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Network-based prediction of drug combinations
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
General Chemistry
General Biochemistry,Genetics and Molecular Biology
General Physics and Astronomy
SDG 3 - Good Health and Well-being
General Biochemistry,Genetics and Molecular Biology
General Physics and Astronomy
SDG 3 - Good Health and Well-being
URI
https://hdl.handle.net/20.500.14018/27154
Abstract
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2019-12-01
Language
ISBN
Identifiers
10.1038/s41467-019-09186-x