LC-MS/MS approaches to characterize oligostilbenes directly from complex crude extract mixtures / Nurhuda Manshoor and Jean-Frederic Faizal Weber Abdullah

LC-MS analysis has become a valuable tool in drug discovery studies from natural products. The technique was used in various studies, such as discrimination of known estrogenic compounds in pomegranate, detection of high molecular weight proanthocyanidins, or differentiation of isomeric of glycosidic flavonoids. Some 400 oligostilbenoids are already known. There are found abundantly in local dipterocarp timber trees, mostly in the form of resveratrol dimers, trimers and tetramers. This project aims at dereplicating dipterocarp extracts. For this purpose, we need to be able to discriminate between compounds from long series of isomers, including closely related diastereoisomers. A standardized LC-MS gradient system will be used to quickly identify known compounds and for initial characterization of unknowns. The detection of chemical constituents from a crude extract of Neobalanocarpus heimii will be performed on a triple quadrupole and/or ion trap liquid chromatography-mass spectrometry (LC-MS) with an electrospray-ionization (ESI) interface. For the efficient use of this technique, an efficient chromatographic analysis will be achieved on a UPLC system similar to the LC component on the LC-MS system. Parameters to be used to generate informative MS/MS spectra will be optimized for the recording of fragmentation pattern in standardized conditions. The collision-induces dissociation (CID) MS/MS spectra of the compounds in the crude plant extract will be recorded and the data will be compared with those of previously obtained. The isolation of chemical constituents will be accomplished on a fully-automated semi-preparative HPLC system, by the same chromatographic condition as in the LC-MS. Their structures and configurations will be established by spectroscopic methods. The identified compounds will be compared to those stored in the library to further confirm the identification of compounds in the crude extract. We expect positive identification of known compounds in mixtures solely from their fragmentation pattern, independently from retention time or other data, thus saving very significant amount of time and resources.