Population Pharmacokinetics of Lamotrigine with Data from Therapeutic Drug Monitoring in German and Spanish Patients with Epilepsy
Population Pharmacokinetics of Lamotrigine with Data from Therapeutic Drug Monitoring in German and Spanish Patients with Epilepsy
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拉莫三嗪的群体药代动力学与德国和西班牙癫痫患者治疗药物监测数据
DOI：
10.1097/ftd.0b013e31817fd4d4
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发表时间：
2008
影响因子：
2.5
通讯作者：
M. García
中科院分区：
文献类型：
--
作者：
N. Rivas;D. S. Buelga;C. Elger;J. Santos;M. Otero;A. Domínguez;M. García
本研究建立了拉莫三嗪（LTG）在西班牙和德国癫痫患者中的群体药代动力学模型。从600例患者中回顾性收集了治疗药物监测的LTG稳态血药浓度数据，共1699个血药浓度。使用非线性混合效应建模程序，根据一室模型分析数据。使用逐步广义加法模型研究了来源（德国或西班牙）、性别、年龄、总体重以及与丙戊酸（VPA）、左乙拉西坦和酶诱导抗癫痫药物（苯巴比妥[PB]、苯妥英[PHT]、扑米酮[PRM]和卡马西平[CBZ]）联合用药的影响。LTG清除率（CL）的最终回归模型如下：CL（L/h）= 0.028* 总体重 *e−0.713*VPA*e0.663*PHT*e0.588*（PB或PRM）*e0.467*CBZ*e0.864*IND，其中IND是指添加到LTG处理中的两种或更多种诱导剂;该因子以及VPA、PHT、PB、PRM和CBZ根据它们的存在或不存在而分别取0或1的值。诱导剂给药导致平均LTG CL显著增加（0.045-0.070 L/h/kg vs.单药治疗中达到0.028 L/h/kg），而VPA导致CL显著降低（0.014 L/h/kg）。因此，与这些分析的药物联合用药可以部分解释群体LTG CL的个体间变异性，其从基本模型下降了40%以上。建议的模型可能是非常有用的临床医生在建立初始LTG剂量指南。然而，最终模型中的个体间变异性（清除率变异系数接近30%）使这些先验剂量预测不精确，并证明需要监测LTG血浆水平以优化给药方案。因此，该最终模型允许在临床药代动力学软件中容易地实施，并且其使用贝叶斯方法在剂量个体化中的应用。
This study develops a population pharmacokinetic model for lamotrigine (LTG) in Spanish and German patients diagnosed with epilepsy. LTG steady-state plasma concentration data from therapeutic drug monitoring were collected retrospectively from 600 patients, with a total of 1699 plasma drug concentrations. The data were analyzed according to a one-compartment model using the nonlinear mixed effect modelling program. The influences of origin (Germany or Spain), sex, age, total body weight, and comedication with valproic acid (VPA), levetiracetam, and enzyme-inducing antiepileptic drugs (phenobarbital [PB], phenytoin [PHT], primidone [PRM], and carbamazepine [CBZ]) were investigated using step-wise generalized additive modelling. The final regression model for LTG clearance (CL) was as follows: CL(L/h) = 0.028*total body weight*e−0.713*VPA*e0.663*PHT*e0.588*(PB or PRM)*e0.467*CBZ*e0.864*IND, where IND refers to two or more inducers added to LTG treatment; this factor as well as VPA, PHT, PB, PRM, and CBZ take a value of zero or one according to their absence or presence, respectively. The administration of inducers led to a significant increase in mean LTG CL (values of 0.045-0.070 L/h/kg vs. 0.028 L/h/kg being reached in monotherapy), whereas VPA led to a significant decrease in CL (0.014 L/h/kg). Thus, comedication with these analyzed drugs can partly explain the interindividual variability in population LTG CL, which decreased from the basic model by more than 40%. The proposed model may be very useful for clinicians in establishing initial LTG dosage guidelines. However, the interindividual variability remaining in the final model (clearance coefficient of variation close to 30%) make these a priori dosage predictions imprecise and justifies the need for LTG plasma level monitoring to optimize dosage regimens. Thus, this final model allows easy implementation in clinical pharmacokinetic software and its application in dosage individualization using the Bayesian approach.