Department of Biopharmaceutics, School of Pharmacy, Shenyang 110016, China.
Biopharm Drug Dispos. 2013 Sep;34(6):321-35. doi: 10.1002/bdd.1848. Epub 2013 Jul 9.
Human effective intestinal membrane permeability (Peff) is one of the two important indicators for drug classification according to the Biopharmaceutical Classification System (BCS), and contributes greatly to the performance of oral drug absorption. Here, a structure-based in silico predictive model of Peff was developed successfully to facilitate in silico BCS classification in the early stage of drug discovery, even before the compound was synthesized. The quantitative structure-Peff relationship for 30 drugs was constructed based on seven structural parameters. Then the model was built by the multiple linear regression method and internally validated by the residual analysis, the normal probability-probability plot and the Williams plot. For the entire data set, the R² and adjusted R² values were 0.782 and 0.712, respectively. The results indicated that the fitted model was robust, stable and satisfied all the prerequisites of the regression models. As for the 102 tested drugs, the predicted Peff values had a good correlation with the experimental human absorbed fraction (Fa). This model was also used to perform high/low Peff classification for 57 drugs that have been classified according to the BCS, and 72% of drugs could be classified correctly, indicating that the developed model can be used for rapid BCS classification in the early stages of drug discovery.
人体有效肠道膜通透性（Peff）是根据生物药剂学分类系统（BCS）进行药物分类的两个重要指标之一，对口服药物吸收的性能有很大贡献。在这里，成功开发了一种基于结构的 Peff 计算预测模型，以促进药物发现早期的计算 BCS 分类，甚至在化合物合成之前。基于七个结构参数，构建了 30 种药物的定量构效关系-Peff 关系。然后，通过多元线性回归方法建立模型，并通过残差分析、正态概率-概率图和 Williams 图进行内部验证。对于整个数据集，R²和调整后的 R²值分别为 0.782 和 0.712。结果表明，拟合模型稳健、稳定，满足回归模型的所有前提条件。对于 102 种测试药物，预测的 Peff 值与人体吸收分数（Fa）具有良好的相关性。该模型还用于对 57 种根据 BCS 分类的药物进行高/低 Peff 分类，其中 72%的药物可以正确分类，表明所开发的模型可用于药物发现早期的快速 BCS 分类。
