Revolutionizing Antiviral Therapeutics: In silico Approaches for Emerging and Neglected RNA Viruses


Дәйексөз келтіру

Толық мәтін

Аннотация

:The 21st century has shown us how rapidly the pandemic can evolve and devastate the life of human beings without differentiating between the continents. Even after the global investment of billions of dollars into the healthcare sector, we are still lacking multiple therapeutics against emerging viruses. World Health Organization (WHO) has listed a number of viruses that could take the form of pandemics at anytime, depending upon their mutations. Among those listed, the SARS-CoV, Ebola, Zika, Nipah, and Chikungunya virus (CHIKV) are the most known viruses in terms of their number of outbreaks. The common feature among these viruses is their RNA-based genome. Developing a new therapeutic candidate for these RNA viruses in a short period of time is challenging. In silico drug designing techniques offer a simple solution to these problems by implementing supercomputers and complicated algorithms that can evaluate the inhibition activity of proposed synthetic compounds without actually doing the bioassays. A vast collection of protein crystal structures and the data on binding affinity are useful tools in this process. Taking this into account, we have summarized the in silico based therapeutic advances against SARS-CoV, Ebola, Zika, Nipah, and CHIKV viruses by encapsulating state-of-art research articles into different sections. Specifically, we have shown that computer- aided drug design (CADD) derived synthetic molecules are the pillars of upcoming therapeutic strategies against emerging and neglected viruses.

Негізгі сөздер

Авторлар туралы

Kirti Sharma

Chitkara University Institute of Engineering and Technology, Chitkara University

Email: info@benthamscience.net

Manjinder Singh

Chitkara College of Pharmacy, Chitkara University

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Sumesh Sharma

Chitkara University Institute of Engineering and Technology,, Chitkara University

Email: info@benthamscience.net

Әдебиет тізімі

  1. Sankaran N, Weiss RA. Viruses: Impact on Science and Society Encyclopedia of Virolog. Amsterdam: Elsevier 2021.
  2. Domingo E. Introduction to virus origins and their role in biological evolution. Virus Populat 2020; 2020: 1-3.
  3. Durmuş S, Ülgen KÖ. Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses. FEBS Open Bio 2017; 7(1): 96-107. doi: 10.1002/2211-5463.12167 PMID: 28097092
  4. Ryu WS. Molecular Virology of Human Pathogenic Viruses. (1st ed.), Amsterdam: Elsevier 2017.
  5. Dimitrov DS. Virus entry: Molecular mechanisms and biomedical applications. Nat Rev Microbiol 2004; 2(2): 109-22. doi: 10.1038/nrmicro817 PMID: 15043007
  6. Richman DD, Nathanson N. Antiviral therapy Viral Pathogenesis. Amsterdam: Elsevier 2016; pp. 271-87.
  7. Bray M. Highly pathogenic RNA viral infections: Challenges for antiviral research. Antiviral Res 2008; 78(1): 1-8. doi: 10.1016/j.antiviral.2007.12.007 PMID: 18243346
  8. (8)Walsh D, Mohr I. Viral subversion of the host protein synthesis machinery. Nat Rev Microbiol 2011; 9(12): 860-75. doi: 10.1038/nrmicro2655 PMID: 22002165
  9. Yang Y, Peng F, Wang R, et al. The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. J Autoimmun 2020; 109: 102434. doi: 10.1016/j.jaut.2020.102434 PMID: 32143990
  10. Yang W, Kandula S, Huynh M, et al. Estimating the infection-fatality risk of SARS-CoV-2 in New York city during the spring 2020 pandemic wave: A model-based analysis. Lancet Infect Dis 2021; 21(2): 203-12. doi: 10.1016/S1473-3099(20)30769-6 PMID: 33091374
  11. Liu WB, Li ZX, Du Y, Cao GW. Ebola virus disease: From epidemiology to prophylaxis. Mil Med Res 2015; 2(1): 7. doi: 10.1186/s40779-015-0035-4 PMID: 26000173
  12. Ioos S, Mallet HP, Leparc Goffart I, Gauthier V, Cardoso T, Herida M. Current Zika virus epidemiology and recent epidemics. Med Mal Infect 2014; 44(7): 302-7. doi: 10.1016/j.medmal.2014.04.008 PMID: 25001879
  13. Sharma V, Kaushik S, Kumar R, Yadav JP, Kaushik S. Emerging trends of Nipah virus: A review. Rev Med Virol 2019; 29(1): e2010. doi: 10.1002/rmv.2010 PMID: 30251294
  14. WHO Media Centre Chikungunya 2020. Available from: https://www.who.int/news-room/fact-sheets/detail/chikungunya
  15. Gibney KB, Fischer M, Prince HE, et al. Chikungunya fever in the United States: A fifteen year review of cases. Clin Infect Dis 2011; 52(5): e121-6. doi: 10.1093/cid/ciq214 PMID: 21242326
  16. Carrasco-Hernandez R, Jácome R, López Vidal Y, Ponce de León S. Are RNA viruse candidate agents for the next global pandemic? A review. ILAR J 2017; 58(3): 343-58. doi: 10.1093/ilar/ilx026 PMID: 28985316
  17. Wongsurawat T, Jenjaroenpun P, Taylor MK, et al. Rapid sequencing of multiple RNA viruses in their native form. Front Microbiol 2019; 10: 260. doi: 10.3389/fmicb.2019.00260 PMID: 30858830
  18. Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-based virtual screening: From classical to artificial intelligence. Front Chem 2020; 8: 343. doi: 10.3389/fchem.2020.00343 PMID: 32411671
  19. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr. Computational methods in drug discovery. Pharmacol Rev 2014; 66(1): 334-95. doi: 10.1124/pr.112.007336 PMID: 24381236
  20. Morawietz T, Artrith N. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications. J Comput Aided Mol Des 2021; 35(4): 557-86. doi: 10.1007/s10822-020-00346-6 PMID: 33034008
  21. Ivanov J, Polshakov D, Kato-Weinstein J, et al. Quantitative structure–activity relationship machine learning models and their applications for identifying viral 3CLpro-and RdRp-targeting compounds as potential therapeutics for COVID-19 and related viral infections. ACS Omega 2020; 5(42): 27344-58. doi: 10.1021/acsomega.0c03682 PMID: 33134697
  22. Cavasotto CN, Adler NS, Aucar MG. Quantum chemical approaches in structure-based virtual screening and lead optimization. Front Chem 2018; 6: 188. doi: 10.3389/fchem.2018.00188 PMID: 29896472
  23. Batra K, Zorn KM, Foil DH, et al. Machine learning algorithms for drug discovery applications. J Chem Inf Model 2021; 61(6): 2641-7. doi: 10.1021/acs.jcim.1c00166 PMID: 34032436
  24. Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today 2018; 23(8): 1538-46. doi: 10.1016/j.drudis.2018.05.010 PMID: 29750902
  25. Danishuddin, Khan AU. Descriptors and their selection methods in QSAR analysis: Paradigm for drug design. Drug Discov Today 2016; 21(8): 1291-302. doi: 10.1016/j.drudis.2016.06.013 PMID: 27326911
  26. Menendez-Arias L, Gago F. Antiviral agents: Structural basis of action and rational design. Subcell Biochem 2013; 68: 599-630. doi: 10.1007/978-94-007-6552-8_20
  27. Monod A, Swale C, Tarus B, et al. Learning from structure-based drug design and new antivirals targeting the ribonucleoprotein complex for the treatment of influenza. Expert Opin Drug Discov 2015; 10(4): 345-71. doi: 10.1517/17460441.2015.1019859 PMID: 25792362
  28. Frecer V, Miertus S. Antiviral agents against COVID-19: Structure-based design of specific peptidomimetic inhibitors of SARS-CoV-2 main protease. RSC Advances 2020; 10(66): 40244-63. doi: 10.1039/D0RA08304F
  29. Aparoy P, Kumar Reddy K, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5-LOX inhibitors. Curr Med Chem 2012; 19(22): 3763-78. doi: 10.2174/092986712801661112 PMID: 22680930
  30. Yu W, MacKerell AD Jr. Computer-aided drug design methods. Methods Mol Biol 2017; 1520: 85-106. doi: 10.1007/978-1-4939-6634-9_5 PMID: 27873247
  31. Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH. QSAR-based virtual screening: Advances and applications in drug discovery. Front Pharmacol 2018; 9: 1275. doi: 10.3389/fphar.2018.01275 PMID: 30524275
  32. Yang Y, Zhu Z, Wang X, et al. Ligand-based approach for predicting drug targets and for virtual screening against COVID-19. Brief Bioinform 2021; 22: 1053-64.
  33. Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agents 2020; 55(3): 105924. doi: 10.1016/j.ijantimicag.2020.105924 PMID: 32081636
  34. van Doremalen N, Bushmaker T, Morris DH, et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med 2020; 382(16): 1564-7. doi: 10.1056/NEJMc2004973 PMID: 32182409
  35. Mittal A, Manjunath K, Ranjan RK, Kaushik S, Kumar S, Verma V. COVID-19 pandemic: Insights into structure, function, and hACE2 receptor recognition by SARS-CoV-2. PLoS Pathog 2020; 16(8): e1008762.
  36. Belouzard S, Millet JK, Licitra BN, Whittaker GR. Mechanisms of coronavirus cell entry mediated by the viral spike protein. Viruses 2012; 4(6): 1011-33. doi: 10.3390/v4061011 PMID: 22816037
  37. Zhu Y, Li J, Pang Z. Recent insights for the emerging COVID-19: Drug discovery, therapeutic options and vaccine development. Asian J Pharmaceut Sci 2021; 16(1): 4-23. doi: 10.1016/j.ajps.2020.06.001 PMID: 32837565
  38. Mody V, Ho J, Wills S, et al. Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents. Commun Biol 2021; 4(1): 93. doi: 10.1038/s42003-020-01577-x PMID: 33473151
  39. V’kovski P, Kratzel A, Steiner S, Stalder H, Thiel V. Coronavirus biology and replication: Implications for SARS-CoV-2. Nat Rev Microbiol 2021; 19(3): 155-70. doi: 10.1038/s41579-020-00468-6 PMID: 33116300
  40. Ju X, Zhu Y, Wang Y, et al. A novel cell culture system modeling the SARS-CoV-2 life cycle. PLoS Pathog 2021; 17(3): e1009439. doi: 10.1371/journal.ppat.1009439 PMID: 33711082
  41. Mostafa-Hedeab G. ACE2 as drug target of COVID-19 virus treatment, simplified updated review. Rep Biochem Mol Biol 2020; 9(1): 97-105. doi: 10.29252/rbmb.9.1.97 PMID: 32821757
  42. Huang Y, Yang C, Xu X, et al. Structural and functional properties of SARS-CoV-2 spike protein: Potential antivirus drug development for COVID-19. Acta Pharmacol Sin 2020; 41(9): 1141-9. doi: 10.1038/s41401-020-0485-4 PMID: 32747721
  43. Williams-Noonan BJ, Todorova N, Kulkarni K, Aguilar MI, Yarovsky I. An active site inhibitor induces conformational penalties for ACE2 recognition by the spike protein of SARS-CoV-2. J Phys Chem B 2021; 125(10): 2533-50. doi: 10.1021/acs.jpcb.0c11321 PMID: 33657325
  44. Jaiswal G, Kumar V. In silico design of a potential inhibitor of SARS-CoV-2 S protein. PLoS One 2020; 15(10): e0240004. doi: 10.1371/journal.pone.0240004 PMID: 33002032
  45. Han Y, Král P. Computational design of ACE2-based peptide inhibitors of SARS-CoV-2. ACS Nano 2020; 14(4): 5143-7. doi: 10.1021/acsnano.0c02857 PMID: 32286790
  46. Yuan H, Ma Q, Ye L, Piao G. The traditional medicine and modern medicine from natural products. Molecules 2016; 21(5): 559. doi: 10.3390/molecules21050559 PMID: 27136524
  47. Borse S, Joshi M, Saggam A, et al. Ayurveda botanicals in COVID-19 management: An in silico multi-target approach. PLoS One 2021; 16(6): e0248479. doi: 10.1371/journal.pone.0248479 PMID: 34115763
  48. Basu A, Sarkar A, Maulik U. Molecular docking study of potential phytochemicals and their effects on the complex of SARS-CoV-2 spike protein and human ACE2. Sci Rep 2020; 10(1): 17699. doi: 10.1038/s41598-020-74715-4 PMID: 33077836
  49. Kumar S, Sharma PP, Shankar U, et al. Rathi, Discovery of new hydroxyethylamine analogs against 3CLpro protein target of SARS-CoV-2: Molecular docking, molecular dynamics simulation, and structure-activity relationship studies. J Chem Inf Model 2020; 60(12): 5754-70. doi: 10.1021/acs.jcim.0c00326 PMID: 32551639
  50. Shawan MMAK, Halder SK, Hasan MA. Luteolin and abyssinone II as potential inhibitors of SARS-CoV-2: An in silico molecular modeling approach in battling the COVID-19 outbreak. Bull Natl Res Cent 2021; 45(1): 27. doi: 10.1186/s42269-020-00479-6 PMID: 33495684
  51. Ritzmann F, Chitirala P, Krüger N, et al. Therapeutic application of alpha-1 antitrypsin in COVID-19. Am J Respir Crit Care Med 2021; 204(2): 224-7. doi: 10.1164/rccm.202104-0833LE PMID: 33961754
  52. Patil SM, Martiz RM, Ramu R, et al. In silico identification of novel benzophenone–coumarin derivatives as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) inhibitors. J Biomol Struct Dyn 2022; 40(23): 13032-48. doi: 10.1080/07391102.2021.1978322 PMID: 34632942
  53. Alkhimova LE, Babashkina MG, Safin DA. Computational analysis of aspirin. J Mol Struct 2022; 1251: 131975. doi: 10.1016/j.molstruc.2021.131975
  54. Abdizadeh R, Hadizadeh F, Abdizadeh T. In silico analysis and identification of antiviral coumarin derivatives against 3-chymotrypsin-like main protease of the novel coronavirus SARS-CoV-2. Mol Divers 2022; 26(2): 1053-76. doi: 10.1007/s11030-021-10230-6 PMID: 34213728
  55. Higashi-Kuwata N, Tsuji K, Hayashi H, et al. Identification of SARS-CoV-2 Mpro inhibitors containing P1′ 4-fluorobenzothiazole moiety highly active against SARS-CoV-2. Nat Commun 2023; 14(1): 1076. doi: 10.1038/s41467-023-36729-0 PMID: 36841831
  56. Sweiti H, Ekwunife O, Jaschinski T, Lhachimi SK. Repurposed therapeutic agents targeting the Ebola virus: A systematic review. Curr Ther Res Clin Exp 2017; 84: 10-21. doi: 10.1016/j.curtheres.2017.01.007 PMID: 28761574
  57. Coltart CEM, Lindsey B, Ghinai I, Johnson AM, Heymann DL. The Ebola outbreak, 2013-2016: Old lessons for new epidemics. Philos Trans R Soc Lond B Biol Sci 2017; 372(1721): 20160297.
  58. Aruna A, Mbala P, Minikulu L, et al. Ebola Virus Disease Outbreak — Democratic Republic of the Congo, August 2018–November 2019. MMWR Morb Mortal Wkly Rep 2019; 68(50): 1162-5. doi: 10.15585/mmwr.mm6850a3 PMID: 31856146
  59. Feldmann H, Jones S, Klenk HD, Schnittler HJ. Ebola virus: From discovery to vaccine. Nat Rev Immunol 2003; 3(8): 677-85. doi: 10.1038/nri1154 PMID: 12974482
  60. Zhu W, Banadyga L, Emeterio K, Wong G, Qiu X. The roles of Ebola virus soluble glycoprotein in replication, pathogenesis, and countermeasure development. Viruses 2019; 11(11): 999. doi: 10.3390/v11110999 PMID: 31683550
  61. Hoenen T, Groseth A, Feldmann H. Therapeutic strategies to target the Ebola virus life cycle. Nat Rev Microbiol 2019; 17(10): 593-606. doi: 10.1038/s41579-019-0233-2 PMID: 31341272
  62. Mirza MU, Vanmeert M, Ali A, Iman K, Froeyen M, Idrees M. Perspectives towards antiviral drug discovery against Ebola virus. J Med Virol 2019; 91(12): 2029-48. doi: 10.1002/jmv.25357 PMID: 30431654
  63. Beniac DR, Booth TF. Structure of the Ebola virus glycoprotein spike within the virion envelope at 11 Å resolution. Sci Rep 2017; 7(1): 46374. doi: 10.1038/srep46374 PMID: 28397863
  64. Brown CS, Lee MS, Leung DW, et al. In silico derived small molecules bind the filovirus VP35 protein and inhibit its polymerase cofactor activity. J Mol Biol 2014; 426(10): 2045-58. doi: 10.1016/j.jmb.2014.01.010 PMID: 24495995
  65. Rai S, Raj U, Varadwaj PK. Systems biology: A powerful tool for drug development. Curr Top Med Chem 2018; 18(20): 1745-54. doi: 10.2174/1568026618666181025113226 PMID: 30360720
  66. Mirza M, Ikram N. Integrated computational approach for virtual hit identification against Ebola viral proteins VP35 and VP40. Int J Mol Sci 2016; 17(11): 1748. doi: 10.3390/ijms17111748 PMID: 27792169
  67. Easton V, McPhillie M, Garcia-Dorival I, et al. Identification of a small molecule inhibitor of Ebola virus genome replication and transcription using in silico screening. Antiviral Res 2018; 156: 46-54.
  68. Khaiboullina S, Uppal T, Martynova E, Rizvanov A, Baranwal M, Verma SC. History of ZIKV infections in India and management of disease outbreaks. Front Microbiol 2018; 9: 2126. doi: 10.3389/fmicb.2018.02126 PMID: 30258421
  69. Musso D, Gubler DJ. Zika virus. Clin Microbiol Rev 2016; 29(3): 487-524. doi: 10.1128/CMR.00072-15 PMID: 27029595
  70. Calvez E, Mousson L, Vazeille M, et al. Zika virus outbreak in the Pacific: Vector competence of regional vectors. PLoS Negl Trop Dis 2018; 12(7): e0006637. doi: 10.1371/journal.pntd.0006637 PMID: 30016372
  71. Heinz FX, Stiasny K. The antigenic structure of Zika virus and its relation to other flaviviruses: Implications for infection and immunoprophylaxis. Microbiol Mol Biol Rev 2017; 81(1): e00055-16. doi: 10.1128/MMBR.00055-16 PMID: 28179396
  72. White MK, Wollebo HS, David Beckham J, Tyler KL, Khalili K. Zika virus: An emergent neuropathological agent. Ann Neurol 2016; 80(4): 479-89. doi: 10.1002/ana.24748 PMID: 27464346
  73. Tan TY, Fibriansah G, Kostyuchenko VA, et al. Capsid protein structure in Zika virus reveals the flavivirus assembly process. Nat Commun 2020; 11(1): 895. doi: 10.1038/s41467-020-14647-9 PMID: 32060358
  74. Millies B, von Hammerstein F, Gellert A, et al. Proline-based allosteric inhibitors of Zika and Dengue virus NS2B/NS3 proteases. J Med Chem 2019; 62(24): 11359-82. doi: 10.1021/acs.jmedchem.9b01697 PMID: 31769670
  75. Choudhry H, Alzahrani FA, Hassan MA, et al. Zika virus targeting by screening inhibitors against NS2B/NS3 protease. BioMed Res Int 2019; 2019: 3947245.
  76. Ramharack P, Soliman MES. Zika virus NS5 protein potential inhibitors: An enhanced in silico approach in drug discovery. J Biomol Struct Dyn 2018; 36(5): 1118-33. doi: 10.1080/07391102.2017.1313175 PMID: 28351337
  77. Ben-Shabat S, Yarmolinsky L, Porat D, Dahan A. Antiviral effect of phytochemicals from medicinal plants: Applications and drug delivery strategies. Drug Deliv Transl Res 2020; 10(2): 354-67. doi: 10.1007/s13346-019-00691-6 PMID: 31788762
  78. Mohd A, Zainal N, Tan KK, AbuBakar S. Resveratrol affects Zika virus replication in vitro. Sci Rep 2019; 9(1): 14336. doi: 10.1038/s41598-019-50674-3 PMID: 31586088
  79. Abrams RPM, Yasgar A, Teramoto T, et al. Therapeutic candidates for the Zika virus identified by a high-throughput screen for Zika protease inhibitors. Proc Natl Acad Sci USA 2020; 117(49): 31365-75. doi: 10.1073/pnas.2005463117 PMID: 33229545
  80. Buendia-Atencio C, Pieffet GP, Montoya-Vargas S, et al. Inverse molecular docking study of NS3-helicase and NS5-RNA polymerase of Zika virus as possible therapeutic targets of ligands derived from Marcetia taxifolia and its implications to Dengue virus. ACS Omega 2021; 6(9): 6134-43. doi: 10.1021/acsomega.0c04719 PMID: 33718704
  81. Singh RK, Dhama K, Chakraborty S, et al. Nipah virus: Epidemiology, pathology, immunobiology and advances in diagnosis, vaccine designing and control strategies - A comprehensive review. Vet Q 2019; 39(1): 26-55. doi: 10.1080/01652176.2019.1580827 PMID: 31006350
  82. Ang BSP, Lim TCC, Wang L. Nipah virus infection. J Clin Microbiol 2018; 56(6): e01875-17. doi: 10.1128/JCM.01875-17 PMID: 29643201
  83. Sen N, Kanitkar TR, Roy AA, et al. Predicting and designing therapeutics against the Nipah virus. PLoS Negl Trop Dis 2019; 13(12): e0007419. doi: 10.1371/journal.pntd.0007419 PMID: 31830030
  84. Lipin R, Dhanabalan AK, Gunasekaran K, Solomon RV. Piperazine-substituted derivatives of favipiravir for Nipah virus inhibition: What do in silico studies unravel? SN Appl Sci 2021; 3(1): 110. doi: 10.1007/s42452-020-04051-9 PMID: 33458565
  85. Ali MH, Anwar S, Kumar Roy P, Ashrafuzzaman M. Ashrafuzzaman, virtual screening for identification of small lead compound inhibitors of Nipah virus attachment glycoprotein. J Pharmacogenomics Pharmacoproteomics 2018; 9(2): 2153-0645. doi: 10.4172/2153-0645.1000180
  86. Yap ML, Klose T, Urakami A, Hasan SS, Akahata W, Rossmann MG. Structural studies of Chikungunya virus maturation. Proc Natl Acad Sci USA 2017; 114(52): 13703-7. doi: 10.1073/pnas.1713166114 PMID: 29203665
  87. Hwu JR, Pradhan TK, Tsay S-C, et al. Antiviral agents towards chikungunya virus: Structures, syntheses, and isolation from natural sources, New Horizons of Process Chemistry. Singapore: Springer 2017; pp. 251-74.
  88. de Bernardi Schneider A, Ochsenreiter R, Hostager R, Hofacker IL, Janies D, Wolfinger MT. Updated phylogeny of Chikungunya virus suggests lineage-specific RNA architecture. Viruses 2019; 11(9): 798. doi: 10.3390/v11090798 PMID: 31470643
  89. Khan N, Bhat R, Patel AK, Ray P. Discovery of small molecule inhibitors of Chikungunya virus proteins (nsP2 and E1) using in silico approaches. J Biomol Struct Dyn 2021; 39(4): 1373-85. doi: 10.1080/07391102.2020.1731602 PMID: 32072865
  90. Crunkhorn S. Targeting T cells to treat Chikungunya virus infections. Nat Rev Drug Discov 2017; 16(4): 237-7. doi: 10.1038/nrd.2017.49 PMID: 28356592
  91. Hwu JR, Kapoor M, Tsay SC, et al. Benzouracil-coumarin-arene conjugates as inhibiting agents for Chikungunya virus. Antiviral Res 2015; 118: 103-9. doi: 10.1016/j.antiviral.2015.03.013 PMID: 25839734
  92. Bissoyi A, Agarwal T, Asthana S. Molecular modeling and docking study to elucidate novel Chikungunya virus nsP2 protease inhibitors. Indian J Pharm Sci 2015; 77(4): 453-60. doi: 10.4103/0250-474X.164769 PMID: 26664062
  93. Ivanova MV, Zhong A, Turken A, Baldo JV, Dronkers NF. Functional contributions of the arcuate fasciculus to language processing. Front Hum Neurosci 2021; 15: 672665. doi: 10.3389/fnhum.2021.672665 PMID: 34248526
  94. Jain J, Kumari A, Somvanshi P, Grover A, Pai S, Sunil S. In silico analysis of natural compounds targeting structural and nonstructural proteins of Chikungunya virus. F1000 Res 2017; 6: 1601. doi: 10.12688/f1000research.12301.2 PMID: 29333236
  95. Kumar D, Meena MK, Kumari K, Patel R, Jayaraj A, Singh P. In silico prediction of novel drug-target complex of nsp3 of CHIKV through molecular dynamic simulation. Heliyon 2020; 6(8): e04720. doi: 10.1016/j.heliyon.2020.e04720 PMID: 32904235
  96. Seyedi SS, Shukri M, Hassandarvish P, et al. Computational approach towards exploring potential anti-chikungunya activity of selected flavonoids. Sci Rep 2016; 6(1): 24027. doi: 10.1038/srep24027 PMID: 27071308
  97. Oo A, Hassandarvish P, Chin SP, Lee VS, Abu Bakar S, Zandi K. In silico study on anti-Chikungunya virus activity of hesperetin. PeerJ 2016; 4: e2602. doi: 10.7717/peerj.2602 PMID: 27812412
  98. Hwu JR, Kapoor M, Gupta NK, et al. Synthesis and antiviral activities of quinazolinamine–coumarin conjugates toward Chikungunya and hepatitis C viruses. Eur J Med Chem 2022; 232: 114164. doi: 10.1016/j.ejmech.2022.114164 PMID: 35176562
  99. Mahajan P, Kaushal J. Epidemic trend of COVID-19 transmission in India during lockdown-1 phase. J Community Health 2020; 45(6): 1291-300. doi: 10.1007/s10900-020-00863-3 PMID: 32578006
  100. Nagu P, Parashar A, Behl T, Mehta V. CNS implications of COVID-19: A comprehensive review. Rev Neurosci 2021; 32(2): 219-34. doi: 10.1515/revneuro-2020-0070 PMID: 33550782

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML

© Bentham Science Publishers, 2024