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.

项目总结 鉴于阿尔茨海默病(AD)发病机制的复杂性和相关的共病条件，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治疗解决方案。药物再利用项目的深度指的是 通过广泛的搜索空间了解疾病机制和药物与靶点的相互作用 剂量和治疗时间相结合。实现深度需要可靠的AD模型系统 全面概括了AD在类人脑环境中的发病机制，以及复杂的 转录图谱，可以揭示疾病逆转表型背后的分子水平变化 跨越多种治疗条件。治疗搜索的广度取决于预测和治疗的效果 从巨大的搜索空间中验证候选化合物的效果。宽度可以从小说中获得 将组学变化与表型变化联系起来的计算算法，从而指导搜索 提高了对机制的了解，避免了对每种可用药物进行详尽的测试。 整合休斯顿卫理公会王实验室的系统医学和药物重新定位专业知识 休斯顿卫理公会医院研究所拥有金和坦兹的阿尔茨海默氏症生物学专业知识 马萨诸塞州总医院的实验室，我们建议对阿尔茨海默病药物进行系统性重新定位 基于生物信息学引导的表型筛选的(SMART)框架。重新格式化一部新的三部- AD(又名：AD)人神经干细胞培养模型盘中的阿尔茨海默氏症)在金氏 为了筛选高含量的药物，Wong实验室筛选了2640种已知药物和生物活性 并获得了一组38个主要靶点，它们强烈抑制β淀粉样蛋白驱动的p-tau积聚。 我们假设，使用我们的新3D细胞模型迭代运行相对较小的屏幕并应用 对筛选命中的转录图谱进行系统的人工智能建模将使我们能够：1) 快速获得AD的强大新药候选小组，以及2)深入了解 来自那些重新定位的候选药物的致病机制，这将随后提高成功率 预测小说点击率。 以最初的38次命中为起点，智能计算模块将更新现有的 NeuriteIQ软件包，用于量化来自高含量筛选的图像数据；它还将合并 公开可用的大数据转录图谱用于预测诱导相似途径的候选化合物 与原来的化合物一样变化，有效地将搜索范围扩大到数万个 化合物，而只需要不到100个候选药物的功能验证。经过验证的 反过来，预测将增加已知的命中结果，从而启动下一轮计算 预测和实验验证，有效地产生新的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图谱与所需的 表型变化。因此，我们将应用系统的图像组学建模来揭示新的机制 驱动着这样的表型。对于所有有效的命中率，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 通过公共和内部生成的转录档案进行建模。我们将使用偏最小二乘法 回归模型识别具有匹配的剂量-反应曲线作为表型的基因模块，因此 使我们能够超越规范路径图的限制，识别新的功能模块 具体涉及感兴趣的表型(目标2)。 从前两个目的中提取的选定化合物将在直接获得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 以加深对AD病理生理学分子机制的了解。此外，智能 药物重新定位的框架将可推广到其他大数据和疾病平台。