Harnessing Diverse Bioinformatic Approaches To Repurpose Drugs For Alzheimers Disease And Related Dementias

摘要 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)通过强大的计算工具发展了新的知识，包括识别路径 以及可能参与疾病启动和/或发展的靶点。挑战是要找到 影响这些通路的药物，然后验证这些通路的重要性-区分主要的 次要事件引发的疾病驱动因素。改变FDA批准的药物的用途是调查的一种方法 概念验证和最终治疗的临床试验中的潜在途径。在此续期申请中，我们 建议通过三个集成的、 互补的信息学方法。具体地说，我们将扩展我们的系统药理学(DRIAD-SP) 经典和网络感知(预先加载)机器学习方法的工具，用于识别路径和 使用加速药物的数据改变AD大脑在疾病进展的不同阶段的靶点 合作伙伴关系-AD可通过Synapse获得(目标1)；我们将使用化学生物学和系统药理学 在人类神经细胞和神经胶质细胞中发现铅激酶抑制剂靶向选择性的方法 使用无偏见的RNA-SEQ、蛋白质组学和成像研究，然后进行途径分析(目标2)。我们会 实施其他因果推理策略，以在电子健康记录(DRIAD- EHR)数据(目标3)，使用三个大数据集得出“预期”结果：英国-TRE，20年 5000万国民健康服务患者的纵向记录和RPDR数据库(基于麻省总医院 Brigham)和以色列的Clalit数据库--每个数据库都有600万个个体，跟踪了20多年。每个目标都有 两种方法：数据驱动、假设生成的分析，以识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。这一协调的计划弥补了每个人的局限性 信息学方法促进已知和新AD的“先导化合物”的发现和批判性评估 小路。为了执行这一战略，我们组建了一支拥有专业知识的多地点、多学科团队 范围从临床护理到计算机科学和系统药理学。团队中的一些成员是AD 专家和其他人带来了局外人的观点。最后，作为可交付成果，我们将继续生产开放的- 源数据包以发布所有具有出处的支持证据、软件和数据 通过Synapse符合公平(可找到、可访问、可互操作和可重现)标准。 这些数据包导致了一项临床试验，并将有助于确定后续临床和翻译的优先顺序 研究包括与行业或社区成员的合作，参与了新的临床试验。

Abstract The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (AD) by powerful computational tools has developed 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 to find drugs that impact those pathways and then validate the importance of those pathways – distinguishing primary disease drivers from secondary events. Repurposing FDA-approved drugs is one approach to probe potential pathways in proof of concept, and ultimately therapeutic, clinical trials. In this renewal application, we propose to discover and validate hypotheses for Drug Repurposing In AD (DRIAD) through three integrated, complementary informatics approaches. Specifically, we will extend our systems pharmacology (DRIAD-SP) tool of classical and network aware (prior-loaded) machine learning approaches to identify pathways and targets altered in AD brains at different stages of disease progression using data from Accelerating Medicines Partnership-AD available through Synapse (Aim 1); we will use chemical biology and systems pharmacology approaches to discover the target selectivity of lead kinase inhibitors within human neuronal and glial cell types using unbiased RNA-seq, proteomic and imaging studies followed by pathway analysis (Aim 2). We will implement additional causal inferential strategies to emulate clinical trials in electronic health records (DRIAD- EHR) data (Aim 3), with “prospective” outcomes using three big data sets: the UK-TRE with 20 year of longitudinal records of 50M National Health Service patients, and the RPDR Database (based at Mass General Brigham),and the Clalit database in Israel – each with 6M individuals followed for over 20 years. Each Aim 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. This coordinated program compensates for the limitations of each individual informatics approach to promote discovery and critical evaluation of “lead compounds” for known and novel AD pathways. To execute this strategy, we have assembled a multi-site, multi-disciplinary 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 continue to produce 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. These data packages have lead to one clinical trial and will help to prioritize follow on clinical and translational studies including collaborations with industry or community members at large involved in new clinical trials.