Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure

Area of Science:

• Pharmacokinetics and Therapeutic Drug Monitoring
• Clinical Pharmacology
• Infectious Diseases

Background:

• Intravenous vancomycin dosing guidelines emphasize drug exposure, measured by AUC, as the key predictor of therapeutic success.
• Accurate estimation of vancomycin's AUC is crucial for optimizing patient outcomes and minimizing toxicity.

Purpose of the Study:

• To evaluate the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0-∞.
• To determine the optimal timing for single blood sample collection for vancomycin AUC estimation in adults with limited sampling.

Main Methods:

• Seven vancomycin population PK models were applied within two Bayesian forecasting programs.
• Adult patients (n=22) with stable renal function underwent intensive blood sampling after a single vancomycin dose.
• AUC was calculated using a two-compartment model (AUCTRUE) and compared to Bayesian-estimated AUC0-∞ derived from single sample concentrations.

Main Results:

• Optimal sampling times for vancomycin AUC estimation varied significantly among different population PK models.
• Earlier sampling times tended to overestimate AUCTRUE, while later times (after 4 hours) led to underestimation.
• Specific models demonstrated precise and unbiased estimations within defined post-infusion windows (e.g., 1.5-6h and 0.75-2h).

Conclusions:

• Single vancomycin concentration measurements for Bayesian AUC estimation are most accurate when samples are collected between 1.5 and 6 hours post-infusion.
• The predictive performance of vancomycin monitoring is dependent on the chosen population PK model and its associated optimal sampling time.
• These findings aid in refining vancomycin therapeutic drug monitoring strategies for improved patient care.