Precision Medicine Digital Twins for Alzheimer’s Target and Drug Discovery and Longevity

用于阿尔茨海默氏症靶点和药物发现及长寿的精准医学数字孪生

• 批准号: 10727793
• 负责人: Feixiong Cheng
• 金额: $ 48.25万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-08-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727793
• 关键词: ATAC-seq; Acceleration; Address; Affect; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease related dementia; American; Artificial Intelligence; Atlases; Binding; Brain; Cause of Death; Cells; Chromatin; Clinic; Clinical; Code; Complex; CpG Islands; Data; Data Science; Data Set; Databases; Dementia; Development; Dimensions; Disease; Disease Outcome; Disease Progression; Disease model; Drug Compounding; Drug Targeting; Electronic Health Record; Enhancers; Ethics; FAIR principles; Foundations; Gene Expression; Genes; Genetic; Genomics; Genotype-Tissue Expression Project; Goals; Graph; Heterogeneity; Histones; Human; Human Genetics; Human Genome; Incidence; Individual; Induced pluripotent stem cell derived neurons; Intelligence; Knowledge Portal; Ligands; Longevity; Machine Learning; Maps; Medical; Medicine; Modeling; Nerve Degeneration; Neurodegenerative Disorders; Pathogenesis; Patients; Phase; Positioning Attribute; Predisposition; Process; Promoter Regions; Proteins; Proteomics; Quantitative Trait Loci; System; Technology; Testing; Therapeutic; Translating; United States; Untranslated RNA; Visualization; autoencoder; cell type; data warehouse; deep learning; design; digital twin; drug candidate; drug discovery; drug repurposing; effective therapy; exome sequencing; genetic architecture; genome wide association study; genome-wide; genomic data; human genome sequencing; innovation; longitudinal analysis; multimodal data; multiple omics; novel; precision medicine; promoter; receptor; single cell analysis; single-cell RNA sequencing; success; therapeutic development; tool; transcription factor; transcriptome sequencing; transcriptomics; translational study

PROJECT SUMMARY Alzheimer’s disease (AD) is a devastating neurodegenerative disease and it is lack of effective disease-modifying treatments. Medical digital twins are computational disease models for target identification and drug discovery. However, how to organize and prioritize drug targets and candidate AD treatments in digital twins at drugome- wide and genome-wide scales are challenging. Our team developed AlzGPS, a genome-wide positioning systems platform to catalyze multi-omics for AD drug discovery. We also created The Alzheimer’s Cell Atlas (TACA), a single-cell transcriptomics and network pathobiology map for target identification and drug repurposing at brain cellulome-wide scales. We demonstrated that systematic identification and characterization of underlying pathogenesis and disease progression at cellulome- and genome-wide scales, will serve as a foundation for identifying and validating disease-modifying targets and treatments in AD or even longevity. We hypothesize that the digital twins tools for coordinated acquisition and seamless curation of multimodal data will be transferrable to any aging therapeutic development domains and will be applicable beyond digital twins, to expand artificial intelligence (AI) and machine learning (AI\ML) workflows in AD target and drug discovery. We thus posit that a drugome-wide and genome-wide, precision medicine digital twins platform that identifies likely causal AD genes and networks from human genome sequencing and multi-omics findings, enables a more complete mechanistic understanding of AD pathobiology and the rapid development of disease-modifying targets and treatments with great success. Our goal is to ethically acquire and responsibly disseminate comprehensive patient-specific multimodal data sets, which will form the basis for scientific, technological, and translational studies to design and evaluate digital twins, and explore their integration to AD target and drug discovery. Aim 1 will develop and test an interpretable mechanistic deep learning framework to identify disease-modifying targets and networks for AD and longevity. We will develop a human protein-protein interactome network topology-based deep learning framework (R21 phase) and identify putative drug targets for AD and longevity through integrating multimodal data (genetics, genomics, transcriptomics, proteomics, and clinical) from AD sequencing project (ADSP), the AD knowledge portal, Longevity Consortium, and the Accelerating Medicines Partnership-AD (R33 phase). Aim 2 will develop and apply AI\ML technologies for collaborative end-to-end analyses of single-cell multi-ome data. We will develop and implement a graph embedded gaussian mixture variational autoencoder network algorithm (R21 phase) and identify AD cell type-specific genes/targets, regulatory networks, and ligand- receptor interactions (R33 phase). Aim 3 will implement and test precision medicine Digital Twins for drug repurposing in AD and AD-related dementia (R33 phase). All Digital Twins codes, toolbox packages, and data developed will be shared through the ADSP and the AD knowledge portal based on the FAIR principles. This project is highly feasible and potentially transformative for both Alzheimer’s data science and precision medicine.

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是一种严重的神经退行性疾病，目前尚无有效的治疗手段 治疗。医学数字双胞胎是用于目标识别和药物发现的计算疾病模型。 然而，如何在drugome的数字双胞胎中组织和优先考虑药物靶点和候选AD治疗， 大范围和全基因组范围是具有挑战性的。我们的团队开发了AlzGPS， 系统平台，以催化AD药物发现的多组学。我们还创建了阿尔茨海默氏症细胞图谱 （TACA），用于靶点识别和药物再利用的单细胞转录组学和网络病理生物学图谱 在脑细胞团范围内。我们证明，系统的识别和表征的基础 发病机制和疾病进展在细胞组和全基因组尺度，将作为一个基础， 识别和验证疾病修饰靶点和治疗AD甚至长寿。我们假设 用于协调采集和无缝管理多模态数据的数字孪生工具将是可转移的 任何老化治疗开发领域，并将适用于数字双胞胎之外，以扩大人工 智能（AI）和机器学习（AI\ML）工作流程在AD靶向和药物发现中的应用。因此，我们认为， 全药物组和全基因组的精准医学数字双胞胎平台，可识别可能的致病AD基因 以及人类基因组测序和多组学发现的网络， 对AD病理生物学的理解以及疾病修饰靶点和治疗的快速发展， 巨大的成功。我们的目标是在道德上获得和负责任地传播全面的患者特异性 多模态数据集，这将成为科学，技术和翻译研究的基础， 评估数字孪生，并探索它们与AD靶标和药物发现的整合。目标1将制定和 测试可解释的机械深度学习框架，以识别疾病修饰目标和网络 AD和长寿。我们将开发一个基于人类蛋白质-蛋白质相互作用组网络拓扑结构的深度 学习框架（R21阶段），并通过整合确定AD和长寿的假定药物靶点 AD测序项目的多模式数据（遗传学、基因组学、转录组学、蛋白质组学和临床） （ADSP）、AD知识门户网站、长寿联盟和加速药物合作伙伴关系-AD（R33 阶段）。目标2将开发和应用AI\ML技术，用于单细胞的端到端协作分析 多组数据。我们将发展并实作一个图嵌入式高斯混合变分自动编码器 网络算法（R21阶段），并确定AD细胞类型特异性基因/靶标，调控网络和配体- 受体相互作用（R33期）。目标3将实施和测试精准医疗数字双胞胎药物 AD和AD相关痴呆（R33期）的再利用。所有Digital Twins代码、工具箱包和数据 将通过ADSP和基于FAIR原则的AD知识门户共享已开发的信息。这 该项目对于阿尔茨海默氏症数据科学和精准医学来说是高度可行的，并且具有潜在的变革性。