Pyrazine-chromene-3-carbohydrazide conjugates: Molecular docking and ADMET predictions on dualacting compounds against SARS-CoV-2 Mpro and RdRp

Drug discovery campaigns against COVID-19 lag far behind vaccine development, but given the low vaccine production rate and unfair distribution, there is still an urgent need to advance reliable and potent anti-SARSCoV-2 agents. We aimed to identify novel and effective molecules with dual-target activity against SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA-polymerase (RdRp). For this, we designed and evaluated the library of hybrid compounds based on pyrazine and 4H-chromen-4-one linked by amide bridges. The synthetic availability of the compounds ranged from 3.08 to 3.40, indicating that these compounds are easy to synthesize. According to in silico ADMET prediction, most of the compounds satisfied all rules of drug-likeness. Compounds CPC-2, 3, 4, 8, 10, 11-14, and 16 were CYP1A2, CYP2C9, and CYP3A4 inhibitors, whereas none of them inhibited CYP2C19 and CYP2D6 isoforms. All designed compounds were predicted to be well-absorbed in the GI tract but not blood-brain barrier permeant and not subject to active efflux. Molecular docking studies against SARS-CoV-2 Mpro showed that compounds CPC-1, 6, 7, 8, and 10 could establish multiple H-bonds with the binding site residues. In the case of SARS-CoV-2 RdRp, compounds CPC-5, 6, 8, 13, 14, and 16 had the most favorable binding orientations and could establish H-bonds, pi-cation, and salt-bridges with the binding tunnel residues and RNA. Compound CPC-6 turned to be the most promising candidate from the dual-action side since it had reasonable docking scores and MM-GBSA ∆Gbind values, and good interaction profiles for both Mpro and RdRp.

PDF

___

[1] Delelis O, Carayon K, Saib A, Deprez E, Mouscadet JF. Integrase and integration: biochemical activities of HIV-1 integrase. Retrovirology. 2008; 5(5): 114-127.
[2] Neamati N, Lin Z, Karki RG, Orr A, Cowansage K, Strumberg D. Metal-dependent inhibition of HIV-1 integrase. J Med Chem. 2002; 45(26): 5661-5670.
[3] Kawasuji T, Yoshinaga T, Sato A, Yodo M, Fujiwara T, Kiyama R. A platform for designing HIV integrase inhibitors. Part 1: 2-hydroxy-3-heteroaryl acrylic acid derivatives as novel HIV integrase inhibitor and modeling of hydrophilic and hydrophobic pharmacophores. Bioorg Med Chem. 2006; 14(24): 8430-8445.
[4] Johns BA, Svolto AC. Advances in two-metal chelation inhibitors of HIV integrase. Expert Opin Ther Pat. 2008; 18(11): 1225-1237.
[5] Rowley M. The discovery of raltegravir, an integrase inhibitor for the treatment of HIV infection. Prog Med Chem. 2008; 46(2008): 1-28.
[6] Sato M, Kawakami H, Motomura T, Aramaki H, Matsuda T, Yamashita M. Quinolone carboxylic acids as a novel monoketo acid class of human immunodeficiency virus type 1 integrase inhibitors. J Med Chem. 2009; 52(15): 4869- 4882.
[7] Elfiky AA. Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): A molecular docking study. Life Sci. 2020; 15(2020): 117592.
[8] Gurung AB, Ali MA, Lee J, Farah MA, Al-Anazi KM. The potential of Paritaprevir and Emetine as inhibitors of SARSCoV-2 RdRp. Saudi J Biol Sci. 2021; 28(2): 1426-1432.
[9] Katlama C, Murphy R. Dolutegravir for the treatment of HIV. Expert Opin Investig Drugs. 2008; 21(4): 523-530.
[10] Di Santo R. Inhibiting the HIV integration process: past, present, and the future. J Med Chem. 2014; 57(3): 539-566.
[11] Liao C, Marchand C, Burke TR, Pommier Y, Nicklaus MC. Authentic HIV-1 integrase inhibitors. Future Med Chem. 2010; 2(7): 1107-1122.
[12] Sirous H, Chemi G, Gemma S, Butini S, Debyser Z, Christ F, Saghaie L, Brogi S, Fassihi A, Campiani G, Brindisi M. Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach. Front Chem. 2019; 7(2019): 1-20.
[13] Hay M, Tomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nature Biotechnol. 2014; 32(2014): 40-51.
[14] Dahlin JL, Inglese J, Walters MA. Mitigating risk in academic preclinical drug discovery. Nature Rev Drug Discov. 2015; 14(2015): 279-294.
[15] Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015; 58(9): 4066-4072.
[16] Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012; 52(11): 3099-3105.
[17] Daina A, Michielin O, Zoete V. iLOGP: A Simple, robust, and efficient description of n-octanol/water partition coefcient for drug design using the GB/SA approach. J Chem Inf Model. 2014; 54(12): 3284-3301.
[18] Di L, Artursson P, Avdeef A, Ecker GF, Faller B, Fischer H, Houston JB, Kansy M, Kerns EH, Krämer SD, Lennernäs H, Sugano K. Evidence-based approach to assess passive diffusion and carrier-mediated drug transport. Drug Discov. 2012; 17(15): 905-912.
[19] Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A web tool for low to ultra high troughput ligand-based virtual screening. J Chem Inf Model. 2016; 56(8): 1399-1404.
[20] Gfeller D. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014; 42(W1): W32–W38.
[21] Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011; 39(2): W270–W277.
[22] Wirth M, Zoete V, Michielin O, Sauer WHB. SwissBioisostere: a database of molecular replacements for ligand design. Nucleic Acids Res. 2013; 41(D1): D1137-D1143.
[23] Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: a fast force feld generation tool for small organic molecules. J Comput Chem. 2011; 32(11): 2359-2368.
[24] 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(2017): 42717-42730.
[25] Liu LJ, Leung KH, Chan DSH, Wang YT, Ma DL, Leung CH. Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening. Cell Death Dis. 2014; 5(6): e1293.
[26] Das S, Sarmah S, Lyndem S, Singha RA. An investigation into the identification of potential inhibitors of SARS-CoV2 main protease using molecular docking study. J Biomol Struct Dyn. 2021; 39(16): 3347-3357.
[27] Kumar Y, Singh H, Natel CN. 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 Health. 2020; 13(9): 1210-1223.
[28] Yu R, Chen L, Lan R, Shen R, Li P. Computational screening of antagonists against the SARS-CoV-2 (COVID-19) coronavirus by molecular docking. Int J Antimicrob Agents. 2020; 56(2): 106012.
[29] Abdolmaleki A, Ghasemi JB, Ghasemi F. Computer aided drug design for multi-target drug design: SAR/QSAR, molecular docking and pharmacophore methods. Curr Drug Targets. 2017; 18(5): 556-575.
[30] Mitra K, Ghanta P, Acharya S, Chakrapani G, Ramajah B, Doble M. Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads. J Biomol Struct Dyn. 2020; 39(16); 6324-6337.
[31] Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007; 25(2007): 197-206.
[32] Pires DE, Blundell TL, Ascher DB. PkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graphbased signatures. J Med Chem. 2015; 58(9): 4066-4072.
[33] Daina A, Zoete V. A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem. 2016; 11(11): 1117–1121.
[34] Selvam P, Chandramohan M, De Clercq E, Pannecouque C, Witrouw M. Synthesis and anti-HIV activity of 4-[(1,2- dihydro-2-oxo-3H-indol-3-ylidene)amino]-N-(4,6-dimethyl-2-pyrimidinyl)-benzene sulphonamide and its derivatives. Eur J Pharm Sci. 2001; 14(4): 313-316.
[35] Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des. 2013; 27(2013): 221-234.
[36] Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004; 47(7): 1739-1749.
[37] Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein− ligand complexes. J Med Chem. 2006; 49(21): 6177-6196.
[38] Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem. 2004; 47(7): 1750-1759.
[39] Debnath B, Ganguly S. Synthesis, biological evaluation, in silico docking, and virtual ADME studies of 2-[2-Oxo-3- (arylimino) indolin-1-yl]-N-arylacetamides as potent anti-breast cancer agents. Monatsh Chem. 2016; 147(2016): 565- 574.