Merging artificial intelligence (AI) and pharmacometrics to elucidate gene-drug interactions linked to clopidogrel responsiveness in Caribbean Hispanic patients

融合人工智能 (AI) 和药理学，阐明与加勒比西班牙裔患者氯吡格雷反应相关的基因药物相互作用

• 批准号: 10626448
• 负责人: Jorge Duconge
• 金额: $ 14.9万
• 依托单位: UNIVERSITY OF PUERTO RICO MED SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626448
• 关键词: Address; Adenosine Diphosphate; Adverse Drug Experience Report; Adverse drug event; Adverse effects; Affect; Age; Area; Artificial Intelligence; Biochemical; Biological Availability; Blood Platelets; Body mass index; CYP2C19 gene; CYP3A5 gene; Cardiometabolic Disease; Cardiovascular system; Caribbean Hispanic; Characteristics; Cilostazol; Clinical; Clinical Research; Collagen; Coronary Arteriosclerosis; Coronary Artery Bypass; Costs and Benefits; Data; Demographic Factors; Development; Diabetes Mellitus; Disparity; Dose; Drug Combinations; Drug Exposure; Drug Interactions; Drug Kinetics; Effectiveness; Elderly; Environment; Environmental Risk Factor; Ethnic Population; European ancestry; Exclusion; Exposure to; Family history of; Food; Gender; Genes; Genetic; Genetic Load; Genetic Polymorphism; Genetic Risk; Genome; Genotype; Guidelines; Guns; Healthcare; Hematocrit procedure; Heritability; Heterogeneity; High Prevalence; Hispanic Populations; Individual; Inflammation; International; Kidney Failure; Knowledge; Left Ventricular Ejection Fraction; Length; Lesion; Link; Machine Learning; Measurement; Measures; Medical; Medical center; Metabolic; Minority Groups; Modeling; Multivariate Analysis; Myocardial Infarction; Obesity; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacodynamics; Pharmacogenetics; Pharmacogenomics; Pharmacotherapy; Phenotype; Play; Population; Precision Medicine Initiative; Procedures; Publishing; Purinoceptor; Recommendation; Regimen; Research; Severities; Smoking; Solid; Stents; System; Techniques; Testing; Time; Translating; Underrepresented Populations; United States National Institutes of Health; Up-Regulation; Variant; absorption; acute coronary syndrome; clinical practice; clinically actionable; clinically relevant; clinically significant; clopidogrel; drug response prediction; expectation; falls; frontier; genetic variant; genome wide association study; health care service; health inequalities; high body mass index; hypercholesterolemia; individualized medicine; inhibitor; insight; inter-individual variation; knowledgebase; machine learning method; medically underserved population; metabolic phenotype; non-genetic; novel; percutaneous coronary intervention; pharmacodynamic model; pharmacokinetics and pharmacodynamics; pharmacometrics; population based; precision medicine; predictive modeling; predictive tools; response; risk variant; sound; success; systematic review; treatment response; Address; Adenosine Diphosphate; Adverse Drug Experience Report; Adverse drug event; Adverse effects; Affect; Age; Area; Artificial Intelligence; Biochemical; Biological Availability; Blood Platelets; Body mass index; CYP2C19 gene; CYP3A5 gene; Cardiometabolic Disease; Cardiovascular system; Caribbean Hispanic; Characteristics; Cilostazol; Clinical; Clinical Research; Collagen; Coronary Arteriosclerosis; Coronary Artery Bypass; Costs and Benefits; Data; Demographic Factors; Development; Diabetes Mellitus; Disparity; Dose; Drug Combinations; Drug Exposure; Drug Interactions; Drug Kinetics; Effectiveness; Elderly; Environment; Environmental Risk Factor; Ethnic Population; European ancestry; Exclusion; Exposure to; Family history of; Food; Gender; Genes; Genetic; Genetic Load; Genetic Polymorphism; Genetic Risk; Genome; Genotype; Guidelines; Guns; Healthcare; Hematocrit procedure; Heritability; Heterogeneity; High Prevalence; Hispanic Populations; Individual; Inflammation; International; Kidney Failure; Knowledge; Left Ventricular Ejection Fraction; Length; Lesion; Link; Machine Learning; Measurement; Measures; Medical; Medical center; Metabolic; Minority Groups; Modeling; Multivariate Analysis; Myocardial Infarction; Obesity; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacodynamics; Pharmacogenetics; Pharmacogenomics; Pharmacotherapy; Phenotype; Play; Population; Precision Medicine Initiative; Procedures; Publishing; Purinoceptor; Recommendation; Regimen; Research; Severities; Smoking; Solid; Stents; System; Techniques; Testing; Time; Translating; Underrepresented Populations; United States National Institutes of Health; Up-Regulation; Variant; absorption; acute coronary syndrome; clinical practice; clinically actionable; clinically relevant; clinically significant; clopidogrel; drug response prediction; expectation; falls; frontier; genetic variant; genome wide association study; health care service; health inequalities; high body mass index; hypercholesterolemia; individualized medicine; inhibitor; insight; inter-individual variation; knowledgebase; machine learning method; medically underserved population; metabolic phenotype; non-genetic; novel; percutaneous coronary intervention; pharmacodynamic model; pharmacokinetics and pharmacodynamics; pharmacometrics; population based; precision medicine; predictive modeling; predictive tools; response; risk variant; sound; success; systematic review; treatment response

PROJECT SUMMARY Gene-drug-drug interactions (GDDIs) are becoming a new frontier in Precision Medicine (PM). Pharmacogenomics can certainly be a good predictive tool to guide drug therapies but is far from being a deterministic measure of phenotypes. A current limitation in most of existing guidelines is their lack of provisions to address the effect of drug combinations, co-medications and, therefore, the risk for GDDIs. Although the high inter-individual variability in the response to clopidogrel has primarily been associated with genetic polymorphisms, multivariate analyses suggest that additional factors (e.g., GDDIs) may contribute to the overall between-subject variability in treatment response. However, the extent to which each of these additional factors contributes to the overall variability, and how they are interrelated, is currently unclear. To this purpose, we propose to derive, for the first time ever, a weighted genetic risk score system based on a genome-wide association study in Caribbean Hispanic patients and machine learning methods. We also plan on developing a novel semi-mechanistic population-based pharmacokinetic/pharmacodynamics (PK-PD) modeling of clopidogrel in individuals with GDDIs with cilostazol in order to identify the clinically relevant factors affecting drug exposure and response, which may ultimately serve as a solid basis for dosing optimization and tailoring therapies. Based on strong preliminary data, we hypothesize that: “The enhancing effect of cilostazol over the clopidogrel-induced anti-platelet activity in Caribbean Hispanic patients exposed to these interacting co-medications is exclusive of those who also are CYP2C19 PMs and CYP3A5 non-expressers” This study is expected to provide important new information on the proportions of individuals from the Caribbean Hispanic population who are likely to have combinations of pharmacogenomics variants and exposure to interacting co-medications that may eventually affect their health care. Hispanics have been largely excluded from PM initiatives, which increase dramatically the disparities in translating benefits from new findings in pharmacogenomics to this medically underserved population, exacerbating the existing inequity in healthcare services. Accordingly, the proposed research will expand our current understanding of the PK-PD interactions between clopidogrel and cilostazol from a pharmacogenetics perspective. Advancing knowledge in the under- investigated area of pharmacogenetics in minority populations will generate results that apply to personalize antiplatelet therapy in the wider population as it moves, inevitably, toward increasing heterogeneity through admixed genomes.

项目摘要 基因 - 药水相互作用（GDDIS）正在成为精密医学（PM）的新领域。 药物基因组学当然可以成为指导药物疗法的好预测工具，但远非 表型的确定性测量。目前在大多数现有准则中的限制是他们缺乏规定 解决药物组合，共同药物的影响，因此是GDDIS的风险。虽然很高 对氯吡格雷反应的个体间变异性主要与遗传有关 多态性，多元分析表明，其他因素（例如，GDDIS）可能有助于整体 治疗反应中受试者之间的变异性。但是，这些其他因素在多大程度上 目前尚不清楚有助于总体可变性及其相互关联的方式。为此，我们 提出基于全基因组的加权遗传风险评分系统，这是有史以来首次得出的提议 加勒比西班牙裔患者和机器学习方法的协会研究。我们还计划开发一个 新型的半机械基于种群的药代动力学/药效学（PK-PD）Clopidagrel的建模 在患有cilostazol的GDDI的个体中，以识别影响药物暴露的临床相关因素 和反应，这最终可能是给药优化和调整疗法的坚实基础。基于 关于强大的初步数据，我们假设： “在加勒比海西班牙裔中氯吡格雷诱导的抗血小板活性上，西洛唑唑的增强作用的增强作用 暴露于这些相互作用的共同药物的患者不包括那些也是CYP2C19 PM的患者 CYP3A5非表达者” 预计这项研究将提供有关加勒比个人比例的重要新信息 西班牙裔人口可能具有药物基因组学的组合并接触 相互作用的共同用品有时可能会影响他们的医疗保健。西班牙裔在很大程度上被排除在外 从总理计划中，从新发现中转化新发现的收益的分布大大增加了 药物基因组学对这一医学服务不足的人群，加剧了医疗保健中现有的不平等现象 服务。彼此之间，拟议的研究将扩大我们当前对PK-PD相互作用的理解 从药物遗传学的角度来看，在氯吡格雷和西洛唑嗪之间。促进知识不足的知识 少数族裔人口中的药物遗传学领域将产生适用于个性化的结果 抗血小板治疗在更广泛的人群中，不可避免地会通过 混合基因组。