TY - JOUR
T1 - Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer
AU - Weiss, Andrea
AU - Ding, Xianting
AU - van Beijnum, Judy R.
AU - Wong, Ieong
AU - Wong, Tse J.
AU - Berndsen, Robert H.
AU - Dormond, Olivier
AU - Dallinga, Marchien
AU - Shen, Li
AU - Schlingemann, Reinier O.
AU - Pili, Roberto
AU - Ho, Chih-Ming
AU - Dyson, Paul J.
AU - van den Bergh, Hubert
AU - Griffioen, Arjan W.
AU - Nowak-Sliwinska, Patrycja
PY - 2015
Y1 - 2015
N2 - 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
AB - 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
U2 - https://doi.org/10.1007/s10456-015-9462-9
DO - https://doi.org/10.1007/s10456-015-9462-9
M3 - Article
C2 - 25824484
SN - 0969-6970
VL - 18
SP - 233
EP - 244
JO - Angiogenesis
JF - Angiogenesis
IS - 3
ER -