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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：
10254376
负责人：
Fuhai Li
金额：
$ 48.61万
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 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10254376
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.

项目总结