レンティヌラ・エドデス抽出物の人工知能支援最適化による生理活性プロファイルと治療的可能性の向上

In this study, extraction parameters to increase the biological activities of Lentinula edodes extracts were optimized using both Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid models. Antioxidant, anticholinesterase, antiproliferative activities, and phenolic contents of the extracts obtained under optimum conditions were determined. According to the findings of the study, it was determined that the extracts obtained with ANN-GA optimization had higher TAS (6.612 mmol/L), FRAP (176.25 mg TE/g), and DPPH (133.00 mg TE/g) values ​​compared to RSM. In addition, TOS (4.167 µmol/L) and OSI (0.063) levels were lower. In anticholinesterase assays, ANN-GA extracts were found to be more effective than RSM in inhibiting AChE (72.47 µg/mL) and BChE (132.13 µg/mL). Furthermore, in antiproliferative assays on A549, MCF-7, and DU-145 cancer cell lines, ANN-GA-optimized extracts were observed to have stronger cytotoxic effects. LC-MS/MS analyses revealed that ANN-GA optimization enriched biologically important phenolic compounds such as gallic acid, protocatechuic acid, and caffeic acid at higher levels. Consequently, AI-assisted optimization offers a powerful strategy for enhancing the biological value of mushroom extracts.