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: 10405522
• 负责人: Dokyoon Kim
• 金额: $ 77.79万
• 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/10405522
• 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; 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; cost; data integration; data resource; deep learning; drug candidate; drug development; drug discovery; drug repurposing; druggable target; early detection biomarkers; graph neural network; 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.

项目概要 阿尔茨海默病（AD）是一种重大的公共卫生危机，目前尚无治愈方法。鉴于最近的失败 许多 AD 临床试验表明，迫切需要制定有效的策略来确定新的 AD 靶点 疾病建模和药物再利用和开发的新候选药物。我们在这里提出一项研究 在转化生物信息学领域开发变革性大数据分析方法的项目， 机器学习和深度学习促进 AD 药物的重新利用。我们的总体目标是发展 创新的机器学习和深度学习方法以及信息学工具和管道 利用相关生物医学领域的大数据。这些大数据包括大规模遗传、多组学、 来自具有里程碑意义的 AD 研究的成像、认知和其他表型数据、之间的功能相互作用数据 药物、蛋白质和疾病、药理学扰动数据、电子健康记录数据和 MarketScan 数据。我们提出的计算研究旨在开发新颖的翻译信息学方法 分析各类分子、临床和其他相关数据以识别单个药物或药物 具有良好疗效和毒性特征的组合作为针对 AD 或 AD-重新定位的候选药物 相关痴呆症（ADRD）。为了实现我们的目标，我们有四个目标。目标1是开发基于网络的多 组学数据整合方法，用于识别基因和通路作为 AD 药物重新定位的新靶点 研究。目标 2 是制定信息学策略，通过以下方式优先考虑和评估有希望的候选目标： 检查它们与 AD 生物标志物和表型的关联。目标3是开发知识驱动的药物 使用网络强化和药物评分重新利用方法来识别 AD 候选药物。目标 4 是 使用药理学对已确定的候选药物进行优先排序和评估，以重新利用 AD/ADRD 扰动、EHR 和 MarketScan 数据。成功完成这些目标将产生新颖的转化 大数据分析方法和工具，以提高我们对遗传、分子和神经生物学的理解 AD 的机制，促进新的有希望的靶点和药物的重新利用的识别，以及 最终对疾病的治疗和预防产生转化影响。这些进步是根本性的 到 2025 年有效治疗或预防 AD/ADRD 的 NIA NAPA 目标。由此产生的方法和工具 预计还将影响一般生物医学研究并有益于公共卫生成果。