Objective Quantitative calculation models for the ratio of daily area under the concentration–time curve (AUC24) to the minimum inhibitory concentration (MIC) (i.e., AUC24/MIC) and the amount of time that concentration stays above the MIC during a dosing interval (i.e., T>MIC%) in regular intermittent i.v. infusion (RIIVI) are currently absent. This work set out to construct the models of AUC24/MIC, T>MIC% and matching daily dosage (Dd) 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. Methods 1-compartment model was used to construct the AUC24/MIC, T>MIC% and matching Dd 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 AUC24/MIC and T>MIC%, 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 AUC24/MIC or T>MIC% model-based efficacy assessment with the Dd model-based regimen optimization to creates a closed loop consisting of efficacy assessment and regimen optimization. Results The AUC24/MIC, T>MIC% and Dd 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 AUC24/MIC and T>MIC%. Sensitivity analysis suggested that MIC was an important factor on AUC24/MIC and T>MIC% variation. Cooperative application of the AUC24/MIC, T>MIC% and Dd model created a closed loop consisting of efficacy assessment and regimen optimization for creation of customized antibiotic regimens. Conclusions The established AUC24/MIC and T>MIC% models displayed better performance relative to the documented models. Cooperative application of these models and the corresponding Dd 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.

Modeling of pharmacokinetic/pharmacodynamic parameters in regular intermittent intravenous infusion and translational application of the models in personalized antibiotics dosing

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2025年7月19日

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Xiangqing Song

摘要

Objective

Quantitative calculation models for the ratio of daily area under the concentration–time curve (AUC₂₄) to the minimum inhibitory concentration (MIC) (i.e., AUC₂₄/MIC) and the amount of time that concentration stays above the MIC during a dosing interval (i.e., T_>MIC%) in regular intermittent i.v. infusion (RIIVI) are currently absent. This work set out to construct the models of AUC₂₄/MIC, T_>MIC% and matching daily dosage (D_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.

Methods

1-compartment model was used to construct the AUC₂₄/MIC, T_>MIC% and matching D_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 AUC₂₄/MIC and T_>MIC%, 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 AUC₂₄/MIC or T_>MIC% model-based efficacy assessment with the D_d model-based regimen optimization to creates a closed loop consisting of efficacy assessment and regimen optimization.

Results

The AUC₂₄/MIC, T_>MIC% and D_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 AUC₂₄/MIC and T_>MIC%. Sensitivity analysis suggested that MIC was an important factor on AUC₂₄/MIC and T_>MIC% variation. Cooperative application of the AUC₂₄/MIC, T_>MIC% and D_d model created a closed loop consisting of efficacy assessment and regimen optimization for creation of customized antibiotic regimens.

Conclusions

The established AUC₂₄/MIC and T_>MIC% models displayed better performance relative to the documented models. Cooperative application of these models and the corresponding D_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.