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Population pharmacokinetics of tacrolimus in Chinese adult liver transplant patients.

Fei TengWeiyue ZhangWei WangJiani ChenShiyi LiuMingming LiLujin LiWenyuan GuoHua Wei
Published in: Biopharmaceutics & drug disposition (2022)
Tacrolimus is widely used in organ transplantation to prevent rejection. However, the narrow therapeutic window and the large inter-and intra-individual variability in the pharmacokinetics (PK) of tacrolimus make it difficult for individualization of dosing. This study aimed at developing a population pharmacokinetic model for estimating the oral clearance of tacrolimus in Chinese liver transplant patients, and identifying factors that contribute to the PK variability of tacrolimus. Data of 151 liver transplant patients who received tacrolimus were analyzed in this study. The population PK model was analyzed and the covariates including population demographic and biochemical characteristics, drug combination, and genetic polymorphism were explored using non-linear mixed-effects modeling approach. A single-compartment population PK model was developed, and the final model was CL/F = (14.6-2.38 × cytochrome P450 (CYP) 3A5-3.72 × WZC+1.04 × (POD/9)+2.48 × COR) × Exp(η i ), where CYP3A5 was 1 for CYP3A5*3/*3, Wuzhi Capsule (WZC) was 1 when patients took tacrolimus combined with WZC, otherwise it was 0, corticosteroids (COR) was 1 when patients take tacrolimus combined with COR, otherwise, it was 0, POD was the post-operative day. Visual inspection and bootstrap indicated that the final model was stable and robust. In this study, we developed the first tacrolimus population PK model in Chinese adult liver transplant patients. We first determined the influence of WZC on tacrolimus in these people, which could provide useful PK information for the drug combination of tacrolimus and WZC. We also revealed the influence of genetic polymorphism of CYP3A5, POD, and a combination of COR on tacrolimus PK. Therefore, these significant factors should be taken into consideration in optimizing dosage regimens.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • healthcare
  • gene expression
  • genome wide
  • deep learning
  • stem cells
  • social media
  • neural network