Exploiting the Complementarity between Dereplication and Computer-Assisted Structure Elucidation for the Chemical Profiling of Natural Cosmetic Ingredients: Tephrosia purpurea as a Case Study.

The aqueous-ethanolic extract of Tephrosia purpurea seeds is currently exploited in the cosmetic industry as a natural ingredient of skin lotions. The aim of this study was to chemically characterize this ingredient by combining centrifugal partition extraction (CPE) as a fractionation tool with two complementary identification approaches involving dereplication and computer-assisted structure elucidation. Following two rapid fractionations of the crude extract (2 g), seven major compounds namely, caffeic acid, quercetin-3-O-rutinoside, ethyl galactoside, ciceritol, stachyose, saccharose, and citric acid, were unambiguously identified within the CPE-generated simplified mixtures by a recently developed (13)C NMR-based dereplication method. The structures of four additional compounds, patuletin-3-O-rutinoside, kaempferol-3-O-rutinoside, guaiacylglycerol 8-vanillic acid ether, and 2-methyl-2-glucopyranosyloxypropanoic acid, were automatically elucidated by using the Logic for Structure Determination program based on the interpretation of 2D NMR (HSQC, HMBC, and COSY) connectivity data. As more than 80% of the crude extract mass was characterized without need for tedious and labor-intensive multistep purification procedures, the identification tools involved in this work constitute a promising strategy for an efficient and time-saving chemical profiling of natural extracts.

Identification of natural metabolites in mixture: a pattern recognition strategy based on (13)C NMR.

Because of their highly complex metabolite profile, the chemical characterization of bioactive natural extracts usually requires time-consuming multistep purification procedures to achieve the structural elucidation of pure individual metabolites. The aim of the present work was to develop a dereplication strategy for the identification of natural metabolites directly within mixtures. Exploiting the polarity range of metabolites, the principle was to rapidly fractionate a multigram quantity of a crude extract by centrifugal partition extraction (CPE). The obtained fractions of simplified chemical composition were subsequently analyzed by (13)C NMR. After automatic collection and alignment of (13)C signals across spectra, hierarchical clustering analysis (HCA) was performed for pattern recognition. As a result, strong correlations between (13)C signals of a single structure within the mixtures of the fraction series were visualized as chemical shift clusters. Each cluster was finally assigned to a molecular structure with the help of a locally built (13)C NMR chemical shift database. The proof of principle of this strategy was achieved on a simple model mixture of commercially available plant secondary metabolites and then applied to a bark extract of the African tree Anogeissus leiocarpus Guill. & Perr. (Combretaceae). Starting from 5 g of this genuine extract, the fraction series was generated by CPE in only 95 min. (13)C NMR analyses of all fractions followed by pattern recognition of (13)C chemical shifts resulted in the unambiguous identification of seven major compounds, namely, sericoside, trachelosperogenin E, ellagic acid, an epimer mixture of (+)-gallocatechin and (-)-epigallocatechin, 3,3'-di-O-methylellagic acid 4'-O-xylopyranoside, and 3,4,3'-tri-O-methylflavellagic acid 4'-O-glucopyranoside.