Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm...

Area of Science:

• Mycology
• Pharmacognosy
• Biotechnology

Background:

• Otidea onotica is a mushroom species with potential medicinal properties.
• Investigating and optimizing the extraction of bioactive compounds from O. onotica is crucial for its therapeutic applications.
• Optimization methods significantly influence the yield and potency of fungal extracts.

Purpose of the Study:

• To evaluate the biological activities of Otidea onotica extracts.
• To compare the efficacy of Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) optimization methods.
• To explore the potential of O. onotica in treating cancer and neurodegenerative diseases.

Main Methods:

• Extraction of O. onotica using RSM and ANN-GA optimization techniques.
• Assays for total phenolic content, antioxidant potential (TAS, DPPH, FRAP), acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibition.
• In vitro antiproliferative testing against A549 lung cancer cell line.

Main Results:

• ANN-GA optimized extracts demonstrated superior antioxidant activity (higher TAS, DPPH, FRAP values) compared to RSM extracts.
• Eight phenolic compounds were identified, with gallic acid being most abundant in ANN-GA extracts and caffeic acid in RSM extracts.
• Both extracts exhibited significant concentration-dependent cytotoxic effects on A549 cells, with ANN-GA showing higher antiproliferative activity.

Conclusions:

• ANN-GA optimization is more effective than RSM for enhancing the biological activities of O. onotica extracts.
• O. onotica possesses significant antioxidant, anticholinesterase, and antiproliferative properties.
• Optimized O. onotica extracts hold promise for future therapeutic applications in oncology and neurodegenerative disease treatment.