Evaluation of antioxidant, anticholinesterase and antiproliferative potential of Artemisia herba-alba by artificial intelligence-assisted extraction optimization.

In this study, in order to maximize the biological activity of Artemisia herba-alba Asso, extraction conditions were optimized by two different methods: Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). A total of 27 experimental conditions were created by evaluating the parameters of extraction temperature (45, 55 and 65 °C), time (5, 10, 15 h) and ethanol/water ratio (0, 50, 100%) and the obtained data were applied to the optimization algorithms. ANN-GA method provided higher biological activity parameters compared to RSM. The TAS value of the extract obtained with ANN-GA was determined as 9.449 mmol/L, DPPH value as 150.673 mg TE/g, FRAP value as 226.580 mg TE/g, TPC value as 303.120 mg GAE/g and TFC value as 363.583 mg QE/g. In anticholinesterase activity tests, AChE IC₅₀ value of ANN-GA extract was 41.923 µg/mL, BChE IC₅₀ value was 61.450 µg/mL, and it showed a stronger inhibitory effect than RSM extract. In antiproliferative assays, both extracts demonstrated a dose-dependent reduction in cell viability on A549 (lung), MCF-7 (breast), and DU-145 (prostate) cancer cell lines. The strongest inhibitory effects were observed at 100 and 200 µg/mL concentrations. Although no statistically significant difference was found between the two methods, the RSM extract exhibited slightly greater antiproliferative efficacy across all three cell lines, particularly at higher con-centrations. These results indicate that both extraction approaches are effective in suppressing cancer cell proliferation, with the RSM method showing a marginal advantage in cytotoxic activity. In the phenolic compound analysis performed by LC-MS/MS, higher levels of pharmacologically important compounds such as naringenin (16,488.02 mg/kg), kaempferol (14,206.24 mg/kg), and caffeic acid (9291.10 mg/kg) were detected in ANN-GA extract. The results show that ANN-GA based optimization approach is an effective strategy to increase antioxidant, anticholinesterase and antiproliferative activities and support the pharmaceutical potential of A. herba-alba.