Identification of active compounds in vegetal extracts based on correlation between activity and HPLC-MS data

Area of Science:

• Analytical Chemistry
• Pharmacology
• Natural Product Chemistry

Background:

• Identifying active compounds in complex natural extracts is challenging.
• High-performance liquid chromatography-mass spectrometry (HPLC-MS) is a powerful tool for analyzing such mixtures.
• Quantifying biological activity alongside chemical analysis is crucial for discovering bioactive molecules.

Purpose of the Study:

• To develop and validate a method for identifying candidate active compounds in vegetal extracts.
• To establish a predictive model for biological activity based on chemical profiles.
• To apply the method to extra virgin olive oil phenolic extracts for breast cancer cell line cytotoxicity.

Main Methods:

• Combined HPLC-MS analysis with biological activity measurements for each sample.
• Performed correlation analysis between chromatographic peak areas (elution time and m/z ratio) and measured activity.
• Developed a predictive model using a training set and validated it on a separate test set.

Main Results:

• Successfully identified three candidate compounds responsible for the cytotoxicity of extra virgin olive oil phenolic extract against JIMT-1 breast cancer cells.
• The developed prediction model accurately estimated the biological activity in new samples.
• Demonstrated the efficacy of the correlation analysis approach in pinpointing bioactive components.

Conclusions:

• The proposed method is effective for identifying potential active compounds in complex natural matrices.
• Correlation analysis of HPLC-MS data with biological activity provides a robust strategy for bioactive compound discovery.
• The predictive model holds promise for accelerating the development of natural product-based therapeutics.