Modeling of pharmacokinetic/pharmacodynamic parameters in regular intermittent intravenous infusion and translational...

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.

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.

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.

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.