Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester M13 9PT, United Kingdom.
J Pharm Sci. 2012 Aug;101(8):2645-52. doi: 10.1002/jps.23202. Epub 2012 Jun 14.
Underprediction of in vivo intrinsic clearance (CL(int)) of unbound drug from human hepatic in vitro systems using physiological extrapolation methodology is accepted as a common outcome. Poulin et al. (2012. J Pharm Sci 101:838-851) recently proposed an approach involving determination of effective fraction unbound in plasma (fu(p)) based on albumin-facilitated hepatic uptake of acidic/neutral drugs which improved prediction accuracy and precision for 25 drugs highly bound to plasma proteins. This approach includes correction of unbound drug according to the ionisation fraction either side of the plasma membrane based on pH difference. Here, we assessed the proposed method using a larger database of predictions of CL(int) for 107 drugs involving hepatocytes (89 drugs) and microsomes (64 drugs). The proposed method was similarly effective in minimising average prediction bias (to within twofold), unlike the conventional fu(p) correction method. However, precision was similar between methods and there was no evidence in the larger database that prediction bias was associated with fu(p). Prediction bias for hepatocytes was clearance dependent by either method, indicating important sources of bias from in vitro methodology. Therefore, to progress beyond empirical correction of bias, there is further need of mechanistic elucidation to improve prediction methodology.
在使用生理外推方法从人体肝体外系统预测未结合药物的体内内在清除率 (CL(int)) 时,预测值低于实际值是一种常见的结果。Poulin 等人(2012. J Pharm Sci 101:838-851)最近提出了一种方法,涉及根据白蛋白促进的酸性/中性药物在肝脏中的摄取来确定血浆中有效游离分数 (fu(p)),该方法提高了 25 种与血浆蛋白高度结合的药物的预测准确性和精密度。该方法包括根据细胞膜两侧的离子化分数对未结合药物进行校正,校正值基于 pH 差异。在这里,我们使用包含 107 种药物的更大的 CL(int)预测数据库(涉及肝细胞 89 种药物和微粒体 64 种药物)评估了该方法。与传统的 fu(p)校正方法不同,该方法同样能够有效地最小化平均预测偏差(在两倍以内)。然而,两种方法的精度相似,并且在更大的数据库中没有证据表明预测偏差与 fu(p)有关。两种方法预测的肝细胞清除率均依赖于清除率,这表明体外方法存在重要的偏差来源。因此,要想超越对偏差的经验性校正,需要进一步阐明机制以改进预测方法。