ABSTRACT
The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.
KEYWORDS
Caco-2; regression; quantitative structure–property relationship (QSPR); KNIME; permeability
AUTHORS
Gabriela Falcón-Cano†, Christophe Molina∥, and Miguel Ángel Cabrera-Pérez †, §, ‡
†Unit of Modeling and Experimental Biopharmaceutics. Centro de Bioactivos Químicos. Universidad Central “Marta Abreu” de las Villas. Santa Clara 54830, Villa Clara, Cuba
∥PIKAÏROS S.A, 31650 Saint Orens de Gameville, France
§Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot 46100, Valencia, Spain
‡Departamento de Ciencias Farmacéuticas, Facultad de Ciencias, Universidad Católica del Norte, Angamos, 0610, Antofagasta, Chile
JOURNAL
Falcón-Cano, G.; Molina, C.; Cabrera-Pérez, M.Á. Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches. Pharmaceutics 2022, 14, 1998. https://doi.org/10.3390/pharmaceutics14101998
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