Discovering novel cancer therapies: A computational modeling and search approach

Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating rationally-designed treatments prevent the discovery of effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the cost of developing the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding interventions that are currently in use and more interestingly, discovering some new approaches that are counter intuitive yet effective.