Implementation and validation of a Bayesian method for accurately forecasting duration of optimal pharmacodynamic target attainment with dalbavancin during long-term use for subacute and chronic staphylococcal infections

Abstract

Dalbavancin is increasingly being used for long-term treatment of subacute and chronic staphylococcal infections. In this study, a new Bayesian model was implemented and validated using MwPharm software for accurately forecasting the duration of pharmacodynamic target attainment above the efficacy thresholds of 4.02 mg/L or 8.04 mg/L against staphylococci. Forecasting accuracy improved substantially with the a posteriori approach compared with the a priori approach, particularly when two measured concentrations were used. This strategy may help clinicians to estimate the duration of optimal exposure with dalbavancin in the context of long-term treatment.

Keywords: Bayesian prediction; MwPharm; TDM; dalbavancin.