Tacrolimus pharmacokinetics in lung transplantation: new strategies for monitoring.
ABSTRACT Tacrolimus (TAC) dosing in lung transplantation is traditionally based on blood trough levels (C0). The best sampling strategy for the estimation of total drug exposure (area-under-the-curve [AUC]) has not been determined.
Thirty-one 12-hour pharmacokinetic profiles were studied in 15 patients (8 men and 7 women, 42.0 +/- 13 years) post-bilateral lung transplantation (7.3 +/- 3.7 months; range, 3-18 months). Twelve-hour AUC (AUC0-12) was calculated by trapezoidal rule. Relationships between individual concentration points or abbreviated kinetics (2-4 concentration points) and AUC0-12 were determined by linear regression analysis (R2; absolute prediction error [APE]).
Pharmacokinetic profiles showed high variability, particularly in the absorption phase. AUC was 221 +/- 47.2 ng/ml (range, 156-329.3 ng/ml) at C0 10 to 15 ng/ml and was independent of TAC dose (R2 = 0.002). C0 was poorly predictive of AUC0-12 (R2 = 0.64; APE, 16.1% +/- 10.9%; range, 1.4%-37.8%). The predictive performance for AUC0-12 was highest with abbreviated kinetics using 4 (C0/C2/C3/C4: R(2) = 0.99; APE, 2.6% +/- 2.0%; range, 0.1%-7%) or 3 concentration points (C0/C2/C4: R2 = 0.98; APE, 2.6% +/- 2.1%; range, 0.1%-9.1%). Of the 2-point kinetics C2/C6 (R2 = 0.96; APE, 5.3% +/- 3.7%; range, 0.1%-12.7%), C2/C4 (R2 = 0.94, APE 6.7% +/- 4.8%; range 0.1%-14.6%) and C0/C4 (R2 = 0.94; APE 4.1% +/- 2.9%; range, 0.5%-11.4%) performed best. Single point strategies (best was C4: R2 = 0.94; APE 7.1% +/- 5.5%, range, 0.2%-24.1%) all had unacceptably high APE (range > 15%).
True TAC exposure shows high variability in stable lung transplant patients and is poorly predicted by C0. Abbreviated kinetics covering at least 2 concentration points between 0 and 4 hours post-drug intake are required for an accurate estimation of AUC.
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ABSTRACT: In literature, a great diversity of limited sampling strategies (LSS) have been recommended for tacrolimus monitoring, however proper validation of these strategies to accurately predict the area under the time concentration curve (AUC0-12) is limited. The aim of this study was to determine whether these LSS might be useful for AUC prediction of other patient populations. The LSS from literature studied were based on regression equations or on Bayesian fitting using MWPHARM 3.50 (Mediware, Groningen, the Netherlands). The performance was evaluated on 24 of these LSS in our population of 37 renal transplant patients with known AUCs. The results were also compared with the predictability of the regression equation based on the trough concentrations C0 and C12 of these 37 patients. Criterion was an absolute prediction error (APE) that differed less than 15% from the complete AUC0-12 calculated by the trapezoidal rule. Thirteen of the 18 (72%) LSS based on regression analysis were capable of predicting at least 90% of the 37 individual AUC0-12 within an APE of 15%. Additionally, all but three LSS examined gave a better prediction of the complete AUC0-12 in comparison with the trough concentrations C0 or C12 (mean 62%). All six LSS based on Bayesian fitting predicted <90% of the 37 complete AUC0-12 correctly (mean 67%). The present study indicated that implementation of LSS based on regression analysis could produce satisfactory predictions although careful evaluation is necessary.European Journal of Clinical Pharmacology 12/2007; 63(11):1039-44. · 2.85 Impact Factor