Brightman F A, Leahy D E, Searle G E, Thomas S
Cyprotex Discovery Ltd., Macclesfield, Cheshire, United Kingdom, SK10 2DR.
Drug Metab Dispos. 2006 Jan;34(1):84-93. doi: 10.1124/dmd.105.004804. Epub 2005 Oct 12.
The routine assessment of xenobiotic in vivo kinetic behavior is currently dependent upon data obtained through animal experimentation, although in vitro surrogates for determining key absorption, distribution, metabolism, and elimination properties are available. Here we present a unique, generic, physiologically based pharmacokinetic (PBPK) model and demonstrate its application to the estimation of rat plasma pharmacokinetics, following intravenous dosing, from in vitro data alone. The model was parameterized through an optimization process, using a training set of in vivo data taken from the literature and validated using a separate test set of in vivo discovery compound data. On average, the vertical divergence of the predicted plasma concentrations from the observed data, on a semilog concentration-time plot, was approximately 0.5 log unit. Around 70% of all the predicted values of a standardized measure of area under the concentration-time curve (AUC) were within 3-fold of the observed values, as were over 90% of the training set t1/2 predictions and 60% of those for the test set; however, there was a tendency to overpredict t1/2 for the test set compounds. The capability of the model to rank compounds according to a given criterion was also assessed: of the 25% of the test set compounds ranked by the model as having the largest values for AUC, 61% were correctly identified. These validation results lead us to conclude that the generic PBPK model is potentially a powerful and cost-effective tool for predicting the mammalian pharmacokinetics of a wide range of organic compounds, from readily available in vitro inputs only.
目前,对外源生物体内动力学行为的常规评估依赖于通过动物实验获得的数据,尽管已有用于确定关键吸收、分布、代谢和消除特性的体外替代方法。在此,我们提出了一个独特的、通用的、基于生理的药代动力学(PBPK)模型,并展示了其仅根据体外数据来估算大鼠静脉给药后血浆药代动力学的应用。该模型通过优化过程进行参数化,使用从文献中获取的体内数据训练集,并使用单独的体内发现化合物数据测试集进行验证。在半对数浓度 - 时间图上,预测血浆浓度与观测数据的垂直偏差平均约为0.5对数单位。浓度 - 时间曲线下面积(AUC)标准化测量值的所有预测值中,约70%在观测值的3倍以内,训练集t1/2预测值的90%以上以及测试集的60%也如此;然而,测试集化合物的t1/2有预测偏高的趋势。还评估了该模型根据给定标准对化合物进行排序的能力:在模型排序为AUC值最大的测试集化合物中,25%里有61%被正确识别。这些验证结果使我们得出结论,通用的PBPK模型可能是一种强大且具有成本效益的工具,仅通过容易获得的体外输入数据就能预测多种有机化合物在哺乳动物体内的药代动力学。