Abstract：
OBJECTIVE: Use of infliximab (IFX) has improved outcomes in children with inflammatory bowel disease (IBD). However, a proportion of patients does not respond to IFX or loses response over time. Population pharmacokinetic (PopPK) modelling is a promising approach for IFX dose optimization, but with the increasing number of PopPK models in literature, model evaluation is essential. The aims of this study are: (i) to validate the predictive performance of existing IFX PopPK models using a cohort of children with IBD; and (ii) to perform a Bayesian estimation of the most suitable model to predict the next IFX concentrations.
METHODS: PubMed was searched for IFX PopPK models in children. Selected models were rebuilt and analysed using R. Model performance was assessed through goodness-of-fit-plots, residuals against time, prediction error and prediction-corrected visual predictive checks. The validation cohort consisted of 73 children with IBD who were treated with IFX in our centre between 2017 and 2023 (340 IFX measurements).
RESULTS: We identified 9 PopPK models. Model bias for individual predicted values ranged from -9.29% to 8.01% compared to bias for population predicted values. The model by Vande Casteele et al. demonstrated superior performance (individual predicted bias 2.13, population predicted bias -6.11); upon Bayesian estimation, it predicted induction trough levels with median error of 12.95% but had a median error of -69% predicting maintenance concentrations.
CONCLUSIONS: The model by Vande Casteele et al. displayed superior performance in initial evaluations but had a high error in estimating next IFX levels and can only be used in practice to predict induction levels.
METHODS: PubMed was searched for IFX PopPK models in children. Selected models were rebuilt and analysed using R. Model performance was assessed through goodness-of-fit-plots, residuals against time, prediction error and prediction-corrected visual predictive checks. The validation cohort consisted of 73 children with IBD who were treated with IFX in our centre between 2017 and 2023 (340 IFX measurements).
RESULTS: We identified 9 PopPK models. Model bias for individual predicted values ranged from -9.29% to 8.01% compared to bias for population predicted values. The model by Vande Casteele et al. demonstrated superior performance (individual predicted bias 2.13, population predicted bias -6.11); upon Bayesian estimation, it predicted induction trough levels with median error of 12.95% but had a median error of -69% predicting maintenance concentrations.
CONCLUSIONS: The model by Vande Casteele et al. displayed superior performance in initial evaluations but had a high error in estimating next IFX levels and can only be used in practice to predict induction levels.
摘要：
目的：使用英夫利昔单抗（IFX）可改善炎症性肠病（IBD）患儿的预后。然而,部分患者对IFX无反应或随时间失去反应。群体药代动力学（PopPK）建模是IFX剂量优化的一种有前途的方法，但是随着文献中PopPK模型的增加，模型评估至关重要。本研究的目的是:(i)使用一组IBD儿童验证现有IFXPopPK模型的预测性能;和(ii)对最合适的模型进行贝叶斯估计以预测下一个IFX浓度。
方法：在儿童中搜索PubMed的IFXPopPK模型。使用R重建和分析选定的模型。通过拟合优度图评估模型性能，相对于时间的残差，预测误差和预测校正的视觉预测检查。验证队列包括2017年至2023年在我们中心接受IFX治疗的73例IBD儿童（340例IFX测量）。
结果：我们确定了9个PopPK模型。与人群预测值的偏差相比，个体预测值的模型偏差范围为-9.29%至8.01%。VandeCasteele等人的模型。表现优异(个体预测偏差2.13，群体预测偏差-6.11)；经贝叶斯估计，它预测诱导谷水平的中位误差为12.95%,但预测维持浓度的中位误差为-69%.
结论：VandeCasteele等人的模型。在初始评估中表现出优异的性能，但在估计下一个IFX水平时误差很大，只能在实践中用于预测诱导水平。
方法：在儿童中搜索PubMed的IFXPopPK模型。使用R重建和分析选定的模型。通过拟合优度图评估模型性能，相对于时间的残差，预测误差和预测校正的视觉预测检查。验证队列包括2017年至2023年在我们中心接受IFX治疗的73例IBD儿童（340例IFX测量）。
结果：我们确定了9个PopPK模型。与人群预测值的偏差相比，个体预测值的模型偏差范围为-9.29%至8.01%。VandeCasteele等人的模型。表现优异(个体预测偏差2.13，群体预测偏差-6.11)；经贝叶斯估计，它预测诱导谷水平的中位误差为12.95%,但预测维持浓度的中位误差为-69%.
结论：VandeCasteele等人的模型。在初始评估中表现出优异的性能，但在估计下一个IFX水平时误差很大，只能在实践中用于预测诱导水平。
