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

Eunhee Ji, Myeong Gyu Kim, Jung Mi Oh

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Purpose: Liver transplantation is the treatment of choice for patients with end-stage liver disease. Due to the between-and within-individual pharmacokinetic variability in tacrolimus, used to prevent rejection after transplantation, it is difficult to predict the dose needed achieve the target levels in the blood. This study aimed to construct a population pharmacokinetic model of tacrolimus dosage prediction for therapeutic drug monitoring in clinical settings for Korean adult patients receiving living donor liver transplantation (LDLT). Methods: A total of 58 Korean adult patients receiving LDLT with tacrolimus administration were enrolled. Demographic, clinical, and CYP3A5*1/*3 polymorphism data were collected. Population pharmacokinetic modeling of tacrolimus during the first 14 days after transplantation was performed using NONMEM program. Parameters were estimated by the first-order conditional estimation with interaction method. The internal validation of the final model was assessed by the bootstrap and visual predictive check methods using 500 samples from the original data. Results: One-compartmental model was selected as a base model. After the stepwise covariate model building process, postoperative day (POD) and combinational CYP3A5 genotype of the recipient and donor were incorporated into clearance (CL/F). The estimated typical values of CL/F and volume of distribution (V/F) were 6.33 L/h and 465 L, respectively. The final model was CL/F =6.33× POD0.257×2.314 (if CYP3A5 expresser recipient grafted from CYP3A5 expresser donor) ×1.523 (if CYP3A5 expresser recipient grafted from CYP3A5 nonexpresser donor) and V/F =465× POD0.322. Conclusion: A population pharmacokinetic model for tacrolimus was established successfully in Korean adult patients receiving LDLT. This model is expected to contribute to improving patient outcomes by optimizing tacrolimus dose adjustment for liver transplant patients.

Original languageEnglish
Pages (from-to)2119-2126
Number of pages8
JournalTherapeutics and Clinical Risk Management
StatePublished - 2018

Bibliographical note

Publisher Copyright:
© 2018 Ji et al.


  • CYP3A5
  • Dosage prediction
  • Living donor liver transplantation
  • Nonlinear mixed-effects modeling
  • Population pharmacokinetics
  • Tacrolimus


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