Song Xiangqing
Department of Pharmacy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, No. 283 Tongzipo Road, Yuelu District, Changsha, Hunan, People's Republic of China.
J Transl Med. 2025 Jul 19;23(1):806. doi: 10.1186/s12967-025-06832-5.
Quantitative calculation models for the ratio of daily area under the concentration-time curve (AUC) to the minimum inhibitory concentration (MIC) (i.e., AUC/MIC) and the amount of time that concentration stays above the MIC during a dosing interval (i.e., T%) in regular intermittent i.v. infusion (RIIVI) are currently absent. This work set out to construct the models of AUC/MIC, T% and matching daily dosage (D) in RIIVI, and further examine their performance by comparing with the documented models currently used widely, and concomitantly create a closed loop for evaluating the original scheme's effectiveness and developing the personalized dosing regimen using these established models.
1-compartment model was used to construct the AUC/MIC, T% and matching D models. 20 designed individuals with different renal functions in different clinical scenarios were employed to examine the models. Bland-Altman plots and Bootstrap analysis were applied to assess the consistency, and the prediction reliability and accuracy of the models in calculating AUC/MIC and T%, respectively. Tornado method based on global sensitivity analysis was used to perform the sensitivity analysis of the models to examine the effect of parameter variation on predictions. Combining the AUC/MIC or T% model-based efficacy assessment with the D model-based regimen optimization to creates a closed loop consisting of efficacy assessment and regimen optimization.
The AUC/MIC, T% and D models in RIIVI were developed. Bland-Altman plots and Bootstrap analysis indicated that the established and the documented models had no consistency and the established models had better prediction reliability and accuracy in calculating AUC/MIC and T%. Sensitivity analysis suggested that MIC was an important factor on AUC/MIC and T% variation. Cooperative application of the AUC/MIC, T% and D model created a closed loop consisting of efficacy assessment and regimen optimization for creation of customized antibiotic regimens.
The established AUC/MIC and T% models displayed better performance relative to the documented models. Cooperative application of these models and the corresponding D model can create a fully closed loop for evaluating the original scheme's effectiveness and developing the optimization regimen, and thus construct a basic framework for the creation of customized antibiotic regimens.
目前缺乏用于定量计算常规间歇性静脉输注（RIIVI）中每日浓度 - 时间曲线下面积（AUC）与最低抑菌浓度（MIC）之比（即AUC/MIC）以及给药间隔期间浓度高于MIC的时间量（即T%）的模型。本研究旨在构建RIIVI中AUC/MIC、T%及匹配每日剂量（D）的模型，并通过与目前广泛使用的已记录模型进行比较来进一步检验其性能，同时创建一个闭环，用于评估原始方案的有效性并使用这些已建立的模型制定个性化给药方案。
采用一室模型构建AUC/MIC、T%及匹配D的模型。选用20名在不同临床场景中具有不同肾功能的设计个体来检验模型。应用Bland - Altman图和Bootstrap分析分别评估模型在计算AUC/MIC和T%时的一致性、预测可靠性和准确性。基于全局敏感性分析的龙卷风方法用于对模型进行敏感性分析，以检验参数变化对预测的影响。将基于AUC/MIC或T%模型的疗效评估与基于D模型的方案优化相结合，创建一个由疗效评估和方案优化组成的闭环。
建立了RIIVI中的AUC/MIC、T%和D模型。Bland - Altman图和Bootstrap分析表明，所建立的模型与已记录的模型不一致，且所建立的模型在计算AUC/MIC和T%时具有更好的预测可靠性和准确性。敏感性分析表明，MIC是影响AUC/MIC和T%变化的重要因素。AUC/MIC、T%和D模型的协同应用创建了一个由疗效评估和方案优化组成的闭环，用于制定定制的抗生素方案。
所建立的AUC/MIC和T%模型相对于已记录的模型表现出更好的性能。这些模型与相应的D模型的协同应用可以创建一个完全闭环，用于评估原始方案的有效性并制定优化方案，从而构建定制抗生素方案的基本框架。
