Artificial intelligence-assisted optimization of Eichhornia crassipes extracts and evaluation of their biological activities

In this research, the extraction conditions for Eichhornia crassipes (Mart.) Solms were optimized using Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) techniques to enhance the biological efficacy of the extracts. The optimization focused on three key variables: extraction temperature, duration, and the ethanol-to-water solvent ratio. Through the ANN-GA model, the optimal parameters were identified as 56.85 °C for temperature, 7.62 h for extraction time, and 23.93% for the ethanol/water proportion. The obtained extracts showed statistically significantly higher values ​​compared to RSM in terms of antioxidant capacity (FRAP: 152.89 mg TE/g; DPPH: 121.48 mg TE/g), total phenolic content (TPC: 209.47 mg GAE/g) and flavonoid content (TFC: 263.86 mg QE/g). In addition, ANN-GA extract exhibited high anticholinesterase activity with lower IC₅₀ values ​​against acetylcholinesterase (AChE: 61.69 µg/mL) and butyrylcholinesterase (BChE: 81.40 µg/mL) enzymes. In in vitro tests on A549 cell line, its antiproliferative effect increased significantly in a dose-dependent manner and significant decreases in cell viability were observed especially at high concentrations. LC-MS/MS analyses revealed that pharmacologically important phenolic compounds such as quercetin (10295.26 mg/kg), kaempferol (8656.31 mg/kg) and naringenin (5364.56 mg/kg) were present in high concentrations in the optimized extracts. In conclusion, ANN-GA based extraction approach stands out as an effective method for obtaining phenolic compound rich and biologically effective extracts of E. crassipes. These findings indicate that this aquatic plant should be evaluated for its pharmaceutical, neuroprotective and anticancer potential.