Pharmacogenomics Workflow: Identifying Biomarkers and Treatment Options

药物基因组学工作流程：识别生物标志物和治疗方案

• 批准号: 10819933
• 负责人: Andreas Scherer
• 金额: $ 39.98万
• 依托单位: GOLDEN HELIX, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10819933
• 关键词: Address; Adopted; Adoption; Adverse effects; Affect; American; Architecture; Automation; Bioinformatics; Clinical; Clinical Decision Support Systems; Computer software; Data; Data Analyses; Data Sources; Databases; Development; Diagnosis; Dose; Drug Labeling; Ensure; Feedback; Genes; Genetic Screening; Genetic Services; Genotype; Goals; Guidelines; Health Care Costs; Health Personnel; Healthcare; Individual; Industry; Laboratories; Learning; Literature; Malignant Neoplasms; Manuals; Medical Genetics; Methods; Mutation; Newborn Infant; Outcome; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Phase; Positioning Attribute; Process; Professional Organizations; Rare Diseases; Recommendation; Reporting; Research; Resources; Role; Safety; Small Business Innovation Research Grant; System; Techniques; Technology; Testing; Time; United States Food and Drug Administration; United States National Institutes of Health; Update; Variant; adverse drug reaction; biomarker identification; clinical decision support; clinical decision-making; clinical practice; clinically relevant; cost; cost effective; data repository; design; dosage; exome; gene interaction; genetic makeup; genetic testing; genome sequencing; genomic data; genotyping technology; health empowerment; improved; individual patient; innovation; knowledge integration; medical schools; molecular pathology; next generation sequencing; personalized approach; personalized medicine; pharmacogenetic testing; reproductive; research and development; research clinical testing; response; support tools; system architecture; tumor; user-friendly; whole genome

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.

摘要 药物基因组学研究个体的基因构成如何影响他们对药物的反应，已经 经历了快速的进步。与此同时，基因分型技术的成本也在下降， 使药物基因组学在临床实践中的实施越来越可行。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 个别患者，远离“一药适用”或“一剂适用于所有人”的策略。这一转变向 更个性化的方法为医疗保健提供者提供了提高临床结果的机会， 减少药物不良反应，通过将药物基因组学整合到 常规的临床实践。 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。这些测试被广泛应用于 诊断罕见疾病，分析肿瘤的突变图谱，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，集成了 临床决策支持工具，以熟练地处理广泛的已识别序列变体。 尽管FDA经常要求在药品标签中包括基因-药物关联，但许多 存在临床相关的数据源(例如，PharmVar和PharmGKB)，目前缺乏集成 将这些资源纳入NGS测试工作流程。这意味着药物与基因的相互作用可能导致严重的 患者的不良反应往往被忽视。 在这个项目中，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用于自动识别的方法 和对药物遗传变异的解释，并将这些发现整合到医疗保健的临床报告中 供应商。此外，该项目还需要对药物遗传学测试的临床实用性进行评估。 下一代测序，评估其对治疗决策、患者结果和医疗保健的影响 成本。总体而言，该项目的目标是将药物遗传测试作为下一代的常规组成部分 测序，为临床医生提供有价值的信息，以优化他们的药物选择和剂量 病人。