Abstract
AbstractMefatinib (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.
Funder
the National Major Science and Technology projects of China
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry
Reference25 articles.
1. Arcila ME, Oxnard GR, Nafa K, Riely GJ, Solomon SB, Zakowski MF, Kris MG, Pao W, Miller VA, Ladanyi M. Rebiopsy of lung cancer patients with acquired resistance to EGFR inhibitors and enhanced detection of the T790M mutation using a locked nucleic acid-based assay. Clin Cancer Res. 2011;17:1169–80.
2. Chan BA, Hughes BG. Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Transl Lung Cancer Res. 2015;4:36–54.
3. Chen J, Xu Y, Lou H, Jiang B, Shao R, Yang D, Hu Y, Ruan Z. Effect of genetic polymorphisms on the pharmacokinetics of deferasirox in healthy Chinese subjects and an artificial neural networks model for pharmacokinetic prediction. Eur J Drug Metab Pharmacokinet. 2020;45:761–70.
4. Dehghani M, Hubálovský Š, Trojovský PJIA. Northern Goshawk optimization: a new swarm-based algorithm for solving optimization problems. IEEE Access. 2021;9:162059–80.
5. Dou Y, Jiang D. Research progress of small molecule anti-angiogenic drugs in non-small cell lung cancer. Chin J Lung Cancer. 2021;24:56–62.
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