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.

项目摘要 药物基因组学指导的药物政策包括个体对其他药物的药物反应的基因组图谱。 临床特征（年龄、体重等）并且可以提高药物治疗的安全性和有效性。 因此，近年来，美国的几个医学中心已经实施了临床药物基因组学 支持这些政策。在可以支持的服务中，抢先临床基因分型 在知道患者可能需要某种药物之前，药物基因组学服务就产生了药物基因组学数据。 先发制人的临床基因分型服务，涵盖主要基于欧洲人群的遗传标记 然而，祖先可能会降低识别耐受性良好的药物的政策的性能， 被忽视的群体在未充分研究的群体中，表现更差的部分原因是， 与欧洲血统组相比，药物反应表型不确定。具有更多 在某些人种亚组中，不确定的药物应答状态转化为更多的“缺失” 在评估个人的药物反应时，“数据”，从而导致种族公平性降低。一个可能 解决这个问题的挑战，知道如果低种族公平是一个问题，是估计药物基因组学- 指导药物政策的性能和公平性，为不同的种族亚组的先验。的具体目标 该项目将使用美国所有研究计划（AoU）的数据来获得潜在的非预期证据 低种族公平性的后果，可能存在一个新的药物基因组学指导的药物政策。的 AoU数据是唯一适合产生这样的证据，因为它包括种族亚组的多样性， 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性能- 指导药物政策，以确定耐受性良好的药物（目标1），并量化差异的潜在影响 患者之间的数据访问性能（目标2）。我们还将研究差异数据访问的影响 药物基因组学指导的药物政策的种族公平性（目标3）。这项工作的结果将证明 一种从真实世界的数据中产生证据的策略，可以在未来进一步扩展和研究 research.在批准药物基因组学指导的药物政策之前提交此类证据 承诺告知药学和治疗委员会的决策。