Identifying the Optimal Sampling Strategy for the Bayesian Estimation of Vancomycin AUC in Adult Hematologic Cancer Patients.

Background And Objective: The latest consensus recommends using the ratio between the area under the curve over 24 h (AUC) and minimal inhibitory concentration (MIC) as the therapeutic target for vancomycin in clinical practice, with a Bayesian approach and population pharmacokinetic (popPK) model being particularly recommended. While using both post-dose peak concentration (C) and pre-dose concentration (C) is more accurate than C alone, the optimal sampling strategy for estimating AUC is still unclear. The objective of this study was to determine the best sampling time(s) to estimate AUC using the Bayesian approach in these specific adult hematologic cancer patients.

Methods: A virtual population (n = 7000) was simulated based on the distribution of the significant covariates (ideal body weight and estimated glomerular filtration rate) from the population used to develop the previous pharmacokinetic model. The dosing regimens from the Le Blanc et al. nomogram were used to generate, with NONMEM (v.7.5), simulated pharmacokinetic (PK) profiles of one loading dose followed by three maintenance doses (steady state). Strategies involving two samples taken during earlier maintenance doses and one sample taken at steady state were tested using the Bayesian approach to predict PK parameters. These strategies were then evaluated for their ability to predict AUC at steady state (AUC) RESULTS: For single-sample strategies, a sample taken anytime from 4 h post-dose can estimate AUC with precision similar to C (R ≈ 0.75), regardless of renal function (R ≈ 0.73-0.77). For two-sample strategies, taking samples at least midway through the dosing interval provides the highest precision for estimating AUC during the first two maintenance doses (R ≈ 0.75-0.77). In both strategies, using C did not yield as precise results as sampling midway through the dosing interval or at C.

Conclusion: This study is the first to test multiple limited sampling strategies using a dosing nomogram stratified by renal function. The results show that vancomycin sampling can extend beyond traditional C and C without compromising the accuracy of maximum a posteriori Bayesian estimation of AUC, thereby providing an opportunity to investigate these limited sampling strategies combined with model-informed precision dosing in a clinical setting.