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Combined network pharmacology and virtual reverse pharmacology approaches for identification of potential targets to treat vascular dementia

机译:组合网络药理学和虚拟逆向药理学方法,用于鉴定治疗血管性痴呆的潜在靶标

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Dementia is a major cause of disability and dependency among older people. If the lives of people with dementia are to be improved, research and its translation into druggable target are crucial. Ancient systems of healthcare (Ayurveda, Siddha, Unani and Sowa-Rigpa) have been used from centuries for the treatment vascular diseases and dementia. This traditional knowledge can be transformed into novel targets through robust interplay of network pharmacology (NetP) with reverse pharmacology (RevP), without ignoring cutting edge biomedical data. This work demonstrates interaction between recent and traditional data, and aimed at selection of most promising targets for guiding wet lab validations. PROTEOME, DisGeNE, DISEASES and DrugBank databases were used for selection of genes associated with pathogenesis and treatment of vascular dementia (VaD). The selection of new potential drug targets was made by methods of NetP (DIAMOnD algorithm, enrichment analysis of KEGG pathways and biological processes of Gene Ontology) and manual expert analysis. The structures of 1976 phytomolecules from the 573 Indian medicinal plants traditionally used for the treatment of dementia and vascular diseases were used for computational estimation of their interactions with new predicted VaD-related drug targets by RevP approach based on PASS (Prediction of Activity Spectra for Substances) software. We found 147 known genes associated with vascular dementia based on the analysis of the databases with gene-disease associations. Six hundred novel targets were selected by NetP methods based on 147 gene associations. The analysis of the predicted interactions between 1976 phytomolecules and 600 NetP predicted targets leaded to the selection of 10 potential drug targets for the treatment of VaD. The translational value of these targets is discussed herewith. Twenty four drugs interacting with 10 selected targets were identified from DrugBank. These drugs have not been yet studied for the treatment of VaD and may be investigated in this field for their repositioning. The relation between inhibition of two selected targets (GSK-3, PTP1B) and the treatment of VaD was confirmed by the experimental studies on animals and reported separately in our recent publications.
机译:痴呆症是老年人残疾和依赖的主要原因。如果要改善患有痴呆症的人们的生命,研究及其翻译成可用的目标是至关重要的。几个世纪以来,几个世纪为治疗血管疾病和痴呆症,古老的医疗保健系统(Ayurveda,Siddha,Unani和Sowa-Rigpa)。这种传统知识可以通过具有逆向药理学(REVP)的网络药理学(NETP)的强大相互作用来转变为新颖的目标,而不忽略切削刃生物医学数据。这项工作展示了近期和传统数据之间的互动,并针对最有前途的目标来指导湿式实验室验证。蛋白质组,脱戊糖和药物银行数据库用于选择与血管痴呆(VAD)的发病机制和治疗相关的基因。通过NETP(钻石算法,KEGG途径的富集分析和基因本体生物过程的富集分析)和手动专家分析,通过NETP(钻石算法,富集分析)的选择选择。来自573个印度药用植物的1976年植物植物的结构用于治疗痴呆和血管疾病的计算估算它们与新的预测VAD相关药物靶标通过REVP方法基于通过(物质的活性光谱预测) 软件。我们发现基于具有基因疾病关联的数据库的分析,我们发现了147个与血管痴呆相关的基因。基于147个基因关联的NETP方法选择六百个新靶。 1976年植物分子和600个NetP预测靶标的预测相互作用的分析导致选择10种潜在药物靶标的VAD。这里讨论了这些目标的翻译价值。从药物银行鉴定了与10种选定靶标相互作用的二十四种药物。这些药物尚未研究过Vad的治疗,并且可以在该领域进行调查,以便它们重新定位。通过对动物的实验研究证实了两种选定靶标(GSK-3,PTP1B)和VAD治疗之间的关系,并在我们最近的出版物中分别报道。

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