Microenvironment-Mediated Modeling of Tumor Response to Vascular-Targeting Drugs

Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 936)

Abstract

The tumor-associated microvasculature is one of the key elements of the microenvironment that helps shape, and is shaped by, tumor progression. Given the important role of the vasculature in tumor progression, and the fact that tumor and normal vasculature are physiologically and molecularly distinct, much effort has gone into the development of vascular-targeting drugs that in theory should target tumors without significant risk to normal tissue. In this chapter, a multiscale hybrid mathematical model of tumor-vascular interactions is presented to provide a theoretical basis for assessing tumor response to vascular-targeting drugs. Model performance is calibrated to quantitative clinical data on tumor response to angiogenesis inhibitors (AIs), preclinical data on response to a cytotoxic chemotherapy, and qualitative preclinical data on response to vascular disrupting agents (VDAs). The calibrated model is then used to explore two questions of clinical interest. First, the hypothesis that AIs and VDAs are complementary treatments, rather than redundant, is explored. The model predicts a minimal increase in antitumor activity as a result of adding a VDA to an AI treatment regimen, and in fact at times the combination can exert less antitumor activity than stand-alone AI treatment. Second, the question of identifying an optimal dosing strategy for treating with an AI and a cytotoxic agent is addressed. Using a stochastic optimization scheme, an intermittent schedule for both chemotherapy and AI administration is identified that can eradicate the simulated tumors. We propose that this schedule may have increased clinical antitumor activity compared to currently used treatment protocols.

Keywords

Tumor-vasculature interactions Hybrid cellular automaton model Angiogenesis inhibitors Vascular disrupting agents Cytotoxic chemotherapy 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematics & StatisticsThe College of New JerseyEwingUSA

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