Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Abstract

Mefatinib (MET306) is a novel second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) designed to address the highly unmet clinical need of gefitinib-induced resistance and irreversibly bind to mutated tyrosine kinase domain of EGFR and human epidermal growth factor receptor 2 (HER2). In this study, a liquid chromatography–tandem mass spectrometry method was established and validated for determining MET306 in non-small cell lung cancer patients and a backpropagation artificial neural network was developed and constructed to predict the pharmacokinetic process. The mobile phase was water containing 5 mM ammonium acetate and acetonitrile at a flow rate of 0.3 mL min−1, within a 4.5 min run time. MET306 was separated on a Hypersil Gold-C18 at 40 °C and subjected to mass analysis using positive electrospray ionization. A total of 524 data were used as development groups and 145 data were used as testing groups. The final established Northern Goshawk Optimization-Backpropagation Artificial Neural Network (NGO-BPANN) model consisted of one input layer with 6 neurons, 1 hidden layer with 10 nodes, and 1 output layer with one node processed by MATLAB2021a.The calibration range of MET306 was 0.5–200 ng mL−1 with the correlation coefficient r ≥ 0.99. Accuracies ranged from 97.20 to 110.80% and the inter- and intra-assay precision were less than 15%. The ranges of extraction recoveries were 104.95% to 112.09% for analyte and internal standard and there was no significant matrix effect. The storage stability under different conditions was in accordance with the bioanalytical guidelines. The time-concentration profiles of the measured and predicted concentrations of MET306 by NGO-BPANN agree well. An NGO-BPANN model was developed to predict the plasma concentration and pharmacokinetic parameters of MET306 in the first time.

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

“…El-Dabah et al [27] utilized NGO for identifying the parameters of the photovoltaic module's triple diode model, and the simulation results showed that NGO accurately extracted the model parameters with superior convergence rate and precision compared to alternative algorithms. Xu et al [28] developed a northern goshawk optimization-backpropagation artificial neural network (NGO-BP) model for forecasting blood concentration and pharmacokinetic parameters of MET306. The NGO has been successfully utilized in resolving a variety of engineering optimization problems, but how to further improve its convergence accuracy and speed is one of the issues, which this study attempts to address.…”