Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimer's Disease

阿尔茨海默病患者脑部基因组、转录组和蛋白质组的研究 (AD)--包括由加速药物伙伴关系(AMP-AD)提供的那些--由POWERFORCE提供 计算工具具有开发新知识的潜力，包括识别路径 以及可能参与疾病启动和/或发展的靶点。该挑战已得到验证 这些途径的重要性-区分主要疾病驱动因素和次要事件-通过发现 影响这些通路的药物。改变FDA批准的药物的用途是探索潜力的一种方法 概念验证和最终治疗的临床试验的途径。在这里，我们建议发现和 通过三个整合、互补的信息学验证AD药物再利用的假说 接近了。这一生物信息学活动，与传统的药物活动平行，使用AMP-AD数据作为 “实验室”和电子健康记录(EHR)作为我们的“临床试验基础设施”。具体来说，我们将申请 经典的和网络感知的(预先加载的)机器学习方法(已在 癌症相关组学数据集)，以确定AD大脑在不同阶段改变的途径和靶点 使用AMP-AD数据的疾病进展(目标1)；我们将使用系统药理学方法来 用无偏见的RNA-发现先导化合物在人类神经元和神经胶质细胞类型中的靶向选择性 SEQ、蛋白质组和成像研究，然后进行通径分析(目标2)。目标1和目标2各有两个 方法：数据驱动、假设生成的分析，以辨别与疾病相关的药物信号；以及 假设检验，其中一种方法的积极结果是使用另一种方法来评估的 评估严格性和重复性。在目标3中，我们将开发新的信息学策略来进行硅胶药物 利用英国20年CPRD通过分析EHR来验证药物临床相关性的试验 1500万人的纵向记录。第二个独立的EHR数据集，RPDR数据库(基于麻省理工学院 综合医院)对600万人进行了20多年的跟踪调查，将进一步验证基于 组学数据集和现存文献。这种协调的信息学计划弥补了这些弱点 每一种单独的信息学方法，以促进发现和批判性评估At的“先导化合物” 至少有一些AD途径。为了执行这一战略，我们组建了一支拥有以下专业知识的团队 计算机科学和系统药理学的临床护理。团队中的一些成员是AD专家和 其他人则带来了局外人的观点。最后，作为可交付成果，我们将创建开源数据包来 发布所有支持证据、软件和数据，并根据公平(可找到的、 可访问、可互操作和可重现)标准，通过Synapse和开发的DataLens平台 在MGH(目标4)。这些数据包将有助于确定后续临床和转化性研究的优先顺序，包括 与业界或更大的生物医学社区成员合作，参与新的临床试验。

The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (AD) – including those provided by the Accelerating Medicines Partnership-AD (AMP-AD) – by powerful computational tools has the potential of developing new knowledge, including the identification of pathways and targets that may be involved in the initiation and/or progression of the disease. The challenge is validate the importance of those pathways – distinguishing primary disease drivers from secondary events – by finding drugs that impact those pathways. Repurposing FDA-approved drugs is one approach to probe potential pathways in proof of concept, and ultimately therapeutic, clinical trials. Here, we propose to discover and validate hypotheses for drug repurposing in AD through three integrated, complementary informatics approaches. This bioinformatics campaign, parallel to a traditional drug campaign, uses AMP-AD data as the “laboratory” and electronic heath records(EHR) as our “clinical trial infrastructure”. Specifically, we will apply classical and network aware (prior-loaded) machine learning approaches (which have demonstrated utility in cancer-related omics datasets) to identify pathways and targets altered in AD brains at different stages of disease progression using AMP-AD data (Aim 1); and we will use systems pharmacology approaches to discover the target selectivity of lead compounds in human neuronal and glial cell types using unbiased RNA- seq, proteomic and imaging studies followed by pathway analysis (Aim 2). Aims 1 and 2 each has two approaches: data-driven, hypothesis-generating analyses to discern disease-relevant drug signals; and hypothesis-testing in which positive findings from one approach are evaluated using the other approaches to assess rigor and reproducibility. In Aim 3, we will develop new informatics strategies to conduct in silico drug trials to validate the clinical relevance of drugs by analyzing EHR, taking advantage of the UK 20 year CPRD longitudinal records of 15M people. A second independent EHR data set, the RPDR Database (based at Mass General Hospital) with 6 M individuals followed for over 20 years, will further validate hypotheses based on the omics data sets and extant literature. This coordinated informatics program compensates for the weaknesses of each individual informatics approach to promote discovery and critical evaluation of “lead compounds” for at least some AD pathways. To execute this strategy, we have assembled a team with expertise ranging from clinical care to computer science and systems pharmacology. Some of the team members are AD experts and others bring an outsider's perspective. Finally, as a deliverable, we will create open-source data packages to release all the supporting evidence, software, and data with provenance in accordance with FAIR (findable, accessible, interoperable and reproducible) standards through Synapse and the DataLens platform developed at MGH (Aim 4). These data packages will help to prioritize follow on clinical and translational studies including collaborations with industry or members of the larger biomedical community involved in new clinical trials.