Endophenotype Network-based Approaches to Prediction and Population-based Validation of In Silico Drug Repurposing for Alzheimer's Disease

基于内表型网络的方法对阿尔茨海默病的计算机药物重新利用进行预测和基于群体的验证

• 批准号: 10409194
• 负责人: Feixiong Cheng
• 金额: $ 32.2万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 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/10409194
• 关键词: 3-Dimensional; Address; Adopted; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease therapeutic; American; Amyloidosis; Anti-Inflammatory Agents; Artificial Intelligence; Bayesian Modeling; Binding; Biology; Cause of Death; Cell Nucleus; Chromatin; Clinic; Clinical Data; Combination Drug Therapy; Combined Modality Therapy; Communication; Communities; Complex; Data; Data Analyses; Data Storage and Retrieval; Databases; Dementia; Development; Disease; Drug Targeting; Etiology; FAIR principles; Foundations; Funding; Future; Gene Expression Regulation; Gene Proteins; Genes; Genetic; Genetic Diseases; Genome; Genomics; Genotype; Goals; High-Throughput Nucleotide Sequencing; Histones; Human; Human Genetics; Immunologic Tests; Incidence; Infrastructure; Investments; Knowledge; Knowledge Portal; Learning; Machine Learning; Medicine; Methodology; Microglia; Molecular; Multiomic Data; Mutate; Nature; Network-based; Neurodegenerative Disorders; Neurosciences; Nucleotides; Parents; Pathogenesis; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacotherapy; Predisposition; Proteins; Proteome; Quantitative Trait Loci; Research; Research Personnel; Role; Signal Transduction; Site; Tauopathies; Techniques; Technology; Testing; Therapeutic Intervention; Translations; United States; United States National Institutes of Health; Validation; Variant; base; cell type; computational platform; deep learning; design; digital; drug discovery; drug repurposing; druggable target; endophenotype; exome sequencing; functional genomics; genetic architecture; genetic variant; genome sequencing; genome wide association study; genome-wide; genomic data; human genome sequencing; human interactome; in silico; innovation; insight; kernel methods; molecular drug target; molecular targeted therapies; multimodality; multiple omics; neuroimaging; neuroinflammation; novel; population based; precision medicine; protein protein interaction; quantum computing; research and development; risk variant; targeted treatment; therapeutic development; therapy design; tool; transcription factor; transcriptome; transcriptomics; user-friendly; web portal

PROJECT SUMMARY Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms and pathobiology, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis, tauopathy and neuroinflammation, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and endophenotype networks, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome using artificial intelligence (AI) and machine learning (ML) are essential for such identification. Given our preliminary results, we posit that AI/ML-based identification of likely risk genes and endophenotype network modules offer unexpected opportunities for drug repurposing and combination therapy design in AD compared to traditional single-target approaches. To address the underlying hypothesis, we propose to establish an AI/ML-based, multimodal analytic framework to repurpose existing genetics, genomics and transcriptomics data generated from NIA-funded AD genome sequencing projects for druggable target identification with two specific aims under the scope of the parent R01 (#R01AG066707). The central unifying hypothesis of this Supplement project is that a genome-wide, AI/ML infrastructure that enables users searching, sharing, visualizing, querying, and analyzing multi-omics (including genetics and genomics) findings can enable emerging development of molecularly targeted treatments for AD. Aim 1 will test common variant-based risk gene and endophenotype network hypothesis in AD using multi-omics evidence aggregation under a multiple kernel learning framework and the FAIR (Findable, Accessible, Interoperable, and Reusable digital objects) principles. We will develop and apply AI/ML approach to identify likely risk genes and endophenotype networks though leveraging genetic, genomic, transcriptomic, and clinical data from AD Sequencing Project (ADSP), the AD Neuroimaging Initiative (ADNI), NIAGADS, and the AD knowledge portal. Aim 2 will test cell type-specific risk genes and anti-inflammatory endophenotype network hypothesis in AD using a network-based deep learning framework. Following FAIR principles, we will implement command-line and web portal to disseminate all AI/ML toolboxes and AI/ML-ready gene/network data from Aims 1 and 2 into the AD knowledge portal and the Cleveland Clinic-IBM Quantum computing platform for accelerating future AD genetic and multi-omics data analyses, an essential goal of the Alzheimer’s precision medicine. 1

项目摘要 尽管研究人员已经进行了400多项人体试验，以寻找治疗阿尔茨海默氏症的潜在方法， 在过去的二十年中，疾病（AD）的流失率估计超过99%。此外，“一个基因， “一种药物，一种疾病”的简化主义范式忽视了疾病的内在复杂性， 继续挑战AD的药物发现。AD的易感性涉及一个复杂的、多基因的、 多效性遗传结构最近的研究表明，AD通常具有共同的潜在 机制和病理生物学，与许多其他复杂疾病共享中间内在表型。 这些内源性表型，如淀粉样变性、tau蛋白病和神经炎症，在许多疾病中具有重要作用。 神经退行性疾病新的潜在发病机制的系统鉴定和表征 与突变基因相比，内表型网络将成为产生可操作的 作为药物再利用和合理设计AD联合治疗的输入目标。融合 使用人工智能（AI）和机器的基因组、转录组、蛋白质组和人类相互作用组 学习（ML）对于这种识别是必不可少的。鉴于我们的初步结果，我们认为基于AI/ML的 识别可能风险基因和内表型网络模块为药物治疗提供了意想不到的机会 与传统的单靶点方法相比，AD的再利用和联合治疗设计。解决 基本假设，我们建议建立一个基于AI/ML的多模态分析框架， 现有的遗传学、基因组学和转录组学数据由国家免疫局资助的AD基因组测序产生 在母R 01范围内具有两个特定目标的可药用目标识别项目 （#R01AG066707）。这个补充项目的中心统一假设是，一个全基因组的AI/ML 使用户能够搜索、共享、可视化、查询和分析多组学（包括 遗传学和基因组学）的发现可以使AD的分子靶向治疗的新兴发展成为可能。 目的1：利用多组学技术验证AD常见变异风险基因和内表型网络假说 多核学习框架下的证据聚合和FAIR（Findable，Discriminable， 可互操作和可重用数字对象）原则。我们将开发和应用AI/ML方法来识别 可能的风险基因和内表型网络，通过利用遗传学、基因组学、转录组学和临床 数据来自AD测序项目（ADSP）、AD神经影像学倡议（ADNI）、NIAGADS和AD 知识门户。目标2将测试细胞类型特异性风险基因和抗炎内表型网络 使用基于网络的深度学习框架在AD中的假设。根据公平原则，我们将实施 命令行和Web门户，用于传播Aims的所有AI/ML工具箱和AI/ML就绪基因/网络数据 1和2进入AD知识门户和Cleveland Clinic-IBM Quantum计算平台， 未来的AD遗传和多组学数据分析，是阿尔茨海默氏症精准医学的一个重要目标。 1