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

项目摘要 迟发性阿尔茨海默病(AD)是一种进展缓慢、无法治疗的神经退行性疾病， 影响了当今相当一部分老龄化人口。数百项临床试验和巨额投资 到目前为止，药物开发的努力还没有产生一种单一的疾病修改疗法，这种疗法显示出 对本病有明显的益气作用。药物重新定位，批准的药物在新药中的应用 疾病背景，作为确定AD治疗选择的有前途的替代方案，已经得到越来越多的关注。 为了成功地对AD进行药物干预，一种药物或药物组合需要针对该复合体 以特定的方式观察到阿尔茨海默病的分子变化。为了识别具有这些预期效果的药物， 对生物系统底层调节层的分子网络有详细的了解 必填项。然而，这些网络并不是现成的，而且分散在数百项研究和 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，它构建 这个分子网络来自遗传关联、共表达/相关网络、代谢途径、 基因调控数据、蛋白质相互作用以及特定于组织的基因和蛋白质表达数据 增加了AD多组学关联，以及药物-药物靶向数据和分子药物签名。 我们将通过利用在以下方面产生的大规模、多组学关联结果的力量来实现这一点 美国国立卫生研究院的大型“加速药物伙伴关系--阿尔茨海默氏症”倡议和其他大规模 以人口为基础的研究。收集的证据将存储在一个可公开访问的图形数据库中，该数据库 然后我们用来识别候选药物或药物组合(“候选药物”)。 通过开发一种新的基于网络的机器学习方法，我们将在 数据库通过它们的概率对AD网络产生有益的影响。高级别的候选人将是 必须经过一个全面的优先顺序流程。为此，我们将回顾调查是否 接受重新定位候选人的个人的纵向AD相关生物标记物档案显示有证据表明 在AD的大型研究中更健康地衰老。这些分析将得到补充，审查后是否- 死亡的神经病理负担支持候选人的有益效果。为了增加权力和 候选人的报道，我们将进一步分析来自英国生物库的电子健康记录，以获得更多 证据。三位最有希望的候选人将由一个专家小组讨论选出。这些 将通过AD动物模型的临床前验证研究进行评估。 总而言之，多学科专业知识、对备受瞩目的数据集的访问和 先进的计算集成管道将使我们能够识别AD中受干扰的分子通路 是药物重新定位候选药物的目标，因此是临床试验中测试的主要候选药物。

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.