Investigating Tacrolimus Disposition in Paediatric Patients with a Physiologically Based Pharmacokinetic Model Incorporating CYP3A4 Ontogeny, Mechanistic Absorption and Red Blood Cell Binding

Abstract

Tacrolimus is a crucial immunosuppressant for organ transplant patients, requiring therapeutic drug monitoring due to its variable exposure after oral intake. Physiologically based pharmacokinetic (PBPK) modelling has provided insights into tacrolimus disposition in adults but has limited application in paediatrics. This study investigated age dependency in tacrolimus exposure at the levels of absorption, metabolism, and distribution. Based on the literature data, a PBPK model was developed to predict tacrolimus exposure in adults after intravenous and oral administration. This model was then extrapolated to the paediatric population, using a unique reference dataset of kidney transplant patients. Selecting adequate ontogeny profiles for hepatic and intestinal CYP3A4 appeared critical to using the model in children. The best model performance was achieved by using the Upreti ontogeny in both the liver and intestines. To mechanistically evaluate the impact of absorption on tacrolimus exposure, biorelevant in vitro solubility and dissolution data were obtained. A relatively fast and complete release of tacrolimus from its amorphous formulation was observed when mimicking adult or paediatric dissolution conditions (dose, fluid volume). In both the adult and paediatric PBPK models, the in vitro dissolution profiles could be adequately substituted by diffusion-layer-based dissolution modelling. At the level of distribution, sensitivity analysis suggested that differences in blood plasma partitioning of tacrolimus may contribute to the variability in exposure in paediatric patients.

Keywords: PBPK modelling; absorption; ontogeny; paediatrics; tacrolimus.

Figure 1

Model development and validation strategy used for the SimCYP adult model building; Bekersky 2001 et al. (1) [25], Möller et al. [3], Mancinelli et al. [27], Bekersky 1999 et al. [28], Bekersky 2001 et al. (2) [29], Bekersky et al. (3) [30], Kropeit et al. [31], Chen et al. [32], Velicković-Radovanović et al. [33], Alloway et al. [34], Arns et al. [35].

Figure 2

Modelling strategy used for the paediatric population.

Figure 3

Ontogeny profiles of CYP3A4 in both the intestine and the liver visualized as the fraction of adult expression as a function of age. Blue = liver CYP3A4 ontogeny profile as published by Emoto et al., red = Simcyp Upreti liver CYP3A4 ontogeny profile, dotted green = no ontogeny, purple = Simcyp Salem liver CYP3A4 ontogeny profile, dotted orange = SimCYP Johnson intestinal CYP3A4 ontogeny profile.

Figure 4

Fitted B_max values for blood plasma partitioning in the paediatric population.

Figure 5

Crystalline and amorphous solubility of tacrolimus in different media. Open bars indicate the crystalline solubility; full black bars indicate the amorphous solubility, measured from the Prograf formulation.

Figure 6

Dissolution profiles of the amorphous tacrolimus formulation Prograf using the dose-to-volume ratios representative for adult and paediatric subpopulations. Blue = Toddler, Red = Infant, Green = Preschool children, Purple = School age children, Orange = Adolescents, Black = Adults.

Figure 7

Tacrolimus exposure in adults: comparison of simulations using the adult PBPK model to selected clinical studies. (A) Möller et al. [3] as a reference study for a single IV administration (0.01 mg/kg). (B) Chen et al. [32] as a reference study for a single oral administration (0.075 mg/kg) to kidney transplant patients (C) Alloway et al. [34] as a reference study for tacrolimus exposure at steady state after oral administration of 2.85 mg in kidney transplant patients twice per day. In (B,C), the diffusion layer model was used to simulate dissolution. Full lines indicate the median simulated profile, dotted lines indicate the 5% and 95% percentile, and open circles represent the average reference data.

Figure 8

Fold error on simulated PK parameters using either the DLM or the input of an in vitro dissolution profile to simulate drug dissolution. Dotted line indicates an ideal prediction, grey coloured area indicates the 2-fold acceptance criteria, while the short full lines indicate the average fold error. Full circles are PK parameters after single dose administrations, open circles with an x inside are at steady state.

Figure 9

Fold error of different paediatric PBPK models for tacrolimus on simulated PK parameters as a function of age. Grey coloured area indicates the 2-fold acceptance criteria. Blue dots = simulations using the Emoto ontogeny, Red squares = simulations using the Upreti ontogeny, Green triangle = simulations using the Upreti liver + intestine ontogeny. Full line indicates the respective AAFE for each PK parameter and model. All fold errors are on concentrations at steady state.

Figure 10

Representative simulations for different ages at steady state for the different models. Open circles indicate the reference data, Purple = simulations using the Upreti ontogeny in liver and intestines assuming constant B_max, Orange = Simulation using the Upreti ontogeny both in liver and intestines and with the assumed age-dependent change in B_max.