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 bindin

SUBMITTER: Ietswaart R 

PROVIDER: S-EPMC7379147 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature