Using Multi-Scale Modeling to Support Preclinical Developments

Using Multi-Scale Modeling to Support Preclinical

Developments

• Systems Pharmacology

• Example

• Lessons learned

Tao You

Computational Biology

__________________________________________________

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Cancer Drug Discovery & Preclinical Development, London 17-18 Sep 2014

2 Tao You [email protected] Computational Biology | Discovery Sciences

Systems Pharmacology (aka multi-scale modeling) – Why?

Tumour biology is multi-scale


in vivo

animal model

In vitro

cell model

Signaling Gene

regulation Metabolism Cell cycle

Sustaining

proliferative

signaling

Resisting cell

death

Genome

instability &

mutation

Inducing

angio-genesis

Deregulating

cellular

energetics

Enabling

replicative

immortality

Evading

growth

suppressor

Activating

invasion &

metastasis

Avoiding

immune

destruction

Tumour

promoting

inflammation

Molecules

Cells Tissues

Organisms

EGFR

inhibitors

Pro-apoptotic

BH3 memetics

PARP

inhibitors

Telomerase

inhibitors

CDK

inhibitors

Aerobic glycolysis

inhibitors

Tumour

growth

Immune

response

ADME

Inspired by Hanahan & Weinberg (2011) Cell. 144: 646-674.

HGF/c-Met

inhibitors

VEGF

inhibitors

Anti-CTLA4

mAb

Anti-inflammatory

drugs

Resistance:

Genetic change

Phenotypic change

Resistance:

Cell-cell interaction

Evolutionary selection

Tumour architecture


Systems Pharmacology – What?

Multi-scale modeling-informed drug discovery & development


Target

Selection POM/POP/POC DFL Launch

Product

Maint. Lead

Optimisation

Lead

Generation

Target Exposure Schedule Tissue Trial Design Dose

PD

Emax

Cmax

EC50

P-Tau

(Brain)

P-GS

(Muscle)

10 100 1000 1000010

100

1000

10000

Plasma Conc (nM)

Bra

in C

on

c (

nM

)

0 4 8 12 160

25

50

75

100

125

Time (h)

P-G

S r

ati

o (

%)

0 4 8 12 160

25

50

75

100

125

Time (h)

P-T

au

rati

o (

%)

0 4 8 12 1610

100

1000

10000

Time (h)

Pla

sm

a C

on

c (

nM

)

seconds minutes hours days months

nm3 μm3 cm3 L

biomarkers cells tissue organ whole body

Systems

Pharmacology

model

Multi-scale modeling - what does it require?


Biology Disease

Molecular Biology

Genetics

Omics

Physiology

Nonlinear

Dynamics Multi-stability

Oscillation

Chaos

Agent-based

Qualitative

Computational

Statistics Model selection

Parameter Inference

Population modeling

Empirical PK/PD

Systems

Pharmacology

Systems Pharmacology Models

Mechanistic

– Relates biomarkers (molecules) with efficacy

(cells)

Integrative

– Links PK (body) with PD with (cells/tissue)

Insightful

– Reconciles in vitro-in vivo differences

– Bridges preclinical-clinical translation

Statistically Robust

– Infers structure, parameter and model behaviours

Predictive

– Validated often with preclinical data

Systems

Biology

Empirical

PK/PD

Predictive

Systems

Modeling


Systems Pharmacology – How?

Target


Product

Maint. Lead

Optimisation

Lead

Generation


Integrates in vitro evidence with in vivo preclinical data Consolidates different information and build confidence in preclinical predictions

Integrates preclinical information with clinical tumours 1. Solid tumour architecture 2.Tumour heterogeneity


Mechanism of

Action

PK

PK/PD/Efficacy

TK/TD/Toxicity in vivo

animal model

In vitro

cell model

PBPK

Clinical

predictions

Signaling

Gene

regulation

Metabolism

Cell cycle

Tumour

growth

ADME

Immune

response

1. Parameter Adjustments

reconciles in vitro-in vivo differences

2. Solid Tumour Architecture

Tumour Heterogeneity

reconciles in vivo-clinical differences

Example – Preclinical & Clinical Dosing & Scheduling

Combination therapy

Preclinical dose selection for Agent 2 Agent 1’s dose is fixed

Preclinical scheduling of Agent 2 Frequency & timing

Minimise toxicity

Maximise efficacy

First-in-human dose scheduling


Agent 1

Agent 2 Arrests cell cycle

Chemotherapy

Example - MOA

Tao You | 15 July 2014 10 iMED | Discovery Sciences

Agent 1

Biomarker 1

Biomarker 2

Biomarkers Cell fate decision

SD

S G1 G2/M

G2D/MD

Biomarker 2 Agent 2

Agent 2

Biomarker 1

G1D

Agent 1

Biomarker 1

Agent 1

Agent 2

Chemotherapy

Abolishes cell cycle arrest

Modeling in vitro data – Biomarker 1


Agent 1

Biomarker 1

Biomarker 2

Biomarkers

Agent 2

Agent 1

Agent 2

0

20

40

60

80

100

0 24 48 72

% +

Bio

mar

ker

1

Time (h)

Biomarker 1 (10nM Agent 1 + Agent 2 @ different conc)

set 1

In vitro data

Chemotherapy


Modeling in vitro data – cell cycle


Base cell cycle

S G1 G2/M

2 1 1

Doubling time: ~24h

Model in vitro data – efficacy for concurrent dosing


Cell fate decision

SD

S G1 G2/M

G2D/MD

Biomarker 2 Agent 2

Biomarker 1

G1D

Agent 1

Biomarker 1

Agent 1

Agent 2 Abolishes cell cycle arrest

0

200

400

600

800

1000

1200

0 24 48

Ce

ll N

um

be

r

Time (h)

In vitro efficacies (Agent 1 @ different conc.)

100nM

30nM

10nM

3nM

1nM

0.1% DMSO

set 1

set 2

Agent 1 (M)

Ce

ll n

um

be

r

In vitro efficacies (Agent 1 + Agent 2) @ 4days

Chemotherapy

Analysis – which parameters were unidentifiable?


Agent 1

Biomarker 1

Biomarker 2


SD

S G1 G2/M

G2D/MD

Biomarker 2 Agent 2

Agent 2

Biomarker 1

G1D

Agent 1

Biomarker 1

Unidentifiable due to lack of washout data

Unidentifiable from data – fitness to data insensitive to changes in the parameters

Unidentifiable due to fast dynamics

Analysis – other questions

Parameter confidence intervals? 1st-order approximation of Fisher information matrix

Population simulations

Model structure – to be discussed


Target


Product

Maint. Lead

Optimisation

Lead

Generation


Integrates in vitro evidence with in vivo preclinical data Consolidates different information and build confidence in preclinical predictions

Integrates preclinical information with clinical tumours 1. Solid tumour architecture 2.Tumour heterogeneity


Mechanism of

Action

PK

PK/PD/Efficacy

TK/TD/Toxicity in vivo

animal model

In vitro

cell model

PBPK

Clinical

predictions

Signaling

Gene

regulation

Metabolism

Cell cycle

Tumour

growth

ADME

Immune

response

1. Parameter Adjustments

reconciles in vitro-in vivo differences

2. Solid Tumour Architecture


reconciles in vivo-clinical differences


Parameter Adjustments reconcile in vitro-in vivo differences

Modeling in vivo data – cell cycle


Base cell cycle

S G1 G2/M

2 1 1

Doubling time: ~7d

Parameter Adjustments – cell cycle duration


Agent 1

Biomarker 1

Biomarker 2


SD

S G1 G2/M

G2D/MD

Biomarker 2 Agent 2

Agent 2

Biomarker 1

G1D

Agent 1

Agent 1

Agent 2

Chemotherapy


Biomarker 1

time

Modeling in vivo data – concurrent dosing

20

Courtesy of Rajesh Odedra

Tao You | 15 July 2014 iMED | Discovery Sciences

Legend Drug 1 Frequency /wk Drug 2 Frequency /wk

Phy Saline 1 DMSO/Captisol 7

Agent 1 1 DMSO/Captisol 7

Phy Saline 1 Agent 2 7

Agent 1 1 Agent 2 1

Agent 1 1 Agent 2 3

Agent 1 1 Agent 2 7

Agent 1

Agent 2 1+7 dosing schedule

Modeling in vivo data – gapped dosing

21 Tao You | 15 July 2014 iMED | Discovery Sciences

Legend Drug 1 Frequency /wk Drug 2 Frequency /wk Gap h

DMSO/Water 1 DMSO/Captisol 3 48

DMSO/Water 1 Agent 2 3 48

Agent 1 1 DMSO/Captisol 3 48

Agent 1 1 Agent 2 3 48



Agent 1

Agent 2

1+3 dosing schedule

Modeling in vivo data – acute PD response

22 Tao You | 15 July 2014 iMED | Discovery Sciences

Agent 1

Agent 1 + Agent 2

h

Bio

mark

er

2

Courtesy of Nicola Broadbent


Predictive – Often validated with preclinical data

24

Model validation – concurrent dosing


Legend Drug 1 Frequency /wk Drug 2 Frequency /wk

Phy Saline 1 DMSO/Captisol 7

Agent 1 1 DMSO/Captisol 7

Phy Saline 1 Agent 2 7

Agent 1 1 Agent 2 7


Agent 1

Agent 2

1+7 dosing schedule

25

Model validation – 24h-gap schedule


Legend Drug 1 Frequency /wk Drug 2 Frequency /wk Gap h






Agent 1

Agent 2

1+3 dosing schedule

Example - summary


• A mechanistic model incorporates biomarkers and cell fate decisions

• Recapitulates 5 in vitro and in vivo datasets

• Validated by 2 in vivo efficacy studies

Agent 1

Biomarker 1

Biomarker 2


SD

S G1 G2/M

G2D/MD

Biomarker 2 Agent 2

Agent 2

Biomarker 1

G1D

Agent 1

Biomarker 1


Solid Tumour Architecture


bridges preclinical-clinical gaps

Clinical tumour architecture


Komlodi-Pasztor E et al. (2012) Inhibitors Targeting Mitosis. Clin Cancer Res 18:51-63

Proliferating rim ~ 1% of tumour mass

Necrotic core

Dormant hypoxic cells Cell cycle durations

in vitro xenograft clinics

1d 1wk 3-12mths

•A multi-scale modeling informed drug development paradigm Clinical PK of Agent 1 – literature

PBPK of Agent 2

Clinical tumour modelling

Biomarker

Cell cycle

Tumour growth

29

Dormant

hypoxic

cells

Proliferating rim

Agent 2

Biomarker 1

time cell

time

Biomarker 2

time

Agent 1

PK PD Preclinical efficacy Clinical efficacy

Biomarker

model

Cell fate

decision

model

Systems Pharmacology paradigm

Tao You [email protected] Computational Biology | Discovery Sciences

Predicted clinical tumour volume responses

30

Assumptions: 3% cells are proliferative (G1: 1.5%; S: 0.75%; G2/M: 0.75%); double time identical to xenograft

6 months 12 months 3 months 9 months

Tao You [email protected] Computational Biology | Discovery Sciences

Systems Pharmacology – Lessons learned


Systems Pharmacology Models

Mechanistic

– Relates biomarkers (molecules) with efficacy (cells)

Be practical: When the exact molecular mechanism is unknown, choose a simple mathematical representation to avoid

unnecessary complexity A) make sure you understand the model, B) avoid unnecessary time spent on computing

Integrative

– Links PK (body) with PD with (cells/tissue)

Be consistent: Make sure you use the best animal PK model so that you don’t have to change the PK part frequently

Insightful

– Reconciles in vitro-in vivo differences

– Bridges preclinical-clinical translation

Be precise: Focus on unidentifiable parameters detected by sensitivity analysis; think about which parameters might be

different from biological knowledge

Statistically Robust

– Infers structure, parameter and model behaviours

Be collaborative: Washout data might be more informative than constant treatments; in vivo efficacy data may help infer

model structure

Predictive

– Validated often with preclinical data

Be confident: Always perform a validation – more convincing than anything else

Acknowledgements

Cross-functional collaboration

Modeling support: James Yates1, Joanne Wilson1, Gary Wilkinson1 In vitro experiments: Linda MacCallum2, Andrew Thomason3 In vivo experiments: Rajesh Odedra3, Nicola Broadbent3, Gareth Hughes3, Elaine Cadogan3

1Oncology DMPK; 2Discovery Sciences; 3Oncology BioScience


Model fitting to in vitro data Evolutionary Algorithm, Parameter Sensitivity


Go

od

ne

ss o

f fi

t

Parameter

Documents

Using Multi-Scale Modeling to Support Preclinical Developments