High-speed countercurrent chromatography with offline detection by electrospray mass spectrometry and nuclear magnetic resonance detection as a tool to resolve complex mixtures: A practical approach using Coffea arabica leaf extract.

INTRODUCTION: Many secondary metabolites isolated from plants have been described in the literature owing to their important biological properties and possible pharmacological applications. However, the identification of compounds present in complex plant extracts has remained a great scientific challenge, is often laborious, and requires a long research time with high financial cost. OBJECTIVES: The aim of this study was to develop a method that allows the identification of secondary metabolites in plant extracts with a high degree of confidence in a short period of time. MATERIAL AND METHODS: In this study, an ethanolic extract of Coffea arabica leaves was used to validate the proposed method. Countercurrent chromatography was chosen as the initial step for extraction fractionation using gradient elution. Resulting fractions presented a variation of compounds concentrations, allowing for statistical total correlation spectroscopy (STOCSY) calculations between liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) and NMR across fractions. RESULTS: The proposed method allowed the identification of 57 compounds. Of the annotated compounds, 20 were previously described in the literature for the species and 37 were reported for the first time. Among the inedited compounds, we identified flavonoids, alkaloids, phenolic acids, coumarins, and terpenes. CONCLUSION: The proposed method presents itself as a valid alternative for the study of complex extracts in an effective, fast, and reliable way that can be reproduced in the study of other extracts.

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more.

INTRODUCTION: We developed Data Base similarity (DBsimilarity), a user-friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience. METHOD: DBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma-Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape. RESULTS: The Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation. CONCLUSION: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification.

Schinus terebinthifolius countercurrent chromatography (Part III): Method transfer from small countercurrent chromatography column to preparative centrifugal partition chromatography ones as a part of method development.

Countercurrent chromatography (CCC) and centrifugal partition chromatography (CPC) are support free liquid-liquid chromatography techniques sharing the same basic principles and features. Method transfer has previously been demonstrated for both techniques but never from one to another. This study aimed to show such a feasibility using fractionation of Schinus terebinthifolius berries dichloromethane extract as a case study. Heptane - ethyl acetate - methanol -water (6:1:6:1, v/v/v/v) was used as solvent system with masticadienonic and 3ß-masticadienolic acids as target compounds. The optimized separation methodology previously described in Part I and II, was scaled up from an analytical hydrodynamic CCC column (17.4mL) to preparative hydrostatic CPC instruments (250mL and 303mL) as a part of method development. Flow-rate and sample loading were further optimized on CPC. Mobile phase linear velocity is suggested as a transfer invariant parameter if the CPC column contains sufficient number of partition cells.

Countercurrent chromatography separation of saponins by skeleton type from Ampelozizyphus amazonicus for off-line ultra-high-performance liquid chromatography/high resolution accurate mass spectrometry analysis and characterisation.

Ampelozizyphus amazonicus Ducke (Rhamnaceae), a medicinal plant used to prevent malaria, is a climbing shrub, native to the Amazonian region, with jujubogenin glycoside saponins as main compounds. The crude extract of this plant is too complex for any kind of structural identification, and HPLC separation was not sufficient to resolve this issue. Therefore, the aim of this work was to obtain saponin enriched fractions from the bark ethanol extract by countercurrent chromatography (CCC) for further isolation and identification/characterisation of the major saponins by HPLC and MS. The butanol extract was fractionated by CCC with hexane - ethyl acetate - butanol - ethanol - water (1:6:1:1:6; v/v) solvent system yielding 4 group fractions. The collected fractions were analysed by UHPLC-HRMS (ultra-high-performance liquid chromatography/high resolution accurate mass spectrometry) and MSn. Group 1 presented mainly oleane type saponins, and group 3 showed mainly jujubogenin glycosides, keto-dammarane type triterpene saponins and saponins with C31 skeleton. Thus, CCC separated saponins from the butanol-rich extract by skeleton type. A further purification of group 3 by CCC (ethyl acetate - ethanol - water (1:0.2:1; v/v)) and HPLC-RI was performed in order to obtain these unusual aglycones in pure form.