Systems Pharmacology Model of Cardiac Hypertrophy

摘要 心力衰竭的定义是心输出量不能满足需求。此外，心力衰竭会导致 严重的发病率和死亡率，影响了600多万美国人，五年内死亡率约为50% 目前的药理学和基于设备的治疗。心肌肥厚，定义为心肌肥厚 心肌细胞大小和心肌质量，导致不良适应性重构，是一个重要的先兆 心力衰竭。因此，在心肌肥厚期间进行早期干预有可能改善健康和 患者的转归。几十年来，研究确定了单个细胞内分子的特征 心肌肥厚的调节剂；然而，针对心肌肥厚的有效临床治疗 仍然难以捉摸。我们的目标是克服过去专注于单一信号分子的障碍 一种系统方法，考虑更广泛的信号交互网络和FDA批准的 有可能成为药物再利用的候选药物。我们的总体目标是识别毒品和网络 作为治疗靶点的机制控制心肌肥厚。为了实现这一目标，我们将测试总体 假设系统药理学网络模型可以准确地预测 体内外治疗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 预测引起心肌细胞上下文相关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批准的药物的药理机制。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。在特定的目标2中，我们将使用培养的大鼠和 以测试对心肌细胞肥大的上下文依赖性抑制。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活体小鼠心肌肥厚模型 并确定该建模是否以依赖于上下文的方式准确地预测药物的效果。 总体而言，这些研究将建立一个系统的药理学模型，对药物如何 调节心肌肥厚，以及大量新的实验数据将验证这些预测。

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.