BACKGROUND: Limited sampling strategies (LSS) for estimating the area under the curve (AUC(0-12h)) of tacrolimus and optimizing dosage adjustment are not currently used or fully validated in pediatric patients, although the method is of real benefit to children. The objective of the present study was to develop and validate reliable and clinically applicable LSS using Bayesian estimation and the multiple regression analysis for estimating tacrolimus AUC in pediatric kidney transplant patients. METHODS: The original tacrolimus pharmacokinetic dataset consists of 50 full profiles from 50 pediatric kidney transplant patients. Two LSS based on Bayesian estimator or multiple regression analysis to calculate tacrolimus AUC were developed and then compared. External validation was prospectively performed in an independent validation group, which consisted of 42 full pharmacokinetic profiles from 20 pediatric kidney transplant patients. RESULTS: Bayesian estimators using C(0h), C(1h) or C(2h), and C(3h) gave the best predictive performance, the external validation having a mean prediction bias of 1% and mean imprecision of 5.5%. The multiple regression analysis using C(0h), C(1h), and C(3h) gave the best correlation (r² = 0.953) between estimated and referenced AUCs with a mean prediction bias of 4.2% and mean precision of 8.3% in external validation dataset. CONCLUSIONS: The prediction of AUC using developed LSS was unbiased and precise. The age and time after transplantation did not influence the predictive performance. Such LSS approach will help guiding tacrolimus therapeutic drug monitoring based on AUC in pediatric kidney transplant patients.
BACKGROUND: Limited sampling strategies (LSS) for estimating the area under the curve (AUC(0-12h)) of tacrolimus and optimizing dosage adjustment are not currently used or fully validated in pediatric patients, although the method is of real benefit to children. The objective of the present study was to develop and validate reliable and clinically applicable LSS using Bayesian estimation and the multiple regression analysis for estimating tacrolimus AUC in pediatric kidney transplant patients. METHODS: The original tacrolimus pharmacokinetic dataset consists of 50 full profiles from 50 pediatric kidney transplant patients. Two LSS based on Bayesian estimator or multiple regression analysis to calculate tacrolimus AUC were developed and then compared. External validation was prospectively performed in an independent validation group, which consisted of 42 full pharmacokinetic profiles from 20 pediatric kidney transplant patients. RESULTS: Bayesian estimators using C(0h), C(1h) or C(2h), and C(3h) gave the best predictive performance, the external validation having a mean prediction bias of 1% and mean imprecision of 5.5%. The multiple regression analysis using C(0h), C(1h), and C(3h) gave the best correlation (r² = 0.953) between estimated and referenced AUCs with a mean prediction bias of 4.2% and mean precision of 8.3% in external validation dataset. CONCLUSIONS: The prediction of AUC using developed LSS was unbiased and precise. The age and time after transplantation did not influence the predictive performance. Such LSS approach will help guiding tacrolimus therapeutic drug monitoring based on AUC in pediatric kidney transplant patients.
Authors: Olivia Campagne; Donald E Mager; Daniel Brazeau; Rocco C Venuto; Kathleen M Tornatore Journal: Br J Clin Pharmacol Date: 2019-01-04 Impact factor: 4.335
Authors: Agnieszka A Prytuła; Karlien Cransberg; Antonia H M Bouts; Ron H N van Schaik; Huib de Jong; Saskia N de Wildt; Ron A A Mathôt Journal: Clin Pharmacokinet Date: 2016-09 Impact factor: 6.447
Authors: Joseph E Rower; Chris Stockmann; Matthew W Linakis; Shaun S Kumar; Xiaoxi Liu; E Kent Korgenski; Catherine M T Sherwin; Kimberly M Molina Journal: BMJ Paediatr Open Date: 2017-11-22