DataSheet1_External evaluation of published population pharmacokinetic models of posaconazole.zip

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.

为实现泊沙康唑（posaconazole）的精准给药，已有研究建立了其群体药代动力学（Population pharmacokinetic, PopPK）模型。然而，此类模型外推至其他中心的性能尚未得到评估。本研究旨在对已发表的泊沙康唑群体药代动力学模型开展外部评价，以考察其预测性能。研究从PubMed及MEDLINE数据库中筛选得到泊沙康唑群体药代动力学模型，并采用来自97例患者的213份谷浓度样本组成的外部数据集对其进行评价。评价分别通过基于预测的诊断方法（预测误差）、基于模拟的诊断方法（可视化预测检验）以及贝叶斯预测开展。此外，本研究分别纳入使用质子泵抑制剂（proton pump inhibitor, PPI）与未使用质子泵抑制剂的外部队列，对模型进行评价。最终共有10个适用于本外部数据集的模型被纳入本研究。在基于预测的诊断分析中，所有模型均未达到预设的预测指标标准。仅模型M4、M6及M10在可视化预测检验中展现出良好的模拟效果。相较于使用全队列的评价结果，未使用质子泵抑制剂队列中M5、M7、M8及M9的预测性能得到了显著提升。与预期一致，当引入2或3次先验观测值时，贝叶斯预测可显著改善模型的预测性能。总体而言，现有已发表的泊沙康唑群体药代动力学模型外推至本中心的适用性并不理想。未来需开展结合治疗药物监测（therapeutic drug monitoring, TDM）的前瞻性研究，以建立适用于中国人群的泊沙康唑群体药代动力学模型，进而推动个体化给药方案的实施。