Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model‐informed precision dosing trial simulations

Abstract

Abstract The recently released revised vancomycin consensus guideline endorsed area under the concentration‐time curve (AUC) guided monitoring. Means to AUC‐guided monitoring include pharmacokinetic (PK) equations and Bayesian software programs, with the latter approach being preferable. We aimed to evaluate the predictive performance of these two methods when monitoring using troughs or peaks and troughs at varying single or mixed dosing intervals (DIs), and evaluate the significance of satisfying underlying assumptions of steady‐state and model transferability. Methods included developing a vancomycin population PK model and conducting model‐informed precision dosing clinical trial simulations. A one‐compartment PK model with linear elimination, exponential between‐subject variability, and mixed (additive and proportional) residual error model resulted in the best model fit. Conducted simulations demonstrated that Bayesian‐guided AUC can, potentially, outperform that of equation‐based AUC predictions depending on the quality of model diagnostics and met assumptions. Ideally, Bayesian‐guided AUC predictive performance using a trough from the first DI was equivalent to that of PK equations using two measurements (peak and trough) from the fifth DI. Model transferability diagnostics can guide the selection of Bayesian priors but are not strong indicators of predictive performance. Mixed versus single fourth and/or fifth DI sampling seems indifferent. This study illustrated cases associated with the most reliable AUC predictions and showed that only proper Bayesian‐guided monitoring is always faster and more reliable than equations‐guided monitoring in pre‐steady‐state DIs in the absence of a loading dose. This supports rapid Bayesian monitoring using data as sparse and early as a trough at the first DI.