Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method

✍ Scribed by Fumiyoshi Yamashita; Suchada Wanchana; Mitsuru Hashida

• Publisher: John Wiley and Sons
• Year: 2002
• Tongue: English
• Weight: 114 KB
• Volume: 91
• Category: Article
• ISSN: 0022-3549
• DOI: 10.1002/jps.10214

✦ Synopsis

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.