  Background: Various population pharmacokinetic models have been developed to
describe the pharmacokinetics of tacrolimus in adult liver transplantation.
However, their extrapolated predictive performance remains unclear in clinical
practice. The purpose of this study was to predict concentrations using a
selected literature model and to improve these predictions by tweaking the
model with a subset of the target population.Methods: A literature review was
conducted to select an adequate population pharmacokinetic model (L).
Pharmacokinetic data from therapeutic drug monitoring of tacrolimus in
liver-transplanted adults were retrospectively collected. A subset of these
data (70%) was exploited to tweak the L-model using the PRIOR subroutine of the
NONMEM software, with 2 strategies to weight the prior information: full
informative (F) and optimized (O). An external evaluation was performed on the
remaining data; bias and imprecision were evaluated for predictions a priori
and Bayesian forecasting.Results: Seventy-nine patients (851 concentrations)
were enrolled in the study. The predictive performance of L-model was
insufficient for a priori predictions, whereas it was acceptable with Bayesian
forecasting, from the third prediction (ie, with $\ge$2 previously observed
concentrations), corresponding to 1 week after transplantation. Overall, the
tweaked models showed a better predictive ability than the L-model. The bias of
a priori predictions was --41% with the literature model versus --28.5% and
--8.73% with tweaked F and O models, respectively. The imprecision was 45.4%
with the literature model versus 38.0% and 39.2% with tweaked F and O models,
respectively. For Bayesian predictions, whatever the forecasting state, the
tweaked models tend to obtain better results.Conclusions: A pharmacokinetic
model can be used, and to improve the predictive performance, tweaking the
literature model with the PRIOR approach allows to obtain better predictions.
