Multiobjective optimization based-approach for discovering novel cancer therapies.
Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding interventions that are currently in use, and more interestingly, discovering potential new approaches that are nonintuitive yet effective.
Mahoney
Arthur W
AW
Utah State University, Logan.
Podgorski
Gregory J
GJ
Flann
Nicholas S
NS
D000465:Algorithms; D020533:Angiogenesis Inhibitors; D035843:Biomedical Research; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D009369:Neoplasms / Q000188:drug therapy*; D009389:Neovascularization, Pathologic / Q000188:drug therapy; D042461:Vascular Endothelial Growth Factor A / Q000037:antagonists & inhibitors
D020533:Angiogenesis Inhibitors; C467484:VEGFA protein, human; D042461:Vascular Endothelial Growth Factor A