RSM and ANN-GA assisted optimization of Morchella importuna extracts and their effects on antioxidant anticholinesterase and antiproliferative activity

This study aimed to determine the extraction conditions that maximize the biological activities of Morchella importuna and to demonstrate the pharmacological potential of the optimized extracts. Ultrasonic-assisted extraction was applied, and the effects of temperature, time, and solvent ratio were evaluated using Response Surface Methodology (RSM) and a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) approach. ANN-GA optimization yielded higher levels of key phenolics, particularly quercetin, protocatechuic acid, and gallic acid, compared to RSM. The antioxidant capacities of the ANN-GA and RSM extracts were determined as TAS: 5.266 ± 0.015 and 5.101 ± 0.031 mmol/L, TOS: 8.144 ± 0.035 and 10.240 ± 0.054 µmol/L, OSI: 0.155 ± 0.001 and 0.201 ± 0.002, DPPH: 121.82 ± 1.30 and 113.26 ± 1.44 mg TE/g, and FRAP: 158.98 ± 0.91 and 141.87 ± 1.80 mg TE/g, respectively. ANN-GA extracts also showed stronger anticholinesterase activity with AChE inhibition values of 66.47 ± 0.85 µg/mL and BChE values of 119.16 ± 1.11 µg/mL. In antiproliferative assays, ANN-GA extracts exhibited higher cytotoxicity against A549, MCF-7, and DU-145 cell lines compared to RSM extracts. Overall, the ANN-GA-assisted optimization approach not only improved extraction efficiency but also enhanced the antioxidant, anticholinesterase, and antiproliferative properties of M. importuna. These findings highlight the potential of ANN-GA optimized extracts as promising natural candidates for functional food, nutraceutical, and future pharmaceutical applications.