Abstract
PURPOSE: Flurbiprofen plays a critical role in clinical pain management. This study aims to elucidate the population pharmacokinetic (PPK) profiles of flurbiprofen's enantiomers, R(-) and S(+)-flurbiprofen in human subjects, following intravenous administration, while investigating the influence of clinical covariates. PATIENTS AND METHODS: PPK modeling of flurbiprofen isomers was based on a prospective study that included a total of 67 patients, each of whom had plasma and cerebrospinal fluid (CSF) samples collected at various time points within 5-50 min. Nonlinear mixed effects modeling was performed using Phoenix NLME. The final PPK model was validated using both Bootstrap and visual prediction checks. Modeling was used to assess the effect of demographic, biological, and pharmacogenetic (12 SNPs relatived to CYP2C9, ABCB1, PXR, POR and UGT1A9) covariates on clearance (CL) and apparent volume of distribution (V(d)) for R(-) and S(+)-flurbiprofen. RESULTS: A two-compartment model best fit the data. All patients were homozygous for the wild-type CYP2C9 allele. Plasma CL of S(+)-flurbiprofen was significantly influenced by ABCB1 (rs1045642) polymorphisms. For R(-)-flurbiprofen, body surface area (BSA) were related to V(d) while POR (rs1057868) polymorphisms were associated with CL. The V(d) for both R(-)- and S(+)-flurbiprofen was found to be larger in CSF compared to plasma. Specifically, the final model estimated the Vd of R(-)-flurbiprofen (CSF 79.1 L VS plasma 17.1 L) and S(+)-flurbiprofen (CSF 32.6 L VS plasma 25.6 L), and the CL of R(-)-flurbiprofen (CSF 0.45L·h(-1) VS plasma 11.8L·h(-1) and S(+)-flurbiprofen (0.39 L·h(-1) VS plasma 16.7 L·h(-1)), respectively. CONCLUSION: Key covariates of S (+)-flurbiprofen was ABCB1 gene polymorphisms and R-flurbiprofen was POR gene polymorphisms and BSA. The findings may provide support for future dose optimization and the development of novel therapeutic approaches.