Abstract
BackgroundDrug-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.MethodsWe 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).ResultsWe 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.ConclusionsThe 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.
Highlights
Drug-drug interactions (DDIs) are a major contributing factor for unexpected adverse drug events (ADEs) [1]
: we developed the mappings between Adverse Event Reporting System (AERS) outcome codes and Common Terminology Criteria for Adverse Event (CTCAE) grades and classified the filtered DDI-ADEs into the CTCAE
We were able to extract a set of putative DDI-ADE pairs and their associated outcome codes for the three target drugs: Warfarin, Clopidogrel and Simvastatin from normalized AERS-DM dataset
Summary
Drug-drug interactions (DDIs) are a major contributing factor for unexpected adverse drug events (ADEs) [1]. A semantically coded knowledge base of DDI-induced ADEs with severity information is critical for clinical decision support systems and translational research applications. In our previous and ongoing study, we developed a standardized knowledge base of ADEs known as ADEpedia (http://adepedia.org) leveraging Semantic Web technologies [7]. The ADEpedia is intended to integrate existing known ADE knowledge for drug safety surveillance from disparate resources such as Food and Drug Administration (FDA) Structured Product Labeling (SPL) [7], FDA Adverse Event Reporting System (AERS) [8], and the Unified Medical Language System (UMLS) [9]. The objective of the study is to develop and evaluate a Semantic Web-based approach for mining severe DDI-induced ADEs. FDA Adverse Event Reporting System (AERS) FDA AERS is a database that provides information on adverse event and medication error reports submitted to FDA [10].
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