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Limited sampling strategies for the estimation of atazanavir daily exposure in HIV‐infected patients
Author(s) -
Cattaneo Dario,
Ripamonti Diego,
Baldelli Sara,
Cozzi Valeria,
Fucile Serena,
Clementi Emilio
Publication year - 2013
Publication title -
fundamental and clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.655
H-Index - 73
eISSN - 1472-8206
pISSN - 0767-3981
DOI - 10.1111/j.1472-8206.2011.01005.x
Subject(s) - atazanavir , raltegravir , medicine , pharmacokinetics , therapeutic drug monitoring , drug , bioavailability , pharmacology , human immunodeficiency virus (hiv) , sampling (signal processing) , bioequivalence , antiretroviral therapy , viral load , virology , computer science , filter (signal processing) , computer vision
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.