Trivariate Linear Regression and Machine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal Permeability Values of 301 Disparate Chemicals
Abstract
A system for predicting apparent bidirectional permeability (Papp) across Caco-2 cells of diverse chemicals has been reported. The present study aimed to investigate the relationship between in silico-generated Papp (from apical to basal side, Papp A to B) for 301 substances with diverse structures and a binary classification of the reported roles of efflux P-glycoprotein or breast cancer resistant protein. The in silico log(Papp A to B/Papp B to A) values of 70 substances with reported active efflux and 231 substances with no reported active efflux were significantly different (p < 0.01). The probabilities of active efflux transport estimated by trivariate analysis with log MW, log DpH 6.0, and log DpH 7.4 for the 70 active-efflux-positive compounds were higher than those of the other 231 substances (p < 0.01); the area under the corresponding receiver operating characteristic (ROC) curve was 0.81. Further probability values estimated using a machine learning algorithm with 30 chemical descriptors as inputs yielded an area under the ROC curve of 0.79. Using a secondary set of 52 efflux-positive and 48 efflux-negative medicines, the final trivariate-generated probabilities resulted in no significant differences between these binary groups (p = 0.09); however, the final machine learning model demonstrated a good area under the ROC curve of 0.79. Consequently, a combination of the previously established system for generating the permeability coefficients across intestinal…
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