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

> **NIH NIH R03** · OHIO STATE UNIVERSITY · 2022 · $78,750

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10415273
- **Project number:** 1R03AT011872-01
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Liva Harinantenaina Rakotondraibe
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $78,750
- **Award type:** 1
- **Project period:** 2022-09-19 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415273

## Citation

> US National Institutes of Health, RePORTER application 10415273, DEVELOPMENT OF AN IN-HOUSE PROTON SPIN NETWORK DATABASE TO CHARACTERIZE THE PHARMACOPHORES OF CENTELLA ASIATICA FOR STANDARDIZATION AND QUALITY CONTROL (1R03AT011872-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10415273. Licensed CC0.

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