In silico safety pharmacology

计算机安全药理学

• 批准号: 10576790
• 负责人: COLLEEN E CLANCY
• 金额: $ 72.64万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-05 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10576790
• 关键词: Action Potentials; Adrenergic Receptor; Agreement; Anti-Arrhythmia Agents; Arrhythmia; Award; Back; Behavior; Binding; Cardiac; Cardiotoxicity; Categories; Cells; Chemical Structure; Chemicals; Chemistry; Clinical Data; Complex; Computer Models; Dangerousness; Data; Development; Discipline; Disease; Disparate; Dissection; Drug Combinations; Drug Design; Drug Interactions; Drug Kinetics; Drug Screening; Drug toxicity; Electrocardiogram; Experimental Models; Exposure to; Goals; Heart; Human; Investigation; Ion Channel; Kinetics; Machine Learning; Maps; Medicine; Methodology; Modeling; Molecular; Molecular Conformation; Movement; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Physicians; Physiological; Poison; Potassium Channel; Process; Property; Research Personnel; Risk; Safety; Screening procedure; Signal Transduction; Structure; Structure-Activity Relationship; System; Therapeutic; Time; Tissues; Validation; Work; Writing; beta-adrenergic receptor; computational pipelines; deep learning; design; drug classification; drug development; drug discovery; drug mechanism; drug structure; heart rhythm; improved; in silico; innovation; insight; learning strategy; multi-scale modeling; novel; novel strategies; predictive modeling; protein function; scale up; screening; side effect; simulation; virtual; voltage

PROJECT SUMMARY: A major factor plaguing drug development is that there is no drug-screening tool that can distinguish between drugs that will induce cardiac arrhythmias from chemically similar safe drugs. The current approaches rely on substitute markers such as action potential duration or QT interval prolongation on the ECG. There is an urgent need to identify a new approach that can predict actual proarrhythmia from the drug chemistry rather than relying on surrogate indicators. We have brought together an expert team to innovate at the interfaces of experimental and computational modeling disciplines and develop an in silico simulation pipeline to predict cardiotoxicity over multiple temporal and spatial scales from the atom to the cardiac rhythm. An essential and unique aspect of our approach is that we propose to utilize atomistic scale simulation to predict the transition rates of ion channels and adrenergic receptors and how they are modified by drug interaction. We hypothesize that it is the subtleties of these interactions that are likely to be the critical determinants of drug associated safety or proarrhythmia. In the last award period, we successfully developed an unprecedented linkage: We connected the highly disparate space and time scales of ion channel structure and function. We utilized atomistic simulation to compute drug kinetic rates were directly used as parameters in a hERG function model. The model components were then integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model validation and showed excellent agreement, demonstrating feasibility of this new approach for cardiotoxicity prediction. In this renewal application we propose to hugely extend this approach to include prediction of the interaction of cardiac channel gating and drug interaction as well as the inclusion of adrenergic receptor interactions with drugs. Another essential aspect of safety pharmacology is the development of new approaches to allow more efficient drug design, screening and prediction of cardiotoxicity. Therefore, we will seek to develop, extend and apply a variety of machine learning and deep learning approaches to improve drug discovery by predicting proarrhythmia from the drug chemistry with an efficient process that identify drug congeners via machine learning to maximize therapy and minimize side effects. Finally, we propose to classify drugs into categories based on proarrhythmia risk in normal and diseased virtual tissue settings. 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.

项目概述：阻碍药物开发的一个主要因素是没有药物筛选工具， 可以区分会诱发心律失常的药物和化学性质相似的安全药物。的 目前的方法依赖于替代标记物，例如动作电位持续时间或QT间期延长， ECG。目前迫切需要确定一种新的方法，可以预测实际的心律失常从药物 而不是依靠替代指标。我们汇集了一个专家团队， 实验和计算建模学科的接口，并开发一个计算机模拟管道 预测从原子到心律的多个时间和空间尺度上的心脏毒性。 我们的方法的一个重要和独特的方面是，我们建议利用原子尺度模拟来预测 离子通道和肾上腺素能受体的转换速率以及它们如何被药物相互作用改变。我们 假设这些相互作用的微妙之处可能是药物的关键决定因素， 相关安全性或致心律失常。在上一个奖项期间，我们成功地开发了一个前所未有的 链接：我们连接了离子通道结构和功能的高度不同的空间和时间尺度。我们 利用原子模拟计算药物动力学速率直接用作hERG函数中的参数 模型然后将模型组件整合到细胞和组织尺度的预测模型中，以暴露 基本的心律失常脆弱性机制和复杂的相互作用潜在的紧急行为。 人类临床数据用于模型验证，并显示出良好的一致性，证明了可行性 心脏毒性预测的新方法。在这个更新的应用程序中，我们建议大大扩展这一点， 方法包括预测心脏通道门控和药物相互作用的相互作用以及 包括肾上腺素能受体与药物的相互作用。安全药理学的另一个重要方面是 开发新的方法，以允许更有效的药物设计，筛选和预测心脏毒性。 因此，我们将寻求开发、扩展和应用各种机器学习和深度学习方法 通过用一种有效的方法从药物化学中预测致心律失常因素来改进药物发现， 通过机器学习识别药物同源物，以最大限度地提高治疗效果并最大限度地减少副作用。最后提出 根据正常和患病虚拟组织环境中的致心律失常风险将药物分类。的 我们将在本申请中开发的用于预测心脏药理学的多尺度模型将被应用于 项目证明其有效性或药物治疗的毒性在复杂的生理 心的系统。