Computational drug repurposing for Alzheimer's disease

阿尔茨海默病的计算药物再利用

• 批准号: 10603878
• 负责人: Monika Eliana Grabowska
• 金额: $ 3.29万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603878
• 关键词: Academic Medical Centers; Aducanumab; Adult; Affect; All of Us Research Program; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Amyloid beta-Protein; Area; Award; COVID-19; Cholinesterase Inhibitors; Clinical; Clinical Data; Cognition; Colorectal Cancer; Complex; Data; Data Set; Databases; Dementia; Development; Diagnosis; Disease; Drug Targeting; Drug usage; Elderly; Electronic Health Record; Evaluation; Failure; Functional disorder; Future; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic study; Glutamates; HIV/AIDS; Health; Healthcare; Human; Hyperlipidemia; Hypertension; Impaired cognition; Impairment; In Vitro; Individual; Informatics; Investigation; Machine Learning; Maps; Medicare; Medicine; Memory; Memory Loss; Mendelian randomization; Methods; Monoclonal Antibodies; Natural Language Processing; Nerve Degeneration; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physicians; Population; Prevalence; Proteins; Research; Research Personnel; Resources; Rheumatoid Arthritis; Safety; Scientist; Short-Term Memory; Therapeutic Effect; Time; United States; United States Centers for Medicare and Medicaid Services; United States Food and Drug Administration; United States National Institutes of Health; Validation; Work; biobank; candidate validation; career; clinical development; cost; drug action; drug candidate; drug development; drug repurposing; electronic health data; genetic approach; genetic variant; genome wide association study; improved; insight; interest; novel; novel therapeutics; pre-clinical; randomized, clinical trials; skills; statistics; success; timeline; tool; transcriptome; transcriptomics; virtual

PROJECT SUMMARY Alzheimer’s disease (AD) is a progressive neurodegenerative condition with profound impacts on memory and cognition that currently affects more than 6 million adults living in the United States. Treatment options for Alzheimer’s remain limited, with high rates of failure in AD drug trials. Drug repurposing to identify new uses for existing drugs offers several advantages over traditional drug development, including higher success rates, lower costs, shorter timelines, and increased assurance of safety. Furthermore, drugs with genetic support of efficacy have been found to be twice as successful in clinical development. This work leverages new developments in genetics and informatics to propose two independent approaches to identify drug repurposing candidates for AD: (1) a virtual transcriptome approach identifying drugs that can reverse gene expression changes observed in AD, and (2) a Mendelian randomization approach identifying drugs acting on genes found to be causally associated with AD. Identified repurposing candidates will be clinically validated using electronic health record (EHR) data from Vanderbilt University Medical Center, as well as external datasets (clinical data from the NIH All of Us Research Program Database or claims data from the Centers for Medicare & Medicaid Services). In AIM 1, we will develop a virtual transcriptomic signature for AD using GWAS summary statistics, and query this signature against large-scale drug perturbation databases. In AIM 2, we will use conditionally independent genetic variants as instrumental variables to perform Mendelian randomization on AD GWAS, and evaluate for causal relationships between actionable druggable genes and AD. Both aims will involve further validation of repurposing candidates using real-world clinical data, which has seldom been done before. Successful completion of this project will yield novel insights into high- priority drug repurposing candidates for AD that can be further investigated in randomized clinical trials, and will establish a high-throughput AD repurposing pipeline integrating genetic, -omics, and EHR data that may be adapted to other diseases of interest.

项目摘要 阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，对 目前影响到美国超过600万成年人的记忆和认知。 阿尔茨海默氏症的治疗方案仍然有限，在AD药物试验中的失败率很高。 识别现有药物的新用途的药物重新使用，比传统药物具有多种优势 开发，包括更高的成功率，较低的成本，较短的时间表和增加的保证 安全。此外，已经发现具有遗传支持的药物是成功的两倍 在临床发展中。 这项工作利用遗传学和信息的新发展来提出两个独立的发展 识别AD的药物重新利用候选者的方法：（1）虚拟转录组方法 鉴定可以逆转基因表达在AD中观察到的变化的药物，（2）Mendelian 随机化方法鉴定作用于被发现与AD相关的基因的药物。 确定的重新利用候选人将使用电子健康记录（EHR）数据在临床上进行验证 来自范德比尔特大学医学中心以及外部数据集（NIH的临床数据 美国研究计划数据库或从医疗保险和医疗补助服务中心索赔数据）。 在AIM 1中，我们将使用GWAS摘要统计数据为AD开发一个虚拟转录组签名，并且 查询此签名针对大型药物扰动数据库。在AIM 2中，我们将有条件使用 独立的遗传变异作为在AD上执行Mendelian随机化的工具变量 GWAS，并评估可操作的可药物基因与AD之间的因果关系。两个目标 将涉及使用现实世界中的临床数据对重新利用候选人的进一步验证， 以前很少做过。成功完成该项目将产生对高级的新见解 优先药物重新利用AD的候选者，可以在随机临床试验中进一步研究 并将建立一个高通量广告重新利用的管道，以整合遗传， - 组和 EHR数据可能适用于其他感兴趣的疾病。