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1.
Article in Chinese | WPRIM | ID: wpr-846473

ABSTRACT

Objective: To explore the effective chemical constituents of Jinhua Qinggan Granules for treatment of coronavirus disease 2019 (COVID-19). Methods: The compounds and action targets of eleven herbal medicines in Jinhua Qinggan Granules were collected via TCMSP. The genes corresponding to the targets were queried by the UniProt database, then the “herbal medicine-compound-target” network was established by Cytoscape software. The gene ontology (GO) function enrichment analysis and KEGG pathway enrichment analysis were performed by DAVID to predict their mechanism. Molecular docking was used to analyze the binding force of the core effective compounds in the “herbal medicine-compound-target” network with SARS-CoV-2 3CL hydrolase and angiotensin converting enzyme II (ACE2). Results: The “herbal medicine-compound-target” network contained 154 compounds and 276 targets, and the key targets involved PTGS2, HSP90AB1, HSP90AA1, PTGS1, NCOA2, etc. GO function enrichment analysis revealed 278 items, including ATP binding, transcription factor activation and regulation of apoptosis process, etc. KEGG pathway enrichment screened 127 signaling pathways, including TNF, PI3K/Akt and HIF-1 signaling pathways related to lung injury protection. The results of molecular docking showed that formononetin, stigmasterol, beta-sitosterol, anhydroicaritin and other key compounds have a certain degree of affinity with SARS-CoV-2 3CL hydrolase and ACE2. Conclusion: The effective compounds in Jinhua Qinggan Granules regulate multiple signaling pathways via binding ACE2 and acting on targets such as PTGS2, HSP90AB1, HSP90AA1, PTGS1, NCOA2 for the prevention of COVID-19.

2.
Article in Chinese | WPRIM | ID: wpr-846394

ABSTRACT

Objective: To study the anti-fatigue mechanism of Epimedii Folium by network pharmacology. Methods: The main active ingredients of Epimedii Folium and the targets of active ingredients were obtained by TCMSP. The GeneCards was used to predict and screen the anti-fatigue targets. The Cytoscape 3.6.1 software was used to construct the active ingredient-disease-target network. The protein interactions network was constructed using the String database. The GO enrichment and KEGG pathways of the targets were analyzed by using DAVID database. Results: Nine active ingredients were screened from Epimedii Folium, including chrysoeriol, kaempferol, anhydroicaritin, C-homoerythrinan,1,6-didehydro-3,15,16-trimethoxy-,(3.beta.)-, 8-(3-methylbut-2-enyl)-2-phenyl- chromone, luteolin, magnograndiolide, quercetin, 8-isopentenyl-kaempferol, which acted on 31 fatigue targets such as PPARG, GABRA1, CASP3, ICAM1, etc. Biological function analysis showed that the targets of Epimedii Folium included cellular response to hypoxia, regulation of apoptotic, positive regulation of nitric oxide biosynthetic, cellular response to hydrogen peroxide, cellular response to hyperoxia, and negative regulation of lipid storage. Signaling pathway analysis showed that Epimedii Folium exerted the anti-fatigue effect by regulating PI3K-Akt, P53, HIF-1, TNF, FoxO, ErbB, MAPK, and other pathways. Conclusion: This study reflects the characteristics of multi-component, multi-target, and multi-pathway of Epimedii Folium, which provides reference for further research on the mechanism of anti-fatigue effects of Epimedii Folium.

3.
Article in Chinese | WPRIM | ID: wpr-852674

ABSTRACT

Objective: To establish a quick method of ultra-performance liquid chromatography-quadruple time-of-fight mass spectrometry (UPLC-Q-TOF-MS) for the identification of chemical constituents in Epimedii Folium. Methods: The separation was performed on the chromatographic column of Waters Acquity UPLC BEH C18 (100 mm × 2.1 mm, 1.7 μm). The mobile phase consisted of 0.1% formic acid-water (A) and 0.1% formic acid-acetonitrile (B) was used as gradient elute. The flow rate was 0.4 mL/min gradient elution and column temperature was 35 ℃. The injection volume was 5 μL. The negative ion mode was used for TOF-MS scanning. The compounds were identified by retention time, accurate relative molecular mass, and fragment ions in mass spectrometry. Results: Based on the MS/MS of standards and compared with reference results, 29 compounds were discovered. Then 10 batches of Epimedii Folium sample were analyzed, and compounds in them were also identified. Finally, 16 constituents exiting in all samples were found. Conclusion: The results demonstrate that UPLC-Q-TOF-MS method is quick, accurate, and efficient for the identification of the compounds in Epimedii Folium. The identification of chemical compositions and the definition of compositions generally existed in Epimedii Folium which provides some experiment foundation for study of its efficiency substances and quality control.

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