AI-Powered Quantitative Systems Pharmacology for AD Drug Repurposing

人工智能驱动的 AD 药物再利用定量系统药理学

• 批准号: 10659412
• 负责人: Lei Xie
• 金额: $ 69.39万
• 依托单位: HUNTER COLLEGE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659412
• 关键词: Address; Alzheimer disease prevention; Alzheimer disease screening; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease therapy; Amyloid beta-Protein; Animal Model; Artificial Intelligence; Binding; Bioinformatics; Biophysics; Brain; Cell Line; Cells; Chemicals; Classification; Clinic; Clinical Trials; Communities; Complex; Computing Methodologies; Consensus; Darkness; Data; Databases; Development; Disease; Disease model; Drug Kinetics; Drug Modelings; Drug Targeting; Drug toxicity; Effectiveness; Failure; Gene Expression; Genes; Graph; Heterogeneity; Human; Inflammation; Knowledge; Lead; Ligands; Link; Machine Learning; Methodology; Methods; Mining; Modeling; Modernization; Molecular; Multiomic Data; Network-based; Pathogenicity; Pathologic; Pathologic Processes; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Phase; Phenotype; Prevalence; Process; Property; Proteins; Public Health; System; Systems Biology; Techniques; Testing; Tissues; Toxic effect; Translating; bioinformatics resource; cell type; computer framework; cost; deep learning; drug action; drug candidate; drug development; drug discovery; drug repurposing; effective therapy; genome wide association study; genome-wide; hyperphosphorylated tau; improved; individual patient; insight; lead optimization; network models; novel; novel therapeutic intervention; novel therapeutics; patient stratification; physiologically based pharmacokinetics; precision medicine; response; screening; side effect; success; therapeutically effective; transcriptome; transcriptome sequencing

Abstract Alzheimer's disease (AD) poses a triple threat to public health, as its prevalence is on the rise, its costs are immense, and there is no effective therapy. However, drug development attempts for the treatment of AD have met with minimal success. The failure is largely attributable to a reductionist concept of "one drug, one gene, one disease." As AD is a multigenic heterogeneous illness, a new therapeutic strategy is urgently required to concurrently target the numerous pathogenic processes involved for the genesis and progression of AD in each individual patient. Many translational bioinformatics strategies for AD drug repurposing have been developed in recent years. Existing target-based, phenotype-based, network-based, and patient-based drug repurposing strategies are unable to fully address the challenges of AD drug repurposing due to the lack of thoroughly validated drug targets, potent lead compounds, and high-throughput phenotype readouts that can characterize the molecular complexity of AD. Over the past decade, we have built an artificial intelligence- based quantitative systems pharmacology (AI-QSP) platform that attempts to predict and characterize genome-wide chemical-protein interactions and functional activities, as well as correlate molecular interactions with phenotypic responses. Our AI-QSP platform integrates diverse omics data synergistically and incorporates machine learning, biophysics, and systems biology methodologies. The AI-QSP platform has been effectively applied to drug repurposing including AD, polypharmacology, side effect prediction, and precision medicine. Established our proof-of-concept studies, we propose to develop and thoroughly evaluate a unique computational methodology that combines target-based and mechanism-driven phenotypic chemical screening for AD individualized drug repurposing. Using a novel domain adaptation strategy, we will expand our context- independent phenotypic compound screening methodologies to AD patient-specific, cell type-specific, transcriptome-based drug repurposing. In addition, we will analyze the ADME features of repurposed pharmaceuticals in the human brain utilizing cutting-edge physiologically based pharmacokinetics (PBPK) techniques. We will improve state-of-the-art drug-gene-disease network models for Alzheimer's disease drug repurposing by incorporating understudied dark proteins that are abundant in the target list suggested by AD omics studies and their inhibitory or activatory effects, and by applying graph mining techniques for drug-gene- disease link predictions. Using cell-based disease models and RNA-seq studies, we will combine complementary phenotype-based and target-based techniques to rank drug candidates and confirm their efficacy and toxicity on AD treatment. In conclusion, the successful completion of this project could provide the scientific community with a novel translational bioinformatics resource for identifying potential therapeutics for effective personalized AD treatments and advancing drug repurposing to a new phase of lead optimizations and clinical trials.

摘要 阿尔茨海默病(AD)对公共卫生构成三重威胁，因为其患病率正在上升，其代价是 巨大的，而且没有有效的治疗方法。然而，治疗阿尔茨海默病的药物开发尝试 只取得了很小的成功。这一失败在很大程度上是由于一种简化论的概念，即一种药物，一个基因， 由于AD是一种多基因异质性疾病，迫切需要一种新的治疗策略来 同时针对阿尔茨海默病的发生和发展涉及的多种致病过程 每个单独的病人。许多用于AD药物再利用的翻译生物信息学策略已经被 近几年发展起来的。现有的基于目标、基于表型、基于网络和基于患者的药物 由于缺乏药物再利用战略，无法完全应对AD药物再利用的挑战 经过充分验证的药物靶点、有效的先导化合物和高通量的表型读数，可以 描述AD的分子复杂性。在过去的十年里，我们构建了一种人工智能-- 基于定量系统药理学(AI-QSP)平台，试图预测和表征 全基因组化学-蛋白质相互作用和功能活性，以及相关的分子相互作用 有表型反应。我们的AI-QSP平台协同集成了不同的组学数据，并整合了 机器学习、生物物理学和系统生物学方法论。AI-QSP平台已经有效地 应用于药物再利用，包括AD、多元药理学、副作用预测和精准医学。 建立了我们的概念验证研究，我们建议开发并彻底评估一个独特的 基于靶点和机制驱动的表型化学筛选相结合的计算方法 用于AD的个体化药物再利用。使用一种新颖的领域适应策略，我们将扩展我们的上下文- 独立的表型化合物筛选方法，用于AD患者特定的、细胞类型特定的 基于转录组的药物再利用。此外，我们还将分析Reputed的ADME特征 利用尖端生理学药物动力学(PBPK)在人脑中的药物 技巧。我们将完善阿尔茨海默病药物的药物-基因-疾病网络模型 通过加入AD建议的靶点列表中丰富的未被研究的暗蛋白来重新定位 组学研究及其抑制或激活作用，并应用图挖掘技术对药物-基因- 疾病关联预测。利用基于细胞的疾病模型和rna-seq研究，我们将结合 基于表型和基于靶点的互补技术来对候选药物进行排名并确认其 治疗阿尔茨海默病的疗效和毒性。总括而言，这项计划若能顺利完成，可提供 科学界拥有一种新的翻译生物信息学资源，用于确定潜在的治疗方法 有效的个性化AD治疗和将药物再利用推进到Lead优化的新阶段 和临床试验。