{"title":"肺移植后早期成人受体中霉酚酸的人群药代动力学及贝叶斯估计。","authors":"Juliette Kauv , Séverine Feuillet , Aurélie Barrail-Tran , Alexandra Delemos , Tiphaine Legrand , Céline Verstuyft , Jérome Le Pavec , Emmanuelle Comets , Valérie Furlan","doi":"10.1016/j.ejps.2025.107290","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div><div>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.</div><div>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.</div></div>","PeriodicalId":12018,"journal":{"name":"European Journal of Pharmaceutical Sciences","volume":"214 ","pages":"Article 107290"},"PeriodicalIF":4.7000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period\",\"authors\":\"Juliette Kauv , Séverine Feuillet , Aurélie Barrail-Tran , Alexandra Delemos , Tiphaine Legrand , Céline Verstuyft , Jérome Le Pavec , Emmanuelle Comets , Valérie Furlan\",\"doi\":\"10.1016/j.ejps.2025.107290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div><div>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.</div><div>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.</div></div>\",\"PeriodicalId\":12018,\"journal\":{\"name\":\"European Journal of Pharmaceutical Sciences\",\"volume\":\"214 \",\"pages\":\"Article 107290\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Pharmaceutical Sciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S092809872500288X\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Pharmaceutical Sciences","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092809872500288X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period
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.
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