Systematic discovery of drug interaction mechanisms
Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807.
Download
Journal Article
| Published
| English
Scopus indexed
Department
Grant
Abstract
Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.
Publishing Year
Date Published
2015-04-01
Journal Title
Molecular Systems Biology
Volume
11
Issue
4
Article Number
807
IST-REx-ID
Cite this
Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098
Chevereau, G., & Bollenbach, M. T. (2015). Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. Nature Publishing Group. https://doi.org/10.15252/msb.20156098
Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology. Nature Publishing Group, 2015. https://doi.org/10.15252/msb.20156098.
G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing Group, 2015.
Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807.
Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology, vol. 11, no. 4, 807, Nature Publishing Group, 2015, doi:10.15252/msb.20156098.
All files available under the following license(s):
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0):
Main File(s)
File Name
IST-2015-395-v1+1_807.full.pdf
1.27 MB
Access Level
Open Access
Date Uploaded
2018-12-12
MD5 Checksum
4289b518fbe2166682fb1a1ef9b405f3