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

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.

Vancomycin area under the curve versus trough only guided dosing and the risk of acute kidney injury: Systematic review and meta‐analysis

“…Out of the 10 studies included in this meta‐analysis, only three utilized Bayesian software for AUC dosing. Bayesian software programs use an established pharmacokinetic model and patient parameters to optimize vancomycin dosing and account for dynamic changes such as renal function 13–15 . Bayesian software programs are the preferred method of AUC monitoring since concentrations do not need to be drawn at steady‐state, and thus, therapeutic drug monitoring can be initiated as early as the first dosing interval.…”

“…Bayesian software programs are the preferred method of AUC monitoring since concentrations do not need to be drawn at steady‐state, and thus, therapeutic drug monitoring can be initiated as early as the first dosing interval. This may quicken the time to effective drug concentrations as trough levels need to be drawn at steady‐state 14 . Additionally, AUC monitoring is beneficial as steady‐state conditions may be difficult to predict in clinical practice since they can be influenced by renal function and other factors such as loading doses and body mass index 14 .…”

“…Area under the curve‐guided dosing can be performed using Bayesian dose optimization software or traditional pharmacokinetic (PK) equations. An advantage of Bayesian dosing is that it accounts for dynamic covariates such as renal function and calculates individualized patient dosing based on Bayesian priors, patients' drug concentrations, and creatinine clearance 13–15 . On the other hand, PK equations provide an estimation of AUC solely based on two vancomycin levels within the same dosing period 13,15 .…”

“…One factor to consider is that these equations rely on serum creatinine (SCr); however, SCr is a delayed marker of renal injury and may take 24–36 h to display a notable change in renal function 9 . Additionally, the Bayesian method concentrations do not need to be drawn at steady‐state and can provide an estimation of AUC prior to reaching steady‐state 13–15 . Disadvantages of the Bayesian dose optimization are the logistics associated with implementation, including cost and education 9,14 .…”

“…Transitioning to AUC‐guided dosing using Bayesian software would require the development and revision of vancomycin dosing and monitoring policies 9 . PK equations also rely on fewer assumptions than Bayesian software programs 14 . The recent shift toward AUC‐guided vancomycin dosing prompts further evaluation to identify the most appropriate method to determine AUC.…”