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Simultaneous determination of ginkgolide A, B, C, bilobalide and rutin in rat plasma by LC-MS/MS and its application to a pharmacokinetic study

Hu Bingying, Sun Yingying, Wang Min, He Zhisheng, Chen Shanshan, Qi Dake, Ge Zhen, Fan Lingling, Chen Jingfang, Wei Yang

Acta Chromatographica

2022

Vol. 34, no. 4

386--393

A reliable LC-MS/MS method for the determination of five bioactive constituents (bilobalide, BLL; ginkgolide A, GLA; ginkgolide B, GLB; ginkgolide C, GLC; rutin) of Ginkgo biloba leaf extracts (GBE) in rat plasma was established, fully validated and applied to an intragastric pharmacokinetic study of a preparation of GBE in rat. Samples were extracted with ethyl acetate. C18 column was selected as analytical column in this method. Mobile phase was water with 0.01% formic acid and acetonitrile. Quantification was performed in negative multiple-reaction monitoring mode. Matrix instability of terpene lactones was noticed and hydrochloric acid was used as a stabilizer. This method showed good precision and accuracy, recovery was reproducible and matrix effect was negligible. Among four terpene lactones, BLL had the highest exposure and the shortest terminal half-life, GLA and GLB had lower exposure and longer terminal half-life, the exposure of GLC was lowest and its terminal half-life was the maximum, and all of them showed rapid absorption. This study provides a reference for determination of terpene lactones and flavonol glycoside prototypes in GBE and offers pharmacokinetic data of flavonol glycoside prototype in GBE.

Data-driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine

Liu Shida, Ji Honghai, Hou Zhongsheng, Zuo Jiashuo, Fan Lingling

International Journal of Applied Mathematics and Computer Science

2021

Vol. 31, no. 2

321--335

A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.