Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimers 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: 10452499
• 负责人: MARK W ALBERS
• 金额: $ 73.31万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452499
• 关键词: Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease therapy; Amyloid beta-Protein; Awareness; Back; Big Data; Bioinformatics; Biological Assay; Brain; Cells; Clinic; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Collaborations; Communities; Complement; Computer Systems; Computer software; Data; Data Science; Data Set; Data Sources; Databases; Diabetes Mellitus; Disease; Disease Pathway; Disease Progression; Drug Design; Drug Exposure; Drug usage; Electronic Health Record; Etiology; Evaluation; Event; Exposure to; FDA approved; Gene Expression; Gene Expression Profile; Generations; Genome; Healthcare; Human; Immune; Individual; Industry; Inflammatory; Informatics; Information Systems; Infrastructure; Knowledge; Laboratories; Lead; Lewy Bodies; Link; Literature; Machine Learning; Mediating; Medicine; Memory; Metformin; Methods; Microglia; Molecular Target; National Health Services; Network-based; Neurofibrillary Tangles; Neuroglia; Neurons; Onset of illness; Outcome; Pathogenicity; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacology; Phenotype; Primary Health Care; Process; Proteome; Proteomics; Public Domains; Records; Regulation; Reproducibility; Senile Plaques; Signal Transduction; Site; Source Code; Statistical Data Interpretation; Synapses; Syndrome; System; Testing; Therapeutic Clinical Trial; Validation; Visualization; base; cell type; cheminformatics; clinical care; clinical translation; cohort; comorbidity; computer science; computerized tools; disease registry; drug candidate; drug repurposing; drug testing; gene discovery; imaging study; improved; in silico; inhibitor; interoperability; kinase inhibitor; large datasets; member; multidisciplinary; neuron loss; novel; open data; predictive modeling; prevent; programs; prospective; protein TDP-43; protein expression; statistical and machine learning; tau phosphorylation; transcriptome; transcriptome sequencing; translational study

Abstract The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (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 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. Here, we propose to discover and validate hypotheses for drug repurposing in AD through three integrated, complementary informatics approaches. Specifically, we will apply 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 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). Each of these two Aims 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. Moreover, RNA-seq and proteomic data collected in cultured human CNS cell types following exposure to potential disease drivers and/or FDA-approved drugs in Aim 2 will be fed back into Aim 1 as CNS-cell type-derived priors to refine the predictive models. In Aim 3, we will develop new informatics strategies to conduct in-silico drug trials in EHR data with “prospective” outcomes to validate hypotheses based on the omics data sets and extant literature, using two big data sets: the UK 20 year CPRD longitudinal records of 20M National Health Service patients, and the RPDR Database (based at Partners Healthcare) with 6 M individuals followed for over 20 years. This integrated informatics 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 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 AlzDataLens 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 community at large involved in new clinical trials.

抽象的 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）强大的计算工具具有开发新知识的潜力，包括 鉴定可能参与疾病主动性和/或进展的途径和目标。 挑战是找到影响这些途径的药物，然后验证这些途径的重要性 - 将原发性疾病驱动因素与次要事件区分开。重新利用FDA批准的药物是一种 探测概念证明和最终治疗性临床试验的潜在途径的方法。在这里，我们 通过三个综合， 完全信息的方法。具体而言，我们将应用经典和网络意识（事先加载） 机器学习方法以识别在广告大脑中在不同阶段发生变化的途径和目标的方法 使用加速药物合作伙伴关系的数据进行疾病进展 - 通过突触获得（目标 1）;我们将使用系统药理学方法来发现铅化合物的目标选择性 使用无偏的RNA-seq，蛋白质组学和成像研究的人类神经元和神经胶质细胞类型，然后是 途径分析（目标2）。这两个目标中的每一个都有两种方法：数据驱动，假设生成 分析以辨别与疾病相关的药物信号；和假设检验，其中一个来自一个的积极发现 使用其他方法评估严格性和可重复性的方法评估方法。此外，RNA-Seq 和潜在疾病驱动因素后培养的人类中枢神经系统细胞类型中收集的蛋白质组学数据 AIM 2中的/或FDA批准的药物将作为CNS-Cell类型衍生的先验送回AIM 1 预测模型。在AIM 3中，我们将制定新的信息策略来在EHR中进行Silico IntiCo药物试验 具有“预期”结果的数据，以基于OMIC数据集和范围文献验证假设， 使用两个大数据集：英国20年CPRD纵向记录2000万名国家卫生服务患者， 以及具有6 m个个人的RPDR数据库（基于Partners Healthcare）超过20年。这 综合信息计划弥补了每个信息信息方法的局限性方法 促进对已知和新型AD途径的“铅化合物”的发现和批判性评估。执行 这种策略，我们组装了一个多站点的多学科团队，具有临床护理的专业知识 用于计算机科学和系统药理学。一些团队成员是广告专家，其他团队成员带来 局外人的观点。最后，作为可交付的，我们将创建开源数据包以发布所有 根据公平（可访问，可访问， 通过突触和MGH开发的Alzdatalens平台可互操作和可重现）标准 （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 与行业或社区成员的合作，参与了新的临床试验。