Methodology improvement for network pharmacology to correct the deviation of deduced medicinal constituents and mechanism: Xian-Ling-Gu-Bao as an example

小桶 计算生物学 系统药理学 计算机科学 虚拟筛选 药物发现 传统医学 药理学 化学 生物信息学 医学 药品 生物 基因 转录组 基因表达 生物化学
作者
Zheng Li,Biao Qu,Xiaowen Wu,Hongwei Chen,Jue Wang,Lei Zhou,Xiaoyi Wu,Wei Zhang
出处
期刊:Journal of Ethnopharmacology [Elsevier BV]
卷期号:289: 115058-115058 被引量:10
标识
DOI:10.1016/j.jep.2022.115058
摘要

Network pharmacology is extremely adaptive for investigating traditional ethnic drugs, especially the herbal medicines. However, challenges still hang over many related studies due to the limitations in the methodology of conventional network pharmacology.Our work was aimed to investigate the methodology limitations of conventional network pharmacology with Xian-Ling-Gu-Bao (XLGB) as a representative, meanwhile, propose the strategies for coping with these issues.Predicted phytochemical constituents formed virtual XLGB. The constituents in realistic XLGB samples was detected by liquid chromatography-mass spectrometry (LC-MS) to correct the constituent deviation resulted from virtual prediction. Multivariate statistical analysis of quantitative target data were used to reveal the relation of target profile between drug and disease. The key constituents and targets were screened and compared between virtual and realistic XLGB through network analysis. After enrichment analysis, reversing network pharmacology was performed to exclude weak targets and re-construct the interaction from key pathways to key targets. Finally, the core constituents and action mechanism of XLGB were deduced.Significant deviation of phytochemical constituents was found between virtual and realistic XLGB. As expected, this deviation led to a cascade of deviation ranging from deduced key constituents to key targets and key pathways. Moreover, many key KEGG pathways were enriched and screened out, however, they were almost irrelevant to the studied disease. These results systemically illustrated the limitations in the methodology of conventional network pharmacology. Importantly, the strategies for coping with these limitations were proposed, such as high-throughput detection of the realistic samples, multivariate analysis of target profile and combined enrichment analysis. Finally, based on the improved network pharmacology, the medicinal constituents and mechanism of XLGB against osteoarthritis were effectively deduced.Our work highlighted the necessity and proposed the strategies for improving the methodology of conventional network pharmacology. The corrected results from improved network pharmacology provided promising directions for future research on XLGB.
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