TargetAD: A systems multi-omics approach to drug repositioning in Alzheimer's disease

TargetAD：一种用于阿尔茨海默病药物重新定位的系统多组学方法

• 批准号: 10474389
• 负责人: Matthias Arnold
• 金额: $ 56.69万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474389
• 关键词: Address; Affect; Age; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Atlases; Attention; Attenuated; Autopsy; Behavior assessment; Behavioral; Binding; Biological Markers; Biomedical Research; Brain; Clinical; Clinical Trials; Cognitive; Collection; Complement; Complex; Data; Data Set; Databases; Development; Disease; Drug Combinations; Drug Exposure; Drug Screening; Drug Targeting; Drug usage; Electronic Health Record; Electrophysiology (science); Exhibits; Future; Gene Expression Regulation; Graph; Immunohistochemistry; Individual; Intake; Intervention; Investments; Late Onset Alzheimer Disease; Link; Longitudinal Studies; Machine Learning; Measures; Medicine; Memory; Metabolic Pathway; Methods; Mining; Molecular; Molecular Profiling; Mus; Network-based; Neurodegenerative Disorders; Outcome; Pathology; Pathway Analysis; Pathway interactions; Pharmaceutical Preparations; Pharmacogenomics; Pharmacologic Substance; Pharmacotherapy; Phase; Population Study; Probability; Process; Proteins; Publishing; Quantitative Trait Loci; Reaction; Research; Resources; Retrospective Studies; Route; Scoring Method; Screening Result; Synapses; System; Testing; Tissue-Specific Gene Expression; Tissues; United States National Institutes of Health; Validation; Visit; aged; aging population; alternative treatment; amyloid pathology; base; biobank; biological systems; candidate identification; clinical biomarkers; data mining; drug candidate; drug development; endophenotype; experimental study; genetic association; genome wide association study; in silico; machine learning method; metabolomics; mouse model; multidisciplinary; multiple omics; neuroimaging; neuropathology; novel; population based; pre-clinical; protein expression; protein protein interaction; religious order study; standardize measure; study population; synaptic function; tau Proteins; treatment strategy; validation studies

Project Summary Late-onset Alzheimer's Disease (AD) is a slowly progressing, untreatable neurodegenerative disorder that affects a substantial fraction of the aging population today. Hundreds of clinical trials and massive investments into drug development efforts have so far not resulted in a single disease-modifying therapy that showed a significant beneficial effect on the disease. Drug repositioning, the application of approved drugs in a novel disease context, has gained increasing attention as a promising alternative to identify treatment options for AD. For successful pharmaceutical intervention in AD, a drug or drug combination needs to target the complex molecular changes observed in AD in a specific manner. To identify drugs exerting these desired effects a detailed understanding of the molecular networks across regulatory layers that underly the biological system is required. However, these networks are not readily available and are scattered across hundreds of studies and complex databases. To address this challenge, we propose TargetAD, a network-based framework that builds this molecular network from genetic associations, co-expression/correlation networks, metabolic pathways, gene regulation data, protein-protein interactions, and tissue-specific gene and protein expression data augmented with AD multi-omics associations, as well as drug-drug target data and molecular drug signatures. We will achieve this by leveraging the power of large-scale, multi-omics association results generated within NIH's large “Accelerating Medicines Partnership - Alzheimer's Disease” initiative and other large-scale population-based studies. The collective evidence will be stored in a publicly accessible graph database, which we then use for the identification of candidate drugs or drug combinations (“candidates”). Through the development of a novel network-based machine-learning method, we will rank candidates in the database by their probability to affect AD networks in a beneficial way. High-ranking candidates will be subjected to a comprehensive prioritization pipeline. To this end, we will retrospectively investigate whether longitudinal AD-related biomarker profiles of individuals who took a repositioning candidate show evidence for healthier aging in large studies of AD. These analyses will be complemented by examining whether the post- mortem neuropathological burden supports a beneficial effect of the candidate. To increase power and coverage of candidates, we will further analyze electronic health records from the UK Biobank for additional evidence. The three most promising candidates will be selected in discussion with a panel of experts. These will be evaluated by preclinical validation studies in animal models of AD. In summary, the unique combination of multidisciplinary expertise, access to high-profile datasets and advanced computational integration pipelines will allow us to identify molecular pathways disturbed in AD that are targetable by drug repositioning candidates, which thus are prime candidates for testing in clinical trials.

项目摘要 晚期的阿尔茨海默氏病（AD）是一种缓慢的进展，不可治疗的神经退行性疾病， 影响当今人口老龄化的很大一部分。数百次临床试验和大量投资 到目前为止，尚未导致单一疾病改良的疗法，该疗法表明 对疾病的显着有益作用。药物重新定位，在小说中应用批准的药物 疾病的环境已成为越来越多的关注，作为识别AD治疗选择的有望替代方案。 为了成功地在AD中进行药物干预，药物或药物组合需要针对复合物 以特定方式观察到AD的分子变化。识别施加这些所需效应的药物 对生物系统下的调节层的分子网络的详细了解是 必需的。但是，这些网络不容易获得，并且散布在数百项研究中 复杂的数据库。为了应对这一挑战，我们提出了Targetad，这是一个基于网络的框架 该分子网络来自遗传关联，共表达/相关网络，代谢途径， 基因调节数据，蛋白质 - 蛋白质相互作用以及组织特异性基因和蛋白质表达数据 随着AD多矩协会以及药物 - 药物靶数据和分子药物特征的增强。 我们将通过利用在内部产生的大规模，多派协会结果的力量来实现这一目标 NIH的大型“加速药物合作伙伴关系 - 阿尔茨海默氏病”倡议和其他大规模 基于人群的研究。集体证据将存储在公共访问的图数据库中，该数据库 然后，我们使用候选药物或药物组合的鉴定（“候选人”）。 通过开发基于网络的新型机器学习方法，我们将在候选人中排名 数据库通过其以有益的方式影响AD网络的可能性。高级候选人将是 进行全面的优先管道。为此，我们将回顾性调查是否 接受重新定位候选人的个人的纵向广告相关的生物标志物概况显示了证据 大型研究中更健康的衰老。这些分析将通过检查后是否完成 Mortem神经病理学负担支持候选人的有益作用。增加力量和 候选人的覆盖范围，我们将进一步分析英国生物银行的电子健康记录以获取更多 证据。将与专家小组讨论中选出三个最有前途的候选人。 将通过AD动物模型中的临床前验证研究评估。 总而言之，多学科专业知识，访问备受瞩目的数据集的独特组合和 先进的计算集成管道将使我们能够确定AD中影响的分子途径 可以通过重新定位候选药物来定位，因此，这是在临床试验中进行测试的主要候选者。