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 A recently issued FDA draft guidance, however, clarified that the effect on symptoms or physical function without a favorable effect on survival or hospitalization could be a basis for approving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）目前是一种全球性流行病，预计未来会增加。