Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

Abstract

Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.Methods: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf ® ) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of K a , V 2 /F, Q/F and V 3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V 2 /F, V 3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.Results: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%.Haematocrit (ΔOFV = À44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing. Conclusion:A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

Tacrolimus population pharmacokinetics in adult heart transplant patients

“…A comparison to the PK parameters of Woillard et al in renal transplant patients showed that in heart transplant patients the apparent clearance was slightly decreased (from 0.37 to 0.20 L·h −1 ·kg −1 ) and the central compartment volume was strongly decreased (from 3.66 to 0.22 L kg −1 ) 22 . Similarly, our apparent clearance is in line with the apparent clearance of other heart transplant patient models (0.27 L·h −1 ·kg −1 for Kirubakaran et al, 1 0.22 L·h −1 ·kg −1 for Gong et al 2 and 0.21 L·h −1 ·kg −1 for Han et al 3 ). The same was observed for our apparent central volume of distribution, which was also strongly decreased compared to the heart transplant model (2.55 L kg −1 for Kirubakaran et al, 1 11.52 L kg −1 for Gong et al 2 and 12.17 L kg −1 for Han et al 3 ).…”

“…Similarly, our apparent clearance is in line with the apparent clearance of other heart transplant patient models (0.27 L·h −1 ·kg −1 for Kirubakaran et al, 1 0.22 L·h −1 ·kg −1 for Gong et al 2 and 0.21 L·h −1 ·kg −1 for Han et al 3 ). The same was observed for our apparent central volume of distribution, which was also strongly decreased compared to the heart transplant model (2.55 L kg −1 for Kirubakaran et al, 1 11.52 L kg −1 for Gong et al 2 and 12.17 L kg −1 for Han et al 3 ). In lung and heart transplant patients, Sikma et al 11 observed an apparent central volume of distribution around 2.99 L kg −1 , which is also far from our value.…”

“…These patients require a higher starting dose compared with CYP3A5*3/*3 carriers to reach the predefined target exposure early after transplantation 35 . The haematocrit has been shown to influence the apparent clearance in renal transplant patients 22 and in heart transplant patients, 1 but its effect was not found to be significant in the present study, even if the range of the haematocrit values was quite large (between 26% and 47%).…”

“…10 Currently, only a few population pharmacokinetic models (POPPK) dedicated to heart transplant patients have been developed. [1][2][3] Other tacrolimus developed models also include lung 11 or liver and kidney 12 transplant patients. In a recent work, Kirubakaran et al 1 built a POPPK based on sparse data (mostly C0 values) on the basis of a previously published POPPK model 13 to inform the effect of antifungal interaction 1 on tacrolimus apparent clearance.…”

“…[1][2][3] Other tacrolimus developed models also include lung 11 or liver and kidney 12 transplant patients. In a recent work, Kirubakaran et al 1 built a POPPK based on sparse data (mostly C0 values) on the basis of a previously published POPPK model 13 to inform the effect of antifungal interaction 1 on tacrolimus apparent clearance. None of the previous models investigated the predicted probability of AUC target attainment using simulations to improve the first dose of tacrolimus.…”