Pharmacokinetic/Pharmacodynamic (PK/PD) Indices of Antibiotics Predicted by a Semimechanistic PKPD Model: a Step toward Model-Based Dose Optimization
Pharmacokinetic/Pharmacodynamic (PK/PD) Indices of Antibiotics Predicted by a Semimechanistic PKPD Model: a Step toward Model-Based Dose Optimization
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DOI：
10.1128/aac.00182-11
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发表时间：
2011-10-01
影响因子：
4.9
通讯作者：
Friberg, Lena E.
中科院分区：
文献类型：
--
作者：
Nielsen, Elisabet I.;Cars, Otto;Friberg, Lena E.
本文评价了表征抗菌药物体外时间-杀灭曲线实验全时程的药代动力学-药效学（PKPD）模型预测先前确定的PK/PD指数的能力。研究了6种药物（苄青霉素、头孢呋辛、红霉素、庆大霉素、阿托沙星和万古霉素），代表了广泛的作用机制和PK和PD特征。对于每种药物，模拟剂量分次研究，使用间歇给药（给药间隔为4、8、12或24 h）或恒定药物暴露的广泛每日总剂量。预测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应的PK/PD指数（最大未结合药物浓度[fC（max）]/MIC、未结合药物浓度-时间曲线下面积[fAUC]/MIC或未结合药物浓度超过MIC的24小时时间段百分比[fT（>MIC）]）。基于体外PKPD模型的计算机模拟预测确定了先前确定的PK/PD指数，其中fT（>MIC）是β-内酰胺类药物作用的最佳预测因子，fAUC/MIC是其余4种评价药物的最佳预测因子。然而，PK/PD指数的选择和幅度对亚群中PK的差异、MIC的不确定性和研究的给药间隔敏感。与使用PK/PD指数相比，基于模型的方法（可预测全时效应过程）对研究设计的敏感性较低，并允许直接考虑亚群中的PK差异。本研究支持使用体外时间-杀灭曲线建立的PKPD模型来开发抗菌药物的最佳给药方案。
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 beta-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.