Pharmacogenomics Workflow: Identifying Biomarkers and Treatment Options

摘要 药物基因组学是一门研究个体基因构成如何影响其对药物反应的学科， 经历了快速的进步。这与基因分型技术成本的降低同时发生， 使得药物基因组学在临床实践中的应用越来越可行。个性化医疗 利用药物基因组学正在获得动力，以优化药物选择，剂量，疗效和安全性， 个体患者，远离“一种药物适合所有人”或“一种剂量适合所有人”的策略。这一转变 更个性化的方法为健康护理提供者提供了增强临床结果的机会， 减少药物不良反应，并通过将药物基因组学整合到 常规临床实践。 随着外显子组和全基因组测序成本的下降，药物基因组学数据分析成为 在基于下一代测序（NGS）的测试中越来越重要。这些测试被广泛采用， 诊断罕见疾病，分析肿瘤的突变谱，提供生殖遗传服务，以及 对新生儿进行基因筛查。NGS测试实验室采用分析软件， 临床决策支持工具，以处理范围广泛的识别序列变异熟练。 尽管FDA经常要求将基因药物关联包括在药物标签中， 存在临床相关的数据源（例如，PharmVar和PharmGKB），目前缺乏整合 将这些资源整合到NGS测试工作流程中。这意味着药物-基因相互作用可能导致严重的 对患者的不良影响往往被忽视。 在本项目中，我们将开始开发药物遗传学分析能力，作为 基于NGS的基因检测这将涉及开发和验证自动识别方法 和药物遗传学变异的解释，并将这些发现整合到医疗保健的临床报告中 提供商此外，该项目需要评价药物遗传学检测在以下方面的临床效用： 下一代测序，评估其对治疗决策、患者结局和医疗保健的影响 成本总的来说，该项目旨在将药物遗传学检测作为下一代遗传学研究的常规组成部分。 测序，为临床医生提供有价值的信息，以优化药物选择和剂量， 患者

Abstract Pharmacogenomics, the study of how an individual's genetic makeup affects their response to drugs, has undergone rapid advancements. This has occurred alongside a decrease in the cost of genotyping technology, making implementation of pharmacogenomics into clinical practice increasingly feasible. Personalized medicine leveraging pharmacogenomics is gaining momentum to optimize drug choice, dosage, efficacy, and safety for individual patients, moving away from the "one drug fits all" or "one dose fits all" strategies. This shift towards a more personalized approach presents an opportunity for healthcare providers to enhance clinical outcomes, reduce adverse drug reactions, and achieve cost-effective healthcare by integrating pharmacogenomics into routine clinical practice. As the cost of exome and whole-genome sequencing declines, pharmacogenomic data analysis becomes increasingly relevant in next-generation sequencing (NGS) based tests. These tests are widely adopted to diagnose rare diseases, to analyze mutation profiles of tumors, to provide reproductive genetic services, and perform genetic screening in newborns. NGS testing laboratories employ analysis software featuring integrated clinical decision support tools to process the extensive range of identified sequence variants proficiently. Although the FDA often requires that gene-drug associations be included in drug labeling, and numerous clinically relevant data sources exist (e.g., PharmVar and PharmGKB), there is currently a lack of integration of these resources into NGS testing workflows. This means that drug-gene interactions that can lead to severe adverse effects in patients are often overlooked. In this project, we will begin developing pharmacogenetics analytics capability as an integrated component of NGS-based genetic testing. This would involve developing and validating methods for automating identification and interpretation of pharmacogenetic variants, and integrating these findings into clinical reports for healthcare providers. Moreover, the project necessitates the evaluation of the clinical utility of pharmacogenetic testing in next-generation sequencing, assessing its influence on treatment decisions, patient outcomes, and healthcare costs. Overall, the project aims to establish pharmacogenetic testing as a routine component of next-generation sequencing, providing clinicians with valuable information to optimize medication selection and dosing for their patients.