DEVELOPMENT OF AN IN-HOUSE PROTON SPIN NETWORK DATABASE TO CHARACTERIZE THE PHARMACOPHORES OF CENTELLA ASIATICA FOR STANDARDIZATION AND QUALITY CONTROL

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PROJECT SUMMARY The benefits of dietary supplements (DS) including botanicals are well documented as they are consumed by about half of the adult population of the United States. For safety and batch-to batch consistency purposes, the identification and characterization of chemical constituents of DS of plant origin have to be performed although the complexity of the mixture and the possible activity contributions of many of the constituting components make the task challenging. Liquid chromatography coupled with mass spectrometry (LC-MS)-based methods are the most reliable so far in the identification and characterization of already known bioactive compounds in a botanical DS. More reliable and updated mass spectrometric databases and methodologies as well as new complementary methods are, however, still needed for rapid metabolite identification, activity consistency, and batch-to-batch quality controls. NMR spectroscopy has long been used with mass spectrometry in metabolomics profiling of natural products and to determine unambiguously structures of organic compounds. Most profiling in many NMR-based metabolomics studies, however, focus only on identifiable (or already known) major compounds, leaving the identification of overlapping signals arising from complex mixture of compounds as major challenges. Furthermore, pure authentic standard compounds display proton and carbon signals with chemical shifts slightly different from those of the same compounds in a mixture and these chemical shift differences make NMR-based metabolomics difficult if not inaccurate. Nevertheless, the shape and the splitting of the signals due to coupled protons in spin networks remain the same, despite mixture-enhanced resonance shifts. These unchanged spin network characteristics can be identified by selective one-dimensional TOCSY (S1DT) experiments, which use pulse sequences that show signal sensitivity increase especially when high-field strength NMR and high number of scans are used. Moreover, many isomers that are undiscernible in most MS analyses can be differentiated using their S1DT fingerprint generated characteristic spin networks. Our overall goal is to couple the identified S1DT fingerprint information with the existing mass spectrometric data information on Centella asiatica (gotu kola) at currently available at BENFRA Botanical Dietary Supplements Research Center (NIH/NCCIH U19 AT010829) and those of compounds isolated during the present study to complement LC-MS for batch-to batch quality control and activity consistencies. Aim 1 on comprehensive untargeted isolation will afford reference standard compounds and a robust LC-MS database that will be used for identification and standardization studies at the center and other research communities working on C. asiatica. In addition, a new application of S1DT that will help to accurately identify chemical constituents and their potential pharmacophores by comparing 1H spin network fingerprints identified in the isolated compounds with those derived from the 1H NMR spectra of the extract will be developed in Aim 2.