Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study

基于模型指导的万古霉素精准给药以快速达到目标浓度-时间曲线下面积:一项模拟研究

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Abstract

In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC(0-24) , AUC(24-48) , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC(24-48) range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC(0-24) range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUC(SS) ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC(24-48) , and first-day sampling may assist in rapidly achieving therapeutic AUC(24-48) , although the AUC(SS) should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.

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