Translational big data analytic approaches to advance drug repurposing for Alzheimer's disease

转化大数据分析方法促进阿尔茨海默氏病的药物再利用

• 批准号: 10175930
• 负责人: Dokyoon Kim
• 金额: $ 80.92万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10175930
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease therapy; Alzheimer’s disease biomarker; American; Amyloid beta-Protein; Area; Big Data; Big Data Methods; Bioinformatics; Biological Markers; Biomedical Research; Cells; Clinical; Clinical Data; Clinical Trials; Cognitive; Communities; Complex; Coupled; Data; Databases; Disease; Disease model; Drug Combinations; Electronic Health Record; Failure; Genes; Genetic; Goals; Graph; Image; Individual; Informatics; Knowledge; Knowledge Portal; Liquid substance; Machine Learning; Medicine; Methods; Molecular; Multimodal Imaging; Multiomic Data; Network-based; Outcome; Pathway interactions; Pharmaceutical Preparations; Pharmacology; Phenotype; Prevention; Proteins; Psychological reinforcement; Public Health; Research; Research Project Grants; Signal Transduction; System; Therapy Clinical Trials; Toxic effect; Translating; Validation; Walking; analytical method; analytical tool; anticancer research; biomarker discovery; biomarker-driven; clinical phenotype; cohort; convolutional neural network; cost; data integration; data resource; deep learning; drug candidate; drug development; drug discovery; drug repurposing; druggable target; early detection biomarkers; improved; informatics tool; innovation; learning strategy; multiple omics; neurobiological mechanism; novel; novel strategies; phenotypic data; population based; prevent; response; success; tool; transcriptome; translational impact; virtual

Project Summary Alzheimer’s disease (AD) is a major public health crisis with no available cure. Given recent failures of many AD clinical trials, there is an urgent need for developing effective strategies to identify new AD targets for disease modeling and new candidates for drug repurposing and development. We propose here a research project to develop transformative big data analytic approaches in the fields of translational bioinformatics, machine learning and deep learning to advance drug repurposing for AD. Our overarching goal is to develop innovative machine learning and deep learning approaches as well as informatics tools and pipelines that leverage big data in relevant biomedical domains. These big data include large-scale genetic, multi-omics, imaging, cognitive and other phenotypic data from landmark AD studies, functional interaction data among drugs, proteins and diseases, pharmacologic perturbation data, electronic health record data, and MarketScan data. Our proposed computational research is aimed at developing novel translational informatics approaches to analyze various types of molecular, clinical and other relevant data to identify individual drugs or drug combinations with favorable efficacy and toxicity profiles as candidates for repositioning against AD or AD- related dementia (ADRD). To achieve our goal, we have four Aims. Aim 1 is to develop network-based multi- omics data integration methods to identify genes and pathways as novel targets for AD drug repositioning research. Aim 2 is to develop informatics strategies to prioritize and evaluate promising candidate targets via examining their associations with AD biomarkers and phenotypes. Aim 3 is to develop knowledge-driven drug repurposing methods using network reinforcement and drug scoring to identify AD candidate drugs. Aim 4 is to prioritize and evaluate the identified candidate drugs for repurposing against AD/ADRD using pharmacologic perturbation, EHR and MarketScan data. Successful completion of these aims will produce novel translational big data analytic methods and tools to improve our understanding of the genetic, molecular and neurobiological mechanisms of AD, facilitate the identification of novel promising targets and drugs for repurposing, and ultimately have a translational impact on disease treatment and prevention. These advances are fundamental to the NIA NAPA goal of effectively treating or preventing AD/ADRD by 2025. The resulting methods and tools are also expected to impact biomedical research in general and benefit public health outcomes.

项目总结