Systems Pharmacology Model of Cardiac Hypertrophy

心脏肥大的系统药理学模型

• 批准号: 10598591
• 负责人: Jeffrey J. Saucerman
• 金额: $ 76.17万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10598591
• 关键词: Address; Adrenergic Agents; Affect; American; Angiotensin II; Angiotensins; Animal Model; Antibodies; Aorta; Arteries; Biochemical; Biological Assay; Cardiac Myocytes; Cardiac Output; Cells; Clinical; Computer Models; Data; Databases; Devices; Drug Combinations; Drug Modulation; Drug Screening; Drug Targeting; Drug usage; Echocardiography; Educational workshop; Event; FDA approved; Goals; Growth; Health; Heart; Heart Hypertrophy; Heart failure; Human; Hypertrophy; In Vitro; Individual; Infusion procedures; Investigation; Investments; Knowledge; Measures; Medical Care Costs; Methods; Microscopy; Modeling; Molecular; Molecular Biology; Morbidity - disease rate; Myocardium; National Heart, Lung, and Blood Institute; Neonatal; Neuregulin 1; Office Visits; Organ; Pathway interactions; Patient-Focused Outcomes; Pharmaceutical Preparations; Pharmacology; Phenylephrine; Preparation; Prevalence; Rattus; Recommendation; Regulation; Renin; Signal Pathway; Signal Transduction; Signaling Molecule; Strategic vision; System; Testing; Translating; United States National Institutes of Health; Validation; Work; Writing; constriction; design; drug candidate; drug mechanism; drug repurposing; falls; improved; in vivo; individualized medicine; innovation; insight; mechanical signal; mortality; mouse model; muscle form; network models; novel therapeutics; pharmacologic; predictive modeling; pressure; response; simulation; targeted treatment; therapeutic target

Summary Heart failure is defined as the inability of cardiac output to meet demand. Moreover, heart failure causes significant morbidity and mortality, affecting over 6 million Americans, with ~50% mortality at five years despite current pharmacologic and device-based therapies. Cardiac hypertrophy, defined as an increase in cardiomyocyte size and heart muscle mass, leads to maladaptive remodeling and is a significant precursor of heart failure. Thus, intervening early during cardiac hypertrophy has the potential to improve the health and outcomes of patients. For decades, investigations have characterized individual intracellular molecular regulators of cardiac hypertrophy; however, effective clinical therapies specifically targeting cardiac hypertrophy remain elusive. We aim to overcome the past obstacles of focusing on a single signaling molecule by employing a systems approach that considers the more extensive network of signaling interactions and FDA-approved drugs that are viable candidates for drug repurposing. Our overall goal is to identify drugs and network mechanisms as therapeutic targets to control cardiac hypertrophy. To achieve this goal, we will test the overall hypothesis that a systems pharmacology network model can accurately predict the context-dependent effects of drugs on cardiomyocyte hypertrophy in vitro and in vivo. In Specific Aim 1, we will apply a systems pharmacology model to predict drugs and drug combinations that cause context-dependent regulation of cardiomyocyte hypertrophy. We will develop a computational model that integrates the cardiomyocyte signaling network with the pharmacologic mechanisms of FDA-approved drugs. We will then use this model to predict the drug combinations and network mechanisms that inhibit cardiomyocyte hypertrophy under distinct environmental contexts. In Specific Aim 2, we will validate our model predictions of candidate drugs using cultured rat and human cardiomyocytes to test the context-dependent inhibition of cardiomyocyte hypertrophy. In Specific Aim 3, we will translate the model and cell-based experimental data to in vivo mouse models of cardiac hypertrophy and determine whether the modeling accurately predicts the effects of drugs in a context-dependent manner. Overall, these studies will establish a systems pharmacology model, new computational insights into how drugs modulate cardiac hypertrophy, and a wealth of new experimental data that will validate these predictions.

概括 心力衰竭定义为无法满足需求的心脏产量。此外，心力衰竭导致 显着的发病率和死亡率，影响超过600万美国人，尽管 当前的药理和基于设备的疗法。心脏肥大，定义为增加 心肌大小和心肌质量，导致适应不良的重塑，是重要的前体 心脏衰竭。因此，在心脏肥大期间的早期介入有可能改善健康状况和 患者的结果。几十年来，研究表征了个体细胞内分子 心脏肥大的调节剂；但是，有效针对心脏肥大的有效临床疗法 保持难以捉摸。我们旨在克服过去通过采用单个信号分子的障碍 一种系统方法，该方法考虑了更广泛的信号交互和FDA批准的网络 是可行的候选药物的药物。我们的总体目标是识别毒品和网络 机制作为控制心脏肥大的治疗靶标。为了实现这一目标，我们将测试总体 系统药理学网络模型可以准确预测上下文依赖性效应的假设 体外和体内心肌细胞肥大的药物。在特定目标1中，我们将应用系统药理学 预测导致心肌细胞上下文依赖性调节的药物和药物组合的模型 肥大。我们将开发一个计算模型，将心肌细胞信号网络集成 FDA批准的药物的药理机制。然后，我们将使用此模型预测药物 在不同环境下抑制心肌细胞肥大的组合和网络机制 上下文。在特定目标2中，我们将使用培养的大鼠和 人体心肌细胞测试情境依赖性抑制心肌细胞肥大。在特定的目标3中 我们将将模型和基于细胞的实验数据转换为心脏肥大的体内小鼠模型 并确定建模是否以上下文依赖性方式准确预测药物的影响。 总体而言，这些研究将建立系统药理学模型，新的计算洞察有关药物的方式 调节心脏肥大，以及大量将验证这些预测的新实验数据。