Disentangling the Complexity of a Hexa-Herbal Chinese Medicine Used for Inflammatory Skin Conditions-Predicting the Active Components by Combining LC-MS-Based Metabolite Profiles and in vitro Pharmacology.

Jennifer B Chang,Min Yang,Michael Heinrich,Majella E Lane

doi:10.3389/fphar.2018.01091

Abstract

Objectives: The purpose of this study is to investigate the anti-inflammatory activity of a hexa-herbal Chinese formula (HHCF) using spontaneously immortalized human epidermal keratinocytes (HaCaT) and to predict the active components by correlating the LC-MS-based metabolite profiles of the HHCF and its 12 varied formulae with their anti-inflammatory activity using partial least-squares regression analysis.Methods: The HHCF comprises the rootstock of Scutellaria baicalensis, Rheum tanguticum, Sophora flavescens, the root bark of Dictamnus dasycarpus, the bark of Phellodendron chinense, and the fruit of Kochia scoparia in equal proportions. Its 12 varied formulae were developed by uniform design with varied proportions of the component botanical drugs. The decoctions of the HHCF and its 12 varied formulae were profiled using liquid chromatography (LC) combined with triple quadrupole mass spectrometry (MS) and their effects on tumor necrosis factor (TNF)-α -plus-interferon (IFN)-γ-induced C-C motif chemokine ligand 17 (CCL17) production in HaCaT were investigated. Partial least-squares regression analysis was conducted to assess the relationship between the LC-MS-based metabolite profiles of the decoctions to anti-CCL17 production in HaCaT.Results: Compounds with potential to promote anti-CCL17 production in HaCaT were identified (e.g., berberine, pyrogallol and catechin dimers) as a result of the developed model and their potential to act as anti-inflammatory agents were also supported by relevant literature.Conclusion: This promising approach should assist in the screening process of active components from complex Chinese herbal preparations and will better inform the necessary pharmacological experiments to take forward.

Highlights

Chinese medicine views a disease condition as the result of different syndromes and treats the diagnosed disorders using a combination of botanical drugs—a formula that has been optimized based on centuries of clinical experiences
The findings of this study underline the power of LC-MSbased metabolite profiling, coupled with partial least-squares regression (PLS-R) to predict potential active components in Chinese herbal medicine (CHM) decoctions
The partial least-squares regression (PLSR) model was developed based on the hypothesis that in vitro activity of CHM decoctions varied with differences in chemical components

Summary

Introduction

Chinese medicine views a disease condition as the result of different syndromes and treats the diagnosed disorders using a combination of botanical drugs—a formula that has been optimized based on centuries of clinical experiences. Instead of isolating and testing pharmacological activities of individual chemical components of a CHM drugs or formulae, here we use a strategy in which we first want to understand the exact composition used in one specific preparation (in this case an aqueous extract). This strategy has been used far less commonly and offers the opportunity to understand the composition and the effects of the preparations used. Advancements in analytical techniques open up the possibility of profiling a multitude of small molecule metabolites in the complex CHM extracts These fingerprints of CHM extracts can potentially be used to assess the composition of preparations and consistency of chemical constituents from batch-to-batch extracts and to ensure reproducible clinical effects by monitoring the bioactive components. While Su et al explored the bioactive components of a CHM formula by analysing the relationship between the peak areas of prominent peaks in its GC fingerprints and the biological effects in vitro (Su et al, 2008)

Objectives

Methods

Results

Conclusion