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

Ranita Kirubakaran; David W Uster; Stefanie Hennig; Jane E Carland; Richard O Day; Sebastian G Wicha; Sophie L Stocker

doi:10.1111/bcp.15566

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

Br J Clin Pharmacol. 2023 Mar;89(3):1162-1175. doi: 10.1111/bcp.15566. Epub 2022 Nov 6.

Authors

Ranita Kirubakaran^{1

2

3}, David W Uster⁴, Stefanie Hennig^{5

6}, Jane E Carland^{1

2}, Richard O Day^{1

2}, Sebastian G Wicha⁴, Sophie L Stocker^{1

2

7}

Affiliations

¹ School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
² Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.
³ Department of Pharmacy, Hospital Seberang Jaya, Penang, Malaysia.
⁴ Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany.
⁵ Certara Inc., Princeton, New Jersey, USA.
⁶ School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia.
⁷ School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.

Abstract

Aim: 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 , V₂ /F, Q/F and V₃ /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, V₂ /F, V₃ /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 (∆OFV = -44, 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.

Keywords: Bayesian; PRIOR; heart transplant; population pharmacokinetic; tacrolimus.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antifungal Agents
Azoles
Cytochrome P-450 CYP3A
Heart Transplantation*
Humans
Immunosuppressive Agents / pharmacokinetics
Models, Biological
Tacrolimus* / pharmacokinetics
Transplant Recipients

Substances

Tacrolimus
Immunosuppressive Agents
Antifungal Agents
Azoles
Cytochrome P-450 CYP3A