Targeting bioactive compounds in natural extracts - Development of a comprehensive workflow combining chemical and biological data

International audienceIn natural product drug discovery, several strategies have emerged to highlight specifically bioactive compound(s) within complex mixtures (fractions or crude extracts) using metabolomics tools. In this area, a great deal of interest has raised among the scientific community on strategies to link chemical profiles and associated biological data, leading to the new field called “biochemometrics”. This article falls into this emerging research by proposing a complete workflow, which was divided into three major steps. The first one consists in the fractionation of the same extract using four different chromatographic stationary phases and appropriated elution conditions to obtain five fractions for each column. The second step corresponds to the acquisition of chemical profiles using HPLC-HRMS analysis, and the biological evaluation of each fraction. The last step evaluates the links between the relative abundances of molecules present in fractions (peak area) and the global bioactivity level observed for each fraction. To this purpose, an original bioinformatics script (encoded with R Studio software) using the combination of four statistical models (Spearman, F-PCA, PLS, PLS-DA) was here developed leading to the generation of a “Super list” of potential bioactive compounds together with a predictive score. This strategy was validated by its application on a marine-derived Penicillium chrysogenum extract exhibiting antiproliferative activity on breast cancer cells (MCF-7 cells). After the three steps of the workflow, one main compound was highlighted as responsible for the bioactivity and identified as ergosterol. Its antiproliferative activity was confirmed with an IC50 of 0.10 μM on MCF-7 cells. The script efficiency was further demonstrated by comparing the results obtained with a different recently described approach based on NMR profiling and by virtually modifying the data to evaluate the computational tool behaviour. This approach represents a new and efficient tool to tackle some of the bottlenecks in natural product drug discovery programs