title: Mining severe drug-drug interaction adverse events using Semantic Web technologies: a case study.
abstract: BACKGROUND
  Drug-drug interactions (DDIs) are a major contributing factor for unexpected adverse drug events (ADEs). However, few of knowledge resources cover the severity information of ADEs that is critical for prioritizing the medical need. The objective of the study is to develop and evaluate a Semantic Web-based approach for mining severe DDI-induced ADEs.
  
  
  METHODS
  We utilized a normalized FDA Adverse Event Report System (AERS) dataset and performed a case study of three frequently prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. We extracted putative DDI-ADE pairs and their associated outcome codes. We developed a pipeline to filter the associations using ADE datasets from SIDER and PharmGKB. We also performed a signal enrichment using electronic medical records (EMR) data. We leveraged the Common Terminology Criteria for Adverse Event (CTCAE) grading system and classified the DDI-induced ADEs into the CTCAE in the Web Ontology Language (OWL).
  
  
  RESULTS
  We identified 601 DDI-ADE pairs for the three drugs using the filtering pipeline, of which 61 pairs are in Grade 5, 56 pairs in Grade 4 and 484 pairs in Grade 3. Among 601 pairs, the signals of 59 DDI-ADE pairs were identified from the EMR data.
  
  
  CONCLUSIONS
  The approach developed could be generalized to detect the signals of putative severe ADEs induced by DDIs in other drug domains and would be useful for supporting translational and pharmacovigilance study of severe ADEs.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA.
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA.
authors.forename: Harold R
authors.initials: HR
authors.lastname: Solbrig
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
keywords: Adverse drug event; Data mining; Drug-drug Interaction; Electronic medical records; Semantic web technology
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