Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches
Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches
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DOI：
10.1007/s11095-004-9015-1
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发表时间：
2005-01-01
影响因子：
3.7
通讯作者：
Houston, JB
中科院分区：
文献类型：
--
作者：
Ito, K;Houston, JB
目的.本研究的目的是比较预测体内固有清除率（克林特）的五种方法和预测肝脏清除率（CLh）在人类使用体外微粒体数据和/或临床前动物数据的准确性。通过5种方法预测33种药物的人克林特，这些方法使用具有生理缩放因子（SF）的体外数据，具有经验SF，具有生理和药物特异性（大鼠体内和体外克林特的比率）SF，或直接使用大鼠克林特和异速生长缩放。使用估计的克林特，根据充分搅拌的肝脏模型计算人体中的CLh。CLh还使用另外两种方法进行预测：使用直接异速生长缩放或药物特异性SF和异速生长。使用体外人微粒体数据与生理SF导致一致的克林特和CLh低估。通过使用经验SF、药物特异性SF或异速生长来减少这种偏倚。然而，异速生长，有一个显着的精度下降。对于药物特异性SF，偏倚减少较少，精密度与经验SF相似。克林特和CLh均使用具有经验SF的体外人微粒体数据进行最佳预测。使用更大的数据集的52种药物与搅拌良好的肝脏模型导致最佳拟合的经验SF，是生理SF的9倍增加。总体而言，经验SF方法和药物特异性SF方法似乎是最好的方法;它们显示出比生理SF更低的偏倚和比异速生长方法更好的精度。使用具有经验SF的体外人微粒体数据可能是优选的，因为它不需要来自临床前研究的额外信息。
Purpose. The aim of this study is to compare the accuracy of five methods for predicting in vivo intrinsic clearance (CLint) and seven for predicting hepatic clearance (CLh) in humans using in vitro microsomal data and/or preclinical animal data.Methods. The human CLint was predicted for 33 drugs by five methods that used either in vitro data with a physiologic scaling factor (SF), with an empirical SF, with the physiologic and drug-specific (the ratio of in vivo and in vitro CLint in rats) SFs, or rat CLint directly and with allometric scaling. Using the estimated CLint, the CLh in humans was calculated according to the well-stirred liver model. The CLh was also predicted using additional two methods: using direct allometric scaling or drug-specific SF and allometry.Results. Using in vitro human microsomal data with a physiologic SF resulted in consistent underestimation of both CLint and CLh. This bias was reduced by using either an empirical SF, a drug-specific SF, or allometry. However, for allometry, there was a substantial decrease in precision. For drug-specific SF, bias was less reduced, precision was similar to an empirical SF. Both CLint and CLh were best predicted using in vitro human microsomal data with empirical SF. Use of larger data set of 52 drugs with the well-stirred liver model resulted in a best-fit empirical SF that is 9-fold increase on the physiologic SF.Conclusions. Overall, the empirical SF method and the drug-specific SF method appear to be the best methods; they show lower bias than the physiologic SF and better precision than allometric approaches. The use of in vitro human microsomal data with an empirical SF may be preferable, as it does not require extra information from a preclinical study.