"External validation with sparse, adaptive-design data for "
External validation with sparse, adaptive-design data for evaluating the predictive performance of a population pharmacokinetic model of tacrolimus Johan E. Wallin1,2, Martin Bergstrand1, Mats O. Karlsson1, Henryk Wilczek3, Christine E. Staatz1,4 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 Prediction corrected visual predictive checks with the three compared models PK model of tacrolimus with the intent of capturing this process. This model has MPE RMSE been used to suggest a revised initial Objectives: Wallin 1.1 5.8 dosing schedule and forms the basis for a To evaluate the predictive performance of Staatz 2.2 7.9 dose adaptation tool. our population model, in comparison to Sam 2.1 7.7 two previously published models (2, 3), Mean prediction error and root mean squared error To validate the model and compare it to using data collected from an independent with the three compared models previously published models, an group of paediatric liver patients and independent dataset was used. The based on model diagnostics suitable for nature of this dataset, comprising of use with TDM data. Accuracy of early Results: sparse adaptive-type TDM data, measurements as well as avoiding Accuracy and precision expressed as necessitate some caution in model fit overprediction was of special concern. MPE and RMSE was better for the evaluation. Population predictions can proposed model compared to the Sam only be used for data prior to and Staatz models. Graphical diagnostics Methods: individualization, and individual confirmed the increased predictive predictions does not serve as an unbiased Data on the PK of tacrolimus in the first capability with the proposed model. guide in model structure discrimination. two weeks following liver transplantation was collected retrospectively from the medical records of 12 paediatric patients. Commonly used simulation based Population predicted drug concentrations diagnostics are also unsuitable when from the three models were compared to using adaptive design data, but visual measured concentrations using samples evaluation of the predictive performance drawn prior to TDM associated dosage can be performed with prediction adaption. Individual predicted drug corrected VPC (pcVPC), where observed concentrations based on all data were and simulated observations are compared to all the measured normalized based on the population concentrations. prediction (1). DV 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 Baysian predictions based on only the previously corrected VPC:s (pcVPC), were measured concentrations, mimicking Bayesian constructed using the PsN software and forecasting. the Xpose graphical analysis toolpack. Conclusions: PRED Population prediction of samples drawn prior Simulation based diagnotics was a to a posteriori dose individualisation 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. 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. Posthoc Bayesian individual predictions of the three compared models representing the overall fit to data