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Current Signal Transduction Therapy

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Research Article

In silico-Based Structural Prediction, Molecular Docking and ADMET Analysis of Novel Imidazo-Quinoline Derivatives as Pf Purine Nucleoside Phosphorylase Inhibitors

Author(s): Chaitali Mallick, Mitali Mishra, Vivek Asati, Varsha Kashaw, Ratnesh Das and Sushil Kumar Kashaw*

Volume 18, Issue 1, 2023

Published on: 11 April, 2023

Article ID: e301122211465 Pages: 30

DOI: 10.2174/1574362418666221130164014

Price: $65

Abstract

Introduction: The prolonged antimalarial therapy with the marketed drug has developed multi-resistant strains of Plasmodium parasites that emerge as a consequential global problem. Therefore, designing new antimalarial agents is an exclusive solution to overcome the alarming situation.

Methods: The integrated computational perspectives, such as pharmacophore mapping, 3D-QSAR and docking studies have been applied to improve the activity of the imidazo-quinoline scaffold. The best hypothesis AARRR_1 (Survival score 5.4609) obtained through pharmacophore mapping revealed that imidazo-quinoline scaffold is found to be vital for antimalarial activity. The significant CoMFA (q2 = 0.728, r2 = 0.909) and CoMSIA (q2 = 0.633, r2 = 0.729) models, developed by using molecular field analysis with the PLS method, showed good predictive ability with r2 pred values of 0.9127 and 0.7726, respectively. Docking studies were performed using Schrodinger and GOLD software with the Plasmodium falciparum purine nucleoside phosphorylase enzyme (PDB ID-5ZNC) and results indicated that the imidazo-quinoline moiety facilitates the interaction with Tyr 160.

Results: In addition, some compounds are screened from the ZINC database based on structural requirements to verify the relevance of the research. Finally, designed molecules and ZINC database compounds were screened through the ADMET tool to evaluate pharmacokinetic and druglikeness parameters.

Conclusion: Thus, these exhaustive studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may lead to potent antimalarial activity for getting rid of resistance issues.

Keywords: World Health Organization, 3D-QSAR, molecular docking, ADMET, Schrodinger and GOLD software, antimalarial activity.

Graphical Abstract

[1]
Quaye IK, Aleksenko L, Oeuvray C, et al. The Pan African Vivax and Ovale Network (PAVON): Refocusing on Plasmodium vivax, ovale and asymptomatic malaria in sub-Saharan Africa. Parasitol Int 2021; 84102415
[http://dx.doi.org/10.1016/j.parint.2021.102415] [PMID: 34216801]
[2]
Oddoux O, Debourgogne A, Kantele A, et al. Identification of the five human Plasmodium species including P. knowlesi by real-time polymerase chain reaction. Eur J Clin Microbiol Infect Dis 2011; 30(4): 597-601.
[http://dx.doi.org/10.1007/s10096-010-1126-5] [PMID: 21161559]
[3]
Chiang AN, Valderramos JC, Balachandran R, et al. Select pyrimidinones inhibit the propagation of the malarial parasite, Plasmodium falciparum. Bioorg Med Chem 2009; 17(4): 1527-33.
[http://dx.doi.org/10.1016/j.bmc.2009.01.024] [PMID: 19195901]
[4]
Anstey NM, Russell B, Yeo TW, Price RN. The pathophysiology of vivax malaria. Trends Parasitol 2009; 25(5): 220-7.
[http://dx.doi.org/10.1016/j.pt.2009.02.003] [PMID: 19349210]
[5]
Lomar AV, Vidal JE, Lomar FP, Barbas CV, de Matos GJ, Boulos M. Acute respiratory distress syndrome due to vivax malaria: Case report and literature review. Braz J Infect Dis 2005; 9(5): 425-30.
[http://dx.doi.org/10.1590/S1413-86702005000500011] [PMID: 16410895]
[6]
Al-Awadhi M, Ahmad S, Iqbal J. Current status and the epidemiology of malaria in the middle east region and beyond. Microorganisms 2021; 9(2): 338.
[http://dx.doi.org/10.3390/microorganisms9020338] [PMID: 33572053]
[7]
Cooper RA, Hartwig CL, Ferdig MT. PFCRT is more than the Plasmodium falciparum chloroquine resistance gene: A functional and evolutionary perspective. Acta Tropica 2005; 94(3): 170-80.
[http://dx.doi.org/10.1016/j.actatropica.2005.04.004]
[8]
Wilson CO, Gisvold O. Wilson and Gisvold’s Textbook of Organic Medicinal and Pharmaceutical Chemistry. Philadelphia: Lippincott Williams & Wilkins 2004.
[9]
Guinovart C, Navia MM, Tanner M, Alonso PL. Malaria: Burden of disease. Curr Mol Med 2006; 6(2): 137-40.
[http://dx.doi.org/10.2174/156652406776055131] [PMID: 16515506]
[10]
Prajapati SP, Kaushik NK, Zaveri M, Mohanakrishanan D, Kawathekar N, Sahal D. Synthesis, characterization and antimalarial evaluation of new β-benzoylstyrene derivatives of acridine. Arab J Chem 2017; 10: 274-80.
[http://dx.doi.org/10.1016/j.arabjc.2012.07.033]
[11]
Hussaini SM. Therapeutic significance of quinolines: A patent review (2013-2015). Expert Opin Ther Pat 2016; 26(10): 1201-21.
[http://dx.doi.org/10.1080/13543776.2016.1216545] [PMID: 27458877]
[12]
Saroa R, Kaushik D, Bagai U, Kaur S, Salunke DB. Efficacy of TLR7 agonistic imidazoquinoline as immunochemotherapeutic agent against P. Berghei ANKA infected rodent host. Bioorg Med Chem Lett 2019; 29(9): 1099-105.
[http://dx.doi.org/10.1016/j.bmcl.2019.02.029] [PMID: 30850167]
[13]
Prathap KC, Kayarmar R, Naveen S, Bhat M, Nagaraja GK, Lokanath NK. Synthesis, characterization, crystal structure and Hirshfeld surface analysis of (1E)-1-Phenylethanone (1-Isobutyl-1H-Imidazo [4, 5-C] Quinolin-4-Yl) hydrazone. J Applicable Chem 2017; 6(3): 400-9.
[14]
Blasco B, Leroy D, Fidock DA. Antimalarial drug resistance: Linking Plasmodium falciparum parasite biology to the clinic. Nat Med 2017; 23(8): 917-28.
[http://dx.doi.org/10.1038/nm.4381] [PMID: 28777791]
[15]
Shi W, Ting LM, Kicska GA, et al. Plasmodium falciparum purine nucleoside phosphorylase: Crystal structures, immucillin inhibitors, and dual catalytic function. J Biol Chem 2004; 279(18): 18103-6.
[http://dx.doi.org/10.1074/jbc.C400068200] [PMID: 14982926]
[16]
Ducati RG, Namanja-Magliano HA, Harijan RK, et al. Genetic resistance to purine nucleoside phosphorylase inhibition in Plasmodium falciparum. Proc Natl Acad Sci USA 2018; 115(9): 2114-9.
[http://dx.doi.org/10.1073/pnas.1525670115] [PMID: 29440412]
[17]
Minnow YVT, Harijan RK, Schramm VL. A resistant mutant of Plasmodium falciparum purine nucleoside phosphorylase uses wild-type neighbors to maintain parasite survival. J Biol Chem 2021; 296100342
[http://dx.doi.org/10.1016/j.jbc.2021.100342] [PMID: 33524395]
[18]
Cui H, Ruda GF, Carrero-Lérida J, Ruiz-Pérez LM, Gilbert IH, González-Pacanowska D. Exploring new inhibitors of Plasmodium falciparum purine nucleoside phosphorylase. Eur J Med Chem 2010; 45(11): 5140-9.
[http://dx.doi.org/10.1016/j.ejmech.2010.08.026] [PMID: 20817362]
[19]
Kagami LP. das Neves GM, Rodrigues RP, da Silva VB, Eifler-Lima VL, Kawano DF. Identification of a novel putative inhibitor of the Plasmodium falciparum purine nucleoside phosphorylase: Exploring the purine salvage pathway to design new antimalarial drugs. Mol Divers 2017; 21(3): 677-95.
[http://dx.doi.org/10.1007/s11030-017-9745-8] [PMID: 28523625]
[20]
Madrid DC, Ting LM, Waller KL, Schramm VL, Kim K. Plasmodium falciparum purine nucleoside phosphorylase is critical for viability of malaria parasites. J Biol Chem 2008; 283(51): 35899-907.
[http://dx.doi.org/10.1074/jbc.M807218200] [PMID: 18957439]
[21]
Downie MJ, Kirk K, Mamoun CB. Purine salvage pathways in the intraerythrocytic malaria parasite Plasmodium falciparum. Eukaryot Cell 2008; 7(8): 1231-7.
[http://dx.doi.org/10.1128/EC.00159-08] [PMID: 18567789]
[22]
Patel PR, Sun W, Kim M, et al. In vitro evaluation of imidazo[4,5-c]quinolin-2-ones as gametocytocidal antimalarial agents. Bioorg Med Chem Lett 2016; 26(12): 2907-11.
[http://dx.doi.org/10.1016/j.bmcl.2016.04.045] [PMID: 27156776]
[23]
Sarvagalla S, Syed SB, Coumar MS. An overview of computational methods, tools, servers, and databases for drug. In: Ray K, Ed. In Silico Drug Design: Repurposing Techniques and Methodologies. Cambridge, Massachusetts: Academic Press 2019; pp. 743-80.
[http://dx.doi.org/10.1016/B978-0-12-816125-8.00025-0]
[24]
Hadni H, Mazigh M, Charif E, Bouayad A, Elhallaoui M. Molecular modeling of antimalarial agents by 3D-QSAR study and molecular docking of two hybrids 4-Aminoquinoline-1, 3, 5-triazine and 4-Aminoquinoline-oxalamide derivatives with the receptor protein in its both wild and mutant types. Biochem Res Int 2018; 20188639173
[http://dx.doi.org/10.1155/2018/8639173] [PMID: 30034881]
[25]
de Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem 2016; 9(1): 1-11.
[http://dx.doi.org/10.2147/AABC.S105289] [PMID: 27390530]
[26]
Rajeswari M, Santhi N, Bhuvaneswari V. Pharmacophore and virtual screening of JAK3 inhibitors. Bioinformation 2014; 10(3): 157-63.
[http://dx.doi.org/10.6026/97320630010157] [PMID: 24748756]
[27]
Vuorinen A, Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods 2015; 71: 113-34.
[http://dx.doi.org/10.1016/j.ymeth.2014.10.013]
[28]
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner RA. PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 2006; 20(10-11): 647-71.
[http://dx.doi.org/10.1007/s10822-006-9087-6] [PMID: 17124629]
[29]
James N, Ramanathan K. Discovery of potent ALK inhibitors using pharmacophore-informatics strategy. Cell Biochem Biophys 2018; 76(1-2): 111-24.
[http://dx.doi.org/10.1007/s12013-017-0800-y] [PMID: 28477056]
[30]
Sakkiah S, Senese S, Yang Q, Lee KW, Torres JZ. Dynamic and multi-pharmacophore modeling for designing polo-box domain inhibitors. PLoS One 2014; 9(7)e101405
[http://dx.doi.org/10.1371/journal.pone.0101405] [PMID: 25036740]
[31]
Giaginis C, Tsantili-Kakoulidou A, Theocharis S. Quantitative Structure-Activity Relationship (QSAR) methodology in forensic toxicology: Modeling postmortem redistribution of structurally diverse drugs using multivariate statistics. Forensic Sci Int 2009; 190(1-3): 9-15.
[http://dx.doi.org/10.1016/j.forsciint.2009.05.003] [PMID: 19482448]
[32]
Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design-a review. Curr Top Med Chem 2010; 10(1): 95-115.
[http://dx.doi.org/10.2174/156802610790232260] [PMID: 19929826]
[33]
Dixit A, Kashaw SK, Gaur S, Saxena AK. Development of CoMFA, advance CoMFA and CoMSIA models in pyrroloquinazolines as thrombin receptor antagonist. Bioorg Med Chem 2004; 12(13): 3591-8.
[http://dx.doi.org/10.1016/j.bmc.2004.04.016] [PMID: 15186843]
[34]
Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron 1980; 36(22): 3219-28.
[http://dx.doi.org/10.1016/0040-4020(80)80168-2]
[35]
Cramer RD III, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988; 110(18): 5959-67.
[http://dx.doi.org/10.1021/ja00226a005] [PMID: 22148765]
[36]
Viswanadhan VN, Ghose AK, Revenkar GR, Robins R. Atomic physicochemical parameters for three dimensional structure directed quantitative structure–activity relationships, Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Model 1989; 29(3): 163-72.
[http://dx.doi.org/10.1021/ci00063a006]
[37]
Xiao A, Zhang Z, An L, Xiang Y. 3D-QSAR and docking studies of 3-arylquinazolinethione derivatives as selective estrogen receptor modulators. J Mol Model 2008; 14(2): 149-59.
[http://dx.doi.org/10.1007/s00894-007-0264-x] [PMID: 18172701]
[38]
Dube PN, Mokale S, Datar P. CoMFA and docking study of 2, N6-disubstituted 1, 2-dihydro-1, 3, 5-triazine-4, 6-diamines as novel PfDHFR enzyme inhibitors for antimalarial activity. Bull Fac Pharm Cairo Univ 2014; 52(1): 125-34.
[http://dx.doi.org/10.1016/j.bfopcu.2014.02.003]
[39]
Shinde MG, Modi SJ, Kulkarni VM. QSAR and molecular docking of phthalazine derivatives as epidermal growth factor receptor (EGFR) inhibitors. J Appl Pharm Sci 2017; 7: 181-91.
[http://dx.doi.org/10.7324/JAPS.2017.70427]
[40]
Tropsha A, Gramatica P, Gombar V. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 2003; 22(1): 69-77.
[http://dx.doi.org/10.1002/qsar.200390007]
[41]
Dunn WJ, Wold S, Edlund V, Helberg S. Multivariate structure-activity relationship between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method Quant Struct Act Relatsh 1984; 131-7.
[http://dx.doi.org/10.1002/qsar.19840030402]
[42]
Wold S, Ruhe A, Wold H, Dunn WJ III. The collinearity problem in linear regression. The Partial Least Squares (PLS) approach to generalized inverses. SIAM J Sci Statist Comput 1984; 5(3): 735-43.
[http://dx.doi.org/10.1137/0905052]
[43]
Clark M, Cramer RD III, Van Opdenbosch N. Validation of the general purpose Tripos 5.2 force field. J Comput Chem 1989; 10(8): 982-1012.
[http://dx.doi.org/10.1002/jcc.540100804]
[44]
Ghosh S, Nie A, An J, Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Curr Opin Chem Biol 2006; 10(3): 194-202.
[http://dx.doi.org/10.1016/j.cbpa.2006.04.002] [PMID: 16675286]
[45]
Mugumbate G, Newton AS, Rosenthal PJ, et al. Novel anti-plasmodial hits identified by virtual screening of the ZINC database. J Comput Aided Mol Des 2013; 27(10): 859-71.
[http://dx.doi.org/10.1007/s10822-013-9685-z] [PMID: 24158745]
[46]
Koes DR, Camacho CJ. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res 2012; 40W409-14
[http://dx.doi.org/10.1093/nar/gks378] [PMID: 22553363]
[47]
Laurie AT, Jackson RM. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 2005; 21(9): 1908-16.
[http://dx.doi.org/10.1093/bioinformatics/bti315] [PMID: 15701681]
[48]
Dziekan JM, Yu H, Chen D, et al. Identifying purine nucleoside phosphorylase as the target of quinine using cellular thermal shift assay. Sci Transl Med 2019; 11(473)eaau3174
[http://dx.doi.org/10.1126/scitranslmed.aau3174] [PMID: 30602534]
[49]
Li Y, Han L, Liu Z, Wang R. Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results. J Chem Inf Model 2014; 54(6): 1717-36.
[http://dx.doi.org/10.1021/ci500081m] [PMID: 24708446]
[50]
Mishra M, Agarwal S, Dixit A, et al. Integrated computational investigation to develop molecular design of quinazoline scaffold as promising inhibitors of Plasmodium lactate dehydrogenase. J Mol Struct 2020; 1207127808
[http://dx.doi.org/10.1016/j.molstruc.2020.127808]
[51]
Meraj K, Mahto MK, Christina NB, Desai N, Shahbazi S, Bhaskar M. Molecular modeling, docking and ADMET studies towards development of novel Disopyramide analogs for potential inhibition of human voltage gated sodium channel proteins. Bioinformation 2012; 8(23): 1139-46.
[http://dx.doi.org/10.6026/97320630081139] [PMID: 23275710]
[52]
Bhachoo J, Beuming T. Beuming, Investigating protein–peptide interactions using the Schrödinger computational suite Modeling peptide-protein interactions 2017; 235-54.
[http://dx.doi.org/10.1007/978-1-4939-6798-8_14]
[53]
Onodera K, Satou K, Hirota H. Evaluations of molecular docking programs for virtual screening. J Chem Inf Model 2007; 47(4): 1609-18.
[http://dx.doi.org/10.1021/ci7000378] [PMID: 17602548]
[54]
Wang Z, Sun H, Yao X, et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: The prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 2016; 18(18): 12964-75.
[http://dx.doi.org/10.1039/C6CP01555G] [PMID: 27108770]
[55]
Guan L, Yang H, Cai Y, et al. ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm 2018; 10(1): 148-57.
[http://dx.doi.org/10.1039/C8MD00472B] [PMID: 30774861]
[56]
Jia CY, Li JY, Hao GF, Yang GF. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov Today 2020; 25(1): 248-58.
[http://dx.doi.org/10.1016/j.drudis.2019.10.014] [PMID: 31705979]
[57]
Giménez BG, Santos MS, Ferrarini M, Fernandes JP. Evaluation of blockbuster drugs under the rule-of-five. Pharmazie 2010; 65(2): 148-52.
[http://dx.doi.org/10.1691/ph.2010.9733] [PMID: 20225662]
[58]
Cheng F, Li W, Zhou Y, et al. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012; 52(11): 3099-105.
[http://dx.doi.org/10.1021/ci300367a]
[59]
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2012; 64: 4-17.
[http://dx.doi.org/10.1016/j.addr.2012.09.019] [PMID: 11259830]
[60]
Ghose AK, Viswanadhan VN, Wendoloski JJ. 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 1999; 1(1): 55-68.
[http://dx.doi.org/10.1021/cc9800071] [PMID: 10746014]
[61]
Martin YC. A bioavailability score. J Med Chem 2005; 48(9): 3164-70.
[http://dx.doi.org/10.1021/jm0492002] [PMID: 15857122]

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