Molecular docking&nbsp;analysis of selected phytochemicals on two&nbsp;SARS-CoV-2 targets

Amaka Ubani; Francis Agwom; Oluwatoyin Ruth Morenikeji; Nathan Yakubu Shehu; Emmanuel Arinze Umera; Uzal Umar; Simeon Omale; John Chinyere Aguiyi; Nnaemeka Emmanuel Nnadi; Pam Dachung Luka

doi:10.12688/f1000research.25076.1

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Research Article

Molecular docking analysis of selected phytochemicals on two SARS-CoV-2 targets

[version 1; peer review: 1 approved, 1 approved with reservations]

Potential lead compounds against two target sites of SARS-CoV-2 obtained from plants

Amaka Ubani¹, Francis Agwom², Oluwatoyin Ruth Morenikeji¹, [...] Nathan Yakubu Shehu³, Emmanuel Arinze Umera¹, Uzal Umar¹, Simeon Omale^1,4, John Chinyere Aguiyi¹, Nnaemeka Emmanuel Nnadi ⁵, Pam Dachung Luka⁶

Amaka Ubani¹, Francis Agwom², [...] Oluwatoyin Ruth Morenikeji¹, Nathan Yakubu Shehu³, Emmanuel Arinze Umera¹, Uzal Umar¹, Simeon Omale^1,4, John Chinyere Aguiyi¹, Nnaemeka Emmanuel Nnadi ⁵, Pam Dachung Luka⁶

PUBLISHED 21 Sep 2020

Author details Author details

¹ African Centre of Excellence in Phytomedicine Research and Development, University of Jos, Jos, Nigeria
² Department of Pharmaceutical & Medicinal Chemistry,Faculty of Pharmaceutical Sciences,, University of Jos, Jos, Nigeria
³ Department of Infectious Medicine, Jos University Teaching Hospital Jos, Jos, Nigeria
⁴ Department of Pharmacology and Toxicology, Faculty of Pharmaceutical Sciences, University of Jos, Jos, Nigeria
⁵ Department of Microbiology, Faculty of Natural and Applied Sciences, Plateau State University, Bokkos, Nigeria
⁶ Biotechnology Centre, National Veterinary Research Institute, Vom, Nigeria, Vom, Nigeria

Amaka Ubani
Roles: Conceptualization, Methodology, Software

Francis Agwom
Roles: Conceptualization, Methodology, Software, Writing – Review & Editing

Oluwatoyin Ruth Morenikeji
Roles: Conceptualization, Methodology, Software

Nathan Yakubu Shehu
Roles: Conceptualization, Methodology, Writing – Review & Editing

Emmanuel Arinze Umera
Roles: Conceptualization, Writing – Review & Editing

Uzal Umar
Roles: Conceptualization, Methodology, Writing – Review & Editing

Simeon Omale
Roles: Conceptualization, Methodology, Writing – Review & Editing

John Chinyere Aguiyi
Roles: Conceptualization, Methodology, Supervision, Writing – Review & Editing

Nnaemeka Emmanuel Nnadi
Roles: Conceptualization, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Pam Dachung Luka
Roles: Conceptualization, Methodology, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Cheminformatics gateway.

This article is included in the Coronavirus (COVID-19) collection.

Abstract

Background: The coronavirus spike (S) glycoprotein and M protease are two key targets that have been identified for vaccines and drug development against COVID-19.
Methods: Virtual screening of some compounds of plant origin that have shown antiviral activities were carried out on the two targets, the M protease (PDB ID 6LU7) and S glycoprotein (PDB ID 6VSB), by docking with PyRx software. The binding affinities were compared with other compounds and drugs already identified as potential ligands for the M protease and S glycoprotein, as well as chloroquine and hydroxychloroquine. The docked compounds with best binding affinities were also filtered for drug likeness using the SwissADME and PROTOX platforms on the basis of physicochemical properties and toxicity, respectively.
Results: The docking results revealed that scopadulcic acid and dammarenolic acid had the best binding affinity for the S glycoprotein and M^pro protein targets, respectively. Silybinin, through molecular docking, also demonstrated good binding affinity for both protein targets making it a potential candidate for further evaluation as repurposed candidate for SARS-CoV-2, with likelihood of having multitarget activity as it showed activities for both targets.
Conclusions: The study proposes that scopadulcic acid and dammarenolic acid be further evaluated in vivo for drug formulation against SARS-COV-2 and possible repurposing of Silybinin for the management of COVIV-19.

Keywords

SARS-CoV-2, Molecular docking, COVID-19

Corresponding author: Nnaemeka Emmanuel Nnadi

Competing interests: No competing interests were disclosed.

Grant information: This research was partially funded by the African Union research grant ASF-RESIST(HRST/ST/AURG-II/CALL1/2016) to P.D.L. and by a World Bank Africa Centre of Excellence in Phytomedicine Research and Development grant (ACE-018) to J.C.A.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2020 Ubani A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Ubani A, Agwom F, Morenikeji OR et al. Molecular docking analysis of selected phytochemicals on two SARS-CoV-2 targets [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2020, 9:1157 (https://doi.org/10.12688/f1000research.25076.1) First published: 21 Sep 2020, 9:1157 (https://doi.org/10.12688/f1000research.25076.1) Latest published: 21 Sep 2020, 9:1157 (https://doi.org/10.12688/f1000research.25076.1)

Introduction

Since 2003, three coronaviruses have been associated with pneumonia, the first was severe acute respiratory syndrome coronavirus (SARS-CoV-1)¹ which affected 8,098 people causing 774 deaths between 2002 and 2003², the second was Middle-East respiratory syndrome coronavirus (MERS-CoV)³ and the third is SARS-CoV-2 which, as at 8^th July, 2020, has affected 11,669,259 globally and is responsible for 539,906 deaths. SARS-CoV-2 is a human pathogen which has been declared a global pandemic by the World Health Organization⁴ and is responsible for the Coronavirus disease 2019 (COVID-19) (1). . The bats have been identified as the possible reservoir and origin for both the SARS-CoV-2 and SARS-COV-1³. Unfortunately, there has been no known cure for COVID-19 to date.³

The entry into the host cell by the coronaviruses is usually mediated by the spike (S) glycoprotein³. This glycoprotein interacts with the angiotensin-converting enzyme 2 (ACE2), enabling the virus penetration into the host. The main protease (M protease, also known as 3CL protease) has also been found to be essential for processing of translated polyproteins for the SARS_CoV-2 virus. The two targets, the S glycoprotein, Sgp and M^Pro proteins have therefore been considered as important drug targets against the SARS-CoV-2 virus.⁵. For this study, these two drug targets were selected and used to virtually screen some phytochemicals for possible activity against the SARS-CoV-2 virus.

Possibly, potent inhibitors of these two targets will be able to interfere with the SARS-CoV-2 replication process and thus serves as potential drugs for the management of the COVID-19. Hence, this work is aimed at identifying some potential lead compounds of plant origin that can serve as candidates for testing against the SARS-CoV-2 virus.

Methods

Mining of compounds from Pubchem

Plant compounds reported in the literatures that have been demonstrated to have antiviral activities (list of all the compounds analyzed is available as Extended data⁶) were selected, alongside also hydroxychloroquine, remdesivir and favipiravir, which have been used in the treatment and management of COVID-19 and mined from the PubChem database.

Protein preparation

Two proteins including crystal structure of the SARS-CoV-2 main protease (PDB ID 6LU7)⁷ in complex with an inhibitor N3 and the S glycoprotein in complex with N-acetyl-D-glucosamine (NAG) (PDB ID 6VSB) were downloaded from the Protein Data Bank (www.rcsb.org). The proteins were prepared by removing water molecules and co-crystalized ligands (highlighting the water molecules and co-crystalized ligands and deleting) on the proteins using Discovery Studio version 2017R2 (19). The UCSF Chimera molecular modeling package is an open access equivalent that could be sued to perform the same function⁸.

Molecular docking and visualization

The already prepared protein and ligands were loaded on to the PyRx docking software (PyRx-Python Prescription 0.8), where molecular docking was done in the AutodockVina mode. Visualization to see how the ligands fitted and bound into the binding pockets on the protein and also the interactions between the protein and the ligands was done using Discovery Studio version 2017R2 (19) by first loading the saved PDBqt output file of the target protein from PyRx and then inserting the output of the binding modes of the different ligands and then viewed under the Receptor-Ligand interaction platform of Discovery Studio software.

Physicochemical properties and toxicity prediction of compounds

The Physicochemical properties and drugability of selected compounds were predicted using the free online versions of SwissADME⁹ and Molinspiration¹⁰ platforms and their predicted toxicity profile also compared using the PROTOX toxicity prediction platform¹¹. In each case the ligands were loaded onto the platforms as SMILES structures obtained from PubChem.

Results

Ligands composition and filtering

A library of 22 compounds of plant origin known to have antiviral activity was obtained from the PubChem database (see Extended data⁶). Though the compounds are chemically diverse, they consist of largely flavonoids and terpenes. Some compounds from the citrus family were found among the library, though they could not make it among the top six selected compounds that demonstrated good binding affinities for the two targets. Most of the compounds has showed similar binding affinities to the selected protein targets (6LU7 (M protease) and 6VSB (S glycoprotein)) compared to the training sets of known ligands to the selected targets (see Table 1).

Table 1. Comparison of Binding affinities of library to some known ligands and the co-crystalized ligand.

Protein target	Ligands used in treatment (kcal/mol)	Phytochemicals (kcal/mol)
6LU7	Remdesivir -7.1	Dammarenolic acid -7.2
	Favipiravir -5.3	Quercetin -7.1
	Chloroquine -5.2	Solanidine -7.0
		Silymarin -6.9
		Silvestrol -6.7
		Shikonin -6.6
6VSB	Remdesivir -7.3	Scopadulcic acid -9.6
	Favipiravir -5.3	Baicalin -9.4
	Chloroquine -5.2	Legalon -9.2
		Solanidine -9.1
		Naringenin -9.0
		Oleanane -9.0
		Silymarin -8.6

However, the top six compounds with most favorable binding affinity were selected for each of the targets. The outcomes of the binding affinities of the selected compounds on the 6LU7 and 6VBS targets are presented in Table 2 and Table 3, respectively.

Table 2. Binding affinities of the compounds on the 6vsb and their Interaction with the binding site.

Serial Number	Ligands	Binding affinity (kcal/Mol)	Hydrogen bond interaction with residues	Hydrophobic bond interaction with residues
1.	Scopadulcic Acid	-9.6	GLN B: 913	TYR C:904, GLY B:1093, VAL B:911, ARG B: 1107, ASN B:907, THR B:912, ASN B:1119, GLN B:1113, GLY B: 910, GLN B:1106, GLU B:1092, PHE A:1121, ARG A:1091
2.	Baicalin	-9.4	ARG B:1039, ARG A:1039, ARG C:1039, ALA B:1020, ASN C:1023	ALA C:1026, LEU B:1024, GLN C:784, SER C:1030, ASP B:1041, LEU C:1024, THR C:1027, PHE B:1042, PHE C:1042, PHE A:1042, THR B:1027, SER B:1021, GLU C:780.
3.	Sylibinin	-9.2	GLU A:954, ARG B:765	GLU A:1017, ARG A:1014, ALA B:766, LYS B:776, LYS A:947, LEU A:948, PRO A:728, VAL A:951, ILE A:1018, ALA B:766, GLN A:957, GLN B:762, GLN A:1010, ILE A:1013, LEU B:1012, ARG B:1019, GLU B:773.
4.	Solanidine	-9.1		TYR A:369, TYR C:489, ARG C:454, PRO C:491, TYR C:421, ASN C:460, LEU C:461
5.	Naringenin	-9.0	GLU C:1092, ARG C:1107, ASN C:1108, GLY C:910, ILE C:909	ASN C:907, THR C:912, GLN C:1113, ARG B:1091, GLU B:1092, GLY C:1093, GLN C:1106, TYR A:904
6.	Oleanane	-9		LEU A:1141, ASP C:1118, LEU C:1141, PRO A:1140, ASP A:1118, THR A:1117, THR B:1116, ASP B:1118, PRO B:1140, GLU B:1144, ASP A:1139, GLU A:1144

Table 3. Binding affinities of the compounds on the 6LU7 and their interaction with the binding site.

S/N	Ligands	Binding affinity (kcal/Mol)	Hydrogen bond interaction with residues	Hydrophobic bond interaction with residues
1.	Dammarenolic acid	-7.2	THR A: 111, GLN A: 110, GLN A: 107	VAL A:104, ARG A: 105, ILE A: 106, THR A: 292, PHE A: 112, GLN A: 127, ASP A: 295, ASN A: 152, PHE A: 294, PHE A: 294, PHE A: 8, ASP A: 153, SER A: 158, ILE A: 152
2.	Quercetin	-7.1	------	-----
3.	Solanidine	-7.0	-------	VAL A: 104, LYS A: 102, SER A: 158, ASP A: 153, ILE A: 152, ASN A: 151, ASN A: 151, PHE A: 8, ARG A: 105, GLN A: 107, ILE A: 106
4.	Silybinin	-6.8	ARG A: 105, ASP A: 176	ASN A: 180, GLU A: 178, PHE A: 103, VAL A: 104, SER A: 158, ASN A: 151, THR A: 111, GLN A: 110, ILE A: 106, GLN A: 107
5.	Loliolide	-6.7	------	------
6.	Shikonin	-6.6	THR A: 111, SER A: 158	PHE A: 294, PHE A: 8, GLN A: 110, ILE A: 106, ASN A: 151, PHE A: 112, LYS A: 102, ASP A: 153, VAL A: 104

Molecular docking analysis

The binding affinities of the top six compounds (Table 1) on the 6vsb target are comparable to each other, i.e. they all lie within a close range of 9 to 9.6 kcal/mol indicating that they might likely have equal or comparable potential as lead compounds for the 6vsb S glycoprotein.

One of the compounds sylibinin¹² is an FDA approved drug, which showed up as active on both M protease and S glycoprotein will make a good candidate of repurposing. Finding Quercetin as a potential inhibitor of the M protease Protein (6LU7) of SARS-CoV-2 corresponds with an earlier report¹³.

Physicochemical screening of ligands

Looking at the octanol–water coefficient (cLogP) of the compounds, there was no correlation observed between the lipophilicity and the interaction with the receptors. However, for the compounds acting on 6LU7 (Serial numbers 3, 4, 7, 8, 9 and 10 in Table 4), interaction with the receptor is correlated with low lipophilicity, with the exception of solanidine and dammarenolic acid, which have high cLogP values, although both compounds also use their polar functional groups in interacting with the receptor. Bacailin (Figure 1) and naringenin showed good hydrogen bond interaction with the 6VSB receptor due to their polarity.

Table 4. Comparison of the calculated cLogP values for the selected compounds.

S/N	Compound	SwissADME cLog P	Molinspiration cLog p	Mean calculated cLogP
1.	Scopodulcic acid	4.57	5.01	4.79
2.	Baicalin	0.22	0.55	0.39
3.	Sylibinin	1.59	1.47	1.53
4.	Solanidine	5.01	5.93	5.47
5.	Naringenin	1.84	2.12	1.98
6.	Oleanane	8.57	8.86	8.72
7.	Dammarenolic acid	6.74	8.08	7.41
8.	Quercetin	1.23	1.68	1.46
9.	Loliolide	1.53	1.84	1.69
10.	Shikonin	2.08	2.02	2.05

cLogP: octanol-water coffecient.

Figure 1.

Best binding pose and interaction of (A) scopodulcic acid and (B) baicalin on the spike glycoprotein.

Drug likeness and predicted toxicity profiles of ligands

Filtering the compounds for drug likeness on the basis of Linpinski’s and/or Veber’s rule showed that all the compounds have drug-like properties except baicalin, which failed the two filtering scales applied (Table 5). This implies that baicalin is not worth considering further without any structural modification. The predicted toxicity profile of the selected compounds shows (Table 6) that all the compounds are likely to be relatively safe, which makes them good potential candidates for anti-infectives because the chances of achieving selective toxicity is high. Baicalin is thus most likely the safest.

Table 5. Drug likeness.

S/N	Compound	Mol. Wt(g/mol)	TPSA	HBA	HBD	RB	cLogP	Lipinski filter	Veber filter
1.	Scopodulcic acid	438.56	80.67	5	1	4	4.79	+	+
2.	Baicalin	446.36	187.12	11	6	4	0.77	-	-
3.	Sylibinin	482.44	155.14	10	5	4	3.06	+	-
4.	Solanidine	397.64	23.47	2	1	0	5.47	+	-
5.	Naringenin	272.25	86.99	5	3	1	1.98	+	+
6.	Oleanane	412.75	0.00	0	0	0	8.72	+	+
7.	Dammarenolic acid	458.72	57.53	3	2	1	7.41	+	+
8.	Quercetin	302.24	131.36	7	5	1	1.46	+	+
9.	Loliolide	196.24	46.53	3	1	0	1.69	+	+
10.	Shikonin	288.3	94.83	5	3	3	2.05	+	+

Mol. Wt.: molecular weight; TPSA: total polar surface area; HBA: number of hydrogen bond acceptors; HBD: number of hydrogen bond donors; RB: number of rotatable bonds; cLogP: mean of calculated logP values.

Table 6. Predicted toxicity profile of the compounds using PROTOX II.

S/N	Compound	Hepatotoxicity	Immunotoxicity	Carcinogenicity	Mutangenicity	Cytoxicity	Possible toxicity targets
1.	Scopodulcic acid	-	++	-	-	-	AR, AO, PGS
2.	Baicalin	--	--	+	-	--	AO, PGS
3.	Sylibinin	--	++	-	--	--	PGS
4.	Solanidine	--	++	-	--	+	AR, PGS
5.	Naringenin	-	--	-	--	+	AR, PGS
6.	Oleanane	--	--	++	--	--
7.	Dammarenolic acid	-	-	--	--	--	AR, AO PGS
8.	Quercetin	-	+	--	-	--	AO, AR, PGS
9.	Loliolide	-	+	--	-	--	AO, PGS
10.	Shikonin	-	-	++	-	+	PG

--: inactive; -: less inactive; +: active; ++: more active; AR: androgen receptor; AO: amine oxidase A; PGS: prostaglandin G/H synthase 1.

Discussion

Two compounds among the top six selected for each target, solanidine and sylibinin, were observed to have good binding affinity on both the 6VSB and the 6FLU7 proteins. This makes them potential multitarget acting inhibitors on the SARS-CoV-2. Solanidine is a steroidal glycoalkaloid found in potatoes¹⁴. Although toxic to humans and animals, solanidine has been reported to be effective against herpes viruses (HSV), herpes genitalis and herpes zoster¹⁵ Its activity against HSV is attributed to the presence of a sugar moiety¹⁶. In silico drug screening using PROTOX II showed that solanidine is very likely to be cytotoxic and immunotoxic. PROTOTOX II is a cost- and time-saving approach for testing and determining the toxicity of a compound to be considered a drug of choice¹⁷. PROTOTOX II predicts the toxicity outcome of a potential drug of choice, it incorporates machine-learning models which use a combination of fragment propensities, molecular similarity, pharmacophores, to predict toxicity endpoints, such as acute toxicity, cytotoxicity, , carcinogenicity , hepatotoxicity, immunotoxicity, mutagenicity and toxicity targets¹⁷

A safe drug must not be toxic to its host target. Based on the PROTOX II evaluation of toxicity, dammarenolic acid emerges as the compound of choice with the least toxicity. Dammarenolic acid has been reported as effective antiviral agents dammarenolic acid potently inhibited the in vitro replication of other retroviruses, including simian immunodeficiency virus and murine leukemic virus in vector-based antiviral screening studies and has been proposed as a potential lead compound in the development of anti-retrovirals¹⁸. The compound is cytotoxic and demonstrate potential against respiratory syncytial virus¹⁹. We therefore propose that the evaluation of dammarenolic acid might hold the key to a safe and effective anti-SARS-CoV-2 drug considering its drugability and low toxicity.

This study proposes a potential re-purposing of silybinin for the management of COVID19 diseases. Silybinin (silymarin) possesses antiviral ability against hepatitis C virus (HCV)^20,21 It has been reported to have activities against a wide range of viral groups including flaviviruses (HCV and dengue virus), togaviruses (Chikungunya virus and Mayaro virus), influenza virus, human immunodeficiency virus, and hepatitis B virus²⁰. In an in vivo and in vitro study, Silymarin has been proposed to inhibit HCV entry, RNA synthesis, viral protein expression and prevent infectious virus production; it can also block cell-to-cell spread of the virus²². In silico analysis of silybinin in this present study has shown that it can likely inhibit SARS-CoV-2 S glycoprotein and M^pro targets, making it a drug to be considered with a possible multi-target activity against the SARS-CoV-2 virus.

Conclusions

From the 22 phytocompounds that were virtually screened, scopodulcic acid and dammarenolic acid showed the best binding energies with the S glycoprotein and M^pro, respectively. This makes them potential lead compounds for development into candidates against the SARS-CoV-2. Furthermore, the FDA-approved drug silybinin (Legalon) had good binding affinity for the two targets, so could be evaluated further for possible repurposing against the SARS-CoV-2 virus.

Data availability

Underlying data

All data underlying the results are available as part of the article and no additional source data are required.

Extended data

Harvard Dataverse: Replication Data for:Molecular docking analysis of some phytochemicals on two SARS-CoV-2 targets. https://doi.org/10.7910/DVN/BB0QQK⁶.

The file within this project contains the compounds obtained from the PubChem database that were analyzed in this study.

Extended data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Faculty Opinions recommended

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