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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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![Figure 1 Modeling process to study drug combinations. (A) Illustration of the drug modelling process. An example enzymatic network with corresponding ODEs is shown. Solid links represent inter-node regulatory relationships, broken lines are background regulations. With the addition of drugs to chosen links (shown by crosses), the equations are modified by incorporating drugs as competitive inhibitors. (B) An example isobologram calculated from the combination of links 1 and 2 in (A). Points on isobloles represent dose combinations with the same efficacy. The black isobole (solid line) is concave, suggesting a synergistic interaction between the two links. The tipping point is the point where CI reaches minimum (or maximum for antagonistic cases). Inhibition strength is defined as [I]/Ki, i. e. the concentration of the inhibitor divided by its inhibition constant. The combination indices calculated from inhibition strengths are identical with those calculated with concentrations since KI's cancel. The whole process depicted here was repeated for all (16,038) networks and 100,000 sampled parameter sets.](/paper-figures/5e94714db0f64d6cb42b4972a23a81ad/synergistic-and-antagonistic-drug-combinations-depend-on-network-topology-figure-1.jpg)
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“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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Synergistic drug combinations tend to improve therapeutically relevant selectivity
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Defining Network Topologies that Can Achieve Biochemical Adaptation
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Self-citationMulticomponent therapeutics for networked systems
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Wu, Y., Chen, W., Xu, Z. P., & Gu, W. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02022 · Cited by 423
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
“For example, according to a corollary of the local hypothesis 13 , the topology of drug combinations is indicative of synergistic or antagonistic relationships 102 .”
NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning
Chen, X., Ren, B., Chen, M., Wang, Q., Zhang, L., & Yan, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004975 · Cited by 284
Computational Multitarget Drug Design
Zhang, W., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00491 · Cited by 189
Self-citationSystems biology approaches for advancing the discovery of effective drug combinations
Ryall, K. A., & Tan, A. C. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0055-9 · Cited by 160
Metadata
- 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
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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
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