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Structure‐based prediction of human intestinal membrane permeability for rapid in silico BCS classification
Author(s) -
Sun Le,
Liu Xiaohong,
Xiang Rongwu,
Wu Chunnuan,
Wang Yongjun,
Sun Yinghua,
Sun Jin,
He Zhonggui
Publication year - 2013
Publication title -
biopharmaceutics and drug disposition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.419
H-Index - 58
eISSN - 1099-081X
pISSN - 0142-2782
DOI - 10.1002/bdd.1848
Subject(s) - in silico , biopharmaceutical , linear regression , membrane permeability , regression analysis , plot (graphics) , mathematics , chemistry , artificial intelligence , biological system , computational biology , statistics , computer science , membrane , biology , biochemistry , microbiology and biotechnology , gene
Human effective intestinal membrane permeability ( P eff ) 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 P eff 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– P eff 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 R 2 and adjusted R 2 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 P eff values had a good correlation with the experimental human absorbed fraction ( F a ). This model was also used to perform high/low P eff 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. Copyright © 2013 John Wiley & Sons, Ltd.