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.
Heart failure (HF) causes pathological changes in multiple organs, thus affecting the pharmacokinetics (PK) of drugs. The aim of this study was to investigate the PK of candesartan in patients with HF while examining significant covariates and their related impact on estimated clearance using a population PK (Pop‐PK) modeling approach. Data from a prospective, multicenter study were used. Modeling and simulations were conducted using Nonlinear Mixed‐Effects Modeling (NONMEM) and R software. A total of 281 white patients were included to develop the Pop‐PK model. The final model developed for apparent oral clearance (CL/F) included weight, estimated glomerular filtration rate (eGFR), and diabetes, which partly explained its interindividual variability. The mean CL/F value estimated was 7.6 L/h (1.7–22.6 L/h). Simulations revealed that an important decrease in CL/F (> 25%) is obtained with the combination of the factors retained in the final model. Considering these factors, a more individualized approach of candesartan dosing should be investigated in patients with HF.
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