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，估计有570万人患有阿尔茨海默氏病（AD）或美国相关痴呆症 相关医疗保健费用约为2770亿美元。但是，目前尚无治愈的AD治疗方法。一个主要挑战是 AD的复杂发病机理尚不清楚，尽管> 42个基因/基因座与AD2,3有关。 对于AD Management 2，这些基因尚不可起作或吸毒2。在临床上测试了超过240种药物 自20031,4以来，试验但未批准AD的新药。这些药物的失败可能部分是 由于单一药物治疗AD的效率有限，而AD是一种普遍复杂的多因素疾病2，即 多通道之间的强大分子信号传导串扰2,5,6,7,8,9，以及复杂的小众因子，例如 氧化应激10,11,12,13和炎症14,15,16，导致神经元去发。因此，组合 消除这些利基因素的疗法，并破坏功能障碍的信号通路和串扰， 在AD患者中，可以比单一药物更有效。 这项研究的目的是填补加速对AD组合疗法的重新定位的空白 以下新型的基因组学和症状数据驱动的模型无缝整合了设计良好的IPSCAβAD 型号。华盛顿大学查尔斯·F·和乔安妮·奈特·阿尔茨海默氏病研究中心 （Knight-Adrc）为大量广告样本生成了全面的OMICS数据。我们建议（在 目的1）通过新颖 信号收敛网络模型，因此发现协同信号网络破坏 药物组合（SNDDC）通过新型药物预测模型整合了异质药物基因组学 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）使用电子健康记录（EHR），可在BJC医疗保健系统中获得（包括14个学术和 密苏里州和伊利诺伊州的社区医院，患有脑损伤疾病的患者，尤其是大脑创伤性疾病 受伤（TBI），通过新型的高级多药效率和安全模型。我们假设急性大脑 TBI的损坏将共享与广告相关的关键关键因素因素。也是因为TBI患者经常 每天需要多种药物（高级多药物使用），我们建议TBI提供适当的 研究可以改善急性脑损伤的组合疗法的协同作用和相互作用的模型 因此，暗示了潜在的神经元受保护的组合。 SNDDC和NPDC的组合提供 新颖有效的AD治疗的候选人。要过滤误报，我们将（在AIM 3中）使用汇总 CRISPR功能基因组学和IPSC神经变性模型，以识别关键信号基因并验证 与APOE4基因型特异性IPSCAβ模型的组合疗法。我们的新型号代表 AD组合疗法发现的潜在突破。