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Pharmacometrics and Systems Pharmacology of Anti-Cancer
Drugs
Donald E. Mager, Pharm.D., Ph.D.Department of Pharmaceutical Sciences
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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
()
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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
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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
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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)
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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)
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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
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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
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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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