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Abstract

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

Keywords

Shenling Baizhu San network pharmacology COVID-19 molecular docking

Introduction

Since the end of 2019, the acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally. The WHO named it COVID-19. This has caused a serious negative impact on the health and economic development of all human beings, as well as social order. At present, there is no specific drug for treatment of this disease. We mainly promote the recovery of patients through supportive management.¹ Traditional Chinese medicine (TCM) is crucial in the COVID-19 outbreak, and plays an integral role in all three processes: the prevention and treatment of COVID-19, and the recovery of patients.² In the preliminary treatment, TCM can significantly inhibit the deterioration of the disease, reduce the patient's symptoms and promote the negative conversion of viral nucleic acid. At the same time, TCM can promote the conversion of severe cases into mild cases.³ This shows that TCM has a positive effect on patients with COVID-19 at different periods.

SBS is composed of ten Chinese medicines: Dolichos lablab L. (Baibiandou), Atractylodes macrocephala Koidz. (Baizhu), Poria cocos(Schw.)Wolf. (Fuling), Glycyrrhiza uralensis Fisch. (Gancao), Platycodon grandiforum(Jacq)A. DC. (Jiegeng), Nelumbo nucifera Gaertn (Lianzi), Panax ginseng C. A. Mey. (Renshen), Amomun villosum Lour. (Sharen), Dioscorea opposita Thunb. (Shanyao), and Coix lacryma-jobi L. var. mayuen. (Roman.) Stapf (Yiyiren). SBS is recorded in the earliest existing Chinese official pharmacopoeia “Tai Ping Hui Min He Ji Ju Fang”.⁴ SBS has the effects of replenishing the spleen, stomach and lungs. It fits the main pathogenesis of COVID-19 “damp, poison, and epidemic”.⁵ Therefore, SBS was used to recover COVID - 19 patients and achieved good results. However, its current mechanism of action is not particularly clear. The motivation of this study was to use network pharmacology to analyze the active ingredients and mechanism of action of SBS, which has been clinically proved to have a therapeutic effect on COVID-19, and to confirm the relevant results by molecular docking. This can not only provide a further theoretical basis for SBS treatment of COVID-19, but also contribute to the development of new drugs.

Network pharmacology includes the technology and content of multiple disciplines such as systems biology, multi-directional pharmacology and computational biology. It can explore the connection between drugs and diseases from the overall perspective.⁶ The holistic, systematic and comprehensive nature of network pharmacology fits well with the characteristics of multiple components, multiple targets, and multiple pathways of traditional Chinese medicine. Therefore, network pharmacology is used to study the mechanism of action of traditional Chinese medicines.⁷ To understand the molecular mechanism of SBS in treating convalescent COVID-19 patients, this study used network pharmacology and molecular docking technology to explore the core targets, pathways and active ingredients of SBS in the treatment of COVID-19, in order to provide new ideas for the prevention and treatment of the disease by TCM. The specific process is shown in Figure 1.

Figure 1.

Flow chart.

Materials and Methods

Screening of Drug Targets of SBS

We used the TCMSP database (http://tcmspw.com/tcmsp.php) to retrieve the chemical constituents of Baibiandou, Baizhu, Fuling, Gancao, Jiegeng, Renshen, Sharen, Shanyao, and Yiyiren.⁸ We used the ETCM database (http://www.tcmip.cn/ETCM/index.php/Home/) to retrieve the chemical constituents of Lianzi.⁹ Then the active ingredients of SBS were screened by the two ADME attribute values: oral bioavailability (OB) ≥ 30%, and drug-likeness (DL) ≥ 0.18.^10,11 The next step was to summarize the protein targets of these active ingredients. For some active ingredients whose target is not recorded in TCMSP, we used SwissTargetPrediction (http://www.swisstargetprediction.ch/) to predict their protein targets.¹² The obtained protein targets were normalized in the Uniprot database (https://www.uniprot.org). Finally, we summarized and deduplicated drug targets of SBS.¹³

Targets for COVID-19

With COVID-19 as the key word, and using the DisGeNET (https://www.disgenet.org/), TTD (http://db.idrblab.net/ttd/), OMIM (http://www.omim.org), Genecards (https://www.genecards.org/) and DRUGBANK databases (https://www.drugbank.ca/), we respectively queried the targets of COVID-19.^14-18 In the Genecards database, the higher the target score, the more closely the target is linked to COVID-19. Therefore, we selected the target with a score greater than or equal to the median as a potential target for the treatment of COVID-19. Finally, all disease targets were summarized and deduplicated.

Establishment of a PPI Network of SBS-COVID-19 Target Proteins

In order to screen out the core targets of SBS for the treatment of COVID-19, we intersected the drug targets and the disease targets by R language, and drew the Venn diagram. Next, the core targets were submitted to the STRING database (https://string-db.org/). We selected multiple proteins for analysis and set the organism to Homo sapiens.¹⁹ By setting the minimum required interaction score to medium confidence (0.400), a PPI network about the SBS and COVID-19 target proteins was established; this was then imported into Cytoscape 3.8.0 for visualization.

Gene Ontology and KEGG Pathway Enrichment Analysis

Through the DAVID database (https://david.ncifcrf.gov/), for the core targets which had been acquired in the previous step, GO function enrichment analysis and KEGG pathway enrichment analysis were performed.^20,21 We used Omicshare tool (http://www.omicshare.com) to draw the bubble chart of the KEGG pathway and GO enrichment analysis for the bar chart.

Construction of the Active Ingredients-Disease Target- KEGG Pathway Network

In order to clarify the relationship between the active ingredients of SBS, the targets of COVID-19, and the pathway of action, Cytoscape 3.8.0 was used to establish a network of active ingredients of SBS-targets of COVID-19-action pathways. In this network diagram, the point (Node) represents the components, targets and pathways, and the edge (Edge) represents the connection between them. Then we analyzed the network topology parameters of the active ingredients and disease targets. Using Degree, Betweenness and Closeness as reference indicators, we chose these parameters to determine the core targets and the main active ingredients that play a role.²²

Molecular Docking Verification

RSCB PBD database (http://www.rcsb.org/) was used to find the PDB structure of the core targets,²³ and TCMSP was used to search for the MOL2 structure of the active ingredients. We used the SwissDock platform (http://www.swissdock.ch/) for online molecular docking.^24,25 The binding strength between the core targets of COVID-19 and the active ingredients of SBS were judged by the binding energy.^26,27 The smaller the binding energy, the more stable was the binding between the ligand and the receptor, and the greater the possibility of interaction.²⁸

Results

The Active Ingredients of SBS and Their Corresponding Targets

After screening by considering the two restrictive conditions of oral bioavailability (OB) and drug-likeness (DL), Baibiandou in SBS has 1 active ingredient, Baizhu has 7 active ingredients, Fuling has 15 active ingredients, Gancao has 92 active ingredients, Jiegeng has 7 active ingredients, Lianzi have 5 active ingredients, Renshen has 22 active ingredients, Sharen has 10 active ingredients, Shanyao has 16 active ingredients, and Yiyiren has 9 active ingredients. By summing up the active ingredients of the ten medicines, 177 were obtained after removing duplication. At the same time, we acquired 681 drug targets after removing duplication. Detailed information of some of the active compounds is shown in Table 1.

Table 1.

Active Ingredients of SBS.

Drug	MOLID	Active ingredient	OB（%）	DL	Codename
Baibiandou	MOL002773	beta-carotene	37.18	0.58	BBD1
Baizhu	MOL000020	12-senecioyl-2E,8E,10E-atractylentriol	62.4	0.22	BZ1
	MOL000021	14-acetyl-12-senecioyl-2E,8E,10E-atractylentriol	60.31	0.31	BZ2
	MOL000022	14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol	63.37	0.3	BZ3
	MOL000028	α-Amyrin	39.51	0.76	BZ4
	MOL000033	(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3, 4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol	36.23	0.78	BZ5
	MOL000049	3β-acetoxyatractylone	54.07	0.22	BZ6
	MOL000072	8β-ethoxy atractylenolide III	35.95	0.21	BZ7
Fuling	MOL000273	(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13, 14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid	30.93	0.81	FL1
	MOL000275	trametenolic acid	38.71	0.8	FL2
	MOL000276	7,9(11)-dehydropachymic acid	35.11	0.81	FL3
	MOL000279	Cerevisterol	37.96	0.77	FL4
	MOL000280	(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13, 14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid	31.07	0.82	FL5
	MOL000282	ergosta-7,22E-dien-3beta-ol	43.51	0.72	FL6
	MOL000283	Ergosterol peroxide	40.36	0.81	FL7
	MOL000285	(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4,10,13, 14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]-5-isopropyl-hex-5-enoic acid	38.26	0.82	FL8
	MOL000287	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	38.7	0.81	FL9
	MOL000289	pachymic acid	33.63	0.81	FL10
	MOL000290	Poricoic acid A	30.61	0.76	FL11
	MOL000291	Poricoic acid B	30.52	0.75	FL12
	MOL000292	poricoic acid C	38.15	0.75	FL13
	MOL000296	hederagenin	36.91	0.75	FL14
	MOL000300	dehydroeburicoic acid	44.17	0.83	FL15
Gancao	MOL001484	Inermine	75.18	0.54	GC1
	MOL001792	DFV	32.76	0.18	GC2
	MOL000211	Mairin	55.38	0.78	GC3
	MOL002311	Glycyrol	90.78	0.67	GC4
	MOL000239	Jaranol	50.83	0.29	GC5
	MOL002565	Medicarpin	49.22	0.34	GC6
	MOL000354	isorhamnetin	49.6	0.31	GC7
	MOL000359	sitosterol	36.91	0.75	A
	MOL003656	Lupiwighteone	51.64	0.37	GC8
	MOL003896	7-Methoxy-2-methyl isoflavone	42.56	0.2	GC9
	MOL000392	formononetin	69.67	0.21	GC10
	MOL000417	Calycosin	47.75	0.24	GC11
	MOL000422	kaempferol	41.88	0.24	B
	MOL004328	naringenin	59.29	0.21	GC12
	MOL004805	(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one	31.79	0.72	GC13
	MOL004806	euchrenone	30.29	0.57	GC14
	MOL004808	glyasperin B	65.22	0.44	GC15
	MOL004810	glyasperin F	75.84	0.54	GC16
	MOL004811	Glyasperin C	45.56	0.4	GC17
	MOL004814	Isotrifoliol	31.94	0.42	GC18
	MOL004815	(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one	39.62	0.35	GC19
	MOL004820	kanzonols W	50.48	0.52	GC20
	MOL004824	(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one	60.25	0.63	GC21
	MOL004827	Semilicoisoflavone B	48.78	0.55	GC22
	MOL004828	Glepidotin A	44.72	0.35	GC23
	MOL004829	Glepidotin B	64.46	0.34	GC24
	MOL004833	Phaseolinisoflavan	32.01	0.45	GC25
	MOL004835	Glypallichalcone	61.6	0.19	GC26
	MOL004838	8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol	58.44	0.38	GC27
	MOL004841	Licochalcone B	76.76	0.19	GC28
	MOL004848	licochalcone G	49.25	0.32	GC29
	MOL004849	3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin	59.62	0.43	GC30
	MOL004855	Licoricone	63.58	0.47	GC31
	MOL004856	Gancaonin A	51.08	0.4	GC32
	MOL004857	Gancaonin B	48.79	0.45	GC33
	MOL004860	licorice glycoside E	32.89	0.27	GC34
	MOL004863	3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone	66.37	0.41	GC35
	MOL004864	5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone	30.49	0.41	GC36
	MOL004866	2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone	44.15	0.41	GC37
	MOL004879	Glycyrin	52.61	0.47	GC38
	MOL004882	Licocoumarone	33.21	0.36	GC39
	MOL004883	Licoisoflavone	41.61	0.42	GC40
	MOL004884	Licoisoflavone B	38.93	0.55	GC41
	MOL004885	licoisoflavanone	52.47	0.54	GC42
	MOL004891	shinpterocarpin	80.3	0.73	GC43
	MOL004898	(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one	46.27	0.31	GC44
	MOL004903	liquiritin	65.69	0.74	GC45
	MOL004904	licopyranocoumarin	80.36	0.65	GC46
	MOL004905	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	34.32	0.55	GC47
	MOL004907	Glyzaglabrin	61.07	0.35	GC48
	MOL004908	Glabridin	53.25	0.47	GC49
	MOL004910	Glabranin	52.9	0.31	GC50
	MOL004911	Glabrene	46.27	0.44	GC51
	MOL004912	Glabrone	52.51	0.5	GC52
	MOL004913	1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone	48.14	0.43	GC53
	MOL004914	1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone	62.9	0.53	GC54
	MOL004915	Eurycarpin A	43.28	0.37	GC55
	MOL004917	glycyroside	37.25	0.79	GC56
	MOL004924	(-)-Medicocarpin	40.99	0.95	GC57
	MOL004935	Sigmoidin-B	34.88	0.41	GC58
	MOL004941	(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one	71.12	0.18	GC59
	MOL004945	(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one	36.57	0.32	GC60
	MOL004948	Isoglycyrol	44.7	0.84	GC61
	MOL004949	Isolicoflavonol	45.17	0.42	GC62
	MOL004957	HMO	38.37	0.21	GC63
	MOL004959	1-Methoxyphaseollidin	69.98	0.64	GC64
	MOL004961	Quercetin der.	46.45	0.33	GC65
	MOL004966	3′-Hydroxy-4′-O-Methylglabridin	43.71	0.57	GC66
	MOL000497	licochalcone a	40.79	0.29	GC67
	MOL004974	3′-Methoxyglabridin	46.16	0.57	GC68
	MOL004978	2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol	36.21	0.52	GC69
	MOL004980	Inflacoumarin A	39.71	0.33	GC70
	MOL004985	icos-5-enoic acid	30.7	0.2	GC71
	MOL004988	Kanzonol F	32.47	0.89	GC72
	MOL004989	6-prenylated eriodictyol	39.22	0.41	GC73
	MOL004990	7,2′,4’-trihydroxy－5-methoxy-3－arylcoumarin	83.71	0.27	GC74
	MOL004991	7-Acetoxy-2-methylisoflavone	38.92	0.26	GC75
	MOL004993	8-prenylated eriodictyol	53.79	0.4	GC76
	MOL004996	gadelaidic acid	30.7	0.2	GC77
	MOL000500	Vestitol	74.66	0.21	GC78
	MOL005000	Gancaonin G	60.44	0.39	GC79
	MOL005001	Gancaonin H	50.1	0.78	GC80
	MOL005003	Licoagrocarpin	58.81	0.58	GC81
	MOL005007	Glyasperins M	72.67	0.59	GC82
	MOL005008	Glycyrrhiza flavonol A	41.28	0.6	GC83
	MOL005012	Licoagroisoflavone	57.28	0.49	GC84
	MOL005013	18α-hydroxyglycyrrhetic acid	41.16	0.71	GC85
	MOL005016	Odoratin	49.95	0.3	GC86
	MOL005017	Phaseol	78.77	0.58	GC87
	MOL005018	Xambioona	54.85	0.87	GC88
	MOL005020	dehydroglyasperins C	53.82	0.37	GC89
	MOL000098	quercetin	46.43	0.28	GC90
Jiegeng	MOL001689	acacetin	34.97	0.24	JG1
	MOL004355	Spinasterol	42.98	0.76	JG2
	MOL004580	cis-Dihydroquercetin	66.44	0.27	JG3
	MOL005996	2-O-methyl-3―O-β-D-glucopyranosyl platycogenate A	45.15	0.25	JG4
	MOL000006	luteolin	36.16	0.25	JG5
	MOL006026	dimethyl 2-O-methyl-3-O-a-D-glucopyranosyl platycogenate A	39.21	0.25	JG6
	MOL006070	robinin	39.84	0.71	JG7
Lianzi	MOL007213	Nuciferin	34.43	0.4	LZ1
	MOL000492	catechin	54.83	0.24	LZ2
	MOL002419	Norcoclaurine	82.54	0.21	LZ3
	MOL007206	Armepavine	69.31	0.29	LZ4
	MOL009172	Pronuciferin	32.75	0.37	LZ5
Renshen	MOL002879	Diop	43.59	0.39	RS1
	MOL000449	Stigmasterol	43.83	0.76	C
	MOL000358	beta-sitosterol	36.91	0.75	D
	MOL003648	Inermin	65.83	0.54	RS2
	MOL000422	kaempferol	41.88	0.24	B
	MOL004492	Chrysanthemaxanthin	38.72	0.58	RS3
	MOL005308	Aposiopolamine	66.65	0.22	RS4
	MOL005314	Celabenzine	101.88	0.49	RS5
	MOL005317	Deoxyharringtonine	39.27	0.81	RS6
	MOL005318	Dianthramine	40.45	0.2	RS7
	MOL005320	arachidonate	45.57	0.2	RS8
	MOL005321	Frutinone A	65.9	0.34	RS9
	MOL005344	ginsenoside rh2	36.32	0.56	RS10
	MOL005348	Ginsenoside-Rh4_qt	31.11	0.78	RS11
	MOL005356	Girinimbin	61.22	0.31	RS12
	MOL005357	Gomisin B	31.99	0.83	RS13
	MOL005360	malkangunin	57.71	0.63	RS14
	MOL005376	Panaxadiol	33.09	0.79	RS15
	MOL005384	suchilactone	57.52	0.56	RS16
	MOL005399	alexandrin_qt	36.91	0.75	RS17
	MOL005401	ginsenoside Rg5_qt	39.56	0.79	RS18
	MOL000787	Fumarine	59.26	0.83	RS19
Sharen	MOL001755	24-Ethylcholest-4-en-3-one	36.08	0.76	SR1
	MOL001771	poriferast-5-en-3beta-ol	36.91	0.75	SR2
	MOL001973	Sitosteryl acetate	40.39	0.85	SR3
	MOL000358	beta-sitosterol	36.91	0.75	D
	MOL003975	icosa-11,14,17-trienoic acid methyl ester	44.81	0.23	SR4
	MOL000449	Stigmasterol	43.83	0.76	C
	MOL007180	vitamin-e	32.29	0.7	SR5
	MOL007514	methyl icosa-11,14-dienoate	39.67	0.23	SR6
	MOL007535	(5S,8S,9S,10R,13R,14S,17R)-17-[(1R,4R)-4-ethyl-1,5-dimethylhexyl]-10, 13-dimethyl-2,4,5,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthrene-3,6-dione	33.12	0.79	SR7
	MOL007536	Stigmasta-5,22-dien-3-beta-yl acetate	46.44	0.86	SR8
Shanyao	MOL001559	piperlonguminine	30.71	0.18	SY1
	MOL001736	(-)-taxifolin	60.51	0.27	SY2
	MOL000310	Denudatin B	61.47	0.38	SY3
	MOL000322	Kadsurenone	54.72	0.38	SY4
	MOL005429	hancinol	64.01	0.37	SY5
	MOL005430	hancinone C	59.05	0.39	SY6
	MOL005435	24-Methylcholest-5-enyl-3belta-O-glucopyranoside_qt	37.58	0.72	SY7
	MOL005438	campesterol	37.58	0.71	SY8
	MOL005440	Isofucosterol	43.78	0.76	SY9
	MOL000449	Stigmasterol	43.83	0.76	C
	MOL005458	Dioscoreside C_qt	36.38	0.87	SY10
	MOL000546	diosgenin	80.88	0.81	SY11
	MOL005461	Doradexanthin	38.16	0.54	SY12
	MOL005463	Methylcimicifugoside_qt	31.69	0.24	SY13
	MOL005465	AIDS180907	45.33	0.77	SY14
	MOL000953	CLR	37.87	0.68	E
Yiyiren	MOL001323	Sitosterol alpha1	43.28	0.78	YYR1
	MOL001494	Mandenol	42	0.19	YYR2
	MOL002372	(6Z,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-hexaene	33.55	0.42	YYR3
	MOL002882	[(2R)-2,3-dihydroxypropyl] (Z)-octadec-9-enoate	34.13	0.3	YYR4
	MOL000359	sitosterol	36.91	0.75	A
	MOL000449	Stigmasterol	43.83	0.76	C
	MOL008118	Coixenolide	32.4	0.43	YYR5
	MOL008121	2-Monoolein	34.23	0.29	YYR6
	MOL000953	CLR	37.87	0.68	E

Targets of COVID-19

In the Genecards database, the highest score with a target was 21.78, the lowest was 0.31, and the median was 11.045. Therefore, the score with a target ≥11.045 was set as the disease target of COVID-19. Next, the disease targets collected in the five databases were summarized and deduplicated. Finally, we obtained 164 disease targets.

Construction of the PPI Network of the Target Protein of SBS for the Treatment of COVID-19

We took the intersection between the targets of active ingredients and the targets of COVID-19, and used R language to draw the Venn diagram (Figure 2); 43 core targets were obtained. These were then submitted to the STRING database, and the PPI network, which is about the targets of SBS for the treatment of COVID-19, was built. It was optimized by Cytoscape 3.8.0 (Figure 3).

Figure 2.

The intersection of the drug targets of SBS and the targets of COVID-19.

Figure 3.

SBS in the treatment of COVID-19 target protein PPI network.

GO Function Enrichment Analysis and KEGG Pathway Enrichment Analysis

In the GO analysis, there were 256 items related to biological process (BP), mainly including inflammatory response, positive regulation of nitric oxide biosynthetic process, positive regulation of transcription from RNA and response to lipopolysaccharide. There were 45 items on molecular function (MF), mainly including Ras guanyl-nucleotide exchange factor activity, receptor binding, drug binding, cytokine activity and steroid hormone receptor activity. There were 25 items related to cellular component (CC), mainly including membrane raft, cell surface and lysosome. According to the P value, we selected the top 10 items about BP, CC, and MF to draw a bar graph. The result is shown in Figure 4. From the enrichment analysis of the KEGG pathway, it can be seen that SBS has 58 pathways in the treatment of COVID-19, mainly including leishmaniasis, tuberculosis, malaria, HIF-1 signaling pathway, Jak-STAT signaling pathway, influenza A, and PI3K-Akt signaling pathway. According to the P value, we selected the top 20 pathway enrichments to draw a bubble chart (Figure 5). From the results of enrichment analysis, it can be seen that SBS treatment of COVID-19 is the result of multiple targets and multiple pathways.

Figure 4.

Go enrichment analysis of SBS in the treatment of COVID-19.

Figure 5.

KEGG pathway enrichment analysis of SBS in the treatment of COVID-19.

Active Ingredients of SBS-Targets of COVID-19-KEGG Pathway Network Diagram

In order to clarify the main active ingredients and core targets of SBS in the treatment of COVID-19, a network diagram which is about the active ingredients of SBS - disease targets - KEGG pathway network was constructed by Cytoscape 3.8.0. Analyzing this network with NetworkAnalyzer, we obtained the main active ingredients and core targets of SBS for the treatment of COVID-19; the network diagram is shown in Figure 6. The results showed that quercetin, 18α-hydroxyglycyrrhetic acid, icosa-11,14,17-trienoic acid methyl ester, kaempferol, 3,22-dihydroxy-11-oxo-olean-12-ene-27α-methoxycarbonyl-29-oic acid, luteolin, gomisin B, 3β-hydroxy-24-methylene-lanost-8-ene-21-oic acid, celabenzine, and denudatin B were the main active ingredients of SBS for the treatment of COVID-19, and that PTGS2, NOS2, PPARG, PTGS1, DPP4, F2, TNF, IFNG, IL6, and NR3C1 were their core targets; their specific network topology parameters are shown in Tables 2 and 3.

Figure 6.

SBS-COVID-19 disease target-action pathway network.

Table 2.

The Main Active Ingredients of SBS in the Treatment of COVID-19.

MOLID	Ingredient	Degree	Betweenness	Closeness
MOL000098	Quercetin	17	0.058275	0.47981
MOL005013	18α-hydroxyglycyrrhetic acid	12	0.019422	0.416495
MOL003975	icosa-11,14,17-trienoic acid methyl ester	12	0.038206	0.453933
MOL000422	Kaempferol	11	0.015495	0.455982
MOL005314	Celabenzine	11	0.016192	0.346484
MOL000310	Denudatin B	11	0.015989	0.362657
MOL004905	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	10	0.02936	0.455982
MOL000006	Luteolin	10	0.022777	0.45805
MOL005357	Gomisin B	9	0.006295	0.331691
MOL000287	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	8	0.013807	0.453933

Table 3.

The Core Targets of SBS in the Treatment of COVID-19.

GENE	Degree	Betweenness	Closeness
PTGS2	119	0.393546	0.60479
NOS2	81	0.176099	0.492683
PPARG	78	0.134258	0.476415
PTGS1	63	0.08263	0.43913
DPP4	42	0.038387	0.391473
F2	35	0.035773	0.40239
TNF	30	0.066564	0.392996
IFNG	18	0.01629	0.35689
IL6	18	0.017761	0.359431
NR3C1	16	0.034032	0.362007

Results of Molecular Docking Between Active Ingredients and Core Targets

The core targets were molecularly docked with the main active ingredients. The likelihood of the ligand interacting with the receptor is determined by the binding energy between them. The results of molecular docking are shown in Table 4, and the details of molecular docking in Figure 7.

Figure 7.

Detailed view of molecular docking.

Table 4.

The Docking Results of the Main Active Ingredients and the Main Core Target Molecules.

GENE	Ingredient	Binding energy（Kcal/MOL）
DPP4(4L72)	18α-hydroxyglycyrrhetic acid	−5.52327
F2(3E6P)	18α-hydroxyglycyrrhetic acid	−6.05165
IFNG(1FG9)	18α-hydroxyglycyrrhetic acid	−5.3842
IL6(4O9H)	18α-hydroxyglycyrrhetic acid	−7.4219
NOS2(5TP6)	18α-hydroxyglycyrrhetic acid	−6.4264
NR3C1(3BQD)	18α-hydroxyglycyrrhetic acid	−7.42189
PPARG(7AWD)	18α-hydroxyglycyrrhetic acid	−8.1274
PTGS2(4RS0)	18α-hydroxyglycyrrhetic acid	−7.48936
DPP4(4L72)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−6.7013
F2(3E6P)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.21466
IFNG(1FG9)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.65426
IL6(4O9H)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.54826
NOS2(5TP6)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−6.3576
NR3C1(3BQD)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.65428
PPARG(7AWD)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.91624
PTGS2(4RS0)	3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid	−7.50472
DPP4(4L72)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−7.47833
F2(3E6P)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−7.65814
IFNG(1FG9)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−7.23068
IL6(4O9H)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−7.8281
NOS2(5TP6)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−6.85416
NR3C1(3BQD)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−7.97035
PPARG(7AWD)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−8.85562
PTGS2(4RS0)	3beta-Hydroxy-24-methylene-8-lanostene-21-oic acid	−8.67365
DPP4(4L72)	Celabenzine	−8.14982
F2(3E6P)	Celabenzine	−7.8051
IFNG(1FG9)	Celabenzine	−7.69632
IL6(4O9H)	Celabenzine	−7.80979
NOS2(5TP6)	Celabenzine	−7.19076
NR3C1(3BQD)	Celabenzine	−7.68351
PPARG(7AWD)	Celabenzine	−8.91299
PTGS2(4RS0)	Celabenzine	−8.17103
DPP4(4L72)	Denudatin B	−7.63409
F2(3E6P)	Denudatin B	−7.14304
IFNG(1FG9)	Denudatin B	−7.09934
IL6(4O9H)	Denudatin B	−7.73778
NOS2(5TP6)	Denudatin B	−6.8616
NR3C1(3BQD)	Denudatin B	−8.98077
PPARG(7AWD)	Denudatin B	−8.32135
PTGS2(4RS0)	Denudatin B	−8.42168
DPP4(4L72)	Gomisin B	−7.59354
F2(3E6P)	Gomisin B	−7.93071
IFNG(1FG9)	Gomisin B	−7.54531
IL6(4O9H)	Gomisin B	−7.43781
NOS2(5TP6)	Gomisin B	−7.37808
NR3C1(3BQD)	Gomisin B	−8.10172
PPARG(7AWD)	Gomisin B	−9.27848
PTGS2(4RS0)	Gomisin B	−8.6602
DPP4(4L72)	icosa-11,14,17-trienoic acid methyl ester	−8.0113
F2(3E6P)	icosa-11,14,17-trienoic acid methyl ester	−8.04964
IFNG(1FG9)	icosa-11,14,17-trienoic acid methyl ester	−7.62945
IL6(4O9H)	icosa-11,14,17-trienoic acid methyl ester	−8.00375
NOS2(5TP6)	icosa-11,14,17-trienoic acid methyl ester	−7.34594
NR3C1(3BQD)	icosa-11,14,17-trienoic acid methyl ester	−9.22402
PPARG(7AWD)	icosa-11,14,17-trienoic acid methyl ester	−9.32201
PTGS2(4RS0)	icosa-11,14,17-trienoic acid methyl ester	−8.93077
DPP4(4L72)	kaempferol	−7.62453
F2(3E6P)	kaempferol	−7.11819
IFNG(1FG9)	kaempferol	−7.4448
IL6(4O9H)	kaempferol	−7.07528
NOS2(5TP6)	kaempferol	−7.0525
NR3C1(3BQD)	kaempferol	−7.77242
PPARG(7AWD)	kaempferol	−7.96199
PTGS2(4RS0)	kaempferol	−8.07337
DPP4(4L72)	luteolin	−7.84251
F2(3E6P)	luteolin	−7.0074
IFNG(1FG9)	luteolin	−7.29347
IL6(4O9H)	luteolin	−7.16725
NOS2(5TP6)	luteolin	−7.57544
NR3C1(3BQD)	luteolin	−7.91857
PPARG(7AWD)	luteolin	−8.35254
PTGS2(4RS0)	luteolin	−7.51851
DPP4(4L72)	quercetin	−7.52243
F2(3E6P)	quercetin	−7.19099
IFNG(1FG9)	quercetin	−7.72354
IL6(4O9H)	quercetin	−7.03858
NOS2(5TP6)	quercetin	−7.14915
NR3C1(3BQD)	quercetin	−8.00782
PPARG(7AWD)	quercetin	−8.58456
PTGS2(4RS0)	quercetin	−7.95985

Discussion

Since the outbreak of COVID-19 at the end of 2019, it has caused great harm to human health all over the world. At present, there is no specific drug for COVID-19 in clinic. Therefore, it is of great significance to screen traditional Chinese medicines, and to know their active components, core targets and active pathways that have a therapeutic effect on COVID-19.

We obtained some core active ingredients of SBS for the treatment of COVID-19, including quercetin, kaempferol, luteolin and gomisin B. Quercetin has anti-inflammatory, antioxidant, antiviral, and protective effects on liver, kidney, and heart, and some studies have shown that it may regulate multiple signaling pathways by inhibiting the activity of recombinant human angiotensin-converting enzyme 2 (ACE2), and then play a therapeutic effect on COVID-19.^29-31 Quercetin can down-regulate the Jak -STAT signal pathway to improve the gas exchange function of the lungs, reduce the release of inflammatory mediators, and reduce lung injury, which is consistent with the results of the enrichment analysis of the KEGG pathway in our study.³² At the same time, quercetin has a high affinity for 3-chymotrypsin-like protease (3CLpro), which is the receptor protein of SARS-CoV-2 like ACE2.^33,34 Both kaempferol and luteolin are flavonols, which have various biological functions such as antiviral, anti-inflammatory and immune regulation.^35,36 Some scholars have found that kaempferol and luteolin can effectively fight SARS-CoV-2 infection through molecular dynamics simulation research and MM-PBSA combined free energy calculation technology, which coincides with our research.³⁷ Gomisin B has a variety of pharmacological effects such as anti-asthma, anti-infection, protection of liver and heart, and can also reduce the inflammatory response of lung injury.³⁸ The above evidence suggests that various components in SBS may play anti-inflammatory and anti-virus roles by inhibiting the binding of SARS-CoV-2 to receptor proteins.

In addition to targeting ACE2 and 3CLpro, SBS has also been shown to regulate IL6 to relieve the symptoms of COVID-19. Among the core targets of SBS for the treatment of COVID-19, PTGS2 and PTGS1 are important targets of the arachidonic acid metabolism pathway. They can directly promote the production of IL-6 by activating PGE2. The level of IL6 is associated with acute respiratory distress syndrome.³⁹ The progressive increase in IL6 is a clinical warning indicator for the deterioration of the disease in the “Chinese Novel Coronavirus Diagnosis and Treatment Program”. Studies have shown that serum TNF-α cytokines in patients with COVID-19 were significantly increased.⁴⁰ The continuous production of pro-inflammatory cytokines such as TNF-α and IL6 can lead to immune disorders and lead to respiratory failure.⁴¹ Some studies have also shown that dipeptidyl peptidase-4 (DPP4) can be another possible receptor for the virus. The existence and severity of COVID-19 are related to the reduction of DPP4, which may be a possibility for disease monitoring in the course of the disease.⁴² A study proved that SARS-CoV-2 also uses DPP4 as a co-receptor when entering cells; the suppression of DPP4 was not only for halting the progression to the hyper-inflammatory state, but also for reducing viral infection to target cells in COVID-19 patients.⁴³ Network pharmacology and molecular docking analysis show that SBS may play a role in an anti-cytokine storm and have an antioxidant effect by regulating IL-6, TNF, DPP4 and other targets after virus infection of host cells, thereby playing a role in the treatment of COVID-19.

In this pharmacological network-based study, we studied the potential mechanism of SBS in the treatment of COVID-19. The results showed that SBS could reduce the inflammatory response, apoptosis and immune defense of SARS-CoV-2 infection. In addition, we provide several potential targets for the treatment of COVID-19, which may help to develop new treatment options. However, our study has several limitations. First, our results need to be further verified by subsequent pharmacodynamic and pharmacokinetic experiments. Secondly, a more comprehensive database of traditional Chinese medicine is needed to make the results of network pharmacological analysis more reliable. Third, even if we combine the results of network pharmacology and molecular docking, we still could not fully understand the accurate treatment mechanism of SBS.

Conclusion

In China's struggle with COVID-19, SBS, a traditional Chinese medicine formula, has been proven to be effective in treating patients with COVID-19. In summary, we obtained the active ingredients of SBS and explored the complex pharmacological mechanism of SBS for the treatment of COVID-19 through network pharmacology. The comprehensive analysis of the active ingredients and core targets of SBS by network pharmacology showed that the effect of SBS is the result of multi-component, multi-target and multi-channel interaction in the treatment of COVID-19.

Footnotes

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 Health Commission of Hubwi Province (grant number ZY2019Z011).

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethical Approval

Ethical Approval is not applicable for this article.

ORCID iDs

Ying Zhang

Li Lu

Statement of Human and Animal Rights

This article does not contain any studies with human or animal subjects.

Statement of Informed Consent

There are no human subjects in this article and informed consent is not applicable.

Trial Registration

Not applicable, because this article does not contain any clinical trials.

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Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

Abstract

Keywords

Introduction

Materials and Methods

Screening of Drug Targets of SBS

Targets for COVID-19

Establishment of a PPI Network of SBS-COVID-19 Target Proteins

Gene Ontology and KEGG Pathway Enrichment Analysis

Construction of the Active Ingredients-Disease Target- KEGG Pathway Network

Molecular Docking Verification

Results

The Active Ingredients of SBS and Their Corresponding Targets

Targets of COVID-19

Construction of the PPI Network of the Target Protein of SBS for the Treatment of COVID-19

GO Function Enrichment Analysis and KEGG Pathway Enrichment Analysis

Active Ingredients of SBS-Targets of COVID-19-KEGG Pathway Network Diagram

Results of Molecular Docking Between Active Ingredients and Core Targets

Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Availability of Data and Materials

Ethical Approval

ORCID iDs

Statement of Human and Animal Rights

Statement of Informed Consent

Trial Registration

References