Failure of Traditional Trough Levels to Predict Tacrolimus Concentrations

The objective of this study was to estimate tacrolimus population parameter values and to evaluate the ability of the maximum a posteriori probability (MAP) Bayesian fitting procedure to predict tacrolimus blood levels, using the traditional strategy of monitoring only trough levels, for dosage individualization in liver transplant patients. Forty patients treated with tacrolimus after liver transplantation were studied during the early posttransplant phase (first 2 weeks). This phase was divided into four time periods (1–4 days, 5–7 days, 8–11 days, 12–14 days). Tacrolimus was administered twice daily. Approximately one determination of a tacrolimus trough level on whole blood was performed each day. The NPEM2 program was used to obtain population pharmacokinetic parameter values. With each individual pharmacokinetic parameter estimated by the MAP Bayesian method for a given period, the authors evaluated the prediction of future levels of tacrolimus for that patient for the next period. This evaluation of Bayesian fitting predictive performance was performed using the USC*PACK clinical software. Mean pharmacokinetic parameter values were in the same general range as previously published values obtained with richer data sets. However, during each period, the percentage of blood levels predicted within 20% did not exceed 40%. The traditional strategy of obtaining only trough whole blood levels does not provide enough dynamic information for the MAP Bayesian fitting procedure (the best method currently available) to be used for adaptive control of drug dosage regimens for oral tacrolimus. The authors suggest modifying the blood concentration monitoring scheme to add at least one other concentration measured during the absorptive or distributive phase to obtain more information about the behavior of the drug. D-Optimal design and similar strategies should be considered.