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

ABSTRACT

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 â†’ m/z 271.3 for febuxostat and m/z 324.3 â†’ 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.