Suein Choi(1,2), Yunjeong Hong(1,2), Sook-Hyun Jung(3), Gaeun Kang(4), Jong-Ryul Ghim(5), Seunghoon Han(1,2)
(1) Pharmacometrics Institute for Practical Education and Training (PIPET), The Catholic University of Korea (2) 2Department of Pharmacology, The Catholic University of Korea (3) Catholic Clinical Research Coordinating Center, Korea (4) Division of Clinical Pharmacology, Chonnam National University Hospital, Korea (5) Department of Pharmacology and Pharmacogenomics Research Center, Inje University Busan Paik Hospital, Inje University College of Medicine, Korea
Objectives: Tacrolimus shows high variability in inter- and intra-individual pharmacokinetics (PK); therefore, it is important to develop an appropriate model for accurate therapeutic drug monitoring (TDM) procedures. This study aimed to develop a pharmacokinetic model for tacrolimus that can be used for TDM procedures in Korean adult transplant recipients by integrating published models with acquired real-world TDM data and evaluating clinically meaningful covariates.
Methods: This study comprised two steps. In the first step, we integrated an observed clinical data of 1,829 trough blood samples from 269 subjects with a simulated dataset from previously published population pharmacokinetic models. We developed the final model from an integrated dataset using a previously built structural model and performed covariate analysis using a nonlinear mixed-effect model. In the second step, the identical integration and parameter estimation procedure using the final model built in Step 1 was repeated 1,000 times. The final parameter was acquired by summarizing 1,000 sets of estimated parameters. Finally, the tacrolimus PK model was developed using the final structural model built in Step 1 and the summarized final parameters estimated in Step 2 The stochastic simulation and estimation (SSE) method was used to obtain the final parameter estimates.
Results: The final estimated values for apparent clearance, the volume of distribution, and absorption rate were 21.2 L/h, 510 L, and 3.1/h, respectively. The number of post-operative days, age, body weight, and type of transplant organs were the major clinical factors affecting tacrolimus PK. Based on the pcVPC results, the central trend and variability in the observed data could be reproduced appropriately by simulating the final PK model, indicating that the predictive performance of the final PK model was acceptable.
Conclusions: A tacrolimus PK model that can incorporate published PK models and newly collected data from the Korean population was developed using the SSE method. Despite the limitations in model development owing to the nature of TDM data, the SSE method was useful in retrieving complete information from the TDM data by integrating published PK models while maintaining the variability of the model.
References:
[1] Staatz CE, Willis C, Taylor PJ, Tett SE. Population pharmacokinetics of tacrolimus in adult kidney transplant recipients. Clin Pharmacol Ther. 2002;72(6):660-669.
[2] Allegaert K, Flint R, Smits A. Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle. Expert Opin Drug Metab Toxicol. 2019;15(9):735-749. [3] Han N, Ha S, Yun HY, et al. Population pharmacokinetic-pharmacogenetic model of tacrolimus in the early period after kidney transplantation. Basic Clin Pharmacol Toxicol. 2014;114(5):400-406. [4] Han N, Yun HY, Hong JY, et al. Prediction of the tacrolimus population pharmacokinetic parameters according to CYP3A5 genotype and clinical factors using NONMEM in adult kidney transplant recipients. Eur J Clin Pharmacol. 2013;69(1):53-63. [5] Leander J, Almquist J, Ahlström C, Gabrielsson J, Jirstrand M. Mixed effects modeling using stochastic differential equations: Illustrated by pharmacokinetic data of nicotinic acid in obese Zucker rats. AAPS J. 2015;17(3):586-596.
Reference: PAGE 30 (2022) Abstr 10122 [www.page-meeting.org/?abstract=10122]
Poster: Methodology - New Modelling Approaches