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.

项目摘要 阿尔茨海默氏病（AD）是主要的公共卫生危机，无法治愈。考虑到最近的失败 许多广告临床试验，迫切需要制定有效的策略来确定新的广告目标 疾病建模和新的候选药物重新利用和发育。我们在这里提出了一项研究 在翻译的生物信息学领域开发变革性的大数据分析方法的项目， 机器学习和深度学习可以推进AD的药物重新利用。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内容丰富的工具和管道 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，多词， 来自地标广告研究的成像，认知和其他表型数据，功能相互作用数据 药物，蛋白质和疾病，药物扰动数据，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丰富的方法 分析各种类型的分子，临床和其他相关数据，以识别单独的药物或药物 结合有利效率和毒性特征作为重新定位AD或AD-的候选者的组合 相关痴呆（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型 法量数据集成方法，以识别基因和途径为AD药物重新定位的新目标 研究。目标2是制定信息策略，以优先考虑和评估承诺候选目标 检查他们与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 使用网络增强和药物评分来识别AD候选药物的重新利用。目标4是 使用药理学优先和评估已确定的候选药物对AD/ADRD进行重新利用 扰动，EHR和MarketScan数据。这些目标的成功完成将产生新颖的翻译 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，促进鉴定新颖有希望的靶标和药物以重新利用，以及 最终对疾病治疗和预防产生翻译影响。这些进步是基本的 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 还期望一般影响生物医学研究，并使公共卫生结果受益。