A Computational Approach for Identifying Synergistic Drug Combinations.

TitleA Computational Approach for Identifying Synergistic Drug Combinations.
Publication TypeJournal Article
Year of Publication2017
AuthorsGayvert KM, Aly O, Platt J, Bosenberg MW, Stern DF, Elemento O
JournalPLoS Comput Biol
Date Published2017 01
KeywordsAntineoplastic Agents, Cell Line, Tumor, Computational Biology, Drug Combinations, Drug Discovery, Drug Synergism, Humans, Melanoma, Models, Theoretical, Proto-Oncogene Proteins B-raf

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.

Alternate JournalPLoS Comput. Biol.
PubMed ID28085880
PubMed Central IDPMC5234777
Grant ListR01 CA194547 / CA / NCI NIH HHS / United States
UL1 TR001863 / TR / NCATS NIH HHS / United States
T32 GM083937 / GM / NIGMS NIH HHS / United States