Genomic Approaches for Predicting Drug Response

预测药物反应的基因组方法

• 批准号: 10589852
• 负责人: PRINCE Joseph KANNANKERIL
• 金额: $ 33.36万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-10 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10589852
• 关键词: Adverse drug effect; Affect; Angiotensin-Converting Enzyme Inhibitors; Antidepressive Agents; Architecture; Automobile Driving; Biological; Child; Clinical; Complex; DNA; Data; Data Set; Discipline; Disease; Disparate; Dose; Drug Kinetics; Drug Modelings; Drug Prescriptions; Drug Transport; Drug usage; Enzymes; Fentanyl; Frequencies; Future; Generations; Genes; Genetic; Genetic Variation; Genome; Genomic approach; Genomics; Genotype; Goals; Individual; Individual Differences; Investigation; Kinetics; Knowledge; Label; Measures; Mediating; Metabolism; Methods; Methotrexate; Mission; Modeling; Modernization; Outcome; Pathway interactions; Patient-Focused Outcomes; Pattern; Pharmaceutical Preparations; Pharmacodynamics; Pharmacogenomics; Phenotype; Population; Population Heterogeneity; Public Health; Research; Role; Sampling; Science; Testing; Therapeutic; United States National Institutes of Health; Validation; Vancomycin; Variant; Whole Organism; Work; clinical implementation; clinical predictors; clinically actionable; clopidogrel; cohort; drug action; drug disposition; drug metabolism; drug response prediction; gain of function; gene interaction; genetic architecture; genetic association; genetic variant; genome sequencing; genome wide association study; genome-wide; genome-wide analysis; genomic predictors; genomic variation; improved; improved outcome; innovation; loss of function; novel; opioid use; precision medicine; prospective test; rare variant; research clinical testing; response; whole genome

PROJECT SUMMARY / ABSTRACT The field of pharmacogenomics has progressed from the discovery of genetic variants that cause variable function of drug metabolism enzymes to clinical implementation of gene-guided drug prescribing. However, only a small number of drugs have clinically valid and actionable genetic associations. One problem with the current pharmacogenomic approach is a focus on genetic variants with a large effect on drug response among a small number of genes. For most drugs, the pathways of drug metabolism and response are complex. For these drugs, the effects of genetic variation on drug disposition and response are also likely to be complex, including effects of hundreds or thousands of genetic variants with variable effect size. The primary objective of this project is to quantitate and characterize the influence of variants throughout the genome on drug response outcomes. Using existing data sets, we will measure the impact of complex polygenic variation on response to a variety of drugs and drug classes. We will also explore rare genetic variation in fentanyl distribution. Aim 1 is to analyze the collective effect of all variants genotyped as part of prior genome-wide association studies for clopidogrel, statins, methotrexate, ACE-inhibitors, antidepressants, and vancomycin, in order to determine the genetic architecture for response to each drug. Through our analysis, we will use mixed models to quantitate the amount of variability in drug response can be attributed to all genetic variation captured using genome-wide genotyping. We will also measure the relative impact of variants with small, moderate, and large effect size. The findings from completion of Aim 1 will guide future efforts in pharmacogenomics. For drugs where nearly all genetic effects are mediated by a small number of well-established variants, the focus can shift from variant discovery to clinical implementation using the current paradigm of targeted genotyping. In contrast, for drugs with genetic effects due to hundreds of variants with variable effect size, validation of the polygenic models across diverse populations is the next step. Aim 2 applies the mixed models approach to the commonly used and highly variable drug, fentanyl. Using data from an ongoing fentanyl pharmacokinetic study, we will define the genomic architecture of fentanyl disposition in order to create a genomic predictor of fentanyl pharmacokinetics. The genomic predictor will then be validated in an independent dataset, providing the opportunity to test the clinical implementation of this genomic predictor in future research. In Aim 3 we will further explore fentanyl disposition, performing whole genome sequencing in individuals with highly atypical fentanyl drug concentrations in order to identify novel genes and rare variants driving fentanyl kinetics. Discovery of these new associations will illuminate biological mechanisms of fentanyl metabolism and transport. Overall, through a shift from drug-gene interactions to drug-genome interactions, the completion of these aims will enable similar investigation of the myriad of drugs with complex biological pathways.

项目摘要/摘要 药物基因组学领域从发现引起变异的遗传变异开始取得进展。 药物代谢酶在临床实施基因导向用药中的作用。然而， 只有一小部分药物具有临床有效和可操作的遗传关联。有一个问题是， 目前的药物基因组学方法关注的是对药物反应有较大影响的遗传变异 少量的基因。对于大多数药物来说，药物代谢和反应的途径是复杂的。为 这些药物，遗传变异对药物处置和反应的影响也可能是复杂的， 包括数百或数千个具有可变效应大小的遗传变异的效应。的主要目标是 这个项目是为了量化和描述基因组中的变异对药物反应的影响。 结果。利用现有的数据集，我们将衡量复杂的多基因变异对应对 各种毒品和毒品类别。我们还将探索芬太尼分布中罕见的遗传变异。目标1是 为了分析作为先前全基因组关联研究的一部分的所有基因分型的变异的集体效应 氯吡格雷、他汀类、甲氨蝶呤、血管紧张素转换酶抑制剂、抗抑郁药和万古霉素，以确定 对每种药物作出反应的遗传结构。通过我们的分析，我们将使用混合模型来量化 药物反应的变异性可以归因于使用全基因组捕获的所有遗传变异 基因分型。我们还将测量具有小、中和大效果大小的变量的相对影响。 完成AIM 1的发现将指导药物基因组学未来的努力。对于毒品来说，几乎 所有的遗传效应都是由一小部分成熟的变异所调节的，焦点可以从变异转移 使用当前的靶向基因分型范例从发现到临床实施。相比之下，对于毒品来说 由于数百个变种的遗传效应具有可变的效应大小，多基因模型的验证 下一步是跨越不同人群。目标2将混合模型方法应用于常用的 以及高度可变的药物芬太尼。使用正在进行的芬太尼药代动力学研究的数据，我们将定义 芬太尼处置的基因组结构以创建芬太尼的基因组预测因子 药物动力学。基因组预测器随后将在独立的数据集中进行验证，提供 有机会在未来的研究中测试这种基因组预测因子的临床实施情况。在《目标3》中，我们将 进一步探索芬太尼的使用情况，对高度不典型的患者进行全基因组测序 芬太尼药物浓度，以确定驱动芬太尼动力学的新基因和罕见变异。 这些新关联的发现将阐明芬太尼代谢和 运输。总体而言，通过从药物-基因相互作用转向药物-基因组相互作用，完成 这些目标将使对具有复杂生物途径的无数药物进行类似的研究成为可能。