Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
Antimicrob Agents Chemother. 2011 Apr;55(4):1571-9. doi: 10.1128/AAC.01286-10. Epub 2011 Jan 31.
We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (E(max)) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.
我们之前描述了一种通用的半机械药代动力学-药效学（PKPD）模型，该模型成功地描述了在静态浓度下五种不同类别的抗菌药物对细菌培养物的抗菌作用随时间的变化过程。在这个 PKPD 模型中，总细菌群体被分为两个亚群，一个是生长的药敏菌群体，另一个是静止的耐药菌群体。药物作用被包括在内，作为对药敏菌的杀伤率的增加，采用最大效应（E（max））模型。本研究的目的是评估该 PKPD 模型描述和预测具有动态浓度-时间曲线的体外实验数据的能力。通过使用体外动力学系统进行动态时间杀伤曲线实验，其中使用不同的起始浓度（MIC 的 2 倍和 16 倍）和消除条件（人体半衰期、半衰期缩短和恒定浓度）将化脓性链球菌的培养物暴露于苯唑西林、头孢呋辛、红霉素、莫西沙星或万古霉素。应用 PKPD 模型，将静态和动态实验的观察结果与基于（i）静态数据、（ii）动态数据和（iii）静态和动态数据组合进行参数估计的模型预测进行比较。静态和动态实验之间的实验设置差异对细菌的生长动力学没有显著影响。通过参数重新估计，我们之前提出的 PKPD 模型的结构可以很好地描述当以不同消除率的五种研究抗生素的动态浓度暴露时细菌的生长和杀伤动力学。此外，仅基于静态时间杀伤曲线实验数据的参数估计的模型可以很好地预测大多数动态实验的时间杀伤曲线。在估计中添加动态实验数据可以提高模型对头孢呋辛和万古霉素的拟合度，表明研究的抗生素之间对实验条件的敏感性存在一些差异。
