The Impact of Sulfonylureas on Tacrolimus Apparent Clearance Revealed by a Population Pharmacokinetics Analysis in Chinese Adult Liver-Transplant Patients

2012 ◽  
Vol 34 (2) ◽  
pp. 126-133 ◽  
Author(s):  
Xiao-qing Zhang ◽  
Zhao-wen Wang ◽  
Jun-wei Fan ◽  
Yu-ping Li ◽  
Zheng Jiao ◽  
...  
Xenobiotica ◽  
2015 ◽  
Vol 45 (9) ◽  
pp. 840-846 ◽  
Author(s):  
LiQin Zhu ◽  
JianWei Yang ◽  
Yuan Zhang ◽  
YaQing Jing ◽  
YanWen Zhang ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Liqin Zhu ◽  
Hao Wang ◽  
Xiaoye Sun ◽  
Wei Rao ◽  
Wei Qu ◽  
...  

Aim. The aim of this study was to establish population pharmacokinetic models of tacrolimus in Chinese adult liver transplantation patients. Methods. Tacrolimus dose and concentration data (n=435) were obtained from 47 Chinese adult liver transplant recipients, and the data were analyzed using a nonlinear mixed-effect modeling (NONMEM) method. Results. The structural model was a two-compartment model with first-order absorption. The typical population values of tacrolimus for the pharmacokinetic parameters of apparent clearance (CL/F), apparent distribution volume of the central compartment (V2/F), intercompartmental clearance (Q/F), apparent distribution volume of the peripheral compartment (V3/F), and absorption rate (ka) were 11.2 L/h, 406 L, 57.3 L/h, 503 L, and 0.723 h−1, respectively. The interindividual variabilities of these parameters were 16.2%, 163%, 19.7%, 199%, and 74.3%, respectively, and the intraindividual variability of observed concentration was 26.54%. The covariates retained in the final models were postoperative days (POD) and dosage per day (DOSE) on CL/F. Conclusion. Population pharmacokinetic models of tacrolimus were developed in Chinese adult liver transplant patients. These results could provide the interpretation of the outcome of pharmacokinetics modeling and the impact of covariate tested on individualized tacrolimus therapy.


2017 ◽  
Vol 42 (6) ◽  
pp. 679-688 ◽  
Author(s):  
B. Chen ◽  
H.-Q. Shi ◽  
X.-X. Liu ◽  
W.-X. Zhang ◽  
J.-Q. Lu ◽  
...  

2017 ◽  
Vol 56 (12) ◽  
pp. 1491-1498 ◽  
Author(s):  
Jean-Baptiste Woillard ◽  
Jean Debord ◽  
Caroline Monchaud ◽  
Franck Saint-Marcoux ◽  
Pierre Marquet

2019 ◽  
Vol 54 (7) ◽  
pp. 652-661 ◽  
Author(s):  
Jia Shao ◽  
Chenyu Wang ◽  
Peng Fu ◽  
Fan Chen ◽  
Yi Zhang ◽  
...  

Background: Tacrolimus (TAC) is widely used after liver transplantation, but the therapeutic window is narrow. Objective: The purpose was to study both donor and recipient CYP3A5*3 genotypes affecting TAC apparent clearance rate (CL/F) and investigate a TAC population pharmacokinetic (PPK) model in Chinese liver transplant recipients for potential starting-dose individualized medication. Methods: A data set of 721 TAC concentrations was obtained from 43 adult liver transplant recipients. The TAC PPK model was analyzed using nonlinear mixed-effects modeling. Potential covariates, including demographic characteristics, physiological and pathological data, concomitant medications, and CYP3A5*3 genotype, were evaluated. The final model was validated using normalized prediction distribution errors and bootstrapping. Results: A 2-compartment model with first-order absorption and elimination was used to describe TAC disposition. Population estimates of TAC, CL/F, apparent central distribution volume (V2/F), rate of absorption (Ka), and apparent peripheral distribution volume (V3/F) were 18.1 L/h (12%), 72.7 L (34%), 0.163 h−1 (17%), and 412 L (21%), respectively. The model and estimated parameters were found to be stable. Other covariates did not influence TAC CL/F. Both donor and recipient CYP3A5*1 genotypes were significantly correlated with TAC clearance, and CL/F was 1.70-fold higher in both donor and recipient CYP3A5*1 carriers than in noncarriers among Chinese liver transplant recipients. Conclusion and Relevance: A PPK model of TAC was established in Chinese adult liver transplantation recipients for starting-dose individualized medication, which can be expanded to optimize clinical efficacy and minimize toxicity with therapeutic drug monitoring.


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