Published in:
Open Access
01-07-2015 | Original Paper
Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer
Authors:
Andrea Weiss, Xianting Ding, Judy R. van Beijnum, Ieong Wong, Tse J. Wong, Robert H. Berndsen, Olivier Dormond, Marchien Dallinga, Li Shen, Reinier O. Schlingemann, Roberto Pili, Chih-Ming Ho, Paul J. Dyson, Hubert van den Bergh, Arjan W. Griffioen, Patrycja Nowak-Sliwinska
Published in:
Angiogenesis
|
Issue 3/2015
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Abstract
Drug combinations can improve angiostatic cancer treatment efficacy and enable the reduction of side effects and drug resistance. Combining drugs is non-trivial due to the high number of possibilities. We applied a feedback system control (FSC) technique with a population-based stochastic search algorithm to navigate through the large parametric space of nine angiostatic drugs at four concentrations to identify optimal low-dose drug combinations. This implied an iterative approach of in vitro testing of endothelial cell viability and algorithm-based analysis. The optimal synergistic drug combination, containing erlotinib, BEZ-235 and RAPTA-C, was reached in a small number of iterations. Final drug combinations showed enhanced endothelial cell specificity and synergistically inhibited proliferation (p < 0.001), but not migration of endothelial cells, and forced enhanced numbers of endothelial cells to undergo apoptosis (p < 0.01). Successful translation of this drug combination was achieved in two preclinical in vivo tumor models. Tumor growth was inhibited synergistically and significantly (p < 0.05 and p < 0.01, respectively) using reduced drug doses as compared to optimal single-drug concentrations. At the applied conditions, single-drug monotherapies had no or negligible activity in these models. We suggest that FSC can be used for rapid identification of effective, reduced dose, multi-drug combinations for the treatment of cancer and other diseases.