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

通过遗传学、电子健康记录和人类 iPSC 模型对阿尔茨海默病进行药物重新定位

基本信息

批准号：
10554325
负责人：
BINGSHAN LI
金额：
$ 78.99万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-15 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10554325
关键词：
Affect Age Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer&apos s disease patient Alzheimer&apos s disease risk Astrocytes Biological Assay Biological Process Cell Death Cell Line Clinical Clinical Trials Code Combined Modality Therapy Data Dementia Development Diagnostic Disease Drug Targeting Drug usage Early Onset Alzheimer Disease Electronic Health Record Electronics Etiology FDA approved Failure Gender Gene Expression Profile Gene Expression Profiling Genes Genetic Genetic study Goals Heritability Human Impaired cognition Individual Late Onset Alzheimer Disease Microglia Mitochondria Modeling Morphology Multiomic Data Mutation Natural Language Processing Neurodegenerative Disorders Neurons Oxidative Stress Pathogenesis Pathologic Pathway interactions Patients Pharmaceutical Preparations Phenotype Positioning Attribute Production Race Recording of previous events Role Structure Synapses Text United States Validation apolipoprotein E-4 candidate identification candidate validation cell type cohort comorbidity computer framework cytokine drug development drug discovery drug repurposing drug use screening efficacy testing gene network genetic architecture genome wide association study genomic data high throughput screening induced pluripotent stem cell innovation mouse model novel phenotyping algorithm presenilin-1 prevent screening sex single cell sequencing stem cell model success tau-1 transmission process virtual

项目摘要

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.

阿尔茨海默氏病 (AD) 是一种进行性神经退行性疾病，也是导致该病的主要原因不幸的是，目前还没有治疗 AD 的药物。遭受重大失败，其中许多是在后期临床试验中，部分原因是我们的能力不足对 AD 病理学的了解以及缺乏与疾病相关和人类相关的发现和这需要具有多样化且互补的专业知识的团队努力来解决。通过从多个角度开发创新方法来共同应对挑战，以实现识别 AD 药物的目标。在此应用中，我们提出了三个互补的具体目标，共同旨在确定 FDA 从不同但互补的角度批准了具有治疗 AD 潜力的药物 AD 是一种高度遗传性疾病，具有协同作用，可提高成功的可能性。估计遗传力为 70%，凸显了遗传学在理解最近的遗传学研究已经确定了 30 多个位点，使我们能够剖析疾病的病因。 AD 的遗传结构，包括疾病涉及的生物过程和细胞类型特别是，我们的目标是将高度多基因的 AD 病因剖析成不同的病因。指导药物再利用的病理生理成分，这只有在最近几年才有可能得益于大规模 GWAS 和公开的大量基因组数据（目标 1）。将挖掘数百万份电子健康记录 (EHR)，以识别可降低 AD 风险和认知能力下降，通过 EHR 开发 AD 相关表型的表型算法（目标 2）。此外，我们将开发高通量筛选（HTS）基因表达谱分析方法和使用人类诱导多能干细胞（iPSC）模型来鉴定候选化合物，并将进一步测试候选药物在源自患者的 iPSC 系和 AD 小鼠模型中的功效（目标 3）从独立的意义上来说，这三个目标是互补和协同的。从截然不同的角度解决同一问题，而其中一个的发现可以作为总而言之，我们希望利用独特且互补的专业知识为他人提供验证。在正交支持下产生真正的可重复利用的 AD 药物。