ApoE Genotype-Directed Drug Repositioning and Combination Therapy for Alzheimer's Disease

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种多因素神经退行性疾病， 遗传和环境因素。载脂蛋白（apo）E4已被确定为主要的遗传危险因素 对于AD。它以基因剂量依赖的方式增加AD的风险并降低AD的发病年龄。遗传 AD的复杂性和多因素性质对开发有效疗法提出了独特的挑战， 这表明需要一种考虑到个体差异的精确医学方法。过去 几十年来，靶向特定AD相关通路的新药开发工作已显示出在 动物实验，结果在人体试验中失败了由于开发AD新药的过程是 复杂且耗时长，相关成本极高，因此迫切需要 考虑非常规药物开发战略，例如重新定位目前用于其他药物的药物， 条件 药物重新定位的方法与新药开发相比具有许多优势， 已成功应用于各种疾病。然而，尝试药物重新定位AD 治疗通常针对特定的AD相关途径或机制，并且在很大程度上是不成功的， 仍在开发中。随着我们了解AD遗传学和发病机制的复杂性， 与共病相关的疾病，越来越清楚的是，AD的有效治疗可能需要靶向 疾病的多个方面，并针对几个致病过程，因此， 可能需要精确的药物和重新使用的药物的联合治疗。 最近两个因素的融合为推进合理用药提供了前所未有的机会 重新定位和数据驱动的药物组合开发。首先是公共数据集的可用性， 大规模基因组、转录组和其他分子分析数据库。二是发展 计算方法和药物靶点的网络概念以及表型筛选的能力， 这使我们能够研究一种或多种治疗剂干扰整个分子的能力， 远离疾病状态的网络。该提案旨在通过实现三个目标来利用这一承诺 目的：（1）分析临床上可用的，大规模的AD患者和年龄匹配的转录组数据集， 对照以鉴定AD的apoE基因型特异性基因表达特征，（2）寻求药物重新定位 基于AD的apoE基因型特异性基因表达特征， apoE基因型特异性AD小鼠模型，以及（3）探索使用药物重新定位的联合治疗 基于AD的apoE基因型特异性特征，并验证apoE中预测的候选组合 AD的基因型特异性小鼠模型。拟议研究的结果将阐明 研究AD的发病机制并潜在地鉴定用于治疗或预防AD的现有药物。

PROJECT SUMMARY Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder caused by interactions among multiple genetic and environmental factors. Apolipoprotein (apo) E4 has been identified as the major genetic risk factor for AD. It increases the risk and lowers the age of onset of AD in a gene dose–dependent manner. The genetic complexity and multifactorial nature of AD pose unique challenges for developing effective therapies and suggest the need for a precision medicine approach that takes into account individual variability. For the past several decades, new drug development efforts to target specific AD-related pathways have shown promise in animal studies, only to fail during human trials. Since the process of developing new drugs for AD is complicated and takes a long time and the related costs are extremely high, there is a pressing need to consider unconventional drug development strategies, such as repositioning drugs currently used for other conditions. The approach of drug repositioning has a number of advantages over the development of new drugs and has been applied successfully to various disease conditions. However, attempts at drug repositioning for AD treatment usually target specific AD-related pathways or mechanisms and have been largely unsuccessful or still under development. As we learn about the complexity of AD genetics and pathogenesis and the associated co-morbid conditions, it is becoming clear that efficacious treatments of AD will likely need to target multiple aspects of the disease and be directed towards several pathogenic processes, and as a result will likely require precision medicine and combination therapy of the repurposed drugs. The recent convergence of two factors presents an unprecedented opportunity to advance rational drug repositioning and data-driven development of drug combinations. First is the availability of public datasets from large-scale genomic, transcriptomic, and other molecular profiling databases. Second is the development of computational approaches and the network concept of drug targets and the power of phenotypic screening, which allows us to investigate the ability of one or more therapeutic agents to perturb entire molecular networks away from disease states. This proposal aims to capitalize on this promise by accomplishing three aims: (1) to analyze publically available, large-scale transcriptomic datasets of AD patients and age-matched controls to identify apoE genotype-specific gene expression signatures of AD, (2) to pursue drug repositioning based on apoE genotype-specific gene expression signatures of AD and validate the top drug candidates in apoE genotype-specific mouse models of AD, and (3) to explore combination therapy using drug repositioning based on apoE genotype-specific signatures of AD and validate the predicted combination candidates in apoE genotype-specific mouse models of AD. The outcomes of the proposed studies will shed light on the pathogenesis of AD and potentially identify existing drugs for treating or preventing AD.