Augmenting Pharmacogenetics with Multi-Omics Data and Techniques to Predict Adverse Drug Reactions to NSAIDs

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Abstract Non-steroidal anti-inflammatory drugs (NSAIDs) are a class of widely used drugs for treatment of pain, fever, and inflammation. NSAID use has been linked to both mild and life-threatening adverse drug reactions (ADRs) including gastrointestinal bleeding and acute coronary syndrome. Given the severity of these outcomes and the large patient pool, there is a great need to predict individual risk of ADR from NSAIDs. Pharmacogenetics is the study of how genetics influence drug response. The Clinical Pharmacogenetics Implementation Consortium has published guidelines for clinicians to modify NSAID treatment in the presence of CYP2C9 loss- of-function variants, which result in reduced clearance of NSAIDs and increased risk of ADRs. However, CYP2C9 alone explains a relatively small proportion of risk of ADR, which is currently better predicted using clinical covariates such as age, sex, concomitant drugs, and comorbidities. We propose to better understand the heritable risk of NSAID ADR by performing a genome-wide association study for NSAID ADR in a diverse population and using it to develop a polygenic risk score (PRS). Furthermore, to improve cross-ancestry performance of our PRS, we will develop a transcriptomic risk score (TRS) based on imputed transcriptomes and integrate it with our PRS. We will then build a multi-modal model that combines pharmacogenetics, genomics, and clinical variables to predict ADR risk. Successful completion of both aims would prevent countless NSAID-induced ADRs and improve our understanding of the risk factors underlying ADR risk. Beyond that, our work will serve as a model for future application of multi-omics to augment pharmacogenetics, bringing us closer to “the right drug, for the right patient, at the right time.”