Systems Pharmacology Model of Cardiac Hypertrophy

心脏肥大的系统药理学模型

基本信息

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
项目类别：
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

项目摘要

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心输出量无法满足需求。此外，心力衰竭还会导致显着的发病率和死亡率，影响了超过 600 万美国人，尽管五年内死亡率约为 50% 当前的药物和基于设备的疗法。心脏肥大，定义为心脏肥大心肌细胞大小和心肌质量会导致适应不良重塑，并且是心脏衰竭。因此，在心脏肥大期间进行早期干预有可能改善健康状况和患者的结果。几十年来，研究已经表征了个体细胞内分子心脏肥大的调节剂；然而，专门针对心脏肥大的有效临床疗法仍然难以捉摸。我们的目标是通过采用一种系统方法，考虑更广泛的信号相互作用网络和 FDA 批准的是药物再利用的可行候选药物。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点。为了实现这个目标，我们将测试整体假设系统药理学网络模型可以准确预测上下文相关的效果体外和体内对心肌细胞肥大的药物。在具体目标 1 中，我们将应用系统药理学预测引起心肌细胞背景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与 FDA 批准药物的药理机制。然后我们将使用这个模型来预测药物不同环境下抑制心肌细胞肥大的组合和网络机制上下文。在具体目标 2 中，我们将使用培养大鼠和人类心肌细胞来测试心肌细胞肥大的背景依赖性抑制。在具体目标 3 中，我们将把模型和基于细胞的实验数据转化为心脏肥大的体内小鼠模型并确定模型是否以上下文相关的方式准确预测药物的作用。总的来说，这些研究将建立一个系统药理学模型，为药物如何发挥作用提供新的计算见解。调节心脏肥大，以及大量新的实验数据将验证这些预测。