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Pharmacometrics and Systems Pharmacology of Anti-Cancer

Drugs

Donald E. Mager, Pharm.D., Ph.D.Department of Pharmaceutical Sciences

1

Overall Trend in R&D Efficiency

Scannell et al. Nat Rev Drug Discov. 11:191 (2012)2

Chemotherapy PK/PD Models

R

DispositionKinetics

BiophaseDistribution

Pharmacokinetics Pharmacodynamics

Drug

keoCP Ce RR

Growth

Cell Cycling

NaturalDeath

Ait-Oudhia et al. (Manuscript in preparation)

Cytotoxic

Cytostatic

()

Mechanismof Action

Nature ofReplication

Cycling andResistance

()

3

Simple Cell Killing

t0AUCk

0

0

eRR

R0R,RCkdtdR

Jusko. J Pharm Sci. 60:892 (1971)

C + R k

C + R kkng

t0ng AUCktk

0 eeRR

4

Modeling of Chemotherapeutic Agents

C S1 SnτS

N2 Nj

τDN1

f(N1,ƩNj)

C: Drug concentration  S: Signal compartment  N: Cancer Cells

Signaling delay

Growth inhibition

Cell differentiation and death

5

PK/PD Modeling of Cell Proliferation and Tumor Dynamics

Signal Distribution Model                   Cell Distribution Model

Lobo and Balthasar. AAPS PharmSci. 2002;4:E42.Mager and Jusko. Clin Pharmacol Ther. 2001;70:210.

Simeoni M et al. Cancer Res. 2004;64:1094. Yang et al. AAPS J. 2010;12:1.   6

Mechanistic Model of Cell Cycle and Apoptosis for Gemcitabine and Birinapant

Zhu et al. JPKPD. 42:477 (2015)

Model predicts greater inhibition with gemcitabine pre-treatment

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Everolimus and Sorafenib Antagonistic Effects in Pancreatic Cancer Cells

Cell Ψ (Model 1) Ψ (Model 2)MiaPaCa-2 1.20 1.48Panc-1 1.01 1.06

Pawaskar et al. AAPSJ. 15:78 (2013)

Ψ = 1 additiveΨ < 1 synergisticΨ > 1 antagonistic

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Everolimus and Sorafenib is Synergistic in LPD Pancreatic Cancer

Xenografts

Pawaskar et al. AAPSJ. 15:78 (2013)

Ψ = 0.321(synergy)

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Translational PK/PD Modeling in Cancer

Haddish-Berhane et al. JPKPD. 40:557 (2013)10

Prediction of PFS for brentuximab-vedotin using an Integrated PK/PD Model

Shah et al. JPKPD. 39:643 (2012)11

Methotrexate Pharmacogenomics

MTXPG PK parameters differ by lineage, ploidy, and molecular subtypes

Panetta et al. PLOS Comp Biol. 6:1-13 (2010)12

PK/PD Model of MyelosuppressionN

eutro

phils

(×10

9 /L)

Vinflunine

Friberg et al. JCO. 20:4713 (2002)

Wallin et al. Comput MethodsPrograms Biomed. 93:283 (2009)

13

Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis

of Phase II Tumor Dynamics

Claret et al. JCO. 27:4103-8 (2009)14

PK/PD Model of Sunitinib ADR and OS in Patients with GIST

Solid lines indicate relationships in the final modelHansson et al. CPT:PSP. 2, e85 (2013)

15

Quantitative Systems Pharmacology

From Sorger et al. NIH QSP Whitepaper (2011)16

Multi-scale Modeling Techniques

Rejniak and Anderson. Sys Biol Med. 3:115 (2011)

Gershenfeld. The Nature of Mathematical Modeling (2003)

“…many efforts fail because of an unintentional attemptto describe either too much or too little.”

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Network-based Approaches in Drug Discovery and Early Development

Harrold et al. Clin Pharmacol Ther. 94:651 (2013)18

Reduced Model of Rituximab Signaling in Ramos Cancer Cells

Jazirehi et al. Cancer Res 2004 64:7117; Vega et al. Immunol 2005 175:2174; Harrold et al. Cancer Res 72:1632-41 (2012).

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Integrated Model Predicts Rituximab-rhApo2L Synergy in Ramos Xenografts

Data: Daniel et al. Blood 110:4037 (2007); Harrold et al. Cancer Res. 72:1632 (2012)

Time (hours)Tu

mor

Vol

ume

(mm

3 )

additive

synergistic

20

Combined ErbB2/3 vs. Combination of MEK and AKT Inhibitors

Kirouac et al. Science Signaling. 6:2 ra68 (2013) 21

Iyengar et al. Sci Trans Med4:126ps7 (2012).

Enhanced Pharmacodynamic Modeling

22

BCL2 Systems Model to Predict Chemotherapy Responses in CRC

Lindner et al. Cancer Res. 73:519-28 (2013)23

SUMMARY

Nonclinical models need to consider, among other things, tumor type, location, target expression, and mechanisms of drug action

Cell systems can provide early indication of drug activity, but xenografts are essential for elucidating PK/PD relationships, with some cancers better represented by LPD (PDX) tumors

Simple indirect response and transit models are useful for describing signal and cell distributions and have been applied to tumor growth kinetics of in vitro and in vivo systems

Mechanisms of action (inhibition vs. stimulation) and onset delays often preclude the adoption of a single model or approach – focus on mechanisms and model fitting criteria

Minimally, simple PK/PD models may suggest clinical target concentrations, but coupling target and causal pathway biomarkers with systems models may improve translation

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SUMMARY

Integration of cell cycle and cellular heterogeneity may improve understanding of variable outcomes, resistance, and role of combinations – will require multiple experimental platforms

Population-based models can provide insights into determinants of interindividual variability in drug exposure and response, but may not be sufficient for detecting multi-dimensional pharmacodynamic covariate relationships

Future efforts will focus on multi-scale, mechanistic systems models for single and combination regimens and disease progression to better understand PK/PD relationships, inter-species differences, differences in modality (cells, xenografts, clinical), toxicity, and complex clinical phenotypes

Multiple model types (e.g., empirical, semi-mechanistic, multi-scale systems) are needed along with new strategies for their effective implementation to enhance drug discovery and use

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