The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Network-based prediction of drug combinations
Open accessAbstract
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.
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Cites
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Cited by
Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
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Metadata
- Authors
- Cheng, F., Kovács, I. A., & Barabási, A.-L.
- Venue
- Nature Communications
- Published
- 2019-03-13
- Volume
- 10
- Issue
- 1
- Pages
- 1197-1197
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2922229918
- Cited by
- 800
- References
- 53
- FWCI
- 27.43
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-04-15)
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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