Real-World Data Estimates of Racial Fairness with Pharmacogenomics-Guided Drug Policy

以药物基因组学为指导的药物政策对种族公平性的真实世界数据估计

• 批准号: 10797705
• 负责人: CASEY OVERBY TAYLOR
• 金额: $ 24.56万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797705
• 关键词: 3-Dimensional; Adoption; Age; All of Us Research Program; Blood Platelets; Body Weight; CYP2C19 gene; Case Study; Characteristics; Clinical; Data; Decision Making; Detection; Dose; Effectiveness; Electronic Health Record; Eligibility Determination; European ancestry; Future; Genetic Markers; Genetic Variation; Genotype; Guidelines; Health; Healthcare Systems; Individual; Measures; Medical center; Modeling; Outcome; Participant; Patients; Performance; Persons; Pharmaceutical Preparations; Pharmacogenomics; Pharmacotherapy; Pharmacy and Therapeutics Committee; Phenotype; Policies; Population; Process; Publishing; Race; Regimen; Research; Safety; Selective Serotonin Reuptake Inhibitor; Services; Structure; TPMT gene; Testing; Therapeutics Committee; Time; Translating; United States; Work; comorbidity; data access; demographics; genome sequencing; genomic profiles; health disparity; health inequalities; improved; insight; interest; lens; observational cohort study; outcome disparities; parity; racial bias; racial diversity; racial population; response; risk mitigation; structural determinants; thiopurine; whole genome

Project Summary A pharmacogenomics-guided drug policy includes the genomic profile of an individual’s drug response with other clinical characteristics (age, body weight, etc.) and may improve the safety and effectiveness of drug therapy. Thus, in recent years several medical centers in the United States have implemented clinical pharmacogenomics services to support such policies. Among the services that can be supported, preemptive clinical genotyping services produce pharmacogenomic data before it is known that a particular drug may be needed by a patient. Preemptive clinical genotyping services that cover genetic markers primarily based on populations of European ancestry, however, can have reduced performance of a policy to identify well-tolerated medications in understudied groups. Worse performance in the understudied groups is, in part, due to being more likely to have an indeterminate drug response phenotype when compared to a European ancestry group. Having more indeterminate drug response statuses in some racial subgroups translates in to more occurrences of “missing data” in assessments of an individuals’ drug response, thus resulting in lower racial fairness. One possible solution to this challenge of knowing if low racial fairness is a problem, is to estimate the pharmacogenomic- guided drug policy performance and fairness for different racial subgroups a priori. The specific objective of this project is to use All of Us research program (AoU) data to derive evidence of the potential unintended consequence of low racial fairness that can exist with a new pharmacogenomic-guided drug policy. The AoU data is uniquely suited to generate such evidence given that it includes a diversity of racial subgroups and a variety of data types, including from electronic health records and clinical whole genome sequencing data. We will conduct an observational cohort study using the AoU data to assess the performance of pharmacogenomics- guided drug policies to identify well-tolerated medications (Aim 1), and quantify the potential impact of differential data access among patients on performance (Aim 2). We will also study the impact of differential data access on the racial fairness of pharmacogenomics-guided drug policy (Aim 3). Outcomes of this work will demonstrate one strategy to produce evidence from real-world data that can be expanded upon and studied further in future research. Presenting this type of evidence prior to approving pharmacogenomics-guided drug policy holds promise to inform Pharmacy & Therapeutics committee decision-making.

项目概要 药物基因组学指导的药物政策包括个体与其他药物反应的基因组图谱 临床特征（年龄、体重等）并可能提高药物治疗的安全性和有效性。 因此，近年来美国多个医疗中心开展了临床药物基因组学研究 支持此类政策的服务。在可以支持的服务中，先发制人的临床基因分型 在得知患者可能需要某种特定药物之前，服务会生成药物基因组数据。 预先临床基因分型服务，涵盖主要基于欧洲人群的遗传标记 然而，血统可能会降低识别耐受良好药物的政策的绩效。 被研究群体。受研究群体表现较差的部分原因是他们更有可能 与欧洲血统群体相比，药物反应表型不确定。拥有更多 某些种族亚群体中不确定的药物反应状态导致更多的“失踪” 数据”来评估个人的药物反应，从而导致种族公平性降低。一种可能的情况是 了解低种族公平性是否是一个问题这一挑战的解决方案是估计药物基因组学 先验指导不同种族亚群体的毒品政策绩效和公平性。具体目标 该项目将使用“我们所有人”研究计划 (AoU) 数据来获取潜在意外事件的证据 新的药物基因组学指导的药物政策可能存在低种族公平性的后果。这 AoU 数据特别适合生成此类证据，因为它包括多样性的种族亚组和 各种数据类型，包括电子健康记录和临床全基因组测序数据。我们 将使用 AoU 数据进行观察性队列研究，以评估药物基因组学的表现- 指导药物政策以确定耐受性良好的药物（目标 1），并量化差异化药物的潜在影响 患者之间关于表现的数据访问（目标 2）。我们还将研究差异数据访问的影响 药物基因组学指导药物政策的种族公平性（目标 3）。这项工作的成果将证明 一种从现实世界数据中生成证据的策略，可以在未来进一步扩展和研究 研究。在批准药物基因组学指导的药物政策之前提出此类证据 承诺告知药剂学和治疗委员会的决策。