Metabolomics-Driven Exploration of the Chemical Drug Space to Predict Combination Antimicrobial Therapies.

ABSTRACT: Alternative to the conventional search for single-target, single-compound treatments, combination therapies can open entirely new opportunities to fight antibiotic resistance. However, combinatorial complexity prohibits experimental testing of drug combinations on a large scale, and methods to rationally design combination therapies are lagging behind. Here, we developed a combined experimental-computational approach to predict drug-drug interactions using high-throughput metabolomics. The approach was tested on 1,279 pharmacologically diverse drugs applied to the gram-negative bacterium Escherichia coli. Combining our metabolic profiling of drug response with previously generated metabolic and chemogenomic profiles of 3,807 single-gene deletion strains revealed an unexpectedly large space

SUBMITTER: Campos AI 

PROVIDER: S-EPMC6591011 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature