Planta Medica International Open 2017; 4(S 01): S1-S202
DOI: 10.1055/s-0037-1608333
Lecture Session – Analytical Studies & Natural Products Chemistry II
Georg Thieme Verlag KG Stuttgart · New York

The Comprehensive Screening of Traditional Chinese Medicine Using a Novel LC/MS Informatics Platform

J Yuk
1   Waters Corporation, Milford, United States
,
G Isaac
1   Waters Corporation, Milford, United States
,
S Nikles
2   Institute of Pharmaceutical Sciences, Department of Pharmacognosy, University of Graz, Graz, Austria
,
M Wrona
1   Waters Corporation, Milford, United States
,
R Bauer
2   Institute of Pharmaceutical Sciences, Department of Pharmacognosy, University of Graz, Graz, Austria
› Author Affiliations
Further Information

Publication History

Publication Date:
24 October 2017 (online)

 

To fully understand the effectiveness of traditional medicines (TM), it is vital to investigate the chemical components of the raw herbal materials. This is a difficult task due to the complexity of the sample which can contain one or multiple herbs. LC/MS is a widely-used analytical technique as it is highly sensitive and is able to separate and identify the diverse chemical components in the TM. However, due to the large amounts of chemical information, the major challenge is actually screening the datasets and generating results rapidly and confidently. Here, we present a novel workflow that enables researchers to quickly identify chemical ingredients from a well-known traditional Chinese medicine, Yu Ping Feng San from a single LC/MS injection. Many active compounds such as furocoumarins, furochromones, isoflavonoids, triterpene saponins, and sesquiterpenes were detected in the different solvent extracts. These compounds were found to have many anti-proliferative, antioxidant and anti-inflammatory properties and through bioactivity tests, have shown to have significant inhibition for the expression of TNF-α, IFN-γ and IL-1β in cells. This presentation will demonstrate a thorough investigation of the complex LC/MS data-set and deduction of the chemical components using an in-depth data analysis of identifying known knowns, unknown knowns, and unknown unknowns.