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.
© 2011 The Authors Fundamental and Clinical Pharmacology © 2011 Société Française de Pharmacologie et de Thérapeutique.
MeSH terms
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Adult
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Anti-HIV Agents / blood
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Anti-HIV Agents / pharmacokinetics*
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Anti-HIV Agents / therapeutic use
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Area Under Curve
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Atazanavir Sulfate
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Biological Availability
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HIV Infections / blood
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HIV Infections / drug therapy*
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HIV Infections / metabolism*
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HIV Protease Inhibitors / blood
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HIV Protease Inhibitors / pharmacokinetics*
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HIV Protease Inhibitors / therapeutic use
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HIV-1*
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Humans
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Oligopeptides / blood
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Oligopeptides / pharmacokinetics*
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Oligopeptides / therapeutic use
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Pyridines / blood
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Pyridines / pharmacokinetics*
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Pyridines / therapeutic use
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Pyrrolidinones / blood
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Pyrrolidinones / pharmacokinetics*
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Pyrrolidinones / therapeutic use
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Raltegravir Potassium
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Young Adult
Substances
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Anti-HIV Agents
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HIV Protease Inhibitors
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Oligopeptides
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Pyridines
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Pyrrolidinones
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Raltegravir Potassium
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Atazanavir Sulfate