Drug repositioning for Alzheimer's disease via genetics, electronic health records, and human iPSC models

阿尔茨海默病(AD)是一种进行性神经退行性疾病，是 美国的痴呆症。不幸的是，阿尔茨海默病没有治愈方法。AD的药物发现已经 遭遇重大失败，许多是在后期临床试验中，部分原因是我们的糟糕 对AD病理的理解以及缺乏与疾病相关和与人类相关的发现和 发展模式。这需要具有多样化和互补性的专业知识的团队努力来解决 共同面对挑战，通过从多个角度开发创新方法来实现 识别AD药物的目标。 在本申请中，我们提出了三个相辅相成的具体目标，它们共同旨在确定 FDA从不同但互补的角度批准了具有AD再用途潜力的药物 协同提高成功的可能性。AD是一种高度可遗传的疾病，具有 估计遗传力为70%，突显了遗传学在理解 疾病病因学。最近的遗传学研究已经确定了30多个基因座，使我们能够剖析 阿尔茨海默病的遗传结构，包括与疾病有关的生物学过程和细胞类型 病因学。特别是，我们的目标是将高度多基因的AD病因学剖析为不同的 用于指导药物再利用的病理生理成分，这只是最近几年才可行的 这要归功于大规模的全球气候变化和可公开获得的大量基因组数据(目标1)。同时，我们 将挖掘数百万电子健康记录(EHR)以识别降低AD风险和 认知衰退，通过开发来自EHR的AD相关表型的表型算法(目标2)。 此外，我们将开发一种高通量筛选(HTS)基因表达谱分析方法和 使用人类诱导多能干细胞(IPSC)模型来识别候选化合物，并将 进一步在患者来源的IPSC系和AD小鼠模型中测试候选药物的有效性 (目标3)。这三个目标是互补和协同的，因为它们是独立的 从截然不同的角度解决同样的问题，而一个人的发现可以作为 对其他人的验证。总之，利用不同和互补的专业知识，我们希望 用正交支撑法生产治疗AD的真正可重复使用的药物。

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia in the United States. Unfortunately, there is no cure for AD. Drug discovery for AD has suffered significant failures, many at late stage clinical trials, partly due to our poor understanding of AD pathology and the lack of disease-relevant and human-relevant discovery and development models. This calls for team efforts with diverse and complementary expertise to tackle the challenges together, by developing innovative approaches from multiple angles to achieve the goal of identifying AD drugs. In this application, we propose three complementary Specific Aims that together aim to identify FDA approved drugs with repurpose potential for AD, from distinct but complementary angles that act synergistically to boost the likelihood of success. AD is a highly heritable disease, with an estimated heritability of 70%, highlighting the critical role of genetics in understanding the disease etiology. Recent genetic studies have identified over 30 loci, enabling us to dissect the genetic architecture of AD, including the biological processes and cell types involved in disease etiology. In particular, we aim to dissect the highly polygenic AD etiology into distinct pathophysiological components to guide drug repurposing, which is only feasible in recent years thanks to large scale GWAS and massive genomics data available publicly (Aim 1). In parallel, we will mine millions of electronic health records (EHRs) to identify drugs that reduce AD risk and cognitive decline, by developing phenotyping algorithms from EHR for AD related phenotypes (Aim 2). In addition, we will develop a high­ throughput screening (HTS) gene expression profiling assay and use human induced pluripotent stem cell (iPSC) models to identify candidate compounds, and will further test the efficacy of the candidates in both patient-derived iPSC lines and AD mouse models (Aim 3). The three aims are complementary and synergistic, in the sense that they independently tackle the same problem from drastically distinct angles, while findings from one can be served as validation for others. Altogether, leveraging distinct and complementary expertise, we expect to yield bona fide repurposable drugs for AD with orthogonal support.