Abstract
Adverse drug events (ADEs) occur when multiple drugs interact within an individual, thus causing effects that were not initially predicted. Such toxic interactions lead to morbidity and mortality. Contemporary research surrounding ADEs has tended to focus on the detection of potential ADEs without great concern for elucidating the associations of drug-drug interaction (DDI) mechanisms that can predict potential adverse drug reactions (ADRs). Such associations are of great practical importance for everyday pharmacovigilance efforts. This study presents a data-driven framework for conducting knowledge-driven data analysis that combines a semantic inference system and enrichment analysis in order to identify potential ADE mechanisms. The framework was used to rank mechanisms according to their relevance for DDIs and also to categorize ADEs based on the number of DDI mechanism associations identified through enrichment analysis. Its validity is demonstrated through using both commercial and publicly available DDI resources. The results of this study solidly prove the framework's effectiveness and highlight potential for future research by way of incorporating additional and broader data to deepen and expand its capabilities.
Highlights
One type of medical error is adverse drug events (ADEs), the occurrence of which is recognized as among the greatest concerns in cases of drug-drug interaction (DDI)
ADEs occur when multiple drugs interact within an individual to cause unanticipated toxic effects, which lead to morbidity and mortality
We demonstrate our framework using both commercial and publicly available DDI resources and show how it can aid clinicians in identifying patients at risk of ADEs stemming from DDIs, including ADEs that have yet to be fully characterized
Summary
One type of medical error is adverse drug events (ADEs), the occurrence of which is recognized as among the greatest concerns in cases of drug-drug interaction (DDI). Other applications have focused on predicting ADEs using computational methods such as text mining [11], machine learning [12], deep learning [13], and network models [14] Such methods often employ inputs that are clinically based, such as electronic patient records [15], clinical notes [16], disease characteristics [17], or drug features [18]; other nonclinical inputs consist of personal messages [19], social media posts [20], and advice from human experts [21]. Materials and Methods e framework is designed around utilizing experimental data from the Human Phenotype Ontology (HPO) [28] in conjunction with the drug-drug interaction discovery and demystification (D3) inference framework by Noor et al [29] It ranks the predictive significance of DDI mechanisms and explores the associations of ADEs with potentially correlated DDI mechanisms, allowing categorization of ADEs. Table 1 shows the mechanisms of interaction covered by the D3 framework. Two experiments are presented here, each employing the system in a different capacity to showcase some of the inferences it could be used to draw and each designed to yield outcomes of potential direct benefit to clinicians. ese are not intended to exhibit the full capacity of what is possible with this system, leaving room for future expansion beyond the framework presented
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