Huang Shuqi, Ding Qin, Yang Nan, Sun Zexu, Cheng Qian, Liu Wei, Li Yejun, Chen Xin, Wu Cuifang, Pei Qi
Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.
Front Pharmacol. 2022 Sep 30;13:1005348. doi: 10.3389/fphar.2022.1005348. eCollection 2022.
Population pharmacokinetic (PopPK) models of posaconazole have been established to promote the precision dosing. However, the performance of these models extrapolated to other centers has not been evaluated. This study aimed to conduct an external evaluation of published posaconazole PopPK models to evaluate their predictive performance. Posaconazole PopPK models screened from the PubMed and MEDLINE databases were evaluated using an external dataset of 213 trough concentration samples collected from 97 patients. Their predictive performance was evaluated by prediction-based diagnosis (prediction error), simulation-based diagnosis (visual predictive check), and Bayesian forecasting. In addition, external cohorts with and without proton pump inhibitor were used to evaluate the models respectively. Ten models suitable for the external dataset were finally included into the study. In prediction-based diagnostics, none of the models met pre-determined criteria for predictive indexes. Only M4, M6, and M10 demonstrated favorable simulations in visual predictive check. The prediction performance of M5, M7, M8, and M9 evaluated using the cohort without proton pump inhibitor showed a significant improvement compared to that evaluated using the whole cohort. Consistent with our expectations, Bayesian forecasting significantly improved the predictive per-formance of the models with two or three prior observations. In general, the applicability of these published posaconazole PopPK models extrapolated to our center was unsatisfactory. Prospective studies combined with therapeutic drug monitoring are needed to establish a PopPK model for posaconazole in the Chinese population to promote individualized dosing.
已建立泊沙康唑的群体药代动力学（PopPK）模型以促进精准给药。然而，这些模型外推至其他中心后的性能尚未得到评估。本研究旨在对已发表的泊沙康唑PopPK模型进行外部评估，以评价其预测性能。使用从97例患者收集的213个谷浓度样本的外部数据集，对从PubMed和MEDLINE数据库筛选出的泊沙康唑PopPK模型进行评估。通过基于预测的诊断（预测误差）、基于模拟的诊断（可视化预测检查）和贝叶斯预测来评估其预测性能。此外，分别使用有和没有质子泵抑制剂的外部队列来评估模型。最终有10个适用于该外部数据集的模型纳入本研究。在基于预测的诊断中，没有一个模型达到预测指标的预定标准。在可视化预测检查中，只有M4、M6和M10表现出良好的模拟效果。与使用整个队列评估相比，使用无质子泵抑制剂队列评估的M5、M7、M8和M9的预测性能有显著改善。与我们的预期一致，贝叶斯预测显著提高了有两到三个先验观察值的模型的预测性能。总体而言，这些已发表的泊沙康唑PopPK模型外推至我们中心的适用性并不理想。需要开展前瞻性研究并结合治疗药物监测，以建立中国人群中泊沙康唑的PopPK模型，以促进个体化给药。
