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. 2024 Jul 14;10(14):e34572.
doi: 10.1016/j.heliyon.2024.e34572. eCollection 2024 Jul 30.

Evidence construction of Jinshuibao capsules against stable chronic obstructive pulmonary disease: A systematic review and network pharmacology

Yongjun Yin  1 , Yilan Wang  1 , Ying Liu  1 , Fei Wang  1 , Zhenxing Wang  1
Affiliations

Evidence construction of Jinshuibao capsules against stable chronic obstructive pulmonary disease: A systematic review and network pharmacology

Yongjun Yin et al. Heliyon. .

Abstract

Background: Jinshuibao capsules has been utilized in treating stable chronic obstructive pulmonary disease (COPD) for a long time. While the evidence-based evidence and network pharmacology to clarify the therapeutic efficacy and pharmacological mechanisms of Jinshuibao capsules have remained elusive.

Objectives: Integrating evidence-based medicine and network pharmacology to explain the therapeutic efficacy and pharmacological mechanisms of Jinshuibao capsules for stable COPD.

Methods: Cochrane Library, Web of Science, EMBASE, PubMed, China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, VIP Information Resource Integration Service Platform (CQVIP), and China Biomedicine (SinoMed) databases were searched. Studies were selected according to the inclusion and exclusion criteria. Statistical analysis was performed using the RevMan 5.3 software (Cochrane, London, UK). In network pharmacology, components of Jinshuibao capsules were screened, stable COPD-related genes were then identified and the 'component-target-pathway' network constructed.

Results: Meta-analysis revealed that Jinshuibao capsules exerts therapeutic effects on stable COPD by increasing the levels of FEV1% pred, FEV1/FVC ratio, FEV1, FVC, and PaO2 while decreasing the level of PaCO2. In addition, Jinshuibao capsules could effectively increase the levels of CD3+, CD4+/CD8+ ratio, Th17/Treg ratio, and SOD while reduce the levels of IL-8 and TNF-α. Network pharmacology identified 22 active compounds and 419 intersection gene targets. AKT1, SRC, MAPK1, STAT3, and MAPK3 were top 5 key target proteins. Besides, 20 potential pathways of Jinshuibao capsules on stable COPD were identified, like endocrine resistance, AGE-RAGE signaling pathway in diabetic complications, and chemical carcinogenesis-receptor activation.

Conclusion: Jinshuibao capsules could positively influence patients with stable COPD, while the efficacy and safety of Jinshuibao capsules in the treatment of COPD could not be reliably confirmed. These findings suggest that Jinshuibao capsules exerts effect on stable COPD through multi-target, multi-component and multi-pathway mechanism. Future studies may explore the active components of Jinshuibao capsules.

Keywords: COPD; Jinshuibao capsules; Meta-analysis; Network pharmacology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart of the study selection.
Fig. 2
Fig. 2
The outcomes of the risk of bias assessment.
Fig. 3
Fig. 3
Forest plot for the FEV1% pred.
Fig. 4
Fig. 4
Funnel plot for the FEV1% pred.
Fig. 5
Fig. 5
Forest plot for the FEV1/FVC ratio.
Fig. 6
Fig. 6
Funnel plot for the FEV1/FVC ratio.
Fig. 7
Fig. 7
Forest plot for the FEV1.
Fig. 8
Fig. 8
Funnel plot for the FEV1.
Fig. 9
Fig. 9
Forest plot for the FVC.
Fig. 10
Fig. 10
Forest plot for the PaO2 level.
Fig. 11
Fig. 11
Forest plot for the PaCO2 level.
Fig. 12
Fig. 12
Forest plot for the number of CD3+ T cells in peripheral blood.
Fig. 13
Fig. 13
Forest plot for the ratio of CD4+/CD8+ T cells in peripheral blood.
Fig. 14
Fig. 14
Forest plot for the ratio of Th17/Treg cells in peripheral blood samples.
Fig. 15
Fig. 15
Forest plot for the IL-8 level.
Fig. 16
Fig. 16
Forest plot for the TNF-α level.
Fig. 17
Fig. 17
Forest plot for the SOD level.
Fig. 18
Fig. 18
Network pharmacology prediction for Jinshuibao capsules treatment in stable COPD. (A) Intersection targets between Jinshuibao capsules and COPD. (B) The intersection targets were used to construct a PPI network.
Fig. 19
Fig. 19
Network construction. (A) compound-target intersection network. (B) hub nodes ranked by degree value. (C) drug-targets-disease network.
Fig. 20
Fig. 20
GO and KEGG enrichment. (A) GO functional annotation. (B) KEGG enrichment analysis.
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