A Data-Driven Medical Decision Framework for Associating Adverse Drug Events with Drug-Drug Interaction Mechanisms

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.

Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

“…Several algorithms and strategies have been proposed by healthcare practitioners and researchers alike in attempts to address this perplexing subject. Our group previously developed a DDI prediction algorithm incorporating ten potential mechanisms [ 14 , 18 ]; other algorithms have been developed to predict DDIs and stratify risk based on available DDI resources [ 19 – 21 ]. A persistent challenge in all of these endeavors is the lack of proper validation data for the developed algorithms [ 9 ].…”

Recent Progress in Traditional Chinese Medicines and Their Mechanism in the Treatment of Allergic Rhinitis

“…These major symptoms may often be accompanied by fatigue from nasal discomfort, itching sensation around the eyes, swelling of the nasal mucosal membranes, postnasal dripping, and cough [ 1 ]. On the far current record, approximately 15–20% of the population around the globe is affected by AR, with a dominating ratio in western countries [ 2 , 3 ]. The treatment of the condition potentially focuses on alleviating the symptoms rather than addressing the root cause of the issue.…”

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

“…Computational methods for predicting DDIs constitute an area of considerable research interest, leading to the development of diverse methods and published resources. These methods have employed a variety of algorithms and utilized many features such as biological effect interactions [ 9 ], protein similarities [ 10 ], clinical and genomic factors [ 11 ], and drug-target [ 12 ] and drug-protein [ 13 ], as well as drug information on web [ 14 , 15 ], text-based data [ 14 ], protein interaction networks [ 16 ], mechanisms of toxicity [ 17 ], and enrichment analysis [ 18 ]. Among the diverse computational methods that have been used in DDI research, rule-based systems have shown especially promising results [ 19 ].…”