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：
10390283
负责人：
BINGSHAN LI
金额：
$ 80万
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/10390283
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 Diagnostic Disease Drug Targeting Drug usage Early Onset Alzheimer Disease Electronic Health Record 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 Morphology Multiomic Data Mutation Natural Language Processing Neurodegenerative Disorders Neurons Oxidative Stress Pathogenesis Pathologic Pathway interactions Patients Pharmaceutical Preparations Phenotype Production Race Recording of previous events Role Structure Synapses Text United States Validation case control cell type cohort comorbidity computer framework cytokine drug candidate 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 model development mouse model novel phenotyping algorithm presenilin-1 prevent screening sex single cell sequencing stem cell model success tau phosphorylation 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，是不幸的是，美国的痴呆症。遭受了严重的失败，许多在后期临床试验，部分是由于我们的差了解AD病理学以及缺乏与疾病相关和与人相关的发现以及开发模型。通过开发倍数的创新方法来实现挑战识别广告药物的目标。在此应用中，我们提出了三个互补的特定目的，旨在确定 FDA批准了具有AD的重新利用潜力的药物，该药物是从独特但压缩的角度起作用的从而提高成功的可能性。估计的遗传力为70％，突出了遗传学在理解你的关键作用疾病的病因学。 AD的遗传结构，包括疾病涉及的生物学过程和细胞类型尤其是病因。病理生理成分指导药物再倍增，这仅在近年来可行多亏了大规模的GWA和大量的基因组数据，我们可以并行将挖掘数百万的电子健康记录（EHR），以识别降低AD风险和和和和和认知能力下降，通过为AD相关表型开发EHR的表型算法（AIM 2）。此外，我们还将开发高含量筛选（HTS）基因表达分析测定法和使用人类诱导的多能干细胞（IPSC）模型来识别候选化合物，并将进一步测试候选人在患者潜水IPSC linsc和AD鼠标模型中的功效（目标3）。从截然不同的角度解决相同的问题，而一个发现的发现可以用作对他人的验证。产生善意的可浮力药物，用于具有正交支持的AD。