The document describes how multi-scale modeling can support preclinical drug development. It provides an example of developing a systems pharmacology model to integrate in vitro biomarker and cell cycle data with in vivo efficacy studies of a combination therapy. The model was able to recapitulate multiple datasets and validate dosing schedules. Lessons learned include the importance of practical yet mechanistic models, consistency, precision, collaboration, and validation.

1. Using Multi-Scale Modeling to Support Preclinical
Developments
• Systems Pharmacology
• Example
• Lessons learned
Tao You
Computational Biology
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AstraZeneca
R&D | Innovative Medicines & Early Development | Discovery Sciences
50S51, Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG
T: +44 (0)1625 514733
tao.you@astrazeneca.com
Cancer Drug Discovery & Preclinical Development, London 17-18 Sep 2014

2. 2 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Systems Pharmacology (aka multi-scale modeling) – Why?

3. Tumour biology is multi-scale
3 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
in vivo
animal model
In vitro
cell model
Signaling
Gene
regulation
MetabolismCell 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

4. 4 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Systems Pharmacology – What?

5. Multi-scale modeling-informed drug discovery & development
5 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Target
Selection
POM/POP/POC DFL Launch
Product
Maint.
Lead
Optimisation
Lead
Generation
Target Exposure ScheduleTissue Trial DesignDose
PD
Emax
Cmax
EC50
P-Tau
(Brain)
P-GS
(Muscle)
0 4 8 12 16
0
25
50
75
100
125
Time (h)
P-GSratio(%)
0 4 8 12 16
0
25
50
75
100
125
Time (h)
seconds minutes hours days months
nm3 μm3 cm3 L
biomarkers cells tissue organ whole body
Systems
Pharmacology
model

6. Multi-scale modeling - what does it require?
6 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
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

7. 7 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Systems Pharmacology – How?

8. Target
Selection
POM/POP/POC DFL Launch
Product
Maint.
Lead
Optimisation
Lead
Generation
Multi-scale modeling-informed drug discovery & development
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
8 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Mechanism of
Action
PK
PK/PD/Efficacy
TK/TD/Toxicityin 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

9. 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
9 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Agent 1
Agent 2 Arrests cell cycle
Chemotherapy

10. Example - MOA
Tao You | 15 July 201410 iMED | Discovery Sciences
Agent 1
Biomarker 1
Biomarker 2
Biomarkers Cell fate decision
SD
SG1 G2/M
G2D/MD
Biomarker 2Agent 2
Agent 2
Biomarker 1
G1D
Agent 1
Biomarker 1
Agent 1
Agent 2
Chemotherapy
Abolishes cell cycle arrest

11. Modeling in vitro data – Biomarker 1
Tao You | 15 July 201411 iMED | Discovery Sciences
Agent 1
Biomarker 1
Biomarker 2
Biomarkers
Agent 2
Agent 1
Agent 2
0
20
40
60
80
100
0 24 48 72
%+Biomarker1
Time (h)
Biomarker 1
(10nM Agent 1 + Agent 2 @ different conc)
set 1
In vitro data
Chemotherapy
Abolishes cell cycle arrest

12. Modeling in vitro data – cell cycle
Tao You | 15 July 201412 iMED | Discovery Sciences
Base cell cycle
SG1 G2/M
2 1 1
Doubling time: ~24h

13. Model in vitro data – efficacy for concurrent dosing
Tao You | 15 July 201413 iMED | Discovery Sciences
Cell fate decision
SD
SG1 G2/M
G2D/MD
Biomarker 2Agent 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
CellNumber
Time (h)
In vitro efficacies
(Agent 1 @ different conc.)
100nM
30nM
10nM
3nM
1nM
0.1% DMSO
set 1
set 2
Agent 1 (M)
Cellnumber
In vitro efficacies
(Agent 1 + Agent 2) @ 4days
Chemotherapy

14. Analysis – which parameters were unidentifiable?
Tao You | 15 July 201414 iMED | Discovery Sciences
Agent 1
Biomarker 1
Biomarker 2
Biomarkers Cell fate decision
SD
SG1 G2/M
G2D/MD
Biomarker 2Agent 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

15. Analysis – other questions
Parameter confidence intervals?
 1st-order approximation of Fisher information matrix
 Population simulations
Model structure – to be discussed
15 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences

16. Target
Selection
POM/POP/POC DFL Launch
Product
Maint.
Lead
Optimisation
Lead
Generation
Multi-scale modeling-informed drug discovery & development
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
16 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Mechanism of
Action
PK
PK/PD/Efficacy
TK/TD/Toxicityin 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

17. 17 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Parameter Adjustments reconcile in vitro-in vivo differences

18. Modeling in vivo data – cell cycle
Tao You | 15 July 201418 iMED | Discovery Sciences
Base cell cycle
SG1 G2/M
2 1 1
Doubling time: ~7d

19. Parameter Adjustments – cell cycle duration
Tao You | 15 July 201419 iMED | Discovery Sciences
Agent 1
Biomarker 1
Biomarker 2
Biomarkers Cell fate decision
SD
SG1 G2/M
G2D/MD
Biomarker 2Agent 2
Agent 2
Biomarker 1
G1D
Agent 1
Agent 1
Agent 2
Chemotherapy
Abolishes cell cycle arrest
Biomarker 1
time

20. 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

21. 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 1 Agent 2 3 72
Courtesy of Rajesh Odedra
Agent 1
Agent 2
1+3 dosing schedule

22. Modeling in vivo data – acute PD response
22 Tao You | 15 July 2014 iMED | Discovery Sciences
Agent 1
Agent 1 + Agent 2
h
Biomarker2
Courtesy of Nicola Broadbent

23. 23 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Predictive – Often validated with preclinical data

24. 24
Model validation – concurrent dosing
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 7
Courtesy of Rajesh Odedra
Agent 1
Agent 2
1+7 dosing schedule

25. 25
Model validation – 24h-gap schedule
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 24
Agent 1 1 Agent 2 3 24
DMSO/Water 1 DMSO/Captisol 3 24
Agent 1 1 Agent 2 3 24
Courtesy of Rajesh Odedra
Agent 1
Agent 2
1+3 dosing schedule

26. Example - summary
Tao You | 15 July 201426 iMED | Discovery Sciences
• 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
Biomarkers Cell fate decision
SD
SG1 G2/M
G2D/MD
Biomarker 2Agent 2
Agent 2
Biomarker 1
G1D
Agent 1
Biomarker 1

27. 27 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Solid Tumour Architecture
Tumour Heterogeneity
bridges preclinical-clinical gaps

28. Clinical tumour architecture
28 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
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

29. •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 tao.you@astrazeneca.com Computational Biology | Discovery Sciences

30. 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 months3 months 9 months
Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences

31. Systems Pharmacology – Lessons learned
31 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
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

32. 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
32 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences

33. Model fitting to in vitro data
Evolutionary Algorithm, Parameter Sensitivity
33 Tao You tao.you@astrazeneca.com Computational Biology | Discovery Sciences
Goodnessoffit
Parameter