Artificial Intelligence Assisted Optimization of Ramaria obtusissima Extracts and Their Integrated Chemical and Biological Characterization

TL;DR

This study uses artificial intelligence to optimize mushroom extracts, finding they have higher antioxidant and anticholinesterase effects compared to traditional methods.

Contribution

AI-assisted optimization significantly enhances the biological activity and chemical profile of Ramaria obtusissima extracts.

Findings

ANN-GA optimized extracts showed higher antioxidant activity with higher FRAP, TAS, and DPPH values.

ANN-GA extracts exhibited stronger anticholinesterase activity with lower IC50 values for both enzymes.

ANN-GA extracts contained higher levels of phenolic compounds like gallic acid and quercetin.

Abstract

In this study, the biological activities of extracts obtained from Ramaria obtusissima were optimized using response surface methodology (RSM) and artificial neural networks-genetic algorithm (ANN-GA) approaches, and the chemical and biological profiles of the obtained extracts were evaluated with a holistic approach. Antioxidant potential was determined using FRAP, DPPH, TAS, TOS, and OSI parameters. It was found that the extract optimized with ANN-GA had significantly higher FRAP (242 ± 3 mg Trolox equivalent/g), TAS (6.64 ± 0.04 mmol/L), and DPPH (154 ± 3 mg Trolox equivalent/g) values compared to the RSM extract, while its OSI value was lower. Anticholinesterase activities were evaluated using IC50 values, and it was determined that the ANN-GA extract exhibited a stronger inhibitory effect on acetylcholinesterase (95 ± 2 µg/mL) and butyrylcholinesterase (125 ± 3 µg/mL) compared to the RSM extract. Antiproliferative effects were investigated in A549, MCF-7, and DU-145 cell lines, and a significant and dose-dependent suppression of cell proliferation was observed in all three cell lines, particularly at concentrations of 100 and 200 µg/mL. The chemical profile was determined using LC-MS/MS and GC-MS techniques. Higher levels of phenolic compounds such as gallic acid (6694.5 ± 4.9 mg/kg), caffeic acid (3374.8 ± 4.9 mg/kg), and quercetin (1563.1 ± 2.3 mg/kg) were found in the ANN-GA extract. GC-MS analyses showed that the ANN-GA extract has a richer lipophilic component profile in terms of biologically active fatty acids and ester derivatives. The findings reveal that AI-assisted optimization offers a powerful and effective approach to enhancing the biological efficacy of mushroom-derived natural products.

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