Adverse 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.

The authors have declared no competing interest.

This work was primarily supported by the National Institutes of Health (NIH), National Institute of General Medical Sciences (NIGMS) grant R35GM131905. Additionally, U.G, M.Z, K.L.B are supported by a NIH National Library of Medicine (NLM) grant T15LM007079, and H.Y.C is supported by the NIH NIGMS grant T32GM145440.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All of the data, code, and models trained and generated to construct the OnSIDES database and all other complementing databases are available and maintained at https://github.com/tatonetti-lab/onsides. Any requests for additional materials can be made via email to the corresponding author.

https://github.com/tatonetti-lab/onsides