Optimization of Hymenopellis radicata extracts using RSM and ANN-GA and evaluation of biological activities

Currently, the utilization of bioactive chemicals derived from natural products is on the rise across various sectors, including pharmaceuticals and the food industry. Our investigation identified the extraction procedures that yield the most biological activity of the medicinal fungus Hymenopellis radicata. The antioxidant, anticholinesterase, and antiproliferative properties of the extracts generated under these conditions were assessed. In this context, temperature, duration, and solvent ratio served as optimization parameters, which were obtained utilizing Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The antioxidant capacities of the optimized extracts were assessed using TAS, TOS, DPPH, and FRAP methodologies. Anticholinesterase activity were evaluated through the inhibition of AChE and BChE. The antiproliferative activity was assessed using the MTT assay on A549 (lung), MCF-7 (breast), and DU-145 (prostate) cancer cell lines. Analyses of phenolic content were conducted by LC-MS/MS. Analyses indicated that extracts optimized using RSM demonstrated superior biological activity relative to those optimized with ANN-GA. They were additionally discovered to possess decreased TOS values. Moreover, studies of phenolic content indicated elevated compound levels in extracts optimized by RSM. Thus, H. radicata was identified as a significant natural resource from both pharmaceutical and biotechnological viewpoints. Moreover, optimization tactics were identified as a crucial factor directly influencing biological activity.