Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques

In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The extracts were tested for phenolic content, antioxidant potential, acetylcholinesterase and butyrylcholinesterase inhibitory activities and antiproliferative effects against A549 lung cancer cell line. The results show that the extracts obtained by ANN-GA optimization exhibited higher antioxidant activity compared to RSM extracts and had higher total antioxidant status (TAS), DPPH and FRAP values. Phenolic content analysis revealed eight phenolic compounds and the compounds with the highest concentrations were caffeic acid (in RSM extract) and gallic acid (in ANN-GA extract), respectively. Both extracts showed strong cytotoxic effects against A549 cells depending on the concentration, with ANN-GA extract showing higher antiproliferative activity. Our study provides important findings on the biological activities and therapeutic potential of O. onotica and particularly reveals that the ANN-GA optimization method plays an important role in increasing biological activity. The findings show that O. onotica extracts can be used in the treatment of cancer and neurodegenerative diseases in the future and that optimization techniques offer an effective strategy for enriching phenolic contents.