Clinical Pharmacology, Advanced PK/PD Modeling and Simulation, Johnson & Johnson Pharmaceutical Research & Development, LLC, Titusville, NJ 08560, USA.
Antimicrob Agents Chemother. 2010 Jun;54(6):2354-9. doi: 10.1128/AAC.01649-09. Epub 2010 Apr 12.
A population pharmacokinetic model of doripenem was constructed using data pooled from phase 1, 2, and 3 studies utilizing nonlinear mixed effects modeling. A 2-compartment model with zero-order input and first-order elimination best described the log-transformed concentration-versus-time profile of doripenem. The model was parameterized in terms of total clearance (CL), central volume of distribution (V(c)), peripheral volume of distribution (V(p)), and distribution clearance between the central and peripheral compartments (Q). The final model was described by the following equations (for jth subject): CL(j) (liters/h) = 13.6.(CL(CR)(j)/98 ml/min)(0.659).(1 + CL(race)(j) [0 for Caucasian]); V(c)(j) (liters) = 11.6.(weight(j)/73 kg)(0.596); Q(j) (liters/h) = 4.74.(weight(j)/73)(1.06); and V(p)(j) (liters) = 6.04.(CL(CR)(j)/98 ml/min)(0.417).(weight(j)/73 kg)(0.840).(age(j)/40 years)(0.307). According to the final model, population mean parameter estimates and interindividual variability (percent coefficient of variation [% CV]) for CL (liters/h), V(c) (liters), V(p) (liters), and Q (liters/h) were 13.6 (19%), 11.6 (19%), 6.0 (25%), and 4.7 (42%), respectively. Residual variability, estimated using three separate additive residual error models, was 0.17 standard deviation (SD), 0.55 SD, and 0.92 SD for phase 1, 2, and 3 data, respectively. Creatinine clearance was the most significant predictor of doripenem clearance. Mean Bayesian clearance was approximately 33%, 55%, and 76% lower for individuals with mild, moderate, or severe renal impairment, respectively, than for those with normal renal function. The population pharmacokinetic model based on healthy volunteer data and patient data informs us of doripenem disposition in a more general population as well as of the important measurable intrinsic and extrinsic factors that significantly influence interindividual pharmacokinetic differences.
使用来自第 1、2 和 3 阶段研究的数据,通过非线性混合效应建模构建了多利培南的群体药代动力学模型。2 室模型,零级输入和一级消除,最佳描述了多利培南的对数转换浓度-时间曲线。该模型以总清除率 (CL)、中央分布容积 (V(c))、外周分布容积 (V(p)) 和中央和外周隔室之间的分布清除率 (Q) 为参数进行参数化。最终模型由以下方程描述(对于第 j 个个体):CL(j)(升/小时)= 13.6.(CL(CR)(j)/98 ml/min)(0.659)。(1 + CL(race)(j) [0 对于白种人]);V(c)(j)(升)= 11.6.(体重(j)/73 kg)(0.596);Q(j)(升/小时)= 4.74.(体重(j)/73 kg)(1.06);和 V(p)(j)(升)= 6.04.(CL(CR)(j)/98 ml/min)(0.417)。(体重(j)/73 kg)(0.840)。(年龄(j)/40 岁)(0.307)。根据最终模型,CL(升/小时)、V(c)(升)、V(p)(升)和 Q(升/小时)的群体平均参数估计值和个体间变异性(%变异系数)分别为 13.6(19%)、11.6(19%)、6.0(25%)和 4.7(42%)。使用三个单独的加性残差误差模型估计的残留变异性分别为第 1、2 和 3 阶段数据的 0.17 标准差 (SD)、0.55 SD 和 0.92 SD。肌酐清除率是多利培南清除率的最重要预测因子。与肾功能正常者相比,肾功能轻度、中度或重度受损者的平均贝叶斯清除率分别降低约 33%、55%和 76%。基于健康志愿者数据和患者数据的群体药代动力学模型使我们能够了解更广泛人群中多利培南的处置情况,以及对个体药代动力学差异有显著影响的重要可测量内在和外在因素。