In silico Safety Pharmacology

项目概述：困扰药物开发的一个主要因素是没有临床前药物筛选 可以准确预测意外药物引起的心律失常。目前的方法依赖于 替代心电图上的QT间期延长等指标。不幸的是，QT延长既不是 特异度或敏感度均不能提示心律失常的可能性。迫切需要确定一种新的方法 这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和最有希望的方法之一 系统组件之间相互作用的机制、模型效应和预测紧急药物效应。 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 相互作用和动力学用于预测药物引起的心脏毒性：1)临床前药物 筛选，2)戒毒康复，以及3)预测药物效应和共存危险因素的交集。 我们的基本假设是，药物相互作用的基本模式源于每种药物的独特 结构活性关系决定了在细胞和组织中对心脏电活动的综合影响。 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 或心脏的电中毒。我们已经召集了一个专家团队来组装和测试新的多标尺 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HERG钾通道在通道、细胞和功能水平的预测 组织。原子结构模拟的预测将被用来告知动力学参数 捕捉药物和离子通道复杂的动力学相互作用的模型。计算性的 然后，将在通道、细胞和组织尺度的预测模型中研究组件，以揭示 突发行为背后的基本机制和复杂的相互作用。实验在 将采用哺乳动物细胞和组织来验证模型预测。药物特性将是 对康复危险药物和降低其潜在毒性的模型感到不安。多尺度模型 对于心脏药理学的预测，我们将在这一应用程序中应用到项目中 在复杂的生理系统中证明其对药物治疗的有效性或毒性 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.