Theoretical investigation of the efficacy of antiangiogenic drugs combined to chemotherapy in xenografted mice.

Antiangiogenic drugs were developed with the aim to inhibit the formation of intratumoral blood vessels and in consequence the growth of solid tumors. As these drugs are generally combined with classical cytotoxic drugs in the treatment of cancer patients, finding the optimal combinations remains a complex challenge due to possible interactions of the antiangiogenic compound with the hemodynamic property of the treated tumor. To analyze this problem, we developed a multi-scale model of vascular tumor growth combining a molecular model of VEGF signaling pathways and a tissue model of the tumor expansion including the dynamics of cellular and tissue processes of tumor growth and response to treatments. We addressed the potential impact of antiangiogenic drug by defining a new index of vasculature quality which depends on the balance between stable and unstable vessels within the tumor mass. Our goal was to investigate the interactions between a chemotherapy and a antiangiogenic treatment, and, by simulating the model, to identify the optimal delay of chemotherapy delivery after the administration of the antiangiogenic compound. This theoretical analysis could be used in the future to optimize antiangiogenic drug delivery in preclinical settings and to facilitate the translation from preclinical to clinical studies.

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

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.