Analysis of structure-Caco-2 permeability relationships using a property landscape approach

Understanding the relationship between the chemical structure of bioactive compounds and Caco-2 permeability is of major importance in modern drug discovery. The purpose of this work was to characterize systematically the Caco-2 permeability landscape of a benchmark dataset of 100 molecules using a novel approach based on the emerging concept of property landscape modeling. Pairwise comparisons of the Caco-2 permeability and chemical structures were calculated for all possible combinations in the dataset. To compare the chemical structures, two distinct manners to represent the molecules were employed, namely, continuous properties previously used to derive QSPR models and molecular fingerprints with different designs. We introduce the concept of "permeability cliffs" discussing cases of compounds with high molecular similarity but large permeability difference. All permeability cliffs were regarded as shallow cliffs, since no extreme difference in Caco-2 permeability (less than two log units) was identified in the dataset. A clear dependence of Caco-2 permeability landscape with molecular representation was observed. The current approach can be further extended to model other ADME relevant landscapes.