A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients

Abstract

Aims: The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.

Methods: Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed-effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.

Results: A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two-compartment model. The mean absorption rate was 3.6 h^-1 , clearance was 23.0 l h^-1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: [Formula: see text] Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.

Conclusions: For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

Keywords: cytochrome P450 enzymes; genetics and pharmacogenetics; immunosuppression Immunology; pharmacokinetics; population analysis; renal transplantation.

Figure 1

Goodness‐of‐fit plots of the final model. (A) DV plotted against PRED. (B) DV plotted against IPRED. (C) The correlation of CWRES with the time after the tacrolimus dose. (D) The correlation of CWRES with PRED. The line represents the line of identity. CWRES, conditional weighted residuals; DV, observed concentrations; IPRED, individual predicted concentration; OBS, observed concentration; PRED, predicted concentration

Figure 2

Prediction‐corrected visual predictive check (VPC) showing how well the average trend of the observations (red line) and how well the variability of the observed data (blue lines) fall within the model simulated (n = 500) average trend (red shaded area) and the model simulated variability (blue shaded areas) represented as 95% confidence interval. The average and the variability of the observed data both fall within the corresponding simulations. (A) Prediction‐corrected VPC of the final model (internal dataset). (B) Prediction‐corrected VPC of the final model (external dataset)

Figure 3

Simulated plasma profiles of tacrolimus at first steady state after transplantation. (A) Simulated plasma profiles of tacrolimus for CYP3A5 nonexpressers (CYP3A5*3/*3) and CYP3A5 expressers (CYP3A5*1/*1 or CYP3A5*1/*3). (B) Simulated plasma profiles of tacrolimus for patients carrying the CYP3A4*1 allele and the CYP3A4*22 allele. (C) Simulated plasma profiles of tacrolimus for patients aged 25, 40, 65 and 80 years. (D) Simulated plasma profiles of tacrolimus for patients with albumin levels of 30, 35, 40, 45 and 50 g l^–1. (E) Simulated plasma profiles of tacrolimus for patients with a BSA of 1.5, 1.75, 2 and 2.25 m². (F) Simulated plasma profiles of tacrolimus for patients with creatinine concentrations of 50, 100, 200 and 500 μmol l^–1. (G) Simulated plasma profiles of tacrolimus for patients with haematocrit levels of 0.25, 0.3, 0.35, 0.4 and 0.45 l l^–1. (H) Simulated plasma profiles of tacrolimus for patients with an LBW of 40, 50, 60, 70 and 80 kg. BSA, body surface area; CYP, cytochrome P450; LBW, lean body weight

Figure 4

Prediction‐corrected visual predictive check (VPC) of the starting dose model. (A) Prediction‐corrected VPC of the starting dose model (internal dataset). (B) Prediction‐corrected VPC of the starting dose model (external dataset)

Figure 5

Boxplot with 10–90 percentile whiskers comparing simulations of the standard bodyweight‐based dose and a dose based on the starting dose model. (A) Simulated predose concentrations. The median tacrolimus C₀ in the bodyweight‐based dose group was 13.9 ng ml^–1, and in the model‐based dose group 12.9 ng ml^–1. (B) Simulated AUCs. The median tacrolimus AUC in the bodyweight‐based dose group was 298.5 ng h ml^–1, and in the model‐based dose group 277.9 ng h ml^–1. AUC, area under the curve