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Pharmacokinetics in rats and tissue distribution in mouse of berberrubine by UPLC-MS/MS

化学 色谱法 甲酸 药代动力学 高效液相色谱法 电喷雾电离 选择性反应监测 定量分析(化学) 萃取(化学) 串联质谱法 质谱法 大气压化学电离 基质(化学分析) 电喷雾 小檗碱 生物利用度 化学电离 离子 电离 生物化学 药理学 医学 有机化学
作者
Xianqin Wang,Shuanghu Wang,Jianshe Ma,Tao Ye,Mengrou Lu,Miao Fan,Mingjie Deng,Lufeng Hu,Zhimou Gao
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier BV]
卷期号:115: 368-374 被引量:55
标识
DOI:10.1016/j.jpba.2015.07.031
摘要

Berberrubine is an isoquinoline alkaloid isolated from Berberis vulgaris L, and it is readily derived from berberine. In this study, a sensitive and selective ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the determination of berberrubine in rat plasma and mouse tissue has been developed. Magnoflorine was employed as an internal standard (IS), and liquid–liquid extraction by ethyl acetate was used for sample preparation. Chromatographic separation was achieved on a UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm) with 0.1% formic acid and acetonitrile as the mobile phase with gradient elution. An electrospray ionization source was applied and operated in positive ion mode; multiple reactions monitoring (MRM) mode was used for quantification using target fragment ions m/z 322.0 → 307.0 for berberrubine and m/z 342.8 → 298.2 for IS. Calibration plots were linear in the range of 2–2000 ng/mL for berberrubine in rat plasma and mouse tissue. Mean recoveries of berberrubine in rat plasma ranged from 79.6% to 84.8%. Intra-day and inter-day precision were less than 11%. The accuracy ranged from 93.6% to 106.8%. The method has also been successfully applied in pharmacokinetics and tissue distribution study of berberrubine. The absolute bioavailability of berberrubine was determined to be 31.6%. The results also show that berberrubine is rapidly absorbed and widely distributed in various tissues. The level of berberrubine in liver is highest, and followed by kidney, spleen and heart. Furthermore, the concentration of berberrubine in various tissues could also be predicted by a BP-ANN model.
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