Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 201203, China; Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610.
Subei People's Hospital, Yangzhou University, Yangzhou, Jiangsu 225001, China; Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
J Pharm Sci. 2018 Jul;107(7):1948-1956. doi: 10.1016/j.xphs.2018.02.021. Epub 2018 Mar 6.
Quantitative prediction of unbound drug fraction (f) is essential for scaling pharmacokinetics through physiologically based approaches. However, few attempts have been made to evaluate the projection of f values under pathological conditions. The primary objective of this study was to predict f values (n = 105) of 56 compounds with or without the information of predominant binding protein in patients with varying degrees of hepatic insufficiency by accounting for quantitative changes in molar concentrations of either the major binding protein or albumin plus alpha 1-acid glycoprotein associated with differing levels of hepatic dysfunction. For the purpose of scaling, data pertaining to albumin and α1-acid glycoprotein levels in response to differing degrees of hepatic impairment were systematically collected from 919 adult donors. The results of the present study demonstrate for the first time the feasibility of physiologically based scaling f in hepatic dysfunction after verifying with experimentally measured data of a wide variety of compounds from individuals with varying degrees of hepatic insufficiency. Furthermore, the high level of predictive accuracy indicates that the inter-relation between the severity of hepatic impairment and these plasma protein levels are physiologically accurate. The present study enhances the confidence in predicting f in hepatic insufficiency, particularly for albumin-bound drugs.
定量预测游离药物分数(f)对于通过基于生理的方法来推算药代动力学至关重要。然而,很少有尝试来评估在病理条件下 f 值的预测。本研究的主要目的是通过考虑主要结合蛋白或白蛋白加α1-酸性糖蛋白的摩尔浓度定量变化,来预测 56 种化合物在不同程度肝不全患者中的 f 值(n=105),这些化合物或有或无结合蛋白的信息。为了进行推算,从 919 位成年供体中系统地收集了与不同程度肝功能障碍相关的白蛋白和α1-酸性糖蛋白水平的数据。本研究首次证明了在验证了具有不同程度肝不全的个体的广泛化合物的实验测量数据后,基于生理的肝功能障碍时 f 值推算的可行性。此外,高预测准确性表明,肝损伤严重程度与这些血浆蛋白水平之间的相互关系在生理上是准确的。本研究增强了在肝不全中预测 f 的信心,特别是对于白蛋白结合药物。