CNR Institute of Neuroscience and Unit of Clinical Pharmacology, Department of Clinical Sciences, Luigi Sacco University Hospital, Università di Milano, via GB Grassi 74, 20157 Milano, Italy.
Fundam Clin Pharmacol. 2013 Apr;27(2):216-22. doi: 10.1111/j.1472-8206.2011.01005.x. Epub 2011 Nov 2.
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.
逐步多元回归分析应用于 44 例同时接受拉替拉韦治疗的 HIV-1 感染患者的阿扎那韦药代动力学曲线，旨在确定有限采样策略以预测药物 AUC（0-12）。基于阿扎那韦谷值的方程无法可靠地预测药物生物利用度低的患者的每日药物暴露量。相反，基于少数样本并与良好相关性、可接受的偏倚和精密度相关的不同算法被确定。这些模型可用于预测阿扎那韦的暴露量，以用于临床或研究目的。
