ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

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• 作者：Ning-Ning Wang ; Jie Dong ; Yin-Hua Deng ; Min-Feng Zhu ; Ming Wen ; Zhi-Jiang Yao ; Ai-Ping Lu ; Jian-Bing Wang ; Dong-Sheng Cao
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2016
• 出版时间：April 25, 2016
• 年：2016
• 卷：56
• 期：4
• 页码：763-773
• 全文大小：499K
• 年卷期：0
• ISSN：1549-960X

文摘

The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure–property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R² = 0.97, RMSE_F = 0.12, Q² = 0.83, RMSE_CV = 0.31 for the training set and R_T² = 0.81, RMSE_T = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.