Simultaneous determination of four phenolic acids and seven alkaloids in rat plasma after oral administration of traditional Chinese medicinal preparation Jinqi Jiangtang Tablet by LC-ESI–MS/MS

Jiang

J of Separation Science

et al. 2019

Berberine alkaloids, a group of protoberberine alkaloids under the classification of isoquinoline alkaloids, include berberine, coptisine, palmatine, columbamine, dehydrocorydaline, jatrorrhizine, and epiberberine from natural sources. Studies have shown that berberine alkaloids have various pharmacological functions, such as antibacterial, antiviral, blood pressure‐lowering, hypoglycaemic, antiarrhythmia, and anticancer effects. Therefore, it is worthwhile to develop analytical methods to investigate the pharmacokinetics and activity mechanisms of berberine alkaloids and to study berberine alkaloids more comprehensively. Current analytical methods for berberine alkaloids include liquid chromatography, thin‐layer chromatography, ultraviolet spectroscopy, capillary electrophoresis, and gas chromatography. The most widely used detection method is mass spectrometry. In order to provide a systematic and comprehensive summary and to serve as a reference for the future pharmacokinetics studies and analysis of berberine alkaloids, analytical methods for natural berberine alkaloids that have been used in the past ten years are reviewed here.

Section: Lc-ms/msmentioning

confidence: 99%

A review on analytical methods for natural berberine alkaloids

Jiang

J of Separation Science

et al. 2019

“…The LC-MS has also been used in pharmacokinetic studies of these acids following the administration of herbal drugs to rats and humans. For example, one such study used the LC-MS/MS method to simultaneously determine phenolic acids and their metabolites in human plasma following the ingestion of instant coffee containing various chlorogenic and phenolic acids; the study used protein precipitation to prepare samples [ 31 ]. The negative [ 24 , 26 , 28 , 29 , 30 , 31 ] and positive [ 25 , 27 ] electrospray ionization (ESI) modes were used for the ionization of phenolic acids in mass spectrometry.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Determination of Chlorogenic Acid Isomers and Metabolites in Rat Plasma Using LC-MS/MS and Its Application to A Pharmacokinetic Study Following Oral Administration of Stauntonia Hexaphylla Leaf Extract (YRA-1909) to Rats

Choi

Kim

et al. 2018

Pharmaceutics

Stauntonia hexaphylla leaf extract (YRA-1909), which is widely used for the antirheumatic properties, has been under phase 2 clinical trials in patients with rheumatoid arthritis since April 2017. Liquid chromatography-tandem mass spectrometric method while using liquid–liquid extraction with ethyl acetate was validated for the simultaneous determination of the major active components of YRA-1909, including chlorogenic acid (CGA), neochlorogenic acid (NCGA), cryptochlorogenic acid (CCGA), and their metabolites (i.e., caffeic acid (CA), caffeic acid 3-O-glucuronide (CA-3-G), caffeic acid 4-O-glucuronide (CA-4-G), and ferulic acid (FA)) in rat plasma and applied to a pharmacokinetic study of YRA-1909 in rats. Seven analytes were separated on Halo C18 while using gradient elution of formic acid and methanol, and then quantified in selected reaction monitoring mode whle using negative electrospray ionization. Following oral administration of YRA-1909 at doses of 25, 50, and 100 mg/kg to male Sprague-Dawley rats, CGA, NCGA, and CCGA were rapidly absorbed and metabolized to CA, CA-3-G, and CA-4-G. The area under the plasma concentration-time curve (AUClast) of CGA, NCGA, CCGA, and three metabolites linearly increased as the YRA-1909 dose increased. Other pharmacokinetic parameters were comparable among three doses studied. AUClast values for CA, CA-3-G, and CA-4-G exceeded those for CGA, NCGA, and CCGA.

“…It consists of three herbal medicines Coptis chinensis, Astragalus membranaceus, and Lonicera japonica, which has been widely used in clinical practice to prevent and treat type 2 diabetes in China for decades [16]. Many efforts have been made to identify bioactive compounds and clarify the anti-diabetes mechanism for the comprehensive understanding of its therapeutic effects [17][18][19]. However, little was known about its Q-markers associated with anti-diabetes bioactivity.…”

Section: Introductionmentioning

confidence: 99%

Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

Yang

Xue

Wei

et al. 2020

Preprint

Background: Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little was known about its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. Methods: This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishing of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled using an untargeted UPLC-LTQ-Orbitrap metabolomic approach. The obtained chemical features associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection, respectively. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).Results: Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of PLSR calibration model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. Using this machine learning model, 15 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.Conclusions: The proposed artificial intelligence approach is suitable for quick and easy discovery of Q-markers with bioactivity from herbal medicines.