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.

项目概要 该提案的重点是确定阿尔茨海默氏病药物重新定位候选药物的挑战。这 这项工作的基础是 ReFRAME 库，这是一组约 13,000 种化合物，其中包括几乎所有小分子 已获得 FDA 批准、达到临床开发或经过重要的临床前分析。重新框架 正在根据体外测定的不同横截面积极筛选文库。 该提案采用三种不同的策略来识别 ReFRAME 集合中的重新定位候选者。 首先，我们将创建并挖掘一个大型且异构的生物医学知识图谱。我们将使用机器学习 根据围绕和连接每个候选者的知识图的属性来识别重新定位候选者的方法 药物和疾病。其次，我们将挖掘海量的保险理赔数据，以找出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 亿条医疗索赔和超过 20 亿条 药房索赔，该数据集代表了可用索赔数据的最大来源。第三，我们将使用基因的概念 表达互补性来识别重新定位候选者。我们将生成一个基因表达签名 与阿尔茨海默病相关的三种细胞系中的每种 ReFRAME 化合物，我们将筛选符合以下条件的化合物： 产生的信号似乎可以逆转阿尔茨海默病中的基因表达变化。 在汇集通过所有这三种方法确定的重新定位候选者后，我们将优先考虑最多 100 个 化合物（或化合物组合）以进行进一步表征和验证。这些后续实验将 最初在五种基于细胞的测定中研究这些化合物的活性，以建立关于 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某些方面的验证 体外（包括 hiPSC 衍生的脑皮质神经元和/或类器官）和体内系统的组合。 我们相信，本提案中描述的多方面方法提供了成功的最佳机会 确定 AD 重新定位候选人。此外，这项工作将创造有用的方法和资源 更广泛的科学界，包括阿尔茨海默病和其他疾病领域。