Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Abstract

Objective: Shiwei Qingwen decoction (SWQWD) is effective in preventing COVID-19. This study examined the active components of SWQWD and its potential targets for preventing COVID-19. The study used network pharmacology and molecular docking technology to verify the role of SWQWD targets through animal experiments and explored the mechanisms that enhance immunity to alleviate mild COVID-19 symptoms. Methods: First, SWQWD- and COVID-19-related targets were retrieved from TCMSP, GeneCards, and OMIM databases. Second, protein–protein interaction networks were established using the String database. The drug active ingredient target network was constructed in Cytoscape to identify the core target proteins. Third, Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the SWQWD mechanism of action. Finally, the targets were validated by molecular docking in an acute lung injury (ALI) rat model. Results: The SWQWD compound target network contained 79 compounds and 277 targets, coinciding with the 73 targets of COVID-19. The most important gene in the core subnetwork was a tumor necrosis factor (TNF). The 3 most potent compounds, quercetin, kaempferol, and luteolin, can enter the active pockets of TNF and have potential therapeutic roles in COVID-19. Conclusion: Quercetin, kaempferol, and luteolin in SWQWD may enhance immunity by regulating multiple TNF signal pathways. After administering SWQWD, the content of tumor necrosis factor-α was significantly reduced in the bronchoalveolar lavage fluid (BALF) of ALI rats in comparison to the model group. We believe SWQWD is able to prevent and control COVID-19 through the target of TNF.

Keywords

Shiwei Qingwen decoction COVID-19 network pharmacology molecular docking TNF ALI

Introduction

Coronavirus disease 2019 (COVID-19) is an acute respiratory disease caused by a novel genus β coronavirus.¹ The disease is highly infectious and may exist with other human respiratory transmitted diseases.² The clinical symptoms include fever, cough, upper respiratory tract congestion, fatigue, and dyspnea. Critically ill patients can show acute respiratory distress syndrome, respiratory failure, multiple organ failure, and even death.³ In addition, COVID-19 patients have reduced counts of white blood cells and lymphocytes, and, thus, low immunity.⁴ Prevention of COVID-19 is crucial because there is no specific drug for treatment, yet new variants are constantly emerging worldwide.⁵ Traditional Chinese Medicine (TCM) has been demonstrated to improve the immune systems and health conditions of patients against COVID-19.⁶

Thus, an expert group from the Jingmen Hospital of TCM considered the characteristics of COVID-19 and how it regulated human immunity and designed Shiwei Qingwen decoction (SWQWD) for preventing and alleviating mild COVID-19 symptoms. Early in 2020, > 1000 medical staff of the Jingmen Hospital of TCM hospital received SWQWD as a preventive strategy. There is no infection report among the SWQWD-treated front-line medical staff to date. Therefore, SWQWD has been promoted in the whole city, and the drug has achieved an excellent preventive effect.⁷ This formula can improve the patient's immune and gastrointestinal functions, and is not only for the inflammation of the lungs. However, the mechanism of SWQWD in preventing COVID-19 is still unclear.

Following the characteristics of the multi-regulation of signaling pathways, multicomponent points, and multiple targets of Chinese herbal medicine, network pharmacology may be a new, well-documented method for finding meaningful information on SWQWD. SWQWD consists of 10 TCMs with complex components and many targets, which aligns with the characteristics and advantages of network pharmacology. Thus, molecular docking was used to identify further the potential SWQWD substances that can bind to the 3 key targets of COVID-19.

Based on the current understanding of COVID-19, the novel coronavirus infection is associated with acute lung injury (ALI), which has massively increased with the COVID-19 pandemic. ALI has common pathological responses with many lung source diseases. For instance, the epithelial cells in ALI patients show alveolar damage with several inflammatory cells such as neutrophils in the lung tissue. In addition, ALI is characterized by excessive release of inflammatory factors, and an imbalance between the inflammatory anti-inflammatory system, and oxidation antioxidant system. Eventually, ALI induces an inflammatory “waterfall”-like cascade, lung injury aggravation, microvascular permeability, pulmonary edema, and lung dysfunction, which is very similar to the “cytokine storm” phenomenon in COVID-19 patients.⁸ Especially, there is a significant increase in pro-inflammatory cytokine serum and local expression, particularly tumor necrosis factor-α (TNF-α).

Here, we used network pharmacology and molecular docking technologies to analyze SWQWD and explore the potential mechanism and targets of COVID-19 prevention. In addition, we constructed an ALI rat model using a tracheal infusion of LPS to simulate the lung lesions in COVID-19 patients.⁹ ELISA was used to detect the content of the core target protein in the bronchoalveolar lavage fluid (BALF) of ALI rats to verify the reliability of the selected potential core targets.

Materials and Methods

Acquisition of SWQWD Compounds Specific to COVID-19 Targets and Screening of Active Components and Targets of SWQWD

The chemical ingredients of each herb (“SaposhnikoviaeRadix,” “Atractylodes macrocephalaKoidz,” “AstragaliRadix,” “Lonicerae japonicae,” “WoodwardiaeRhizoma,” “PhragmitisRhizoma,” “EupatoriiHerba,” “AtractylodisRhizoma,” “Citri reticulatae Pericarpium,” and “JujubaeFructus”) in SWQWD were obtained from the TCMSP (http://tcmspw.com/tcmsp.php).¹⁰ The chemical properties of the compounds in each SWQWD herb were then analyzed, including the oral bioavailability (OB) and drug-likeness (DL). OB ≥ 30% and DL ≥ 0.18 were used for screening the bioactive components. The gene names of the SWQWD target proteins were used to search the UniPort database and standardize the target protein.¹¹ We also use SwissTargetPrediction (http://www.swisstargetprediction.ch/) to search the targets.

We chose “COVID-19” and “SARS-COV-2” as keywords for searching Genecards (https://www.genecards.org)¹² and OMIM (https://www.omim.org)¹³ databases to identify COVID-19-related targets. A Wayne diagram was used to compare the relationship between COVID-19 disease targets and SWQWD component targets.

Establishing the SWQWD Active Ingredient Target Network

The active components of the drug and the potential action targets (in section “Acquisition of SWQWD Compounds Specific to COVID-19 Targets and Screening of Active Components and Targets of SWQWD”) were analyzed using the Network Analyzer function of Cytoscape3.6.0 software.¹⁴ Thus, the “TCM-component-target-disease” network was constructed, and the core nodes were screened following the network topological characteristics. Therefore, the top 10 key compounds with topological parameters were extracted.

GO and KEGG Enrichment Analysis

The GO and KEGG enriched functions and pathways of the target genes were determined in the DAVID database (https://david.ncifcrf.gov/home.jsp;using P < .05 as the threshold) to establish the functions of the target genes.¹⁵ The top 10 entries from GO (only biological processes) and KEGG enrichment analyses were plotted using an online drawing station (Omishare; http://www.omicshare.com/tools)generating a bubble chart for visual processing.

Constructing the Protein–Protein Interaction (PPI) and Core Subnetwork of Key Target Proteins

Target proteins were analyzed in the STRING database (http://string-db.org/cgi/input.pl; at > 0.4, which implies medium confidence) to obtain the PPI network-related information.¹⁶ The STRING output was imported into Cytoscape3.6.0 to construct and visualize the PPI and core networks. Next, CytoNca, and CytoHubba plugins were used to obtain the highest rated key target protein.¹⁷

Components Target Molecular Docking

The key target proteins were downloaded and analyzed by their core subnetwork 3D structures (in section “Constructing the PPI and Core Subnetwork of Key Target Proteins”) from PDB files of the RSCB PDB database (https://ww.rcsb.org/).¹⁸ Using the AutoDock Tools version 1.5.6, the key target proteins were used as receptors and the active ingredient candidates as ligands. Discovery Studio software determined the active pocket of the receptor, and Autodock Vina¹⁹ determined the docking. The docking results were visualized in PyMol software. We also use Protein Ligand Interaction Profiler (PLIP) to check the formed interactions between the protein and the ligand.²⁰

Animal Models and Decoction Preparation

Sixty SPF SD male rats were randomly divided into 6 groups: control, model, SWQWD high dose (36.8 g/kg), SWQWD medium dose (18.4 g/kg), SWQWD low dose (9.2 g/kg), and positive drug groups (dexamethasone 0.001 g/kg), respectively. Treatment administration started 3 d after adaptive feeding with 1 mL/100 g for 7 d. One hour after the last administration, rats were anesthetized with a 3% pentobarbital 40 mg/kg solution. In contrast, control rats were intratracheally instilled with sterile PBS 100 μL, and the rats in the other groups were intratracheally instilled with 1 mg/mL LPS 100 μL. The rats were killed after 6 h of anesthesia, and BALF was collected from each animal. TNF-α was detected in BALF samples using an ELISA detection kit (Bioss, lot: BA06185133) (Bioss Antibodies, MA, USA), following the manufacturer's protocol. The Hubei Experimental Animal Center provided the animals under the experimental animal quality certificate: no. 42000600043471. The rats were maintained in cages at ambient temperature (20 °C-25 °C and 50%-60% relative humidity). Animal bedding was replaced weekly, and used beddings was washed and disinfected accordingly. The caged rats were illuminated for 10 to 14 h a day, fed, and provided drinking water every morning. The animal experiments were conducted in strict compliance with the ethical requirements of animal experimentation, ethical batch number: HUCMS202109001.

SWQWD was prepared from a mixture of 10 kg SaposhnikoviaeRadix, 16.3 kg AstragaliRadix, 2.2 kg PhragmitisRhizoma, 10 kg Lonicerae japonicae, 10 kg EupatoriiHerba, 6.6 kg WoodwardiaeRhizoma, 6.6 kg AtractylodisRhizoma, 6.6 kg Citri reticulataePericarpium, 2.2 kg JujubaeFructus, and 10 kg Atractylodes macrocephalaKoidz. The mixture (80.5 kg) was put in an extraction tank and soaked in 805 L of water (10 × the total weight) for 1 h. The second decoction was in 644 L (8 × times the total weight), and this was cooked twice at a constant temperature of 100 °C, first for 30 min, followed by 20 min. Afterward, the supernatant of the secondary decoction was collected by filtration and combined with the first decoction. The filtrate was vapor distilled, and the volatile liquid was incorporated into the concentration tank to make vacuum concentrations (temperature 60 °C-80 °C) of approximately 1.1 to 1.3. The vacuum concentrate was dried in a Galanz microwave oven (P7021TP-6) (Galanz, Shandong, China) to obtain the infusion extracts weighing 28.6 kg (yield 35.5%). Next, the infusion extracts were vacuum-sealed in aluminum packaging plastic bags for later use.

Statistical Analysis

The experimental data were statistically analyzed using Graphpad Prism software, and pairwise comparisons between groups were performed using the LSD t-test with P < .05 as the test level.

The flowchart of this study is shown in Figure 1.

Figure 1.

The flowchart of this study.

Results

Screening the Active Compounds in SWQWD

A total of 918 compounds were retrieved from the TCMSP database and SwissTargetPrediction database, yielding 113 active compounds with OB ≥ 30% and DL ≥ 0.18 as the screening standard.²¹ Table 1shows the literature-based information on the main active compounds in each SWQWD constituent medicinal material.

Table 1.

Basic Information on the Main Active Compounds of the Various Herbs in Shiwei Qingwen Decoction.

Mol ID	Chemical component	OB (%)	DL	Herb
MOL000049	3β-acetoxyatractylone	54.07	0.22	Atractylodes macrocephala Koidz
MOL000072	8β-ethoxy atractylenolideIII	35.95	0.21	Atractylodes macrocephala Koidz
MOL000173	Wogonin	30.68	0.23	Atractylodes Lancea (Thunb.)Dc
MOL000188	3β-acetoxyatractylone	40.57	0.22	Atractylodes Lancea (Thunb.)Dc
MOL004328	Naringenin	59.29	0.21	Citrus Reticulata
MOL005828	Nobiletin	61.67	0.52	Citrus Reticulata
MOL001454	Berberine	36.86	0.78	Jujubae Fructus
MOL000358	Beta-sitosterol	36.91	0.75	Jujubae Fructus
MOL011753	5-O-Methylvisamminol	37.99	0.25	Saposhnikoviae Radix
MOL000011	(2R,3R)-3-(4-hydroxy-3-methoxy-phenyl)-5-methoxy-2-methylol-2,3-dihydropyrano[5,6-h][1,4]benzodioxin-9-one	68.83	0.66	Saposhnikoviae Radix
MOL000422	Kaempferol	41.88	0.24	Fortunes Bossfern Rhizome
MOL002610	ZINC00035529	58.39	0.22	Fortunes Bossfern Rhizome
MOL000392	Formononetin	69.67	0.21	Hedysarum Multijugum Maxim
MOL000417	Calycosin	47.75	0.24	Hedysarum Multijugum Maxim
MOL000354	Isorhamnetin	49.6	0.31	Hedysarum Multijugum Maxim
MOL000378	7-O-methylisomucronulatol	74.69	0.3	Hedysarum Multijugum Maxim
MOL003059	Kryptoxanthin	47.25	0.57	Lonicerae japonicae Flos
MOL002773	Beta-carotene	37.18	0.58	Lonicerae japonicae Flos
MOL003044	Chryseriol	35.85	0.27	Lonicerae japonicae Flos
MOL000449	Stigmasterol	43.83	0.76	Phragmitis Rhizoma
MOL000006	Luteolin	36.16	0.25	Eupatorium Fortunei Turcz

TCM-Compounds-Target-Disease Interaction Network

The Venn diagram above shows 73 common action targets between the SWQWD active compounds and COVID-19 genes (Figure 2a). The TCM Compounds-Target-Disease network chart has 162 nodes (including 10 TCMs, 79 compounds, and 73 common targets) and 693 edges. The top 10 chemical components are quercetin, kaempferol, luteolin, wogonin, beta-sitosterol, nobiletin, formononetin, naringenin, isorhamnetin, and 7-O-methylisomucronulatol (Table 2). The specific topological parameters show that these 10 chemical components may be the main active components preventing COVID-19 (Figure 2b).

Figure 2.

(A) Venn diagram showing the common targets between COVID-19 and Shiwei Qingwen decoction (SWQWD). (B) A herbal compound target disease network consisting of 162 nodes and 693 edges. The blue diamond-shaped nodes on the outer periphery represent potential genes. The octagonal nodes with different colors on the inner circumference represent the effective compounds of different herbal medicines, respectively. The square nodes represent the 10 herbs of the Ten Flavors Clear Distemper Soup. The node size is positively correlated with the degree value; the larger the node, the larger the degree value.

Table 2.

Specific Topological Parameters of the Top 10 Chemical Components.

Mol ID	Compound	CAS	BC	CC	DC
MOL000098	Quercetin	117-39-5	3.31 × 10⁻¹	5.18 × 10⁻¹	155
MOL000422	Kaempferol	520-18-3	5.94 × 10⁻²	4.25 × 10⁻¹	60
MOL000006	Luteolin	491-70-3	1.21 × 10⁻¹	4.36 × 10⁻¹	58
MOL000173	Wogonin	632-85-9	6.27 × 10⁻²	4.20 × 10⁻¹	40
MOL000358	Beta-sitosterol	474-58-8	3.44 × 10⁻²	4.12 × 10⁻¹	32
MOL005828	Nobiletin	480-41-1	4.23 × 10⁻²	4.04 × 10⁻¹	16
MOL000392	Formononetin	485-72-3	2.82 × 10⁻²	4.04 × 10⁻¹	15
MOL004328	Naringenin	480-41-1	6.75 × 10⁻²	3.98 × 10⁻¹	15
MOL000354	Isorhamnetin	480-19-3	1.44 × 10⁻²	4.04 × 10⁻¹	14
MOL000378	7-O-methylisomucronulatol	90-19-7	1.30 × 10⁻²	4.04 × 10⁻¹	13

GO and KEGG Enrichment Analysis

The 73 common targets enriched 470 GO entries (P < .05), and the top 10 terms are illustrated in Figure 3. The GO terms suggested that these target genes are essential in hypoxia, inflammation, and immune responses (Figure 3ato c). Moreover, the KEGG enrichment analysis identified 119 (P < .05) signaling pathways, including immune-related pathways such as TNF, Toll-like receptor, and PI3K-AKT (Figure 3d). The relationship between the above top 10 immune-related pathways, the genes that enrich these pathways, and TCM and their major active compounds are shown in Figure 4.

Figure 3.

GO and KEGG enrichment analysis of the top 20 SWQWD targets. (A) GO-BP enrichment analysis; (B) GO-CC enrichment analysis; (C) GO-MF enrichment analysis; (D) KEGG enrichment analysis. The more red the color, the smaller the P-value, the larger the point, the greater the number of genes enriched into the pathway.

Figure 4.

The SWQWD-herbs-components-targets-pathways COVID-19 composite network diagram. Elliptical and yellow nodes represent TCM, hexagonal and green nodes represent active components, rhombic and blue nodes represent targets, while rectangular and pink nodes represent pathways.

PPI and Core Subnetworks Predicting the Potential Action Target

A PPI network derived from the STRING database showed that the proteins encoded by the target genes had complex interactions. The PPI network generated 2 core subnetworks of 15 targets using CytoNca and CytoHub, ranking TNF on top (Figure 5).

Figure 5.

Identification of the key subnetwork using Cytoscape. (A) The protein–protein interaction (PPI) network and the first filtration by CytoNca. The yellow nodes were screened using a score higher than the median. (B) The subnetwork was constructed by a second filtration via CytoNca. The yellow nodes were screened using a score higher than the median. (C) The core sub-networks filtered using the CytoNca plugin. (D) Core sub-networks analyzed using the CytoHubba plugin; the darker the color, the higher the ranking.

Active Ingredient-Target Molecular Docking Results

We screened the key target TNF through the core sub-network to verify whether the main active ingredients in SWQWD fought neocoronavirus through TNF as a potential target. The molecularly docked protein 1DU3 (encoded by TNF) with the top 10 compounds as potentially active compounds had low binding free energy to the TNF (Table 3). Quercetin, kaempferol, and lignan docked with the TNF receptor, showing that quercetin, kaempferol, and lignan enter the TNF binding pocket and maybe exert their anti-new coronary pneumonia effects by acting on the TNF receptor. The docking details are shown in Figure 6. Supplemental Figure S1represents the interactions formed between the best 3 herbal compounds with the target receptor-1DU3, while Supplemental Table S1shows the details of the interactions formed.

Figure 6.

Molecular docking between quercetin (A, B), kaempferol (C, D), luteolin (E, F), and protein 1DU3 (encoded by tumor necrosis factor [TNF]).

Table 3.

The Binding Energies of the First 10 Compounds.

CAS	Molecule names	Molecular formula	TNF docking score (kcal/mol)
117-39-5	Quercetin	C₁₅H₁₀O₇	−8.2
520-18-3	Kaempferol	C₁₅H₁₀O₆	−8.1
491-70-3	Luteolin	C₁₅H₁₀O₆	−8.6
632-85-9	Wogonin	C₁₆H₁₂O₅	−8.1
83-46-5	Beta-sitosterol	C₂₉H₅₀O	−9.7
478-01-3	Nobiletin	C₂₁H₂₂O₈	−7.5
485-72-3	Formononetin	C₁₆H₁₂O₄	−8.4
480-41-1	Naringenin	C₁₅H₁₂O₅	−8.6
480-19-3	Isorhamnetin	C₁₆H₁₂O₇	−8.4
90-19-7	7-O-methylisomucronulatol	C₁₆H₁₂O₇	−8.5

Abbreviation: TNF, tumor necrosis factor.

TNF-α in BALF and ALI Rats

Further investigation of the TNF action through animal experiments showed that TNF-α significantly increased in BALF compared to the normal group (P < .01). Moreover, TNF-α was significantly reduced in BALF from SWQWD high, SWQWD medium, and dexamethasone groups (P < .05; Table 4).

Table 4.

Contents of TNF-α in BALF of ALI Rats.

Group	Dose (g/kg)	TNF-α
Normal group	–	99.8 ± 12.1**
Model group	–	484.1 ± 320.5
High-dose group of SWQWD	36.8	144.3 ± 71.0**
Middle-dose group of SWQWD	18.4	187.1 ± 51.9*
Low-dose group of SWQWD	9.2	215.5 ± 120.1*
Positive drug (dexamethasone) group	0.001	116.1 ± 34.1**

Abbreviations: TNF-α, tumor necrosis factor-α; BALF, bronchoalveolar lavage fluid; ALI, acute lung injury; SWQWD, Shiwei Qingwen decoction.

Discussion

Patients with COVID-19 show low immunity, fever, cough, sore throat, and diarrhea.²² Based on the characteristics of this disease, the expert group of Jingmen TCM Hospital used a compound formula composed of anti-diarrheal drugs such as SaposhnikoviaeRadix, Atractylodes macrocephala, AstragaliRadix, Lonicerae japonicae, EupatoriiHerba, and AtractylodisRhizoma. SaposhnikoviaeRadix, Atractylodes macrocephala, and AstragaliRadix and found that this can enhance the immune function, while Lonicerae japonicae, EupatoriiHerba, and AstragaliRadix played an important role in the 2003 SARS outbreak. This formula improves the immunity of susceptible people as a whole while fighting the inflammation caused by COVID-19.

We screened several ingredients of SWQWD in the TCMSP database. This study generated 10 potential active compounds: quercetin, kaempferol, luteolin, wogonin, beta-sitosterol, nobiletin, formononetin, naringenin, isorhamnetin, and 7-O-methylisomucronulatol from a TCM-medicine-compound-target-disease composite network screened by network topological parameters. Quercetin, abundantly found in JujubaeFructus, AstragaliRadix, and Lonicerae japonicae, has antioxidant, anti-inflammatory, and immune promotion effects,²³ thus, inhibiting the expression of inflammatory factor IL-6 and reducing the inflammatory response.²⁴ Recent studies have also shown that quercetin has the potential to treat COVID-19.²⁵ Kaempferol is commonly found in WoodwardiaeRhizoma and Lonicerae japonicaeand has anti-oxidation, anti-inflammatory, and antiviral effects,²⁶ increasing autophagia and reducing silica-induced pulmonary fibrosis.²⁷ Moreover, luteolin is abundant in Lonicerae japonicaeand EupatoriiHerba and has anti-inflammatory, anti-oxidation, and antiviral properties,²⁸ which inhibit the expression of the NF-κB signaling pathway, thus reducing the contents of cytokines like TNF-α, IL-1β, and IL-6. Besides, luteolin is protective against LPS-induced ALI in mice.²⁹ Wogonin is abundant in SaposhnikoviaeRadix and AstragaliRadix and exhibits anti-inflammatory effects.³⁰ Wogonin inhibits iNOS expression, reduces NF-κB activity, and weakens the LPS-induced inflammatory response in macrophages. However, beta-sitosterol, commonly found in SaposhnikoviaeRadix, JujubaeFructus, and Lonicerae japonicae, has anti-inflammatory effects and inhibits the activation of NLRP3 in macrophages.³¹ Beta-sitosterol significantly reduces the activation of cellular inflammatory factors such as TNF-α, IL-1β, and IL-6 and protects against ALI.³² In summary, SWQWD contains active compounds that may prevent COVID-19 through its anti-inflammatory and immune-enhancing properties.

The enriched GO functions and KEGG pathways demonstrated that the SWQWD active components mainly regulate apoptosis, inflammatory response, oxidative stress processes, and other regulations, including enhancing immunity, anti-inflammation, and antiviral responses. The enriched functions and pathways are highly related to the PI3K-ATK, Toll-like receptor (TLRs), and TNF signaling pathways. Moreover, viral stimulation of PI3K-AKT and TLRs signaling pathways leads to the release of inflammatory mediators, enabling the inflammatory response.³³ Stimulating PI3K-AKT and TLRs signaling pathways also activate NF-κB and regulate the transcription of inflammatory cytokine genes,³⁴ thus, generating inflammatory factors such as IL-6 and TNF-α. When TNF reverses its effect on the expression of PI3K-AKT and TLRs, the inflammatory response and immune destruction escalate. Therefore, a possible mechanism is that SWQWD active components exert anti-inflammatory and immune-modulating functions by acting on the PI3K-AKT-NF-κB-TNF and TLRs-NF-κB-TNF signaling pathways preventing COVID-19 disease development.

The above analysis implied that SWQWD has a COVID-19 preventive effect, so we further discovered the core genes by generating core subnetworks. Tumor necrosis factor (TNF) was the highest-ranked target gene in the 2 core subnetworks. In addition, the molecular docking results showed that TNF has a high affinity toward the top ten potentially active compounds. TNF, as an endogenous pyrogen, mainly regulates immune cells that cause fever, preventing tumorigenesis and viral replication.³⁵ Besides, TNF is a pro-inflammatory cytokine that can mediate MAPK, ERK, and NF-κB signaling pathways that are widely involved in physiological and pathological cell differentiation processes, apoptosis, and induced inflammation.^36–38 Overexpressing TNF and IL-6 causes lung injury and increases the COVID-19 fatality rate. Therefore, inhibiting the overproduction of cytokines (such as TNF and IL-6) is the main treatment strategy for COVID-19.^39,40 Rat experiments for ALI showed that SWQWD significantly reduces TNF-α contents in the BALF of ALI rats, possibly inhibiting the production of inflammatory factors and providing an anti-pneumonia effect.

Conclusion

In summary, this study demonstrated the preventive mechanisms of SWQWD on COVID-19. The study focused on the role of SWQWD in improving immunity and decreasing the inflammatory responses of infection and oxidative stress. In addition, we proposed a potential target for COVID-19 and verified the role of TNF targets in preventing COVID-19 in vivo and in vitro. We believe SWQWD can to prevent and control COVID-19 through the target of TNF and these findings may aid the global fight against the COVID-19 pandemic.

Supplemental Material

sj-docx-2-npx-10.1177_1934578X221125089 - Supplemental material for Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Supplemental material, sj-docx-2-npx-10.1177_1934578X221125089 for Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms by Chen-xiong Yang, Shang-zhi Ma, Qian Zhang, Shu-yun Guo, Xiao-di Hu, Yan-ju Liu, Li Wen and Zhong-shi Zhou in Natural Product Communications

Supplemental Material

sj-docx-3-npx-10.1177_1934578X221125089 - Supplemental material for Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Supplemental material, sj-docx-3-npx-10.1177_1934578X221125089 for Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms by Chen-xiong Yang, Shang-zhi Ma, Qian Zhang, Shu-yun Guo, Xiao-di Hu, Yan-ju Liu, Li Wen and Zhong-shi Zhou in Natural Product Communications

Footnotes

Acknowledgements

We thank MogoEdit for its linguistic assistance during the preparation of this manuscript.

Authors’ Contributions

All authors have read and agreed to the published version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Department of Science and Technology of Hubei Province (grant no. NO2020DEB016).

ORCID iD

Li Wen

Supplemental Material

Supplemental material for this article is available online.

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