丙戊酸盐的群体药代动力学模型及癫痫患儿丙戊酸盐血清浓度预测
Population pharmacokinetic model of valproate and prediction of valproate serum concentrations in children with epilepsy.
作者信息
Jiang De-chun, Wang Li
机构信息
Department of Pediatrics, Peking University First Hospital, Therapeutic Drug Monitoring and Clinical Toxicology, Center of Peking University, Beijing 100034, China.
出版信息
Acta Pharmacol Sin. 2004 Dec;25(12):1576-83.
AIM
Using sparse data of valproate (VPA) serum concentrations to build a population pharmacokinetic (PPK) model of VPA in Chinese children with epilepsy and to predict serum concentrations for new patients using a Bayesian approach.
METHODS
Two hundred epileptic children, whose VPA serum concentrations were collected, were divided randomly into two groups (A and B, n=100 each). The PPK parameter values of group A were calculated to establish a PPK Model by using the NPEM Program of USCPACK software. Based on it, VPA serum concentrations of group B were predicted with the Bayesian Fitting Program of the USCPACK software. To assess the accuracy and precision of prediction, a paired-comparisons t-test was run between predicted and observed concentrations, and then the mean prediction error (MPE), mean square prediction error (MSPE), root mean square prediction error (RMSPE), and coincidence rates for different percentages of prediction error were all calculated.
RESULTS
Optimum PPK parameters were: Ka, 2.522+/-2.743 h(-1); Vs, 0.329+/-0.496 L/kg; and Kel, 0.0438+/-0.0384 h(-1). For group B, there was no significant difference between predicted and observed concentrations. MPE was -0.43 mg/L, MSPE was 115.40 (mg/L)2, and RMSPE was 5.47 mg/L. The coincidence rates for percentages of prediction error, which were less than 5 %, 10 %, 15 %, 20 %, 25 %, and 30 %, were 62 %, 74 %, 82 %, 85 %, 89 %, and 93 %, respectively.
CONCLUSION
A PPK model of VPA in epileptic children was successfully established. Based on it, VPA serum concentrations can be predicted accurately with a Bayesian approach.
目的
利用丙戊酸（VPA）血清浓度的稀疏数据，建立中国癫痫儿童VPA的群体药代动力学（PPK）模型，并采用贝叶斯方法预测新患者的血清浓度。
方法
收集200例癫痫儿童的VPA血清浓度，随机分为两组（A组和B组，每组n = 100）。使用USCPACK软件的NPEM程序计算A组的PPK参数值，以建立PPK模型。在此基础上，使用USCPACK软件的贝叶斯拟合程序预测B组的VPA血清浓度。为评估预测的准确性和精确性，对预测浓度和实测浓度进行配对比较t检验，然后计算平均预测误差（MPE）、平均平方预测误差（MSPE）、均方根预测误差（RMSPE）以及不同预测误差百分比的符合率。
结果
最佳PPK参数为：Ka，2.522±2.743 h⁻¹；Vs，0.329±0.496 L/kg；Kel，0.0438±0.0384 h⁻¹。对于B组，预测浓度与实测浓度之间无显著差异。MPE为 -0.43 mg/L，MSPE为115.40 (mg/L)²，RMSPE为5.47 mg/L。预测误差百分比小于5%、10%、15%、20%、25%和30%时的符合率分别为62%、74%、82%、85%、89%和93%。
结论
成功建立了癫痫儿童VPA的PPK模型。基于该模型，采用贝叶斯方法可准确预测VPA血清浓度。
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