Rapid optimization of drug combinations for the angiostatic treatment of cancer

Combination of drugs can improve angiostatic cancer treatment efficacy and enable the reduction of side effects and drug resistance. Current clinical approaches to combination therapy are mainly based on trial and error and reveal major toxicity issues. 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 to identify optimal low-dose drug combinations. This implied an iterative approach of in vitro testing of endothelial cell (EC) viability and algorithm-based analysis. A second order linear regression model gave a good fit to the resulting measurements of EC viability versus drug combinations at varying doses. The regression coefficients obtained in this fit were used to reduce the number of drugs and to finally identify an optimal and synergistic drug combination containing three compounds, i.e. erlotinib, BEZ-235 and RAPTA-C. The drug doses in the best combination were reduced by 5-to 11-fold as compared to optimal single drug efficacy. 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. Single drug monotherapies had no or negligible activity in these models. We suggest that the current FSC technique can be used for rapid identification of effective, reduced dose, multi-drug combinations for the treatment of cancer and other diseases.