Identification of chemical constituents of Qingjin Yiqi granules and comparative study on pharmacokinetics of 23 main bioactive components in normal and Lung-Qi deficiency rats by UPLC-MS/MS method.

Qingjin Yiqi granules (QJYQ granules) are hospital preparations derived from ancient prescriptions under the guidance of academician Zhang Boli; they have the effect of invigorating qi and nourishing yin, strengthening the spleen and harmonizing the middle, clearing heat, and drying dampness, and are mainly used for patients with coronavirus disease 2019 (COVID-19) during the recovery period. However, their chemical constituents and pharmacokinetic characteristics in vivo have not been systematically investigated. In this study, 110 chemical constituents of QJYQ granules were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and a fast and sensitive ultra-high-performance liquid chromatography-mass spectrometry method was developed and validated for the target analytes. A rat model of lung-qi deficiency was established by subjecting mice to passive smoking combined with cold baths, and 23 main bioactive components of QJYQ granules were analyzed in normal and model rats after oral administration. The results showed that, compared to the normal group, there were significant differences in the pharmacokinetics of baicalin, schisandrin, ginsenoside Rb1, naringin, hesperidin, liquiritin, liquiritigenin, glycyrrhizic acid, and hastatoside in the model rats (P < 0.05), indicating that the in vivo processes of the above components changed under pathological conditions, suggesting that they may have pharmacological effects as active components. This study has helped identify QJYQ particulate substances and further supports their clinical application..

Simultaneous determination of multiple components in rat plasma by UHPLC-sMRM for pharmacokinetic studies after oral administration of Qingjin Yiqi Granules.

As a Traditional Chinese Medicine prescription, Qingjin Yiqi Granules (QJYQ) provides an effective treatment for patients recovering from COVID-19. However, the pharmacokinetics characteristics of the main components of QJYQ in vivo are still unknown. An efficacious ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was developed and validated for the simultaneous determination of 33 components in rat plasma after oral administration of QJYQ. The plasma samples were precipitated with 400 µL methanol/acetonitrile (1/1, v/v) and analyzed in scheduled multiple reaction monitoring mode. The linear relationship of the 33 components was good (r > 0.9928). The lower limit of quantification for 33 components ranged from 0.4-60.5 ng/mL. The average recoveries and matrix effects of the analytes ranged from 72.9% to 115.0% with RSD of 1.4%-15.0%. All inter-day and intra-day RSDs were within 15.0%. After oral administration (3.15 g/kg), the validated approach was effectively applied to the pharmacokinetics of main components of QJYQ. Finally, fifteen main constituents of QJYQ with large plasma exposure were obtained, including baicalin, wogonoside, wogonin, apigenin-7-O-glucuronide, verbenalin, isoferulic acid, hesperidin, liquiritin, harpagide, protocatechuic acid, p-Coumaric acid, ferulic acid, sinapic acid, liquiritin apioside and glycyrrhizic acid. The present research lays a foundation for clarifying the therapeutic material basis of QJYQ and provides a reference for further scientific research and clinical application of QJYQ.

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules.

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.