Skip to content
2000
Volume 31, Issue 7
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Background

New strains of SARS-CoV-2 are continually emerging worldwide. Recently, WHO warned of a severe new wave in Europe. Current vaccines cannot fully prevent reinfection in vaccinated individuals.

Aim

Given this issue, recent research focuses on new antiviral candidates with high efficacy and minimal side effects.

Objectives

Screen natural compounds as inhibitors of Mpro SARS-CoV-2 protein using molecular dynamics.

Methods

In this study, we have screened the potential of plant-based natural anti-viral compounds. A library of the 579 compounds was generated using currently available literature and online databases. All these compounds were screened based on their binding affinities as predicted by molecular docking analysis and compounds having binding affinity values ≤ -10 Kcal/mol were considered for analysis. Furthermore, from physicochemical assessment, drug-likeness initially nine compounds were identified as the antiviral targets for the selected viral proteins. After ADMET analysis and simulations, the compound 9064 with the lowest RMSD, Coul-SR interaction energy (-71.53 kJ/mol), and LJ-SR energy (-95.32 kJ/mol) was selected as the most stable drug candidate against COVID-19 main protease Mpro.

Results

The ΔG value, calculated using MMGBSA also revealed strong binding of the compound with Mpro. The selected antiviral compound 9064 is an antioxidant flavonoid (Catechin or Cianidanol), which was previously known to have significant immunomodulatory, anti-inflammatory, and antioxidant properties.

Conclusion

Considering the limitations of currently available vaccines, our study may provide new insight into potential drugs that may prevent SARS-CoV-2 infection in humans.

Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128315762240828052002
2024-11-19
2025-05-08
Loading full text...

Full text loading...

References

  1. Comas-GarciaM. Packaging of genomic RNA in positive-sense single-stranded RNA viruses: A complex story.Viruses201911325310.3390/v1103025330871184
    [Google Scholar]
  2. LimY. NgY. TamJ. LiuD. Human coronaviruses: A review of virus–host interactions.Diseases2016432610.3390/diseases403002628933406
    [Google Scholar]
  3. PeirisJ.S.M. LaiS.T. PoonL.L.M. Coronavirus as a possible cause of severe acute respiratory syndrome.Lancet200336193661319132510.1016/S0140‑6736(03)13077‑212711465
    [Google Scholar]
  4. XieQ. CaoY. SuJ. Genomic sequencing and analysis of the first imported Middle East Respiratory Syndrome Coronavirus (MERS CoV) in China.Sci. China Life Sci.201558881882010.1007/s11427‑015‑4903‑726199186
    [Google Scholar]
  5. LiQ. GuanX. WuP. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia.N. Engl. J. Med.2020382131199120710.1056/NEJMoa200131631995857
    [Google Scholar]
  6. LiuC. ZhouQ. LiY. Research and development on therapeutic agents and vaccines for COVID-19 and related human coronavirus diseases.ACS Cent. Sci.20206331533110.1021/acscentsci.0c0027232226821
    [Google Scholar]
  7. RabaanA.A. Al-AhmedS.H. SahR. SARS-CoV-2/COVID-19 and advances in developing potential therapeutics and vaccines to counter this emerging pandemic.Ann. Clin. Microbiol. Antimicrob.20201914010.1186/s12941‑020‑00384‑w
    [Google Scholar]
  8. GuJ. GuiY. ChenL. YuanG. LuH.Z. XuX. Use of natural products as chemical library for drug discovery and network pharmacology.PLoS One201384e6283910.1371/journal.pone.006283923638153
    [Google Scholar]
  9. MishraK. SharmaN. DiwakerD. GanjuL. SinghS. Plant derived antivirals: A potential source of drug development.J. Virol. Antivir. Res.2013229
    [Google Scholar]
  10. BibiY. NisaS. ChaudharyF.M. ZiaM. Antibacterial activity of some selected medicinal plants of Pakistan.BMC Complement. Altern. Med.20111115210.1186/1472‑6882‑11‑5221718504
    [Google Scholar]
  11. MustafaG. ArifR. AttaA. SharifS. JamilA. Bioactive compounds from medicinal plants and their importance in drug discovery in Pakistan.Matrix Sci Pharma201711172610.26480/msp.01.2017.17.26
    [Google Scholar]
  12. ShinwariZ.K. QaiserM. Efforts on conservation and sustainable use of medicinal plants of Pakistan.Pak. J. Bot.201143510
    [Google Scholar]
  13. SubbarayappaB.V. The roots of ancient medicine: An historical outline.J. Biosci.200126213514310.1007/BF0270363711426049
    [Google Scholar]
  14. ManiJ.S. JohnsonJ.B. SteelJ.C. Natural product-derived phytochemicals as potential agents against coronaviruses: A review.Virus Res.202028419798910.1016/j.virusres.2020.19798932360300
    [Google Scholar]
  15. OrhanI.E. DenizF.S.S. Natural products as potential leads against coronaviruses: Could they be encouraging structural models against SARS-CoV-2?Nat. Prod. Bioprospect.202010171186
    [Google Scholar]
  16. TerstappenG.C. ReggianiA. In silico research in drug discovery.Trends Pharmacol. Sci.2001221232610.1016/S0165‑6147(00)01584‑411165668
    [Google Scholar]
  17. BoruahL. DasA. NainwalL.M. AgarwalN. ShankarB. In silico drug design: A revolutionary approach to change the concept of current drug discovery process.Indian J Pharmaceut Biol Res201312607310.30750/ijpbr.1.2.11
    [Google Scholar]
  18. WuC. LiuY. YangY. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods.Acta Pharm. Sin. B202010576678810.1016/j.apsb.2020.02.00832292689
    [Google Scholar]
  19. WuF. ZhaoS. YuB. A new coronavirus associated with human respiratory disease in China.Nature2020579779826526910.1038/s41586‑020‑2008‑332015508
    [Google Scholar]
  20. RaniR. SinghA. PareekA. TomarS. In silico guided drug repurposing to combat SARS-CoV-2 by targeting Mpro, the key virus specific protease.ChemRxiv202010.26434/chemrxiv.12030345.v1
    [Google Scholar]
  21. KumarY. SinghH. PatelC.N. In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular docking and dynamics simulation based drug-repurposing.J. Infect. Public Health20201391210122310.1016/j.jiph.2020.06.016
    [Google Scholar]
  22. KligerY. LevanonE.Y. GerberD. From genome to antivirals: SARS as a test tube.Drug Discov. Today200510534535210.1016/S1359‑6446(04)03320‑315749283
    [Google Scholar]
  23. SalvatoriG. LubertoL. MaffeiM. SARS-CoV-2 SPIKE PROTEIN: An optimal immunological target for vaccines.J. Transl. Med.202018122210.1186/s12967‑020‑02392‑y32493510
    [Google Scholar]
  24. GilC. GinexT. MaestroI. COVID-19: Drug targets and potential treatments.J. Med. Chem.20206321123591238610.1021/acs.jmedchem.0c0060632511912
    [Google Scholar]
  25. PickettB.E. SadatE.L. ZhangY. ViPR: An open bioinformatics database and analysis resource for virology research.Nucleic Acids Res.201240D1D593D59810.1093/nar/gkr85922006842
    [Google Scholar]
  26. BensonD. LipmanD.J. OstellJ. GenBank.Nucleic Acids Res.199321132963296510.1093/nar/21.13.29638332518
    [Google Scholar]
  27. BermanH.M. WestbrookJ. FengZ. The protein data bank.Nucleic Acids Res.200028123524210.1093/nar/28.1.23510592235
    [Google Scholar]
  28. SieversF. HigginsD.G. Clustal omega.Curr. Protoc. Bioinformatics2014481310.1002/0471250953.bi0313s48
    [Google Scholar]
  29. ZhangY. I-TASSER server for protein 3D structure prediction.BMC Bioinformatics2008914010.1186/1471‑2105‑9‑4018215316
    [Google Scholar]
  30. Accelrys Software Inc. SD, CA, USA. Visualizer, Discovery Studio Release 4.0.2013Available from: https://www.sciepub.com/reference/263156 (accessed on 6-8-2024)
  31. LüthyR. BowieJ.U. EisenbergD. Assessment of protein models with three-dimensional profiles.Nature19923566364838510.1038/356083a01538787
    [Google Scholar]
  32. VilarS. CozzaG. MoroS. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery.Curr. Top. Med. Chem.20088181555157210.2174/15680260878678662419075767
    [Google Scholar]
  33. LabuteP. Protonate3D: Assignment of ionization states and hydrogen coordinates to macromolecular structures.Proteins200975118720510.1002/prot.2223418814299
    [Google Scholar]
  34. JoshiT. JoshiT. SharmaP. In silico screening of natural compounds against COVID-19 by targeting Mpro and ACE2 using molecular docking.Eur. Rev. Med. Pharmacol. Sci.20202484529453632373991
    [Google Scholar]
  35. MauryaV.K. KumarS. PrasadA.K. BhattM.L.B. SaxenaS.K. Structure-based drug designing for potential antiviral activity of selected natural products from Ayurveda against SARS-CoV-2 spike glycoprotein and its cellular receptor.Virusdisease202031217919310.1007/s13337‑020‑00598‑832656311
    [Google Scholar]
  36. ShahSA AkhtarN AkramM Pharmacological activity of Althaea officinalis L.J. Med. Plant. Res.2011556625626
    [Google Scholar]
  37. BudratP. ShotiprukA. Extraction of phenolic compounds from fruits of bitter melon (Momordica charantia) with subcritical water extraction and antioxidant activities of these extracts.Warasan Khana Witthayasat Maha Witthayalai Chiang Mai200835123130
    [Google Scholar]
  38. MukhtarM. ArshadM. AhmadM. PomerantzR.J. WigdahlB. ParveenZ. Antiviral potentials of medicinal plants.Virus Res.2008131211112010.1016/j.virusres.2007.09.00817981353
    [Google Scholar]
  39. AnilakumarK.R. KumarG.P. IlaiyarajaN. SciencesF. Nutritional, pharmacological and medicinal properties of Momordica charantia.Int. J. Nutr.201547583
    [Google Scholar]
  40. BensoB. Evaluation of the antibacterial, anti-inflammatory, antiosteoclastogenic and anti-HIV activities of Malva sylvestris: State University of Campinas.UNICAMP2016
    [Google Scholar]
  41. MuhammadD.R.A. DewettinckK. Cinnamon and its derivatives as potential ingredient in functional food-A review.Int. J. Food Prop.20172012710.1080/10942912.2017.1369102
    [Google Scholar]
  42. SaeedS. TariqP. CINNAMON (Cinnamomum zeylanicum L.).Int. J. Biol. Biotechnol.200851722
    [Google Scholar]
  43. BodekerG. BurfordG. ChamberlainJ. ChamberlainJ. BhatK. The underexploited medicinal potential of Azadirachta indica A. Juss. (Meliaceae) and Acacia nilotica (L.) Willd. ex Del. (Leguminosae) in sub-Saharan Africa: A case for a review of priorities.Int. For Rev.2001285298
    [Google Scholar]
  44. HussainW. HaleemK.S. KhanI. Medicinal plants: A repository of antiviral metabolites.Future Virol.201712629930810.2217/fvl‑2016‑0110
    [Google Scholar]
  45. SoleimanyV. BanaeeM. MohiseniM. Nematdoost HagiB. Mousavi DehmourdiL. Evaluation of pre-clinical safety and toxicology of Althaea officinalis extracts as naturopathic medicine for common carp (Cyprinus carpio).Iran. J. Fish. Sci.201615613629
    [Google Scholar]
  46. TalleiT.E. TumilaarS.G. NiodeN.J. Potential of plant bioactive compounds as SARS-CoV-2 main protease (Mpro) and spike (S) glycoprotein inhibitors: A molecular docking study.Scientifica (Cairo)2020202011810.1155/2020/630745733425427
    [Google Scholar]
  47. BöhmeK. Barros-VelázquezJ. Calo-MataP. AubourgS.P. Antibacterial, antiviral and antifungal activity of essential oils: Mechanisms and applications. VillaT. Veiga-CrespoP. Antimicrobial compounds.Springer2014518110.1007/978‑3‑642‑40444‑3_3
    [Google Scholar]
  48. AshfaqU.A. IdreesS. Medicinal plants against hepatitis C virus.World J. Gastroenterol.201420112941294710.3748/wjg.v20.i11.294124659884
    [Google Scholar]
  49. JurcaT. PallagA. MarianE. EugeniaM. The histo-anatomical investigation and the polyphenolic profile of antioxidant complex active ingredients from three Viola species.Farmacia201967463464010.31925/farmacia.2019.4.12
    [Google Scholar]
  50. KimS. ThiessenP.A. BoltonE.E. PubChem substance and compound databases.Nucleic Acids Res.201644D1D1202D121310.1093/nar/gkv95126400175
    [Google Scholar]
  51. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.2017714271710.1038/srep4271728256516
    [Google Scholar]
  52. YangH. LouC. SunL. admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties.Bioinformatics20193561067106910.1093/bioinformatics/bty70730165565
    [Google Scholar]
  53. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b0010425860834
    [Google Scholar]
  54. AbrahamM.J. MurtolaT. SchulzR. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  55. YuW. HeX. VanommeslaegheK. MacKerellA.D.Jr Extension of the CHARMM general force field to sulfonyl‐containing compounds and its utility in biomolecular simulations.J. Comput. Chem.201233312451246810.1002/jcc.2306722821581
    [Google Scholar]
  56. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.103293625835573
    [Google Scholar]
  57. CaseD.A. CheathamT.E.III DardenT. The Amber biomolecular simulation programs.J. Comput. Chem.200526161668168810.1002/jcc.2029016200636
    [Google Scholar]
  58. GuanL. YangH. CaiY. ADMET-score - A comprehensive scoring function for evaluation of chemical drug-likeness.MedChemComm201910114815710.1039/C8MD00472B30774861
    [Google Scholar]
  59. ChillistoneS. HardmanJ.G. Factors affecting drug absorption and distribution.Anaesth. Intensive Care Med.201718733533910.1016/j.mpaic.2017.04.007
    [Google Scholar]
  60. ZangerU.M. SchwabM. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation.Pharmacol. Ther.2013138110314110.1016/j.pharmthera.2012.12.00723333322
    [Google Scholar]
  61. KönigJ. ZolkO. SingerK. HoffmannC. FrommM.F. Double‐transfected MDCK cells expressing human OCT1/MATE1 or OCT2/MATE1: Determinants of uptake and transcellular translocation of organic cations.Br. J. Pharmacol.2011163354655510.1111/j.1476‑5381.2010.01052.x20883471
    [Google Scholar]
  62. OtsukaM. MatsumotoT. MorimotoR. AriokaS. OmoteH. MoriyamaY. A human transporter protein that mediates the final excretion step for toxic organic cations.Proc. Natl. Acad. Sci. USA200510250179231792810.1073/pnas.050648310216330770
    [Google Scholar]
  63. MasudaS. TeradaT. YonezawaA. Identification and functional characterization of a new human kidney-specific H+/organic cation antiporter, kidney-specific multidrug and toxin extrusion 2.J. Am. Soc. Nephrol.20061782127213510.1681/ASN.200603020516807400
    [Google Scholar]
  64. OmoteH. HiasaM. MatsumotoT. OtsukaM. MoriyamaY. The MATE proteins as fundamental transporters of metabolic and xenobiotic organic cations.Trends Pharmacol. Sci.2006271158759310.1016/j.tips.2006.09.00116996621
    [Google Scholar]
  65. TaniharaY. MasudaS. SatoT. KatsuraT. OgawaO. InuiK. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H+-organic cation antiporters.Biochem. Pharmacol.200774235937110.1016/j.bcp.2007.04.01017509534
    [Google Scholar]
  66. TeradaT. InuiK. Physiological and pharmacokinetic roles of H+/organic cation antiporters (MATE/SLC47A).Biochem. Pharmacol.20087591689169610.1016/j.bcp.2007.12.00818262170
    [Google Scholar]
  67. SakanoT. MahamoodM.I. YamashitaT. FujitaniH. Molecular dynamics analysis to evaluate docking pose prediction.Biophys. Physicobiol.201613018119410.2142/biophysico.13.0_18127924273
    [Google Scholar]
  68. WangX. KleerekoperQ. RevtovichA.V. KangD. KirienkoN.V. Identification and validation of a novel anti-virulent that binds to pyoverdine and inhibits its function.Virulence20201111293130910.1080/21505594.2020.181914432962519
    [Google Scholar]
  69. RazaM.A.A. YanC. AbbasH.S.M. UllahA. COVID‐19 pandemic control and administrative issues in Pakistan: How Pakistan mitigated both pandemic and administration issues?J. Public Aff.202222S1e276010.1002/pa.276034899059
    [Google Scholar]
  70. RehmanS. ShafiqueL. IhsanA. LiuQ. Evolutionary trajectory for the emergence of novel coronavirus SARS-CoV-2.Pathogens20209324010.3390/pathogens903024032210130
    [Google Scholar]
  71. CalligariP. BoboneS. RicciG. BocediA. Molecular investigation of SARS-CoV-2 proteins and their interactions with antiviral drugs.Viruses202012444510.3390/v1204044532295237
    [Google Scholar]
  72. YangH. XieW. XueX. Design of wide-spectrum inhibitors targeting coronavirus main proteases.PLoS Biol.2005310e32410.1371/journal.pbio.003032416128623
    [Google Scholar]
  73. KhaerunnisaS. KurniawanH. AwaluddinR. SuhartatiS. SoetjiptoS. Potential inhibitor of COVID-19 main protease (Mpro) from several medicinal plant compounds by molecular docking study.Preprints202020202020030226
    [Google Scholar]
  74. AdemS. EyupogluV. SarfrazI. RasulA. AliM. Identification of potent COVID-19 main protease (Mpro) inhibitors from natural polyphenols: An in silico strategy unveils a hope against CORONA.Preprints202020202020030333
    [Google Scholar]
  75. JinZ. DuX. XuY. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors.Nature2020582781128929310.1038/s41586‑020‑2223‑y32272481
    [Google Scholar]
  76. BhattacharyaM. SharmaA.R. PatraP. Development of epitope‐based peptide vaccine against novel coronavirus 2019 (SARS‐CoV‐2): Immunoinformatics approach.J. Med. Virol.202092661863110.1002/jmv.2573632108359
    [Google Scholar]
  77. KalitaP. PadhiA.K. ZhangK.Y.J. TripathiT. Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2.Microb. Pathog.202014510423610.1016/j.micpath.2020.10423632376359
    [Google Scholar]
  78. KarT. NarsariaU. BasakS. A candidate multi-epitope vaccine against SARS-CoV-2.Sci. Rep.20201011089510.1038/s41598‑020‑67749‑132616763
    [Google Scholar]
  79. DagurH.S. DhakarS.S. GuptaA. Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2.PLoS One2020173e026470010.14744/ejmo.2020.01978
    [Google Scholar]
  80. HomansS. Dynamics and thermodynamics of ligand-protein interactions. Bioactive Conformation.Leeds, England, UKSpringer20065182
    [Google Scholar]
  81. PalmK. StenbergP. LuthmanK. ArturssonP.I. Polar molecular surface properties predict the intestinal absorption of drugs in humans.Pharm. Res.199714556857110.1023/A:10121886250889165525
    [Google Scholar]
  82. PajouheshH. LenzG.R. Medicinal chemical properties of successful central nervous system drugs.NeuroRx20052454155310.1602/neurorx.2.4.54116489364
    [Google Scholar]
  83. LoboS. Is there enough focus on lipophilicity in drug discovery?Expert Opin. Drug Discov.202015326126310.1080/17460441.2020.1691995
    [Google Scholar]
  84. JorgensenW.L. DuffyE.M. Prediction of drug solubility from structure.Adv. Drug Deliv. Rev.200254335536610.1016/S0169‑409X(02)00008‑X11922952
    [Google Scholar]
  85. LipinskiC.A. Drug-like properties and the causes of poor solubility and poor permeability.J. Pharmacol. Toxicol. Methods200044123524910.1016/S1056‑8719(00)00107‑611274893
    [Google Scholar]
  86. LipinskiC.A. LombardoF. DominyB.W. FeeneyP.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. 1PII of original article: S0169-409X(96)00423-1.Adv. Drug Deliv. Rev.2001461-332610.1016/S0169‑409X(00)00129‑011259830
    [Google Scholar]
  87. GhoseA.K. ViswanadhanV.N. WendoloskiJ.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.J. Comb. Chem.199911556810.1021/cc980007110746014
    [Google Scholar]
  88. MueggeI. HealdS.L. BrittelliD. Simple selection criteria for drug-like chemical matter.J. Med. Chem.200144121841184610.1021/jm015507e11384230
    [Google Scholar]
  89. EganW.J. MerzK.M.Jr BaldwinJ.J. Prediction of drug absorption using multivariate statistics.J. Med. Chem.200043213867387710.1021/jm000292e11052792
    [Google Scholar]
  90. ZhongH.A. ADMET properties: Overview and current topics. GroverA. Drug Design: Principles and Applications.SingaporeSpringer201711313310.1007/978‑981‑10‑5187‑6_8
    [Google Scholar]
  91. GleesonM.P. Generation of a set of simple, interpretable ADMET rules of thumb.J. Med. Chem.200851481783410.1021/jm701122q18232648
    [Google Scholar]
  92. AbdullahiW. DavisT.P. RonaldsonP.T. Functional expression of P-glycoprotein and organic anion transporting polypeptides at the blood-brain barrier: Understanding transport mechanisms for improved CNS drug delivery?AAPS J.201719493193910.1208/s12248‑017‑0081‑928447295
    [Google Scholar]
  93. FurgeL.L. GuengerichF.P. Cytochrome P450 enzymes in drug metabolism and chemical toxicology: An introduction.Biochem. Mol. Biol. Educ.2006342667410.1002/bmb.2006.4940340206621638641
    [Google Scholar]
  94. MotohashiH. InuiK. Organic cation transporter OCTs (SLC22) and MATEs (SLC47) in the human kidney.AAPS J.201315258158810.1208/s12248‑013‑9465‑723435786
    [Google Scholar]
  95. KramerJ.A. SagartzJ.E. MorrisD.L. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates.Nat. Rev. Drug Discov.20076863664910.1038/nrd237817643090
    [Google Scholar]
  96. GleesonP. BraviG. ModiS. LoweD. ADMET rules of thumb II: A comparison of the effects of common substituents on a range of ADMET parameters.Bioorg. Med. Chem.200917165906591910.1016/j.bmc.2009.07.00219632124
    [Google Scholar]
  97. BaeJ. KimN. ShinY. KimS.Y. KimY.J. Activity of catechins and their applications.BMC Dermatol.20204110
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128315762240828052002
Loading
/content/journals/cpd/10.2174/0113816128315762240828052002
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test