Compound repositioning for Alzheimer's Disease using knowledge graphs, insurance claims data, and gene expression complementarity

使用知识图、保险索赔数据和基因表达互补性对阿尔茨海默病进行复合重新定位

• 批准号: 10338152
• 负责人: ANDREW I SU
• 金额: $ 88.65万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10338152
• 关键词: Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Area; Biological Assay; Blue Cross; Blue Shield; Cell Line; Cells; Chemicals; Clinical; Collection; Communities; Control Groups; Data; Data Set; Databases; Diagnosis; Disease; Disease Outcome; Drug Prescriptions; Drug usage; FDA approved; Follow-Up Studies; Foundations; Future; Gene Expression; Gene Expression Profile; Genetic Transcription; Goals; Graph; Health; In Vitro; Incidence; Individual; Knowledge; Libraries; Machine Learning; Manuals; Medical; Methods; Mining; Neurons; Organoids; Outcome; Output; Pharmaceutical Preparations; Pharmacy facility; Property; Publishing; Recommendation; Research; Resources; Severities; Source; Statistical Data Interpretation; System; Testing; Therapeutic; Training; United States National Institutes of Health; Validation; Work; base; clinical development; cost; experimental study; follow-up; in vitro Assay; in vivo; insurance claims; interest; knowledge graph; machine learning method; novel therapeutics; pre-clinical; preclinical development; screening; small molecule; symposium; tool; transcriptome sequencing; trend; validation studies

PROJECT SUMMARY This proposal focuses on the challenge of identifying drug repositioning candidates for Alzheimer’s Disease. The foundation of this work is the ReFRAME library, a set of ~13,000 compounds that includes nearly all small molecules that have been FDA-approved, reached clinical development, or undergone significant preclinical profiling. The ReFRAME library is being actively screened against a diverse cross-section of in vitro assays. This proposal pursues three distinct strategies for identifying repositioning candidates among the ReFRAME collection. First, we will create and mine a large and heterogeneous biomedical knowledge graph. We will use machine learning methods to identify repositioning candidates based on properties of the knowledge graph surrounding and joining each drug and disease. Second, we will mine a massive data set of insurance claims data for associations between drug use and the incidence or severity of Alzheimer’s Disease. Containing almost 7 billion medical claims and over 2 billion pharmacy claims, this data set represents the largest source of claims data available. Third, we will use concept of gene expression complementarity to identify repositioning candidates. We will generate a gene expression signature for every ReFRAME compound in three cell lines relevant to Alzheimer’s Disease, and we will screen for compounds that produce a signature that appear to reverse gene expression changes seen in Alzheimer’s Disease. After assembling repositioning candidates identified through all three of these methods, we will prioritize up to 100 compounds (or compound combinations) for further characterization and validation. These follow-up experiments will initially investigate the activity of these compounds in five cell-based assays to establish a mechanistic hypothesis on their mechanism of action in Alzheimer’s Disease. Secondary follow-up experiments may include validation in some combination of in vitro (including hiPSC-derived cerebrocortical neurons and/or organoids) and in vivo systems. We believe that the multifaceted approach described in this proposal offers the best possible chance at successfully identifying AD repositioning candidates. Moreover, this work will create methods and resources that will be useful to the broader scientific community, both for Alzheimer’s Disease and for other disease areas.

项目总结 这项提案的重点是确定阿尔茨海默病的药物重新定位候选药物的挑战。这个 这项工作的基础是重组文库，这是一组约13,000种化合物，几乎包括所有 已获得FDA批准，达到临床开发，或经历了重大的临床前分析。重新定格 文库正在积极地进行筛选，以对抗不同的体外分析横截面。 该建议采用三种不同的策略来确定重新定位集合中的重新定位候选对象。 首先，我们将创建和挖掘一个巨大的、异质的生物医学知识图谱。我们将使用机器学习 基于知识图周围和连接每个对象的属性来标识重新定位候选对象的方法 毒品和疾病。其次，我们将挖掘保险索赔数据的海量数据集，以寻找药物使用之间的关联 以及阿尔茨海默病的发病率或严重程度。包含近70亿份医疗索赔和20多亿份 药房索赔，此数据集代表了可用的索赔数据的最大来源。第三，我们将使用基因的概念 表达互补性，以确定重新定位的候选对象。我们将为其生成一个基因表达签名 与阿尔茨海默病相关的三个细胞系中的每一种重组化合物，我们将筛选出 产生一种似乎可以逆转阿尔茨海默病基因表达变化的信号。 在集合了通过这三种方法确定的重新定位候选对象后，我们将对多达100个进行优先排序 用于进一步表征和验证的化合物(或化合物组合)。这些后续实验将 初步研究这些化合物在五个基于细胞的分析中的活性，以建立一个关于 它们在阿尔茨海默病中的作用机制。二次后续实验可能包括在某些方面的验证 体外(包括HiPSC来源的大脑皮层神经元和/或类器官)和体内系统的组合。 我们相信，这项提案中描述的多方面方法提供了成功的最好机会 确定广告重新定位候选者。此外，这项工作将创建有用的方法和资源 更广泛的科学界，包括阿尔茨海默氏症和其他疾病领域。