Genomic Approaches for Predicting Drug Response

预测药物反应的基因组方法

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要药物基因组学领域的发展源于发现导致变异的遗传变异药物代谢酶的功能对临床实施基因指导药物处方的影响。然而，只有少数药物具有临床有效且可操作的遗传关联。一个问题是目前的药物基因组学方法侧重于对药物反应有很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，包括数百或数千个具有不同效应大小的遗传变异的影响。主要目标该项目旨在量化和表征整个基因组变异对药物反应的影响结果。使用现有的数据集，我们将测量复杂的多基因变异对响应的影响各种药物和药物类别。我们还将探索芬太尼分布中罕见的遗传变异。目标 1 是分析作为先前全基因组关联研究的一部分进行基因分型的所有变异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，以确定对每种药物反应的遗传结构。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量可归因于使用全基因组捕获的所有遗传变异基因分型。我们还将衡量效应大小较小、中等和较大的变异的相对影响。目标 1 的完成结果将指导药物基因组学的未来工作。对于药物来说，几乎所有遗传效应都是由少数已确定的变异介导的，焦点可以从变异转移使用当前的靶向基因分型范式从发现到临床实施。相比之下，对于药物由于数百个具有可变效应大小的变异而具有遗传效应，多基因模型的验证下一步是跨不同人群。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，提供在未来的研究中测试该基因组预测因子的临床实施的机会。在目标 3 中，我们将进一步探索芬太尼的处置，对高度非典型的个体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的生物学机制和运输。总体而言，通过从药物-基因相互作用到药物-基因组相互作用的转变，完成了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。