10.1371/journal.pcbi.1005308
Kaitlyn M. Gayvert
Kaitlyn M.
Gayvert
Omar Aly
Omar
Aly
James Platt
James
Platt
Marcus W. Bosenberg
Marcus
W. Bosenberg
David F. Stern
David
F. Stern
Olivier Elemento
Olivier
Elemento
A Computational Approach for Identifying Synergistic Drug Combinations
Public Library of Science
2017
right combinations
synergy predictions
combination therapies
Synergistic Drug Combinations
BRAF melanoma
drug combination testing
drug function
drug efficacy
anticancer molecules
Computational Approach
drug resistance
drug synergy
combinatorial screens
2017-01-13 17:34:36
Dataset
https://plos.figshare.com/articles/dataset/A_Computational_Approach_for_Identifying_Synergistic_Drug_Combinations/4554829
<div><p>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.</p></div>