Study on the improvement of Qingdaipowder Gel for external use on mice with specific dermatitis / 中国临床药理学与治疗学

AIM: To study the effect of Qingdaipowder Gel (QDPG) on mice specific dermatitis (AD) model and the antibacterial effect of the ethanol extract of Qingdaipowder. METHODS: AD model of mice was established by repeated skin induction with 2,4-dinitrochlorobenzene (DNCB). Fifty-six mice were randomly divided into blank group, model group, Hydrocortisone Butyrate Cream group (Hyd, 1.5 mg/cm

Mechanism of compound kushen injection in the treatment of lung cancer based on serum metabolomics and network pharmacology / 中国临床药理学与治疗学

AIM: To explore the mechanism of action of alkaloid components of compound kushen Injection (CKI) in the treatment of lung cancer based on serum metabolomics, network pharmacology, and molecular docking techniques. METHODS: A lung cancer model was established in C57 mice by inoculation of Lewis mouse lung cancer tumor strain. Thirty male mice were randomly divided into normal group, model group and CKI group. The drug was administered by tail vein injection once daily for 17 consecutive days. Mouse serum was examined by ultrahigh performance liquid chromatography tandem mass spectrometry (LC-MS) metabolomics, and several multivariate statistical analyses including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), combined with databases such as the human metabolic database (HMDB) and related literature to identify and identify differential metabolites, the relevant metabolic pathways were searched for by the metaboanalyst online tool. Using network pharmacology, construct the“component-target-disease”network of CKI in the treatment of lung cancer. Molecular docking method was used to verify the interaction between potential active ingredients and core targets. Serum metabolomics was jointly analyzed with network pharmacology to construct a“metabolite-germinal-enzyme-gene” network. RESULTS: Through metabolomics technology, 16 differential metabolites associated with lung cancer were screened from serum, and CKI addback these differential metabolite levels compared with the model group. Metabolic pathways mainly involve retinol metabolism, tryptophan metabolism, glycerophospholipid metabolism and other metabolic pathways. Network pharmacology analysis indicated that CKI treatment of lung cancer mainly targets STAT3, MAPK3, and MAPK1, which are closely related to proteoglycans, cellular senescence, and HIF − 1 signaling pathways in cancer. CONCLUSION: This article explains the mechanism of CKI in treating lung cancer from the perspective of metabonomics and network pharmacology, and provides basis for further study of CKI.