Predictability of individualized dosage regimens of carbamazepine and valproate mono- and combination therapy.
Predictability of individualized dosage regimens of carbamazepine and valproate mono- and combination therapy.
复制标题
卡马西平和丙戊酸盐单药和联合治疗个体化剂量方案的可预测性。
DOI：
10.1111/j.1365-2710.2010.01215.x
复制
发表时间：
2011
影响因子：
2
通讯作者：
Bondareva,KI
中科院分区：
文献类型：
--
作者：
Bondareva,IB;Jelliffe,RW;Andreeva,OV;Bondareva,KI
已知内容和目的：许多研究者认为，适当合理使用治疗药物监测（TDM）和贝叶斯反馈剂量调整有助于卡马西平（CBZ）和/或丙戊酸盐（VPA）治疗癫痫，增加癫痫发作控制和安全性，以及降低治疗成本。在之前的工作中，我们已经开发并在临床实践中使用了不同剂型的VPA和诱导后CBZ行为的群体药代动力学（PK）模型，以及CBZ与另一种“旧”抗癫痫药物（AED）的联合治疗。外部验证的一个重要步骤是评估基于拟议的群体PK模型和稀疏TDM数据的贝叶斯个体化AED给药方案程序的效果，以及它在真实的临床环境中的帮助程度。本研究的目的是评估诱导期后CBZ单药治疗或VPA单药治疗的个体化给药方案的可预测性，以及基于癫痫患者的TDM数据和早期开发的群体模型，对CBZ和VPA作为联合治疗进行分析。方法：使用USC*PACK软件对4组TDM数据进行PK/PD分析：CBZ单药治疗成人癫痫患者的556项预测，VPA单药治疗的662项预测，CBZ+VPA联合治疗的成人癫痫患者的402个CBZ血清水平预测值和430个VPA血清水平预测值。预测误差（PE）和加权PE的统计特征被用来估计预测的偏差和精度。结果和讨论：本研究表明，在CBZ和VPA单药治疗和联合治疗的大多数情况下，基于早期开发的群体PK模型、TDM数据和患者特异性最大后验概率贝叶斯后验参数值对未来AED浓度的预测提供了临床可接受的估计值。残差的统计分析表明，残差和加权残差的分布接近正态分布（Kolmogorov-Smirnov检验，P> 0·05），其平均值在所有预测组中均与零无统计学显著性差异（无统计学显著性偏倚，P> 0·05）。 在VPA+CBZ联合治疗期间观察到的VPA浓度预测质量下降，尤其是当CBZ剂量改变时，可能很好地解释了其PK相互作用。对于所有组，在线性回归分析中，在不同的未来预测时间范围内观察到的预测质量下降趋势被认为具有统计学显著性（P<0.05）。 在1.5至3.5年的观察期内，未来血清水平的预测精度低于接近目前的预测精度。没有偏见的预测是与time-horizontal.What是新的和结论：我们的验证结果表明，较早开发的人口模型的预测性能良好，和相当可接受的预测未来AED血清水平的CBZ和VPA治疗的个性化剂量方案在真实的临床设置。
What is known and Objective:Many investigators agree that appropriate rational utilization of therapeutic drug monitoring (TDM) with Bayesian feedback dosage adjustment facilitates epilepsy treatment with carbamazepine (CBZ) and/or valproate (VPA) by increasing the seizure control and safety, as well as by reducing treatment costs. In previous works we have developed and used in clinical practice population pharmacokinetic (PK) models of different dosage forms for VPA and post‐induction CBZ behaviour, as well as for combined therapy with CBZ plus another ‘old’ antiepileptic drug (AED). An important step of external validation is to evaluate how well a procedure of Bayesian individualizing AED dosage regimens based on a proposed population PK model and sparse TDM data ‘works’, and how helpful it is in real practical clinical settings.The aim of this study was to evaluate the predictability of individualized dosage regimens for monotherapy with CBZ in the post‐induction period or with VPA, as well as for CBZ and VPA given as combination therapy based on TDM data of epileptic patients and the earlier developed population models.Methods:Four groups of TDM data were analysed using the USC*PACK software for PK/PD analysis: 556 predictions for adult epileptic patients on CBZ monotherapy, 662 predictions for VPA monotherapy, 402 predictions of CBZ serum levels and 430 predictions of VPA serum levels for adult epileptic patients on CBZ+VPA combination therapy. Statistical characteristics of the prediction errors (PE) and weighted PE were used to estimate bias and precision of predictions. Intraindividual and interoccasional variability of predictions were also estimated.Results and Discussion:This study demonstrated that in most cases of CBZ and VPA monotherapy and combination therapy, predictions of future AED concentrations based on the earlier developed population PK models, TDM data and patient‐specific maximum a posteriori probability Bayesian posterior parameter values provided clinically acceptable estimates.Statistical analysis of the residuals demonstrated that the distributions of residual and weighted residual were close to the normal distribution (Kolmogorov–Smirnov test,P> 0·05) and their mean values did not differ statistically significant from zero (no statistically significant bias,P> 0·05) for all groups of predictions. The observed decreased quality of predictions of VPA concentrations during VPA+CBZ combination therapy, especially when CBZ dosages were changed, might well be explained by their PK interactions. For all groups, in linear regression analysis, the observed trend of decreasing of the prediction quality over various future prediction time horizons was considered statistically significant (P< 0·05). Prediction of serum levels further in future was less precise than those closer to the present for a 1·5‐ to 3·5‐year observation period. No bias in predictions was associated with the time horizons.What is new and Conclusion:Our validation results suggest good predictive performance of the population models developed earlier, and quite acceptable predictions of future AED serum levels for individualized dosage regimens of CBZ and VPA therapy in real clinical settings.