Literature DB >> 28833329

Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients.

B Chen1, H-Q Shi1, X-X Liu1, W-X Zhang1, J-Q Lu1, B-M Xu1, H Chen2.   

Abstract

WHAT IS KNOWN AND
OBJECTIVES: Tacrolimus (TAC) is widely used as part of immunosuppressive regimens. There is great interindividual variation on the disposition of TAC. The aim of this study was to develop a population pharmacokinetic (PPK) model for Chinese liver transplant patients and evaluate genetic polymorphism and other possible factors on the PK parameters. The exposure of TAC is to be estimated through Bayesian modelling.
METHODS: A total of 47 sets of rich-time PK and 1234 conventional therapeutic drug monitoring (TDM) data were collected from 125 Chinese liver transplant patients. The pathophysiological data of these patients were recorded. CYP3A5*3 and ABCB1 genotypes were determined for each patient. The PPK model for TAC was established by nonlinear mixed-effects modelling (nonmem). The impact of pathophysiology and genotype on PPK parameters was evaluated. Bayesian estimators for the area under concentration-time curve (AUC) of TAC were validated.
RESULTS: A two-compartment model with lag time was found to be the most suitable model for the pooled full PK and TDM data for Chinese liver transplant patients. The CL/F, V2 /F, Q/F, V3 /F, Ka and lag time were 17.4±0.81 L/h, 165±44.1 L, 54.9±25.8L/h, 594±87.5 L, 0.51±0.095 L/h and 1.57±0.34 h. Post-operative day (POD), creatinine clearance (CLcr) and ABCB1 C3435T genotypes were found to have significant influences on CL/F (P<.01). ABCB1 C3435T genotypes showed a significant correlation with V2 /F (P<.01). C0 -C2 and C0 -C2 -C4 were shown to be suitable for the estimation of AUC in Chinese liver transplant patients. WHAT IS NEW AND
CONCLUSION: A PPK model for TAC was established successfully in Chinese liver transplant patients. POD, CLcr and ABCB1 C3435T genotypes were shown to have significant effects on CL/F. The AUC of TAC in Chinese liver transplant patients could be estimated through Bayesian modelling, based on which individualized immunosuppressive regimens can be designed.
© 2017 John Wiley & Sons Ltd.

Entities:  

Keywords:  zzm321990ABCB1zzm321990; Bayesian; genetic polymorphism; nonlinear mixed-effects modelling; population pharmacokinetics; tacrolimus

Mesh:

Substances:

Year:  2017        PMID: 28833329     DOI: 10.1111/jcpt.12599

Source DB:  PubMed          Journal:  J Clin Pharm Ther        ISSN: 0269-4727            Impact factor:   2.512


  6 in total

1.  Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients.

Authors:  Camille Riff; Jean Debord; Caroline Monchaud; Pierre Marquet; Jean-Baptiste Woillard
Journal:  Br J Clin Pharmacol       Date:  2019-06-14       Impact factor: 4.335

2.  Population pharmacokinetics and pharmacogenomics of tacrolimus in Chinese children receiving a liver transplant: initial dose recommendation.

Authors:  Xiao Chen; Dong-Dong Wang; Hong Xu; Zhi-Ping Li
Journal:  Transl Pediatr       Date:  2020-10

3.  Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach.

Authors:  Tom M Nanga; Thao T P Doan; Pierre Marquet; Flora T Musuamba
Journal:  Br J Clin Pharmacol       Date:  2019-12-17       Impact factor: 4.335

4.  CYP3A5 genotype-based model to predict tacrolimus dosage in the early postoperative period after living donor liver transplantation.

Authors:  Eunhee Ji; Myeong Gyu Kim; Jung Mi Oh
Journal:  Ther Clin Risk Manag       Date:  2018-10-25       Impact factor: 2.423

5.  Population pharmacokinetic analysis of linezolid in patients with different types of shock: Effect of platelet count.

Authors:  Dongdong Wang; Xiaofei Zheng; Yang Yang; Xiao Chen
Journal:  Exp Ther Med       Date:  2019-07-08       Impact factor: 2.447

Review 6.  Importance of genetic polymorphisms in liver transplantation outcomes.

Authors:  Tomislav Kelava; Petra Turcic; Antonio Markotic; Ana Ostojic; Dino Sisl; Anna Mrzljak
Journal:  World J Gastroenterol       Date:  2020-03-28       Impact factor: 5.742

  6 in total

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