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: 9974450
• 负责人: MARK W ALBERS
• 金额: $ 75.93万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974450
• 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 testing; gene discovery; imaging study; improved; in silico; inhibitor/antagonist; 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.

摘要 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)通过强大的计算工具，有可能开发新的知识，包括 鉴定可能参与疾病的起始和/或进展的途径和靶标。 挑战在于找到影响这些途径的药物，然后验证这些途径的重要性 - 区分原发性疾病和继发性疾病。重新使用FDA批准的药物是一个 在概念验证和最终治疗临床试验中探索潜在途径的方法。这里我们 建议通过三个综合的、 互补信息学方法。具体来说，我们将应用经典和网络感知（预先加载） 机器学习方法来识别AD大脑在不同阶段改变的途径和目标， 使用Synapse（Aim）提供的加速药物伙伴关系-AD的数据进行疾病进展 1）;我们将使用系统药理学方法来发现先导化合物的靶选择性， 使用无偏的RNA-seq、蛋白质组学和成像研究， 路径分析（目标2）。这两个目标都有两种方法：数据驱动，假设生成 分析以辨别疾病相关的药物信号;以及假设检验，其中来自一个 使用其他方法对这些方法进行评估，以评估严谨性和再现性。此外，RNA-seq 和暴露于潜在疾病驱动因素后在培养的人CNS细胞类型中收集的蛋白质组学数据 和/或FDA批准的药物将作为CNS细胞类型衍生的先验反馈到Aim 1中，以细化Aim 2中的药物。 预测模型在目标3中，我们将开发新的信息学策略，在EHR中进行计算机药物试验 具有“预期”结果的数据以验证基于组学数据集和现存文献的假设， 使用两个大数据集：英国20年CPRD纵向记录的2000万国民健康服务患者， 和RPDR数据库（基于Partners Healthcare），其中有600万人随访了20多年。这 综合信息学计划弥补了每个单独的信息学方法的局限性， 促进对已知和新的AD途径的“先导化合物”的发现和批判性评价。执行 为此，我们组建了一个多地点、多学科的团队，其专业知识包括临床护理、 到计算机科学和系统药理学。有些团队成员是广告专家，有些则带来了 局外人的视角最后，作为一个可交付成果，我们将创建开源数据包， 根据FAIR（可查找，可访问， 通过Synapse和MGH开发的AlzDataLens平台， (Aim 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 与参与新临床试验的行业或社区成员合作。