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.

项目概要 鉴于阿尔茨海默氏病 (AD) 发病机制的复杂性和相关的共病状况，两者 目前可用的药物再利用解决方案的“深度”和“宽度”需要改进，以便 提供有效的 AD 治疗方案的深度是指药物再利用项目的水平。 在广泛的搜索空间中了解疾病机制和药物靶点相互作用 剂量和治疗时间的结合需要可靠的 AD 模型系统。 全面总结了类人脑环境下的AD发病机制，并提出了复杂的 转录组图谱，可以揭示疾病逆转表型背后的分子水平变化 跨多种治疗条件的治疗搜索的宽度取决于预测和治疗的效果。 从新颖的搜索空间中验证候选化合物的效果可以实现。 连接组学变化与表型变化的计算算法，从而指导搜索 提高对机制的了解并避免对每种可用药物进行详尽的测试。 整合休斯顿卫理公会黄实验室的系统医学和药物重新定位专业知识 休斯敦卫理公会医院研究所拥有 Kim 和 Tanzi 的阿尔茨海默病生物学专业知识 在马萨诸塞州总医院的实验室，我们提出了一种系统性阿尔茨海默病药物重新定位 （SMART）基于生物信息学指导的表型筛选的框架。 金氏开发的AD（又名培养皿中的阿尔茨海默病）的三维人类神经干细胞培养模型 和 Tanzi 实验室进行高内涵筛选，Wong 实验室筛选了 2,640 种已知药物和生物活性物质 化合物并获得了一组 38 个初级命中，可强烈抑制 β-淀粉样蛋白驱动的 p-tau 积累。 我们勇敢地使用新颖的 3D 单元模型迭代运行相对较小的屏幕，并应用 对筛选命中的转录组图谱进行系统的人工智能建模将使我们能够：1) 快速获得一组强大的 AD 候选新药，并且 2) 深入了解 从那些重新定位的候选药物中了解疾病机制，这将随后提高成功率 预测小说点击率。 以最初的 38 个命中作为起点，SMART 计算模块将更新现有的 NeuriteIQ 软件包用于量化高内涵筛选的图像数据； 公开的大数据转录组图谱可预测诱导相似途径的候选化合物 随着那些原始化合物的变化，将搜索宽度扩展到数万个 化合物，同时只需要不到 100 种候选药物的功能验证。 反过来，预测将添加到已知命中的面板中，从而启动下一轮计算 预测和实验验证，有效生成新型 AD 疗法的候选药物（目标 1）。 SMART 的迭代预测验证方案有效地将更多转录组谱与所需的转录组谱连接起来 因此，我们将应用系统的图像组学模型来揭示新的机制。 对于所有经过验证的命中，pTau 抑制表型的剂量反应将是： 使用 3D 培养模型获得；而单个基因和途径的剂量反应将是 我们将使用偏最小二乘法通过公共和内部生成的转录组图谱进行建模。 回归模型识别具有匹配剂量反应曲线的基因模块作为表型，从而 使我们能够超越规范通路图的限制并识别新的功能模块 与感兴趣的表型特别相关（目标 2）。 来自前两个目标的选定化合物将在直接衍生的人类神经元中进行评估 来自 AD 患者和动物模型（目标 3）。 这项工作的成功也将导致新的 AD 治疗化合物准备好转化为临床试验 作为对AD病理生理学分子机制的更深入了解，SMART。 药物重新定位框架将推广到其他大数据和疾病平台。

项目成果

OCIAD1 contributes to neurodegeneration in Alzheimer's disease by inducing mitochondria dysfunction, neuronal vulnerability and synaptic damages.

OCIAD1 通过诱导线粒体功能障碍、神经元脆弱性和突触损伤，导致阿尔茨海默病的神经变性。

• 发表时间: 2020-01
• 影响因子: 11.1
• 作者: Li, Xuping;Wang, Lin;Cykowski, Matthew;He, Tiancheng;Liu, Timothy;Chakranarayan, Joshua;Rivera, Andreana;Zhao, Hong;Powell, Suzanne;Xia, Weiming;Wong, Stephen T C
• 通讯作者: Wong, Stephen T C