Population pharmacokinetic modeling of flurbiprofen, the active metabolite of flurbiprofen axetil, in Chinese patients with postoperative pain
Received 6 June 2018
Accepted for publication 5 November 2018
Published 30 November 2018 Volume 2018:11 Pages 3061—3070
Checked for plagiarism Yes
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Peer reviewer comments 3
Editor who approved publication: Dr E Alfonso Romero-Sandoval
Jingru Zhang,1,2,* Hong Zhang,1,* Libo Zhao,3 Jian Gu,4 Yi Feng,1 Haiyan An1
1Department of Anesthesiology, Peking University People’s Hospital, Beijing 100044, China; 2Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China; 3Beijing Children’s Hospital, Capital Medical University, Beijing 100045, China; 4Department of Pharmacy, Peking University People’s Hospital, Beijing 100044, China
*These authors contributed equally to this work
Background: Flurbiprofen axetil, a lipid-microsphere-carrier targeting preparation, is a nonsteroidal anti-inflammatory drug indicated for the treatment of postoperative pain.
Aim: The aim of the study was to develop a population pharmacokinetic (PPK) model of flurbiprofen, the active metabolite of flurbiprofen axetil, and optimize the treatment of flurbiprofen axetil in Chinese patients.
Methods: A total of 144 therapeutic drug-monitoring samples of flurbiprofen axetil from 72 patients were included in this study. The pharmacologically active metabolite flurbiprofen was used as the analytical target and determined 5–45 minutes after intravenous administration. The PPK model for flurbiprofen was developed using Phoenix NLME 1.3 with a nonlinear mixed-effect model. Bootstrap and visual predictive checks were used simultaneously to validate the final PPK model. Potential covariates of age, sex, body weight, height, and body-mass index were tested for PK parameters.
Results: The PPK model of flurbiprofen was explained by a one-compartment model with first-order elimination, in which a hypothetical-effect compartment was linked to a PK compartment. Population mean values of PK parameters estimated in the final model were θKe=0.0015/h, θVd=7.91 L, and θCL=1.55 L/h. Analysis of covariates showed that height and weight influenced the Ke of flurbiprofen. The final model was proved to be robust.
Conclusion: The final PPK model was demonstrated to be appropriate and effective, and can be used to assess the PK parameters of flurbiprofen in Chinese patients with postoperative pain.
Keywords: population pharmacokinetics, flurbiprofen, postoperative pain, weight, height
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