Drug-drug interaction discovery and demystification using Semantic Web technologies. (23rd December 2016)

• Record Type: Journal Article
• Authors: Noor, Adeeb; Assiri, Abdullah; Ayvaz, Serkan; Clark, Connor; Dumontier, Michel
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 3(2017:May)
• Page Start: 556
• Page End: 564
• Publication Date: 2016-12-23
• Subjects: drug interactions -- pharmacovigilance -- pharmacologic actions -- Semantic Web; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocw128
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. Materials and Methods: A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. Results: The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. Discussion: The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. Conclusion: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.