Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, State University of New York, Amherst, NY 14260, USA.
Biopharm Drug Dispos. 2012 Jan;33(1):1-14. doi: 10.1002/bdd.1761. Epub 2012 Jan 24.
The aim of this study was to evaluate the prediction performance of various allometric scaling methods in predicting human biliary clearance (CL(b)) from data in rats or multiple animal species and to compare the prediction performance with that of quantitative structure pharmacokinetic relationship (QSPKR) models. CL(b) data of parent drugs in rats and humans were collected from the literature for 18 compounds. A simple allometric approach was applied to CL(b) or unbound CL(b) using 0.75 or 0.66 as the allometric exponent. For scaling from rat studies alone, the prediction using 0.66 as the exponent was better than that using 0.75, and a better prediction was obtained for unbound CL(b) than CL(b). For a subset of compounds, six multiple-species scaling methods were compared, with the best prediction achieved with the simple unbound CL(b) approach. However, in the absence of protein binding data, the correction with maximum life-span potential (MLP) or 'Rule of exponent' (ROE) method offered the best prediction. Overall, multiple species had better predictability than scaling with the rat alone. Comparison of predicted human CL(b) values using multiple animal species and QSPKR offered similar prediction performance. In conclusion, the results of the present study, although based on limited data, suggested that the prediction for human CL(b) by allometry was greatly improved by the incorporation of protein binding. Human CL(b) prediction using rat data alone was not satisfactory. Additionally, QSPKR provides an alternative approach to allometry for the prediction of human biliary clearance.
本研究旨在评估各种体型预测方法在预测大鼠或多种动物物种中人体胆汁清除率（CL(b)）方面的预测性能，并将其预测性能与定量构效关系（QSPKR）模型进行比较。从文献中收集了 18 种化合物在大鼠和人体内的母体药物 CL(b)数据。采用简单的体型预测方法，以 0.75 或 0.66 作为体型指数，预测 CL(b)或未结合 CL(b)。对于仅从大鼠研究进行预测，使用 0.66 作为指数的预测优于使用 0.75 的预测，且未结合 CL(b)的预测优于 CL(b)。对于一组化合物，比较了六种多物种缩放方法，其中简单的未结合 CL(b)方法的预测效果最佳。然而，在没有蛋白结合数据的情况下，使用最大寿命潜力（MLP）或“指数规则”（ROE）方法进行校正可提供最佳预测。总体而言，与单独使用大鼠相比，多物种具有更好的预测能力。使用多种动物物种和 QSPKR 预测的人类 CL(b)值具有相似的预测性能。总之，尽管本研究基于有限的数据，但结果表明，通过结合蛋白结合，体型预测对人体 CL(b)的预测得到了极大的改善。仅使用大鼠数据预测人体 CL(b)的效果并不令人满意。此外，QSPKR 为预测人体胆汁清除率提供了一种替代体型预测的方法。
