Leveraging the Microbiome, Local Admixture, and Machine Learning to Optimize Anticoagulant Pharmacogenomics in Medically Underserved Patients

利用微生物组、局部混合物和机器学习来优化医疗服务不足的患者的抗凝药物基因组学

• 批准号: 10270784
• 负责人: Jason Hansen Karnes
• 金额: $ 41.91万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270784
• 关键词: 16S ribosomal RNA sequencing; Accounting; Address; Admixture; Adverse drug event; Adverse event; Affect; African; African American; Algorithms; Anticoagulants; Anticoagulation; CYP2C9 gene; Cardiovascular Diseases; Characteristics; Clinic Visits; Clinical; Clinical Research; Collection; DNA; Data; Data Set; Development; Diet Records; Dose; Drug Prescriptions; Drug Receptors; Emergency Situation; Enrollment; Enzymes; Epigenetic Process; Escherichia coli; European; Failure; Genes; Genetic; Genotype; Guidelines; Hispanics; Hospitalization; Hour; Individual; International; Investigation; Latino; Linear Regressions; Machine Learning; Measures; Medical Research; Mission; Modeling; Native Americans; Oral; Outcome; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Population; Population Heterogeneity; Public Health; Race; Randomized Controlled Trials; Research; Research Project Grants; Role; Safety; Sampling; Source; Techniques; Testing; Therapeutic; Training; United States; United States National Institutes of Health; Variant; Vitamin K; Warfarin; Work; adverse drug reaction; bacterial community; bacterial genome sequencing; base; cohort; dietary; disparity reduction; genome-wide; gut bacteria; gut microbiome; improved; medically underserved; microbial community; microbiome; novel; personalized predictions; response; sample collection; support vector machine; treatment disparity

ABSTRACT Warfarin remains one of the most commonly prescribed drugs and a leading cause of emergency hospitalizations. Warfarin use is especially common in medically underserved patients such as African Americans (AAs) and Latinos, which is particularly concerning since AAs and Latinos suffer worse outcomes due to suboptimal warfarin therapy. Thus AAs and Latinos can derive a distinct benefit from warfarin pharmacogenomic (PGx) algorithms, which maximize safety and efficacy by predicting individualized warfarin dose. However, currently available PGx algorithms have critical limitations, including a lack of generalizability to non-white populations and a failure to account for 50 percent of variability in warfarin dose. Under-representation in clinical studies, the propensity to cause adverse events, and a lack of consideration of admixed populations in clinical PGx guidelines are all factors that contribute to limited utility of warfarin PGx algorithms in diverse populations. Many potential sources of warfarin stable dose variability remain critically unexplored, including the role of vitamin K biosynthesizing bacterial species, the influence of local ancestry at warfarin pharmacogenes, and the potential for machine learning techniques to enable accurate warfarin dosing algorithms in diverse populations. This proposal addresses the overarching hypothesis that warfarin stable dose prediction can be improved by incorporation of gut microbiome data, measures of local ancestry, and machine learning in diverse populations. We will pursue three Specific Aims (SAs) to test this hypothesis: (SA1) Determine the impact of abundance of vitamin K biosynthesizing bacteria from the gut microbiome on warfarin stable dose and; (SA2) Determine the influence of local admixture on warfarin stable dose in admixed populations; (SA3) Optimize warfarin PGx algorithms for diverse populations using machine learning. In SA#1, we will conduct a clinical study with fecal sample collection at anticoagulation clinic visits and perform whole genome bacterial sequencing to identify the effect of vitamin K biosynthesizing bacterial species on warfarin stable dose. In SA#2, we will estimate African, European, and Native American local ancestry in warfarin pharmacogenes in a large, admixed population (n=1194) and determine its effects on warfarin stable dose. In SA#3, a large, diverse population of warfarin treated patients (n=7366) will be used to develop machine learning models and test improved prediction of warfarin stable dose over existing linear regression models. Our studies overcome major limitations of previous warfarin PGx studies by leveraging gut microbiome data, local ancestry, machine learning, and diverse, admixed populations. The outcomes of this work will provide a framework for local ancestry investigation with other PGx drug-gene pairs, enabling use of clinical PGx guidelines in admixed populations. This research has the potential to identify new sources of variability in warfarin dose, improve the safety and efficacy of warfarin treatment, and reduce disparities in PGx research for medically underserved patients.

摘要 华法林仍然是最常用的处方药之一，也是导致紧急情况的主要原因 住院治疗。华法林的使用在非洲等医疗服务不足的患者中尤其常见。 美国人(AA)和拉丁裔，这一点特别令人担忧，因为AA和拉丁裔遭受的后果更糟 由于华法林治疗效果不佳。因此，AA和拉丁裔可以从华法林中获得明显的好处 药物基因组学(PGx)算法，通过预测个性化华法林来最大化安全性和有效性 剂量。然而，当前可用的PGx算法具有严重的局限性，包括缺乏对 非白人人群以及未能解释华法林剂量50%的变异性。代表不足 在临床研究中，导致不良事件的倾向，以及缺乏对混合人群的考虑 在临床上，PGx指南是导致华法林PGx算法在不同领域的应用受限的所有因素 人口。华法林稳定剂量可变性的许多潜在来源仍然严重未被探索，包括 维生素K生物合成菌种的作用，华法林产药菌本地血统的影响， 以及机器学习技术的潜力，使准确的华法林剂量算法在不同的 人口。这项建议解决了华法林稳定剂量预测可以 通过将肠道微生物组数据、当地血统测量和机器学习纳入不同领域进行了改进 人口。我们将追求三个特定目标(SA)来检验这一假设：(SA1)确定 华法林稳定剂量下肠道微生物群维生素K生物合成菌的丰度；(SA2) 确定局部混合对混合人群中华法林稳定剂量的影响；(SA3)优化 使用机器学习的不同种群的华法林PGx算法。在SA#1中，我们将进行临床研究 在抗凝门诊采集粪便样本，并进行全基因组细菌测序 确定维生素K生物合成菌种对华法林稳定剂量的影响。在SA#2中，我们将估计 华法林药原中的非洲人、欧洲人和美洲原住民的地方血统 人群(n=1194)，确定其对华法林稳定剂量的影响。在SA#3，一个庞大的、多样化的人口 接受华法林治疗的患者(n=7366)将被用于开发机器学习模型和测试改进的预测 与现有的线性回归模型相比，华法林稳定剂量。我们的研究克服了以下主要限制 以前的华法林PGx研究利用肠道微生物组数据、当地血统、机器学习和多样性， 混杂种群。这项工作的成果将为当地的祖先调查提供一个框架 其他PGx药物-基因对，使临床PGx指南能够在混合人群中使用。这项研究已经 找出华法林剂量变异的新来源，提高华法林的安全性和有效性 治疗，并减少医疗服务不足患者在PGx研究方面的差异。