A reference set of clinically relevant adverse drug-drug interactions

Bhaskar Dutta,Munir Pirmohamed,Simon Maskell,Elpida Kontsioti

doi:10.1038/s41597-022-01159-y

Bhaskar Dutta, Munir Pirmohamed + Show 2 more

Open Access

https://doi.org/10.1038/s41597-022-01159-y

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Abstract

The accurate and timely detection of adverse drug-drug interactions (DDIs) during the postmarketing phase is an important yet complex task with potentially major clinical implications. The development of data mining methodologies that scan healthcare databases for drug safety signals requires appropriate reference sets for performance evaluation. Methodologies for establishing DDI reference sets are limited in the literature, while there is no publicly available resource simultaneously focusing on clinical relevance of DDIs and individual behaviour of interacting drugs. By automatically extracting and aggregating information from multiple clinical resources, we provide a scalable approach for generating a reference set for DDIs that could support research in postmarketing safety surveillance. CRESCENDDI contains 10,286 positive and 4,544 negative controls, covering 454 drugs and 179 adverse events mapped to RxNorm and MedDRA concepts, respectively. It also includes single drug information for the included drugs (i.e., adverse drug reactions, indications, and negative drug-event associations). We demonstrate usability of the resource by scanning a spontaneous reporting system database for signals of DDIs using traditional signal detection algorithms.

Highlights

Background & SummaryPolypharmacy has become a common phenomenon in the Western world
As life expectancy is increasing around the world, leading to more people living with multiple chronic diseases, together with new medicines being launched onto the market each year, giving rise to a growing volume of possible drug combinations, the implications of drug-drug interactions (DDIs) in clinical practice have become a matter of concern
A definitive reference standard including the complete set of DDIs cannot exist, the automatic extraction and aggregation of information from multiple clinical resources on DDIs and the individual behaviour of interacting drugs, along with scanning the scientific literature for negative controls, enabled us to construct, share, and advocate CRESCENDDI (Clinically-relevant REference Set CENtered around Drug-Drug Interactions), a dataset that can be used to facilitate research in signal detection algorithms (SDAs) and allow common ground for comparing methodologies

Summary

Background & Summary

Polypharmacy (i.e., the concomitant use of multiple medications in an individual) has become a common phenomenon in the Western world. Efforts to automate the generation of a reference set for single-drug ADRs by combining multiple sources of evidence identified a number of limitations, including: size; consideration of a single data source for extracting positive controls; availability (i.e., not being open access); and inclusion of only a limited number of drugs and adverse events (AEs)[18]. A definitive reference standard including the complete set of DDIs cannot exist, the automatic extraction and aggregation of information from multiple clinical resources on DDIs and the individual behaviour of interacting drugs, along with scanning the scientific literature for negative controls, enabled us to construct, share, and advocate CRESCENDDI (Clinically-relevant REference Set CENtered around Drug-Drug Interactions), a dataset that can be used to facilitate research in SDAs and allow common ground for comparing methodologies.

Methods

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