Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use

• Published in: Drug safety Vol. 43; no. 6; pp. 567 - 582
• Main Authors: Jerome, Rebecca N., Joly, Meghan Morrison, Kennedy, Nan, Shirey-Rice, Jana K., Roden, Dan M., Bernard, Gordon R., Holroyd, Kenneth J., Denny, Joshua C., Pulley, Jill M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2020; Springer Nature B.V
• Subjects: Biological effects; Chemical compounds; Clinical trials; Codes; Drug Safety and Pharmacovigilance; Electronic health records; Electronic medical records; FDA approval; Genes; Genetics; Genomes; Genotype & phenotype; Laboratories; Medicine; Medicine & Public Health; Methods; Original Research Article; Patients; Pharmacology; Pharmacology/Toxicology; Phenotypes; Product safety; Proteins; Regulatory approval; Safety; Side effects; Studies; United States > US
• ISSN: 0114-5916; 1179-1942
• DOI: 10.1007/s40264-020-00915-6

Introduction When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work. Objective We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects. Methods We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product. Results Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene–drug class pairs. Conclusions By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.