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.

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 RMSE510.3%. 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.