Drug repurposing for Alzheimer's disease using structural systems pharmacology

利用结构系统药理学重新调整阿尔茨海默病的药物用途

• 批准号: 9559932
• 负责人: Lei Xie
• 金额: $ 77.08万
• 依托单位: HUNTER COLLEGE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2018-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9559932
• 关键词: Address; Adoption; Adverse effects; Algorithms; Alzheimer' s Disease; Amyloid; Amyloid beta-Protein; Animal Model; Antineoplastic Agents; Binding; Bioinformatics; Biological; Biophysics; CASP2 gene; Cell model; Chemicals; Clinical; Collaborations; Complex; Computer Simulation; Computing Methodologies; Data; Dementia; Diazoxide; Disease; Disease Pathway; Drug Combinations; Drug Targeting; FDA approved; Failure; Genes; Health; Human; Inflammation; Laboratories; Lead; Link; Machine Learning; Methods; Modeling; Pathologic Processes; Pathology; Pathway interactions; Performance; Pharmaceutical Preparations; Pharmacology; Phenotype; Phosphotransferases; Prevalence; Process; Proteins; Proteome; Resolution; System; Systems Biology; Testing; Therapeutic; base; beta-site APP cleaving enzyme 1; computer framework; cost; drug discovery; effective therapy; experimental study; genome wide association study; genome-wide; genome-wide analysis; improved; innovation; multiple omics; network models; new therapeutic target; novel; pre-clinical trial; precision medicine; preclinical development; response; success; tau Proteins

Abstract Alzheimer’s disease (AD) is a triple health threat – with soaring prevalence, enormous costs and lack of effective treatment. However, efforts in drug discovery and repurposing for the treatment of AD have had limited success. The failure is largely attributed to the adoption of a reductionist model of “one-drug-one-gene- one-disease”. As AD is a multi-facet complex disease, a new treatment approach is urgently needed to simultaneously target multiple pathological processes responsible for the onset and progress of AD, some of which are also common to other diseases that cause dementia. In this application, we will develop an innovative translational bioinformatics approach to addressing challenges in AD drug discovery. Our approach is based on a new paradigm of systems pharmacology, which focuses on defining multiple targets to a single drug or a drug combination, and studying the effect of the drug(s) on perturbing disease-causing networks. Over the last ten years, we have developed a novel structural systems pharmacology (SSP) platform that can predict genome-wide high-resolution protein-chemical interactions and correlate molecular interactions with phenotype responses. The SSP platform synergistically combines novel methods from machine learning, bioinformatics, biophysics, and systems biology. We have successfully applied the SSP platform to drug repurposing, polypharmacology, side effect prediction, precision medicine, and Genome-Wide Association Studies. Building on our successful proof-of-concept studies, and in close collaborations with experimental laboratories, we will develop, and rigorously test a novel SSP approach to AD drug repurposing and polypharmacology. Firstly, we will develop a multi-layered drug-gene-pathway-disease-side effect network model (MULAN) that links FDA-approved drugs with dementia and side effects through protein-chemical interactions, gene-disease associations, chemical-disease associations, and dementia-associated biological pathways through integrating multiple omics data. Secondly, we will improve and apply our proven successful SSP platform, which can accurately infer novel relations from sparse and noisy MULAN, to identify safe FDA- approved drugs that can be repurposed for AD treatment. Finally, we will experimentally test FDA-approved drugs identified for their binding activity of drug targets and anti-AD potency in cell and animal models. The successful completion of this project will provide an integrated computational modeling framework for AD drug repurposing and polypharmacology as well as identify novel targeted anti-AD therapeutics toward pre-clinical trials.

摘要 阿尔茨海默病（AD）是一种三重健康威胁-患病率飙升，成本巨大，缺乏治疗。 有效治疗。然而，在药物发现和再利用以治疗AD方面的努力已经取得了进展。 有限的成功。失败的主要原因是采用了“一药一基因”的简化模型， 一种疾病”。由于AD是一种多方面的复杂疾病，因此迫切需要一种新的治疗方法， 同时靶向导致AD发病和进展的多种病理过程， 这也是导致痴呆症的其他疾病的常见原因。在这个应用程序中，我们将开发一个 创新的翻译生物信息学方法，以应对AD药物发现的挑战。我们的方法 基于系统药理学的新范式，其重点是将多个靶点定义为单个靶点。 药物或药物组合，并研究药物对扰乱致病网络的影响。 在过去的十年中，我们开发了一种新的结构系统药理学（SSP）平台，可以 预测全基因组高分辨率蛋白质化学相互作用，并将分子相互作用与 表型反应SSP平台协同结合了机器学习的新方法， 生物信息学、生物物理学和系统生物学。我们已经成功地将SSP平台应用于药物 再利用，多药理学，副作用预测，精准医学和全基因组协会 问题研究在我们成功的概念验证研究的基础上， 实验室，我们将开发并严格测试一种新的SSP方法来重新利用AD药物， 多元药理学首先，我们将建立一个多层次的药物-基因-通路-疾病-副作用网络 该模型（MULAN）通过蛋白质化学物质将FDA批准的药物与痴呆症和副作用联系起来， 相互作用，基因-疾病关联，化学-疾病关联，以及痴呆相关的生物 通过整合多个组学数据的途径。其次，我们将改进和应用我们的成功经验， SSP平台，它可以准确地从稀疏和嘈杂的MULAN中推断出新的关系，以识别安全的FDA- 批准的药物，可以重新用于AD治疗。最后，我们将实验性地测试FDA批准的 在细胞和动物模型中鉴定药物靶点结合活性和抗AD效力的药物。的 该项目成功完成将为AD药物提供一个完整的计算建模框架 再利用和多药理学以及确定新的靶向抗AD治疗药物， 审判

项目成果

VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in Genome-Wide Association Studies: applied to Alzheimer's Disease.

• DOI: 10.1109/bibm.2017.8217995
• 发表时间: 2017-11
• 期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
• 作者: Liu Q;Chen C;Gao A;Tong HH;Xie L;Alzheimer’s Disease Neuroimaging Initiative
• 通讯作者: Alzheimer’s Disease Neuroimaging Initiative