Limited Sampling Strategies for Monitoring Tacrolimus in Pediatric Liver Transplant Recipients
ABSTRACT To develop and validate limited sampling strategies (LSSs) for tacrolimus in pediatric liver transplant recipients.
Thirty-six 12-hour pharmacokinetic profiles from 28 pediatric liver transplant recipients (0.4-18.5 years) were collected. Tacrolimus concentrations were measured by immunoassay and area under the curve (AUC0-12) was determined by trapezoidal rule. LSSs consisting of 1, 2, 3, or 4 concentration-time points were developed using multiple regression analysis. Eight promising models (2 per category) were selected based on the following criteria: r2 ≥ 0.90, inclusion of trough concentration (C0), and time points within 4 hours postdose. The predictive performance of these LSSs was evaluated in an independent set of data by measuring the mean prediction error and the root mean squared prediction error.
Five models including 2-4 time points predicted AUC0-12 with a ±15% error limit. Bias (mean prediction error) and precision (root mean squared prediction error) of LSS involving C0, C1, and C4 (AUCpredicted = 9.30 + 3.69 × C0 + 2.19 × C1 + 4.69 × C4) were -4.98% and 8.29%, respectively. Among single time point LSSs, the model using C0 had a poor correlation with AUC0-12 (r2 = 0.53), whereas the one with C4 had the highest correlation with tacrolimus exposure (r2 = 0.84).
Trough concentration is a poor predictor of tacrolimus AUC0-12 in pediatric liver transplant recipients. However, LSSs using 2-4 concentration-time points obtained within 4 hours postdose provide a reliable and convenient method to predict tacrolimus exposure in this population. The proposed LSSs represent an important step that will allow the undertaking of prospective trials aiming to better define tacrolimus target AUC in pediatric liver transplant recipients and to determine whether AUC-guided monitoring is superior to C0-based monitoring in terms of efficacy and safety.
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ABSTRACT: BACKGROUND: A limited sampling strategy (LSS) for estimating the area under the curve (AUC) of the prolonged-release formulation of tacrolimus (tacrolimus(PR)) is not available in pediatric patients, although the method is of real benefit to children. The objective of this study was to develop and validate a reliable and clinically applicable LSS using Bayesian estimation for estimating tacrolimus(PR) AUC in pediatric kidney transplant patients METHODS: The original tacrolimus pharmacokinetic dataset consisted of 22 full profiles from 22 pediatric kidney transplant patients. The Bayesian estimation method was used to develop the LSS. External validation was performed in an independent validation group which consisted of 20 full pharmacokinetic profiles from 12 pediatric kidney transplant patients. RESULTS: Bayesian estimator using C(0h) C(2h) and C(3h) gave the best predictive performance with a mean prediction error of 2.2 % in the external validation dataset. There was no correlation between the prediction error and age. The Bland-Altman analysis showed that the mean difference between the reference and Bayesian-estimated AUC(0-24) was 3.5 (95 % confidence interval -3.5-10.5) ng h/mL CONCLUSIONS: A reliable and clinically applicable LSS for estimating AUC(0-24) of tacrolimus(PR) was determined and validated in children. The prediction was unbiased and precise. It can be used as a routine procedure to perform AUC-based tacrolimus(PR) dosage optimization in pediatric renal transplant patients.European Journal of Clinical Pharmacology 12/2012; 69(5). DOI:10.1007/s00228-012-1457-5 · 2.70 Impact Factor
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ABSTRACT: To develop limited sampling strategies (LSSs) to predict total tacrolimus exposure (AUC0-24) after the administration of Advagraf® and Prograf® to pediatric patients with stable liver or kidney transplants.Forty-one pharmacokinetic profiles were obtained after Prograf® and Advagraf® administration. LSSs predicting AUC0-24 were developed by linear regression using 3 extraction time points. Selection of the most accurate LSS was made on the basis of the r2, Mean Error and Mean Absolute Error.All selected LSSs had higher correlation with AUC0-24 than the correlation found between C0 and AUC0-24. Best LSS for Prograf® in liver transplants was C0_1.5_4 (r2=0.939), and for kidney transplants C0_1_3 (r2=0.925). For Advagraf®, the best LSS in liver transplants was C0-1-2.5 (r2=0.938) and for kidney transplants was C0-0.5-4 (r2=0.931). Excluding transplant type variable, the best LSS for Prograf® is C0_1_3 (r2=0.920), and the best LSS for Advagraf® was C0_0.5_4 (r2=0.926). Considering transplant type irrespective of the formulation used, the best LSS for liver transplants was C0-2-3 (r2=0.913) and for kidney transplants was C0-0.5-4 (r2=0.898). Best LSS, considering all data together was C0_1_4 (r2=0.898).We developed several LSSs to predict AUC0-24 for Tacrolimus in children and adolescents with kidney or liver transplants after Prograf® and/or Advagraf® treatment.This article is protected by copyright. All rights reserved.Transplant International 05/2014; 27(9). DOI:10.1111/tri.12362 · 3.16 Impact Factor
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ABSTRACT: AimsThe objectives of this study were to develop a population pharmacokinetic (PopPK) model for tacrolimus in paediatric liver transplant patients and determine optimal sampling strategies to estimate tacrolimus exposure accurately.Methods Twelve hour intensive pharmacokinetic profiles from 30 patients (age 0.4–18.4 years) receiving tacrolimus orally were analysed. The PopPK model explored the following covariates: weight, age, sex, type of transplant, age of liver donor, liver function tests, albumin, haematocrit, drug interactions, drug formulation and time post-transplantation. Optimal sampling strategies were developed and validated with jackknife.ResultsA two-compartment model with first-order absorption and elimination and lag time described the data. Weight was included on all pharmacokinetic parameters. Typical apparent clearance and central volume of distribution were 12.1 l h−1 and 31.3 l, respectively. The PopPK approach led to the development of optimal sampling strategies, which allowed estimation of tacrolimus pharmacokinetics and area under the concentration–time curve (AUC) on the basis of practical sampling schedules (three or four sampling times within 4 h) with clinically acceptable prediction error limit. The mean bias and precision of the Bayesian vs. reference (trapezoidal) AUCs ranged from −2.8 to −1.9% and from 7.4 to 12.5%, respectively.Conclusions The PopPK of tacrolimus and empirical Bayesian estimates represent an accurate and convenient method to predict tacrolimus AUC(0–12) in paediatric liver transplant recipients, despite high between-subject variability in pharmacokinetics and patient demographics. The developed optimal sampling strategies will allow the undertaking of prospective trials to define the tacrolimus AUC-based therapeutic window and dosing guidelines in this population.British Journal of Clinical Pharmacology 06/2014; 77(6). DOI:10.1111/bcp.12276 · 3.69 Impact Factor