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 可以区分会导致心律失常的药物和化学上类似的安全药物。这个 目前的方法依赖于替代标记物，如动作电位时程或QT间期延长 心电图仪。迫切需要确定一种新的方法，可以从药物中预测实际的心律失常 化学，而不是依赖于替代指示剂。我们汇聚了一支创新的专家团队 实验和计算建模学科的接口，并开发了一个在线模拟流水线 在从原子到心律的多个时间和空间尺度上预测心脏毒性。 我们方法的一个基本和独特的方面是，我们提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移率以及药物相互作用如何改变它们。我们 假设正是这些相互作用的微妙之处可能是药物的关键决定因素 相关的安全性或心律失常。在上一届颁奖期间，我们成功地开发出了前所未有的 联系：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 模特。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 基本的心律失常易损性机制和潜在紧急行为的复杂相互作用。 人类临床数据用于模型验证，并显示出极好的一致性，证明了可行性 这一新的心脏毒性预测方法。在这份续订申请中，我们建议大幅延长这一期限 包括预测心脏通道门控和药物相互作用的方法以及 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基本方面是 开发新的方法，以实现更有效的药物设计、心脏毒性的筛选和预测。 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 通过从药物化学预测心律失常的有效过程来改进药物发现 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将副作用降至最低。最后，我们建议 根据正常和疾病虚拟组织环境中的心律失常风险将药物分类。这个 我们将在本应用中开发的用于心脏药理学预测的多尺度模型将应用于 展示其在复杂生理学药物治疗中的有效性或毒性的项目 心脏系统。