An Integrated Reverse Engineering Approach Toward Rapid drug Re positioning for Alzheimer's Disease

一种针对阿尔茨海默病快速药物重新定位的综合逆向工程方法

• 批准号: 10220714
• 负责人: Rong Xu
• 金额: $ 60.76万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10220714
• 关键词: Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Amyloid deposition; Animal Model; Biological Assay; Biology; Blood - brain barrier anatomy; Case-Control Studies; Cell model; Cells; Chronic; Clinical; Clinical Trials; Clinical effectiveness; Computational algorithm; Computer Models; Computing Methodologies; Data; Dementia; Diagnosis; Disease; Electronic Health Record; Engineering; Experimental Models; FDA approved; Genetic; Genomics; Hospitals; Human; Knowledge; Modeling; Modification; Morbidity - disease rate; Mus; Neurodegenerative Disorders; Output; Patients; Permeability; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Positioning Attribute; Provider; Source; Testing; Therapeutic; Translating; United States; Work; aging population; base; blood-brain barrier permeabilization; clinical efficacy; drug candidate; drug efficacy; economic cost; in silico; innovation; knowledge base; mortality; mouse model; neurodegenerative phenotype; novel; phenomics; prospective; social; tau Proteins; therapeutic evaluation; web app

PROJECT SUMMARY Alzheimer disease (AD) is the most common cause of dementia and one of the leading sources of morbidity and mortality in the aging population. Despite enormous social and economic costs associated with AD, current drugs are directed towards symptomatic relief and none are curative. In this project titled “An integrated reverse engineering approach toward rapid drug repositioning for Alzheimer’s disease,” we propose to develop an innovative integrated drug repositioning strategy that combines computation-based drug prediction, computation-based human brain-blood-barrier (BBB) permeability prediction, retrospective large-scale clinical corroboration, and prospective experimental testing to rapidly identify anti-AD drug candidates. First, we will develop novel computational approaches to identify repositioning anti-AD candidates from all (>2,600) FDA-approved drugs. Second, we will develop novel multifaceted biology-based computational methods to predict which repositioned drug candidates can cross BBB in humans. Third, we will perform large-scale retrospective case-control studies to corroborate the clinical efficacy of repositioned drug candidates using patient electronic health record (EHR) data of >50 million patients. Finally, we will evaluate the therapeutic potential of promising repositioned candidates in experimental models. Our study will generate a large amount of data/knowledge/hypotheses that could serve as a starting point for us and others to conduct hypothesis-driven drug repositioning studies in other animal models of AD and in AD patients. We will build a comprehensive Alzheimer Drug Repositioning Knowledge Base (ADRKB) and develop interactive web applications to make ADRKB publicly available. The unique and powerful strength of our project is our ability to seamlessly combine novel computational predictions, retrospective clinical corroboration using patient EHRs, and experimental testing in animal models of AD to rapidly identify innovative drug candidates that may work in real-world AD patients. The repositioned drug candidates will have interpretable mechanisms of action, are highly likely to cross BBB in humans, have clinical effectiveness evidence gathered from ‘real-world’ AD patients, and have demonstrated efficacy in mouse models of AD. We anticipate that these findings can be expeditiously translated into clinical trials and benefit 5.4 million AD patients in United States and 47 million AD patients worldwide.

项目概要 阿尔茨海默病 (AD) 是痴呆症最常见的原因，也是发病的主要来源之一 和人口老龄化的死亡率。尽管 AD 带来了巨大的社会和经济成本， 目前的药物都是针对缓解症状的，没有一种药物能够治愈。 在这个名为“一种针对快速药物重新定位的集成逆向工程方法”的项目中， 阿尔茨海默病”，我们建议开发一种创新的综合药物重新定位策略， 结合了基于计算的药物预测、基于计算的人脑血屏障（BBB） 渗透率预测、回顾性大规模临床验证和前瞻性实验测试 快速识别抗 AD 候选药物。首先，我们将开发新的计算方法来识别 从 FDA 批准的所有（> 2,600 种）药物中重新定位抗 AD 候选药物。二、我们将开发新颖 基于多方面生物学的计算方法来预测哪些重新定位的候选药物可以交叉 人类的 BBB。第三，我们将进行大规模回顾性病例对照研究，以证实临床研究结果。 使用超过 5000 万的患者电子健康记录 (EHR) 数据重新定位候选药物的功效 患者。最后，我们将在实验中评估有希望的重新定位候选药物的治疗潜力 模型。我们的研究将产生大量数据/知识/假设，可以作为起点 我们和其他人在 AD 动物模型中进行假设驱动的药物重新定位研究的要点 以及 AD 患者。我们将建立一个全面的阿尔茨海默病药物重新定位知识库（ADRKB） 并开发交互式 Web 应用程序以使 ADRKB 公开可用。 我们项目的独特而强大的优势是我们能够无缝结合新颖的计算 预测、使用患者 EHR 进行回顾性临床验证以及动物模型实验测试 AD 快速识别可能对现实世界 AD 患者有效的创新候选药物。重新定位的 候选药物将具有可解释的作用机制，极有可能跨越人类的血脑屏障， 从“真实世界”AD 患者收集的临床有效性证据，并已证明其疗效 AD小鼠模型。我们预计这些发现可以迅速转化为临床试验和 使美国 540 万 AD 患者和全球 4700 万 AD 患者受益。