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

Weiss, Andrea ; Ding, Xianting ; van Beijnum, Judy ; Wong, Ieong ; Wong, Tse ; Berndsen, Robert ; Dormond, Olivier ; Dallinga, Marchien ; Shen, Li ; Schlingemann, Reinier ; Pili, Roberto ; Ho, Chih-Ming ; Dyson, Paul ; van den Bergh, Hubert ; Griffioen, Arjan ; Nowak-Sliwinska, Patrycja

In: Angiogenesis, 2015, vol. 18, no. 3, p. 233-244

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    Titre
    • Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer
    Auteur
    • Weiss, Andrea. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
    • Ding, Xianting. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China
    • van Beijnum, Judy. Department of Medical Oncology, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands
    • Wong, Ieong. Department of Mechanical and Aerospace Engineering, University of California, 90095, Los Angeles, CA, USA
    • Wong, Tse. Department of Medical Oncology, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands
    • Berndsen, Robert. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
    • Dormond, Olivier. Department of Visceral Surgery, Centre Hospitalier Universitaire Vaudois, 1011, Lausanne, Switzerland
    • Dallinga, Marchien. Ocular Angiogenesis Group, Departments of Ophthalmology and Cell Biology and Histology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
    • Shen, Li. Department of Medicine, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA
    • Schlingemann, Reinier. Ocular Angiogenesis Group, Departments of Ophthalmology and Cell Biology and Histology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
    • Pili, Roberto. Department of Medicine, Roswell Park Cancer Institute, 14263, Buffalo, NY, USA
    • Ho, Chih-Ming. Department of Mechanical and Aerospace Engineering, University of California, 90095, Los Angeles, CA, USA
    • Dyson, Paul. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
    • van den Bergh, Hubert. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
    • Griffioen, Arjan. Department of Medical Oncology, VU University Medical Center, 1007 MB, Amsterdam, The Netherlands
    • Nowak-Sliwinska, Patrycja. Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL), 1015, Lausanne, Switzerland
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:333100
    Summary
    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.