摘要
目的:基于指纹图谱和网络药理学,预测补肾培元汤潜在的质量标志物成分.方法:运用高效液相色谱法建立10批补肾培元汤指纹图谱,指认并归属其共有峰,进行相似度评价和聚类分析,评价不同批次样品间质量差别,基于化学成分有效性,可测性和网络药理学对其潜在的质量标志物预测分析.结果:指纹图谱共确认43个共有峰,指认12个共有峰,包括绿原酸、芦丁、山柰酚、苦杏仁苷、京尼平苷酸、大黄酚、大黄酸、芦荟大黄素、京尼平龙胆双糖苷、积雪草苷、槲皮素、毛蕊异黄酮,并且其相似度均大于0.9,样品可聚为2类,通过网络药理学预测可知,12个成分能通过RELA、MMP3和GSK3B等多个靶点调控癌症,肾病,粥样硬化等多条通路.结论:预测绿原酸、芦丁、山柰酚、苦杏仁苷、京尼平苷酸、大黄酚、大黄酸、芦荟大黄素、京尼平龙胆双糖苷、积雪草苷、槲皮素、毛蕊异黄酮12个化学成分可作为潜在的质量标志物,以期为补肾培元汤质量控制及后续的研究提供参考.
Abstract
OBJECTIVE Prediction of potential quality marker components of Bushen Peiyuan Decoction based upon finger-printing and network pharmacology.METHODS High performance liquid chromatography(HPLC)was utilized for establishing the fingerprints of 10 batches of Bushen Peiyuan Decoction,identifying and attributing the common peaks,performing similarity evaluation and cluster analysis,evaluating the quality differences between different batches of samples and to predicting potential quality markers based upon the validity of chemical compositions,measurability and cyber-pharmacology.RESULTS A total of 43 shared peaks were identified by fingerprinting and 12 shared peaks were confirmed,including chlorogenic acid,rutin,kaemp-ferol,bitter amygdalin,genipin glycoside,rhubarb phenol,rhubarbic acid,aloe rhubarbin,genipin gentian bisaccharide glyco-side,cumulus glandulosus glycoside,quercetin and mulligatawny isoflavones.All of them were more than 0.9 similarity.The samples could be clustered into 2 classes.Pedictions of network pharmacology showed that these 12 constituents could regulate such multiple pathways as cancer,nephropathy and atherosclerosis through multiple targets of RELA,MMP3 and GSK3B.CONCLUSION The chemical components of chlorogenic acid,rutin,kaempferol,bitter amygdalin,genipin glycoside,rhubarb phenol,rhubarbic acid,aloe rhubarbin,genipin gentian disaccharide glycoside,cumene glycoside,quercetin and trichothecenes isoflavones may be employed as potential quality markers for references for quality control of Bushen Peiyuan Decoction and for subsequent studies.
基金项目
吉林省教育厅科学研究项目(JJKH20230979KJ)
NETL
NSTL
万方数据