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

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.