Integration of QSAR modeling and in vitro validation for identification of food-derived Nrf2 activators against arsenic-induced toxicity.

Arsenic exposure is a major public health concern due to its toxicity and carcinogenicity, largely mediated by oxidative stress, and activation of the Nrf2 signaling pathway is considered a promising strategy to mitigate such damage. In this study, a quantitative structure-activity relationship (QSAR) model was developed to identify potential Nrf2 activators from food-derived compounds. Machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and a stacking model, were developed and optimized. The optimized RF, SVM, and LDA models were subsequently integrated into a stacking framework, which was selected as the final predictive model for virtual screening based on its robust and balanced predictive performance. The stacking model achieved the highest accuracy during five-fold cross-validation (0.77) and demonstrated competitive performance on the independent test set (0.742 ± 0.040), with balanced precision, sensitivity, and F-measure. Consequently, the stacking model was selected as the final model for virtual screening. A total of 70,926 compounds retrieved from a food-derived database were filtered based on physicochemical criteria, and QSAR-based virtual screening predicted 920 compounds as potential activators of the Nrf2 signaling pathway, from which 5,7-Dimethylchrysin (FooDB ID: FDB015537) and Demethylvestitol (FooDB ID: FDB012218) were selected for experimental validation. In vitro assays using HepG2 cells demonstrated that 5,7-Dimethylchrysin significantly enhanced cell viability against sodium arsenite (150 μM)-induced cytotoxicity, with an effective concentration (EC50) of 0.54 μM, whereas Demethylvestitol exhibited only limited protective effects. Furthermore, luciferase reporter assays confirmed that 5,7-Dimethylchrysin activated antioxidant response element (ARE)-driven transcription in a concentration-dependent manner, producing a 1.5 to 2.4-fold increase at concentrations ranging from 0.78 to 6.25 μM. Overall, these findings highlight the effectiveness of integrating QSAR modeling with experimental validation to identify food-derived compounds with protective potential against arsenic-induced toxicity, with 5,7-Dimethylchrysin emerging as a promising candidate for further investigation.