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.

探索源自阿尔茨海默氏病的大脑的基因组，转录组和蛋白质组织 （AD） - 包括加速药物合作伙伴关系（AMP-AD）提供的（AMP-AD） - 强大 计算工具具有开发新知识的潜力，包括识别途径 以及可能参与疾病的主动性和/或进展的目标。挑战是验证 通过发现这些途径的重要性 - 将原发性疾病驱动因素与次要事件区分开 影响这些途径的药物。重新利用FDA批准的药物是一种探测潜力的方法 概念证明的途径，最终是治疗性的临床试验。在这里，我们建议发现和 通过三个综合，完整的信息验证在AD中验证药物重新利用的假设 方法。这项与传统药物运动平行的生物信息学运动使用AMP-AD数据作为 “实验室”和电子热记录（EHR）作为我们的“临床试验基础设施”。具体来说，我们将申请 经典和网络意识（提前加载）机器学习方法（已证明了实用性 与癌症相关的OMICS数据集）识别AD大脑中不同阶段的途径和目标改变的途径 使用AMP-AD数据进行疾病进展（AIM 1）；我们将使用系统药理学方法来 使用无偏的RNA-发现人神经元和神经胶质细胞类型中铅化合物的靶标选择性 SEQ，蛋白质组学和成像研究，然后进行途径分析（AIM 2）。目标1和2每个有两个 方法：数据驱动的，假设生成的分析，以辨别与疾病相关的药物信号；和 假设检验，其中通过其他方法评估了一种方法的积极发现 评估严格和可重复性。在AIM 3中，我们将制定新的信息策略来进行计算机药物 试验通过分析EHR来验证药物的临床相关性，利用英国20年CPRD 1500万人的纵向记录。第二个独立的EHR数据集，RPDR数据库（基于质量） 综合医院）有6 m的个人紧随其后20年以上，将进一步验证基于 法学数据集和现有文献。这个协调的信息计划弥补了弱点 每种信息的方法来促进发现和对AT的“铅化合物”的批判性评估 至少有一些广告路径。为了执行此策略，我们组建了一个专业知识的团队 计算机科学和系统药理学的临床护理。一些团队成员是广告专家， 其他人带来了局外人的观点。最后，作为可交付的，我们将创建开源数据包 按照公平（可发现， 通过突触可访问，可互操作和可重现的）标准，Datalens平台开发了 在MGH（目标4）。这些数据包将有助于优先考虑临床和翻译研究，包括 与参与新临床试验的行业或较大的生物医学界的成员的合作。