Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method.
Caco-2 cell monolayers are widely used systems for predicting human intestinal absorption. This study was carried out to develop a quantitative structure-property relationship (QSPR) model of Caco-2 permeability using a novel genetic algorithm-based partial least squares (GA-PLS) method. The Caco-2 permeability data for 73 compounds were taken from the literature. Molconn-Z descriptors of these compounds were calculated as molecular descriptors, and the optimal subset of the descriptors was explored by GA-PLS analysis. A fitness function considering both goodness-of-fit to the training data and predictability of the testing data was adopted throughout the genetic algorithm-driven optimization procedure. The final PLS model consisting of 24 descriptors gave a correlation coefficient (r) of 0.886 for the entire dataset and a predictive correlation coefficient (r(pred)) of 0.825 that was evaluated by a leave-some-out cross-validation procedure. Thus, the GA-PLS analysis proved to be a reasonable QSPR modeling approach for predicting Caco-2 permeability.
Yamashita
Fumiyoshi
F
Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, Yoshidashimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan.
Wanchana
Suchada
S
Hashida
Mitsuru
M
D000465:Algorithms; D018938:Caco-2 Cells; D002463:Cell Membrane Permeability / Q000502:physiology*; D030541:Databases, Genetic; D005823:Genetics / Q000706:statistics & numerical data*; D006801:Humans; D016018:Least-Squares Analysis; D008970:Molecular Weight; D021281:Quantitative Structure-Activity Relationship