Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach.
Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach.
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DOI：
10.1021/acsomega.1c01526
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发表时间：
2021-06-01
期刊：
影响因子：
4.1
通讯作者：
Kumar P
中科院分区：
文献类型：
--
作者：
Advani D;Kumar P
目的/假设：多种病理特征的复杂性和异质性使阿尔茨海默病（AD）成为全球健康的主要罪魁祸首。药物再利用是将现有药物重新用于新适应症的廉价且可靠的方法。本研究旨在研究将已批准的抗癌药物用于AD治疗的可能性。我们提出了一个基于“组学”数据挖掘的计算机管道，它结合了基因组学、转录组学和代谢组学研究。我们的目的是验证再利用药物的神经保护特性，并确定拟议药物在AD中的可能作用机制。结果如下：我们生成了AD相关基因的列表，然后搜索DrugBank数据库和治疗靶点数据库，以寻找与潜在AD靶点相关的抗癌药物。具体而言，我们研究了现有的已批准抗癌药物，并排除了研究和实验药物的信息。我们开发了一个计算管道来优先考虑与AD靶点密切相关的抗癌药物。通过数据挖掘，我们获得了2914个AD相关基因的列表，并通过功能富集分析获得了49个潜在的药物靶点。这些基因的蛋白质-蛋白质相互作用（PPI）研究揭示了641个相互作用。我们发现15个AD风险/直接PPI基因与30种已批准的肿瘤药物相关。候选药物-靶点相互作用的计算验证、结构和功能分析、相关分子机制的研究以及基于文献的分析产生了四种再利用候选药物，其中三种药物是表皮生长因子受体（EGFR）抑制剂。结论：我们的计算药物再利用方法提出EGFR抑制剂作为AD的潜在再利用药物。因此，我们提出的框架可以用于以经济有效的方式用于不同适应症的药物再利用。
Aim/Hypothesis: The complexity and heterogeneity of multiple pathological features make Alzheimer’s disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on “omics” data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD. Results: We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein–protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug–target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors. Conclusion: Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.
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