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<p>This study aimed to determine the extraction conditions that maximize the biological activities of <i>Morchella importuna</i> and to demonstrate the pharmacological potential of the optimized extracts. Ultrasonic-assisted extraction was applied, and the effects of temperature, time, and solvent ratio were evaluated using Response Surface Methodology (RSM) and a hybrid Artificial Neural Network–Genetic Algorithm (ANN-GA) approach. ANN-GA optimization yielded higher levels of key phenolics, particularly quercetin, protocatechuic acid, and gallic acid, compared to RSM. The antioxidant capacities of the ANN-GA and RSM extracts were determined as TAS: 5.266 ± 0.015 and 5.101 ± 0.031 mmol/L, TOS: 8.144 ± 0.035 and 10.240 ± 0.054 µmol/L, OSI: 0.155 ± 0.001 and 0.201 ± 0.002, DPPH: 121.82 ± 1.30 and 113.26 ± 1.44&#xa0;mg TE/g, and FRAP: 158.98 ± 0.91 and 141.87 ± 1.80&#xa0;mg TE/g, respectively. ANN-GA extracts also showed stronger anticholinesterase activity with AChE inhibition values of 66.47 ± 0.85&#xa0;µg/mL and BChE values of 119.16 ± 1.11&#xa0;µg/mL. In antiproliferative assays, ANN-GA extracts exhibited higher cytotoxicity against A549, MCF-7, and DU-145 cell lines compared to RSM extracts. Overall, the ANN-GA-assisted optimization approach not only improved extraction efficiency but also enhanced the antioxidant, anticholinesterase, and antiproliferative properties of <i>M. importuna</i>. These findings highlight the potential of ANN-GA optimized extracts as promising natural candidates for functional food, nutraceutical, and future pharmaceutical applications.</p>

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RSM and ANN-GA assisted optimization of Morchella importuna extracts and their effects on antioxidant anticholinesterase and antiproliferative activity

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  • 发表日期：

    2025年12月27日

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  • Ilgaz Akata,
  • Ayşenur Gürgen,
  • Tetiana Krupodorova,
  • Aras Fahrettin Korkmaz,
  • Emre Cem Eraslan,
  • Mustafa Sevindik

摘要

This study aimed to determine the extraction conditions that maximize the biological activities of Morchella importuna and to demonstrate the pharmacological potential of the optimized extracts. Ultrasonic-assisted extraction was applied, and the effects of temperature, time, and solvent ratio were evaluated using Response Surface Methodology (RSM) and a hybrid Artificial Neural Network–Genetic Algorithm (ANN-GA) approach. ANN-GA optimization yielded higher levels of key phenolics, particularly quercetin, protocatechuic acid, and gallic acid, compared to RSM. The antioxidant capacities of the ANN-GA and RSM extracts were determined as TAS: 5.266 ± 0.015 and 5.101 ± 0.031 mmol/L, TOS: 8.144 ± 0.035 and 10.240 ± 0.054 µmol/L, OSI: 0.155 ± 0.001 and 0.201 ± 0.002, DPPH: 121.82 ± 1.30 and 113.26 ± 1.44 mg TE/g, and FRAP: 158.98 ± 0.91 and 141.87 ± 1.80 mg TE/g, respectively. ANN-GA extracts also showed stronger anticholinesterase activity with AChE inhibition values of 66.47 ± 0.85 µg/mL and BChE values of 119.16 ± 1.11 µg/mL. In antiproliferative assays, ANN-GA extracts exhibited higher cytotoxicity against A549, MCF-7, and DU-145 cell lines compared to RSM extracts. Overall, the ANN-GA-assisted optimization approach not only improved extraction efficiency but also enhanced the antioxidant, anticholinesterase, and antiproliferative properties of M. importuna. These findings highlight the potential of ANN-GA optimized extracts as promising natural candidates for functional food, nutraceutical, and future pharmaceutical applications.