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.

项目摘要 该提案的重点是确定阿尔茨海默病药物重新定位候选人的挑战。的 这项工作的基础是ReFRAME库，一组约13，000种化合物，包括几乎所有的小分子， 已获得FDA批准，达到临床开发阶段，或经历了重要的临床前分析。ReFrame 文库正在针对体外测定的不同截面进行积极筛选。 该建议追求三个不同的战略，以确定重新定位的ReFRAME集合中的候选人。 首先，我们将创建和挖掘一个大型的异构生物医学知识图。我们将使用机器学习 基于围绕和连接每个重新定位候选的知识图的属性来识别重新定位候选的方法 药物与疾病第二，我们将挖掘大量的保险索赔数据集，以了解药物使用 以及阿尔茨海默病的发病率和严重程度。包含近70亿医疗索赔和超过20亿 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 表达互补性以鉴定重新定位候选物。我们将生成一个基因表达签名， 三种与阿尔茨海默病相关的细胞系中的每一种ReFRAME化合物，我们将筛选出 产生一个信号，似乎可以逆转阿尔茨海默病中的基因表达变化。 在集合通过所有这三种方法确定的重新定位候选人后，我们将优先考虑多达100个 化合物（或化合物组合）用于进一步表征和验证。这些后续实验将 在五种基于细胞的测定中初步研究这些化合物的活性， 它们在阿尔茨海默病中的作用机制。二次随访实验可能包括一些验证 体外（包括hiPSC衍生的皮质神经元和/或类器官）和体内系统的组合。 我们认为，这项建议中所述的多方面办法提供了成功解决这一问题的最佳机会， 识别AD重新定位候选者。此外，这项工作将创造方法和资源， 更广泛的科学界，无论是阿尔茨海默病和其他疾病领域。