Harnessing Diverse Bioinformatic Approaches To Repurpose Drugs For Alzheimers Disease And Related Dementias

利用多种生物信息学方法重新利用治疗阿尔茨海默病和相关痴呆症的药物

基本信息

批准号：
10744875
负责人：
MARK W ALBERS
金额：
$ 105.47万
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 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10744875
关键词：
Accelerated Phase Acceleration Affinity Alzheimer&apos s Disease Alzheimer&apos s Disease Pathway Alzheimer&apos s disease brain Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Award Awareness Big Data Bioinformatics Biological Markers Biology Brain Brain region Cells Chemicals Clinical Clinical Investigator Clinical Research Clinical Trials Clinical Trials Design Collaborations Communities Compensation Computer Systems Computer software Data Data Set Databases Dementia Diagnosis Disease Disease Progression Drug Prescriptions Drug Targeting Drug usage Economics Electronic Health Record Evaluation Event Exons FDA approved Gene Expression Gene Expression Profile Genetic Complementation Test Genome Human Individual Industry Inflammatory Informatics Infrastructure Israel Kidney Failure Knowledge Laboratories Lead Literature Machine Learning Medicine Mendelian randomization Methodology Methods Modeling National Health Services Neurofibrillary Tangles Neuroglia Neurons New Agents Onset of illness Outcome Pathologic Pathology Pathway Analysis Pathway interactions Patients Pattern Peripheral Pharmaceutical Preparations Pharmacology Phase II Clinical Trials Phenotype Placebo Control Prevention Production Proteome Proteomics Proxy Public Domains Randomized Records Reproducibility Risk Running Signal Transduction Single Nucleotide Polymorphism Site Statistical Data Interpretation Symptoms Synapses System Target Populations Testing Therapeutic Therapeutic Clinical Trial Update Work biobank candidate identification cell type cheminformatics clinical care clinically relevant computer science computerized tools cost dementia care drug action drug candidate drug repurposing federated learning gene discovery imaging study improved inhibitor interoperability kinase inhibitor large datasets meetings member multidisciplinary novel open data open source patient population phase III trial predictive marker preference prevent programs prospective protein TDP-43 protein expression response tool 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 developed 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. In this renewal application, we propose to discover and validate hypotheses for Drug Repurposing In AD (DRIAD) through three integrated, complementary informatics approaches. Specifically, we will extend our systems pharmacology (DRIAD-SP) tool of 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 chemical biology and systems pharmacology approaches to discover the target selectivity of lead kinase inhibitors within human neuronal and glial cell types using unbiased RNA-seq, proteomic and imaging studies followed by pathway analysis (Aim 2). We will implement additional causal inferential strategies to emulate clinical trials in electronic health records (DRIAD- EHR) data (Aim 3), with “prospective” outcomes using three big data sets: the UK-TRE with 20 year of longitudinal records of 50M National Health Service patients, and the RPDR Database (based at Mass General Brigham),and the Clalit database in Israel – each with 6M individuals followed for over 20 years. Each Aim 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. This coordinated 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 continue to produce 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. These data packages have lead to one clinical trial and will help to prioritize follow on clinical and translational studies including collaborations with industry or community members 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开发了新知识，包括路径的识别以及可能参与疾病发生和/或进展的靶点。挑战在于找到可能涉及疾病发生和/或进展的靶点。影响这些途径，然后验证药物这些途径的重要性——区分主要途径重新利用 FDA 批准的药物是探索次要事件的疾病驱动因素的一种方法。在这个更新的应用程序中，我们探索了概念验证和最终治疗临床试验的潜在途径。提议通过三个综合的、具体来说，我们将扩展我们的系统药理学（DRIAD-SP）。经典和网络感知（预先加载）机器学习方法的工具，用于识别路径和使用加速药物的数据，在疾病进展的不同阶段，AD 大脑中的目标发生了变化通过 Synapse 提供合作伙伴 AD（目标 1）；我们将使用化学生物学和系统药理学；发现人类神经细胞和神经胶质细胞类型中先导激酶抑制剂的靶点选择性的方法使用无偏见的 RNA 测序、蛋白质组学和成像研究，然后进行通路分析（目标 2）。实施额外的因果推理策略来模拟电子健康记录中的临床试验（DRIAD- EHR）数据（目标 3），使用三个大数据集得出“预期”结果：具有 20 年历史的 UK-TRE 5000 万国民医疗服务患者的纵向记录，以及 RPDR 数据库（基于麻省总医院） Brigham）和以色列的 Clalit 数据库——每个 Aim 都有 600 万人被跟踪了 20 多年。两种方法：数据驱动的假设生成分析，以识别与疾病相关的药物信号；以及假设检验，其中使用其他方法评估一种方法的积极结果评估严谨性和可重复性。这个协调的计划弥补了每个人的局限性。信息学方法促进已知和新型AD“先导化合物”的发现和批判性评估为了执行这一战略，我们组建了一支具有专业知识的多地点、多学科团队。范围从临床护理到计算机科学和系统药理学，一些团队成员是AD。最后，作为一个可交付成果，我们将继续提供开放的内容。源数据包，以发布所有支持证据、软件和数据，其出处为通过 Synapse 符合 FAIR（可查找、可访问、可互操作和可复制）标准。这些数据包已导致一项临床试验，并将有助于优先考虑临床和转化的后续工作研究包括与参与新临床试验的行业或社区成员合作。