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

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.

Keywords: CCL17; Chinese herbal medicine formula; HaCaT; LC-MS-based metabolite profiles; chemometric; inflammation; partial least-squares regression.

Figure 1

The schematic diagram of the proposed approach. N9, Nll, N13, N21 and N27, and P14, P16, PIS, P26, and P31 are the top 5 most abundant metabolites in the HHCF in negative and positive ionization mode, respectively. Chemical structures of these metabolites are shown in Figure 2.

Figure 2

The top 5 most abundant metabolites in the HHCF in positive and negative ionization mode.

Figure 3

TICs of the 12 varied formulae (Vl-V12) of the HHCF in positive ionization mode.

Figure 4

TICs of the 12 varied formulae (Vl-V12) of the HHCF in negative ionization mode.

Figure 5

Effect of the HHCF (15, 30, 60, and 120 μ/ml) and its twelve varied formulae (Vl-V12; 30 and 60 μ/ml), SB202190 monohydrochloride hydrate (positive control; 2.5 and 5 μ) on TNF-a plus IFN-y-induced CCL17 production in HaCaT. Data are represented as mean ± standard error of three independent experiments (n = 3). Statistical significance was determined using one-way analysis of variance with Dunnett's multiple comparisons test. *p < 0.05 vs. TNF-a plus IFN-y treatment alone.