Evaluation of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients
Authors: Barraclough Katherine A. , Isbel Nicole M. , Kirkpatrick Carl M. , Lee Katie J. , Taylor Paul J. , Johnson David W. , Campbell Scott B. , Leary Diana R. , Staatz Christine E. 
Published: September 2010

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• Tacrolimus pre‐dose (C0) concentrations are currently used to guide tacrolimus dosing.

• However, conflicting data exist regarding the relationship of C0 with tacrolimus area under the concentration–time curve from 0 to 12 h post‐dose (AUC0–12) and clinical outcomes.

• Previous literature suggests that limited sampling methods, such as multiple linear regression‐derived limited sampling strategies or maximum a posteriori (MAP) Bayesian analyses, may provide more reliable estimations of tacrolimus exposure.

WHAT THIS STUDY ADDS

• For the first time, the predictive performances of all published limited sampling methods for tacrolimus are compared in an independent cohort of adult kidney transplant recipients.

• Limited sampling methods better predict tacrolimus exposure compared with measurement of C0.

• However, the predictive power of the methods is highly variable, highlighting the importance of validating any method prior to applying it to an alternative population.

AIMS To examine the predictive performance of limited sampling methods for estimation of tacrolimus exposure in adult kidney transplant recipients.

METHODS Twenty full tacrolimus area under the concentration–time curve from 0 to 12 h post‐dose (AUC0–12) profiles (AUCf) were collected from 20 subjects. Predicted tacrolimus AUC0–12 (AUCp) was calculated using the following: (i) 42 multiple regression‐derived limited sampling strategies (LSSs); (ii) five population pharmacokinetic (PK) models in the Bayesian forecasting program TCIWorks; and (iii) a Web‐based consultancy service. Correlations (r2) between C0 and AUCf and between AUCp and AUCf were examined. Median percentage prediction error (MPPE) and median absolute percentage prediction error (MAPE) were calculated.

RESULTS Correlation between C0 and AUCf was 0.53. Using the 42 LSS equations, correlation between AUCp and AUCf ranged from 0.54 to 0.99. The MPPE and MAPE were <15% for 29 of 42 equations (62%), including five of eight equations based on sampling taken ≤2 h post‐dose. Using the PK models in TCIWorks, AUCp derived from only C0 values showed poor correlation with AUCf (r2 = 0.27–0.54) and unacceptable imprecision (MAPE 17.5–31.6%). In most cases, correlation, bias and imprecision estimates progressively improved with inclusion of a greater number of concentration time points. When concentration measurements at 0, 1, 2 and 4 h post‐dose were applied, correlation between AUCp and AUCf ranged from 0.75 to 0.93, and MPPE and MAPE were <15% for all models examined. Using the Web‐based consultancy service, correlation between AUCp and AUCf was 0.74, and MPPE and MAPE were 6.6 and 9.6%, respectively.

CONCLUSIONS Limited sampling methods better predict tacrolimus exposure compared with C0 measurement. Several LSSs based on sampling taken 2 h or less post‐dose predicted exposure with acceptable bias and imprecision. Generally, Bayesian forecasting methods required inclusion of a concentration measurement from >2 h post‐dose to adequately predict exposure.