Purpose: To statistically model the permeability across Caco-2 cell monolayers using theoretically computed molecular descriptors and multivariate statistics.
Methods: Seventeen structurally diverse compounds were investigated. The program MolSurf was used to compute theoretical molecular descriptors related to physico-chemical properties such as lipophilicity, polarity, polarizability and hydrogen bonding. The multivariate Partial Least Squares Projections to Latent Structures (PLS) method was used to delineate the relationship between the permeability across Caco-2 cell monolayers and the theoretically computed molecular descriptors.
Results: Excellent statistical models were derived. Properties associated with hydrogen bonding had the largest impact on diffusion through the monolayers and should be kept at a minimum to promote high permeability. High lipophilicity and the presence of surface electrons, i.e. valence electrons, which are not tightly bonded to the molecule, were also found to have a favorable influence to achieve high permeability.
Conclusions: The results indicate that theoretically computed molecular MolSurf descriptors in conjunction with multivariate statistics of PLS type can be used to successfully model permeability across Caco-2 cell monolayers and, thus, differentiate drugs with poor permeability from those with acceptable permeability at an early stage of the preclinical drug discovery process.