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. 2023 Dec 21;16(1):17.
doi: 10.3390/pharmaceutics16010017.

Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Jasper Kamp  1 , Tom C Zwart  1 , Soufian Meziyerh  2   3 , Paul J M van der Boog  2   3 , Esther E Nijgh  2   3 , Koen van Duin  2   3 , Aiko P J de Vries  2   3 , Dirk Jan A R Moes  1
Affiliations

Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Jasper Kamp et al. Pharmaceutics. .

Abstract

Background: Meltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation.

Methods: A total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0-24h and an abbreviated dried blood spot (DBS) AUC0-24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods.

Results: The PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h.

Conclusions: The developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.

Keywords: elderly; kidney transplantation; population pharmacokinetics; prolonged release; tacrolimus.

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Conflict of interest statement

D.J.A.R.M. received a speakers honorarium from Chiesi Pharmaceutici S.p.A, paid to the institution. The other authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Measured tacrolimus concentrations vs. time after dosing. The red data points show tacrolimus concentrations obtained using DBS sampling (A). The orange data points show tacrolimus concentrations obtained using whole blood sampling (B).
Figure 2
Figure 2
Overview of the structural pharmacokinetic model. First, a mixture model assigns an absorption lag time of either 0 h (no lag time) or an estimation of the lag time, depending on the model fit for that specific individual. After modeling of the absorption lag time, the drug dose is inputted in the depot compartment. Subsequently, the administered dose passes two additional absorption transit compartments, Absorption transit 1 and Absorption transit 2, before reaching the central volume of distribution. The drug is cleared from the central volume of distribution via elimination clearance, CL and intercompartmental clearance, Q.
Figure 3
Figure 3
Basic goodness-of-fit plots. Observed tacrolimus concentrations vs. model-predicted concentrations (A) and observed tacrolimus concentrations vs. model individual-predicted concentrations (B).
Figure 4
Figure 4
Time to steady-state tacrolimus concentrations. Median simulated fraction of the 90% steady state (IQR, error bars) vs. time after initiation of tacrolimus (A). Probability of reaching at least 90% (blue line) and 97% (black line) of the steady-state concentration vs. time after the start of tacrolimus dosing (B). Median simulated tacrolimus concentrations vs. time after tacrolimus initiation (IQR, shaded area) (C).
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