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

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,
RMSEF = 0.12, Q2 = 0.83, RMSECV = 0.31 for the training set and RT2 = 0.81, RMSET = 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.