Screening Of Active Ingredients Of Qingkailing Injection Based On Metabolomics And Its Mechanism

Qingkailing injection (QKLI) palys an important role in the treatments of upper respiratory infection, fever and other diseases due to its obvious detoxification and sedative effecs. In order to explore the antipyretic active components and clarify the antipyretic mechanisms of QKLI, the tissue-targeted metabolic strategy was applied in this study to elucidate the underlying biochemical basis of hypothalamus in the fever process by analyzing the global metabolic profile of the hypothalamus in yeast-induced pyrexia rats. In addition, microdialysis was introduced to collect dialysates of hypothalamus after tail intravenous injection of QKLI. The concentration of QKLI in the hypothalamus was quantified and the pharmacokinetic parameters were calculated. Then the antipyretic components were initially identified. Finally, the conclusions were further validated by the regression analysis between potential biomarkers and the pharmacokinetic parameters. Therefore, the antipyretic components could be selected and the antipyretic mechanisms could also be explained.1. Hypothalamus targeted metabolic analysisIn the previous reports regarding thermoregulation, the hypothalamus is thought to be the primary centre in the central nervous system for controlling the body temperature. In the current study, we utilized a tissue-targeted metabolomics strategy to elucidate the underlying biochemical mechanisms of thermoregulation in the fever process by analyzing the global metabolic profile of the hypothalamus in yeast-induced pyrexia rats. Data acquisition was completed using the HPLC-LTQ-Orbitrap/MS in both positive and negative ion mode. Principal component analysis was used to observe the cluster characteristics between the control group and the pyrexia group. Potential biomarkers were screened using orthogonal partial least-squares-discriminant analysis. Seventeen potential biomarkers were identified in the hypothalamus samples to discriminate the control and pyrexia groups, including amino acids, nucleic acids, vitamins, carbohydrates, and phospholipids. As a result, purine metabolism was enhanced pronouncedly, and perturbation of lipid metabolism was also observed. Meanwhile, amino acid metabolism and energy metabolism were also activated significantly. In conclusion, the study indicated that hypothalamus-targeted metabolomics could provide a powerful tool to further understand the pathogenesis of febrile response.2. Pharmacokinetic study of main active components of Qingkailing Injection in the hypothalamus by microdialysisIn order to quantify the main active components of Qingkailing Injection(QKLI) and to compare the pharmacokinetic behaviours of these compound in normal control group and the yeast induced fever rats, a rapid and sensitive method based on microdialysis combined with liquid chromatography-tandem mass spectrometry was developed for the determination of QKLI in the hypothalamus of yeast induced pyrexia rats. Pharmacokinetic parameters were performed using Phoenix WinNonlin Ver.6.3.The statistical analysis were completed using SPSS 16.0. The results demonstrated that only baicalin and geniposide could be detected in the dialysates. However, the Cmax and AUC (area under the curve) value of baicalin were greater in fevered rats than in the normal rats, and the same results were observed from geniposide. Therefore, the baicalin and geniposide were most likely to be the potential active components that had antipyretic effects in QKLI.3. Regression analysis between potential biomarkers and Cmax or AUCThe pharmacokinetic analysis of QKLI in the hypothalamus initially identified baicalin and geniposide as the potential markers that owned the antipyretic effects. To further validate the conclusions, the regression analysis between potential biomarkers and Cmax or AUC was applied in this study. According to the accuracy and stability of the the acquired regression equations, the conclusions could be proved. The areas of these potential biomarkers and the Cmax or AUC of baicalin and geniposide were imported into SPSS 16.0 to calculate the regression equations. A good correlation between the potential biomarkers and baicalin can be observed. Thus, the conclusion that baicalin were the main active antipyretic components could be verified.