title: A Computational Approach for Identifying Synergistic Drug Combinations.
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.
authors.affiliation: Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States of America.|Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States of America.|Tri-Institutional Graduate Program on Computational Biology and Medicine, New York, NY, United States of America.
authors.forename: Kaitlyn M
authors.initials: KM
authors.lastname: Gayvert
authors.affiliation: Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States of America.|Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States of America.
authors.forename: Omar
authors.initials: O
authors.lastname: Aly
authors.affiliation: Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America.|Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: James
authors.identifier: 0000-0002-4286-446X
authors.initials: J
authors.lastname: Platt
authors.affiliation: Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America.|Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: Marcus W
authors.initials: MW
authors.lastname: Bosenberg
authors.affiliation: Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America.
authors.forename: David F
authors.initials: DF
authors.lastname: Stern
authors.affiliation: Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States of America.|Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States of America.
authors.forename: Olivier
authors.initials: O
authors.lastname: Elemento
mesh_terms: D000970:Antineoplastic Agents; D045744:Cell Line, Tumor; D019295:Computational Biology; D004338:Drug Combinations; D055808:Drug Discovery / Q000379:methods*; D004357:Drug Synergism; D006801:Humans; D008545:Melanoma / Q000188:drug therapy / Q000235:genetics; D008962:Models, Theoretical; D048493:Proto-Oncogene Proteins B-raf / Q000235:genetics
chemical_list: D000970:Antineoplastic Agents; D004338:Drug Combinations; C482119:BRAF protein, human; D048493:Proto-Oncogene Proteins B-raf