Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimers Disease

基于内表型网络的方法对阿尔茨海默病的计算机药物重新利用进行预测和基于群体的验证

• 批准号: 10569077
• 负责人: Feixiong Cheng
• 金额: $ 72.2万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10569077
• 关键词: AD transgenic mice; Acceleration; Address; Adherence; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease therapy; American; Amyloid; Amyloidosis; Animal Model; Bayesian Modeling; Biological Assay; Biological Availability; Blood - brain barrier anatomy; Brain; Case/Control Studies; Cause of Death; Cells; Central Nervous System; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Complement; Complex; Computerized Medical Record; Computers; Data; Databases; Dementia; Disease; Disease Outcome; Drug Combinations; Drug Delivery Systems; Drug Targeting; Drug user; Enhancers; Evaluation; Foundations; Genes; Genome; Heritability; Hi-C; Hippocampus; Human; Human Genetics; In Situ; In Vitro; Incidence; Intelligence; Interdisciplinary Study; Investments; Knowledge; Late Onset Alzheimer Disease; Mediating; Medicine; Methodology; Microglia; Molecular; Multiomic Data; Mutate; Network-based; Neurodegenerative Disorders; Neuroglia; Pathogenesis; Patient Care; Patients; Penetration; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Physiological; Population; Positioning Attribute; Predisposition; Prevention; Preventive therapy; Proteins; Proteome; Publications; Publishing; Quality of life; Rattus; Records; Regimen; Research Personnel; Resected; Role; System; Tauopathies; Testing; Therapeutic; Transgenic Animals; United States; Validation; Visualization; Work; bioinformatics tool; brain endothelial cell; brain tissue; clinical efficacy; clinically relevant; dementia care; drug development; drug discovery; drug efficacy; drug repurposing; drug testing; effective therapy; efficacious treatment; endophenotype; functional genomics; genetic architecture; genome wide association study; genome-wide; genomic data; human genome sequencing; human interactome; improved outcome; in silico; in vivo; individual patient; informatics tool; innovation; mouse model; multiple omics; novel; novel therapeutics; patient health information; pharmacokinetic model; pharmacologic; population based; predictive tools; promoter; rational design; research and development; risk variant; side effect; single cell sequencing; single-cell RNA sequencing; symptom treatment; tau Proteins; therapeutic development; tool; transcriptome; transgenic model of alzheimer disease; trial design; web app

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome are essential for such identification. Given our preliminary results, we posit that network- based identification of novel risk genes and endophenotype modules that share degree between amyloid and tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and combination therapies for AD using network proximity approaches -- We will emphasize the uses of network proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims- Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate brain penetration and target network engagement for repurposable drugs -- We will use the humanized in vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats) to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to minimizing side effects. The successful completion of this project will offer powerful network methodologies and bioinformatics tools for prediction and population-based validation of in silico drug repurposing. It will also allow for the identification of novel repurposable drugs and clinically relevant combination therapies toward AD trials.

尽管研究人员已经针对阿尔茨海默病的潜在治疗方法进行了 400 多项人体试验 在过去的二十年中，疾病（AD）的损耗率估计超过 99% 此外，“一个基因， “一种药物，一种疾病”的还原论范式忽视了疾病固有的复杂性， AD 的易感性涉及复杂的、多基因的和复杂的因素。 最近的研究表明 AD 通常具有共同的基础。 机制，与许多其他复杂疾病共享中间内表型。 淀粉样变性和 tau 蛋白病等内表型在许多神经退行性疾病中具有重要作用。 新的潜在发病机制和疾病模块的系统识别和表征，更是如此 比突变基因，将作为产生可操作靶标的基础，作为药物再利用的输入 以及基因组、转录组、蛋白质组和 AD 联合治疗的合理设计。 鉴于我们的初步结果，人类相互作用组对于这种识别至关重要。 基于淀粉样蛋白和淀粉样蛋白之间共享程度的新风险基因和内表型模块的鉴定 与分别针对淀粉样蛋白和 tau 蛋白相比，tau 蛋白为 AD 药物治疗提供了意想不到的机会。 为了解决潜在的假设，我们建议建立一个综合的跨学科研究计划 目标 1 将探索淀粉样蛋白和 tau 介导的 AD 内表型模块——我们将进行测试。 使用我们最近开发的贝叶斯框架的淀粉样蛋白和 tau 蛋白的网络模块假设 整合多组学数据（即全基因组关联研究 [GWAS] 位点、单细胞测序和 人类大脑 Hi-C 数据）和人类相互作用组 Aim 2 将能够搜索现有药物和药物。 使用网络邻近方法治疗 AD 的联合疗法——我们将强调网络的使用 邻近方法（即全基因组定位系统网络 [GPSnet]）来识别可重复利用的药物 这将通过整合 AD 内表型模块来实现。 研究结果、公共药物靶点数据库、人类相互作用组和大规模患者纵向索赔- 电子病历数据（来自 MarketScan 数据库的超过 2 亿患者将进行评估）。 可重复利用药物的大脑渗透和目标网络参与——我们将在中使用人性化 体外血脑屏障、切除脑组织（离体/原位）和转基因 AD 模型（即 TgF344-AD 大鼠） 实验评估大脑渗透性和目标网络参与度。 与 AD 相关内表型模块的网络接近度，这些模块与最大化功效相关并 该项目的成功完成将提供强大的网络方法和效果。 用于预测和基于群体的计算机模拟药物再利用验证的生物信息学工具也将允许。 为 AD 试验确定新型可重复利用药物和临床相关联合疗法。