Department of Clinical Sciences and Administration, College of Pharmacy, University of Houston, Houston, Texas, United States of America.
PLoS Comput Biol. 2011 Jan 6;7(1):e1001043. doi: 10.1371/journal.pcbi.1001043.
Pharmacodynamic modeling has been increasingly used as a decision support tool to guide dosing regimen selection, both in the drug development and clinical settings. Killing by antimicrobial agents has been traditionally classified categorically as concentration-dependent (which would favor less fractionating regimens) or time-dependent (for which more frequent dosing is preferred). While intuitive and useful to explain empiric data, a more informative approach is necessary to provide a robust assessment of pharmacodynamic profiles in situations other than the extremes of the spectrum (e.g., agents which exhibit partial concentration-dependent killing). A quantitative approach to describe the interaction of an antimicrobial agent and a pathogen is proposed to fill this unmet need. A hypothetic antimicrobial agent with linear pharmacokinetics is used for illustrative purposes. A non-linear functional form (sigmoid Emax) of killing consisted of 3 parameters is used. Using different parameter values in conjunction with the relative growth rate of the pathogen and antimicrobial agent concentration ranges, various conventional pharmacodynamic surrogate indices (e.g., AUC/MIC, Cmax/MIC, %T>MIC) could be satisfactorily linked to outcomes. In addition, the dosing intensity represented by the average kill rate of a dosing regimen can be derived, which could be used for quantitative comparison. The relevance of our approach is further supported by experimental data from our previous investigations using a variety of gram-negative bacteria and antimicrobial agents (moxifloxacin, levofloxacin, gentamicin, amikacin and meropenem). The pharmacodynamic profiles of a wide range of antimicrobial agents can be assessed by a more flexible computational tool to support dosing selection.
药效动力学模型已越来越多地被用作决策支持工具，以指导药物开发和临床环境中的剂量方案选择。抗菌药物的杀菌作用传统上被分类为浓度依赖性（这将有利于较少分割的方案）或时间依赖性（这更倾向于更频繁的给药）。虽然直观且有助于解释经验数据，但在除了极端情况之外的情况下，需要更具信息性的方法来对药效动力学特征进行稳健评估（例如，表现出部分浓度依赖性杀菌作用的药物）。提出了一种定量方法来描述抗菌药物与病原体的相互作用，以满足这一未满足的需求。为了说明问题，使用了具有线性药代动力学的假设性抗菌药物。使用 3 个参数的非线性功能形式（最大效应 sigmoid）来表示杀菌作用。结合病原体的相对生长速率和抗菌药物浓度范围，使用不同的参数值，可以将各种传统药效动力学替代指标（例如 AUC/MIC、Cmax/MIC、%T>MIC）与结果进行满意地关联。此外，可以推导出剂量方案的平均杀灭率所代表的给药强度，可用于定量比较。我们之前使用各种革兰氏阴性菌和抗菌药物（莫西沙星、左氧氟沙星、庆大霉素、阿米卡星和美罗培南）进行的研究的实验数据进一步支持了我们方法的相关性。通过更灵活的计算工具，可以评估广泛的抗菌药物的药效动力学特征，以支持剂量选择。
