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.