RT Journal Article SR Electronic T1 Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology JF bioRxiv FD Cold Spring Harbor Laboratory SP 750950 DO 10.1101/750950 A1 Robert Ietswaart A1 Seda Arat A1 Amanda X. Chen A1 Saman Farahmand A1 Bumjun Kim A1 William DuMouchel A1 Duncan Armstrong A1 Alexander Fekete A1 Jeffrey J. Sutherland A1 Laszlo Urban YR 2019 UL http://biorxiv.org/content/early/2019/09/27/750950.abstract AB 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. Here, we analyze 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. 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. Among 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. These associations provide a comprehensive resource to support drug development and human biology studies.