Article Text

Download PDFPDF
Comparison of area under the curve for vancomycin from one- and two-compartment models using sparse data
  1. Nyein Hsu Maung1,
  2. Janthima Methaneethorn2,
  3. Thitima Wattanavijitkul1,
  4. Tatta Sriboonruang1
  1. 1Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand
  2. 2Department of Pharmacy Practice, Faculty of Pharmaceutical sciences, Naresuan University, Phitsanulok, Thailand
  1. Correspondence to Dr. Tatta Sriboonruang, Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand; Tatta.s{at}pharm.chula.ac.th

Abstract

Background and objective Vancomycin pharmacokinetics have been described by both one- and two-compartment models. One-compartment models are widely used to predict the area under the curve (AUC), a useful parameter for determining the efficacy and safety of vancomycin, based on sparse data collected during therapeutic drug monitoring. It is uncertain whether AUCs from one-compartment models with sparsely sampled data can sufficiently represent the true AUC. This study aimed to compare AUC estimates from one- and two-compartment models using sparse data. The reliability of AUCs from models constructed with trough-only data was also assessed.

Methods A previously published robust model was used to simulate vancomycin concentration points at 15 min intervals in 100 patients. From these simulated data, the reference AUC (AUCref) was calculated and two depleted dataset versions (trough-only and peak-trough datasets) were also created. One- and two-compartment models were built from the depleted datasets with the use of NONMEM. Vancomycin 24-hour AUC was calculated from concentration–time profiles of each model by a linear trapezoidal formula at three different time periods: 0–24 hours (AUC0–24), 24–48 hours (AUC24–48) and 0–48 hours (AUCavg). The deviation of each of the AUCs from the AUCref was examined to assess the AUC predictability of models from sparse data. The difference in AUCs between one- and two-compartment models was analysed from statistical and clinical perspectives.

Results When assessing the deviation of each AUC from the AUCref, the one-compartment model from both peak-trough and trough-only data could adequately represent the true AUC with no statistically significant differences. Two-compartment model from peak-trough data also provided similar AUC estimates with the AUCref. However, AUCs from the two-compartment model with trough-only data did not adequately represent the true AUC, with significant differences of 25.16% for AUC0–24, 15.92% for AUC24–48 and 19.45% for AUCavg.

Conclusion Regardless of statistically significant differences between AUCs from one- and two-compartment models, the level of difference was acceptable from the clinical perspective, being <17% in models from peak-trough data. Therefore, both one- and two-compartment models with sparse data having at least a pair of peak-trough data per patient could be reliable for predicting AUC. Furthermore, AUCs of the one-compartment model from trough-only data did not show a significant difference from the AUCref. Hence, one-compartment models developed from trough-only data could be useful for predicting AUC when models with rich data are not available for the intended population. However, it is suggested that the use of the two-compartment model built from trough-only data should be avoided.

  • drug monitoring
  • microbiology
  • clinical medicine
  • education
  • pharmacy
  • pharmacy service
  • hospital

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

Statistics from Altmetric.com

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

View Full Text

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.