Drug repurposing for Alzheimer's disease using structural systems pharmacology

摘要 阿尔茨海默病(AD)是对健康的三重威胁--发病率飙升，成本巨大，缺乏 有效的治疗。然而，治疗阿尔茨海默病的药物发现和改变用途的努力已经 有限的成功。这一失败在很大程度上是由于采用了一药一基因的简约主义模式。 一种疾病“。由于AD是一种多方面的复杂疾病，迫切需要一种新的治疗方法来治疗 同时针对导致AD发生和发展的多个病理过程，其中一些 这在其他导致痴呆症的疾病中也很常见。在此应用程序中，我们将开发一个 创新的翻译生物信息学方法，以应对AD药物发现中的挑战。我们的方法 是基于一种新的系统药理学范式，其重点是将多个靶点定义为一个 药物或药物组合，并研究药物(S)对扰乱致病网络的作用。 在过去的十年中，我们开发了一个新的结构系统药理学(SSP)平台，它可以 预测全基因组范围的高分辨率蛋白质-化学相互作用并将分子相互作用与 表型反应。SSP平台协同结合了来自机器学习的新方法， 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平台应用于药品 再利用、多元药理学、副作用预测、精确医学和全基因组关联 研究。以我们成功的概念验证研究为基础，并与实验部门密切合作 实验室，我们将开发并严格测试一种新的用于AD药物再用途和 多元药理学。首先，我们将建立多层次的药物-基因-途径-疾病副作用网络 通过蛋白质化学将FDA批准的药物与痴呆症和副作用联系起来的模型(木兰) 相互作用、基因-疾病关联、化学-疾病关联和痴呆症相关生物学 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已被证明成功的 SSP平台，可以准确地从稀疏和嘈杂的花木兰中推断出新的关系，以识别安全的FDA- 已批准的可用于AD治疗的药物。最后，我们将试验性地测试FDA批准的 在细胞和动物模型中确定其药物靶标的结合活性和抗AD效力的药物。这个 该项目的成功完成将为AD药物提供一个完整的计算模型框架 重新调整用途和多元药理学，以及确定新的靶向抗AD疗法，以达到临床前 审判。

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.