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

项目摘要 药物基因组学指导的药物政策包括个人与其他人的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，美国的几个医学中心已经实施了临床药物基因组学 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 在知道患者可能需要一种特定的药物之前，服务就会产生药物基因组数据。 先发制人的临床基因分型服务，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执行情况 未得到充分研究的群体。在研究不足的群体中表现较差，部分原因是更有可能 与欧洲血统群体相比，一种不确定的药物反应表型。拥有更多 某些种族亚群中不确定的药物反应状态会转化为更多的“失踪”事件 在评估个人的药物反应时使用“数据”，从而导致较低的种族公平。一种可能 要知道种族不公平是否是一个问题，解决方案是评估药物基因组学- 先验地指导不同种族亚群的药品政策绩效和公平性。的具体目标 这个项目是使用所有美国研究计划(AOU)的数据来获得潜在的意外证据 新的药物基因组导向的药物政策可能存在的低种族公平性的后果。这个 AOU数据特别适合于生成此类证据，因为它包括各种种族亚群和 各种数据类型，包括来自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)，并量化差异的潜在影响 患者对绩效的数据访问(目标2)。我们还将研究差异数据访问的影响 论药物基因组学指导的药物政策的种族公平性(目标3)。这项工作的成果将证明 一种从真实世界的数据中产生证据的策略，这些证据可以在未来进行扩展和进一步研究 研究。在批准药物基因组学指导的药物政策之前提交这类证据 承诺告知药学和治疗委员会的决策。

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.