Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimers Disease

基于内表型网络的方法对阿尔茨海默病的计算机药物重新利用进行预测和基于群体的验证

• 批准号: 10339430
• 负责人: Feixiong Cheng
• 金额: $ 77.14万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10339430
• 关键词: AD transgenic mice; Address; Adherence; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease therapy; American; Amyloid; Amyloidosis; Animal Model; Bayesian Modeling; Biological Assay; Biological Availability; Blood - brain barrier anatomy; Brain; Case-Control Studies; Cause of Death; Cells; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Complement; Complex; Computerized Medical Record; Computers; Data; Databases; Dementia; Disease; Disease Outcome; Drug Combinations; Drug Delivery Systems; Drug Targeting; Drug user; Enhancers; Evaluation; Foundations; Genes; Genome; Heritability; Hi-C; Hippocampus (Brain); Human; Human Genetics; In Situ; In Vitro; Incidence; Intelligence; Interdisciplinary Study; Investments; Knowledge; Late Onset Alzheimer Disease; Mediating; Medicine; Methodology; Microglia; Molecular; Multiomic Data; Mutate; Network-based; Neuraxis; Neurodegenerative Disorders; Neuroglia; Pathogenesis; Patient Care; Patients; Penetration; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Physiological; Population; Positioning Attribute; Predisposition; Prevention; Preventive therapy; Proteins; Proteome; Publications; Publishing; Quality of life; Rattus; Records; Regimen; Research Personnel; Resected; Role; System; Tauopathies; Testing; Therapeutic; Transgenic Animals; United States; Validation; Work; base; bioinformatics tool; brain endothelial cell; brain tissue; clinical efficacy; clinically relevant; dementia care; drug development; drug discovery; drug efficacy; drug repurposing; drug testing; effective therapy; efficacious treatment; endophenotype; functional genomics; genetic architecture; genome wide association study; genome-wide; genomic data; human genome sequencing; human interactome; improved outcome; in silico; in vivo; individual patient; informatics tool; innovation; mouse model; multiple omics; novel; novel therapeutics; patient health information; pharmacokinetic model; population based; promoter; rational design; research and development; risk variant; side effect; single cell sequencing; single-cell RNA sequencing; symptom treatment; tau Proteins; therapeutic development; tool; transcriptome; transgenic model of alzheimer disease; trial design; web app

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome are essential for such identification. Given our preliminary results, we posit that network- based identification of novel risk genes and endophenotype modules that share degree between amyloid and tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and combination therapies for AD using network proximity approaches -- We will emphasize the uses of network proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims- Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate brain penetration and target network engagement for repurposable drugs -- We will use the humanized in vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats) to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to minimizing side effects. The successful completion of this project will offer powerful network methodologies and bioinformatics tools for prediction and population-based validation of in silico drug repurposing. It will also allow for the identification of novel repurposable drugs and clinically relevant combination therapies toward AD trials.

尽管研究人员已经对阿尔茨海默氏症的潜在治疗方法进行了400多次人体试验