Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
Antimicrob Agents Chemother. 2011 Oct;55(10):4619-30. doi: 10.1128/AAC.00182-11. Epub 2011 Aug 1.
A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fC(max)]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT(>MIC)]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices, with fT(>MIC) being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.
这里评估了一种能够描述抗菌药物体外时间杀菌曲线实验全过程的药代动力学-药效学（PKPD）模型，以评估其预测先前确定的 PK/PD 指标的能力。研究了 6 种药物（青霉素、头孢呋辛、红霉素、庆大霉素、莫西沙星和万古霉素），它们代表了广泛的作用机制和 PK/PD 特征。对于每种药物，使用广泛的总日剂量进行了剂量分割研究，这些剂量作为间歇性剂量（给药间隔为 4、8、12 或 24 小时）或作为恒定药物暴露给予。预测了药物浓度的时间过程（PK 模型）以及细菌对药物暴露的反应（体外 PKPD 模型）。非线性最小二乘回归分析确定了最能预测效果的 PK/PD 指标（最大游离药物浓度 [fC(max)]/MIC、游离药物浓度-时间曲线下面积 [fAUC]/MIC 或游离药物浓度超过 MIC 的 24 小时时间百分比 [fT(>MIC)])。基于体外 PKPD 模型的计算机预测确定了先前确定的 PK/PD 指标，对于β-内酰胺类药物，fT(>MIC)是效果的最佳预测指标，对于其余 4 种评估药物，fAUC/MIC 是最佳预测指标。然而，PK/PD 指数的选择和大小被证明对亚人群 PK 的差异、MIC 的不确定性和研究中使用的剂量间隔敏感。与使用 PK/PD 指数相比，基于模型的方法可以更准确地预测效果的全过程，并且对研究设计的敏感性较低，还可以直接考虑亚人群中的 PK 差异。这项研究支持使用从体外时间杀菌曲线建立的 PKPD 模型来制定抗菌药物的最佳给药方案。
