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Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved May 9, 2018 (received for review February 9, 2018)
Significance
The conventional single-agent approach to therapeutics is challenged by the redundancy and feedback in biological networks. The use of multiple drugs in combination might improve desired functional outcomes while reducing toxicity and overcoming drug resistance. However, the complexity and resources required to test many combinations have slowed discovery efforts. Here, we introduce a screening platform that automatically constructs combinations of drugs from nanoliter-scale droplets, greatly simplifying screening logistics and reducing resource consumption. We applied our platform to discover drug pairs that act synergistically against the model pathogen Escherichia coli. Our platform can be further developed to support many types of phenotypic assays in a variety of disease models.
Abstract
Combinatorial drug treatment strategies perturb biological networks synergistically to achieve therapeutic effects and represent major opportunities to develop advanced treatments across a variety of human disease areas. However, the discovery of new combinatorial treatments is challenged by the sheer scale of combinatorial chemical space. Here, we report a high-throughput system for nanoliter-scale phenotypic screening that formulates a chemical library in nanoliter droplet emulsions and automates the construction of chemical combinations en masse using parallel droplet processing. We applied this system to predict synergy between more than 4,000 investigational and approved drugs and a panel of 10 antibiotics against Escherichia coli, a model gram-negative pathogen. We found a range of drugs not previously indicated for infectious disease that synergize with antibiotics. Our validated hits include drugs that synergize with the antibiotics vancomycin, erythromycin, and novobiocin, which are used against gram-positive bacteria but are not effective by themselves to resolve gram-negative infections.
Footnotes
↵1A.K. and J.K. contributed equally to this work.
↵2Present address: Department of Bioengineering, University of California, Berkeley, CA 94720.
↵3Present address: Bain & Company, Boston, MA 02116.
- ↵4To whom correspondence should be addressed. Email: pblainey{at}broadinstitute.org.
Author contributions: A.K., J.K., and P.C.B. designed research; A.K., J.K., J.E.H., and P.T. performed research; A.K. and J.K. analyzed data; and A.K., J.K., and P.C.B. wrote the paper.
Conflict of interest statement: Broad Institute and MIT may seek to commercialize aspects of this work; related applications for intellectual property have been filed.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1802233115/-/DCSupplemental.
Published under the PNAS license.
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Combinatorial drug discovery in nanoliter droplets
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