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Objective <p>This study aims to identify the bioactive components of Qing-Wei-San (QWS) for treating periodontitis and to uncover their mechanisms using computational pharmacology. By constructing a comprehensive pharmacological network, this study seeks to identify key targets and active components associated with periodontitis, providing scientific evidence for the optimization, mechanistic analysis, and clinical application of TCM.</p> Methods <p>Through bioinformatics analysis, periodontitis-associated pathogenic genes were identified from the DisGeNET and GEO databases. Meanwhile, all components of QWS were retrieved from the TCMSP database, TCM Integrated Database, and TCM Database@Taiwan. These components were then subjected to further screening to identify potential bioactive compounds. A component-target-target network was constructed using protein-protein interaction data, pathogenic genes, and active components. The network was validated using a computational network pharmacology model to identify equivalent component groups. The anti-inflammatory effects of these components on RAW264.7 cells were assessed via CCK8, NO, qRT-PCR, and Western Blot assays. Additionally, molecular docking was performed with AutoDock Tools to evaluate the binding affinity between equivalent components and core targets.</p> Results <p>We designed a novel computational systems pharmacology model that integrates the targets of the traditional Chinese medicine QWS with periodontitis-associated pathogenic genes, forming a Core Effect Space (CES). Node importance was calculated using a new method to identify active proteins within this space. Subsequently, approximate equivalent component group (AECG) capable of mediating these active proteins was screened based on a cumulative contribution rate model. The systems pharmacology results indicate that the targets of the AECG derived from the optimized CES effectively cover both drug targets and pathogenic genes at the functional level. Experimental results demonstrate that Ethyl ferulate, Methyl protocatechuate, and quercetin significantly reduce the expression levels of inflammatory factors in an inflammatory environment.</p> Conclusion <p>This study can predict the key bioactive components and mechanisms of action of QWS in the treatment of periodontitis, providing a methodological reference for the optimization, mechanistic analysis, and further development of TCM.</p>

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Using computational pharmacology and experimental verification to decode mechanism of Qing-Wei-San in treating periodontitis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月9日

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  • Yanhong Liu,
  • Wenqi Li,
  • Jingxiao Chen,
  • Yi Li,
  • Jieqi Cai,
  • Yongxi Luo,
  • Jiahao Lin,
  • Lu Chen,
  • Aiping Lu,
  • Daogang Guan,
  • Huiyong Xu

摘要

Objective

This study aims to identify the bioactive components of Qing-Wei-San (QWS) for treating periodontitis and to uncover their mechanisms using computational pharmacology. By constructing a comprehensive pharmacological network, this study seeks to identify key targets and active components associated with periodontitis, providing scientific evidence for the optimization, mechanistic analysis, and clinical application of TCM.

Methods

Through bioinformatics analysis, periodontitis-associated pathogenic genes were identified from the DisGeNET and GEO databases. Meanwhile, all components of QWS were retrieved from the TCMSP database, TCM Integrated Database, and TCM Database@Taiwan. These components were then subjected to further screening to identify potential bioactive compounds. A component-target-target network was constructed using protein-protein interaction data, pathogenic genes, and active components. The network was validated using a computational network pharmacology model to identify equivalent component groups. The anti-inflammatory effects of these components on RAW264.7 cells were assessed via CCK8, NO, qRT-PCR, and Western Blot assays. Additionally, molecular docking was performed with AutoDock Tools to evaluate the binding affinity between equivalent components and core targets.

Results

We designed a novel computational systems pharmacology model that integrates the targets of the traditional Chinese medicine QWS with periodontitis-associated pathogenic genes, forming a Core Effect Space (CES). Node importance was calculated using a new method to identify active proteins within this space. Subsequently, approximate equivalent component group (AECG) capable of mediating these active proteins was screened based on a cumulative contribution rate model. The systems pharmacology results indicate that the targets of the AECG derived from the optimized CES effectively cover both drug targets and pathogenic genes at the functional level. Experimental results demonstrate that Ethyl ferulate, Methyl protocatechuate, and quercetin significantly reduce the expression levels of inflammatory factors in an inflammatory environment.

Conclusion

This study can predict the key bioactive components and mechanisms of action of QWS in the treatment of periodontitis, providing a methodological reference for the optimization, mechanistic analysis, and further development of TCM.