Characterization of the Active Ingredient and Prediction of the Potential Mechanism of Dahuoluo Pill via Mass Spectrometry with the Network Pharmacology Method

Xu, Haoran; Bi, Yuelin; Feng, Xin; Wang, Jiaqi; Yu, Gengyuan; Zhang, Tonghua; Li, Tianyi; Gao, Xuhua; Liu, Runhua; Sun, Yu; Wu, Hao; Fang, Linlin; Zhang, Chenning; Sun, Yikun

doi:https://doi.org/10.1155/2023/8819534

Journal of Analytical Methods in Chemistry

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Abstract Introduction Materials and Methods Results Discussion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 8819534 | https://doi.org/10.1155/2023/8819534

Characterization of the Active Ingredient and Prediction of the Potential Mechanism of Dahuoluo Pill via Mass Spectrometry with the Network Pharmacology Method

Haoran Xu,¹Yuelin Bi,¹Xin Feng,¹Jiaqi Wang,¹Gengyuan Yu,¹Tonghua Zhang,¹Tianyi Li,¹Xuhua Gao,¹Runhua Liu,¹Yu Sun,¹Hao Wu,¹and Linlin Fang² et al.

Academic Editor: Antony C. Calokerinos

Received17 Mar 2023

Revised21 Sept 2023

Accepted11 Oct 2023

Published07 Nov 2023

Abstract

The Dahuoluo pill (DHLP) is a classic Chinese patent medicine used to treat rheumatoid arthritis and other conditions. However, there has been no research on the chemical components of DHLP and the mechanisms by which it ameliorates rheumatoid arthritis. Hence, we analysed the chemical components of DHLP and the DHLP components absorbed in blood by using ultraperformance liquid chromatography-Q-exactive-orbitrap-mass spectrometry. We then used network pharmacology to predict the underlying mechanisms by which DHLP ameliorates rheumatoid arthritis. We identified 153 chemical compounds from DHLP, together with 27 prototype components absorbed in blood. We selected 48 of these compounds as potential active ingredients to explore the mechanism. These compounds are related to 88 significant pathways, which are linked to 18 core targets. This study preliminarily reveals the potential mechanisms by which DHLP ameliorates rheumatoid arthritis and provides a basis for further evaluation of the drug’s efficacy.

1. Introduction

Rheumatoid arthritis (RA) is a chronic and systemic inflammatory disease with a prevalence ranging from 0.4% to 1.3% of the population, depending on sex (women are affected 2–3 times more often than men) and age (the frequency of new RA diagnoses peaks in the sixth decade of life) [1]. The cause of RA is still unknown. The main symptoms of this disease are pain and swelling in the joints of the hands or feet, which interfere with quality of life. These clinical manifestations of RA are stimulated by receptor activators of nuclear factor κB ligand (RANKL), tumour necrosis factor (TNF), and interleukin (IL)-6 [2]. Many drugs have been used to treat RA, including nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and disease-modifying antirheumatic drugs (DMARDs) [3]. In recent years, the use of traditional Chinese medicine (TCM) to treat RA has been effective and very popular with patients. TCM can avoid some adverse drug reactions, including infections, nausea, and vomiting, and is more suitable for long-term use [4].

The Dahuoluo pill (DHLP) is made from 50 ingredients, including Cinnamomum cassia, Ephedra, Rhizoma coptidis, and Angelica sinensis, which are collected in the Medicine Standards of the Ministry of Health of People’s Republic of China Traditional Chinese Medicine Prescriptions (Volume 17). DHLP is widely used clinically for its effects of dispelling wind and dampness and relaxing muscles and collaterals, among others. As a famous Chinese patent medicine, the clinical application of DHLP is the treatment of cerebral infarction [5], frozen shoulder [6], and RA [7]. Modern experiments have proved that DHLP can promote peripheral vascular microcirculation, relax blood vessels, and relieve thrombosis [8]. The ingredients in DHLP, such as chlorogenic acid, ligustride I, and Z-ligustilide, have proven anti-inflammatory effects [9]. However, the mechanism by which DHLP ameliorates RA is not completely clear, and further exploration is required.

In recent years, network pharmacology has been widely used in TCM. The disease-gene-target-drug network is a common approach of network pharmacology, and the characteristics of integrity and comprehensiveness of network pharmacology are compatible with the feature of complex components, numerous targets, and diverse regulatory mechanisms in TCM. Therefore, we selected the main chemical components from DHLP as the active ingredients and employed network pharmacology to explore the mechanism by which DHLP ameliorates RA. In the current study, we used ultraperformance liquid chromatography-Q-exactive-orbitrap-mass spectrometry (UPLC-Q-exactive-orbitrap-MS) to identify the chemical components in vitro and the components from DHLP absorbed in rat plasma. Furthermore, we screened the main identified components to explore the target and mechanism of DHLP in the treatment of RA through network pharmacology. This experiment provides a new method for the quality control of DHLP and a theoretical basis for its development and utilisation.

2. Materials and Methods

2.1. Materials

Acetonitrile (MS grade) was supplied by China Thermo Fisher Scientific Co. Ltd. Formic acid (MS grade) was purchased from US ROE company. DHLP (lot no. 19013262) was obtained from Beijing Tongrentang Pharmaceutical Group Co. Ltd. Water was derived from a Milli-Q Ultrapure water purification system (Millipore, Bedford, MA, USA).

Six male Sprague–Dawley rats (300–320 g) were procured from Si Pei Fu Biotechnology Co. Ltd. (Beijing, China). The rats were divided into an experimental group and a normal group and housed in the experimental animal center of the Beijing University of Chinese Medicine (Beijing, China) at 23 ± 2°C and 60% ± 5% relative humidity, with access to water and a normal diet ad libitum. The animal experiment protocol was approved by the Animal Care and Use Committee of the Beijing University of Chinese Medicine.

2.2. Analysis of the DHLP Chemical Components In Vitro

2.2.1. Preparation of the Sample Solution

A portion of DHLP (3.5 g) was cut into small pieces and dissolved in 35 mL of 75% methanol at a ratio of 1 : 10. The stock solution was extracted by the heating reflux method for 1 h. The decoction liquid was centrifuged for 10 min at 12000 rpm and diluted five times, taking 5 μL of the diluent for MS analysis.

2.2.2. Chromatographic Conditions

An ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 × 150 mm; Waters, Milford, MA, USA) was used, with a column temperature of 40°C, an injection volume of 5 μL, a flow rate of 0.3 mL/min, and a mobile phase containing 0.1% formic acid aqueous solution (A) and acetonitrile (B). The gradient elution program was as follows: 0–2 min, 5% B; 2–17 min, 5%–98% B; 17–19 min, 98% B; 19–23 min, 98%–5% B; and 23–25 min, 5% B.

2.2.3. MS Conditions

Electrospray ionisation (ESI) was used for the ion source, positive and negative ions were alternately scanned, the scan mode was a full scan/data-dependent two-stage scan (full scan/ddMS²), the scan range was 100–1300 Da, and the capillary temperature was 350°C. The spray voltage in the positive mode was 3200 V, the spray voltage in the negative mode was 3800 V, the sheath gas was 35 arb, and the auxiliary gas was 15 arb. MS² uses three collision energies, low, medium, and high, to perform the second level of the precursor ion. The positive ion mode was 30, 40, and 50 V, and the negative ion mode was 30, 40, and 50 V. The resolution of the primary mass spectra was full scan 70000 full width at half maximum (FWHM), and the resolution of the secondary mass spectra was MS/MS17500 FWHM.

2.2.4. Compound Identification

The raw data were processed with Thermo Xcalibur Qual Browser 3.0.63, which could detect the mass, retention time, and intensity of the peaks in each chromatogram. The chemical components of DHLP were determined by comparison to the relevant data.

2.3. Analysis of Absorbed Components In Vivo

2.3.1. Drug Intervention

The DHLP sample was dissolved in distilled water. The three rats in the experimental group were given the suspension by intragastric administration at a dose of 10.33 g/kg (clinical five-fold measurement) twice a day for 3 days; the three rats in the normal group were given normal saline via intragastric administration twice a day for 3 days. After the last administration, 0.2 mL of blood was collected in a heparinised microcentrifuge tube from the tail vein at the following time points: 15 min, 30 min, 1 h, 2 h, 4 h, and 6 h. The samples were then centrifuged at 4000 rpm for 10 min, and the supernatant was frozen at −80°C until analysis.

2.3.2. Preparation of Plasma Samples

Plasma samples frozen at −80°C were thawed at room temperature. The plasma samples from different time points were mixed in equal amounts. Then, 360 µL of methanol solution was added to 120 µL of the mixed plasma sample, and the resulting sample was vortexed for 3 min, sonicated in an ice-filled ultrasonic water bath for 10 min, and centrifuged at 4000 rpm for 10 min. The supernatant was removed and blow-dried at 40°C under nitrogen. The residue was dissolved in 100 µL of methanol, vortexed for 30 s, and centrifuged at 12000 rpm for 10 min. The injection volume was 5 µL.

2.3.3. Detection and Analysis

The samples were injected according to the above detection conditions. Qualitative analysis of the prototype composition in the plasma sample was based on the DHLP chemical composition identification results, combined with the retention time and fragment ion information.

2.4. Network Pharmacology

2.4.1. Screening of Active Ingredients

The identified compounds from the DHLP chromatogram with an m/z intensity of >10⁸ kg/C were screened as the active ingredients. Meanwhile, to avoid ignoring highly recognised active ingredients, the Chinese Pharmacopoeia (2020 edition) and the relevant literature were used to obtain the ingredients. These findings were merged with the screening results from the ion current diagram. The SwissTargetPrediction website (https://www.swisstargetprediction.ch/) was used for target prediction.

2.4.2. Target Prediction of RA

The human Online Mendelian Inheritance in Man (OMIM, https://www.omim.org/) knowledge base and a human gene database (GeneCards, https://www.genecards.org/) were used to acquire disease targets by entering the keyword “rheumatoid arthritis.”

2.4.3. Construction of the Protein-Protein Interaction (PPI) Network

A bioinformatics website (https://www.bioinformatics.com.cn/) was used to visualise the intersection between the component targets and the disease targets with a Venn diagram. The common targets were imported into the STRING website (https://string-db.org/) to draw the PPI network, and then, Cytoscape 3.7.1 was used to visualise the protein network structure and to analyse the topological characteristics. The medians of the three parameters of degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) were used as the screening condition. The core targets for the component treatment of RA were obtained by screening twice.

2.4.4. Gene Ontology (GO) Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis

The direct targets of the ingredients on RA were imported into the DAVID website (https://david.ncifcrf.gov/). GO and KEGG enrichment maps were made through a bioinformatics website (https://www.bioinformatics.com.cn/).

2.4.5. Network Construction

The TCM, components, targets, and pathways of DHLP were imported into Cytoscape 3.7.1 to establish a TCM-component-target-pathway network.

3. Results

3.1. Identification of the Chemical Constituents

Based on the optimised chromatographic and MS analysis conditions, we analysed the components of DHLP. We identified that 153 compounds were under the positive and negative ion modes, including 34 alkaloids, 27 flavonoids, 19 terpenes, 16 glycosides, and 10 phenylpropanoids. The base peak integration is shown in Figure 1. The classification diagrams of the identified compounds are shown in Figure 2. The chemical composition lists are shown in Table 1 for the negative ion mode and Table 2 for the positive ion mode.

(a)

(b)

3.1.1. Alkaloids

Hypaconitine is a terpene alkaloid, and the diagnostic ions related to it include [M + H − CH₃OH]⁺, [M + H − OCOCH₃]⁺, and [M + H − OCOCH₃ − CH₃OH]⁺. In the positive ion flow diagram, the retention time of peak 67 is 10.25 min, and its quasi-molecular ion peak is m/z 616.3111 [M + H]⁺. The fragment ions coincide with that of hypaconitine, so we determined that the compound is hypaconitine. The cleavage law of hypaconitine and the secondary mass spectrum are shown in Figure 3(a).

(a)

(b)

(c)

(d)

(e)

(f)

Ephedrine is an organic amine alkaloid. The N atom in its side chain is relatively active, and the substituents attached to the N atom are easily lost by MS collisions. In the positive ion flow diagram, the retention time of peak 11 is 5.38 min, and its quasi-molecular ion peak is m/z 166.1227 [M + H]⁺. At the same time, the characteristic fragments of m/z 148.1120 [M + H − H₂O]⁺, [M + H − H₂O − CH₃]⁺, and [M + H − H₂O − NH₂CH₃]⁺ can be observed. Hence, we determined that the compound is ephedrine. The cleavage law of ephedrine and the secondary mass spectrum are shown in Figure 3(b).

Berberine is an isoquinoline alkaloid. It is prone to [M + H − CH₃]⁺, and then, further removal of the H connected to N produces [M + H − CH₄]⁺, which can remove CO at the same time to get [M + H − CH₄ − CO]⁺ and can also remove CH₂ to get [M + H − CH₄ − CH₂]⁺. The quasi-molecular ion peak and the fragment ions of compound 54 coincide with berberine. The cleavage law of berberine and the secondary mass spectrum are shown on the left side of Figure 3(c).

3.1.2. Flavonoids

Puerarin is an isoflavone, which mainly undergoes cleavage of the D ring-glycosidic bond, forming a stable conjugate between the A, B, and C rings, which is not easy to break. The pyrolysis characteristics of puerarin are m/z 399, 381, 363, 321, 297, and 279, which coincide with compound 22. The cleavage law of puerarin and the secondary mass spectrum are shown on the right side of Figure 3(d).

Baicalein is an isoflavone, and its cleavage characteristics are similar to that of puerarin. In the positive ion flow diagram, compound 59 coincides with baicalein, whose fragment ions include m/z 253.0493 [M + H − H₂O]⁺, 241.0493 [M + H − CO]⁺, 225.0544 [M + H − H₂O − CO]⁺, and 195.0436 [M + H − 2CO − H₂O]⁺. At the same time, the compound can undergo an RDA reaction to generate the fragment ion m/z 169.0646. The cleavage law of baicalein and the secondary mass spectrum are shown on the left side of Figure 3(e).

Kaempferol is a flavonol, which loses CO through the cleavage of the C ring to produce m/z 257.0452 [M − H − CO]⁻ (Table 3). Further loss of CO or H₂O produces m/z 229.0510 [M − H − 2CO]⁻ or 239.0349 [M − H − CO − H₂O]⁻, respectively. It also can undergo an RDA reaction to generate fragment ion m/z 151.0027. The cleavage law of kaempferol and the secondary mass spectrum are shown on the right side of Figure 3(f).

3.2. Network Pharmacology

3.2.1. Chemical Composition Screening and Target Prediction

We selected a total of 48 compounds: 36 of the identified compounds with the intensity >10⁸ from the DHLP chromatogram and 12 compounds from the Chinese Pharmacopoeia (2020 edition) and the related literature. We used the SwissTargetPrediction website (https://www.swisstargetprediction.ch/) to predict targets, thereby obtaining a total of 1480 targets.

3.2.2. Target Prediction for RA

We found 1196 disease targets after searching the OMIM and GeneCards databases with “rheumatoid arthritis” as a keyword, and then screening, merging, and removing duplicates (Figure 4).

(a)

(b)

(c)

(d)

3.2.3. Construction of the PPI Network

We generated the Venn diagram through the bioinformatics website (Figure 5(a)). There are 167 common targets.

(a)

(b)

(c)

(d)

(e)

The STRING website is a functional protein association network that can predict PPIs. After importing all the targets into the website, we obtained and saved the PPI network in TSV format. We used Cytoscape 3.7.1 to analyse the topological characteristics of the protein network structure and to screen the core targets of the components for the treatment of diseases, as shown in Figure 5(b) and Table 4. The middle and innermost targets in Figure 5(b) are the results after the first screening, while the innermost layer is the result of the second screening. This later shows the core targets, sorted by the degree value from deep to shallow and from large to small. After sorting the 18 core targets, we found that AKT1 (degree = 52), MAPK1 (degree = 50), STAT3 (degree = 50), VEGFA (degree = 48), MAPK8 (degree = 45), EGFR (degree = 44), and TNF (degree = 43) are the key targets in this network.

3.2.4. GO Analysis and KEGG Enrichment Analysis

With a false discovery rate (FDR) <0.001 as the screening condition, we obtained 82 GO biological items, including response to the drug, response to lipopolysaccharide, the inflammatory response, positive regulation of nitric oxide, biosynthetic process, and positive regulation of ERK1 and ERK2 cascade. The 15 biological processes ranked according to the FDR value are shown in Figure 5(c). We speculate that DHLP may ameliorate RA via complex multichannel synergy.

To further explore the mechanism of action of DHLP in treating RA, we conducted KEGG enrichment analysis on 167 targets and screened out 88 related pathways according to FDR <0.01. After sorting according to the FDR value, the top 15 ranked pathways are shown in Figure 5(d). They include the TNF signalling pathway, hepatitis B, pathways in cancer, Chagas disease (American trypanosomiasis), the toll-like receptor signalling pathway, and other related pathways.

3.2.5. Construction of the TCM-Component-Target-Pathway Network

To clarify the relationship between medicinal materials, ingredients, targets, and pathways, we used Cytoscape 3.7.1 to construct a TCM-component-target-pathway network (Figure 5(e)). Through this network, we can visually demonstrate the effective substances in DHLP that treat RA, as well as their possible mechanisms of action.

4. Discussion

DHLP is a famous TCM that contains a wide variety of medicinal materials. However, DHLP is a complex mixture of a wide variety of herbal medicines, and this composition may cause problems such as difficult drug quality control and the restriction of medicines [38, 39]. UPLC-Q-exactive-orbitrap-MS has high sensitivity and selectivity and is widely used in the analysis of chemical components in complex chemical systems of TCM. Therefore, we used UPLC-Q-exactive-orbitrap-MS in the positive and negative ion modes to quickly identify the chemical components in DHLP and the DHLP components absorbed in blood. According to the retention time, molecular ion peaks, and fragments, as well as the chemical composition of DHLP based on the related literature, we identified 117 compounds in the positive ion mode and 41 compounds in the negative ion mode, including 34 alkaloids, 27 flavonoids, 19 terpenes, 16 glycosides, and 10 phenylpropanoids. Furthermore, we identified 27 prototype components absorbed in blood based on comparison with the in vitro ingredients, including 11 flavonoids, 3 glycosides, and 2 alkaloids. This information provides an experimental basis to find new active ingredients of DHLP, to improve quality control standards, and to guide the rational clinical use of drugs.

Network pharmacology is based on biological networks that reveal the interconnections between complex diseases, symptoms, and prescriptions. Many scholars have used this approach to conduct in-depth research on TCM. Based on network pharmacology, we identified 48 compounds, 18 core targets, and 88 related pathways in DHLP that may be related to the treatment of RA. Among them, the top seven core targets are AKT1, MAPK1, STAT3, VEGFA, MAPK8, EGFR, and TNF. MAPK1 is also called ERK2, and a high concentration of epidermal growth factor (EDF) promotes the expression of COX-2 by stimulating the activity of ERK1/2-MAPK to participate in the inflammatory response of RA [40]. TNF participates in the formation of pannus and can promote cartilage destruction and aggravate inflammation to promote the formation and development of RA [41]. Therefore, the chemical components in DHLP may treat RA by regulating related proteins such as MAPK1 and TNF. KEGG enrichment analysis revealed enrichment of osteoclast differentiation and TNF, toll-like receptor, and HIF-1 signalling pathways, among others. Mesenchymal stem cells can directly inhibit osteoclast differentiation by producing NF-κB receptor activator ligands to induce osteoprotegerin production [42]. The toll-like receptor signalling pathway plays an important role in joint destruction caused by chronic expression of proinflammatory cytokines and chemokines [43]. The HIF-1 signalling pathway is an important angiogenesis pathway in RA. HIF-1 affects cell reactivity in synovial tissue under hypoxia to promote the infiltration of macrophages and other inflammatory cells, producing vascular endothelial growth factor (VEGF) and increasing the release of TNF inflammatory mediators, which leads to the persistence of synovitis [44]. Therefore, we speculate that DHLP may ameliorate RA by regulating osteoclast differentiation, by altering the toll-like receptor and HIF-1 signalling pathways, and by acting through other mechanisms.

The prototype components absorbed in blood include glycosmisic acid, costunolide, wogonoside, and chlorogenic acid. Chrysophanic acid could alleviate the pathological changes of osteonecrosis of the femoral head by augmenting osteogenesis and retarding adipogenesis in the scenario of ethanol administration, which may appear during the course of RA [45]. We identified glycosmisic acid, which is found in Glycyrrhiza uralensis, as a prototype component and an active ingredient. It exerts anti-inflammatory effects by inhibiting the expression of NF-κB, IL-6, and IL-8 [32]. Wogonoside, which is mainly contained in Radix scutellariae, attenuates IL-1β-induced extracellular matrix degradation and hypertrophy in mouse chondrocytes by suppressing activation of NF-κB/HIF-2α in the PI3K/AKT pathway [33] and can also suppress lipopolysaccharide-stimulated production of inflammatory factors by repressing the activation of the JNK/c-Jun signalling pathway in macrophages [36]. The absorbed components mainly ameliorate RA by acting on targets such as STAT3, IL-2, and MMP12 through the toll-like receptor, MAPK, T-cell receptor, and other pathways.

In summary, we employed UPLC-Q-extactive-orbitrap-MS and identified 153 chemical compounds from DHLP and 27 prototype components of DHLP absorbed in blood, and we explored the potential mechanism for the treatment of RA through the method of network pharmacology. We screened 48 potential active ingredients based on the MS results and the related literature. We identified 1480 potential targets based on these 48 compounds, with 1196 RA-related disease targets, including 167 common targets for DHLP and RA. The GO biological process and KEGG signalling pathway enrichment analysis for these targets predicted that DHLP could regulate MAPK1, STAT3, AKT1, MAPK8, TNF, and other targets and that DHLP could regulate TNF, toll-like receptor, HIF-1, and other signalling pathways as well as osteoclast differentiation to suppress inflammation and to regulate immune function to treat RA. The results of the current study provide important experimental data and bioinformatics analysis for further development and rational use of DHLP.

Data Availability

The data used to support the findings of this study are included within the article.

Disclosure

Haoran Xu and Yuelin Bi are the co-first authors of the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Haoran Xu and Yuelin Bi contributed equally to this manuscript.

Acknowledgments

The financial support was provided by the National Natural Science Foundation of China (NSFC) (Grant no. 81373942), the 2022 Medical and Health Science and Technology Plan of Xiangyang City, Hubei Province (Grant no. 2022YL30A), the Hubei Province Natural Science Foundation (Grant no. 2022CFB867), and the Education Department Foundation of Liaoning Province (Grant no. LZ2020074). The authors are very grateful for the support of the Analysis and Testing Center of Beijing University of Chinese Medicine. Funding was provided for the graduate training of the Beijing University of Traditional Chinese Medicine.

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Copyright © 2023 Haoran Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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