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Research Article

2D-QSAR, 3D-QSAR, molecular docking and ADMET prediction studies of some novel 2-((1H-indol-3-yl)thio)-N-phenyl-acetamide derivatives as anti-influenza A virus

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Pages 510-532 | Received 13 Apr 2022, Accepted 29 Jul 2022, Published online: 11 Aug 2022

References

  • Avila G, Cruz-Licea V, Rojas-Espinosa K, et al. Influenza A H1N1 virus 2009 synthetic hemagglutinin and neuraminidase peptides for antibody detection. Arch Med Res. 2020;51(5):436–443.
  • Korsten K, Adriaenssens N, Coenen S, et al. World Health Organization influenza-like illness underestimates the burden of respiratory syncytial virus infection in community-dwelling older adults. J Infect Dis. 2021.
  • Zhang G-N, Li Q, Zhao J, et al. Design and synthesis of 2-((1H-indol-3-yl) thio)-N-phenyl-acetamides as novel dual inhibitors of respiratory syncytial virus and influenza virus A. Eur J Med Chem. 2020;186:111861.
  • Aleebrahim-Dehkordi E, Molavi B, Mokhtari M, et al. T helper type (Th1/Th2) responses to SARS-CoV-2 and influenza A (H1N1) virus: from cytokines produced to immune responses. Transpl Immunol. 2022;70:101495.
  • Abed Y, Bouhy X, L’Huillier AG, et al. The E119D neuraminidase mutation identified in a multidrug-resistant influenza A(H1N1)pdm09 isolate severely alters viral fitness in vitro and in animal models. Antiviral Res. 2016;132:6–12.
  • Adams SE, Lee N, Lugovtsev VY, et al. Effect of influenza H1N1 neuraminidase V116A and I117V mutations on NA activity and sensitivity to NA inhibitors. Antiviral Res. 2019;169:104539.
  • Hayden FG, Asher J, Cowling BJ, et al. Reducing influenza virus transmission: the potential value of antiviral treatment. Clin Infect Dis. 2022;74(3):532–540.
  • Abdullahi M, Das N, Adeniji SE, et al. In-silico design and ADMET predictions of some new imidazo [1, 2-a] pyridine-3-carboxamides (IPAs) as anti-tubercular agents. J Clin Tuberculosis Other Mycobacterial Dis. 2021;25:100276.
  • Al-Attraqchi OHA, Venugopala KN. 2D- and 3D-QSAR modeling of imidazole-based glutaminyl cyclase inhibitors. Curr Comput Aided Drug Des. 2020;16(6):682–697.
  • Ibrahim MT, Uzairu A, Shallangwa GA, et al. Structure-based design and activity modeling of novel epidermal growth factor receptor kinase inhibitors; an in silico approach. Sci Afr. 2020;9:e00503.
  • Abdullahi M, Shallangwa GA, Uzairu A. In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype. Beni-Suef Univ J Basic Appl Sci. 2020;9(1):1–12.
  • Abdullahi M, Adeniji SE, Arthur DE, et al. Quantitative structure-activity relationship (QSAR) modelling study of some novel carboxamide series as new anti-tubercular agents. Bull National Res Centre. 2020;44(1):1–13.
  • Yap CW. PaDEL‐descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466–1474.
  • Ahamad S, Islam A, Ahmad F, et al. 2/3D-QSAR, molecular docking and MD simulation studies of FtsZ protein targeting benzimidazoles derivatives. Comput Biol Chem. 2019;78:398–413.
  • Roy K, Kar S, Das RN. Statistical methods in QSAR/QSPR. A primer on QSAR/QSPR modeling. Springer; 2015. p. 37–59.
  • Apablaza G, Montoya L, Morales-Verdejo C, et al. 2D-QSAR and 3D-QSAR/CoMSIA studies on a series of (R)-2-((2-(1H-Indol-2-yl)ethyl)amino)-1-phenylethan-1-ol with human beta(3)-adrenergic activity. Molecules. 2017;22(3).
  • Roy K, Das RN, Ambure P, et al. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr Intell Lab Syst. 2016;152:18–33.
  • Bouakkadia A, Kertiou N, Amiri R, et al. Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides. J Serb Chem Soc. 2021;86(7–8):673–684.
  • Umar BA, Uzairu A, Shallangwa GA, et al. QSAR modeling for the prediction of pGI50 activity of compounds on LOX IMVI cell line and ligand-based design of potent compounds using in silico virtual screening. Network Model Anal Health Inf Bioinf. 2019;8(1):1–10.
  • El Aissouq A, Toufik H, Lamchouri F, et al., editors. QSAR study of isonicotinamide derivatives as Alzheimer's disease inhibitors using PLS-R and ANN methods. 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS); 2019: IEEE.
  • Gramatica P. Principles of QSAR modeling: comments and suggestions from personal experience. Int J of Quantitative Struct-Pro Relat (IJQSPR). 2020;5(3):61–97.
  • Vucicevic J, Nikolic K, Mitchell JBO. Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening Approaches. Curr Med Chem. 2019;26(21):3874–3889.
  • Vishwakarma K, Bhatt H. Molecular modelling of quinoline derivatives as telomerase inhibitors through 3D-QSAR, molecular dynamics simulation, and molecular docking techniques. J Mol Model. 2021;27(2):30.
  • Goudzal A, El Aissouq A, El Hamdani H, et al. 3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors. J Biomol Struct Dyn. 2022;1–15.
  • Aouidate A, Ghaleb A, Ghamali M, et al. Computer aided drug design based on 3D-QSAR and molecular docking studies of 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amine derivatives as PIM2 inhibitors: a proposal to chemists. Silico Pharmacol. 2018;6(1):5.
  • Vavricka CJ, Li Q, Wu Y, et al. Structural and functional analysis of laninamivir and its octanoate prodrug reveals group specific mechanisms for influenza NA inhibition. PLoS Pathog. 2011;7(10):e1002249.
  • Ahmed MF, Santali EY, El-Haggar R. Novel piperazine–chalcone hybrids and related pyrazoline analogues targeting VEGFR-2 kinase; design, synthesis, molecular docking studies, and anticancer evaluation. J Enzyme Inhib Med Chem. 2021;36(1):307–318.
  • Shakour N, Hadizadeh F, Kesharwani P, et al. 3D-QSAR studies of 1,2,4-oxadiazole derivatives as sortase A inhibitors. Biomed Res Int. 2021;2021:6380336.
  • Aziz M, Ejaz SA, Tamam N, et al. Identification of potent inhibitors of NEK7 protein using a comprehensive computational approach. Sci Rep. 2022;12(1):1–17.
  • Kar S, Roy K, Leszczynski J. In silico tools and software to predict ADMET of new drug candidates. In: Silico methods for predicting drug. Toxicity: Springer; 2022. p. 85–115.
  • Babalola S, Igie N, Odeyemi I. Structure-based discovery of multitarget directed anti-inflammatory p-nitrophenyl hydrazones; molecular docking, drug-likeness, in-silico pharmacokinetics, and toxicity studies. 2022.
  • Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6‐7):476–488.
  • Thompson CG, Kim RS, Aloe AM, et al. Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Appl Social Psychol. 2017;39(2):81–90.
  • Satpathy R, Guru RK, Behera R. Computational QSAR analysis of some physiochemical and topological descriptors of Curcumin derivatives by using different statistical methods. J Chem Pharm Res. 2010;2(6):344–350.
  • Wang T, Tang L, Luan F, et al. Prediction of the toxicity of binary mixtures by QSAR approach using the hypothetical descriptors. Int J Mol Sci. 2018;19(11):3423.
  • Begum S, Jaswanthi P, Lakshmi BV, et al. QSAR studies on indole-azole Analogues using DTC tools; imidazole ring is more favorable for aromatase inhibition. J Indian Chem Soc. 2021;98(1):100016.
  • Consonni V, Todeschini R. Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices. References: John Wiley & Sons; 2009.
  • Shirvani P, Fassihi A. In silico design of novel FAK inhibitors using integrated molecular docking, 3D-QSAR and molecular dynamics simulation studies. J Biomol Struct Dyn. 2021;1–19.
  • Gu X, Wang Y, Wang M, et al. Computational investigation of imidazopyridine analogs as protein kinase B (Akt1) allosteric inhibitors by using 3D-QSAR, molecular docking and molecular dynamics simulations. J Biomol Struct Dyn. 2021;39(1):63–78.
  • Chauhan K, Singh P, Kumar V, et al. Investigation of Ugi-4CC derived 1H-tetrazol-5-yl-(aryl) methyl piperazinyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid: synthesis, biology and 3D-QSAR analysis. Eur J Med Chem. 2014;78:442–454.
  • Ahmed A, Saeed A, Ejaz SA, et al. Novel adamantyl clubbed iminothiazolidinones as promising elastase inhibitors: design, synthesis, molecular docking, ADMET and DFT studies. RSC Adv. 2022;12(19):11974–11991.
  • Arámburo-Gálvez JG, Arvizu-Flores AA, Cárdenas-Torres FI, et al. Prediction of ACE-I inhibitory peptides derived from chickpea (Cicer arietinum L.): in silico assessments using simulated enzymatic hydrolysis, molecular docking and ADMET evaluation. Foods. 2022;11(11):1576.
  • Adianingsih OR, Khasanah U, Anandhy KD, et al. In silico ADME-T and molecular docking study of phytoconstituents from Tithonia diversifolia (Hemsl.) A. Gray on various targets of diabetic nephropathy. J Pharm & Pharmacogn Res. 2022;10(4):571–594.
  • Dowdy SF, Setten RL, Cui X-S, et al. Delivery of RNA therapeutics: the great endosomal escape! Nucleic Acid Ther. 2022.
  • Xiong G, Wu Z, Yi J, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49(W1):W5–W14.