Usefulness of population pharmacokinetics to optimize the dosage regimen of infliximab in inflammatory bowel disease patients
Usefulness of population pharmacokinetics to optimize the dosage regimen of infliximab in inflammatory bowel disease patients
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DOI：
10.17235/reed.2020.6857/2020
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发表时间：
2020-01-01
影响因子：
2
通讯作者：
Iniesta Navalon, Carles
中科院分区：
文献类型：
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作者：
Gil Candel, Mayte;Gascon Canovas, Juan Jose;Iniesta Navalon, Carles
前言：英夫利昔单抗用于炎症性肠病，具有较大的个体间药动学变异性。因此，在许多情况下，有必要对治疗进行个体化。我们研究的主要目的是比较两种使用治疗性药物监测的剂量调整策略的方法：a)基于算法和b)基于贝叶斯预测，以达到炎症性肠病患者的最佳英夫利昔单抗水平。第二个目标是评估英夫利昔单抗在炎症性肠病患者中的群体药代动力学模型的预测性能，从而评估其临床应用价值。方法：对炎症性肠病患者和有效血清英夫利昔单抗水平进行回顾性观察队列分析。比较两组谷浓度与给药策略的关系。使用NONMEM软件对先前发表的群体药代动力学模型进行外部验证。通过计算平均预测误差和平均绝对预测误差来评价模型的预测性能。结果：共获得47例患者的94份英夫利昔单抗血清样本。调整后，共有30名患者(63.8%)达到最佳英夫利昔单抗低谷水平。基于贝叶斯的给药策略与英夫利昔单抗的最佳低谷水平相关，后者高于基于算法的策略(OR：8.94[95%CI：2.24-35.6]，p=0.001)。对于个体预测，平均预测误差为0.118 mU g/ml(95%CI：-0.149~0.384)，平均绝对预测误差为0.935 mU g/ml(95%CI：0.569~1.075)。结论：基于贝叶斯预测的群体药代动力学模型的应用是优化英夫利昔单抗剂量治疗炎症性肠病的重要进展。
Introduction: infliximab is used in inflammatory bowel disease, which has a great inter-individual pharmacokinetic variability. Thus, it is necessary to individualize the therapy in many cases. The main objective of our study was to compare two methods of a dose adjustment strategy using therapeutic drug monitoring: a) based on an algorithm and b) based on Bayesian prediction, to achieve an optimal infliximab trough level in patients with inflammatory bowel diseases. The secondary objective was to evaluate the predictive performance of a population pharmacokinetic model of infliximab in patients with inflammatory bowel diseases and therefore, its clinical utility. Furthermore, the factors associated with a suboptimal adjustment of the model were analyzed.Methods: a retrospective observational cohort analysis was performed of patients with inflammatory bowel disease and available serum levels of infliximab. The relationship between trough concentration and dosing strategy was compared in both groups. The external validation of a previously published population pharmacokinetic model was performed using the NONMEM software. The mean prediction error and mean absolute prediction error were calculated to evaluate the predictive performance of the model.Results: a total of 94 infliximab serum samples were obtained from 47 patients. After the adjustment, a total of 30 patients (63.8 %) achieved optimal infliximab trough levels. A dosing strategy based on Bayesian was associated with optimal infliximab trough levels that were higher than the strategy based on an algorithm (OR: 8.94 [95 % CI: 2.24 - 35.6], p = 0.001). For the individual predictions, the mean prediction error was 0.118 mu g/ml (95 % CI: -0.149-0.384) and the mean absolute prediction error was 0.935 mu g/ml (95 % CI: 0.569-1.075).Conclusions: the application of a population pharmacokinetic model based on Bayesian prediction is an important advance in the optimization of infliximab dosage in the treatment of inflammatory bowel disease.