van Rij Catharina M, Huitema Alwin D R, Swart Eleonora L, Greuter Henricus N J M, Lammertsma Adriaan A, van Loenen Arie C, Franssen Eric J F
Department of Pharmacy, Groene Hart Hospital, PO Box 1098, 2800 BB Gouda, The Netherlands.
Br J Clin Pharmacol. 2005 Nov;60(5):477-85. doi: 10.1111/j.1365-2125.2005.02487.x.
The objectives of the study were to develop a population pharmacokinetic model for (11)C-flumazenil at tracer concentrations, to assess the effects of patient-related covariates and to derive an optimal sampling protocol for clinical use.
A population pharmacokinetic model was developed using nonlinear mixed effects modelling (NONMEM) with data obtained from 51 patients with either depression or epilepsy. Each patient received approximately 370 MBq (1-4 microg) of (11)C-flumazenil. The effects of selected covariates (gender, weight, type of disease and age) were investigated. The model was validated using a bootstrap method. Finally, an optimal sampling design was established.
The population pharmacokinetics of tracer quantities of (11)C-flumazenil were best described by a two compartment model. Type of disease and weight were identified as significant covariates (P < 0.002). Mean population pharmacokinetic parameters (percent coefficient of variation) were: CL 1530 mL min(-1) (6.6%), V(1) 24.8 x 10(3) mL (3.8%), V(2) 27.3 x 10(3) mL (5.4%), and Q 2510 mL min(-1) (6.5%). CL was 20% lower in patients with epilepsy, and the influence of weight on V(1) was 0.55% kg(-1). For the prediction of the AUC, a combination of two time points at t = 30 and 60 min post injection was considered optimal (bias -0.7% (95% CI -2.2 to 0.8%), precision 5.7% (95% CI 4.5-6.9%)). The optimal sampling strategy was cross-validated (observed AUC = 296 MBql(-1) min(-1) (95% CI 102-490), predicted AUC = 288 MBql(-1) min(-1) (95% CI 70-506)).
The population pharmacokinetics of tracer quantities of (11)C-flumazenil are well described by a two-compartment model. Inclusion of weight and type of disease as covariates significantly improved the model. Furthermore, an optimal sampling procedure may increase the feasibility and applicability of (11)C-flumazenil PET.
本研究的目的是建立示踪剂浓度下(11)C-氟马西尼的群体药代动力学模型,评估患者相关协变量的影响,并推导临床使用的最佳采样方案。
采用非线性混合效应建模(NONMEM),利用51例抑郁症或癫痫患者的数据建立群体药代动力学模型。每位患者接受约370 MBq(1 - 4 μg)的(11)C-氟马西尼。研究了选定协变量(性别、体重、疾病类型和年龄)的影响。采用自助法对模型进行验证。最后,建立了最佳采样设计。
示踪剂量的(11)C-氟马西尼的群体药代动力学最佳用二室模型描述。疾病类型和体重被确定为显著协变量(P < 0.002)。群体药代动力学参数均值(变异系数百分比)为:清除率(CL)1530 mL·min⁻¹(6.6%),中央室容积(V₁)24.8×10³ mL(3.8%),周边室容积(V₂)27.3×10³ mL(5.4%),以及流量(Q)2510 mL·min⁻¹(6.5%)。癫痫患者的CL降低20%,体重对V₁的影响为0.55% kg⁻¹。对于曲线下面积(AUC)的预测,注射后t = 30和60分钟的两个时间点组合被认为是最佳的(偏差 -0.7%(95%置信区间 -2.2至0.8%),精密度5.7%(95%置信区间4.5 - 6.9%))。最佳采样策略经交叉验证(观察到的AUC = 296 MBq·l⁻¹·min⁻¹(95%置信区间102 - 490),预测的AUC = 288 MBq·l⁻¹·min⁻¹(95%置信区间70 - 506))。
示踪剂量的(11)C-氟马西尼的群体药代动力学可用二室模型很好地描述。将体重和疾病类型作为协变量纳入显著改善了模型。此外,最佳采样程序可能会提高(11)C-氟马西尼正电子发射断层扫描(PET)的可行性和适用性。