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PLoS Comput Biol. 2017 Jan 13;13(1):e1005308. doi: 10.1371/journal.pcbi.1005308. eCollection 2017 Jan.

A Computational Approach for Identifying Synergistic Drug Combinations.

Author information

1
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States of America.
2
Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States of America.
3
Tri-Institutional Graduate Program on Computational Biology and Medicine, New York, NY, United States of America.
4
Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America.
5
Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, United States of America.

Abstract

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.

PMID:
28085880
PMCID:
PMC5234777
DOI:
10.1371/journal.pcbi.1005308
[Indexed for MEDLINE]
Free PMC Article

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