Predictive Networks-based in-silico approach for Precision Medicine-repurposing for Alzheimer's Disease

基于预测网络的精密医学方法 - 重新利用阿尔茨海默病

基本信息

批准号：
10017130
负责人：
Rui Chang
金额：
$ 77.77万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10017130
关键词：
Affect Aftercare Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease pathology Amyloid beta-Protein Apolipoprotein E Astrocytes Behavior Binding Bioinformatics CASP3 gene Cell model Cells Cleaved cell Clinic Clinical Clinical Data Coculture Techniques Cognition Data Data Set Databases Dementia Disease Docking Drug Combinations Drug Screening Drug Targeting Drug toxicity Drug usage Endothelium Evaluation F Factor FDA approved Genetic Genomics Genotype Goals Human Cell Line Image In Vitro Investigation Investments Lead Measures Messenger RNA Microglia Modeling Mus Network-based Neurons Oligodendroglia Pathogenesis Pathogenicity Pathologic Pathway interactions Patients Peptides Pharmaceutical Preparations Process Proteins Proteomics Risk Risk Factors Safety Subgroup Systems Biology Testing Therapeutic Toxic effect Treatment Efficacy cell type cost efficient drug development drug efficacy genomic data in silico in vivo in vivo Model interest metabolomics model design mouse model multiple omics network models neuron apoptosis novel patient stratification patient subsets personalized approach precision drugs precision medicine recruit screening sex tau Proteins tau-1 therapeutic target therapy development trait transcriptome sequencing uptake whole genome

项目摘要

Project Summary Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide. This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. Recently, NIA organized large-scale efforts, through AMP-/M2OVE-AD consortia, has generated the richest genotype, genomic and clinical data, which enabled an unprecedented opportunity to explore the enormous complexity of AD pathogenesis. On the other hand, through all failed clinic trials, we learned that an efficacious treatment would need to target multiple aspects of the disease and be directed towards several pathogenic processes in AD. Moreover, patients with different sex and risk factor will respond differently to the same treatment due to distinct pathological mechanisms, therefore, it became extremely critical to develop patient-specific therapeutic targets and precision medicine for each patient sub-group. However, despite tremendous interests in advancing therapy and drug development for AD, there is a paucity of advanced bioinformatics approaches available to guide the effective and efficient development of drugs and de-risk investment in these expensive therapeutic approaches. We respond to the PAR (PAR-17-032) with the goals 1) to apply novel computational systems biology approach, i.e. top-down and bottom-up predictive network for short), to analyze the existing rich genetics, genomics, proteomics, metabolomics, and clinical datasets in AMP-AD and other datasets in AD and 2) to build network models and to predict therapeutic targets of single-cell type and multi-cell cross-talk pathways contributing to the onset and progression of AD pathology; 3) to stratify patients into sub-groups according to Sex, APOE and disease-stage (whenever clinical data available) and to predict therapeutic targets for each sub-group of patients towards precision medicine (drug repurposing) in AD; 4) to use novel in- silico prediction pipeline to prioritize therapeutic targets; 5) to repurpose FDA-approved, investigational, and experimental drugs binding to prioritized therapeutic targets through (known) on-targets and/or (predicted by docking) off-targets; 6) to in-silico evaluate repurposed drugs: efficacy, toxicity, mechanism, transability through BBB; 7) to evaluate prioritized drug/combination using in-vitro and in-vivo AD models.

项目摘要阿尔茨海默氏病是最常见的痴呆症形式，估计会影响全球3600万人。除非开发出有效的治疗性，否则预计到2050年，该数字将增加到1.15亿。最近， NIA通过AMP-/M2OVE-AD联盟组织了大规模的努力，它产生了最富有的基因型，即基因组和临床数据，这使一个前所未有的机会探索了巨大的复杂性 AD发病机理。另一方面，通过所有失败的诊所试验，我们了解到有效的治疗需要针对疾病的多个方面，并针对多个致病过程广告。此外，由于性别和危险因素不同的患者对同一治疗的反应将有所不同因此，不同的病理机制，开发患者特异性治疗变得极为至关重要每个患者子组的靶标和精度药物。然而，尽管对推进AD的疗法和药物开发，很少有先进的生物信息学方法可用于指导药物的有效开发和在这些昂贵的治疗方法。我们对目标（第17-032杆）的响应（目标）1）应用新颖的计算系统生物学方法，即自上而下和自下而上的预测网络，以分析现有 AMP-AD和其他数据集中的丰富遗传学，基因组学，蛋白质组学，代谢组学和临床数据集 2）建立网络模型并预测单细胞类型和多细胞串扰的治疗目标有助于AD病理的发作和进展的途径； 3）将患者分为子组根据性别，APOE和疾病阶段（每当可用的临床数据）并预测治疗 AD中每个患者每个子组的靶标（药物重新利用）； 4）使用新颖计算机预测管道以优先考虑治疗靶标； 5）重新利用FDA批准，研究和实验药物通过（已知）靶向和/或（通过 docking）脱靶； 6）硅内评估重新利用的药物：疗效，毒性，机制，可移植性通过BBB； 7）使用体内和体内AD模型评估优先的药物/组合。