Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer

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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10456-015-9462-9) contains supplementary material, which is available to authorized users.

In-Silico Methodologies for Cancer Multidrug Optimization

“…Weiss et al [78] applied differential evolution to iteratively test the endothelial cell viability. They identified an optimal effective three-drug combination which was translated successfully into several in vivo tumor models.…”

Development of bioluminescent chick chorioallantoic membrane (CAM) models from primary pancreatic cancer cells: A platform for drug testing

“…Previous studies assessed tumor dimension through size measurements and weight as well as total tumor cell counts [10][11][12] . These methods could potentially be complemented with bioluminescence (BLI), which is a low-cost longitudinal imaging method.…”

Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model

“…Optimal experimental design has been utilized for decades in a variety of settings in which it is of interest to maximize efficiency of resource use and obtain a significant amount of information from experiments with acceptable cost [8][9][10][11][12][13][14][15][16]. Recently, as biological modeling and systems biology have emerged as an important area in biomedical research, optimal experimental design applied to biological experimental systems has become more popular [17][18][19][20][21][22][23][24][25][26][27][28]; additionally, optimal experimental design has been recognized as a valuable tool in optimal control for several decades [29].…”

“…Recently, as biological modeling and systems biology have emerged as an important area in biomedical research, optimal experimental design applied to biological experimental systems has become more popular [17][18][19][20][21][22][23][24][25][26][27][28]; additionally, optimal experimental design has been recognized as a valuable tool in optimal control for several decades [29]. For example, Jones et al [13] maximized production of an exogenous commodity chemical in metabolically engineered E. coli using an empirical modeling method similar to those used in [15,16] to maximize the efficacy of drug delivery. Weber [26] utilized optimal experimental design to maximize model prediction accuracy for a model of vesicle transport via the trans-Golgi network.…”