Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease

结合基因组学和症状数据驱动模型来发现阿尔茨海默病的协同组合疗法

• 批准号: 10228346
• 负责人: Fuhai Li
• 金额: $ 50.03万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10228346
• 关键词: Acute; Acute Brain Injuries; Adverse effects; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease therapy; Amyloid beta-Protein; Brain Diseases; Brain Injuries; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Combined Modality Therapy; Community Hospitals; Complex; Data; Data Set; Dementia; Disease; Dose; Drug Combinations; Drug Targeting; Electronic Health Record; Epidemiology; FDA approved; Failure; Generations; Genes; Genomics; Genotype; Goals; Health Care Costs; Healthcare Systems; Hospitals; Illinois; Investigation; Knowledge; Medical Records; Mining; Missouri; Modeling; Molecular; Multiomic Data; Nerve Degeneration; Network-based; Neurons; Neuroprotective Agents; Pathogenesis; Pathologic; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacy facility; Polypharmacy; Protective Agents; Reporting; Research; Safety; Sampling; Signal Pathway; Signal Transduction; Symptoms; System; Testing; Time; Traumatic Brain Injury; Universities; Washington; apolipoprotein E-4; curative treatments; deep learning; design; disorder subtype; effective therapy; experience; functional genomics; genomic locus; induced pluripotent stem cell; interest; long short term memory; medical schools; medication safety; multidisciplinary; network models; neurite growth; novel; novel therapeutics; predictive modeling; response; screening; synergism; targeted treatment; vector

Project Summary In 2018, an estimated 5.7 million people have Alzheimer's Disease (AD) or a related dementia in the U.S., with related healthcare costs of ~$277 billion1. However, there is no cure yet for AD. One major challenge is that the complicated pathogenesis of AD remains unclear, though >42 genes/loci have been associated with AD2,3. These genes are not actionable or druggable yet for AD management2. Over 240 drugs were tested in clinical trials but no new drugs have been approved for AD since 20031,4. The failure of these drugs is likely, in part, due to the limited efficacy of single agents to treat AD that is a genetically complex, multifactorial disease2, i.e., robust molecular signaling crosstalks among multi-pathways2,5,6,7,8,9, as well as complicated niche factors, e.g., oxidative stress10,11,12,13, and inflammation14,15,16, leading to neuron de-generation. Therefore, combination therapies eliminating these niche factors, and disrupting the dysfunctional signaling pathways and cross-talks, can be more effective than single agents for in AD patients. The goal of this study is to fill the gap of accelerating repositioning of combination therapies for AD using following novel genomics and symptoms data-driven models seamlessly integrating well designed iPSC Aβ AD models. The Washington University Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC), has generated comprehensive omics data for a large group of AD samples. We propose to (in Aim 1) uncover core signaling pathways and crosstalks of ApoE4 genotype-specific AD subtypes via a novel signaling convergence network model, and consequently to discover synergistic Signaling Network Disruption drug combinations (SNDdc) via novel drug prediction models integrating heterogenous pharmacogenomics datasets. On the other hand, we propose to (in Aim 2) discover potential Neuron Protective drug combinations (NPdc) using electronic health records (EHR), available in BJC HealthCare system (includes 14 academic and community hospitals in Missouri and Illinois), of patients with brain injury diseases, especially Traumatic Brain Injury (TBI), via a novel high-order poly-pharmacy efficacy and safety model. We hypothesize that acute brain damage in TBI will share the aforementioned key AD-related niche factors. Also because TBI patients often require multiple drugs daily (high-order poly-pharmacy use), we propose that TBI provides an appropriate model to study synergy and interactions of combination therapies that can ameliorate acute brain injury, and thus suggest potentially neuron protective combinations. Combinations in SNDdc and NPdc provide candidates for novel and effective AD treatment. To filter the false positives, we will (in Aim 3) utilize pooled CRISPR functional genomics and iPSC neurodegeneration model to identify key signaling genes, and validate combination therapies with ApoE4 genotype-specific iPSC Aβ models. Our new models represent a potential breakthrough in AD combination therapies discovery.

项目摘要 2018年，美国估计有570万人患有阿尔茨海默病(AD)或相关痴呆症， 相关的医疗费用约为2770亿美元1。然而，目前还没有治愈阿尔茨海默病的方法。一个主要的挑战是， 阿尔茨海默病复杂的发病机制尚不清楚，尽管已有42个基因/位点与AD2，3相关。 这些基因还不能用于AD治疗，也不能用药治疗2。超过240种药物在临床上进行了测试 试验，但自2003年以来没有新药被批准用于AD，4.这些药物的失败可能在一定程度上， 由于单一药物治疗AD的疗效有限，AD是一种遗传上复杂的多因素疾病2，即， 多通路2、5、6、7、8、9之间的强健分子信号串扰，以及复杂的生态位因素，例如， 氧化应激10，11，12，13和炎症14，15，16，导致神经元退化。因此，组合 消除这些利基因素，扰乱功能失调的信号通路和串扰的治疗方法， 可以比单一药物更有效地治疗AD患者。 这项研究的目的是填补加速AD综合疗法重新定位的空白 遵循新的基因组学和症状数据驱动模型无缝集成精心设计的IPSC AβAD 模特们。华盛顿大学查尔斯·F·奈特和乔安妮·奈特阿尔茨海默病研究中心 (Knight-ADRC)，已经为一大组AD样本生成了全面的组学数据。我们建议(在) 目的1)揭示载脂蛋白E4基因特异性AD亚型的核心信号通路和串扰 信令融合网络模型，从而发现协同信令网络中断 通过整合异源药物基因组学的新型药物预测模型进行药物组合(SNDdc) 数据集。另一方面，我们建议(在目标2中)发现潜在的神经元保护药物组合 (NPDC)使用北京医疗保健系统中提供的电子健康记录(EHR)(包括14个学术和 密苏里州和伊利诺伊州的社区医院)，为脑损伤疾病，特别是创伤性脑疾病的患者提供服务 伤害(TBI)，通过一种新的高阶多药物疗效和安全性模型。我们假设急性脑 TBI的损害将分享上述与AD相关的关键利基因素。也是因为脑外伤患者经常 每天需要多种药物(高阶多药房使用)，我们建议TBI提供适当的 研究可以改善急性脑损伤的联合治疗的协同和相互作用的模型，以及 因此暗示了潜在的神经元保护性组合。SNDDC和NPDC的组合提供 寻求新颖有效的AD治疗方法。为了过滤误报，我们将(在目标3中)使用Pooled CRISPR功能基因组学和IPSC神经变性模型，以确定关键信号基因，并验证 载脂蛋白E4基因型特异性IPSCAβ模型的联合治疗。我们的新型号代表了一种 阿尔茨海默病联合疗法发现的潜在突破。