OnSIDES (ON-label SIDE effectS resource) Database : Extracting Adverse Drug Events from Drug Labels using Natural Language Processing Models

Abstract

AbstractAdverse drug events (ADEs) are the fourth leading cause of death in the US and cost billions of dollars annually in increased healthcare costs. However, few machine-readable databases of ADEs exist, limiting the opportunity to study drug safety on a broader, systematic scale. Recent advances in Natural Language Processing methods, such as BERT models, present an opportunity to accurately extract relevant information from unstructured biomedical text. As such, we fine-tuned a PubMedBERT model to extract ADE terms from descriptive text in FDA Structured Product Labels for prescription drugs. With this model, we achieve an F1 score of 0.90, AUROC of 0.92, and AUPR of 0.95 at extracting ADEs from the labels’ “Adverse Reactions”. We further utilize this method to extract serious ADEs from labels’ “Boxed Warnings”, and ADEs specifically noted for pediatric patients. Here, we present OnSIDES (ON-label SIDE effectS resource), a compiled, computable database of drug-ADE pairs generated with this method. OnSIDES contains more than 3.6 million drug-ADE pairs for 3,233 unique drug ingredient combinations extracted from 47,211 labels. Additionally, we expand this method to extract ADEs from drug labels of other major nations/regions - Japan, the UK, and the EU - to build a complementary OnSIDES-INTL database. To present potential applications, we used OnSIDES to predict novel drug targets and indications, analyze enrichment of ADEs across drug classes, and predict novel ADEs from chemical compound structures. We conclude that OnSIDES can be utilized as a comprehensive resource to study and enhance drug safety.One Sentence SummaryOnSIDES is a large, comprehensive database of adverse drug events extracted from drug labels using natural language processing methods.