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首页> 外文期刊>Journal of pharmaceutical sciences. >PhRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, Part 3: Comparative Assessement of Prediction Methods of Human Clearance
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PhRMA CPCDC Initiative on Predictive Models of Human Pharmacokinetics, Part 3: Comparative Assessement of Prediction Methods of Human Clearance

机译：PhRMA CPCDC关于人类药代动力学预测模型的倡议，第3部分：人类清除率预测方法的比较评估

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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 IVTVE 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 (AUC_(p.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. IVTVE 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. IVTVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUC_(p.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.
机译：这项研究的目的是评估在有和没有血浆蛋白结合校正的情况下用于预测人静脉内（i.v.）清除率（CL）的各种异构和体外-体内外推（IVIVE）方法的性能。目的还在于用动物数据评估IVTVE预测方法。从文献中选择方法。美国药品研究与制造商成员公司提供了针对108种化合物的临床前和临床研究的盲数据集，其中19种药物具有i.v.临床药代动力学数据并用于分析。使用体内和体外临床前数据通过29种不同方法预测CL。对于许多化合物，只有两种物种（通常是大鼠和狗）的体内数据可用和/或缺少所需的体外数据，这意味着某些方法无法正确评估。此外，使用i.v.的合理组合评估了107种化合物的曲线下预测口服（p.o.）面积的方法（AUC_（p.o）。）。 CL和生物利用度（F），以及观察到的p.o的直接缩放。来自临床前物种的CL。各种统计和异常值技术被用来评估每种方法的可预测性。在所有方法中，预测这19种药物的人类CL的最大成功率分别是该化合物的100％，94％和78％，预测值分别落在观察到的CL的10倍，3倍和2倍误差之内。通常，体内方法的效果比IVIVE方法略好（至少在相关性和整体一致性方面），其中fu截获法和基于两种物种的异速测量法（鼠狗）是表现最好的方法。使用微粒体（结合血浆和微粒体结合）和肝细胞（不结合结合）的IVTVE方法分别导致75％和78％的预测落在两倍误差内。使用结合假设的其他组合的IVTVE方法的准确性要低得多。预测AUC_（p.o）的结果。与i.v.一致CL。然而，成功预测人类p.o的最大挑战。 CL是人体中F的估计值。总体而言，该计划的结果证实了用于预测人类CL的常用方法的预测性能。

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