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: 10454235
• 负责人: Jason Hansen Karnes
• 金额: $ 44万
• 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/10454235
• 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; 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; base; cohort; dietary; disparity reduction; genome-wide; gut bacteria; gut microbiome; improved; machine learning model; 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.

抽象的 华法林仍然是最常用的处方药物之一，也是紧急情况的主要原因 住院治疗。华法林的使用在医疗服务不足的患者中尤其常见，例如非洲 美国人 (AA) 和拉丁裔，这尤其令人担忧，因为 AA 和拉丁裔的结果更糟 由于华法林治疗不理想。因此，AA 和拉丁裔可以从华法林中获得明显的益处 药物基因组 (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，包括缺乏通用性 非白人群体以及未能解释华法林剂量 50% 的变异性。代表性不足 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 临床 PGx 指南中的所有因素都导致华法林 PGx 算法在不同领域的效用有限 人口。华法林稳定剂量变异性的许多潜在来源仍未得到充分探索，包括 维生素 K 生物合成细菌种类的作用、当地血统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剂量算法的潜力 人口。该提案提出了一个总体假设，即华法林稳定剂量预测可以 通过整合肠道微生物组数据、当地血统测量和不同领域的机器学习来改进 人口。我们将追求三个具体目标 (SA) 来检验这一假设： (SA1) 确定 华法林稳定剂量下肠道微生物组中维生素 K 生物合成细菌的丰度； (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 使用机器学习针对不同人群的华法林 PGx 算法。在SA#1中，我们将进行一项临床研究 在抗凝诊所就诊时收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、欧洲和美洲原住民当地血统中的华法林药物基因存在于一个大型、混合的地区 人群（n=1194）并确定其对华法林稳定剂量的影响。在 SA#3 中，有大量多样化的人口 华法林治疗的患者 (n=7366) 将用于开发机器学习模型并测试改进的预测 华法林稳定剂量与现有线性回归模型的比较。我们的研究克服了主要局限性 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、机器学习和多样化， 混合人群。这项工作的成果将为当地血统调查提供一个框架 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。这项研究有 识别华法林剂量变异新来源的潜力，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的 PGx 研究中的差异。