Prediction of caco-2 cell permeability using bilinear indices and multiple linear regression

Huong Le-Thi-Thu, Yudith Canizares-Carmenate, Yovani Marrero-Ponce, Francisco Torrens, Juan A. Castillo-Garit

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

11 Citas (Scopus)

Resumen

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 R2ext 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.

Idioma originalInglés
Páginas (desde-hasta)161-169
Número de páginas9
PublicaciónLetters in Drug Design and Discovery
Volumen13
N.º2
DOI
EstadoPublicada - 1 ene. 2016
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Prediction of caco-2 cell permeability using bilinear indices and multiple linear regression'. En conjunto forman una huella única.

Citar esto