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Meta-Analysis
. 2024 Sep 13;103(37):e39679.
doi: 10.1097/MD.0000000000039679.

Network pharmacological mechanism analysis and evidence-based medical validation of Dahuang Mudan Decoction in the treatment of acute pancreatitis

Jinhan Chen  1 , Mengjie Jiang  2 , Yuou Ying  1 , Yuan Ji  1 , Yuying Chi  1 , Linghui Tao  3 , Fuping Wu  1 , Mingxian Chen  4
Affiliations
Meta-Analysis

Network pharmacological mechanism analysis and evidence-based medical validation of Dahuang Mudan Decoction in the treatment of acute pancreatitis

Jinhan Chen et al. Medicine (Baltimore). .

Abstract

Background: Dahuang Mudan Decoction is commonly used in China for the treatment of acute pancreatitis. Nevertheless, the therapeutic efficacy of the drug remains a subject of debate, and its active ingredients and potential therapeutic targets remain to be determined. The present study used a network pharmacological approach to investigate the active ingredients and possible targets of the drug, and illustrated the clinical effectiveness of Dahuang Mudan Decoction in the treatment of acute pancreatitis by meta-analysis.

Methods: The present study investigated the active ingredients of the constituent herbs of Dahuang Mudan Decoction using the TCMID database. In order to further identify molecular targets, Swiss Target Prediction, OMIM and Genecards databases was be used. The present study used metascape database for gene ontology function enrichment analysis and Kyoto Genome Encyclopedia pathway enrichment analysis. A gene interaction network diagram was established for predicting the main targets and mechanism of action to Dahuang Mudan Decoction for acute pancreatitis. To further illustrate the validity of the gene targets and the clinical efficacy of the drug, 13 relevant studies were included for meta-analysis and analyzed using the Cochrane Collaboration's Review Manager 5.4 software.

Result: After a thorough screening process, the present study identified three main components of Dahuang Mudan Decoction: kaempferol, quercetin and eupatin. These three major components have the potential to target 5 important proteins: AKT1, TNF-a, IL-6, TP53, HIF1A. In addition, pathway analyses by the Kyoto Genome Encyclopedia showed that Dahuang Mudan Decoction is active through the Pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, PI3K-Akt signaling pathway, etc signaling pathway to act on acute pancreatitis. The results of meta-analysis showed that compared with the control group, the experimental group had superior performance in terms of overall treatment efficacy, reduction of hospital stays and inflammatory factor levels after treatment.

Conclusion: In summary, network pharmacological studies have shown that Dahuang Mudan Decoction affects acute pancreatitis through different components, targets, and mechanisms. In addition, the meta-analysis study strongly supported the effectiveness of Dahuang Mudan Decoction in the treatment of acute pancreatitis.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
Venn diagram for screening of Chinese her ingredient targets and disease targets.
Figure 2.
Figure 2.
Construction of the compounds-target visual network of acute pancreatitis.
Figure 3.
Figure 3.
A diagram of the interactions between the active ingredient targets, where darker colors indicate that the targets have more complex interactions and may play a greater role in the treatment.
Figure 4.
Figure 4.
(A) GO enrichment analysis of cross genes between Dahuang Mudan Decoction and acute pancreatitis; (B) KEGG enrichment analysis of cross genes between Dahuang Mudan Decoction and acute pancreatitis. BP = biological process, CC = cell composition, GO = Gene ontology, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular function.
Figure 5.
Figure 5.
Study flow diagram for patient inclusion, screening, and final cohort selected for analysis.
Figure 6.
Figure 6.
Cochrane collaboration risk of bias summary: evaluation of bias risk items for each included study.
Figure 7.
Figure 7.
Forest plot comparing overall response rates between the 2 groups.
Figure 8.
Figure 8.
Forest plot comparing Length of hospitalization between the 2 groups.
Figure 9.
Figure 9.
Forest plot comparing TNF-a between the 2 groups.
Figure 10.
Figure 10.
Forest plot comparing IL-6 between the 2 groups.
Figure 11.
Figure 11.
Funnel plot of all the included studies for assessing publication bias.
See this image and copyright information in PMC

References

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