Predictive multiscale in silico cardio-pharmacology

描述（由申请人提供）：长期寻求的目标是开发药物来管理心律失常，当心脏中的电脉冲变得紊乱时发生心律失常。心律失常的药理学管理失败的主要原因是因为目前无法预测具有固有复杂性质、活性代谢物和脱靶效应的离子通道阻断药物将如何改变心脏中产生的紧急电行为。迫切需要新的方法，在亚细胞尺度上的复杂药物相互作用的过程和由此产生的对器官水平电行为的紧急影响之间提供预测性联系。为了开始弥合差距，我们召集了一个专家团队来组装和测试一个新的多尺度模型框架，该框架首次将用于预测药物和离子通道的原子尺度相互作用的详细数学模型连接到通道，细胞，组织和器官水平的功能尺度预测。当我们计划使用原子结构模拟的预测来告知捕获药物和离子通道的复杂动力学相互作用的模型的动力学参数时，将形成前所未有的联系。然后将在细胞、组织和器官尺度的预测模型中研究计算组件，以揭示涌现行为的基本机制和复杂相互作用。将在人诱导多能干细胞（hiPSC）衍生的心肌细胞（hiPSC-CM）和哺乳动物细胞、组织和器官中进行实验，以验证细胞和组织中的模型预测。然后将在模型中扰动药物性质，以确定改善治疗潜力的药物性质变化。这些数据将被反馈到预测模拟和结构模型中，以鉴定具有预测的必要功能的小分子类似物，用于改善治疗。我们将在本申请中开发的用于预测药理学的多尺度模型将应用于证明其在1）药物预测，2）药物筛选和3）药物治疗方面的有用性的项目。最终目标是一个可扩展的自动化平台，该平台将与其他尖端技术相互作用，以服务于政府监管，工业，学术界和临床医学的目的，可以扩展到预测其他常见心脏疾病和兴奋性疾病（如癫痫，共济失调和疼痛）的药理学。

DESCRIPTION (provided by applicant): A long sought goal has been to develop drugs to manage cardiac arrhythmia, which occurs when electrical impulses in the heart become disordered. A primary reason that pharmacological management of cardiac arrhythmia has failed is because there is currently no way to predict how ion channel blocking drugs with intrinsically complex properties, active metabolites and off-target effects will alter emergent electrical behavior generated in the heart. New approaches that provide a predictive link between the processes of complex drug interactions at the subcellular scale and the resultant emergent effects on organ level electrical behavior are desperately needed. In order to begin to bridge the gap, we have brought together an expert team to assemble and test a new multiscale model framework that connects for the first time detailed mathematical models to predict atomic scale interactions of drugs and ion channels to functional scale predictions at the level of the channel, cell, tissue and organ. An unprecedented link will be formed as we plan to use predictions from the atomic structure simulations to inform the kinetic parameters of models that capture the complex dynamical interactions of drugs and ion channels. The computational components will then be studied in predictive models at the cell, tissue and organ scales to expose fundamental mechanisms and complex interactions underlying emergent behaviors. Experiments in human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) and mammalian cells, tissues and organs will be undertaken to validate model predictions in cells and tissue. Drug properties will then be perturbed in models to identify changes to drug properties that improve therapeutic potential. These data will be fed back to predictive simulations and structure models to identify small molecules analogs with predicted requisite function for improved therapy. The multiscale model for prediction of pharmacology that we will develop in this application will be applied to projects demonstrating its usefulness for 1) drug prediction, 2) drug screening and 3) drug therapy. The eventual goal is a scalable, automated platform that will interact with other cutting edge technologies to serve purposes in governmental regulation, industry, academia and in clinical medicine that can expand to predict pharmacology of other common cardiac diseases and disorders of excitability such as epilepsy, ataxia and pain.