Study on metabolites derived from Zhideke granules in rats in vivo / 中国药房

OBJECTIVE To analyze the metabolites of Zhideke granules and speculate its metabolic pathway in rats in vivo. METHODS Male SD rats were randomly divided into blank group and administration group （Zhideke granules， 9.45 g/kg）； they were given ultrapure water or relevant medicine， twice a day， every 6-8 h， for 3 consecutive days. Serum， urine and feces samples of rats were collected， and their metabolites were identified by UPLC-Q-Exactive-MS technique after intragastric administration of Zhideke granules； their metabolic pathways were speculated. RESULTS After intragastric administration of Zhideke granules， 16 prototype components （i.g. irisflorentin， baicalin， chlorogenic acid） and 11 metabolites （i.g. hydration products of kaempferol or luteolin， methylation products of chlorogenic acid， and hydroxylation products of baicalin） were identified in serum， urine and feces of rats. Among them， 8 prototype components and 4 metabolites were identified in serum samples； 10 prototype components and 7 metabolites were identified in urine samples； 8 prototype components and 5 metabolites were identified in the fecal samples. CONCLUSIONS The metabolites of Zhideke granules in rats mainly include baicalin， irisflorentin，chlorogenic acid， and the main metabolic pathways included methylation， hydroxylation， glucuronidation.

Integrating Network Pharmacology Based on UPLC-Q-Exactive/MS Technology to Explore the Mechanism of Chaihu Guizhi Decoction in the Treatment of Secondary Bacterial Pneumonia Caused by Influenza / 世界科学技术-中医药现代化

Objective To study the mechanism of Chaihu Guizhi Decoction(CGD)in the treatment of influenza and staphylococcus aureus co-infection.Methods The co-infection model of influenza and staphylococcus aureus was established and CGD was used to intervene.The chemical components of CGD were qualitatively analyzed by UPLC-Q-Exactive/MS technology.The potential action targets of chemical components in CGD and the related targets of influenza Staphylococcus aureus co-infection were mined by network pharmacology method.The"component target disease"network was constructed.Core targets were selected according to degree ranking.Core action pathways were enriched by KEGG analysis and GO annotation analysis.The core target was verified by RT-qPCR,and the interaction between the core component and the key target was verified by molecular docking.Results CGD could significantly improve the decrease of body weight and thymus index(P<0.05)caused by co-infection.The lung index(P<0.05),relative amount of MmRNA expression(P<0.05)and bacterial load(P<0.05)were decreased,and the survival rate was improved.51 chemical constituents were identified from CGD.Through network pharmacological analysis,107 related targets corresponding to CGD treatment of bacterial pneumonia secondary to influenza were excavated.TNF,AKT1,ALB,VEGFA,MAPK3,PTGS2,STAT3,EGFR and other targets with strong correlation,mainly involved Fc epsilon RI signal pathway,GnRH signal pathway,NF-κB signal path,etc.Molecular docking study showed that the main active component of CGD,including oroxyloside,baicalein and wogonin have strong affinity with TNF,PTGS2 and EGFR targets.Compared with co-infection model group,in CGD group TNF-α、EGFR and PTGS2 increased significantly(P<0.05).Conclusion The main active ingredient of CGD is oroxyloside,baicalein and wogonin.TNF-α,PTGS2,EGFR and other targets to played a role in the treatment of influenza staphylococcus aureus co-infection.

UPLC-Q-Exactive MS and GC-MS Metabonomics Technology Revealing Difference of Constituents in Callerya speciosa Flowers at Different Flowering Periods / 世界科学技术-中医药现代化

Objective To investigate the chemical composition from the flowers of Callerya speciosa,and reveal the metabolites difference at different flowering periods based on metabolomics technology.Methods The primary and secondary metabolites,volatile chemical components in flowers of C.speciosa were analyzed combined by GC-MS and UPLC-Q-Exactive MS.Principal component analysis(PCA),orthogonal partial least squares-discriminant analysis(OPLS-DA),and hierarchical cluster analysis(HCA)were performed to identify differential metabolites.Results A total of 332 compounds were identified by UPLC-Q-Exactive MS,mainly including secondary metabolites such as flavonoids,triterpenoids,phenylpropanoids.A total of 297 compounds were identified by GC-MS,mainly including primary metabolites and volatile chemical components,such as organic acids,amino acids,saccharides,heterocycles,alcohols.The PCA analysis demonstrated that the metabolites of the four flowering periods were divided into two groups:bud,initial bloom and blooming periods clustered into one group,while wilting period clustered into the other group,the main differences were filtered and identified as flavonoids and triterpenoids,organic acids,respectively.Compared to the upright type,the flowers of vine type contained more characteristic flavonoids as differential metabolites during the bud,initial bloom and blooming periods,and some flavonoids decrease gradually with the development of flowering.Conclusion The results indicated that the flowers of C.speciosa possessed abundant active flavonoid metabolites for further utilization,and the best harvest stage is initial bloom,the best harvest plant is vine type.This study provides a scientific basis for the scientific development and rational use of the flowers of C.speciosa.

Active components and mechanism of Jinwugutong Capsules in treatment of osteoporosis: a study based on UPLC-Q-Exactive-MS/MS combined with network pharmacology / 中国中药杂志

UPLC-Q-Exactive-MS/MS and network pharmacology were employed to preliminarily study the active components and mechanism of Jinwugutong Capsules in the treatment of osteoporosis. Firstly, UPLC-Q-Exactive-MS/MS was employed to characterize the chemical components of Jinwugutong Capsules, and network pharmacology was employed to establish the "drug-component-target-pathway-disease" network. The key targets and main active components were thus obtained. Secondly, AutoDock was used for the molecular docking between the main active components and key targets. Finally, the animal model of osteoporosis was established, and the effect of Jinwugutong Capsules on the expression of key targets including RAC-alpha serine/threonine-protein kinase(AKT1), albumin(ALB), and tumor necrosis factor-alpha(TNF-α) was determined by enzyme-linked immunosorbent assay(ELISA). A total of 59 chemical components were identified from Jinwugutong Capsules, among which coryfolin, 8-prenylnaringenin, demethoxycurcumin, isobavachin, and genistein may be the main active components of Jinwugutong Capsules in treating osteoporosis. The topological analysis of the protein-protein interaction(PPI) network revealed 10 core targets such as AKT1, ALB, catenin beta 1(CTNNB1), TNF, and epidermal growth factor receptor(EGFR). The Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment showed that Jinwugutong Capsules mainly exerted the therapeutic effect by regulating the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT) signaling pathway, neuroactive ligand-receptor interaction, mitogen-activated protein kinase(MAPK) signaling pathway, Rap1 signaling pathway and so on. Molecular docking showed that the main active components of Jinwugutong Capsules well bound to the key targets. ELISA results showed that Jinwugutong Capsules down-regulated the protein levels of AKT1 and TNF-α and up-regulated the protein level of ALB, which preliminarily verified the reliability of network pharmacology. This study indicates that Jinwugutong Capsules may play a role in the treatment of osteoporosis through multiple components, targets, and pathways, which can provide reference for the further research.

Chemical profiling and rapid discrimination of Blumea riparia and Blumea megacephala by UPLC-Q-Exactive-MS/MS and HPLC / 中草药·英文版

OBJECTIVE@#To rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis.@*METHODS@#UPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation.@*RESULTS@#A total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC-MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala.@*CONCLUSION@#This study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.

Analysis of chemical constituents in ethyl acetate extract of Taxilli Herba by UPLC-Q-Exactive-MS and screening of potential xanthine oxidase inhibitors / 中国中药杂志

The present study analyzed and identified the chemical constituents from ethyl acetate(EA) extract of Taxilli Herba with UPLC-Q-Exactive-MS and screened active xanthine oxidase(XO) inhibitors with HPLC. The analysis was performed on an Hypersil GOLD C_(18) reversed-phase column(2.1 mm×50 mm, 1.9 μm), with the mobile phase of water containing 1% formic acid(A) and methanol(B) under gradient elution, the flow rate of 0.3 mL·min~(-1), and the injection volume of 5 μL. ESI source was used for MS and the compounds were collected in positive and negative ion modes. Xcalibur 4.1 was used to analyze the retention time, accurate relative molecular weight, and fragmentation of the compounds. The inhibitory activity of some known compounds on XO was screened by HPLC. Thirty chemical constituents were identified, including phenolic acids and flavonoids by experimental data combined with information of standards, data reported previously, and databases, such as MzCloud and ChemSpider. The activities of 10 chemical components were screened. Gallic acid and naringenin chalcone had strong inhibitory activities on XO with IC_(50) of 57 μg·mL~(-1) and 108 μg·mL~(-1). UPLC-Q-Exactive-MS allows the accurate, rapid, and comprehensive identification of main chemical constituents from Taxilli Herba. Gallic acid and naringenin chalcone may be the active components of XO inhibitors.