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European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025

Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period.

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Kauv Juliette, Feuillet Séverine, Barrail-Tran Aurélie, Delemos Alexandra, Legrand Tiphaine, Verstuyft Céline, Le Pavec Jérome, Comets Emmanuelle, Furlan Valérie

Аннотация

Mycophenolic acid (MPA), administered as its prodrug Mycophenolate mofetil (MMF), is widely used to prevent rejection after organ solid transplantation. Few population pharmacokinetic studies of MPA in lung transplantation are available, so the present study was designed to describe the population pharmacokinetics (PK) of MPA and develop a maximum a posteriori Bayesian estimator of MPA area under the curve (AUC0-12h) in lung transplant (LT) recipients in the early post-transplant period. We conducted a single-center retrospective study in the Plessis-Robinson hospital (France), including LT recipients receiving MMF in whom at least one concentration-time profile was available during the first 3 months post-transplantation. MPA was measured before intake and 0.5, 1, 2, 4 ± 6 hours post-administration. Patients were divided into an index group (N=72) and a validation group (N=46). A population PK model and a Bayesian estimator with three sampling times were built with the index dataset and externally evaluated with the validation dataset. Analyses were performed using nonlinear mixed-effects models with Monolix® software. The PK model was built using 97 MPA profiles in the index dataset. MPA PK was best described using a two-compartment model with a lag time and first-order absorption and elimination. The significant covariates on the MPA apparent clearance were creatinine, body weight and ciclosporin co-administration and the posttransplantation time on the apparent volume of the central compartment. The model was successfully evaluated in the 60 MPA profiles from the validation dataset. The best Bayesian estimator included samples just before intake, 1 and 4 hours post-administration. The prediction of AUC was unbiased in both datasets and had a precision around 20 %. This is the first Bayesian estimator allowing the prediction of AUC in LT patients without cystic fibrosis in the early post-transplant period, providing a tool to improve the therapeutic drug monitoring of MPA.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты