In silico safety pharmacology

计算机安全药理学

• 批准号: 10480737
• 负责人: COLLEEN E CLANCY
• 金额: $ 72.09万
• 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/10480737
• 关键词: Action Potentials; Address; 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; 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; Prodrugs; Property; Research Personnel; Risk; Safety; Screening procedure; Signal Transduction; Structure; Structure-Activity Relationship; System; Therapeutic; Time; Tissues; Validation; Work; base; beta-adrenergic receptor; computational pipelines; deep learning; design; 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.

项目摘要：困扰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相似的安全药物。这 目前的方法依赖于替代标记，例如动作电位持续时间或 QT 间期延长 心电图。迫切需要找到一种新方法来预测药物引起的实际致心律失常 化学而不是依赖替代指标。我们汇聚了一支专家团队，致力于创新 实验和计算建模学科的接口并开发计算机模拟管道 预测从原子到心律的多个时间和空间尺度的心脏毒性。 我们的方法的一个重要且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率以及它们如何通过药物相互作用进行修改。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 相关的安全性或致心律失常。在上一届颁奖期间，我们成功开发了前所未有的 联动：我们将离子通道结构和功能的高度不同的空间和时间尺度连接起来。我们 利用原子模拟来计算药物动力学速率并直接用作 hERG 函数中的参数 模型。然后将模型组件集成到细胞和组织尺度的预测模型中，以揭示 基本心律失常脆弱性机制和紧急行为背后的复杂相互作用。 使用人体临床数据进行模型验证并显示出良好的一致性，证明了可行性 这种心脏毒性预测新方法的研究。在此续订申请中，我们建议大幅扩展此 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 开发新方法以实现更有效的药物设计、筛选和心脏毒性预测。 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 通过有效的过程从药物化学中预测致心律失常来改进药物发现 通过机器学习识别药物同系物，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，我们建议 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。这 我们将在本应用中开发的用于预测心脏药理学的多尺度模型将应用于 证明其在复杂生理学中药物治疗的功效或毒性方面的有用性的项目 心脏的系统。