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

Arat, Seda, Ietswaart, Robert, Chen, Amanda, Farahmand, Saman, Kim, Bunjum, Armstrong, Duncan, Urban, Laszlo, DuMouchel, William, Jeffrey, Sutherland and Fekete, Alexander (2020) Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology. EBioMedicine., 57. pp. 1028-1037. ISSN eBioMedicine


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 250 target-ADR associations. Among these are established relations, such as the association of in vitro hERG binding with cardiac arrhythmias, which 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, skin, respiratory tract and central nervous system disorders. Unexpectedly, our model suggests PDE3 is associated with 44 ADRs, including congenital renal disorders. These associations provide a comprehensive resource to support drug development and human biology studies.

Item Type: Article
Keywords: Adverse drug reactions, adverse event report, FAERS, secondary pharmacology, machine learning, statistical modeling, drug discovery & development, drug safety.
Date Deposited: 29 Sep 2020 00:45
Last Modified: 29 Sep 2020 00:45


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