Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning

For medicines, the apparent membrane permeability coefficients (P-app) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P-app values of 29 industrial chemicals were found to have an inverse association with their reported no-observed effect levels for hepatotoxicity in rats. In the current study, we expanded our influx permeability predictions for the 90 previously investigated chemicals to both influx and efflux permeability predictions for 207 diverse primary compounds, along with those for 23 secondary compounds. Trivariate linear regression analysis found that the observed influx and efflux logP(app) values determined by in vitro experiments significantly correlated with molecular weights and the octanol-water distribution coefficients at apical and basal pH levels (pH 6.0 and 7.4, respectively) (apical to basal, r = 0.76, n = 198; and basal to apical, r = 0.77, n = 202); the distribution coefficients were estimated in silico. Further, prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM) to estimate influx and efflux logP(app) values that incorporated 17 and 19 in silico chemical descriptors (r = 0.83-0.84, p < 0.001). The determination in vitro and/or prediction in silico of permeability coefficients across intestinal cell monolayers of a diverse range of industrial chemicals/food components/medicines could contribute to the safety evaluations of oral intakes of general chemicals in humans. Such new alternative methods could also reduce the need for animal testing during toxicity assessment.

Automated summary

The study found that the observed influx and efflux logP(app) values determined by in vitro experiments significantly correlated with molecular weights and the octanol-water distribution coefficients at different pH levels. Prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM).