Ring Barbara J, Chien Jenny Y, Adkison Kimberly K, Jones Hannah M, Rowland Malcolm, Jones Rhys Do, Yates James W T, Ku M Sherry, Gibson Christopher R, He Handan, Vuppugalla Ragini, Marathe Punit, Fischer Volker, Dutta Sandeep, Sinha Vikash K, Björnsson Thorir, Lavé Thierry, Poulin Patrick
Drug Disposition, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285.
J Pharm Sci. 2011 Oct;100(10):4090-110. doi: 10.1002/jps.22552. Epub 2011 May 3.
The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.
本研究的目的是评估各种异速生长法和体外-体内外推法（IVIVE）在有或无血浆蛋白结合校正情况下预测人体静脉注射清除率（CL）的性能。该目的还包括使用动物数据评估IVIVE预测方法。方法是从文献中选取的。美国制药研究与制造商协会成员公司提供了来自108种化合物的临床前和临床研究的盲态数据集，其中19种药物有静脉注射临床药代动力学数据并用于分析。体内和体外临床前数据用于通过29种不同方法预测CL。对于许多化合物，仅可获得来自两个物种（通常为大鼠和犬）的体内数据和/或缺少所需的体外数据，这意味着一些方法无法得到恰当评估。此外，使用静脉注射CL和生物利用度（F）的合理组合以及直接按比例缩放临床前物种观察到的口服CL，对107种化合物评估了66种预测口服曲线下面积（AUCp.o.）的方法。采用了各种统计和离群值技术来评估每种方法的可预测性。在所有方法中，对于这19种药物，预测人体CL的最大成功率分别为：预测值落在观察到的CL的10倍、3倍和2倍误差范围内的化合物占比为100%、94%和78%。总体而言，体内方法的表现略优于IVIVE方法（至少在相关性和整体一致性方面），其中游离药物浓度截距法和基于两个物种（大鼠-犬）的异速生长法是表现最佳的方法。使用微粒体（结合血浆和微粒体结合）和肝细胞（不结合）的IVIVE方法分别有75%和78%的预测值落在2倍误差范围内。使用其他结合假设组合的IVIVE方法准确性要低得多。预测AUCp.o.的结果与静脉注射CL一致。然而，成功预测人体口服CL的最大挑战在于对人体F的估计。总体而言，该项目的结果证实了用于预测人体CL的常用方法的预测性能。
