Model-informed precision dosing of vancomycin via continuous infusion: a clinical fit-for-purpose evaluation of published PK models

Background: Model-informed precision dosing is an innovative approach used to guide bedside vancomycin dosing. The use of Bayesian software requires suitable and externally validated population pharmacokinetic (popPK) models. Objectives: This study aimed to identify suitable popPK models for a priori prediction and a posteriori forecasting of vancomycin in continuous infusion. Additionally, model averaging (MAA) and model selection approach (MSA) were compared with the identified popPK models. Methods: Clinical pharmacokinetic data were retrospectively collected from patients receiving continuous vancomycin therapy and admitted to a general ward of three large Belgian hospitals. The predictive performance of the popPK models, identified in a systematic literature search, as well as the MAA/MSA were evaluated for the a priori and a posteriori scenarios using bias, root mean square errors, normalised prediction distribution errors and visual predictive checks. Results: The predictive performance of 23 popPK models was evaluated based on clinical data from 169 patients and 923 therapeutic drug monitoring samples. Overall, the best predictive performance was found using the Okada et al. model (bias < -0.1 mg/L) followed by the Colin et al. model. The MAA/MSA predicted with a constantly high precision and low inaccuracy and were clinically acceptable in the Bayesian forecasting. Conclusion: This study identified the two-compartmental models of Okada et al. and Colin et al. as most suitable for non-ICU patients to forecast individual exposure profiles after continuous vancomycin infusion. The MAA/MSA performed equally as well as the individual popPK models; therefore, both approaches could be used in clinical practice to guide dosing decisions.