Modeling and Simulation as a Preclinical to Clinical ... Webinar_Series...Modeling and Simulation as...

Modeling and Simulation as a Preclinical to Clinical

Translational Tool

9-Apr-2025

Martin Beliveau, PhD

Director, Pharmacometrics

Pharsight Consulting Services

[email protected]

mailto:[email protected]

© 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)


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


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


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


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 ?

http://images.google.com/imgres?imgurl=http://www.apredica.com/images/dmpk.jpg&imgrefurl=http://www.apredica.com/dmpk.php&usg=___O5l1rcYyNtJ7g3cDZQWn9fNFYM=&h=335&w=484&sz=15&hl=en&start=2&sig2=iN898JsxJ-ZdwhLddNw4lg&um=1&tbnid=xoipt_GXT67QBM:&tbnh=89&tbnw=129&prev=/images?q=dmpk&gbv=2&hl=en&sa=N&um=1&ei=FencSfyJG83VlQec1PXrDQ


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


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


(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 !


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 ?


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


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


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


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





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





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.


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








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


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)


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…


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.


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








(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


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


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


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)


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


Role of Preclinical PK-PD


Q & A

28

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