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CPT Pharmacometrics Syst Pharmacol. 2015 Dec;4(12):720-7. doi: 10.1002/psp4.12045. Epub 2015 Dec 11.

Modeling and predicting optimal treatment scheduling between the antiangiogenic drug sunitinib and irinotecan in preclinical settings.

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Inria, project-team Numed, Ecole Normale Supérieure de Lyon Lyon France.
EMR 3738, Ciblage Thérapeutique en Oncologie, Faculté de Médecine et de Maïeutique Lyon-Sud, Université Lyon 1 Oullins France.
Department of Biological Regulation Weizmann Institute of Science Rehovot Israel.
CellVax Laboratory facility Bâtiment Marcenac, Ecole Nationale Vétérinaire d'Alfort Maisons Alfort France.


We present a system of nonlinear ordinary differential equations used to quantify the complex dynamics of the interactions between tumor growth, vasculature generation, and antiangiogenic treatment. The primary dataset consists of longitudinal tumor size measurements (1,371 total observations) in 105 colorectal tumor-bearing mice. Mice received single or combination administration of sunitinib, an antiangiogenic agent, and/or irinotecan, a cytotoxic agent. Depending on the dataset, parameter estimation was performed either using a mixed-effect approach or by nonlinear least squares. Through a log-likelihood ratio test, we conclude that there is a potential synergistic interaction between sunitinib when administered in combination with irinotecan in preclinical settings. Model simulations were then compared to data from a follow-up preclinical experiment. We conclude that the model has predictive value in identifying the therapeutic window in which the timing between the administrations of these two drugs is most effective.

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