title: Drug-drug interaction discovery and demystification using Semantic Web technologies.
abstract: OBJECTIVE
  To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs.
  
  
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, KSA.
authors.forename: Adeeb
authors.initials: A
authors.lastname: Noor
authors.affiliation: School of Pharmacy, Purdue University, West Lafayette, Indiana, USA.|School of Pharmacy, King Khalid University, Abha, KSA.
authors.forename: Abdullah
authors.initials: A
authors.lastname: Assiri
authors.affiliation: Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul 34353, Turkey.
authors.forename: Serkan
authors.initials: S
authors.lastname: Ayvaz
authors.affiliation: Unaffiliated Researcher, Mountain View, California, USA.
authors.forename: Connor
authors.initials: C
authors.lastname: Clark
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford, California.
authors.forename: Michel
authors.initials: M
authors.lastname: Dumontier
mesh_terms: D016208:Databases, Factual; D004347:Drug Interactions; D006801:Humans; D051188:Knowledge Bases; D010597:Pharmacogenetics; D010599:Pharmacokinetics; D010600:Pharmacology; D060735:Pharmacovigilance; D000075403:Semantic Web; D017432:Unified Medical Language System
keywords: Semantic Web; drug interactions; pharmacologic actions; pharmacovigilance