A Computational Approach for Identifying Synergistic Drug Combinations. Academic Article uri icon

Overview

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.

publication date

  • January 13, 2017

Research

keywords

  • Drug Combinations
  • Drug Discovery
  • Drug Synergism

Identity

PubMed Central ID

  • PMC5234777

Scopus Document Identifier

  • 85011321685

Digital Object Identifier (DOI)

  • 10.1200/JCO.2012.43.4233

PubMed ID

  • 28085880

Additional Document Info

volume

  • 13

issue

  • 1