Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models

Abstract

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC_inf) of dalbavancin is a key parameter and AUC_inf/MIC ratio is a critical pharmacodynamic marker.

2. Using end of intravenous infusion concentration (i.e. C_max) C_max versus AUC_inf relationship for dalbavancin was established by regression analyses (i.e. linear, log–log, log–linear and power models) using 21 pairs of subject data.

3. The predictions of the AUC_inf were performed using published C_max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment.

4. MAE and RMSE values for the various models were comparable. The C_max versus AUC_inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84–1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC_inf with a RMSE of 3.02–27.46% with fold difference largely contained within 0.64–1.48.

5. Regardless of the regression models, a single time point strategy of using C_max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC_inf of dalbavancin in patients.

Keywords:

• AUC
• dalbavancin
• regression analysis

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.