A Population Pharmacokinetic Model to Predict the Individual Starting Dose of Tacrolimus Following Pediatric Renal Transplantation

Abstract

Background: Multiple clinical, demographic, and genetic factors affect the pharmacokinetics of tacrolimus in children, yet in daily practice, a uniform body-weight based starting dose is used. It can take weeks to reach the target tacrolimus pre-dose concentration.

Objectives: The objectives of this study were to determine the pharmacokinetics of tacrolimus immediately after kidney transplantation and to find relevant parameters for dose individualization using a population pharmacokinetic analysis.

Methods: A total of 722 blood samples were collected from 46 children treated with tacrolimus over the first 6 weeks after renal transplantation. Non-linear mixed-effects modeling (NONMEM^®) was used to develop a population pharmacokinetic model and perform a covariate analysis. Simulations were performed to determine the optimal starting dose and to develop dosing guidelines.

Results: The data were accurately described by a two-compartment model with allometric scaling for bodyweight. Mean tacrolimus apparent clearance was 50.5 L/h, with an inter-patient variability of 25%. Higher bodyweight, lower estimated glomerular filtration rate, and higher hematocrit levels resulted in lower total tacrolimus clearance. Cytochrome P450 3A5 expressers and recipients who received a kidney from a deceased donor had a significantly higher tacrolimus clearance. The model was successfully externally validated. In total, these covariates explained 41% of the variability in clearance. From the significant covariates, the cytochrome P450 3A5 genotype, bodyweight, and donor type were useful to adjust the starting dose to reach the target pre-dose concentration. Dosing guidelines range from 0.27 to 1.33 mg/kg/day.

Conclusion: During the first 6 weeks after transplantation, the tacrolimus weight-normalized starting dose should be higher in pediatric kidney transplant recipients with a lower bodyweight, those who express the cytochrome P450 3A5 genotype, and those who receive a kidney from a deceased donor.

Fig. 1

Goodness-of-fit plots of the final model. a Observed concentration (DV) plotted against predicted concentration (PRED). b DV plotted against individual predicted concentration (IPRED). c The correlation of conditional weighted residuals (CWRES) with the time after the tacrolimus dose. d The correlation of CWRES with PRED. The line represents the line of identity. OBS observed concentration

Fig. 2

Prediction-corrected visual predictive check (VPC) showing how well the average trend of the observations (solid line) and how well the variability of the observed data (two dashed lines) fall within the model simulated average trend (red shaded area) and the model-simulated variability (blue shaded areas) represented as a 95% confidence interval (CI). The average and the variability of the observed data both fall within the corresponding simulations. a Internal VPC of the final model. b External VPC of the final model

Fig. 3

Simulated plasma profiles of tacrolimus at first steady state after transplantation. a Simulated plasma profiles of tacrolimus for cytochrome P450 (CYP) 3A5 non-expressers (CYP3A5*3/*3) and CYP3A5 expressers (CYP3A5*1/*1 or CYP3A5*1/*3). b Simulated plasma profiles of tacrolimus for patients receiving a kidney from a living or deceased donor. c Simulated plasma profiles of tacrolimus for patients with a bodyweight of 15, 30, 55, or 80 kg. d Simulated plasma profiles of tacrolimus for patients with plasma hematocrit (Ht) levels of 0.2, 0.25, and 0.3 L/L. e Simulated plasma profiles of tacrolimus for patients with an estimated glomerular filtration rate (eGFR) of 10, 50, or 90 mL/min. conc concentration