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

利用多种生物信息学方法重新利用治疗阿尔茨海默病的药物

• 批准号: 9565013
• 负责人: MARK W ALBERS
• 金额: $ 83.54万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2018-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9565013
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease model; Amyloid beta-Protein; Automobile Driving; Awareness; Big Data to Knowledge; Bioinformatics; Biological Assay; Brain; Brain Diseases; Cells; Cerebrovascular Disorders; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Collaborations; Communities; Comorbidity; Computer Simulation; Computer Systems; Computer software; Data; Data Set; Databases; Diabetes Mellitus; Disease; Disease Pathway; Disease Progression; Electronic Health Record; Etiology; Evaluation; Event; FDA approved; Gene Expression; General Hospitals; Generations; Genome; Human; Imagery; Immune; Individual; Industry; Inflammatory; Informatics; Knowledge; Laboratories; Lead; Lewy Bodies; Link; Literature; Machine Learning; Malignant Neoplasms; Medicine; Metformin; Methods; Microglia; Molecular Target; Mono-S; Network-based; Neurofibrillary Tangles; Neuroglia; Neurons; Onset of illness; Outcome; Pathogenicity; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Pharmacology; Phenotype; Primary Health Care; Process; Proteome; Proteomics; Public Domains; Records; Reproducibility; Research Infrastructure; Senile Plaques; Signal Transduction; Statistical Data Interpretation; Structure; Synapses; Syndrome; System; Testing; Therapeutic Clinical Trial; Validation; base; cell type; clinical care; clinically relevant; cohort; computer science; computerized tools; disease registry; drug testing; imaging study; interoperability; kinase inhibitor; member; neuron loss; open data; programs; protein TDP-43; protein expression; software development; tau phosphorylation; transcriptome; transcriptome sequencing; translational study

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.

探索阿尔茨海默氏病大脑的基因组、转录组和蛋白质组 (AD) – 包括加速药品合作伙伴关系-AD (AMP-AD) 提供的那些 – 由强大的 计算工具具有开发新知识的潜力，包括识别路径 以及可能参与疾病发生和/或进展的目标挑战是有效的。 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 重新利用 FDA 批准的药物是探索潜力的一种方法。 在这里，我们建议发现和临床试验的概念验证和最终治疗的途径。 通过三个综合、互补的信息学验证 AD 药物再利用的假设 这种生物信息学活动与传统的药物活动并行，使用 AMP-AD 数据作为数据。 具体来说，我们将应用“实验室”和电子健康记录（EHR）作为我们的“临床试验基础设施”。 经典和网络感知（预先加载）的机器学习方法（已在 癌症相关的组学数据集）来识别 AD 大脑在不同阶段改变的途径和目标 使用 AMP-AD 数据确定疾病进展（目标 1）；我们将使用系统药理学方法来 使用无偏 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标选择性 seq、蛋白质组学和成像研究，然后进行通路分析（目标 1 和 2）各有两个。 方法：数据驱动的假设生成分析，以辨别疾病相关的药物信号；以及 假设检验，其中使用其他方法评估一种方法的积极结果 在目标 3 中，我们将开发新的信息学策略以进行计算机模拟药物评估。 利用英国 20 年 CPRD 的优势，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 Mass）。 综合医院）对 600 万人进行了 20 多年的跟踪，将进一步验证基于 这种协调的信息学程序弥补了组学数据集和现有文献的弱点。 每个单独的信息学方法，以促进“先导化合物”的发现和批判性评估 为了执行这一战略，我们组建了一支具有以下专业知识的团队： 一些团队成员是 AD 专家和计算机科学和系统药理学的临床护理。 最后，作为可交付成果，我们将创建开源数据包。 根据 FAIR（可查找的、 通过 Synapse 和 DataLens 平台开发的标准 MGH（目标 4）将有助于优先考虑临床和转化研究的后续工作。 与参与新临床试验的行业或更大的生物医学界成员合作。