415742 Chemical-Genetic Inference of Antibiotic Interactions for Combination Therapies

Wednesday, November 11, 2015: 12:30 PM
155A (Salt Palace Convention Center)
Sriram Chandrasekaran1,2, James J Collins2,3,4 and Murat Cokol5, (1)Harvard Society of Fellows, Harvard University, Cambridge, MA, (2)Broad Institute of Harvard and MIT, Cambridge, MA, (3)Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, (4)Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, (5)Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

Combination antibiotic therapies are being increasingly used in response to a global rise in drug resistance. Mapping synergistic and antagonistic interactions between antibiotics is essential for designing effective combinations that enhance potency and counter resistance. However, the large search-space of candidate drugs and dosage regimes makes such approaches highly challenging. Here, we present a computational approach that integrates publicly available chemogenomic data to predict dose-specific antibiotic interactions. We experimentally tested interactions for 171 antibiotic pairs in nine dose combinations in Escherichia coli to validate our approach. Our analysis revealed a core set of cellular pathways (e.g., central metabolism) and biophysical factors (e.g., drug lipophilicity) that are predictive of synergy and antagonism. Using an evolutionary approach, we modified our chemical-genetic model of E. coli, a Gram-negative bacterium, to accurately predict drug-drug interactions in the Gram-positive pathogen Staphylococcus aureus. This study provides a framework for chemogenomics-driven discovery and development of effective combination therapies.

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