Synergistic and Antagonistic Drug Combinations Depend on Network Topology
Open accessAbstract
Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems.
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Graph representation learning methods such as GNNs have been developed to learn robust and meaningful molecular representations despite the incomplete interactome and to inductively infer new edges between node pairs.
“To this end, graph representation learning methods, such as GNNs, have been developed to learn robust and meaningful representations of molecules despite the incomplete interactome [102] and to inductively infer new edges between pairs of nodes [154].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [102]
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- Authors
- Yin, N., Ma, W., Pei, J., Ouyang, Q., Tang, C., & Lai, L.
- Affiliations
- Peking University (CN)
- Harvard University (US)
- Corresponding author
- Lai, Luhua
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-04-08
- Volume
- 9
- Issue
- 4
- Pages
- e93960
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3979733
- OpenAlex
- W2015789827
- Cited by
- 139
- References
- 36
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 12 · 2023: 22 · 2022: 12 · 2021: 15 · 2020: 12 · 2019: 15 · 2018: 8 · 2017: 9 · 2016: 11 · 2015: 7 · 2014: 2
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- National Natural Science Foundation of China (91313302, 90913021, 81273436, 2012AA020301, 2012AA020308)
- National Institute on Alcohol Abuse and Alcoholism (R01AA020308)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- combination therapy, drug synergy, drug antagonism, network topology, drug-target network, three-node network, positive feedback loop, network motifs
- MeSH terms
- Drug Combinations, Drug Interactions, Drug Synergism, Models, Theoretical, Drug Design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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