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.

阿尔茨海默病患者脑组织基因组、转录组和蛋白质组的研究 (AD)- 包括由加速药物伙伴关系-AD（AMP-AD）提供的药物-由强大的 计算工具具有开发新知识的潜力，包括识别途径 以及可能参与疾病启动和/或进展的靶点。挑战在于验证 这些途径的重要性-区分原发性疾病驱动因素和继发性事件-通过发现 影响这些通路的药物。重新使用FDA批准的药物是探索潜在的方法之一 在概念验证和最终的治疗临床试验中的途径。在这里，我们建议发现和 通过三个集成的、互补的信息学验证AD中药物再利用的假设 接近。这项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，我们将应用 经典的和网络感知的（预先加载的）机器学习方法（已经证明了在 癌症相关的组学数据集），以确定在不同阶段AD大脑中改变的通路和靶点。 使用AMP-AD数据进行疾病进展（目标1）;我们将使用系统药理学方法， 使用无偏的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、蛋白质组学和成像研究，然后进行途径分析（Aim 2）。目标1和2各有两个 方法：数据驱动的假设生成分析，以识别疾病相关的药物信号;以及 假设检验，其中一种方法的阳性结果使用其他方法进行评估， 评估严谨性和再现性。在目标3中，我们将开发新的信息学策略， 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（基于马萨诸塞州 综合医院），对600万人进行了超过20年的随访，将进一步验证基于 组学数据集和现存文献。这个协调的信息学计划弥补了 每一个单独的信息学方法，以促进发现和关键评价的“先导化合物”， 至少有一些AD途径。为了执行这一战略，我们组建了一个专业团队， 临床护理到计算机科学和系统药理学。一些团队成员是AD专家， 其他人则带来了局外人的视角。最后，作为交付成果，我们将创建开源数据包， 根据FAIR（findable， 通过Synapse和DataLens平台开发的可访问、可互操作和可复制）标准 在MGH（目标4）。这些数据包将有助于优先考虑后续临床和转化研究，包括 与行业或参与新临床试验的更大生物医学界成员合作。