Mathematical modeling techniques are now widely employed in the field of cancer research. In this talk, a computational tool is presented that seeks to provide a theoretical basis for helping drug design teams assess the most promising drug targets and design optimal cancer treatment strategies. The tool is grounded in a previously-validated hybrid cellular automaton model of tumor growth in a vascularized environment. I will demonstrate how computer simulations of the mathematical model can be used to study the anti-tumor activity of several vascular-targeting compounds, as well as a chemotherapeutic agent. When possible, simulation results will be directly compared to preclinical and clinical data. Further, I will illustrate how techniques from optimization theory can be employed to identify a dosing protocol that minimizes the number of cancer cells remaining after treatment with a vascular-disrupting and chemotherapeutic drug. The treatment regimen identified can successfully halt simulated tumor growth, even after the cessation of therapy. I will end with some ongoing work and open questions.
Mathematical Modeling of Tumor-Vasculature Interactions: Implications for Treatment
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