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.

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 住院治疗在医疗服务不足的患者中，如非洲人， 美国人（AAs）和拉丁美洲人，这是特别令人关注的，因为AAs和拉丁美洲人遭受更糟糕的结果 因为华法林治疗效果欠佳因此，AA和拉丁美洲人可以从华法林中获得明显的益处 药物基因组学（PGx）算法，通过预测个体化华法林来最大限度地提高安全性和疗效 次给药结束然而，目前可用的PGx算法具有关键的局限性，包括缺乏可推广性， 非白人人群和未能解释华法林剂量变异性的50%。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PGx指南中，所有因素都导致华法林PGx算法在各种疾病中的效用有限 人口。华法林稳定剂量变异性的许多潜在来源仍然未被探索，包括 维生素K生物合成细菌物种的作用，当地祖先对华法林药物基因的影响， 以及机器学习技术在不同领域实现准确华法林给药算法的潜力。 人口。该提案解决了总体假设，即华法林稳定剂量预测可以 通过整合肠道微生物组数据，当地血统的测量和机器学习， 人口。我们将追求三个特定目标（SA）来检验这一假设：（SA 1）确定 华法林稳定剂量时肠道微生物组中维生素K生物合成细菌的丰度;（SA 2） 确定混合人群中局部混合对华法林稳定剂量的影响;（SA 3）优化 华法林PGx算法，用于使用机器学习的不同人群。在SA#1中，我们将进行一项临床研究 在抗凝临床访视时采集粪便样本，并进行全基因组细菌测序， 确定维生素K生物合成细菌种类对华法林稳定剂量的影响。在SA#2中，我们将估计 非洲人、欧洲人和美洲原住民的当地祖先在华法林药物基因组中存在大量的混合 人群（n=1194），并确定其对华法林稳定剂量的影响。在SA#3中，一个庞大的，多样化的 华法林治疗患者（n=7366）将用于开发机器学习模型并测试改善的预测 华法林稳定剂量的线性回归模型。我们的研究克服了 之前的华法林PGx研究通过利用肠道微生物组数据，当地血统，机器学习和多样性， 混合种群这项工作的结果将为当地祖先调查提供一个框架， 其他PGx药物-基因对，使得能够在混合人群中使用临床PGx指南。本研究 确定华法林剂量变异性的新来源，提高华法林的安全性和有效性的可能性 治疗，并减少PGx研究的医疗服务不足的患者的差距。