Multiscale modeling of inherited cardiomyopathies and therapeutic interventions

遗传性心肌病的多尺度建模和治疗干预

• 批准号: 9980457
• 负责人: Kenneth S Campbell
• 金额: $ 64.48万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-03 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980457
• 关键词: Acute; Affect; Algorithms; American; Biological Assay; Biology; Cardiac; Cardiac Death; Cardiomyopathies; Cardiovascular Diseases; Cells; Chronic; Computer Models; Computer Simulation; Computer software; Coupled; DNA Sequence Alteration; Data; Data Analyses; Development; Drug usage; Elements; Engineering; Fiber; Future; Genetic; Geometry; Goals; Growth; Heart; Heart Abnormalities; Heart Diseases; Heart Hypertrophy; Heart failure; Histology; Hypertrophy; Inherited; Intervention; Kinetics; Laws; Magnetic Resonance Imaging; Mathematics; Measurement; Measures; Modeling; Molecular; Motor; Mutation; Myocardial; Myosin ATPase; Myosin Regulatory Light Chains; Organ; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacotherapy; Proteins; Records; Research; Research Personnel; Sarcomeres; Scientist; Structure; Techniques; Testing; Therapeutic Intervention; Time; Tissues; Transgenic Animals; Transgenic Mice; Treatment Failure; Validation; Ventricular; Ventricular Remodeling; Wild Type Mouse; Work; cell motility; drug testing; experience; experimental study; genetic regulatory protein; heart function; improved; in silico; inherited cardiomyopathy; innovation; interest; kinetic model; mathematical model; model development; molecular scale; multi-scale modeling; novel therapeutics; personalized medicine; predictive modeling; response; simulation; skills; software development; treatment optimization; treatment planning

ABSTRACT The goal of this research is to develop a predictive multiscale model that will improve understanding of familial cardiomyopathies and that can be used to help screen potential new therapies for cardiac disease. Familial cardiomyopathies are the most frequently inherited heart defect and affect about 700,000 Americans. Most of the genetic mutations affect myosin or regulatory proteins that modulate myosin function. The majority of these mutations also induce abnormal cardiac growth termed hypertrophy. This project will develop, calibrate, and validate an innovative multiscale model that uses data quantifying myosin-level function to predict how hearts hypertrophy over time. This is a critical step on the path to developing patient-specific computer models that can be used to optimize treatments for heart failure and to predict the effects of different types of pharmaceutical intervention. In the future, one could envision clinicians testing drug treatments in silico and selecting the intervention that produces the greatest long-term benefit for their patient. The research team consists of two physiologists/biophysicists (Campbell & Yengo) and two engineers (Wenk & Lee) who share a common interest in cardiac biology. Together, their research skills span from structure-function analysis of myosin molecules to computer simulations of hearts that grow and remodel over time. The research plan integrates state-of-the-art hierarchically-coupled mathematical models with validation experiments that range from stopped-flow molecular kinetic assays to magnetic resonance imaging of myocardial strain patterns. The model will be tested using molecular to organ-level experimental data obtained from wild-type mice and from transgenic animals that develop cardiac hypertrophy because of a K104E mutation in myosin regulatory light chain. Additional tests will be performed using drugs that enhance (omecamtiv mecarbil) and inhibit (MYK-461) myosin-level contractile function. There are three specific aims. Aim 1: Integrate a multistate kinetic model of myosin into an organ-level finite framework to predict the effects of genetic and/or pharmaceutical modulation of myosin function. Aim 2: Develop growth and remodeling algorithms to predict chronic changes in ventricular structure and function resulting from genetic and/or pharmaceutical modulation of myosin function. Aim 3: Calibrate and validate the model using experimental data quantifying different spatial and temporal scales.

抽象的 这项研究的目的是开发一个预测性的多尺度模型，以提高对家族的理解 心肌病，可用于帮助筛查潜在的心脏病疗法。家族 心肌病是最常见的心脏缺陷，影响约70万美国人。大多数 遗传突变会影响调节肌球蛋白功能的肌球蛋白或调节蛋白。其中大多数 突变还诱导异常的心脏生长称为肥大。这个项目将开发，校准，并且 验证一种创新的多尺度模型，该模型使用量化肌球蛋白级功能的数据来预测心脏 随着时间的推移肥大。这是开发特定于患者的计算机模型的路径的关键步骤 用于优化心力衰竭的治疗方法，并预测不同类型的药物的影响 干涉。将来，可以设想临床医生在硅中测试药物治疗并选择 干预为患者带来最大的长期利益。 研究小组由两名生理学家/生物物理学家（Campbell＆Yengo）和两名工程师组成（Wenk＆ Lee）在心脏生物学上有共同的兴趣。他们的研究技能跨越结构功能 分析肌球蛋白分子对随着时间的推移而生长和重塑的心脏的计算机模拟。研究 计划将最先进的层次结合耦合数学模型与验证实验进行 范围从停止流量分子动力学测定到心肌应变模式的磁共振成像。 该模型将使用从野生型小鼠获得的分子至器官级实验数据进行测试 由于肌球蛋白调节光中的K104E突变，会发展心脏肥大的转基因动物 链。将使用增强的药物（Omecamtiv Mecarbil）进行其他测试并抑制（Myk-461） 肌球蛋白级收缩功能。 有三个特定的目标。 AIM 1：将肌球蛋白的多态动力学模型整合到器官水平的有限框架中，以预测 肌球蛋白功能的遗传和/或药物调节。 目标2：发展生长和重塑算法以预测心室结构和功能的慢性变化 肌球蛋白功能的遗传和/或药物调节导致。 AIM 3：使用实验数据量化不同的空间和时间尺度的实验数据校准和验证模型。