Diagnostic Tools for Targeted Heart Failure Treatments

心力衰竭靶向治疗的诊断工具

• 批准号: 10546035
• 负责人: Tetsuro Wakatsuki
• 金额: $ 49.37万
• 依托单位: INVIVOSCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546035
• 关键词: 3-Dimensional; Action Potentials; Affect; Biomechanics; Biopsy; Calcium; Cardiac; Cardiac Catheterization Procedures; Cardiovascular system; Cells; Cessation of life; Clinical; Clinical Data; Clinical Trials; Coupling; Data; Data Set; Data Store; Development; Diabetes Mellitus; Drug Screening; Drug Targeting; EFRAC; Echocardiography; Electronic Health Record; Environmental Risk Factor; Epidemic; Failure; Fatty Acids; Functional disorder; Future; Genes; Genetic; Glucose; Goals; Heart failure; Hospitalization; Hypertrophic Cardiomyopathy; In Vitro; Inherited; Insulin; Left; Left Ventricular Hypertrophy; Life Style; Measures; Metabolic; Mitochondria; Modeling; Molecular Profiling; Monitor; Morbidity - disease rate; Myocardial; Myocardium; Non-Insulin-Dependent Diabetes Mellitus; Oncology; Patient Rights; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Physical Function; Physical activity; Population Heterogeneity; Predisposition; Property; Protocols documentation; Reporting; Risk; Smoking; Specificity; Subgroup; Symptoms; Syndrome; Systems Biology; Technology; Testing; Thyroid Hormones; Time; Tissues; Transforming Growth Factor beta; Treatment Failure; Ventricular; Woman; cardiac tissue engineering; cardiovascular risk factor; chronotropic; cost; diabetic patient; diagnostic catheterization; diagnostic platform; diagnostic tool; drug candidate; drug development; drug discovery; efficacy validation; feasibility testing; hemodynamics; high risk; induced pluripotent stem cell; innovation; machine learning method; mechanical properties; men; mortality; novel; patient population; patient stratification; precision medicine; preservation; reconstitution; sample collection; screening; targeted treatment; trend

Heart failure (HF) is a global epidemic at present and is projected to increase in the future. Despite the critical needs, HF drug discovery efforts are declining because of the requirement of large and long clinical trials to validate efficacy in morbidity and mortality endpoints. A recently issued FDA draft guidance, however, clarified that the effect on symptoms or physical function without a favorable effect on survival or hospitalization could be a basis for approving drugs to treat heart failure. Developing start diagnostic tools to monitor functional properties of cardiovascular systems, including myocardium function, will support a new trend of HF drug development. Annually >1 million diagnostic catheterizations have been performed; numerous data are stored in the electronic health record. Analyzing those data could provide an unprecedented opportunity to identify patterns of functional changes in the hemodynamics of various HFs. Our approach will combine computational and machine learning methods to achieve this goal. Diabetes mellitus (DM) in men and women have a 2X and 4X, respectively, higher risk of heart failure (HF) incident. A recent phenogrouping study identified a subgroup of HFpEF with diabetes having the highest risk of cardiovascular death and hospitalization among other groups. This study brought up an opportunity to develop a targeted therapy for HFpEF with DM (dHFpEF) by developing diagnostic tools to identify candidate dHFpEF patients. Here, we will test the feasibility of diagnostic tools to stratify HFpEF and model it in vitro. Aim 1 of the study is to optimize an already developed diagnostic platform, AI-Assisted, Systems-biology Integrated patient Stratification Technology (AASIST), to analyze data collected by trans-thoracic echocardiography (TTE) and right heart catheterization (RHC) for the purpose of classifying dHFpEF phenotypes. We expect to identify a few groups of dHFpEF defined by their mechanical properties of the myocardium (e.g., elevated left ventricular stiffness). Aim 2 of this study is to analyze corresponding parameters of LV stiffness and contractility in vitro using engineered heart tissues, NuHeart, reconstituted derived from DM patients’ cells. We will culture NuHeart with various environmental challenges to model dHFpEF. While lifestyle risks (e.g., smoking, low physical activities) may outweigh genetic influences, we hypothesize that NuHeart derived from a diabetic patient is susceptible to develop HFpEF phenotype depending on its culture conditions.

心力衰竭（HF）目前是一种全球流行病，预计未来会增加。 尽管需求迫切，但由于对以下方面的要求，高频药物发现工作正在下降： 最近进行了大规模和长期的临床试验来验证发病率和死亡率终点的疗效。 然而，FDA 发布的指南草案澄清了对症状或身体功能的影响 对生存或住院治疗没有有利影响可以作为批准药物的基础 治疗心力衰竭。开发启动诊断工具来监测心力衰竭的功能特性。 心血管系统，包括心肌功能，将支持心力衰竭药物的新趋势 每年进行超过 100 万次诊断导管插入术； 分析这些数据可以提供存储在电子健康记录中的数据。 前所未有的机会来识别血流动力学功能变化的模式 我们的方法将结合计算和机器学习方法来 实现这一目标的男性和女性糖尿病 (DM) 分别为 2 倍和 4 倍。 最近的一项表型分组研究确定了心力衰竭 (HF) 事件的一个亚组。 患有糖尿病的 HFpEF 患者心血管死亡和住院的风险最高 这项研究为开发 HFpEF 靶向治疗提供了机会。 通过开发诊断工具来识别候选 dHFpEF 患者，以识别 DM (dHFpEF)。 我们将测试诊断工具对 HFpEF 进行分层并对其进行体外建模的可行性。 该研究的目标 1 是优化已经开发的诊断平台 AI 辅助， 系统生物学综合患者分层技术 (AASIST)，用于分析数据 通过经胸超声心动图（TTE）和右心导管插入术（RHC）收集 对 dHFpEF 表型进行分类的目的我们期望识别几组 dHFpEF。 由心肌的机械特性定义（例如，升高的左心室 刚性）。 本研究的目的 2 是分析 LV 硬度和收缩性的相应参数 使用源自 DM 患者细胞的工程心脏组织 NuHeart 进行体外实验。 将培养 NuHeart 应对各种环境挑战，以模拟 dHFpEF 生活方式。 我们发现，风险（例如吸烟、体力活动不足）可能超过遗传影响 来自糖尿病患者的 NuHeart 容易出现 HFpEF 表型 取决于其文化条件。