Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

Abstract

Background: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.

Methods: Here, we analyse in vitro secondary pharmacology of common (off) targets for 2134 marketed drugs. To associate these drugs with human ADRs, we utilized FDA Adverse Event Reports and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.

Findings: By evaluating Gini importance scores of model features, we identify 221 target-ADR associations, which co-occur in PubMed abstracts to a greater extent than expected by chance. Amongst these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which further validate our machine learning approach. Evidence on bile acid metabolism supports our identification of associations between the Bile Salt Export Pump and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 40 ADRs.

Interpretation: These associations provide a comprehensive resource to support drug development and human biology studies.

Funding: This study was not supported by any formal funding bodies.

Keywords: Adverse drug reactions; Adverse event report; Drug discovery & development; Drug safety; FAERS; Machine learning; Secondary pharmacology; statistical modeling.