Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Department of Pharmacy, The Affiliated Hospital of Guilin Medical University, Guilin, China.
J Clin Pharm Ther. 2021 Jun;46(3):820-831. doi: 10.1111/jcpt.13363. Epub 2021 Mar 22.
Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set.
The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models.
Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F ) ≥35% and PE% within ±30% (F ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models.
The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.
已经构建了各种儿童和青少年万古霉素群体药代动力学（PopPK）模型，以优化万古霉素的治疗方案。然而，当外推到不同的临床中心时，对这些模型的预测性能知之甚少。因此，本研究的目的是验证万古霉素 PopPK 模型外推到不同临床中心时的可预测性，并验证其在独立数据集上的适用性。
从文献中筛选出已发表的模型，并使用总共 220 例中国儿科患者的 451 个万古霉素血药浓度的外部数据集进行评估。进行预测和基于模拟的诊断以及贝叶斯预测，以评估模型的预测性能。
评估了 10 个已发表的 PopPK 模型。预测基于诊断显示，没有一个模型符合所有标准（中位数预测误差（MDPE）≤±20%，中位数绝对预测误差（MAPE）≤30%，PE%在±20%（F）≥35%，PE%在±30%（F）≥50%），表明预测性能不佳。在基于模拟的诊断中，所有模型的视觉预测检查（VPC）和归一化预测分布误差（NPDE）都表明存在模型不拟合。贝叶斯预测结果表明，通过一个或两个先验观察值，可以显著提高个体预测的准确性和精密度，但在临床实践中可能不需要频繁监测，因为贝叶斯预测表明，增加样本数量并不能显著提高预测性能。Moffett 等人建立的模型 3 显示出比其他模型更好的预测能力。
10 个已发表的模型在预测和基于模拟的诊断中表现不佳；没有一个已发表的模型适合设计万古霉素的初始给药方案。在推断万古霉素模型时，应考虑药代动力学特征和协变量，如体重、肾功能、年龄和基础疾病。基于模型 3 的贝叶斯预测结合治疗药物监测可用于调整万古霉素的给药方案。
