Systematic Alzheimer's disease drug repositioning (SMART) based on bioinformatics-guided phenotype screening and image-omics

基于生物信息学引导的表型筛选和图像组学的系统性阿尔茨海默病药物重新定位（SMART）

• 批准号: 10431823
• 负责人: STEPHEN TC WONG
• 金额: $ 68.96万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431823
• 关键词: 3-Dimensional; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease therapeutic; Alzheimer' s disease therapy; Amyloid beta-Protein; Animal Model; Artificial Intelligence; Automobile Driving; Back; Big Data; Bioinformatics; Biological Assay; Biological Models; Biology; Brain; Cell Culture Techniques; Cell Line; Cell model; Cellular Assay; Clinical; Clinical Research; Clinical Trials; Communities; Computational algorithm; Computer software; Data; Databases; Disease; Dose; Drug Targeting; Drug usage; Ensure; Environment; Event; Functional disorder; Funding; Future; General Hospitals; Genes; Hospitals; Human; Image; In Vitro; Institutes; Knowledge; Least-Squares Analysis; Libraries; Literature; Maps; Massachusetts; Medicine; Methodist Church; Methods; Modeling; Molecular; Network-based; Neurons; Pathogenesis; Pathogenicity; Pathology; Pathway interactions; Persons; Pharmaceutical Preparations; Phase; Phenotype; Reporting; Research Institute; Running; Scheme; Series; Signal Transduction; Synapses; System; Tauopathies; Techniques; Testing; Therapeutic; Time; Toxicology; Translations; United States; United States National Institutes of Health; Update; Validation; Width; Work; base; bench-to-bedside translation; comorbidity; computational platform; cost; dosage; drug candidate; drug development; drug discovery; drug efficacy; drug repurposing; exhaustion; feeding; improved; in vitro Model; in vivo; independent component analysis; individual response; interest; knowledge base; nerve stem cell; neuron loss; novel; novel therapeutics; public health relevance; relating to nervous system; response; screening; success; symptom treatment; tau Proteins; tau aggregation; tau-1; three dimensional cell culture; transcriptomics

PROJECT SUMMARY Given the complexity of Alzheimer's Disease (AD) pathogenesis and the associated co-morbid conditions, both the “depth” and the “width” of currently available drug repurposing solutions need to be improved in order to deliver effective AD therapeutic solutions. The depth of a drug-repurposing project refers to the level of understanding of disease mechanism and drug-target interactions across a wide searching space for the combination of dosage and treatment time. Achieving depth requires a reliable AD model system that comprehensively recapitulates AD pathogenesis in a human brain-like environment, and sophisticated transcriptomic profiles, which can reveal molecular-level changes underlying disease-reversing phenotypes across multiple treatment conditions. The width of a therapy search relies on the efficacy of predicting and validating effects of candidate compounds from an enormous search space. Width can be achieved from novel computational algorithms connecting –omics changes with phenotypic changes, thus guiding the search with improved knowledge on mechanisms and avoiding exhaustive testing of every available drug. Integrating the systems medicine and drug repositioning expertise of the Wong Lab at the Houston Methodist Research Institute of Houston Methodist Hospital with the Alzheimer's biology expertise of the Kim and Tanzi labs at Massachusetts General Hospital, we propose a SysteMatic Alzheimer's disease drug ReposiTioning (SMART) framework based on bioinformatics-guided phenotype screening. Reformatting a novel three- dimensional human neural stem cell culture model of AD (a.k.a. Alzheimer's in a dish) developed in the Kim and Tanzi labs for high content screening, the Wong lab screened 2,640 known drugs and bioactive compounds and obtained a panel of 38 primary hits that strongly inhibit β-amyloid-driven p-tau accumulation. We hypothesize that iteratively running relatively small screens with our novel 3D cell model and applying systematic artificial intelligence modeling to the transcriptomic profiles of the screening hits will allow us to: 1) quickly obtain a panel of robust novel drug candidates for AD, and 2) gain an in-depth understanding of disease mechanisms from those repositioned drug candidates, which will subsequently improve the success rate of predicting novel hits. Using the primary 38 hits as a starting point, the SMART computational modules will update the existing NeuriteIQ software package to quantify the image data from high content screening; it will also incorporate publicly available big data transcriptomic profiles to predict candidate compounds inducing similar pathway changes as those original compounds, effectively expanding the search width to tens of thousands of compounds while only requiring functional validation of less than 100 drug candidates. The validated predictions will, in turn, add to the panel of known hits that will launch the next round of computational predictions and experimental validations, efficiently generating candidates for novel AD therapies (Aim 1). SMART's iterative prediction-validation scheme effectively connects more transcriptomic profiles to desirable phenotypic changes. Thus, we will apply systematic image-omics modeling to uncover novel mechanisms driving such phenotypes. For all the validated hits, dose-responses for the phenotype of pTau inhibition will be obtained using the 3D culture model; while the dose-responses for individual genes and pathways will be modeled through public and in-house generated transcriptomic profiles. We will use Partial Least Square Regression models to identify gene modules with matching dose-response curves as the phenotypes, thus allowing us to go beyond the confinement of canonical pathway maps and identify novel functional modules specifically related to phenotypes of interest (Aim 2). Selected compounds derived from the previous two aims will be evaluated in human neurons directly derived from AD patients and in animal models (Aim 3). Success of this work will lead to new AD therapeutic compounds ready for translation into clinical trials, as well as a deeper understanding of the molecular mechanisms of AD pathophysiology. In addition, the SMART framework for drug repositioning will be generalizable to other big data and disease platforms.

项目摘要 鉴于阿尔茨海默氏病（AD）发病机理和相关的合并症的复杂性 需要改进当前可用的药物再利记解决方案的“深度”和“宽度”才能 提供有效的AD治疗解决方案。 了解疾病机制和药物目标相互作用，跨越广泛的搜索空间 剂量和治疗时间的结合需要一个可靠的广告模型系统 综合性在类似人脑的环境中重组AD发病机理，并精致 转录组曲线，可以揭示分子水平的变化，而逆转疾病的表型 在多种治疗条件下。 从巨大的搜索空间验证候选化合物的效果。 计算算法连接 - 组随表型变化而变化，从而指导搜索 改进了机制上的诺奇奇，并避免了彻底的avery药物测试。 在Houseon卫理公会上整合Wong实验室的系统医学和药物报告 休斯顿卫理公会研究所，阿尔茨海默氏症的生物学实验法 马萨诸塞州综合医院的实验室，我们提出了一个系统的阿尔茨海默氏病药物重新介绍 （智能）基于生物信息学引导的表型筛查的框架。 KIM中开发的AD（又名阿尔茨海默氏症）的维度人神经干细胞培养模型（又名阿尔茨海默氏症） Wong Lab筛选了2,640种已知药物和生物活性的Andzi实验室 化合物并获得了一组38个主要命中率，可强烈抑制β-淀粉样蛋白驱动的P-TAU积累。 我们假设迭代运行的运行相对小屏幕与我们的新型3D细胞模型模型应用 针对筛选命中的转录组轮廓的系统人工智力建模将使我们能够：1） 快速获取一组鲁棒毒品候选物进行AD，2）获得深入的未融合 重新定位的候选药物的疾病机制，随后将改善成功 预测新颖的命中率。 将主要的38命中作为起点，智能计算模块将更新现有 神经INURITEIQ软件包可以量化高内容筛选的图像数据； 公开可用的大数据转录组概况到配置文件，以构图候选人组合相似的途径 随着原始化合物的变化，有效地将搜索宽度扩展到数万个 在ONLE的同时，需要少于100个药物的功能验证 预测又将添加到已知命中的面板中，这些命中将启动下一轮计算 预测和实验验证，有效地生成了新型AD疗法的候选者（AIM 1）。 Smart的迭代预测验证方案有效将更多的转录组概况连接到理想的 表型变化。 驱动所有经过验证的命中的表型。 使用3D培养模型获得； 虽然公开和内部生成的转录组概况。 回归模型以鉴定具有匹配剂量响应曲线的基因为表型，因此 使我们超越了规范途径图的限制并识别新型功能模块 与感兴趣的表型特别相关（AIM 2）。 将在直接得出的人类神经元中评估源自两个目标的选定化合物 来自AD患者和动物模型（AIM 3）。 这种磨损的成功将导致新的AD治疗化合物，也可以转化为临床试验 作为对AD病理生理学的分子机制的更深入的理解。 药物报告的框架将推广到其他大数据和疾病平台。