Systems Pharmacology Model of Cardiac Hypertrophy

总结 心力衰竭定义为心输出量不能满足需求。此外，心力衰竭会导致 显著的发病率和死亡率，影响了超过600万美国人，尽管 目前的药理学和基于设备的治疗。心脏肥大，定义为 心肌细胞大小和心肌质量，导致适应不良的重塑，是一个重要的前体， 心衰因此，在心脏肥大期间的早期干预具有改善健康和 患者的结果。几十年来，研究已经表征了单个细胞内分子 心脏肥大的调节剂;然而，专门针对心脏肥大的有效临床治疗 仍然难以捉摸我们的目标是克服过去的障碍，集中在一个单一的信号分子，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 这些药物是药物再利用的可行候选药物。我们的总体目标是识别药物和网络 作为治疗靶点来控制心脏肥大的机制。为了实现这一目标，我们将全面测试 假设系统药理学网络模型可以准确地预测上下文相关的影响， 药物对心肌细胞肥大的影响。在具体目标1中，我们将应用系统药理学 用于预测引起心肌细胞的情境依赖性调节的药物和药物组合的模型 肥厚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 在不同环境下抑制心肌细胞肥大的组合和网络机制 contexts.在具体目标2中，我们将使用培养的大鼠和 人心肌细胞以测试心肌细胞肥大的环境依赖性抑制。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肌肥大的体内小鼠模型 并确定该建模是否以上下文相关的方式准确地预测药物的作用。 总的来说，这些研究将建立一个系统药理学模型，新的计算见解如何药物 调节心脏肥大，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.