Predictive multiscale in silico cardio-pharmacology

描述(申请人提供)：一个长期寻求的目标是开发药物来管理心律失常，当心脏的电脉冲变得紊乱时就会发生心律失常。心律失常的药物治疗失败的一个主要原因是，目前还没有办法预测具有内在复杂性质、活性代谢物和非靶点效应的离子通道阻滞剂将如何改变心脏产生的紧急电行为。迫切需要新的方法，在亚细胞级别的复杂药物相互作用过程和由此产生的器官水平电行为的紧急效应之间提供预测联系。为了开始弥合这一差距，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，该框架首次将详细的数学模型连接起来，以预测药物和离子通道的原子尺度相互作用，以及在通道、细胞、组织和器官水平上的功能尺度预测。随着我们计划使用原子结构模拟的预测来告知捕捉药物和离子通道复杂动力学相互作用的模型的动力学参数，将形成一种前所未有的联系。然后，将在细胞、组织和器官尺度上的预测模型中研究计算组件，以揭示紧急行为背后的基本机制和复杂的相互作用。将在人类诱导多能干细胞(HiPSC)来源的心肌细胞(hiPSC-CMS)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，药物属性将在模型中受到干扰，以确定提高治疗潜力的药物属性的变化。这些数据将被反馈到预测性模拟和结构模型，以识别具有预测所需功能的小分子类似物，以改进治疗。我们将在这一应用中开发的用于药理学预测的多尺度模型将被应用于展示其在1)药物预测、2)药物筛选和3)药物治疗中的有效性的项目。最终目标是一个可扩展的自动化平台，它将与其他尖端技术互动，服务于政府监管、工业、学术界和临床医学，可以扩展到预测其他常见心脏疾病和兴奋性疾病的药理，如癫痫、共济失调和疼痛。

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.