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: 10223922
• 负责人: Kenneth S Campbell
• 金额: $ 57.63万
• 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 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10223922
• 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.

摘要 本研究的目标是开发一个预测性多尺度模型，以提高对家族性疾病的理解。 这可以用来帮助筛选潜在的心脏病新疗法。家族性 心肌病是最常见的遗传性心脏缺陷，影响约70万美国人。大部分 基因突变影响肌球蛋白或调节肌球蛋白功能的调节蛋白。大多数这些 突变还诱导称为肥大的异常心脏生长。该项目将开发、校准和 验证一种创新的多尺度模型，该模型使用量化肌球蛋白水平功能的数据来预测心脏如何 随着时间的推移，这是开发患者特异性计算机模型的关键一步， 用于优化心力衰竭的治疗，并预测不同类型药物的效果。 干预在未来，人们可以设想临床医生在计算机上测试药物治疗， 为患者带来最大长期利益的干预措施。 研究小组由两名生理学家/生物药理学家（坎贝尔和延戈）和两名工程师（温克和 Lee），他们对心脏生物学有着共同的兴趣。他们的研究技能涵盖了结构-功能 从肌球蛋白分子的分析到随着时间的推移心脏生长和重塑的计算机模拟。研究 该计划将最先进的分层耦合数学模型与验证实验相结合， 范围从停流分子动力学分析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和从小鼠获得的分子到器官水平的实验数据进行测试。 由于肌球蛋白调节光中的K104 E突变而发生心脏肥大的转基因动物 链将使用增强（Omecamtiv Mecarbil）和抑制（MYK-461）的药物进行额外检测 肌球蛋白水平的收缩功能。 有三个具体目标。 目的1：将肌球蛋白的多状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。 目的2：开发生长和重塑算法，以预测心室结构和功能的慢性变化 由肌球蛋白功能的遗传和/或药物调节引起。 目标3：使用量化不同空间和时间尺度的实验数据校准和验证模型。