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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos 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

项目摘要

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.

项目概要 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，其中相关医疗费用约为 2,770 亿美元1 然而，目前尚无治疗 AD 的方法。尽管超过 42 个基因/位点与 AD2,3 相关，但 AD 的复杂发病机制仍不清楚。这些基因尚不能用于 AD 管理240 多种药物进行了临床测试。试验，但自 20031 年以来尚未批准用于 AD 的新药物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 是一种遗传复杂、多因素的疾病2，即多途径之间强大的分子信号串扰2,5,6,7,8,9，以及复杂的利基因素，例如，氧化应激10,11,12,13和炎症14,15,16，导致神经元变性因此，组合。消除这些利基因素并破坏功能失调的信号通路和串扰的疗法，对于 AD 患者来说，比单一药物更有效。本研究的目标是填补加速 AD 联合疗法重新定位的空白遵循新颖的基因组学和症状数据驱动模型，无缝集成精心设计的 iPSC Aβ AD 华盛顿大学查尔斯·F. 和乔安妮·奈特阿尔茨海默病研究中心的模型。 (Knight-ADRC) 已为大量 AD 样本生成了全面的组学数据。目标 1) 通过一种新颖的方法揭示 ApoE4 基因型特异性 AD 亚型的核心信号通路和串扰信令汇聚网络模型，从而发现协同信令网络中断通过整合异质药物基因组学的新型药物预测模型进行药物组合（SNDdc）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 HealthCare 系统中使用（包括 14 个学术和密苏里州和伊利诺伊州的社区医院），治疗脑损伤疾病患者，尤其是脑外伤患者损伤（TBI），通过一种新颖的高阶多药疗效和安全模型。 TBI 的损害将共享上述关键的 AD 相关生态位因素。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疗法的协同作用和相互作用的模型，以及因此表明 SNDdc 和 NPdc 的组合具有潜在的神经元保护作用。为了过滤误报，我们将（在目标 3 中）利用汇总。 CRISPR 功能基因组学和 iPSC 神经变性模型可识别关键信号基因并进行验证我们的新模型代表了 ApoE4 基因型特异性 iPSC Aβ 模型的联合疗法。 AD 联合疗法发现的潜在突破。