ArticleSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society2020
A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.
Article in Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Innovative horizons: harnessing drug repositioning for targeted therapeutics in colorectal cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Temporal offset association between the number of irinotecan-related adverse reactions and pharmacogenomic studies: A cross-correlation analysis.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making.Journal of healthcare engineering · 2022Article
- A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions.Computational and mathematical methods in medicine · 2022Article
- A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms.Journal of healthcare engineering · 2022Article
- Improving bioinformatics software quality through incorporation of software engineering practices.PeerJ. Computer science · 2022Article
- A novel computational drug repurposing approach for Systemic Lupus Erythematosus (SLE) treatment using Semantic Web technologies.Saudi journal of biological sciences · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-drug interactions (DDIs) are a potentially distressing corollary of drug interventions, and may result in discomfort, debilitating illness, or even death. Existing research predominantly considers only a single level of interaction; however, serious health complications may result from multi-pathway DDIs, and so new methods are needed to enable predicting and preventing complex DDIs. This article introduces a novel method for the prediction of DDIs at two pharmacological levels (metabolic and transporter interactions) by means of a rule-based model implemented with Semantic Web technologies. The chemotherapy agent irinotecan is used as a case study for demonstrating the validity of this approach. Mechanistic and interaction data were mined from available sources and then used to predict interactors of irinotecan, including potential DDIs mediated by previously unidentified mechanisms. The findings also draw attention to the profound variation between DDI resources, indicating that clinical practice would see significant value from the development of an evidence-based resource to support DDI identification.
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