Department of Chemical & Biomolecular Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Department of Pharmacy Practice and Translational Research, University of Houston, 4349 Martin Luther King Boulevard, Houston TX 77204, United States of America.
Comput Methods Programs Biomed. 2022 Dec;227:107212. doi: 10.1016/j.cmpb.2022.107212. Epub 2022 Nov 1.
Model-based analysis of longitudinal optical density measurements from a bacterial suspension exposed to antibiotics has been proposed as a potentially efficient and effective method for extracting useful information to improve the individualized design of treatments for bacterial infections. To that end, the authors developed in previous work a mathematical modeling framework that can use such measurements for design of effective dosing regimens.
Here we further explore ways to extract information from longitudinal optical density measurements to predict bactericidal efficacy of clinically relevant antibiotic exposures.
Longitudinal optical density measurements were collected in an automated instrument where Acinetobacter baumannii, ATCC BAA747, was exposed to ceftazidime concentrations of 1, 4, 16, 64, and 256 mg/L and to ceftazidime/amikacin concentrations of 1/0.5, 4/2, 16/8, 64/32, and 256/128 (mg/L)/(mg/L) over 20 h. Calibrated conversion of measurements produced total (both live and dead) bacterial cell concentration data (CFU/mL equivalent) over time. Model-based data analysis predicted the bactericidal efficacy of ceftazidime and of ceftazidime/amikacin (at ratio 2:1) for periodic injection every 8 h and subsequent exponential decline with half-life of 2.5 h. Predictions were experimentally tested in an in vitro hollow-fiber infection model, using peak concentrations of 60 and 150 mg/L for injected ceftazidime and of 40/20 (mg/L)/(mg/L) for injected ceftazidime/amikacin.
Model-based analysis predicted low (<62%) confidence in clinically relevant suppression of the bacterial population by periodic injections of ceftazidime alone, even at high peak concentrations. Conversely, analysis predicted high (>95%) confidence in bacterial suppression by periodic injections of ceftazidime/amikacin combinations at a wide range of peak concentrations ratioed at 2:1. Both predictions were experimentally confirmed in an in vitro hollow fiber infection model, where ceftazidime was periodically injected at peak concentrations 60 and 150 mg/L (with predicted suppression confidence 38% and 59%, respectively) and a combination of ceftazidime/amikacin was periodically injected at peak concentrations 40/20 (mg/L)/(mg/L) (with predicted suppression confidence 98%).
The paper highlights the potential of clinicians using the proposed mathematical framework to determine the utility of different antibiotics to suppress a patient-specific isolate. Additional studies will be needed to consolidate and expand the utility of the proposed method.
从暴露于抗生素的细菌悬浮液中进行的纵向光密度测量的基于模型的分析已被提议为一种从测量中提取有用信息以改善细菌感染的个体化治疗设计的潜在有效和有效的方法。为此，作者在之前的工作中开发了一种数学建模框架，该框架可用于设计有效的给药方案。
在这里，我们进一步探讨了从纵向光密度测量中提取信息以预测临床相关抗生素暴露的杀菌效果的方法。
在自动仪器中收集纵向光密度测量值，其中鲍曼不动杆菌，ATCC BAA747 暴露于头孢他啶浓度为 1、4、16、64 和 256mg/L 和头孢他啶/阿米卡星浓度为 1/0.5、4/2、16/8、64/32 和 256/128(mg/L)/(mg/L)，持续 20 小时。经校准的测量转换产生了总（活的和死的）细菌细胞浓度数据（CFU/mL 当量）随时间的变化。基于模型的数据分析预测了头孢他啶和头孢他啶/阿米卡星（比例为 2:1）的杀菌效果，每隔 8 小时进行周期性注射，随后以半衰期为 2.5 小时的指数下降。在体外中空纤维感染模型中进行了实验测试，其中注射用头孢他啶的峰值浓度为 60 和 150mg/L，注射用头孢他啶/阿米卡星的峰值浓度为 40/20(mg/L)/(mg/L)。
基于模型的分析预测，单独使用周期性注射头孢他啶对细菌种群的抑制作用置信度低（<62%），即使在高峰值浓度下也是如此。相反，分析预测周期性注射头孢他啶/阿米卡星组合在广泛的峰值浓度比为 2:1 时具有高（>95%）的细菌抑制置信度。这两种预测都在体外中空纤维感染模型中得到了实验证实，其中周期性注射头孢他啶的峰值浓度为 60 和 150mg/L（预测抑制置信度分别为 38%和 59%），而头孢他啶/阿米卡星的组合周期性注射的峰值浓度为 40/20(mg/L)/(mg/L)（预测抑制置信度为 98%）。
本文强调了临床医生使用所提出的数学框架来确定不同抗生素抑制患者特定分离物的效用的潜力。需要进一步的研究来巩固和扩展所提出方法的效用。
