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: 10626114
• 负责人: Jason Hansen Karnes
• 金额: $ 39.46万
• 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/10626114
• 关键词: 16S ribosomal RNA sequencing; Accounting; Address; Admixture; Adverse drug event; Adverse event; Affect; African; African American population; 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; European ancestry; Failure; Genes; Genetic; Genotype; Guidelines; Hospitalization; Hour; Individual; International; Investigation; Latino; Latino Population; 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; cohort; dietary control; disparity reduction; diverse data; genome-wide; gut bacteria; gut microbiome; improved; machine learning model; medically underserved; microbial community; microbiome; multi-racial; 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.

抽象的 华法林仍然是最常见的药物之一，也是紧急事件的主要原因 住院。华法林使用在医疗服务不足的患者（例如非洲）中尤其常见 美国人（AAS）和拉丁美洲人，这特别令人担忧，因为AAS和Latinos的结果较差 由于次优华法林治疗。因此，AAS和Latinos可以从华法林获得明显的好处 药物基因组学（PGX）算法，通过预测个性化华法林，可以最大程度地发挥安全性和功效 剂量。但是，当前可用的PGX算法有关键的局限性，包括缺乏可普遍性 非白人人群，未能占华法林剂量变异性的50％。代表性不足 在临床研究中，引起不良事件的倾向以及缺乏对混合种群的考虑 在临床PGX指南中，所有因素都导致华法林PGX算法有限的多样性 人群。华法林稳定剂量可变性的许多潜在来源仍然尚未探索，包括 维生素K生物合成细菌物种的作用，局部祖先在华法林药物基因源的影响， 以及机器学习技术的潜力，以使多样化的精确华法林给药算法 人群。该提议探讨了华法林稳定剂量预测的总体假设 通过纳入肠道微生物组数据，局部血统的度量以及各种各样的机器学习来改进 人群。我们将追求三个具体目标（SAS）来检验这一假设：（SA1）确定 华法林稳定剂量上的肠道微生物组中的丰富性维生素K生物合成细菌； （SA2） 确定当地混合物对混合式种群中华法林稳定剂量的影响； （SA3）优化 使用机器学习的Warfarin PGX算法用于不同人群。在SA＃1中，我们将进行临床研究 在抗凝诊所就诊时收集粪便样品，并进行整个基因组细菌测序 确定维生素K生物合成细菌物种对华法林稳定剂量的影响。在SA＃2中，我们将估计 华法林药物基因源的非洲，欧洲和美国原住民当地血统 人口（n = 1194），并确定其对华法林稳定剂量的影响。在SA＃3中，大量的人口 华法林治疗的患者（n = 7366）将用于开发机器学习模型和测试改进的预测 在现有线性回归模型上的华法林稳定剂量。我们的研究克服了主要局限 以前的华法林PGX通过利用肠道微生物组数据，本地血统，机器学习和多样化的研究 混合种群。这项工作的结果将为当地血统调查提供一个框架 其他PGX药物对，可以在混合种群中使用临床PGX指南。这项研究有 确定华法林剂量可变性的新来源的潜力，提高华法林的安全性和功效 治疗，并减少医疗服务不足的患者PGX研究的差异。