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:
- 金额:$ 4.77万
- 依托单位:
- 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:
- 关键词:AccelerationAddressAdoptedAdoptionAdverse drug effectAdverse effectsAdverse eventAgeAllelesAntineoplastic AgentsAreaCYP2C9 geneCementationClinicalClinical DataCommunitiesDataData SetData SourcesDrug ModelingsDrug usageElasticityElectronic Health RecordEnzymesEventFeverFutureGastrointestinal HemorrhageGastrointestinal tract structureGene ExpressionGenesGeneticGenetic studyGenomicsGenotypeGoalsGuidelinesHemorrhageHeritabilityIndividualInflammationJointsLaboratoriesLifeLinkLogistic RegressionsMeasurementMeasuresMedicineMethodsModalityModelingMultiomic DataNon-Prescription DrugsNon-Steroidal Anti-Inflammatory AgentsOutcomeOutputPain managementParticipantPatientsPerformancePharmaceutical PreparationsPharmacogeneticsPharmacogenomicsPharmacotherapyPolygenic TraitsPopulation HeterogeneityPublishingROC CurveResearchRiskRisk AssessmentRisk FactorsScoring MethodSeveritiesSumTechniquesTimeTrainingUnited States National Institutes of HealthUpper digestive tract structureVariantWeightWorkacute coronary syndromeadverse drug reactionanticancer researchbiobankcase controlclinical phenotypeclinically relevantcohortcomorbiditydiverse datadrug metabolismdrug response predictionexperimental studyfrontiergenome wide association studyimprovedloss of functionmultimodalitymultiple data sourcesmultiple omicsnon-geneticnovelnovel strategiespain reductionphenomicspolygenic risk scoreportabilityprecision medicinepreventresponserisk variantsexstatisticssuccesstraittranscriptometranscriptomicsvector
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
项目成果
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