Limited sampling strategies for the estimation of atazanavir daily exposure in HIV-infected patients

Abstract

Stepwise multiple regression analyses were applied to 44 atazanavir pharmacokinetic profiles from 44 HIV-1 infected patients concomitantly treated with raltegravir with the goal of identifying limited sampling strategies for the prediction of drug AUC(0-12) . Atazanavir trough-based equations failed to reliably predict daily drug exposure in patients with low drug bioavailability. Conversely, different algorithms based on few samples and associated with good correlation, acceptable bias and imprecision with the measured atazanavir AUC(0-12) were identified. These models could be used to predict atazanavir exposure for clinic or research purposes.