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Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

Delineating Potential de novo Therapeutics and Repurposed Drugs Against Novel Protein LRRC15 to Treat SARS-CoV-2

Author(s): Maliha Afroj Zinnia and Abul Bashar Mir Md. Khademul Islam*

Volume 21, Issue 9, 2024

Published on: 18 April, 2023

Page: [1502 - 1520] Pages: 19

DOI: 10.2174/1570180820666230223120829

Price: $65

Abstract

Introduction: Sudden SARS-CoV-2 pandemic disrupted global public health; hence, searching for more effective treatments is urgently needed.

Objective: Recently, a new host protein LRRC15 has been identified, facilitating viral attachment and cellular invasion and hence can be a good target against SARS-CoV-2. In this study, design some potential inhibitors against LRRC15.

Methods: Here, we explored three strategies to find potential inhibitors against LRRC15, including the repurposing of ACE2 inhibitors, structure-based de novo drug generation, and virtual screening of three chemical libraries (ZINC Trial, ZINC Fragments, and Enamine HTSC).

Results: Based on binding affinity Benazepril (-7.7 kcal/mol) was chosen as a final repurpose drug candidate, and ten de novo drugs (-8.9 to -8.0 kcal/mol) and 100 virtually screened drugs (-11.5 to -10.7 kcal/mol) were elected for further ADMET and drug likeliness investigation. After filtering, Z131403838 and Z295568380 were chosen as final drug candidates, and de novo drugs were further optimized. Optimization, re-docking, and pharmacokinetic analysis confirmed L-2 and L-36 as the best hit de novo drug candidates. Furthermore, all five final drugs demonstrated stable receptor-drug complex stability in molecular dynamics simulation.

Conclusion: Effective treatment options are necessary to combat the SARS-CoV-2 epidemics. All the compounds presented in this study appeared to be promising inhibitorpromising inhibitors against LRRC15, though the future clinical investigation is needed toensure the biological effectiveness.

Keywords: SARS-CoV-2, LRRC15, ACE2, molecular docking, molecular dynamics simulation, ADMET analysis.

Graphical Abstract
[1]
World Health Organization. WHO COVID-19 dashboard. 2022. Available from: https://covid19.who.int/
[2]
Hoffmann, M.; Kleine-Weber, H.; Schroeder, S.; Krüger, N.; Herrler, T.; Erichsen, S.; Schiergens, T.S.; Herrler, G.; Wu, N.H.; Nitsche, A.; Müller, M.A.; Drosten, C.; Pöhlmann, S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell, 2020, 181(2), 271-280.e8.
[http://dx.doi.org/10.1016/j.cell.2020.02.052] [PMID: 32142651]
[3]
Ibrahim, I.M.; Abdelmalek, D.H.; Elfiky, A.A. GRP78: A cell’s response to stress. Life Sci., 2019, 226, 156-163.
[http://dx.doi.org/10.1016/j.lfs.2019.04.022] [PMID: 30978349]
[4]
Belouzard, S.; Millet, J.K.; Licitra, B.N.; Whittaker, G.R. Mechanisms of coronavirus cell entry mediated by the viral spike protein. Viruses, 2012, 4(6), 1011-1033.
[http://dx.doi.org/10.3390/v4061011] [PMID: 22816037]
[5]
Li, F.; Li, W.; Farzan, M.; Harrison, S.C. Structure of SARS coronavirus spike receptor-binding domain complexed with receptor. Science, 2005, 309(5742), 1864-1868.
[http://dx.doi.org/10.1126/science.1116480] [PMID: 16166518]
[6]
Walls, A.C.; Park, Y.J.; Tortorici, M.A.; Wall, A.; McGuire, A.T.; Veesler, D. Structure, function, and antigenicity of the SARS-COV-2 spike glycoprotein. Cell, 2020, 181(2), 281-292.e6.
[http://dx.doi.org/10.1016/j.cell.2020.02.058] [PMID: 32155444]
[7]
Lelis, D.F.; Freitas, D.F.; Machado, A.S.; Crespo, T.S.; Santos, S.H.S. Angiotensin-(1-7), adipokines and inflammation. Metabolism, 2019, 95, 36-45.
[http://dx.doi.org/10.1016/j.metabol.2019.03.006] [PMID: 30905634]
[8]
Zhang, H.; Penninger, J.M.; Li, Y.; Zhong, N.; Slutsky, A.S. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: Molecular mechanisms and potential therapeutic target. Intensive Care Med., 2020, 46(4), 586-590.
[http://dx.doi.org/10.1007/s00134-020-05985-9] [PMID: 32125455]
[9]
Wang, G.; Yang, M-L.; Duan, Z-L.; Liu, F-L.; Jin, L.; Long, C-B. Dalbavancin binds ACE2 to block its interaction with SARS-CoV-2 spike protein and is effective in inhibiting SARS-CoV-2 infection in animal models. Cell Res., 2021, 31(1), 17-24.
[PMID: 33262453]
[10]
Puray-Chavez, M.; LaPak, K.M.; Schrank, T.P.; Elliott, J.L.; Bhatt, D.P.; Agajanian, M.J.; Jasuja, R.; Lawson, D.Q.; Davis, K.; Rothlauf, P.W.; Liu, Z.; Jo, H.; Lee, N.; Tenneti, K.; Eschbach, J.E.; Shema Mugisha, C.; Cousins, E.M.; Cloer, E.W.; Vuong, H.R.; VanBlargan, L.A.; Bailey, A.L.; Gilchuk, P.; Crowe, J.E., Jr; Diamond, M.S.; Hayes, D.N.; Whelan, S.P.J.; Horani, A.; Brody, S.L.; Goldfarb, D.; Major, M.B.; Kutluay, S.B. Systematic analysis of SARS-CoV-2 infection of an ACE2-negative human airway cell. Cell Rep., 2021, 36(2), 109364.
[http://dx.doi.org/10.1016/j.celrep.2021.109364] [PMID: 34214467]
[11]
Hikmet, F.; Méar, L.; Edvinsson, Å.; Micke, P.; Uhlén, M.; Lindskog, C. The protein expression profile of ACE2 in human tissues. Mol. Syst. Biol., 2020, 16(7), e9610.
[http://dx.doi.org/10.15252/msb.20209610] [PMID: 32715618]
[12]
Osuchowski, M.F.; Winkler, M.S.; Skirecki, T.; Cajander, S.; Shankar-Hari, M.; Lachmann, G.; Monneret, G.; Venet, F.; Bauer, M.; Brunkhorst, F.M.; Weis, S.; Garcia-Salido, A.; Kox, M.; Cavaillon, J.M.; Uhle, F.; Weigand, M.A.; Flohé, S.B.; Wiersinga, W.J.; Almansa, R.; de la Fuente, A.; Martin-Loeches, I.; Meisel, C.; Spinetti, T.; Schefold, J.C.; Cilloniz, C.; Torres, A.; Giamarellos-Bourboulis, E.J.; Ferrer, R.; Girardis, M.; Cossarizza, A.; Netea, M.G.; van der Poll, T.; Bermejo-Martín, J.F.; Rubio, I. The COVID-19 puzzle: Deciphering pathophysiology and phenotypes of a new disease entity. Lancet Respir. Med., 2021, 9(6), 622-642.
[http://dx.doi.org/10.1016/S2213-2600(21)00218-6] [PMID: 33965003]
[13]
Shilts, J.; Crozier, T.W.M.; Greenwood, E.J.D.; Lehner, P.J.; Wright, G.J. No evidence for basigin/CD147 as a direct SARS-CoV-2 spike binding receptor. Sci. Rep., 2021, 11(1), 413.
[http://dx.doi.org/10.1038/s41598-020-80464-1] [PMID: 33432067]
[14]
Shilts, J.; Crozier, T.W.M.; Teixeira-Silva, A.; Gabaev, I.; Greenwood, E.J.D.; Watson, S.J. LRRC15 mediates an accessory interaction with the SARS-CoV-2 spike protein. bioRxiv, 2021.
[http://dx.doi.org/10.1101/2021.09.25.461776]
[15]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with Alpha Fold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[16]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[17]
Jiménez, J.; Doerr, S.; Martínez-Rosell, G.; Rose, A.S.; De Fabritiis, G. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 2017, 33(19), 3036-3042.
[http://dx.doi.org/10.1093/bioinformatics/btx350] [PMID: 28575181]
[18]
Volkamer, A.; Kuhn, D.; Rippmann, F.; Rarey, M. DoGSiteScorer: A web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics, 2012, 28(15), 2074-2075.
[http://dx.doi.org/10.1093/bioinformatics/bts310] [PMID: 22628523]
[19]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein–ligand docking and virtual drug screening with the autodock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[20]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951] [PMID: 26400175]
[21]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open babel: An open chemical toolbox. J. Cheminform., 2011, 3(1), 33.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]
[22]
Skalic, M.; Jiménez, J.; Sabbadin, D.; De Fabritiis, G. Shape-based generative modeling for de novo drug design. J. Chem. Inf. Model., 2019, 59(3), 1205-1214.
[http://dx.doi.org/10.1021/acs.jcim.8b00706] [PMID: 30762364]
[23]
Jiménez, J.; Škalič, M.; Martínez-Rosell, G.; De Fabritiis, G. KDEEP: Protein–ligand absolute binding affinity prediction via 3d-convolutional neural networks. J. Chem. Inf. Model., 2018, 58(2), 287-296.
[http://dx.doi.org/10.1021/acs.jcim.7b00650] [PMID: 29309725]
[24]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2009, 31(2), 455-461.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[25]
Viswanathan, U.; Tomlinson, S.M.; Fonner, J.M.; Mock, S.A.; Watowich, S.J. Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. J. Chem. Inf. Model., 2014, 54(10), 2816-2825.
[http://dx.doi.org/10.1021/ci500531r] [PMID: 25263519]
[26]
Yang, H.; Lou, C.; Sun, L.; Li, J.; Cai, Y.; Wang, Z.; Li, W.; Liu, G.; Tang, Y. admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 2019, 35(6), 1067-1069.
[http://dx.doi.org/10.1093/bioinformatics/bty707] [PMID: 30165565]
[27]
Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021, 49(W1), W5-W14.
[http://dx.doi.org/10.1093/nar/gkab255] [PMID: 33893803]
[28]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[29]
Villar, H.O.; Yan, J.; Hansen, M.R. Using NMR for ligand discovery and optimization. Curr. Opin. Chem. Biol., 2004, 8(4), 387-391.
[http://dx.doi.org/10.1016/j.cbpa.2004.05.002] [PMID: 15288248]
[30]
Pawar, S.S.; Rohane, S.H. Review on discovery studio: An important tool for molecular docking. Asian J. Res. Chem, 2021, 14(1), 1-3.
[http://dx.doi.org/10.5958/0974-4150.2021.00014.6]
[31]
Zinnia, M.A.; Khademul, I.A.B.M.M. Fenugreek steroidal saponins hinder osteoclastogenic bone resorption by targeting CSF-1R which diminishes the RANKL/OPG ratio. Int. J. Biol. Macromol., 2021, 186, 351-364.
[http://dx.doi.org/10.1016/j.ijbiomac.2021.06.197] [PMID: 34217743]
[32]
Peele, K.A.; Potla Durthi, C.; Srihansa, T.; Krupanidhi, S.; Ayyagari, V.S.; Babu, D.J.; Indira, M.; Reddy, A.R.; Venkateswarulu, T.C. Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study. Informatics in Medicine Unlocked, 2020, 19, 100345.
[http://dx.doi.org/10.1016/j.imu.2020.100345] [PMID: 32395606]
[33]
Choudhary, M.I.; Shaikh, M. tul-Wahab, A.; ur-Rahman, A. In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation. PLoS One, 2020, 15(7), e0235030.
[http://dx.doi.org/10.1371/journal.pone.0235030] [PMID: 32706783]
[34]
Berendsen, H.J.C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91(1-3), 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[35]
Siu, S.W.I.; Pluhackova, K.; Böckmann, R.A. Optimization of the opls-aa force field for long hydrocarbons. J. Chem. Theory Comput., 2012, 8(4), 1459-1470.
[http://dx.doi.org/10.1021/ct200908r] [PMID: 26596756]
[36]
Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF chimera a visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25(13), 1605-1612.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[37]
Dodda, L.S.; Cabeza de Vaca, I.; Tirado-Rives, J.; Jorgensen, W.L. LigParGen web server: An automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res., 2017, 45(W1), W331-W336.
[http://dx.doi.org/10.1093/nar/gkx312] [PMID: 28444340]
[38]
Berendsen, H.J.C.; Grigera, J.R.; Straatsma, T.P. The missing term in effective pair potentials. J. Phys. Chem., 1987, 91(24), 6269-6271.
[http://dx.doi.org/10.1021/j100308a038]
[39]
Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; DiNola, A.; Haak, J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys., 1984, 81(8), 3684-3690.
[http://dx.doi.org/10.1063/1.448118]
[40]
Hess, B.; Bekker, H.; Berendsen, H.J.C.; Fraaije, J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 1997, 18(12), 1463-1472.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463:AID-JCC4>3.0.CO;2-H]
[41]
Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 1981, 52(12), 7182-7190.
[http://dx.doi.org/10.1063/1.328693]
[42]
Bondi, A. van der waals volumes and radii. J. Phys. Chem., 1964, 68(3), 441-451.
[http://dx.doi.org/10.1021/j100785a001]
[43]
Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 2007, 9(3), 90-95.
[http://dx.doi.org/10.1109/MCSE.2007.55]
[44]
Harris, C.R.; Millman, K.J.; van der Walt, S.J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N.J.; Kern, R.; Picus, M.; Hoyer, S.; van Kerkwijk, M.H.; Brett, M.; Haldane, A.; del Río, J.F.; Wiebe, M.; Peterson, P.; Gérard-Marchant, P.; Sheppard, K.; Reddy, T.; Weckesser, W.; Abbasi, H.; Gohlke, C.; Oliphant, T.E. Array programming with NumPy. Nature, 2020, 585(7825), 357-362.
[http://dx.doi.org/10.1038/s41586-020-2649-2] [PMID: 32939066]
[45]
R Core Team. R Core Team (2020). — European environment agency. 2020. Available from: https://www.eea.europa.eu/data-and-maps/indicators/nutrients-infreshwater/r-core-team-2013
[46]
a) Osorio, D.; Rondón-Villarreal, P.; Torres, R. Peptides: A package for data mining of antimicrobial peptides. R J., 2015, 7(1), 4.
[http://dx.doi.org/10.32614/RJ-2015-001];
(b) Towler, P.; Staker, B.; Prasad, S. G.; Menon, S.; Tang, J.; Parsons, T.; Ryan, D.; Fisher, M.; Williams, D.; Dales, N. A.; Patane, M. A.; Pantoliano, M. W. ACE2 X-Ray Structures Reveal a Large Hinge-Bending Motion Important for Inhibitor Binding and Catalysis. J. Biol. Chem., 2004, 279(17), 17996-18007.
[http://dx.doi.org/10.1074/jbc.m311191200]
[47]
Wang, N.N.; Dong, J.; Deng, Y.H.; Zhu, M.F.; Wen, M.; Yao, Z.J.; Lu, A.P.; Wang, J.B.; Cao, D.S. ADME properties evaluation in drug discovery: Prediction of caco-2 cell permeability using a combination of NSGA-II and boosting. J. Chem. Inf. Model., 2016, 56(4), 763-773.
[http://dx.doi.org/10.1021/acs.jcim.5b00642] [PMID: 27018227]
[48]
Radchenko, E.V.; Dyabina, A.S.; Palyulin, V.A.; Zefirov, N.S. Prediction of human intestinal absorption of drug compounds. Russ. Chem. Bull., 2016, 65(2), 576-580.
[http://dx.doi.org/10.1007/s11172-016-1340-0]
[49]
Wang, N.N.; Huang, C.; Dong, J.; Yao, Z.J.; Zhu, M.F.; Deng, Z.K.; Lv, B.; Lu, A-P.; Chen, A.F.; Cao, D-S. Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Advances, 2017, 7(31), 19007-19018.
[http://dx.doi.org/10.1039/C6RA28442F]
[50]
Abdallah, H.M.; Al-Abd, A.M.; El-Dine, R.S.; El-Halawany, A.M. P-glycoprotein inhibitors of natural origin as potential tumor chemo-sensitizers: A review. J. Adv. Res., 2015, 6(1), 45-62.
[http://dx.doi.org/10.1016/j.jare.2014.11.008] [PMID: 25685543]
[51]
Zhu, J.; Wang, J.; Yu, H.; Li, Y.; Hou, T. Recent developments of in silico predictions of oral bioavailability. Comb. Chem., 2011, 14(5), 362-374.
[PMID: 21470180]
[52]
Kazmi, S.R.; Jun, R.; Yu, M.S.; Jung, C.; Na, D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med., 2019, 106, 54-64.
[http://dx.doi.org/10.1016/j.compbiomed.2019.01.008] [PMID: 30682640]
[53]
Clark, D.E. In silico prediction of blood–brain barrier permeation. Drug Discov. Today, 2003, 8(20), 927-933.
[http://dx.doi.org/10.1016/S1359-6446(03)02827-7] [PMID: 14554156]
[54]
Varma, M.; Khandavilli, S.; Ashokraj, Y.; Jain, A.; Dhanikula, A.; Sood, A.; Thomas, N.; Pillai, O.; Sharma, P.; Gandhi, R.; Agrawal, S.; Nair, V.; Panchagnula, R. Biopharmaceutic classification system: a scientific framework for pharmacokinetic optimization in drug research. Curr. Drug Metab., 2004, 5(5), 375-388.
[http://dx.doi.org/10.2174/1389200043335423] [PMID: 15544432]
[55]
Cook, J.; Addicks, W.; Wu, Y.H. Application of the biopharmaceutical classification system in clinical drug development--an industrial view. AAPS J., 2008, 10(2), 306-310.
[http://dx.doi.org/10.1208/s12248-008-9036-5] [PMID: 18500563]
[56]
Prentis, R.A.; Lis, Y.; Walker, S.R. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). Br. J. Clin. Pharmacol., 1988, 25(3), 387-396.
[http://dx.doi.org/10.1111/j.1365-2125.1988.tb03318.x] [PMID: 3358900]
[57]
Kennedy, T. Managing the drug discovery/development interface. Drug Discov., 1997, 2(10), 436-444.
[http://dx.doi.org/10.1016/S1359-6446(97)01099-4]
[58]
Duan, L.; Zheng, Q.; Zhang, H.; Niu, Y.; Lou, Y.; Wang, H. The SARS-CoV-2 spike glycoprotein biosynthesis, structure, function, and antigenicity: implications for the design of spike-based vaccine immunogens. Front. Immunol., 2020, 11, 576622.
[http://dx.doi.org/10.3389/fimmu.2020.576622] [PMID: 33117378]
[59]
Ibrahim, M.A.A.; Abdeljawaad, K.A.A.; Abdelrahman, A.H.M.; Hegazy, M-E.F. Natural-like products as potential SARS-CoV-2 Mpro inhibitors: In-silico drug discovery. J. Biomol. Struct. Dyn., 2021, 39(15), 5722-5734.
[PMID: 32643529]
[60]
Ji, H.; Zhang, W.; Zhang, M.; Kudo, M.; Aoyama, Y.; Yoshida, Y.; Sheng, C.; Song, Y.; Yang, S.; Zhou, Y.; Lü, J.; Zhu, J. Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14α-demethylase of fungi. J. Med. Chem., 2003, 46(4), 474-485.
[http://dx.doi.org/10.1021/jm020362c] [PMID: 12570370]
[61]
Vardhan, S.; Sahoo, S.K. In silico ADMET and molecular docking study on searching potential inhibitors from limonoids and triterpenoids for COVID-19. Comput. Biol. Med., 2020, 124, 103936.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103936] [PMID: 32738628]
[62]
van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2(3), 192-204.
[http://dx.doi.org/10.1038/nrd1032] [PMID: 12612645]
[63]
Norinder, U.; Bergström, C.A.S. Prediction of ADMET properties. ChemMedChem, 2006, 1(9), 920-937.
[http://dx.doi.org/10.1002/cmdc.200600155] [PMID: 16952133]
[64]
Ota, M. Will we see protection or reinfection in COVID-19? Nat. Rev. Immunol., 2020, 20(6), 351-1.
[http://dx.doi.org/10.1038/s41577-020-0316-3] [PMID: 32303697]
[65]
Kommoss, F.K.F.; Schwab, C.; Tavernar, L.; Schreck, J.; Wagner, W.L.; Merle, U.; Jonigk, D.; Schirmacher, P.; Longerich, T. The pathology of severe COVID-19 related lung damage-mechanistic and therapeutic implications. Dtsch. Arztebl. Int., 2020, 117(29-30), 500-506.
[PMID: 32865490]
[66]
Walters, W.P.; Murcko, A.A.; Murcko, M.A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol., 1999, 3(4), 384-387.
[http://dx.doi.org/10.1016/S1367-5931(99)80058-1] [PMID: 10419858]
[67]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[68]
Ma, B.; Kumar, S.; Tsai, C.J.; Nussinov, R. Folding funnels and binding mechanisms. Protein Eng. Des. Sel., 1999, 12(9), 713-720.
[http://dx.doi.org/10.1093/protein/12.9.713] [PMID: 10506280]
[69]
Kumar, S.; Ma, B.; Tsai, C.J.; Wolfson, H.; Nussinov, R. Folding funnels and conformational transitions via hinge-bending motions. Cell Biochem. Biophys., 1999, 31(2), 141-164.
[http://dx.doi.org/10.1007/BF02738169] [PMID: 10593256]
[70]
Tsai, C.J.; Kumar, S.; Ma, B.; Nussinov, R. Folding funnels, binding funnels, and protein function. Protein Sci., 1999, 8(6), 1181-1190.
[http://dx.doi.org/10.1110/ps.8.6.1181] [PMID: 10386868]
[71]
Chakraborty, C.; Sharma, A.R.; Bhattacharya, M.; Agoramoorthy, G.; Lee, S.S. The drug repurposing for covid-19 clinical trials provide very effective therapeutic combinations: Lessons learned from major clinical studies. Front. Pharmacol., 2021, 12, 704205.
[http://dx.doi.org/10.3389/fphar.2021.704205] [PMID: 34867318]
[72]
Na Takuathung, M.; Sakuludomkan, W.; Khatsri, R.; Dukaew, N.; Kraivisitkul, N.; Ahmadmusa, B.; Mahakkanukrauh, C.; Wangthaweesap, K.; Onin, J.; Srichai, S.; Buawangpong, N.; Koonrungsesomboon, N. Adverse effects of angiotensin-converting enzyme inhibitors in humans: A systematic review and meta-analysis of 378 randomized controlled trials. Int. J. Environ. Res. Public Health, 2022, 19(14), 8373.
[http://dx.doi.org/10.3390/ijerph19148373] [PMID: 35886227]
[73]
de Wit, E.; van Doremalen, N.; Falzarano, D.; Munster, V.J. SARS and MERS: Recent insights into emerging coronaviruses. Nat. Rev. Microbiol., 2016, 14(8), 523-534.
[http://dx.doi.org/10.1038/nrmicro.2016.81] [PMID: 27344959]

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