Genomic Approaches for Predicting Drug Response

预测药物反应的基因组方法

• 批准号: 10375408
• 负责人: PRINCE Joseph KANNANKERIL
• 金额: $ 37.44万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10375408
• 关键词: Adverse drug effect; Affect; Angiotensin-Converting Enzyme Inhibitors; Antidepressive Agents; Architecture; Automobile Driving; Biological; Child; Clinical; Complex; Contrast Media; DNA; Data; Data Set; Discipline; Disease; Dose; Drug Interactions; 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; Lead; 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; base; 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 药物基因组学领域已从发现引起变量的遗传变异的发现发展 药物代谢酶在基因引导药物处方的临床实施方面的功能。然而， 只有少数药物具有临床有效且可起作用的遗传关联。一个问题 当前的药物基因组方法是对遗传变异的重点，对药物反应的影响很大 少数基因。对于大多数药物，药物代谢和反应的途径很复杂。为了 这些药物，遗传变异对药物处置和反应的影响也可能很复杂， 包括数百或数千种具有变化效应大小的遗传变异的影响。主要目标 该项目是为了定量和表征整个基因组中变体对药物反应的影响 结果。使用现有数据集，我们将衡量复杂多基因变化对响应的影响 多种药物和药物类别。我们还将探索芬太尼分布的罕见遗传变异。目标1是 分析所有基因组基因组关联研究的一部分的所有变体的集体效应 氯吡格雷，他汀类药物，甲氨蝶呤，ACE抑制剂，抗抑郁药和万古霉素，以确定 遗传结构，以应对每种药物。通过我们的分析，我们将使用混合模型来定量 药物反应的可变性量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，中和较大效果大小的变体的相对影响。 AIM 1完成后的发现将指导未来的药物基因组学努力。对于毒品几乎 所有遗传效应均由少数建立的变体介导，焦点可以从变体中转移 发现使用靶向基因分型的当前范式进行临床实施。相反，对于药物 由于数百种具有变化效应大小的变体引起的遗传效应，验证了多基因模型 下一步是跨不同人口。 AIM 2将混合模型方法应用于常用 和高度可变的药物，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指标 药代动力学。然后，基因组预测因子将在独立数据集中进行验证，以提供 在未来研究中测试该基因组预测指标的临床实施的机会。在目标3中，我们将 进一步探索芬太尼的处置，在具有高度非典型的个体中进行整个基因组测序 芬太尼药物浓度是为了鉴定新的基因和罕见的变体驱动芬太尼动力学。 这些新关联的发现将阐明芬太尼代谢的生物学机制和 运输。总体而言，通过从吸毒的相互作用到药物 - 基因组相互作用的转变， 这些目标将能够对具有复杂生物学途径的无数药物进行类似的研究。