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Modeling and Simulation as a Preclinical to Clinical
Translational Tool
9-Apr-2025
Martin Beliveau, PhD
Director, Pharmacometrics
Pharsight Consulting Services
© Copyright 2014 Certara, L.P. All rights reserved.
Goal of Preclinical Drug Development
• Pharmacokinetics (ADME)
• Pharmacodynamics/Efficacy in Appropriate
Animal Model
• Safety Pharmacology & Toxicology
To ultimately..
• Select the right molecule and
• Support FIH Dosing (Phase I)
© Copyright 2014 Certara, L.P. All rights reserved.
What are we trying to model ?
DOSE INTERNAL
DOSE
EFFECT/
RESPONSE PK PD
Dose E
ffect
Effect
Internal Dose (Concentration)
Inte
rnal D
ose
Concentr
atio
n)
Time
© Copyright 2014 Certara, L.P. All rights reserved.
What is a Model?
• An abstraction/oversimplification of reality used to
explore the structures, behaviors and relationships
– Newton’s Second Law, F=ma
– Einstein’s E=mc2
© Copyright 2014 Certara, L.P. All rights reserved.
Why Model ?
Cmax
AUC0-T
AUCT-∞
0 6 12 18 24
Time (h)
50
Pla
sma C
oncentr
ati
on
1
100
lz (Kel)
Tmax
AUC0-∞
CL/F = Dose / AUC0-∞
Group Time (h)
0 1 2 4 6 8
1 x x x
2 x x x
Sparse and
staggered
sampling
© Copyright 2014 Certara, L.P. All rights reserved.
6
•One concept of Translational Science is to break down the barriers between Discovery
and Development. Several companies have reorganized along these lines.
Slide 6
Bioinformatics
DIS
DMPK
Cheminformatics Preclinical
TOX
DMPK
P I P IIa P IIb P III Post Mkt
Clinical
Discovery Development
Translational Science
Proof of
Concept
Data
Repositories
PK/PD Modeling
Molecular
Modeling Tools
Molecular Modeling
Pred. Met.
Pred. Tox Tools
Genomics
Modeling Tools
ADME/Tox/
Pharmacology
Microarray, Protein
Gene Expression
Analysis
Tools
Population Modeling
Why Model ?
© Copyright 2014 Certara, L.P. All rights reserved.
Applications of Modeling and Simulation (M&S)
• This is nothing new…M&S has been fully developed in other
fields such as engineering (aerospace, naval) for the last 30
years.
• So we are not reinventing the wheel.
• This framework is applicable for any therapeutic area
Anti infectives
Oncology
and others…
7
© Copyright 2014 Certara, L.P. All rights reserved.
Workflow
Dose
Effect
Response
Exposure
Inte
rnal D
ose
Concentr
atio
n)
Time
1
2
3
Inte
rnal D
ose
Co
nce
ntr
atio
n)
Time
4
•PK in animal / humans
•Determine internal dose in
efficacy animal model = >
EXPOSURE / RESPONSE !
•Simulate PK in humans
•Determine efficacy in humans
© Copyright 2014 Certara, L.P. All rights reserved.
(1) PK Modeling in Animals and Humans
Generate Population PK Database
•Dataset construction for the population PK analysis of sparse serum concentrations.
•The dataset will include study identifier, patient ID, visit identifier, actual date and time of dosing (i.e. times of infusion start and end), actual date and time of
blood sample collection, concentration values, and intrinsic (e.g. age, sex, race, renal function, hepatic function, body weight) and extrinsic factors (e.g.
concomitant medications) that may explain sources of PK variability.
•
Population PK Model – Structural PK Model
•Various PK models are tested to model concentration-time profiles. As required, the population PK model structure will be revised to account for the different
population, dosing strategy, and sampling strategy adopted. Goodness of fit is assessed by various statistical criterion (AIC) and visual diagnostics.
PRED
Cobs
0 50 100 150 200
0100
200
300
IDENTLOESS
IPRED
Cobs
0 50 100 150 200 250 300
0100
200
300
IDENTLOESS
Time after dose
Cobs
0 20 40 60
0100
200
300
LOESS PREDLOESS Cobs
TIME
WR
ES
0 500 1000 1500 2000
-6-4
-20
24
6 ZEROLOESS
Vc/F Vp/F
SC Dose
Ka
CL/F
Structural PK Model Goodness of Fit
=> ESTABLISHING A BASELINE !
© Copyright 2014 Certara, L.P. All rights reserved.
PK Modeling and Discovering Factors that Will Impact the Pharmacokinetics
Apply Existing Population PK model – Covariate Analysis and Model Evaluation/Qualification
•Covariate analysis will be performed using a stepwise forward additive inclusion (alpha level of 0.05) and a backward elimination (alpha level of
0.01) approach to identify potential sources of variability.
•The effect of specific covariates will be evaluated. Examples of intrinsic and extrinsic factors that may be tested include, but are not limited to:
Intrinsic Factors Potentially Relevant to PK
• Age
• Body weight
• Sex
• Race
• Markers of renal function: Creatinine clearance (Cockroft-Gault)
• or serum creatinine
• Markers of hepatic function (ALT, AST, bilirubin)
• Other
Extrinsic Factors Potentially Relevant to PK
• Use of concomitant medications
• Other
CL
50 100 150 200 0.35 0.45 60 80 120
48
12
16
50
100
150
200
r = 0.75
p value <0.001
V
r = -0.16
p value <0.001
r = 0.46
p value <0.001
KA
1.0
2.0
3.0
4.0
0.3
50.4
5
r = 0.27
p value <0.001
r = 0.095
p value 0.0063
r = -0.37
p value <0.001
ALAG
r = -0.3
p value <0.001
r = -0.18
p value <0.001
r = 0.17
p value <0.001
r = -0.13
p value <0.001
AGE
20
40
60
4 8 12 16
60
80
120
r = 0.18
p value <0.001
r = 0.27
p value <0.001
1.0 2.0 3.0 4.0
r = 0.2
p value <0.001
r = -0.049
p value 0.16
20 40 60
r = 0.059
p value 0.09
WGHT
10Feb2009
=> IS CMAX CONSISTANTLY HIGHER IN FEMALES ? BY WHAT
FACTOR ?
=> IMPACT IN HUMANS ?
© Copyright 2014 Certara, L.P. All rights reserved.
Example of a PK Model in Monkeys
Correlation between Vc/F and CL/F = 0.954
CL/F
Q/F
Vc/F Vp/F
Oral Dose
Ka Tlag
Inter-Individual
Variability
(IIV)
Infected
Monkeys
t1/2 α = 1.46 hr
t1/2 β = 9.1hr
Uninfected Monkeys
t1/2 α = 1.48 hr
t1/2 β = 9.8 hr
The effect of INFECTION and DOSE on CL/F do not have a “significant” impact on CL /F values
Population PK Parameters Population Estimates
Inter-Individual
Variability (%)
Inter-Occasion
Variability (%)
Ka (h-1) Infected 0.868 * (DOSE/10)0.160
11.13% 31.78% Uninfected 0.586 * (DOSE/10)0.160
Tlag (hr) 0.302 NA ---
CL/F (L/h) Infected 2.846 * (WT/3.105)0.75 * (DOSE/10) 0.093
31.01% 14.37%
Uninfected 2.827* (WT/3.105)0.75 * (DOSE/10) 0.093
Vc /F(L) 20.054 * (WT/3.105)0.75 * (DOSE/10) 0.623 46.61% ---
Q /F(L/h) 3.244 * (WT/3.105)0.75 74.51% ---
Vp/F (L) 13.338* (WT/3.105) 54.57% ---
Error Model
Add Error 0.133 NA
Proportional Error (%) 30.06% NA
Correlation between Vc/F and CL/F = 0.945
Leeds, J. M., Fenneteau, F., Gosselin, N. H., Mouksassi, M.-S., Kassir, N., Marier, J. F., … Jordan, R. (2013). Pharmacokinetic and
pharmacodynamic modeling to determine the dose of ST-246 to protect against smallpox in humans. Antimicrobial Agents and
Chemotherapy, 57(3), 1136–43. doi:10.1128/AAC.00959-12
© Copyright 2014 Certara, L.P. All rights reserved.
Example of a PK Model in Humans
Population PK
Parameters Population Estimates
Inter-Individual
Variability (%)
Ka (h-1) 1.06 41.03%
Tlag (hr) 1.46 16.74%
CL/F (L/h) 41.15x (WT/78.35)0.75 30.87%
Vc /F(L)
Female 281.51x (WT/78.35)1
28.12%
Male 217.44x (WT/78.35)1
Q /F(L/h) 36.79x (WT/78.35)0.75
Vp/F (L) 413.53x (WT/78.35)1 54.39%
Error Model 53.58%
Additive error (μg/L) 10.92
Proportional Error (%) 26.76% NA
Male
t1/2 = 1.71 h
t1/2 = 16.7 h
Correlation between Vc/F and CL/F = 0.887
CL/F
Q/F
Vc/F Vp/F
Oral Dose
Ka
Tlag Frel
Inter-Individual
Variability
(IIV)
Female
t1/2 = 2.13 h
t1/2 = 17.4 h
The gender effect does not have a
significant impact on half-life nor
on total exposure
Leeds, J. M., Fenneteau, F., Gosselin, N. H., Mouksassi, M.-S., Kassir, N., Marier, J. F., … Jordan, R. (2013). Pharmacokinetic and
pharmacodynamic modeling to determine the dose of ST-246 to protect against smallpox in humans. Antimicrobial Agents and
Chemotherapy, 57(3), 1136–43. doi:10.1128/AAC.00959-12
© Copyright 2014 Certara, L.P. All rights reserved.
Effect of Infection Human Exposure: How to predict
In order to simulate exposure in infected patients, same infection effects on CL/F and Ka than those
previously found for monkeys were applied in the human population PK model.
No significant effect of infection on human exposure was predicted. 0
10
00
20
00
30
00
40
00
Infected (n= 528)
Uninfected (n= 528)
AU
C (
µg
.hr/
L)/
Do
se
(m
g/k
g)
of
ST
-24
6
01
00
20
03
00
40
05
00
60
0
Infected (n= 528)
Uninfected (n= 528)
Cm
ax a
t S
S (
µg
/L)/
Do
se (
mg
/kg
) o
f S
T-2
46
02
04
06
08
01
00
Infected (n= 528)
Uninfected (n= 528)
Cm
in a
t S
S (
µg
/L)/
Do
se (
mg
/kg
) o
f S
T-2
46
ST-246 Exposure in Humans - Steady State
Leeds, J. M., Fenneteau, F., Gosselin, N. H., Mouksassi, M.-S., Kassir, N., Marier, J. F., … Jordan, R. (2013). Pharmacokinetic and
pharmacodynamic modeling to determine the dose of ST-246 to protect against smallpox in humans. Antimicrobial Agents and
Chemotherapy, 57(3), 1136–43. doi:10.1128/AAC.00959-12
© Copyright 2014 Certara, L.P. All rights reserved.
In Summary, Why model the PK Aspects ?
• Identify trends…
• Quantify trends…
• Pooling studies together increases power
• Allows to mechanistically link applicability to the human
experience.
• Provides confidence in the human PK prediction since ALL
of the data is used, not just “selected” data.
© Copyright 2014 Certara, L.P. All rights reserved.
Workflow
Dose
Effect
Response
Exposure
Inte
rnal D
ose
Concentr
atio
n)
Time
1
2
3
Inte
rnal D
ose
Co
nce
ntr
atio
n)
Time
4
•PK in animal / humans
•Determine internal dose in
efficacy animal model = >
EXPOSURE / RESPONSE !
•Simulate PK in humans
•Determine efficacy in humans
© Copyright 2014 Certara, L.P. All rights reserved.
Fundamental PK/PD Concepts: Dose vs. Exposure Analyses
Dose-Safety Exposure-Safety
Dose-Efficacy Exposure-Efficacy
Exposure (AUC) at any timepoint
E max
Effic
acy (
Su
rviv
al)
Dose (mg)
0 250 500 1000
E max ?
Sa
fety
(P
rob
AE
)
Exposure at any timepoints
Sa
fety
(P
rob
AE
)
?
0 250 500 1000
Dose (mg)
Effic
acy (
Su
rviv
al)
buckets continuous
© Copyright 2014 Certara, L.P. All rights reserved.
Logistic Regression - Definition
17
Logistic regression is a type of regression analysis used to predict the outcome of a binary
variable based on a predictor variable as presented in the formula below.
Binary Variable
• Clinical Biomarker
Response (yes/no)
Predictor Variable
• Drug AUC (mg*h/L)
For example, the following binary variable and predictor variable can be used.
© Copyright 2014 Certara, L.P. All rights reserved. slide 18
(2) Exposure Response (Efficacy and Safety)
What is your
benchmark ?
Do you accept >80%
responders ?
Do you accept >99%
responders ?
The more strict, the
more you have to
“pump up” the dose.
Therefore, do you
accept the trade off
in increase in
safety/toxicology
concerns ? In cost to
manufacture more
drug ?
Predicted Probability of Liver Damage (Driven by Exposure)
© Copyright 2014 Certara, L.P. All rights reserved.
Example of an Exposure Response
Any exposure metric can be used.
Traditional metrics of goodness of fit
and domain knowledge would apply to
select an appropriate internal dose
surrogate for each endpoint of
interest.
Allows for exploration and testing of
relevant clinical biomarkers in
preclinical studies => What is a good
biomarker ? Application of what if
scenarios…
© Copyright 2014 Certara, L.P. All rights reserved.
Example of an Exposure Response: AUC of Viral Load by Dose (or
Exposure)
20
Here, the external dose can be related
to the internal dose because we have
a PK model that can predict any
internal dose surrogate metric as a
function of time and dose.
Of course Cmax and AUC are popular
surrogates for acute and chronic
effects, respectively.
This example is from an anti infective.
© Copyright 2014 Certara, L.P. All rights reserved.
Workflow
Dose
Effect
Response
Exposure
Inte
rnal D
ose
Concentr
atio
n)
Time
1
2
3
Inte
rnal D
ose
Co
nce
ntr
atio
n)
Time
4
•PK in animal / humans
•Determine internal dose in
efficacy animal model = >
EXPOSURE / RESPONSE !
•Simulate PK in humans
•Determine efficacy in humans
© Copyright 2014 Certara, L.P. All rights reserved.
(3 and 4) Overall Approach of Simulation in Humans
307 AUC
Probability 50
25
1
50 @ 8 mg/kg
75 @ 10 mg/kg
99 @ 15 mg/kg
From the exposure-response graph, you can estimate a point estimate of a mean exposure (AUC,
Cmax, Other) of associated with a 90% probability of survival from a particular challenge (let’s say, 307
mg.h/L). This mean AUC corresponds to a particular dose based on the PK model in humans (let’s say,
8 mg/kg).
Therefore, a mean AUC value of 307 mg.h/L implies that approximately 50% of the population will
display AUC values lower and above 307 mg.h/L for a normal distribution (bell shaped). As presented in
the figure below, if we want to protect more than 50% of the population, from a PK perspective, we
need higher doses to obtain 99% of the population AUC values above 307 mg.h/L.
90%
% Below % Above
© Copyright 2014 Certara, L.P. All rights reserved.
0.0008 0.028 0.004
Simulation Rationale - Targeting a Human Effective Dose
1- Body weight values in a total of 100 normal healthy subjects were randomly sampled from known
distribution of body weight reported in literature
Reference: Mouksassi MS, Marier JF, Cyran J, Vinks AA: Clinical trial simulations in pediatric patients using realistic covariates: application to
teduglutide, a glucagon-like peptide-2 analog in neonates and infants with short-bowel syndrome, Clin Pharmacol Ther 2009, 86:667-67
2- Individual CL were derived based on simulated concentration-time profiles of the mAb in humans.
Overall, 10000 profiles were generated using the Monte Carlo approach.
Step 1 Step 2
© Copyright 2014 Certara, L.P. All rights reserved.
Simulation Results – Targeting Human Effective Dose
3- The population PK model was used to scale exposure of in adult healthy male (18-55 yrs, body
weight within 20% of BMI).
4- The selected human target dose corresponded to 99% of population having at least the exposure
necessary to achieve 90% responders (307 mg.h/L identified using the exposure response model)
5- Different doses were tested. The 32 mg/kg administration was selected as the optimal dose that
would provide exposure >307 mg.h/L in 99% of the target population.
Dose 99% Population AUC
Achieved Target
10 75 307
12 93 307
30 290 307
32 308 307
40 415 307
© Copyright 2014 Certara, L.P. All rights reserved.
32 mg/kg AUC Distribution
203 1971 619
1% = 308 mg*h/L
Simulation Results – Human Effective Dose (32 mg/kg)
1- The distribution of drug exposure following administration of a single dose of 32 mg/kg in 10000
subjects resulted in an exposure distribution ranging from 203 to 1971 mg.h/L with a median value of
619 mg.h/L. (See histogram)
2- The 32 mg/kg dose is associated with at least 99% of a typical human population having at least an
exposure of 308 mg.h/L which is necessary to achieve a 90% response rate.
AUC (mg_h_L)
© Copyright 2014 Certara, L.P. All rights reserved.
Take Home…
Modeling abolishes silos and provides the “big picture” using all of the available data
=> Perhaps its not required or perhaps its absolutely required
Simulations can provide a mechanistic understanding of efficacy to ultimately support
dosing recommendations in humans.
=> Simulations can result in dose recommendations such that 90% survival (for
example) is achieved in at least 99% of the target population (for
example).
This simple simulation framework provides the necessary tools for providing suggested
doses in any other target population, such as pediatric or elderly patients.
=> In Modeling and Simulation the number of “what-if?” scenarios is unlimited,
the model is continuously improved, always providing the “latest and greatest”
estimate.
=> Although important, the objective is not necessarily predictability, but
understanding. We need to tease out the drugs that are not viable clinical
candidates for safety, efficacy and economical reasons.
Modeling and simulation is a framework that can be used:
for any type of molecule (small and large)
any stage of DD
for any indication
that can provide predictions at an early stage
© Copyright 2014 Certara, L.P. All rights reserved.
Role of Preclinical PK-PD
© Copyright 2014 Certara, L.P. All rights reserved.
Q & A
28