Systematic prediction of drug combinations based on clinical side-effects
Open accessAbstract
Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
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High-throughput screening using sophisticated models can identify compounds that mitigate disease symptoms without necessitating pre-existing knowledge about drug-target interactions.
“Notably, high-throughput screening using sophisticated models can identify compounds that mitigate disease symptoms without necessitating pre-existing knowledge about the drug-target interactions.29,30”
From Drug repurposing for cancer therapy (2024) · cites this paper as [29]
The Abstract (page unavailable) describes a computational model that uses clinical side effects to predict drug combinations, rather than requiring drug-target interaction data. But it does not show that the predicted compounds mitigate disease symptoms; the Discussion (page unavailable) says the predictions need experimental testing. The Abstract also distinguishes this approach from high-throughput screening, which it says is impractical for all possible combinations. (LLM, conf=0.86)
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“29,30 Current computational methodologies, such as merging drug effects with clinical disease signatures and model systems that predict disease-modifying effects, are available for the selection of drug candidates suitable for drug repurposing in cancer.”
BackgroundSelf-citation
- Authors
- Huang, H., Zhang, P., Qu, X. A., Sanseau, P., & Yang, L.
- Affiliations
- GlaxoSmithKline (United States) (US)
- IBM (United States) (US)
- Corresponding author
- Yang, Lun
- Venue
- Scientific Reports
- Published
- 2014-11-24
- Volume
- 4
- Issue
- 1
- Pages
- 7160-7160
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4241517
- OpenAlex
- W2054768549
- Cited by
- 73
- References
- 0
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 12 · 2022: 7 · 2021: 12 · 2020: 3 · 2019: 8 · 2018: 8 · 2017: 4 · 2016: 3 · 2015: 2
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- drug combination prediction, drug-drug interactions, post-marketing surveillance, drug labels, black box warning, pneumonia, fixed-dose combination, confounding factors
- MeSH terms
- Drug Combinations, Drug Interactions, Gastrointestinal Hemorrhage, Humans, Models, Theoretical, Pneumonia, Product Surveillance, Postmarketing, Retinal Hemorrhage, ROC Curve, Databases, Factual, Area Under Curve, Drug-Related Side Effects and Adverse Reactions
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