Optimization of biological activities of Agaricus species: an artificial intelligence-assisted approach.

This study aims to determine the optimum extraction conditions that maximize the biological activities of Agaricus campestris and Agaricus bisporus species. In the study, a total of 64 extraction experiments were carried out at different temperatures, time and solvent concentrations and the obtained data were modeled with Artificial Neural Network (ANN) and optimized with Genetic Algorithm (GA). After determining the optimum extraction parameters, antioxidant, anticholinesterase and antiproliferative activities and phenolic contents of the produced extracts were analyzed. As a result of single and multi-objective optimization studies of A. campestris and A. bisporus, extract concentration was determined. In antioxidant analyses, it was observed that A. campestris extracts had higher total antioxidant capacity (TAS) and lower total oxidant level (TOS) compared to A. bisporus extracts. Anticholinesterase activity tests revealed that A. campestris extracts showed stronger inhibitory effect compared to A. bisporus. In addition, in the antiproliferative activity analyses performed on the A549 lung cancer cell line, it was determined that the extracts produced with ANN-GA optimization suppressed cell proliferation. Phenolic compound analyses showed that antioxidant compounds such as gallic acid, protocatechuic acid and caffeic acid were found at high levels in A. campestris extracts. The results show that ANN-GA supported optimization processes enrich the bioactive components of mushroom extracts and that these methods may be effective in biotechnological applications.