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

利用多组学数据和技术增强药物遗传学，预测 NSAID 的药物不良反应

基本信息

批准号：
10748642
负责人：
Karl Ernst Keat
金额：
$ 4.77万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-18 至 2026-09-17
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10748642
关键词：
Acceleration Address Adopted Adoption Adverse drug effect Adverse effects Adverse event Age Alleles Antineoplastic Agents Area CYP2C9 gene Cementation Clinical Clinical Data Communities Data Data Set Data Sources Drug Modelings Drug usage Elasticity Electronic Health Record Enzymes Event Fever Future Gastrointestinal Hemorrhage Gastrointestinal tract structure Gene Expression Genes Genetic Genetic study Genomics Genotype Goals Guidelines Hemorrhage Heritability Individual Inflammation Joints Laboratories Life Link Logistic Regressions Measurement Measures Medicine Methods Modality Modeling Multiomic Data Non-Prescription Drugs Non-Steroidal Anti-Inflammatory Agents Outcome Output Pain management Participant Patients Performance Pharmaceutical Preparations Pharmacogenetics Pharmacogenomics Pharmacotherapy Polygenic Traits Population Heterogeneity Publishing ROC Curve Research Risk Risk Assessment Risk Factors Scoring Method Severities Sum Techniques Time Training United States National Institutes of Health Upper digestive tract structure Variant Weight Work acute coronary syndrome adverse drug reaction anticancer research biobank case control clinical phenotype clinically relevant cohort comorbidity diverse data drug metabolism drug response prediction experimental study frontier genome wide association study improved loss of function multimodality multiple data sources multiple omics non-genetic novel novel strategies pain reduction phenomics polygenic risk score portability precision medicine prevent response risk variant sex statistics success trait transcriptome transcriptomics vector

项目摘要

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.”

摘要