Eales Brianna M, Smith James, Pouya Nazanin, Hudson Cole S, Tam Vincent H, Nikolaou Michael
Department of Pharmacological and Pharmaceutical Sciences, University of Houston College of Pharmacy, Houston, TX 77204.
Department of Pharmacy Practice and Translational Research, University of Houston College of Pharmacy, Houston, TX 77204.
Comput Chem Eng. 2025 Jan;192. doi: 10.1016/j.compchemeng.2024.108884. Epub 2024 Sep 24.
Treatment of serious bacterial infections with antimicrobial agents, such as antibiotics, is a major clinical challenge, because of growing bacterial resistance to multiple agents. Combination therapy (i.e. combined dosing of more than one agent) is often used for the purpose, but its systematic design remains a challenge. To address this, we recently reported a method to mathematically model bacterial response to antimicrobial agents, and to use this model for systematic design of clinically relevant combination therapy. The method relies on (a) longitudinal data of bacterial load, estimated from optical density measurements during time-kill experiments in an automated instrument, and (b) use of these data to fit a mathematical model for combination therapy design. In this work, we studied an application of the proposed method to (a) an important bacterial pathogen () and (b) two cases of antibiotic combinations (ceftazidime / amikacin and ceftazidime / avibactam) in synchronous and asynchronous dosing, not otherwise studied to date. Following the proposed method, optical density based data of bacterial load under antibiotic exposure for 20 h were used to calibrate the mathematical model and subsequently predict outcomes of various dosing regimens with clinically relevant pharmacokinetics. Representative predictions by the mathematical model were tested in a hollow fiber infection model over 120 h. Test outcomes validated these predictions. Collectively, this study both provides guidance for design of infection treatments with the agents studied and underscores the broader applicability of the proposed method for design of clinically relevant combination therapy.
使用抗生素等抗菌药物治疗严重细菌感染是一项重大临床挑战，因为细菌对多种药物的耐药性不断增强。联合治疗（即联合使用多种药物）常用于此目的，但其系统设计仍然是一个挑战。为了解决这个问题，我们最近报道了一种方法，通过数学模型来模拟细菌对抗菌药物的反应，并利用该模型进行临床相关联合治疗的系统设计。该方法依赖于：（a）在自动仪器的时间杀灭实验过程中，通过光密度测量估算出的细菌载量纵向数据；（b）利用这些数据来拟合联合治疗设计的数学模型。在这项工作中，我们研究了所提出方法在以下方面的应用：（a）一种重要的细菌病原体（）；（b）两种抗生素联合用药情况（头孢他啶/阿米卡星和头孢他啶/阿维巴坦），采用同步和异步给药方式，这些情况迄今尚未进行过其他研究。按照所提出的方法，利用抗生素暴露20小时后基于光密度的细菌载量数据来校准数学模型，并随后预测具有临床相关药代动力学的各种给药方案的结果。通过数学模型进行的代表性预测在中空纤维感染模型中进行了120小时的测试。测试结果验证了这些预测。总体而言，本研究既为所研究药物的感染治疗设计提供了指导，也强调了所提出方法在临床相关联合治疗设计方面更广泛的适用性。
