Adverse drug reactions are one of the leading causes of morbidity and mortality in health care. We used machine learning to identify which drug target genes are associated with adverse drug reactions.
Drugs often hit many different targets and some drug side effects become clear only after drug approval. Machine learning can systematically identify connections between drug characterizations in the lab and the drug effects on humans using adverse event reporting from a large population. We analyzed an in vitro secondary pharmacology database provided by Urban and colleagues from Novartis Institutes of Biomedical Research of common (off) targets for 2134 marketed drugs.
To associate these drugs with human adverse drug reactions (ADRs), we queried the US FDA Adverse Event Reports database (FAERS).
Next, we developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.
By evaluating Gini importance scores of model features, we identify 221 target-ADR associations.
We perform several systematic prediction validation analyses: our associations co-occur in PubMed abstracts to a greater extent than expected by chance. So, many predictions have supporting evidence from previous clinical, animal model or in vitro studies. We also predicted new adverse events for drugs that were observed in chronologically later event reports, benchmarked on OMOP. And for drugs that did not have reports, we correctly predicted the adverse events as listed on their drug labels.
We find established causal relations, eg in vitro hERG binding with cardiac arrhythmia. Evidence on bile acid metabolism supports our identification of associations between BSEP and renal, thyroid, lipid metabolism, respiratory tract and central nervous system disorders. Our model suggests PDE3 is associated with 40 ADRs. Understanding which drug targets are linked to ADRs can ultimately lead to the development of safer medicines. Altogether we provide a comprehensive resource to support drug development and future human biology studies.
Citation
Robert Ietswaart*,#, Seda Arat*,#, Amanda X. Chen*, Saman Farahmand*, Bumjun Kim, William DuMouchel, Duncan Armstrong, Alexander Fekete, Jeffrey J. Sutherland#, Laszlo Urban#
Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology, EBioMedicine, 57, Pp. 102837 https://doi.org/10.1016/j.ebiom.2020.102837.
# corresponding author; * These authors contributed equally. Data is freely available as part of code repository on GitHub: https://github.com/samanfrm/ADRtarget