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

基本信息

批准号：
10212939
负责人：
MARK W ALBERS
金额：
$ 74.81万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
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/10212939
关键词：
Alzheimer&apos s Disease Alzheimer&apos s Disease Pathway Alzheimer&apos s disease brain Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos 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/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

项目摘要

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.

抽象的探索阿尔茨海默氏病大脑的基因组、转录组和蛋白质组（AD）通过强大的计算工具具有开发新知识的潜力，包括识别可能参与疾病发生和/或进展的途径和靶点。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 – 区分原发疾病驱动因素和次要事件是重新利用 FDA 批准的药物的原因之一。探索概念验证和最终治疗临床试验中潜在途径的方法。提议通过三个综合的、具体来说，我们将应用经典和网络感知（预先加载）的方法。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 提供的加速药物合作伙伴关系-AD 数据进行疾病进展（目标 1);我们将使用系统药理学方法来发现先导化合物的靶点选择性使用无偏 RNA-seq、蛋白质组学和成像研究对人类神经元和神经胶质细胞类型进行研究，然后路径分析（目标 2）。这两个目标都有两种方法：数据驱动、假设生成。分析以辨别与疾病相关的药物信号；以及假设检验，其中一个阳性结果此外，RNA-seq 正在使用其他方法来评估 ligor 和重现性。暴露于潜在疾病驱动因素后在培养的人类中枢神经系统细胞类型中收集的蛋白质组数据和/或目标 2 中 FDA 批准的药物将作为 CNS 细胞类型衍生的先验反馈到目标 1 中，以细化在目标 3 中，我们将开发新的信息学策略以在 EHR 中进行计算机模拟药物试验。具有“前瞻性”结果的数据，以验证基于组学数据集和现有文献的假设，使用两个大数据集：英国 2000 万国民医疗服务患者的 20 年 CPRD 纵向记录， RPDR 数据库（位于 Partners Healthcare）有 600 万人关注此数据库 20 多年。综合信息学方案弥补了每种信息学方法的局限性促进已知和新颖 AD 途径的“先导化合物”的发现和严格评估。根据这一战略，我们组建了一支多地点、多学科团队，其专业知识涵盖临床护理一些团队成员是 AD 专家，其他人则来自计算机科学和系统药理学。最后，作为一个可交付成果，我们将创建开源数据包来发布所有的数据。根据 FAIR（可查找、可访问、通过 MGH 开发的 Synapse 和 AlzDataLens 平台实现可互操作和可复制的标准（目标 4）。这些数据包将有助于优先考虑临床和转化研究，包括与参与新临床试验的行业或广大社区成员合作。