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儿童拉莫三嗪剂量推荐:基于既往和新的群体药代动力学模型评估。

Dosing Recommendations for Lamotrigine in Children: Evaluation Based on Previous and New Population Pharmacokinetic Models.

机构信息

Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France.

Réanimation néonatale et néonatologie, Centre Hospitalier Intercommunal de Créteil, Créteil, France.

出版信息

J Clin Pharmacol. 2021 May;61(5):677-687. doi: 10.1002/jcph.1791. Epub 2020 Dec 14.

DOI:10.1002/jcph.1791
PMID:33244764
Abstract

Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9 mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7 kg (8-110 kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5 mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration.

摘要

拉莫三嗪是一种广谱抗癫痫药物,儿童的血清浓度个体间差异很大。本研究旨在评估已发表的拉莫三嗪儿童群体药代动力学(PPK)模型的预测性能,利用我们的监测数据建立新模型,并评估当前推荐剂量。验证队列包括接受拉莫三嗪治疗且在治疗药物监测(TDM)期间进行了血清水平检测的患者。首先将文献中发表的 PPK 模型应用于验证队列。我们使用平均预测误差、均方根误差和可视化预测检查来评估它们的预测性能。然后使用数据建立新模型。进行剂量模拟以评估推荐剂量。我们纳入了来自 175 名患者的 270 个 0.5 至 17.9 mg/L 的拉莫三嗪浓度。中位数(范围)年龄和体重分别为 11.8 岁(0.8-18 岁)和 32.7 kg(8-110 kg)。我们测试了 6 个 PPK 模型;大多数模型具有可接受的偏差和精度,但低估了队列的变异性。我们建立了一个 1 室模型,具有 1 阶吸收和消除、比例缩放以及抑制剂和诱导共药物的影响。在我们的队列中,22.6%的谷浓度低于 2.5 mg/L。总之,我们提出了一个可用于儿童拉莫三嗪 TDM 的 PPK 模型。在我们的人群中,很大一部分儿童的拉莫三嗪谷浓度较低。由于推荐剂量的间隔较大,我们建议将目标定在较高的剂量范围以达到目标浓度。

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引用本文的文献

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Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population.群体药代动力学与机器学习联合应用于优化儿科抗癫痫治疗
Ther Adv Drug Saf. 2023 Jun 21;14:20420986231181337. doi: 10.1177/20420986231181337. eCollection 2023.