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.

翻译：背景：已有多种群体药代动力学模型用于描述成人肝移植术后他可莫司的药代动力学特征，但其在临床实践中的外推预测性能仍不明确。本研究旨在利用筛选文献模型进行浓度预测，并通过目标人群子集优化模型来提升预测准确性。方法：通过文献综述筛选合适的群体药代动力学模型（L模型）。回顾性收集成人肝移植患者他可莫司治疗药物监测的药代动力学数据，利用其中70%数据，采用NONMEM软件的PRIOR子程序以两种先验信息加权策略（完全信息型F与优化型O）对L模型进行优化。剩余30%数据用于外部验证，通过先验预测和贝叶斯预测评估偏差与精密度。结果：共纳入79例患者（851个血药浓度数据）。L模型先验预测性能不足，但从第三个预测点（即含≥2个先前观测浓度，对应移植术后1周）起，贝叶斯预测性能可接受。整体而言，优化模型的预测能力优于L模型：文献模型先验预测偏差为-41%，而F优化模型和O优化模型分别为-28.5%和-8.73%；文献模型不精密度为45.4%，F优化模型和O优化模型分别为38.0%和39.2%。无论预测阶段如何，贝叶斯预测中优化模型均倾向于获得更优结果。结论：群体药代动力学模型可用于临床，而采用PRIOR方法优化文献模型可显著提升预测性能，获得更优预测结果。