Abstract

Jinshui-Huanxian granules (JHGs), a Chinese herbal compound prescription, have shown a therapeutic effect in reducing lung tissue damage, improving the degree of pulmonary fibrosis, replenishing lungs and kidneys, relieving cough and asthma, reducing phlegm, and activating blood circulation. However, these active compounds' pharmacokinetics and metabolic processes were unclear. This study aimed to compare the pharmacokinetics, reveal the metabolic dynamic changes, and obtain the basic pharmacokinetic parameters of 16 main bioactive compounds after intragastric administration of JHGs in control and pulmonary fibrosis (PF) model rats by using Orbitrap Fusion MS. After administration of JHGs, the rat plasma was collected at different times. Pretreating the plasma sample with methanol and internal standard (IS) solution carbamazepine (CBZ), and it was then applied to a C18 column by setting gradient elution with a mobile phase consisting of methanol 0.1% formic acid aqueous solution. Detection was performed on an electrospray ionization source (ESI), and the scanning mode was SIM. Pharmacokinetic parameters were analyzed according to the different analytes' concentrations in plasma. The matrix effect was within the range of 79.01-110.90%, the extraction recovery rate was 80.37-102.72%, the intra-day and inter-day precision relative standard deviation (RSD) was less than 7.76%, and the stability was good, which met the requirements of biological sample testing. The method was validated (r ≥ 0.9955) and applied to compare the pharmacokinetic profiles of the control group and PF model group after intragastric administration of the JHGs. The 16 analytes exhibited different pharmacokinetic behaviors in vivo. In the pathological state of the PF model, most of the components were more favorable for metabolism and absorption, and it was more meaningful to study the pharmacokinetics. Above all, this study provided an essential reference for exploring the mechanism of action of JHGs and guided clinical medication as well.

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