Department of Anesthesiology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB, Groningen, The Netherlands.
Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
Clin Pharmacokinet. 2019 Jun;58(6):767-780. doi: 10.1007/s40262-018-0727-5.
Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.
A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.
The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL (73.4 µmol L) has a V, V, CL and Q of 42.9 L, 41.7 L, 4.10 L h and 3.22 L h. Clearance matures with age, reaching 50% of the maximal value (5.31 L h 70 kg) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.
A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.
由于不确定最佳的万古霉素剂量方案在不同的患者人群中，导致了大量的亚组特异性药代动力学模型和衍生的剂量方案。我们旨在研究是否可以基于涵盖患者特征极端情况的广泛数据集开发万古霉素的单一模型。此外，作为当前剂量推荐的基准，我们评估并优化了整个生命周期和特定患者亚组的预期万古霉素暴露。
在 NONMEM 中构建了一个群体药代动力学模型，该模型基于来自不同患者人群的 14 项不同研究的数据。对两个美国食品和药物管理局/欧洲药品管理局批准的药物标签的稳态暴露进行了模拟，并比较了不同患者亚组的情况，并推导了优化剂量。
最终模型使用经产妇年龄、体重和血清肌酐作为协变量。对于血清肌酐水平为 0.83mg/dL（73.4µmol/L）的 35 岁、70kg 患者，V、V、CL 和 Q 分别为 42.9L、41.7L、4.10L/h 和 3.22L/h。清除率随年龄成熟，在经产妇年龄 46.4 周时达到最大值（5.31L/h·70kg）的 50%，然后随年龄下降至 61.6 岁时的 50%。目前的剂量指南未能在不同患者亚组中实现令人满意的稳态暴露。经优化后，美国食品和药物管理局标签的增加剂量可在不同患者亚组中实现一致的目标达标，且剂量过低和过高的风险最小（±20%）。
开发了一种群体模型，可用于进一步制定万古霉素的年龄和肾功能分层剂量方案，并通过治疗药物监测和贝叶斯预测进行个体化治疗。
