Division of Pharmacology, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Clin Pharmacol Ther. 2018 Apr;103(4):663-673. doi: 10.1002/cpt.777. Epub 2017 Aug 30.
Pharmacokinetic (PK) models exist for most antiepileptic drugs (AEDs). Yet their use in clinical practice to assess interindividual differences and derive individualized doses has been limited. Here we show how model-based dosing algorithms can be used to ensure attainment of target exposure and improve treatment response in patients. Using simulations, different treatment scenarios were explored for 11 commonly used AEDs. For each drug, five scenarios were considered: 1) all patients receive the same dose. 2) Individual clearance (CL), as predicted by population PK models, is used to personalize treatment. 3-5) Individual CL, obtained by therapeutic drug monitoring (TDM) according to different sampling schemes, is used to personalize treatment. Attainment of steady-state target exposure was used as the performance criterion to rank each scenario. In contrast to current clinical guidelines, our results show that patient demographic and clinical characteristics should be used in conjunction with TDM to personalize the treatment of seizures.
PK 模型存在于大多数抗癫痫药物（AEDs）中。然而，它们在临床实践中用于评估个体间差异并得出个体化剂量的应用受到限制。在这里，我们展示了如何使用基于模型的剂量算法来确保目标暴露的实现，并改善患者的治疗反应。使用模拟，我们探讨了 11 种常用 AED 的不同治疗方案。对于每种药物，我们考虑了五个方案：1）所有患者接受相同的剂量。2）根据群体 PK 模型预测的个体清除率（CL）用于个性化治疗。3-5）根据不同的采样方案，通过治疗药物监测（TDM）获得的个体 CL 用于个性化治疗。稳态目标暴露的实现被用作绩效标准来对每个方案进行排名。与当前的临床指南相比，我们的结果表明，患者的人口统计学和临床特征应与 TDM 一起用于癫痫的个体化治疗。
