4 5 6 7 8 9 10
Normal
Medium
with
Calcium
Ca2+-free
Medium
with 1 mM
EGTA
Uptake
Cell
Lysis
and
Sampli
ng
3
T. Lavé
Pharmaceuticals
log(Solubility) [mg/mL]
PBPK models have many applications in
preclinical drug development because of the
need to integrate data
In silico estimates
Pharmaceuticals
Outline
■
■
■
■
PBPK models for generating hypotheses
Oral absorption modeling
PBPK modeling for candidate selection
Predicting exposure window using PBPK/PD
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Pharmaceuticals
PBPK modeling is an evolving process
■ New paradigm: See if you can predict in preclinical
species. If you cannot, perform appropriate experiments
to fill the knowledge gap.
■ Mechanistic knowledge and availability of key data
determine the confidence you can have in model
predictions.
5
Pharmaceuticals
Fundamental value of modeling
■ Models allow you to integrate multiple sources of data and
information with a mechanistic description based on your
fundamental understanding of the system.
■ If the model prediction is correct, it is useful.
● Instead of making predictions based on rat data, you can
take it a step further. After testing your understanding of
the system with rat, you can extend that knowledge to
make predictions in man.
■ If the model prediction is incorrect, it is also useful.
● The disconnect between the model and data suggests
your understanding of the system is flawed.
● The model can be used to rule out hypotheses that are
inconsistent with the data and determine additional
experimentation that is most likely to be informative.
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Pharmaceuticals
Example: Lack of IVIVC from nonlinear CL
Rat PO Profile at 3 mpk
Problem: Modeling
indicated lack of good
IVIVC
Concentration (ng/mL)
1000
PBPK Simulation
Measured PK
100
10
1
0
10
20
30
0.1
Time Post Dose (h)
Experiments:
• Rat and dog bile-duct cannulated studies to better understand clearance
mechanisms: No significant biliary secretion
• Using lower concentration of drug (0.1 µM instead of 1 µM) for clearance
measurement in vitro to prevent saturation of metabolic enzymes in
hepatocyte or microsome to obtain accurate intrinsic clearance
D. Lu
7
Pharmaceuticals
Outcome: IVIVC achieved for the compound within 2-fold
uncertainty using newly obtained microsome clearance.
Proceeded with PBPK simulation in human.
Rat PO Profile at 3 mpk
Concentration (ng/mL)
1000
PBPK Simulation
100
Measured PK
10
1
0
5
10
15
20
25
30
0.1
Time Post Dose (h)
D. Lu
8
Courtesy of Dr. Werner Rubas
Pharmaceuticals
Example: Impact of enterohepatic
circulation (EHC) on PK
9
Pharmaceuticals
For this compound, CL is overpredicted and t1/2
is underpredicted by the in vitro model parameters
Observed
1000
Modeled
Conc. [ng/mL]
100
10
1
0.1
0
5
10
15
20
25
30
Time [h]
Hypothesis: Enterohepatic circulation of parent
Estimation of fraction EHC = (CLobs – CLpred)/CLobs = 0.25
10
W. Rubas
1000
Pharmaceuticals
Inclusion of predicted EHC (25%)
improves simulation
Observed
Modeled w/o EHC
Modeled w EHC
Conc. [ng/mL]
100
10
1
0.1
0
5
10
15
20
25
30
Time [h]
Experiment using bile duct cannulated rats:
Fraction EHC was determined to be 0.33.
11
W. Rubas
Pharmaceuticals
PK profiles are well simulated when
enterohepatic circulation is included (33%)
PO 2 mg/kg
IV 0.5 mg/kg
1000
1000
Observed
Observed
Modeled w/o EHC
Modeled
Modeled w EHC
100
Conc. [ng/mL]
Conc. [ng/mL]
100
10
10
1
1
0.1
0.1
0
5
10
15
20
25
30
Time [h]
Predicted AUC and t1/2 are
within 10 % of observed
0
5
10
15
20
25
30
Time [h]
Cmin prediction is
improved
12
W. Rubas
Pharmaceuticals
Outline
■ PBPK models for generating hypotheses
■ Oral absorption modeling
● Validating the model
● Using the model
■ Compound prioritization based on early preclinical data
■ Predicting exposure window using PBPK/PD
13
Example: ACAT model in GastroPlus
Pharmaceuticals
Recently, several powerful PBPK approaches
have been developed to predict oral absorption.
The model can also include degradation of compound passing through the GI tract, the
impact of transporters, and differences in permeability with region.
Species differences in regional pH, compartment volume, etc. are included in the model.
Other examples:
ADAM (advanced dissolution, absorption & metabolism) model (Jamei et al., in press),
GI tract described as tube with spatially varying properties (Willman et al., 2003)
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Data (in vitro/in vivo)
Validation model (few compounds)
Pharmaceuticals
How can M&S increase efficacy and
speed in drug discovery?
Screening funnel
Multiple series
Sensitivity analysis
Critical parameters
Not critical
Which one to focus on first?
•Metabolic stability?
•Protein binding?
•Solubility?
Stop experiment
Clinical lead
Front load
15
Pharmaceuticals
PBPK modeling for oral absorption
■ PBPK modeling approaches for predicting human oral absorption
can be used to simulate
● absorption as a function of time
● the amount absorbed from various regions of the GI tract
● plasma concentrations as a function of time
■ Approach:
● Develop a model in a preclinical species and check it against
whatever data is available (e.g., po SDPK data)
● Determine how to improve the model based on mismatches
between model and data; perform critical experiments to
elucidate key mechanisms
● Develop the human model based on what was learned in the
preclinical species
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Pharmaceuticals
Applications of oral absorption modeling
■ Predicting bioavailability
■ Evaluate potential absorption and bioavailability for large
■
■
■
■
number of compounds with very limited data
Determine the sensitivity of bioavailability and PK to various
input parameters
Maximum absorbable dose
Predicting whether a food effect is expected
Determining an appropriate formulation strategy
17
Pharmaceuticals
An absorption model can be developed
using permeability and solubility data
Physicochemical properties
Parameter
Early in development (LO)
Late in development (CLS, EIGLP)
cLogP / Log D @
reference pH
In silico
Measured Log D-pH profile
pKa’s
In silico
Measured
Permeability
(human Peff)
In silico
Optional: cell-based
permeability assay (e.g.,
MDCK, Caco-2) or PAMPA
Cell-based permeability assay (e.g.,
MDCK, Caco-2) or PAMPA
Solubility @
reference pH
Aqueous; HT Fessif/Fassif
for low solubility compounds
(e.g., < 20 ug/ml aqu. sol.)
pH-solubility profile, including
Fessif/Fassif
Solubility factor
In silico
Based on solubility data for range of
pH’s
18
Pharmaceuticals
Outline
■ PBPK models for generating hypotheses
■ Oral absorption modeling
● Validating the model
● Using the model
■ PBPK modeling for candidate selection
■ Predicting exposure window using PBPK/PD
19
Pharmaceuticals
To check your absorption model
Simulation results can be compared to:
Bioavailability for a single experiment
Bioavailability as a function of dose
Fraction absorbed into the portal vein
Plasma time-course concentrations (requires a PK study)
Before using absorption modeling to make decisions,
it is good practice to check the model results against
PK data at multiple doses and in one or more species if
possible.
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Pharmaceuticals
Using absorption modeling to predict bioavailability
■ F = Fabs x Fh x Fg x … where
● Fabs is the fraction that is absorbed into enterocytes
● Fh is the fraction that makes it through the liver
● Fg is the fraction that makes it through the enterocytes
without being metabolized
■ There could be additional sources of loss (e.g., metabolism by
microflora in the gut, instability in the stomach, etc.).
■ Absorption modeling provides a prediction of fraction
absorbed, but to predict in vivo bioavailability, first-pass
processes must also be incorporated.
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Pharmaceuticals
Definition of Bioavailability (F)
■ Bioavailability (F) is the fraction (or percent) of administered dose
that reaches systemic circulation intact.
AUCpo AUCiv
■ Bioavailability is typically calculated
F=
by comparing AUC from iv PK data
D po
D iv
to AUC from po PK data.
■ Often bioavailability is < 1 because loss can occur from factors like
● incomplete absorption (e.g., from low permeability or solubility)
● first pass extraction by the gut and liver
● chemical degradation in the lumen
■ Sometimes estimated F is > 1 than because of factors such as
● saturable metabolism
● enterohepatic circulation
● incomplete administration of iv dose
22
■ One mechanism that often has a large impact on bioavailability is
first-pass hepatic extraction.
■ At a minimum, the effect of first-pass extraction (Eh) should be
combined with Fabs for estimating bioavailability. (Assume Fg=1 if no
phenotype data are available.)
F = Fh x Fabs
Fh = fraction that makes it past the liver, 1 - Eh
Fabs = fraction absorbed
■ When can Fabs be compared directly to bioavailability?
● For compounds that are cleared by mechanisms other than
●
hepatic metabolism or biliary excretion
For low-clearance compounds (CL < 0.1 x QL)
23
Pharmaceuticals
Comparing Fabs to Bioavailability
■ Fh can be calculated as: Fh = 1 – Eh = 1 – CL / QL
■ Eh can be calculated from hepatocyte or microsome in vitro data
■ Eh can also be estimated using CL from an iv SDPK study (CL / QL)
● Caution:
Is the mechanism of CL hepatic metabolism?
■ Which method to use? Do scaled hepatocyte or microsome data:
● predict IV CL? Either method can be used.
● underpredict IV CL? Metabolism is probably not the only
mechanism of CL, and so scaled hepatocyte or microsome data
should be used.
● overpredict IV CL? Most CL is probably from metabolism, and
the IV CL can be used as the more accurate CL estimate.
CL = CLplasma / BPR where BPR = blood to plasma ratio
In some cases (e.g., high extraction compounds that do not partition into red
blood cells) it is critical to have BPR data for interpretation of PO PK data!
24
Pharmaceuticals
Methods for calculating Fh
Pharmaceuticals
Checking model prediction of bioavailability
Selected drug properties and F from rat SDPK data with Fabs from
GastroPlus and Fh from an appropriate method.
Solubility,
µg/ml a
Caco2 Peff,
cm/sx10-6
ER
F
Fabs
Fh
FabsxFh
Compound 1
145 (Aq)
13.2
1.2
0.26
0.96
0.23
0.22
Compound 2
21.2 (Aq)
16.3
1.1
0.34
0.55
0.34
0.19
Compound 3
2 (Aq)
15.3
0.77
0.46
0.079 0.31
0.024
Compound 3
38.7 (Fa)
15.3
0.77
0.46
0.78
0.24
Compound 4
6.1 (Aq)
1.52
8.4
0.38
0.077 0.37
0.029
Compound 4
32.9 (Fa)
1.52
8.4
0.38
0.35
0.37
0.13
Compound 4
32.9 (Fa)
13.2 (w/ elac.b)
0.87
0.38
0.62
0.37
0.23
a
Aq = aqueous solubility at pH=6.5. Fa = FaSSIF solubility at pH=6.5.
b
Elacridar is a P-gp inhibitor.
0.31
25
Multiple mechanisms affect exposure as a function of dose.
However, in terms of Fabs, one would generally expect the
following:
■ BCS Class 2 and 4 may exhibit dose-proportional or lessthan-dose-proportional increases in AUC and Cmax as the
dose increases.
● If the model predicts that dose-limited absorption occurs
(i.e., simulated Fabs decreases with increasing dose),
there should be a less-than-dose-proportional increases in
AUC and Cmax as the dose increases in po SDPK studies.
■ BCS Class 1 and 3 may exhibit dose-proportional or greaterthan-dose-proportional increases in AUC and Cmax as the
dose increases in po SDPK studies.
26
Pharmaceuticals
Checking bioavailability prediction for a
range of doses
Pharmaceuticals
Checking fraction absorbed into the
portal vein *
■ The Fabs into the portal vein will not reflect the effects of first-pass
Cumulative Aabs, mg
hepatic metabolism (although other first-pass processes, such as
metabolism by enterocytes, might still have an impact).
■ Therefore, Fabs into
mg/kg POPOdose
700100
mg ITMN-191
Dose
the portal vein can be
300
used more easily to
250
check the model
200
prediction of Fabs.
150
100
KFA
KFB
KFC
KFD
GP sim
50
0
0
4
8
12
16
20
24
28
time, h
* Useful for compounds with high hepatic extraction!
27
Pharmaceuticals
Calculation: Amount Absorbed
■ The amount absorbed as a function of time can be calculated from
the concentrations in the portal and peripheral vein.
d Aabs = Qpv x (Cpv – Cp) x BPR
dt
Aabs =
amount absorbed
t
=
time
Qpv =
portal vein blood flow rate (~85% of liver blood flow)
Cpv =
plasma concentration in portal vein
Cp
=
plasma concentration in peripheral vein
BPR =
blood-to-plasma ratio
28
Pharmaceuticals
Checking the model against plasma
time-course concentrations
Rat, 20 mg/kg in Suspension
Rat, 2 mg/kg in Suspension
100000
Cp, ng/ml
1000
Rat 1
Rat 2
100
Rat 3
Sim
10
10000
Cp, ng/ml
10000
Rat 1
1000
Rat 2
100
Rat 3
Sim
10
1
1
0
4
8
12
16
20
24
0
4
8
time, h
16
20
24
time, h
Monkey, 20 mg/kg
Monkey, 2 mg/kg
KF 1
10000
KF 2
KF 3
1000
Sim
Cp, ng/ml
100000
100000
Cp, ng/ml
12
KF 1
10000
KF 2
KF 3
1000
Sim
100
100
0
4
8
12
time, h
16
20
24
0
4
8
12
time, h
16
20
24
29
■ For rat PO PK, first use a compartment model to simulate iv
PK to remove one source of uncertainty and check the
absorption prediction.
● Assumption: linear kinetics
● First-pass Eh will need to be calculated and included
● After checking absorption model, redo calculation to check
the PO PK profile using the PBPK model.
0.5 mg/kg iv PK
Compartment model
for systemic PK
Change to po
exposure,
parameterize
oral absorption
model
2 mg/kg po PK
30
Pharmaceuticals
Approach: Reduce confounding factors by
using a compartment model for systemic PK.
Pharmaceuticals
Next, switch to the PBPK model for systemic
PK to verify that the model is still predictive.
■ After checking the absorption model, redo the calculation
to check the PO PK profile using the PBPK model.
0.5 mg/kg iv PK
PBPK model for
systemic PK
Change to po
exposure,
parameterize
oral absorption
model
2 mg/kg po PK
31
Pharmaceuticals
What do you do if the model does not
match the data?
Several avenues of exploration
■ User error
● Double check input, paying close attention to units
● Are solubility, permeability, and compound properties
entered correctly?
■ Inappropriate input for BCS Class 2, 3 or 4
● BCS Class 2, 4: need appropriate solubility data
● BCS Class 3, 4: carefully consider best way to estimate
Peff
■ Important mechanism impacting absorption or PK not yet
identified (e.g., metabolism by gut, nonlinear metabolism)
● Need to design appropriate experiments
32
Pharmaceuticals
Absorption models for BCS Class 1 compounds
tend to be reliable, but issues can still arise.
Example: Dissolution data was necessary to
simulate absorption of diltiazem (BCS Class I)
■ The initial model overpredicted Cmax and underpredicted tmax.
Dissolution data was required to simulate absorption.
■ Input: Dissolution data, solubility = 3000 µg/ml @pH = 6.4, PAMPA
Peff = 1.9x10-6 cm/s, hepatic extraction = 54%
Weibull function
Dog PO PK, Teva IR 120 mg Tablet
Formulation=IR, Teva
1600
100
Cp, ng/mL
1200
Dog 1
80
Dog 2
Dog 3
800
Mean
Sim, generic
400
Sim, disso data
0
60
Observed
40
Predicted
20
0
0
4
8
12
16
Time (hours)
20
24
0
50
100
time (min)
150
200
33
Pharmaceuticals
For human simulation, dissolution data and first-pass
extraction by the gut had to be incorporated.
Weibull function
Cardizem IR - 120 mg dose
Formulation=IR, Cardizem
120
100
Cp, ng/ml
100
80
60
40
No GFJ
GFJ
80
Sim, no GFJ
60
Sim, GFJ
40
Observed
Predicted
20
0
0
4
8
12
time, h
16
20
24
Data from
Christensen
et al. 2002
20
0
0
500
1000
1500
time (min)
One way to determine the impact of first pass extraction by the gut is to do a
grapefruit juice (GFJ) study because it inhibits CYP3A4 in the gut (Yang et al.
2007). Fg < 1 for many CYP3A4 substrates, e.g., midazolam (Fg = 0.57),
felodipine (Fg = 0.58), and saquinavir (Fg = 0.67).
* Most PBPK software packages include CYP expression in the gut and
can predict Fg if reaction phenotyping data is available.
34
Pharmaceuticals
Considerations for dissolution data
■ Dissolution data might
improve your prediction,
depending on how the study
is designed and whether
dissolution is rate limiting.
■ Considerations for study
design:
● Sink conditions
● Physiologically relevant
media (FeSSIF, FaSSIF,
FeSGF, FaSGF)
● Two-stage design (e.g.,
pH 1 and then pH 6)
Key plot for diagnosing whether
dissolution might be rate-limiting
Dissolved
Absorbed
BCS Class II
compound
35
Pharmaceuticals
BCS Class 2
Low solubility, high permeability
■ For BCS Class 2 solubility is a very sensitive model parameter,
■
■
■
■
and appropriate (i.e., physiologically relevant) solubility data is
critical.
FeSSIF and FaSSIF solubility data have been shown to
dramatically improve the predictive accuracy of the absorption
model for low-solubility compounds.
If the dosing solution contains solubility enhancers, solubility data
with solubility enhancers might also be appropriate.
Different batches of drug can have very different solubilities. A
mismatch between Fabs in a po SDPK study and the model
prediction of Fabs could be because the compound administered
to the animals and the compound used to determine the solubility
were from different batches.
Efflux transporters might be an issue.
36
Pharmaceuticals
Verify that sufficient data are
available to set solubility
If a compound
has a
physiologically
relevant pKa,
carefully
consider if
solubility data
have been
generated for a
sufficient range
of pH values.
37
Pharmaceuticals
Early stage prediction of bioavailability
in rats for BCS Class 2 compounds
Used to verify understanding of factors impacting series %F
Compounds:
-CLogP, pKa from ADMET Predictor;
45.0
-Dose form: IR suspension;
40.0
12 within 2-fold
2 over-predicted
2 under-predicted
35.0
-Solubility: phosphate (if >20 ug/mL)
and FaSSIF
-particle size 25 µm
-Peff from Caco2 AB
Physiology: Rat (fasted), Opt LogD Model
30.0
%F (cal)
-Particle density 1.2 g/mL
y = 1.0x
25.0
20.0
15.0
10.0
5.0
0.0
Simulation: 24h
0
5
10
15
20
25
30
35
40
45
%F (measured)
First-pass extraction: In vivo IV clearance
38
Courtesy of Q. Hu
■ For BCS Class 3, the effect of transporters may have a large, and
not easily predicted, impact on PK.
● At low doses, the impact of transporters will be largest.
● At high doses, transporters could saturate.
● Solution: include saturable transport into the model.
■ For estimating Peff for Pgp substrates, Caco2 (A>B) data with
elacridar is likely more predictive. But for lower solubility or
permeability compounds, Caco2 (A>B) data without elacridar could
be more predictive. Looking at the Fabs calculation using both Peff
values could provide a reasonable range of values.
■ In the intestines (duodenum, proximal jejunum scrapings), CYP3A4
was the most abundant P450 (80% of intestinal P450), followed by
CYP2C9 (15%) (Paine et al. 2006). Gut metabolism may be most
important for low permeability CYP3A4 substrates (Yang et al. 2007).
39
Pharmaceuticals
BCS Class 3
Low permeability, high solubility
Pharmaceuticals
The gut expresses many efflux
and uptake transporters.
Expression levels can vary
depending on location in GI
tract.
Figure from:
Predicting drug disposition,
absorption/elimination/
transporter interplay and the
role of food on drug absorption.
Custodio JM, Wu CY, Benet LZ
(2008). Adv Drug Deliv Rev
60(6): 717-733.
40
Pharmaceuticals
Early stage prediction of bioavailability
in rats for BCS Class 3 compounds
Used to verify understanding of factors impacting series %F
5 over-predicted
8 within 2-fold
Data: In silico cLogP, pKa; Caco2 Peff with no inhibitor, THESA solubility,
Eh calculated using Mx data
Courtesy of S. Larrabee
41
■ For BCS Class 4, the issues that occur with BCS Class 2 and
3 might both occur.
■ For BCS Class 4, it might be important to do more checking
than for the other BCS classes, and to be more careful with
interpretation of results, particularly if transporters are
involved.
■ Absorption modeling can still be useful, but results should be
carefully interpreted. The key is to interpret more qualitatively
than quantitatively unless you have validated the model
appropriately.
42
Pharmaceuticals
BCS Class 4
Low solubility, low permeability
Pharmaceuticals
Outline
■ PBPK models for generating hypotheses
■ Oral absorption modeling
● Validating the model
● Using the model
■ PBPK modeling for candidate selection
■ Predicting exposure window using PBPK/PD
43
■ Peters (2008): Assess any “lineshape” mismatch between
simulated and observed oral profiles to gain mechanistic
insights into processes impacting absorption and PK
● for example, drug-induced delays in gastric emptying and
regional variation in gut absorption.
■ Jones et al. (2006): GastroPlus human oral absorption
models were established for six compounds. Food effects
were predicted for a range of doses and compared to the
results from human food effect studies.
● In general, the models were able to predict whether a food
effect would be major (i.e., for two compounds) or minor
(i.e., for four compounds).
44
Pharmaceuticals
Examples from the literature
Pharmaceuticals
Example: Is a CR formulation feasible?
■ CR formulation development is expensive, but can be
necessary for a compound with a short t1/2 in humans.
■ Extensive PK experimentation is typically performed to
determine whether a CR formulation is feasible (i.e.,
whether the compound can be absorbed in the colon)
before resources are spent on the effort.
● Surgically modified animals with cannulas for
administering compound to various parts of the GI tract
● Cross-over IV and PO PK studies for deconvolution to
determine the amount absorbed as a function of time
● EnterionTM capsule, Pharmaceutical Profiles
■ Modeling can help to focus and reduce the experimentation.
45
Simulated Human Site-of-Absorption Data
100 mg Furosemide
PO
1
PO
IJ
IC
0.8
Cp, ug/ml
Pharmaceuticals
Based on the po PK profiles
Site-of-absorption study
alone, it is not clear where
Example: Furosemide (BCS Class 4) absorption occurs.
0.6
0.4
0.2
0
0
4
8
12
16
20
24
time, h
IJ
IC
46
Simulations performed with GastroPlus demo database.
■ If iv and po PK data are available, numerical deconvolution
can be used to estimate the fraction of drug absorbed as a
function of time.
● The results are sometimes noisy and difficult to interpret.
● Better results are obtained with a cross-over design so that
iv and po data are available in the same animal.
■ This analysis will not necessarily result in clear information on
where drug is absorbed (i.e., results may be difficult to
interpret).
■ For compounds that are well absorbed in the small intestines,
the po data will have no information on the rate of absorption
from the colon.
47
Pharmaceuticals
Deconvolution of po PK data (an empirical approach)
can also be useful for understanding absorption.
Pharmaceuticals
Example: Ketoprofen (BCS Class 1)
Simulation for humans administered a 50 mg dose of ketoprofen:
Most ketoprofen absorption occurs in the duodenum and jejunum. The po
PK data contains no information on rate of absorption from the large
intestines.
Simulations can provide useful information about
whether deconvolution of po PK data will yield useful information.
48
Simulation performed with GastroPlus demo database.
Pharmaceuticals
100
PO
80
60
Peff (x10-6 cm/s) =
40
0.3
1.2
1.7
3.4
20
0
Cumulative Aabs, mg
Cumulative Aabs, mg
Example: Should a CR formulation be
developed for a compound with a short t1/2?
100
Peff (x10-6 cm/s) =
80
IC
0.3
1.2
1.7
3.4
60
40
20
0
0
4
8
12
16
time, h
20
24
0
4
8
12
16
20
24
time, h
For this compound, Caco2 Peff data had been measured and
exhibited 10-fold variability.
Simulations indicate that the intracolonic route in humans may
result in 31-46% of the PO AUC. Sensitivity analysis shows
that Peff is critical for making the prediction.
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Pharmaceuticals
Model refinement with new data suggests that
CR development would be very challenging.
■ Peff was a sensitive model parameter.
■ Additional experimentation revealed that the compound had
permeability in the low end of the range.
● Refined simulations indicated that the IC route in humans
would likely result in 31% of the PO AUC or less.
■ In a recent PharmaProfiles poster by Connor et al. (2008),
CR development is classified as very challenging for
compounds with a relative bioavailability of < 30% upon
administration to the colon.
■ Based on this assessment, CR development would likely be
very challenging.
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Pharmaceuticals
Outline
■
■
■
■
■
PBPK models for generating hypotheses
Oral absorption modeling
PBPK modeling for a parent compound and metabolite
PBPK modeling for candidate selection
Predicting exposure window using PBPK/PD
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Pharmaceuticals
PBPK For Candidate Selection
■ Aim: to use physiologically based simulation tools to combine the
available PK and PD data and assist in selection of the optimal
clinical candidate
■ Steps taken:
● Verification: compare simulated and observed PK in rat
● Predict PK: estimate human PK
● Predict PD: effective concentrations in human
● Estimation: clinical doses and exposures
■ Outcome:
● Balanced comparison of PK/PD incorporating variability
and uncertainty to be weighed with other factors (early
tox, synthetic tractability, formulation ease etc…)
N. Parrott
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Pharmaceuticals
PBPK For Candidate Selection
1.5
40
1
30
0.5
20
0
10
Clint human hepatocytes
[uL/min/M cells]
0
CL Rat (ml/min/kg)
40
30
20
10
0
Ki rat (nM)
Ki human (nM)
Available data for 5 compounds
•Physicochemical
0.40
•In vitro hepatocytes rat & man
0.30
•Protein binding rat & man
0.20
0.10
•In vivo PK i.v. and p.o. in rat
0.00
•Effect versus concentration in rat
fu% rat
fu% man
6
4
2
0
ID90 rat
(mg/kg)
N. Parrott
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Pharmaceuticals
PBPK For Candidate Selection
Simulated Cmax in human for a dose of 25mg including variability
in both CL and V based upon in vitro and in vivo data
Mean with 95% limits shown
400
Cmax [ng/mL]
350
300
250
200
150
100
50
ROCHE_5
ROCHE_4
ROCHE_3
ROCHE_2
N. Parrott
ROCHE_1
0
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Pharmaceuticals
PBPK For Candidate Selection
1000
Variability is
based on the
observed
variability in
the rat
100
10
N. Parrott
ROCHE_5
ROCHE_4
ROCHE_3
ROCHE_2
1
ROCHE_1
Conc for 90% reversal [ng/mL]
Effective concentration in human estimated from rat PD data rat and
in vitro data in both species (Fu% rat & human, Ki rat & human)
55
40
Prediction in Human
30
20
10
0
CL Rat (ml/min/kg)
Simulation
1000
Pharmaceuticals
Multiple Discovery Data
1.5
100
1
0.5
10
0
Clint human hepatocytes
[uL/min/M cells]
0.40
0.30
0.20
•Integrates
multiple PK and
PD data
1
Conc. for 90% effect in
human (ng/mL)
0.10
0.00
fu% rat
fu% man
40
30
20
10
•Aids rational
and balanced
decision
80
60
40
0
Ki rat (nM)
Ki human (nM)
•Focuses on
expected human
PK/PD
6
4
2
0
ID90 rat
(mg/kg)
20
0
Daily dose [mg]
56
N. Parrott
Pharmaceuticals
Outline
■
■
■
■
■
PBPK models for generating hypotheses
Oral absorption modeling
PBPK modeling for a parent compound and metabolite
PBPK modeling for candidate selection
Predicting exposure window using PBPK/PD
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Pharmaceuticals
Hypothetical example
Predicting therapeutic window for drug
with safety issue and nonlinear PK
■ A BCS Class II compound has dose-limited absorption.
■ Efficacy for the compound is related to percent inhibition,
which can be described using a simple Emax model with
EC50 = 40 ng/ml.
■ The compound has an off-target effect, and the PD for this
effect also can be described using a simple Emax model with
EC50 = 2000 ng/ml.
■ A simple modeling approach can be used to understand
whether efficacy can be achieved while avoiding the PD that
is a safety concern.
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Pharmaceuticals
Even at low doses, dose-limited
absorption is expected to occur.
Cp, ug/ml
0.16
20 mg
40 mg
0.12
Fabs = 31%
0.08
60 mg
80 mg
100 mg
0.04
Fabs = 62%
0
0
4
8
12
16
20
24
time, h
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Pharmaceuticals
Dose-limited absorption might make it
difficult to achieve efficacious Cp
Efficacy, % of Emax
■ For efficacy, a minimum of 50% inhibition is required.
100
80
20 mg
40 mg
60
40
60 mg
80 mg
20
100 mg
0
0
4
8
12
16
20
24
time, h
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Pharmaceuticals
At a 100 mg dose, the safety PD would
likely be detectable.
■ The safety effect cannot be determined as different from
Safety PD, % change
from baseline
the baseline for < 5% response, and so doses up to 60 mg
might be acceptable.
7
6
5
4
3
2
1
0
20 mg
40 mg
60 mg
80 mg
100 mg
0
4
8
12
16
20
24
time, h
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Pharmaceuticals
100
0.16
100 mg dose
Cp, ug/ml
0.14
80
0.12
0.1
60
0.08
40
0.06
0.04
20
0.02
0
Efficacy/safety PD, %
Despite a relatively low peak-to-trough ratio, the
drug does not have a therapeutic window.
Cp
Efficacy PD
Safety PD
0
0
4
8
12 16 20 24
time, h
62
■ PBPK modeling can be useful for multiple applications
throughout the preclinical development process.
● Generating hypotheses and prioritizing experimentation
● Verifying that processes impacting absorption are
understood
● Selecting the most efficacious compound to move forward
■ The key is to use the model appropriately for the amount of
validation that was possible.
■ PBPK modeling can be used to integrate multiple forms of
preclinical data and to make a prediction of human PK/PD for
both efficacy and safety end points.
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Summary
Pharmaceuticals
Acknowledgements
■
■
■
■
■
■
■
■
■
■
■
Neil Parrott
Thierry Lave
Werner Rubas
Ying Ou
Dan Lu
Pam Berry
Paul Weller
Karen Olocco
Dimitrios Stefanidis
Qingyan Hu
Susan Larrabee
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Pharmaceuticals
References
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■
■
■
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Pharmaceuticals
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