Clinical efficacy evaluation and potential mechanism prediction on Pudilan Xiaoyan oral liquid in treatment of mumps in children based on meta-analysis, network pharmacology, and molecular docking

Abstract

Background: Mumps is caused by the mumps virus and is characterized by pain and parotid gland swelling. Although its incidence has declined due to vaccines, outbreaks still occur among children. In addition, it can lead to severe complications, so it has a certain perniciousness. Pudilan Xiaoyan oral liquid (PDL), a Chinese patent medicine, commonly treats children with mumps. However, its safety, efficacy, and specific mechanisms lack relevant evaluation and analysis. Therefore, we did a meta-analysis of the randomized controlled trials combined with a network pharmacology analysis to assess the efficacy and safety of PDL in relieving symptoms of mumps in children and investigate its pharmacological mechanisms. Methods: This study systematically searched the China National Knowledge Infrastructure (CNKI), WanFang Data Knowledge Service Platform, VIP Database, Sinomed, Chinese Medical Journal Full-text Database, PubMed, Embase, Cochrane Library, Web of Science, and Google Scholar for the published randomized controlled trials (date up to 3 March 2022; studies in both English and Chinese) comparing PDL and antiviral drug combination treatment to standalone antiviral drug treatment. The primary outcomes in this study were the effective rate and duration of five characteristic symptoms of children's mumps. We assessed the pooled data by using a fix-effect or random-effect model. We illustrated an odds ratio (OR) or standardized mean difference (SMD) with a 95% confidence interval (CI) using the Stata 15 software. In network pharmacology, active components of PDL were collected from the traditional Chinese medicine system pharmacology technology platform and the CNKI studies, while mumps' targets were collected from databases of the Genecards and Online Mendelian Inheritance in Man (OMIM), and then we constructed a "drug-component-target" network and a protein-protein interaction network using Cytoscape 3.9.0 for screening the core components and targets. Next, we ran Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of intersection targets of PDL and mumps. Finally, molecular docking was performed between core components and targets. Results: Of 70 identified studies, 12 were eligible and included in our analysis (N = 1,307 participants). Compared with the antiviral drug treatments, combination treatment using PDL and antiviral drugs provided higher effective rates (OR = 5.94), shorter symptom durations for fever (SMD = -1.05), headache (SMD = -0.69), parotid gland swelling (SMD = -1.30), parotid gland pain (SMD = -2.53), and loss of appetite (SMD = -0.56) with fewer reported side effects. Of the 113 active components of PDL and 57 mumps' targets, 11 core components like quercetin, isoetin, and seven core targets such as albumin (ALB) and interleukin-6 were obtained. Moreover, the potential pathways identified included cytokine-cytokine receptor interaction and T helper cell 17 (Th17 cell) differentiation. Molecular docking results revealed that most core components and targets could form stable structures. The core components, including isoetin, quercetin, and luteolin, and core targets involving heat shock protein HSP 90-alpha (HSP90AA1), estrogen receptor (ESR1), and ALB showed the best affinities. Conclusion: The combined use of PDL and antiviral drugs could effectively improve the efficacy of mumps among children and rapidly alleviate mumps-related symptoms. This efficacy may be associated with the anti-inflammatory and antiviral mechanisms by which PDL acts using multiple components, multiple targets, and multiple pathways. However, these results should be confirmed by further studies.

Keywords: Pudilan Xiaoyan oral liquid; meta-analysis; molecular docking; mumps; network pharmacology; randomized controlled trials; traditional Chinese medicine.

FIGURE 1

Flow diagram of the study selection.

FIGURE 2

Overall risk (A) and detailed risk (B) of bias in the included studies.

FIGURE 3

Forest plot of effective rate comparison among groups.

FIGURE 4

Forest plot of duration of fever comparison among groups.

FIGURE 5

Forest plot of duration of the headache.

FIGURE 6

Forest plot of duration of the parotid gland swelling.

FIGURE 7

Forest plot of the duration of comparing the parotid gland pain among the groups.

FIGURE 8

Forest plot of duration of appetite symptom.

FIGURE 9

Begg’s and Egger’s funnel plots for publication bias.

FIGURE 10

Results of sensitivity analysis.

FIGURE 11

Mumps–PDL intersection targets.

FIGURE 12

Drug-component-target network.

FIGURE 13

PPI network of intersection targets.

FIGURE 14

GO enrichment analysis.

FIGURE 15

KEGG enrichment analysis.

FIGURE 16

(A) Heat map of the molecular docking results between core components and targets; (B) docking models of the best combinations (from top left to bottom right: quercetin and HSP90AA1, taraxacin and ESR1, quercetin and MuV, and luteolin and ALB).