In silico Safety Pharmacology

项目概述：阻碍药物开发的一个主要因素是没有临床前药物筛选 可以准确预测药物引起的心律失常目前的方法依赖于 替代标记物，如ECG上的QT间期延长。不幸的是，QT间期延长 对心律失常可能性的指示既不特异也不敏感。迫切需要确定一种新的办法 可以预测实际的心律失常而不是替代指标。数学建模与仿真 构成了揭示基本生物学原理的最有前途的方法之一， 机制，模型系统组件之间的相互作用的影响，并预测紧急的药物作用。 因此，我们提出了一种新的基于药物通道结构的多尺度方法的发展 相互作用和动力学，旨在预测药物诱导的心脏毒性：1）临床前药物 筛查，2）药物康复，3）药物作用与共存危险因素交叉的预测。 我们的基本假设是，药物相互作用的基本模式来自于每种药物的独特性。 构效关系决定了对细胞和组织中心脏电活动的最终影响。 通过在模型中捕捉这些复杂的药物通道相互作用，我们期望能够预测药物安全性 或心脏电中毒。我们已经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，连接详细的数学模型，以预测药物在原子尺度上的相互作用。 混杂的hERG钾通道在通道、细胞和 组织.从原子结构模拟的预测将被用来通知的动力学参数， 捕获药物和离子通道的复杂动力学相互作用的模型。计算 然后将在通道、细胞和组织尺度的预测模型中研究组分， 涌现行为背后的基本机制和复杂互动。实验 哺乳动物细胞和组织将被用来验证模型预测。药物性质将是 在模型中扰动，以恢复危险药物并降低其潜在毒性。多尺度模型 我们将在此应用中开发的心脏药理学预测将应用于项目 证明了其对于药物治疗在复杂生理系统中的功效或毒性的有用性， 心脏

PROJECT SUMMARY: A major factor plaguing drug development is that there is no preclinical drug screen that can accurately predict unintended drug induced cardiac arrhythmias. The current approaches rely on substitute markers such as QT interval prolongation on the ECG. Unfortunately, QT prolongation is neither specific nor sensitive to indicate likelihood of arrhythmias. There is an urgent need to identify a new approach that can predict actual proarrhythmia rather than surrogate indicators. Mathematical modeling and simulation constitutes one of the most promising methodologies to reveal fundamental biological principles and mechanisms, model effects of interactions between system components and predict emergent drug effects. Thus, we propose the development of a novel multiscale approach based on drug-channel structural interactions and kinetics intended to predict drug induced cardiotoxicity in the context of: 1) preclinical drug screening, 2) drug rehabilitation, and 3) prediction of the intersection of drug effects and coexistent risk factors. Our underlying hypothesis is that the fundamental mode of drug interaction derived from each drug’s unique structure activity relationship determines the resultant effects on cardiac electrical activity in cells and tissue. By capturing these complex drug channel interactions in a model, we expect to be able to predict drug safety or electro-toxicity in the heart. We have brought together an expert team to assemble and test a new multiscale model framework that connects detailed mathematical models to predict atomic scale interactions of drugs on the promiscuous hERG potassium channel to functional scale predictions at the level of the channel, cell and tissue. Predictions from the atomic structure simulations will be used 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 channel, cell and tissue scales to expose fundamental mechanisms and complex interactions underlying emergent behaviors. Experiments in mammalian cells and tissues will be undertaken to validate model predictions. Drug properties will be perturbed in models to rehabilitate dangerous drugs and reduce their potential toxicity. The multiscale model for prediction of cardiopharmacology that we will develop in this application will be applied to projects demonstrating its usefulness for efficacy or toxicity of drug treatments in the complex physiological system of the heart.