Purpose:

The single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various creatinine clearance rates (CLcr).

Methods:

The PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUCR) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUCR of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria.

Results:

Four CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUCR value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various CLcr were predicted.

Conclusion:

CDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting "mathematical knowledge transfer and application" and also providing reference for quantitative and personalized research on similar drugs.