Prediction of Caco-2 Cell Permeability Using Bilinear Indices and Multiple Linear Regression

Author(s): Huong Le-Thi-Thu, Yudith Canizares-Carmenate, Yovani Marrero-Ponce, Francisco Torrens, Juan A. Castillo-Garit.

Journal Name: Letters in Drug Design & Discovery

Volume 13 , Issue 2 , 2016

Graphical Abstract:


Abstract:

The qualitative relationship between in vitro Caco-2 cellular transport and in vivo drug permeability allow using Caco-2 cell assay for intestinal absorption studies. In this work, atom-based bilinear indices and multiple linear regression (MLR) are applied to obtain models useful for the prediction of Caco-2 cell absorption. Making use of a previously reported database, we obtain four statistically significant MLR models, the best models shown R2=0.72 (s=0.435) for nonstochastic indices and R2=0.66 (s=0.464) for stochastic indices. No significant difference was found when comparing to previous reported studies. The models were internally validated using leave-one-out cross-validation, bootstrapping, as well as Y-scrambling experiments. Additionally, we performed an external validation using a test set, which yields significant values of R2 ext of 0.70 and 0.72 for stochastic models, showing a better predictive power. Furthermore, we define a domain of applicability for our models. These results suggest that our approach could offer an appropriate tool as an alternative to predict the absorption in Caco-2 cells in a short time and decrease experimental costs.

Keywords: Bilinear indices, TOMOCOMD-CARDD, ADME, Caco-2 cell, QSAR.

Rights & PermissionsPrintExport

Article Details

VOLUME: 13
ISSUE: 2
Year: 2016
Page: [161 - 169]
Pages: 9
DOI: 10.2174/1570180812666150630183511
Price: $58

Article Metrics

PDF: 19
HTML: 0
EPUB: 0
PRC: 0