Development of LC‐MS/MS determination method and backpropagation artificial neural networks pharmacokinetic model of febuxostat in healthy subjects

• Published in: Journal of clinical pharmacy and therapeutics Vol. 46; no. 2; pp. 333 - 342
• Main Authors: Xu, Yichao, Chen, Jinliang, Yang, Dandan, Hu, Yin, Hu, Xinhua, Jiang, Bo, Ruan, Zourong, Lou, Honggang
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.04.2021
• Subjects: Acetonitrile; Back propagation; Bioequivalence; BPANN; febuxostat; Formic acid; Gout; healthy subjects; Hyperuricemia; Ions; LC‐MS/MS; Liquid chromatography; Mass spectroscopy; Neural networks; pharmacokinetic; Pharmacokinetics; LC-MS/MS; febuxostat; BPANN; pharmacokinetic; healthy subjects
• ISSN: 0269-4727; 1365-2710; 1365-2710
• DOI: 10.1111/jcpt.13285

What is known and objective Febuxostat is a well‐known drug for treating hyperuricemia and gout. The published methods for determination of febuxostat in human plasma might be unsuitable for high‐throughput determination and widespread application. We need to develop a highly selective, sensitive and rapid liquid chromatography‐tandem mass spectrometry method. Methods The chromatographic separation was achieved on a Hypersil Gold‐C18 (2.1 mm × 100 mm, 1.9 μm) column with mobile phase A (Water containing 0.1% formic acid) and mobile phase B (acetonitrile containing 0.1% formic acid). Multiple reaction monitoring (MRM) mode was used for quantification using target ions at m/z 315.3 → m/z 271.3 for febuxostat and m/z 324.3 → m/z 280.3 for Febuxostat‐d9 (IS). A backpropagation artificial neural network (BPANN) pharmacokinetic model was constructed by the data of bioequivalence study. Results and Discussion After the LC‐MS/MS method validated, it was successfully applied to the bioequivalence study of 30 human volunteers under fed condition. The predicted concentrations generated by BPANN model had a high correlation coefficient with experimental values. What is new and conclusion A sensitive LC‐MS/MS method had been developed and validated for determination of febuxostat in healthy subjects under fed condition, and a BPANN model was developed that can be used to predict the plasma concentration of febuxostat. A UPLC‐MS/MS method was developed for determining febuxostat in human plasma with ultralow volume (10 μL) and a short run time (3.0 min). A backpropagation artificial neural networks model was developed that can be used to predict the plasma concentration of febuxostat.