Improvement and prediction of the extraction parameters of lupeol and stigmasterol metabolites of Melia azedarach with response surface methodology


BMC Biotechnol. 2024 Jun 7;24(1):39. doi: 10.1186/s12896-024-00865-2.


BACKGROUND: Melia azedarach is known as a medicinal plant that has wide biological activities such as analgesic, antibacterial, and antifungal effects and is used to treat a wide range of diseases such as diarrhea, malaria, and various skin diseases. However, optimizing the extraction of valuable secondary metabolites of M. azedarach using alternative extraction methods has not been investigated. This research aims to develop an effective, fast, and environmentally friendly extraction method using Ultrasound-assisted extraction, methanol and temperature to optimize the extraction of two secondary metabolites, lupeol and stigmasterol, from young roots of M. azedarach using the response surface methodology.

METHODS: Box-behnken design was applied to optimize different factors (solvent, temperature, and ultrasonication time). The amounts of lupeol and stigmasterol in the root of M. azedarach were detected by the HPLC-DAD. The required time for the analysis of each sample by the HPLC-DAD system was considered to be 8 min.

RESULTS: The results indicated that the highest amount of lupeol (7.82 mg/g DW) and stigmasterol (6.76 mg/g DW) was obtained using 50% methanol at 45 °C and ultrasonication for 30 min, and 50% methanol in 35 °C, and ultrasonication for 30 min, respectively. Using the response surface methodology, the predicted conditions for lupeol and stigmasterol from root of M. azedarach were as follows; lupeol: 100% methanol, temperature 45 °C and ultrasonication time 40 min (14.540 mg/g DW) and stigmasterol 43.75% methanol, temperature 34.4 °C and ultrasonication time 25.3 min (5.832 mg/g DW).

CONCLUSIONS: The results showed that the amount of secondary metabolites lupeol and stigmasterol in the root of M. azedarach could be improved by optimizing the extraction process utilizing response surface methodology.

PMID:38849803 | DOI:10.1186/s12896-024-00865-2