Experimental Therapeutic Branch, Department of Clinical Pharmacology, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA.
Division of Infectious Disease Pharmacology, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland, USA.
Antimicrob Agents Chemother. 2020 Aug 20;64(9). doi: 10.1128/AAC.01308-20.
Effective bacterial infection eradication requires not only potent antibacterial agents but also proper dosing strategies. Current practices generally utilize point estimates of the effects of therapeutic agents, even though the actual kinetics of exposure are much more complex and relevant. Here, we use a full time course of the observed effects to develop a semimechanistic pharmacokinetic-pharmacodynamic model for eravacycline against multiple Gram-negative bacterial pathogens. This model incorporates components such as pharmacokinetics, bacterial life cycle, and drug effects to quantitatively describe the time course of antibacterial killing and the emergence of resistance. Model discrimination was performed by comparing goodness of fit, convergence diagnostics, and objective function values. Models were validated by assessing their abilities to describe bacterial count time courses in visual predictive checks. The final model describes 576 bacterial counts (expressed in log CFU per milliliter) from 144 time-kill experiments with low residual error and high precision. We characterize antibacterial susceptibility as a function of the MIC and adaptive resistance. In doing so, we show that the MIC is proportional to initial susceptibility at 0 h and the development of resistance over the course of 16 h. Altogether, this model may be useful in supporting dose selection, since it incorporates pharmacodynamics and clinically observed individual drug susceptibilities.
有效清除细菌感染不仅需要强有力的抗菌药物，还需要适当的给药策略。目前的实践通常利用治疗药物效果的点估计值，尽管实际的暴露动力学要复杂得多且相关。在这里，我们使用观察到的效应的完整时间过程来开发针对多种革兰氏阴性细菌病原体的依拉环素的半机械药代动力学-药效动力学模型。该模型结合了药代动力学、细菌生命周期和药物作用等成分，定量描述了抗菌杀伤和耐药性出现的时间过程。通过比较拟合优度、收敛诊断和目标函数值来进行模型区分。通过评估其在视觉预测检查中描述细菌计数时间过程的能力来验证模型。最终模型描述了来自 144 次时间杀伤实验的 576 个细菌计数（以每毫升对数 CFU 表示），残差误差低，精度高。我们将抗菌敏感性描述为 MIC 和适应性耐药性的函数。这样，我们表明 MIC 与 0 h 时的初始敏感性成正比，以及 16 h 内耐药性的发展。总的来说，该模型可能有助于支持剂量选择，因为它结合了药效动力学和临床观察到的个体药物敏感性。
