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In the last decade, a number of drugs targeting specific proteins have been developed that are becoming common in cancer research as a basis for personalized therapy. How-ever, the numerous aberrations in molecular pathways that can produce cancer necessitate the use of drug combinations as compared to single drugs for treatment of individual cancers. In this article, we consider the design of combination therapy based on tumor sensitivity measurements over a panel of targeted drugs. We consider the following two optimization criteria (a) generating drug combinations with high sensitivity and minimal toxicity and (b) generating drug combinations targeting multiple parallel pathways for avoiding resistance. The optimization problem is solved using a set cover approach and a sequential search hill climbing technique. The effectiveness of our optimization procedure is illustrated on both synthetic and experimental models.