1. Results: Accuracy and precision expressed as MPE and RMSE was better for the proposed model compared to the Sam and Staatz models. Graphical diagnostics confirmed the increased predictive capability with the proposed model. External validation with sparse, adaptive-design data for evaluating the predictive performance of a population pharmacokinetic model of tacrolimus Johan E. Wallin 1,2, Martin Bergstrand 1, Mats O. Karlsson 1, Henryk Wilczek 3, Christine E. Staatz 1,4 1. Department of Pharmaceutical Biosciences, Uppsala University, Sweden, 2. PK/PD/TS, Eli Lilly, Erl Wood Windlesham, UK, 3. Division of Transplantation Surgery, Karolinska Institute, Stockholm, Sweden 4. School of Pharmacy, University of Queensland, Brisbane, Australia. Introduction: Tacrolimus is a potent immunosuppr- essant used to prevent and treat organ rejection in paediatric liver transplantation. Tacrolimus has a narrow therapeutic window and displays considerable between and within-subject pharmaco- kinetic (PK) variability. The PK of tacrolimus change markedly in the immediate post-transplant period. We have previously developed a population PK model of tacrolimus with the intent of capturing this process. This model has been used to suggest a revised initial dosing schedule and forms the basis for a dose adaptation tool. To validate the model and compare it to previously published models, an independent dataset was used. The nature of this dataset, comprising of sparse adaptive-type TDM data, necessitate some caution in model fit evaluation. Population predictions can only be used for data prior to individualization, and individual predictions does not serve as an unbiased guide in model structure discrimination. Commonly used simulation based diagnostics are also unsuitable when using adaptive design data, but visual evaluation of the predictive performance can be performed with prediction corrected VPC (pcVPC), where observed and simulated observations are normalized based on the population prediction (1). Objectives: To evaluate the predictive performance of our population model, in comparison to two previously published models (2, 3), using data collected from an independent group of paediatric liver patients and based on model diagnostics suitable for use with TDM data. Accuracy of early measurements as well as avoiding overprediction was of special concern. Methods: Data on the PK of tacrolimus in the first two weeks following liver transplantation was collected retrospectively from the medical records of 12 paediatric patients. Population predicted drug concentrations from the three models were compared to measured concentrations using samples drawn prior to TDM associated dosage adaption. Individual predicted drug concentrations based on all data were compared to all the measured concentrations. To evaluate the models’ potential for Bayesian forecasting in dose adaptation, individual predicted drug concentrations based on prior samples were compared to measured concentrations. Model predictive performance was compared by calculation of MPE and RMSE. Prediction corrected VPC:s (pcVPC), were constructed using the PsN software and the Xpose graphical analysis toolpack. Posthoc Bayesian individual predictions of the three compared models representing the overall fit to data Conclusions: Simulation based diagnotics was a valuable aid in determining that the proposed PK model predicted the validation data set reasonably well, and performing better than the previously published models in this early post- transplantation phase. Prediction corrected visual predictive checks with the three compared models Baysian predictions based on only the previously measured concentrations, mimicking Bayesian forecasting. Population prediction of samples drawn prior to a posteriori dose individualisation MPERMSE Wallin1.15.8 Staatz2.27.9 Sam2.17.7 References: 1.M Bergstrand, A.C Hooker, J.E Wallin, M.O Karlsson. Prediction Corrected Visual Predictive Checks. ACoP (2009) Abstr F7. [http://www.go-acop.org/sites/all/assets/webform/Poster_ACoP_VPC_091002_two_page.pdf] 2.Sam WJ, Aw M, Quak SH, et al. Population pharmacokinetics of tacrolimus in Asian paediatric liver transplant patients. Br J Clin Pharmacol 2000; 50 (6): 531. 3.Staatz CE, Taylor PJ, Lynch SV, Willis C, Charles BG, Tett SE. Population pharmacokinetics of tacrolimus in children who receive cut-down or full liver transplants. Transplantation 2001; 72 (6): 1056. Mean prediction error and root mean squared error with the three compared models DV PRED