Developing a quantitative structure-property relationships (QSPR) model using Caco-2 cell bioavailability indicators (BA) to predict the BA of phytochemicals.

BACKGROUND

The present study aimed to measure bioavailability (BA) indicators, including epithelial barrier function, apparent permeability (P) and efflux ratio, of 84 types of phytochemicals using Caco-2 cell and to develop predictive model systems using machine learning with a quantitative structure-property relationship (QSPR) model based on BA indicators and an Isomeric Simplified Molecular Input Line Entry System (SMILES). Analysis of phytochemicals was carried out with a validated HPLC analytical method.

RESULTS

With these BA indicators, Isomeric SMILES including information such as the stereochemistry, chemical structure and properties of phytochemicals was encoded to molecular descriptors using PaDEL-Descriptor and alvaDesc. The validity of the dataset was verified using principal component analysis, leverage plot and Williams plot. In the case of transepithelial electrical resistance (TEER), R is 0.86, root mean square error (RMSE) is 55.25, R is 0.63 and RMSE is 74.77, respectively. Regarding the P, the model demonstrated strong performance on the training set with RMSE of 4.54 × 10 and R of 0.95 with the test set results (RMSE = 6.23 × 10 and R  = 0.91). For the efflux ratio, the modle explains 92% of the variance with RMSE of 0.39, R of 0.92, R of 0.85 and RMSE of 0.71.

CONCLUSION

The present study suggests that a prediction system for bioavailability, including TEER, P and efflux ratio, can be developed using a QSPR model, which could contribute to advancements in discover of functional ingredients and drugs. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.