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A Computational Approach for Identifying Synergistic Drug Combinations

2017; Public Library of Science; Volume: 13; Issue: 1 Linguagem: Inglês

10.1371/journal.pcbi.1005308

1553-7358

Kaitlyn Gayvert, Omar M. Aly, James T. Platt, Marcus Bosenberg, David F. Stern, Olivier Elemento,

Bioinformatics and Genomic Networks

A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.