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2000
Volume 32, Issue 29
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Introduction

In our quest to identify potent inhibitors against SARS-CoV-2, an extensive investigation was conducted for the binding and inhibitory efficacy of Rutin against nine SARS-CoV-2 proteins.

Methods

Structure Similarity, flexible alignment, Molecular Docking, molecular dynamics (MD) simulations and assays against the RdRp and SARS-CoV-2 have been conducted.

Results

The first step of our analysis involved a comprehensive examination of structural similarity among the co-crystallized ligands associated with those proteins. A substantial structural similarity was observed between Rutin and Remdesivir, the ligand of the SARS-CoV-2 RNAdependent RNA polymerase (RdRp). This similarity was validated through a flexible alignment study. Molecular docking studies, involving superimposition, revealed a notable resemblance in the mode of binding between Rutin and Remdesivir inside the active site of the RdRp. A 200 ns MD simulation confirmed that the RdRp-Rutin complex is more stable than the RdRp-Remdesivir complex. The MM-GBSA studies showed that Rutin had much more favorable binding energies, with a significantly lower value of -7.76 kcal/mol compared to Remdesivir's -2.15 kcal/mol. This indicates that the RdRp-Rutin binding is more robust and stable. PLIP and ProLIF studies helped clarify the 3D binding interactions and confirmed the stable binding seen in MD simulations. PCAT gave more insights into the dynamic behavior of the RdRp-Rutin complex. tests showed that Rutin has a strong inhibitory effect on RdRp with an IC of 60.09 nM, significantly outperforming Remdesivir, which has an IC of 24.56 μM. Remarkably, against SARS-CoV-2, Rutin showed a superior IC of 0.598 μg/ml compared to Remdesivir (12.47 μg/ml). The values of the selectivity index underscored the exceptional margin of safety of Rutin (SI: 1078) compared to Remdesivir (SI: 5.8).

Conclusion

In conclusion, our comprehensive analysis indicates Rutin’s promising potential as a potent SARS-CoV-2 RdRp inhibitor, providing a valuable insight for developing an effective COVID-19 treatment.

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References

  1. WHO COVID-19 dashbord.2024Available from: https://data.who.int/dashboards/covid19/cases?n=c (accessed March, 2nd 2024).
  2. GrimmeS. SchreinerP.R. Computational chemistry: The fate of current methods and future challenges.Angew. Chem. Int. Ed.201857164170417610.1002/anie.20170994329105929
     [Google Scholar]
  3. YangX. WangY. ByrneR. SchneiderG. YangS. Concepts of artificial intelligence for computer-assisted drug discovery.Chem. Rev.201911918105201059410.1021/acs.chemrev.8b0072831294972
     [Google Scholar]
  4. RekerD. SchneiderG. Active-learning strategies in computer-assisted drug discovery.Drug Discov. Today201520445846510.1016/j.drudis.2014.12.00425499665
     [Google Scholar]
  5. WestbrookJ.D. BurleyS.K. How structural biologists and the Protein Data Bank contributed to recent FDA new drug approvals.Structure201927221121710.1016/j.str.2018.11.00730595456
     [Google Scholar]
  6. González-DíazH. New experimental and computational tools for drug discovery. Part – XII.Curr. Top. Med. Chem.202121978910.2174/15680266210921052610361434086545
     [Google Scholar]
  7. AminS.A. BanerjeeS. SinghS. QureshiI.A. GayenS. JhaT. First structure–activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: An endeavor on COVID-19 drug discovery.Mol. Divers.20212531827183810.1007/s11030‑020‑10166‑333400085
     [Google Scholar]
  8. WillemsH. De CescoS. SvenssonF. Computational chemistry on a budget: Supporting drug discovery with limited resources.J. Med. Chem.20206318101581016910.1021/acs.jmedchem.9b0212632298123
     [Google Scholar]
  9. IdrisM.O. YekeenA.A. AlakanseO.S. DurojayeO.A. Computer-aided screening for potential TMPRSS2 inhibitors: A combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches.J. Biomol. Struct. Dyn.202139155638565610.1080/07391102.2020.179234632672528
     [Google Scholar]
  10. LuY. LiM. A new computer model for evaluating the selective binding affinity of phenylalkylamines to T-Type Ca2+ channels.Pharmaceuticals202114214110.3390/ph1402014133578931
     [Google Scholar]
  11. LuX. YangH. ChenY. LiQ. HeS. JiangX. FengF. QuW. SunH. The development of pharmacophore modeling: Generation and recent applications in drug discovery.Curr. Pharm. Des.201824293424343910.2174/138161282466618081016294430101699
     [Google Scholar]
  12. FanJ. FuA. ZhangL. Progress in molecular docking.Quant. Biol.20197838910.1007/s40484‑019‑0172‑y
     [Google Scholar]
  13. PinziL. RastelliG. Molecular docking: Shifting paradigms in drug discovery.Int. J. Mol. Sci.20192018433110.3390/ijms2018433131487867
     [Google Scholar]
  14. AlRawashdehS. BarakatK.H. Applications of molecular dynamics simulations in drug discovery.Methods Mol. Biol.2024271412714110.1007/978‑1‑0716‑3441‑7_737676596
     [Google Scholar]
  15. HospitalA. GoñiJ.R. OrozcoM. GelpíJ.L. Molecular dynamics simulations: Advances and applications.Adv. Appl. Bioinform. Chem.20158374726604800
     [Google Scholar]
  16. ChengF. LiW. LiuG. TangY. In silico ADMET prediction: Recent advances, current challenges and future trends.Curr. Top. Med. Chem.201313111273128910.2174/1568026611313999003323675935
     [Google Scholar]
  17. RanjanS. DevarapalliR. KunduS. SahaS. DeolkaS. VangalaV.R. ReddyC.M. Isomorphism: ‘Molecular similarity to crystal structure similarity’ in multicomponent forms of analgesic drugs tolfenamic and mefenamic acid.IUCrJ20207217318310.1107/S205225251901604X32148846
     [Google Scholar]
  18. TruongP.L. YinY. LeeD. KoS.H. Advancement in COVID-19 detection using nanomaterial-based biosensors.Exploration2023312021023210.1002/EXP.2021023237323622
     [Google Scholar]
  19. PopowskiK.D. López de Juan AbadB. GeorgeA. SilkstoneD. BelcherE. ChungJ. GhodsiA. LutzH. DavenportJ. FlanaganM. PiedrahitaJ. DinhP.U.C. ChengK. Inhalable exosomes outperform liposomes as mRNA and protein drug carriers to the lung.Extracell. Vesicle2022110000210.1016/j.vesic.2022.10000236523538
     [Google Scholar]
  20. KabiA.K. PalM. GujjarappaR. MalakarC.C. RoyM. Overview of hydroxychloroquine and remdesivir on severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2).J. Heterocycl. Chem.202260216518210.1002/jhet.454135942205
     [Google Scholar]
  21. AmaniB. AmaniB. Efficacy and safety of nirmatrelvir/ritonavir (Paxlovid) for COVID-19: A rapid review and meta-analysis.J. Med. Virol.2023952e2844110.1002/jmv.2844136576379
     [Google Scholar]
  22. HwangY.C. LuR.M. SuS.C. ChiangP.Y. KoS.H. KeF.Y. LiangK.H. HsiehT.Y. WuH.C. Monoclonal antibodies for COVID-19 therapy and SARS-CoV-2 detection.J. Biomed. Sci.2022291110.1186/s12929‑021‑00784‑w34983527
     [Google Scholar]
  23. Hadj HassineI. COVID-19 vaccines and variants of concern: A review.Rev. Med. Virol.2022324e231310.1002/rmv.231334755408
     [Google Scholar]
  24. WangZ. CuiK. CostabelU. ZhangX. Nanotechnology-facilitated vaccine development during the coronavirus disease 2019 (COVID-19) pandemic.Exploration2022252021008210.1002/EXP.2021008235941992
     [Google Scholar]
  25. MetwalyA.M. LianlianZ. LuqiH. DeqiangD. Black ginseng and its saponins: Preparation, phytochemistry and pharmacological effects.Molecules20192410185610.3390/molecules2410185631091790
     [Google Scholar]
  26. MetwalyA.M. GhoneimM.M. EissaI.H. ElsehemyI.A. MostafaA.E. HegazyM.M. AfifiW.M. DouD. Traditional ancient Egyptian medicine: A review.Saudi J. Biol. Sci.202128105823583210.1016/j.sjbs.2021.06.04434588897
     [Google Scholar]
  27. BadshahS.L. FaisalS. MuhammadA. PoulsonB.G. EmwasA.H. JaremkoM. Antiviral activities of flavonoids.Biomed. Pharmacother.202114011159610.1016/j.biopha.2021.11159634126315
     [Google Scholar]
  28. WangL. SongJ. LiuA. XiaoB. LiS. WenZ. LuY. DuG. Research progress of the antiviral bioactivities of natural flavonoids.Nat. Prod. Bioprospect.202010527128310.1007/s13659‑020‑00257‑x32948973
     [Google Scholar]
  29. MouffoukC. MouffoukS. MouffoukS. HambabaL. HabaH. Flavonols as potential antiviral drugs targeting SARS-CoV-2 proteases (3CLpro and PLpro), spike protein, RNA-dependent RNA polymerase (RdRp) and angiotensin-converting enzyme II receptor (ACE2).Eur. J. Pharmacol.202189117375910.1016/j.ejphar.2020.17375933249077
     [Google Scholar]
  30. MorimotoR. HanadaA. MatsubaraC. HorioY. SumitaniH. OgataT. IsegawaY. Anti-influenza A virus activity of flavonoids in vitro: A structure–activity relationship.J. Nat. Med.202377121922710.1007/s11418‑022‑01660‑z36357821
     [Google Scholar]
  31. NaderiM. SalavatihaZ. GogoiU. MohebbiA. An overview of anti-Hepatitis B virus flavonoids and their mechanisms of action.Front Cell Infect. Microbiol.202414135600310.3389/fcimb.2024.1356003
     [Google Scholar]
  32. ŠudomováM. HassanS.T.S. Flavonoids with anti-herpes simplex virus properties: Deciphering their mechanisms in disrupting the viral life cycle.Viruses20231512234010.3390/v1512234038140581
     [Google Scholar]
  33. GoyalJ. VermaP.K. An overview of biosynthetic pathway and therapeutic potential of rutin.Mini Rev. Med. Chem.202323141451146010.2174/138955752366623012510410136698235
     [Google Scholar]
  34. MazikM. Promising therapeutic approach for SARS-CoV-2 infections by using a rutin-based combination therapy.Chem. Med. Chem.20221711e20220015710.1002/cmdc.20220015735489042
     [Google Scholar]
  35. HollmanP. C. H. Absorption, bioavailability, and metabolism of flavonoids.Pharma. Biol.200442sup1748310.3109/13880200490893492
     [Google Scholar]
  36. ChenL. CaoH. HuangQ. XiaoJ. TengH. Absorption, metabolism and bioavailability of flavonoids: A review.Crit. Rev. Food Sci. Nutr.202262287730774210.1080/10408398.2021.191750834078189
     [Google Scholar]
  37. NaeemA. MingY. PengyiH. JieK.Y. YaliL. HaiyanZ. ShuaiX. WenjingL. LingW. XiaZ.M. ShanL.S. QinZ. The fate of flavonoids after oral administration: A comprehensive overview of its bioavailability.Crit. Rev. Food Sci. Nutr.202262226169618610.1080/10408398.2021.189833333847202
     [Google Scholar]
  38. TengH. ZhengY. CaoH. HuangQ. XiaoJ. ChenL. Enhancement of bioavailability and bioactivity of diet-derived flavonoids by application of nanotechnology: A review.Crit. Rev. Food Sci. Nutr.202363337839310.1080/10408398.2021.194777234278842
     [Google Scholar]
  39. YuanD. GuoY. PuF. YangC. XiaoX. DuH. HeJ. LuS. Opportunities and challenges in enhancing the bioavailability and bioactivity of dietary flavonoids: A novel delivery system perspective.Food Chem.202443013711510.1016/j.foodchem.2023.13711537566979
     [Google Scholar]
  40. ElkaeedE.B. EissaI.H. SalehA.M. AlsfoukB.A. MetwalyA.M. Computer-aided drug discovery of natural antiviral metabolites as potential SARS-CoV-2 helicase inhibitors.J. Chem. Res.20244811747519823122125310.1177/17475198231221253
     [Google Scholar]
  41. ElkaeedE.B. YoussefF.S. EissaI.H. ElkadyH. AlsfoukA.A. AshourM.L. El HassabM.A. Abou-SeriS.M. MetwalyA.M. Multi-step in silico discovery of natural drugs against COVID-19 targeting main protease.Int. J. Mol. Sci.20222313691210.3390/ijms2313691235805916
     [Google Scholar]
  42. ElkaeedE.B. EissaI.H. ElkadyH. AbdelalimA. AlqaisiA.M. AlsfoukA.A. ElwanA. MetwalyA.M. A multistage in silico study of natural potential inhibitors targeting SARS-CoV-2 main protease.Int. J. Mol. Sci.20222315840710.3390/ijms2315840735955547
     [Google Scholar]
  43. ElkaeedE.B. MetwalyA.M. AlesawyM.S. SalehA.M. AlsfoukA.A. EissaI.H. Discovery of potential SARS-CoV-2 papain-like protease natural inhibitors employing a multi-phase in silico approach.Life (Basel)2022129140710.3390/life1209140736143445
     [Google Scholar]
  44. MetwalyA. M. AlesawyM. S. AlsfoukB. A. IbrahimI. M. ElkaeedE. B. EissaI. H. Computer-assisted drug discovery of potential african anti-SARS-COV-2 natural products targeting the helicase protein.Nat. Prod. Commun.20241941934578X24124673810.1177/1934578X241246738
     [Google Scholar]
  45. ElkaeedE.B. AlsfoukB.A. IbrahimT.H. ArafaR.K. ElkadyH. IbrahimI.M. EissaI.H. MetwalyA.M. Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach.Antivir. Ther.20232851359653523119983810.1177/1359653523119983837669909
     [Google Scholar]
  46. ElkaeedE.B. KhalifaM.M. AlsfoukB.A. AlsfoukA.A. El-AttarA.A.M.M. EissaI.H. MetwalyA.M. The discovery of potential SARS-CoV-2 natural inhibitors among 4924 african metabolites targeting the papain-like protease: A multi-phase in silico approach.Metabolites20221211112210.3390/metabo1211112236422263
     [Google Scholar]
  47. ElkaeedE.B. ElkadyH. BelalA. AlsfoukB.A. IbrahimT.H. AbdelmoatyM. ArafaR.K. MetwalyA.M. EissaI.H. Multi-phase in silico discovery of potential SARS-CoV-2 RNA-dependent RNA polymerase inhibitors among 3009 clinical and FDA-approved related drugs.Processes202210353054910.3390/pr10030530
     [Google Scholar]
  48. MetwalyA. M. ElkaeedE. B. AlsfoukA. A. IbrahimI. M. SolimanO. A. ElkadyH. EissaI. H. Repurposing FDA-approved drugs as potential inhibitors of SARS-CoV-2 PLpro: A comprehensive computational study.J. Comput. Biophy. Chem.2024230912310.1142/S273741652450039X
     [Google Scholar]
  49. EissaI.H. SalehA.M. Al-RashoodS.T. El-AttarA.A.M.M. MetwalyA.M. PenoniA. Multistaged in silico discovery of the best SARS-CoV-2 main protease inhibitors amongst 3009 clinical and FDA-approved compounds.J. Chem.2024202411910.1155/2024/5084553
     [Google Scholar]
  50. EissaI. H. AlesawyM. S. SalehA. M. ElkaeedE. B. AlsfoukB. A. El-AttarA.-A. M. M. MetwalyA. M. Ligand and structure-based in silico determination of the most promising SARS-CoV-2 nsp16-nsp10 2'- o-methyltransferase complex inhibitors among 3009 FDA approved drugs.Molecules20222772287229910.3390/molecules27072287
     [Google Scholar]
  51. MetwalyA.M. ElwanA. El-AttarA.A.M.M. Al-RashoodS.T. EissaI.H. Structure-based virtual screening, docking, ADMET, molecular dynamics, and MM-PBSA calculations for the discovery of potential natural SARS- CoV-2 helicase inhibitors from the traditional chinese medicine.J. Chem.2022202212310.1155/2022/7270094
     [Google Scholar]
  52. MetwalyA.M. ElkaeedE.B. AlsfoukB.A. SalehA.M. MostafaA.E. EissaI.H. The computational preventive potential of the rare flavonoid, patuletin, isolated from tagetes patula, against SARS-CoV-2.Plants20221114188610.3390/plants1114188635890520
     [Google Scholar]
  53. MetwalyA.M. El-FakharanyE.M. AlsfoukA.A. IbrahimI.M. MostafaA.E. ElkaeedE.B. EissaI.H. Comprehensive structural and functional analysis of Patuletin as a potent inhibitor of SARS-CoV-2 targeting the RNA-dependent RNA polymerases.J. Mol. Struct.2024131113842410.1016/j.molstruc.2024.138424
     [Google Scholar]
  54. AbrahamM. J. MurtolaT. SchulzR. PállS. SmithJ. C. HessB. LindahlE. J. S. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151192510.1016/j.softx.2015.06.001
     [Google Scholar]
  55. BrooksB.R. BrooksC.L.III MackerellA.D.Jr NilssonL. PetrellaR.J. RouxB. WonY. ArchontisG. BartelsC. BoreschS. CaflischA. CavesL. CuiQ. DinnerA.R. FeigM. FischerS. GaoJ. HodoscekM. ImW. KuczeraK. LazaridisT. MaJ. OvchinnikovV. PaciE. PastorR.W. PostC.B. PuJ.Z. SchaeferM. TidorB. VenableR.M. WoodcockH.L. WuX. YangW. YorkD.M. KarplusM. CHARMM: The biomolecular simulation program.J. Comput. Chem.200930101545161410.1002/jcc.2128719444816
     [Google Scholar]
  56. JoS. ChengX. IslamS.M. HuangL. RuiH. ZhuA. LeeH.S. QiY. HanW. VanommeslaegheK. MacKerellA.D.Jr RouxB. ImW. CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues.Adv. Protein Chem. Struct. Biol.20149623526510.1016/bs.apcsb.2014.06.00225443960
     [Google Scholar]
  57. TuccinardiT. What is the current value of MM/PBSA and MM/GBSA methods in drug discovery?Expert Opin. Drug Discov.202116111233123710.1080/17460441.2021.194283634165011
     [Google Scholar]
  58. Valdés-TresancoM.S. Valdés-TresancoM.E. ValienteP.A. MorenoE. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS.J. Chem. Theory Comput.202117106281629110.1021/acs.jctc.1c0064534586825
     [Google Scholar]
  59. BouyssetC. FiorucciS. ProLIF: A library to encode molecular interactions as fingerprints.J. Cheminform.20211317210.1186/s13321‑021‑00548‑634563256
     [Google Scholar]
  60. SalentinS. SchreiberS. HauptV.J. AdasmeM.F. SchroederM. PLIP: Fully automated protein–ligand interaction profiler.Nucleic Acids Res.201543W1W443W44710.1093/nar/gkv31525873628
     [Google Scholar]
  61. TubianaT. CarvailloJ.C. BoulardY. BressanelliS. TTClust: A versatile molecular simulation trajectory clustering program with graphical summaries.J. Chem. Inf. Model.201858112178218210.1021/acs.jcim.8b0051230351057
     [Google Scholar]
  62. AmadeiA. LinssenA.B.M. BerendsenH.J.C. Essential dynamics of proteins.Proteins199317441242510.1002/prot.3401704088108382
     [Google Scholar]
  63. PapaleoE. MereghettiP. FantucciP. GrandoriR. De GioiaL. Free-energy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: The myoglobin case.J. Mol. Graph. Model.200927888989910.1016/j.jmgm.2009.01.00619264523
     [Google Scholar]
  64. El-FakharanyE.M. El-GendiH. El-MaradnyY.A. Abu-SerieM.M. Abdel-WahhabK.G. ShabanaM.E. AshryM. Inhibitory effect of lactoferrin-coated zinc nanoparticles on SARS-CoV-2 replication and entry along with improvement of lung fibrosis induced in adult male albino rats.Int. J. Biol. Macromol.202324512555210.1016/j.ijbiomac.2023.12555237356684
     [Google Scholar]
  65. MosmannT. Rapid colorimetric assay for cellular growth and survival: Application to proliferation and cytotoxicity assays.J. Immunol. Methods1983651-2556310.1016/0022‑1759(83)90303‑46606682
     [Google Scholar]
  66. MostafaA. KandeilA. A M M ElshaierY. KutkatO. MoatasimY. RashadA.A. ShehataM. GomaaM.R. MahrousN. MahmoudS.H. GabAllahM. AbbasH. TaweelA.E. KayedA.E. KamelM.N. SayesM.E. MahmoudD.B. El-SheshenyR. KayaliG. AliM.A. FDA-approved drugs with potent in vitro antiviral activity against severe acute respiratory syndrome coronavirus 2.Pharmaceuticals (Basel)2020131244310.3390/ph1312044333291642
     [Google Scholar]
  67. HassellA.M. AnG. BledsoeR.K. BynumJ.M. CarterH.L.III DengS.J.J. GampeR.T. GrisardT.E. MadaussK.P. NolteR.T. RocqueW.J. WangL. WeaverK.L. WilliamsS.P. WiselyG.B. XuR. ShewchukL.M. Crystallization of protein–ligand complexes.Acta Crystallogr. D Biol. Crystallogr.2007631727910.1107/S090744490604702017164529
     [Google Scholar]
  68. NantasenamatC. Isarankura-Na-AyudhyaC. NaennaT. PrachayasittikulV. A practical overview of quantitative structure-activity relationship.EXCLI J.20098748810.17877/DE290R‑690
     [Google Scholar]
  69. MaggioraG. VogtM. StumpfeD. BajorathJ. Molecular similarity in medicinal chemistry.J. Med. Chem.20145783186320410.1021/jm401411z24151987
     [Google Scholar]
  70. TurchiM. CaiQ. LianG. An evaluation of in-silico methods for predicting solute partition in multiphase complex fluids – A case study of octanol/water partition coefficient.Chem. Eng. Sci.201919715015810.1016/j.ces.2018.12.003
     [Google Scholar]
  71. SullivanK.M. EnochS.J. EzendamJ. SewaldK. RoggenE.L. CochraneS. An adverse outcome pathway for sensitization of the respiratory tract by low-molecular-weight chemicals: Building evidence to support the utility of in vitro and in silico methods in a regulatory context.Appl. In Vitro Toxicol.20173321322610.1089/aivt.2017.0010
     [Google Scholar]
  72. AltamashT. AmhamedA. AparicioS. AtilhanM. Effect of hydrogen bond donors and acceptors on CO2 absorption by deep eutectic solvents.Processes (Basel)20208121533154310.3390/pr8121533
     [Google Scholar]
  73. WanY. TianY. WangW. GuS. JuX. LiuG. In silico studies of diarylpyridine derivatives as novel HIV-1 NNRTIs using docking-based 3D-QSAR, molecular dynamics, and pharmacophore modeling approaches.RSC Advances2018871405294054310.1039/C8RA06475J35557880
     [Google Scholar]
  74. Escamilla-GutiérrezA. Ribas-AparicioR.M. Córdova-EspinozaM.G. Castelán-VegaJ.A. In silico strategies for modeling RNA aptamers and predicting binding sites of their molecular targets.Nucleosides Nucleotides Nucleic Acids202140879880710.1080/15257770.2021.195175434323642
     [Google Scholar]
  75. KaushikA.C. KumarA. BharadwajS. ChaudharyR. SahiS. Ligand-based approach for In-silico drug designing. Bioinformatics Techniques for Drug Discovery.Springer2018111910.1007/978‑3‑319‑75732‑2_2
     [Google Scholar]
  76. ZhangH. RenJ.X. MaJ.X. DingL. Development of an in silico prediction model for chemical-induced urinary tract toxicity by using naïve Bayes classifier.Mol. Divers.201923238139210.1007/s11030‑018‑9882‑830294757
     [Google Scholar]
  77. ErlundI. KosonenT. AlfthanG. MäenpääJ. PerttunenK. KenraaliJ. ParantainenJ. AroA. Pharmacokinetics of quercetin from quercetin aglycone and rutin in healthy volunteers.Eur. J. Clin. Pharmacol.200056854555310.1007/s00228000019711151743
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673339634241210151734
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Integrated in Silico and in Vitro Studies of Rutin's Potential against SARS-CoV-2 through the Inhibition of the RNA-dependent RNA Polymerase
Curr. Med. Chem. 32, 6353 (2025); https://doi.org/10.2174/0109298673339634241210151734
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  • Article Type:     Research Article
Keyword(s): in vitro; MD Simulations; RdRp; ritonavir; Rutin; SARS-CoV-2

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