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