Post-marketing Evaluation Study Of Reduning Injection In The Treatment Of Viral Infectious Diseases Based On Integrated Big Data

ObjectiveReduning injection(RDNI)is a TCM injection extracted from three Chinese herbal medicines:the dry aboveground part of Artemisia annua L.,the flower of Lonicera japonica Thunb.and the fruit of Gardenia jasminoides J.Ellis.It has the functions of clearing away wind,clearing away heat,as well as detoxifying toxin.Moreover,it is mainly used in the treatment of upper respiratory tract infection(URTI).Recently,many clinical studies focused on the treatment of URTI by RDNI were published.However,small sample size and inconsistent outcome indexes are the limitations in these studies.RDNI is also clinically used in the treatment of influenza,hand,foot and mouth disease(HFMD)and Corona Virus Disease 2019(COVID-19).Nevertheless,due to the characteristics of multi-component and multi-target,its molecular mechanisms are still unclear.Therefore,to provide more stable evidence for clinical application,this study adopted the method of Meta-analysis to evaluate the clinical efficacy and safety of RDNI in the treatment of URTI.Chip analysis method was applied to analysis the differentially expressed genes(DEGs)between influenza A(H3N2)symptomatic patients and their health status.Network pharmacology was used to explore the molecular mechanisms of RDNI in treatment of influenza,influenza A(H3N2),COVID-19 and HFMD.Molecular docking was employed to preliminary verify the results.It provides a direction for future mechanism research and experiment.Methods1.Meta-analysisRandomized controlled trials(RCTs)of RDNI in the treatment of URTI were comprehensively and systematically searched from CNKI,Wanfang database,VIP database,Sinomed,PubMed,Embase,The Cochrane Library and Web of Science.According to the inclusion and exclusion criteria.The literature was strictly screened and the information was extracted.Outcome indicators of the study included clinical efficacy,mean antipyretic time,disappearance time of herpes,disappearance time of nasal congestion and runny nose,disappearance time of pharyngeal congestion,and cessation time of cough.The "risk of bias assessment" tool recommended by Cochrane Handbook 5.1 was used to assess the quality of the literature.RevMan 5.3 and Stata 13.0 were used to analyze the data.Forest maps were drawn,sensitivity,funnel plots,and publication bias analysis were performed.The adverse reaction information was also recorded and summarized.2.Chip analysisGene expression profiles of influenza A(H3N2)infected patients were retrieved and downloaded from the GEO database.The genes were grouped by symptom and infection time.After that,The Limma package of R software was adopted to analyze the DEGs of each group.A Venn diagram was drawn to observe the relationship between each group.The DEGs between symptomatic patients and healthy people were analyzed for correlation.Then the proteinprotein interaction analysis of DEGs was performed through STRING or HINT.The results were then imported into Cytoscape for plotting.Modular analysis and core gene analysis were carried out through MCODE and cytoHubba plugin.The clusterProfiler package of R was adopted to perform GO and KEGG analysis.At last,Network pharmacological methods were used to further predict the interaction between RDNI and DEGs and other genes related to influenza A(H3N2).3.Network pharmacologyTo obtain the chemical components of RDNI,Chinese and English literature were searched systematically and comprehensively.Compound targets were obtained through literature,SwissTargetPrediction.STITCH as well as SuperPred,and disease-related genes were obtained by searching Disgenet,GeneCards and Digsee.The interaction relationship between proteins was get from STRING.Cytoscape was used to plot the networks and the Merge plugin was used to obtain potential targets.Module analysis of protein interaction network and core gene analysis was carried out by Mcode and cytoHubba plugin.GO and KEGG enrichment and disease cluster analysis were carried out by clusterProfiler of R software and DAVID.AutoDock Vina was used to conduct molecular docking of key compounds and their corresponding target targets to verify the binding ability between comounds and targets.The visualization was carried out by PyMOL software.Results1.Meta-analysis of RDNI in the treatment of URTI compared with ribavirinA total of 118 studies involving 15,461 patients were included.Meta-analysis results showed that the clinical efficacy,mean antipyretic time,herpes disappearance time,nasal obstruction and runny nose disappearance time,pharyngeal congestion disappearance time,and cough cessation time of RDNI were better than those of ribavirin based on conventional treatment(P<0.00001).Safety analysis results showed that RDNI had a lower incidence of adverse reactions(P<0.00001).Additionally,the adverse reactions of RDNI are milder.2.The result of the network of pharmacology-based strategy deciphers the multitarget pharmacological mechanism of RDNI in the treatment of influenzaEight potential targets of influenza were obtained by merging the "compound-target"network and "disease-target" network,including CXCL10.CCL2,IL6,STAT1,PTPN11,TNF,BRAF,and MMP9.GO enrichment analysis showed that potential targets were mainly concentrated in biological processes "positive regulation of ERK1 and ERK2 cascade" and "cellular response to lipopolysaccharide".KEGG results indicated that potential targets were mainly enriched in three pathways:"TNF signaling pathway","influenza A",and " herpes simplex infection".The results of molecular docking showed that all the compounds had a good binding ability with the targets.3.Mechanism study of RDNI in the treatment of influenza A(H3N2)based on integrated bioinformaticsGSE30550 microarray analysis showed that there were 48 up-regulated genes in the symptomatic infected group compared with the healthy group.Ten genes were considered the core genes in the DEGs' PPI network,respectively were XAF1,IFI44L,RSAD2,OAS1,MX1,IFIT2,OAS2,IFIT3,IFIT1,and IFI44.They were significantly enriched in the influenza A pathway.A total of 8 potential targets of RDNI on influenza A(H3N2)were identified,namely LPO,IL1B,EGFR,CCL2,CXCL10,LAP3,PTPN11,and CSF2.The molecular docking results showed that the corresponding compounds of RDNI had a good binding ability with target,among which EGFR had the best binding ability with rutin.4.Network of pharmacology-based strategy deciphers the multitarget pharmacological mechanism of RDNI in the treatment of COVID-19Among the compound targets of RDNI,26 were associated with cytokine storm,5 were associated with fever,and 251 targets were co-expressed with ACE2.The three targets with the highest degree were CA2,CA12,and CA1.In the PPI network of compound targets,HSP90AB1 has the highest degree.A total of 1491 items of FDR<1×10-6 were obtained by GO enrichment,and 113 pathways of FDR<1×10-6 were obtained by KEGG enrichment analysis,including 18 signal transduction related pathways,12 immune system related pathways,6 cell growth and death related pathways,and 10 pathways of viral infectious diseases.In the results of disease cluster analysis,3 of the 7 items in the cluster with the highest enrichment level were lung disease.Molecular docking results showed that RDNI compounds have good binding ability with COVID-19 targets PLP,ACE2,and MPRO.5.Network of pharmacology-based strategy deciphers the multitarget pharmacological mechanism of RDNI in the treatment of HFMDA total of 130 potential targets of Reduning for HFMD were obtained.The top ten targets with the highest degree in compound-target network were respectively MMP2?CA6?MMP13?ELANE?MMP1?MMP9?EGFR?TYR?ABCB1,and APP.However,AKT1,MAPK1,VEGFA,IL6,STAT3,TP53,IGF1,EGFR,HRAS,and TNF were10 core targets in PPI network.The results of molecular docking showed that RDNI had good binding ability with the corresponding targets.A total of 3542 GO functional entries and 147 KEGG pathways were obtained by enrichment analysis.The results of molecular docking showed that Reduning injection had good binding ability with the corresponding targets.ConclusionsMeta-analysis results showed that RDNI had better efficacy and higher safety than ribavirin in the treatment of URTI.Mechanism study of RDNI based on chip analysis and network pharmacology showed that RDNI can regulate multiple targets through the synergistic action of multiple active ingredients.For infectious diseases,it can regulate cytokine storm and achieve the purpose of antipyretic by regulating cytokines related to pyrexia.It achieves the purpose of treating influenza,HFMD,and COVID-19 through regulating multiple targets and pathways together.This study provides a large sample of evidence-based medical evidence for the treatment of URTI by RDNI,which can be a reference for clinical medication.It also provides ideas and references for further research on the mechanism of RDNI in the treatment of influenza,HFMD,and COVID-19.