Targeting Vancomycin AUC in Neonates − A Model‐Based Bayesian Approach for Personalized Therapeutic Drug Monitoring

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BACKGROUND: When treating methicillin-resistant Staphylococcus aureus (MRSA) infections with vancomycin, national guidelines recommend targeting an AUC24/MIC ≥400 to ensure adequate drug exposure. To optimize neonatal vancomycin dosing, accurate AUC24 estimates are needed. The objective of this study was to assess the utility of a model based Bayesian approach for estimating vancomycin AUC24 in neonates.

METHODS: Neonates who received vancomycin and had ≥1 ‘peak’ and ≥1 ‘trough’ concentrations at two healthcare systems (2006-2013) were studied. Bayesian estimates of clearance were calculated for each neonate using a published, externally validated population pharmacokinetic model in NONMEM (external validation was not completed until October 2014). AUC24 was calculated as the daily dose Û clearance. The percent prediction error (PE) and the percent absolute prediction error (APE) of the AUC24 estimates were compared for: 1) the full dataset, 2) a dataset with only the first peak and trough concentrations, and 3) a dataset with only the first trough concentration.

RESULTS: A total of 427 neonates were studied (median [IQR] postmenstrual age 36 [29-41] weeks and weight 2.3 [1.0-3.4] kg). Compared with the full dataset, Bayesian estimates of AUC24 using only the first trough concentration had a median PE of -0.7% (95%CI: -1.3% to 0.0%) and a median APE of 4.1% (95% CI: 3.5% to 4.8%). AUC24 predictions were within 15% of the full dataset for 90% of neonates. The addition of a peak concentration provided no substantial predictive benefit.

CONCLUSION: A model based therapeutic monitoring strategy using only a single trough concentration can adequately predict vancomycin AUC24 in neonates. Application of this approach can help clinicians personalize vancomycin therapy and warrants further study.


Presented at the 115 American Society for Clinical Pharmacology and Therapeutics in Atlanta, GA.