Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Abstract

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure-activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q² = 0.8735, R²_(train) = 0.9096) and ANN [3-2-1] (Q² = 0.8987, R²_(train) = 0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S + E (Q² = 0.559, R²_(train) = 0.939) and CoMSIA_S + E (Q² = 0.577, R²_(train) = 0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Keywords

QSAR / Molecular docking / Molecular dynamics / Binding cavity / Anti-IAV activity prediction

References

[1] OberemokVV, Andreeva OA, AlievaEE, BilykAI. New advances and perspectives of influenza prevention: current state of the art. Sci Pharm. 2023;91(2):28.

[2] YangS, XieY-M, WangL-X. RDN for the treatment of influenza in children: a randomized, double-blinded, parallel-controlled clinical trial. BMC Compl. Med. Ther. 2023;23(1):1–8.

[3] AkhtarZ, IslamMA, AleemMA, et al. SARS-CoV-2 and influenza virus coinfection among patients with severe acute respiratory infection during the first wave of COVID-19 pandemic in Bangladesh: a hospital-based descriptive study. BMJ Open. 2021;11(11):e053768.

[4] BhaleraoU, MaviAK, ManglicS, et al. An updated review on influenza viruses. Emerging human viral diseases. Respir Haemor Fev. 2023;I:71–106.

[5] DemirdenSF, Alptekin K, Kimiz-GebologluI, OncelSS. Influenza vaccine: an engineering vision from virological importance to production. Biotechnol Bioproc Eng. 2022;27(5):740–764.

[6] NeumannG, Kawaoka Y. The COVID-19 pandemic—a potential role for antivirals in mitigating pandemics. Viruses. 2023;15(2):303.

[7] MarandinoA, Tomás G, PanzeraY, et al. Spreading of the high-pathogenicity avian influenza (H5N1) virus of clade 2.3. 4.4 b into Uruguay. Viruses. 2023;15(9):1906.

[8] BatoolS, Chokkakula S, SongM-S. Influenza treatment: limitations of antiviral therapy and advantages of drug combination therapy. Microorganisms. 2023;11(1):183.

[9] ScalaMC, Marchetti M, SupertiF, AgamennoneM, Campiglia P, SalaM. Rational design of novel peptidomimetics against influenza A virus: biological and computational studies. Int J Mol Sci. 2023;24(18):14268.

[10] AdamsonCS, Chibale K, GossRJ, JasparsM, NewmanDJ, DorringtonRA. Antiviral drug discovery: preparing for the next pandemic. Chem Soc Rev. 2021;50(6):3647–3655.

[11] SivaphongthongchaiA, Sayorwan W, SiripornpanichV, PalanuvejC, Kanchanakhan N, RuangrungsiN. Olfactory effects of d-Borneol on psychophysiological parameters among healthy participants. J Curr Sci Technol. 2022;12(3):492–504.

[12] AliB, Al-Wabel NA, ShamsS, AhamadA, KhanSA, AnwarF. Essential oils used in aromatherapy: a systemic review. Asian Pac J Trop Biomed. 2015;5(8):601–611.

[13] RajputA, KasarA, ThoratS, Kulkarni M. Borneol: a plant-sourced terpene with a variety of promising pharmacological effects. Nat Prod J. 2023;13(1):13–28.

[14] SokolovaA, Yarovaya O, SemenovaM, et al. Synthesis and in vitro study of novel borneol derivatives as potent inhibitors of the influenza A virus. MedChemComm. 2017;8(5):960–963.

[15] BorisevichSS, GureevMA, YarovayaОI, et al. Can molecular dynamics explain decreased pathogenicity in mutant camphecene-resistant influenza virus? J Biomol Struct Dyn. 2022;40(12):5481–5492.

[16] AlamgirA, Alamgir A. Secondary metabolites: secondary metabolic products consisting of C and H;C, H, and O;N, S, and P elements;and O/N heterocycles. Therapeutic Use of Medicinal Plants and their Extracts. Phytochem Bio Comp. 2018;2:165–309.

[17] GuptaPK, NawazMH, MishraSS, Parappa K, SillaA, HanumegowdaR. New age approaches to predictive healthcare using in silico drug design and internet of things (IoT). Sustainable and Energy Efficient Computing Paradigms for Society. 2021:127–151.

[18] DouB, ZhuZ, MerkurjevE, et al. Machine learning methods for small data challenges in molecular science. Chem Rev. 2023;123(13):8736–8780.

[19] AbdullahiM, Shallangwa GA, UzairuA. 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.

[20] AbdullahiM, UzairuA, ShallangwaGA, et al. Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase. J Biomol Struct Dyn. 2023:1–15.

[21] AbdullahiM, UzairuA, ShallangwaGA, et al. In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents. J Biomol Struct Dyn. 2023:1–21.

[22] IbrahimMT, UzairuA, ShallangwaGA, UbaS. Structure-based design and activity modeling of novel epidermal growth factor receptor kinase inhibitors;an in silico approach. Sci Afr. 2020;9:e00503.

[23] AbdullahiM, Adeniji SE, ArthurDE, MusaS. Quantitative structure-activity relationship (QSAR) modelling study of some novel carboxamide series as new antitubercular agents. Bull Natl Res Cent. 2020;44(1):1–13.

[24] DongJ, CaoD-S, MiaoH-Y, et al. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminf. 2015;7(1):1–10.

[25] YapCW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011;32(7):1466–1474.

[26] AhamadS, IslamA, AhmadF, Dwivedi N, HassanMI. 2/3D-QSAR, molecular docking and MD simulation studies of FtsZ protein targeting benzimidazoles derivatives. Comput Biol Chem. 2019;78:398–413.

[27] HadniH, Elhallaoui M. 2D and 3D-QSAR, molecular docking and ADMET properties in silico studies of azaaurones as antimalarial agents. New J Chem. 2020;44(16):6553–6565.

[28] GoodarziM, Dejaegher B, HeydenYV. Feature selection methods in QSAR studies. J AOAC Int. 2012;95(3):636–651.

[29] KubinyH. Variable selection in QSAR studies. I. An evolutionary algorithm. Quant Struct-Act Relat. 1994;13(3):285–294.

[30] KhanPM, RoyK. Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR). Expet Opin Drug Discov. 2018;13(12):1075–1089.

[31] GonzalezMP, TeranC, Saiz-UrraL, Teijeira M. Variable selection methods in QSAR: an overview. Curr Top Med Chem. 2008;8(18):1606–1627.

[32] ForrestS. Genetic algorithms: principles of natural selection applied to computation. Science. 1993;261(5123):872–878.

[33] DebK. An introduction to genetic algorithms. Sadhana. 1999;24:293–315.

[34] RendersJ-M, FlasseSP. Hybrid methods using genetic algorithms for global optimization. IEEE Trans Syst Man Cyber Part B. 1996;26(2):243–258.

[35] KhaledK, Abdel-Shafi N. Quantitative structure and activity relationship modeling study of corrosion inhibitors: genetic function approximation and molecular dynamics simulation methods. Int J Electrochem Sci. 2011;6:4077–4094.

[36] FriedmanJH. Multivariate adaptive regression splines. Ann Stat. 1991;19(1):1–67.

[37] XieW, Wiriyarattanakul S, RungrotmongkolT, ShiL, Wiriyarattanakul A, MaitaradP. Rational design of a low-data regime of pyrrole antioxidants for radical scavenging activities using quantum chemical descriptors and QSAR with the GAMLR and ANN concepts. Molecules. 2023;28(4):1596.

[38] SefiddashtiFM, Asadpour S, HaddadiH, NasabSG. QSAR analysis of pyrimidine derivatives as VEGFR-2 receptor inhibitors to inhibit cancer using multiple linear regression and artificial neural network. Res Pharm Sci. 2021;16(6):596.

[39] AbdullahiM, UzairuA, ShallangwaGA, MamzaPA, Ibrahim MT. Computational modelling of some phenolic diterpenoid compounds as anti-influenza A virus agents. Sci Afr. 2023;19:e01462.

[40] BouakkadiaA, Kertiou N, AmiriR, DrioucheY, Messadi D. Use of GA-ANN and GASVM for a QSPR study on the aqueous solubility of pesticides. J Serb Chem Soc. 2021;86(7–8):673–684.

[41] UmarAB, UzairuA. Molecular modeling strategy to design novel anticancer agents against UACC-62 and UACC-257 melanoma cell lines. Egypt J Basic Appl Sci. 2023;10(1):157–173.

[42] AbdullahiM, UzairuA, ShallangwaGA, MamzaPA, Ibrahim MT. 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. Egypt J Basic Appl Sci. 2022;9(1):510–532.

[43] RoyK, DasRN, AmbureP, Aher RB. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr Intell Lab Syst. 2016;152:18–33.

[44] RoyK, KarS, AmbureP. On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab Syst. 2015;145:22–29.

[45] GolbraikhA, ShenM, XiaoZ, Xiao Y-D, LeeK-H, TropshaA. Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des. 2003;17:241–253.

[46] TropshaA. Best practices for QSAR model development, validation, and exploitation. Mol Inf. 2010;29(6-7):476–488.

[47] GramaticaP. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007;26(5):694–701.

[48] IbrahimMM, UzairuA, IbrahimMT, Umar AB. Modelling PIP4K2A inhibitory activity of 1, 7-naphthyridine analogues using machine learning and molecular docking studies. RSC Adv. 2023;13(6):3402–3415.

[49] RoyK, KarS, DasRN. Statistical Methods in QSAR/QSPR. A Primer on QSAR/QSPR Modeling. Springer;2015:37–59.

[50] VermaJ, Khedkar VM, CoutinhoEC. 3D-QSAR in drug design-a review. Curr Top Med Chem. 2010;10(1):95–115.

[51] HadniH, Elhallaoui M. 3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents. Heliyon. 2020;6(4).

[52] AkamatsuM. Current state and perspectives of 3D-QSAR. Curr Top Med Chem. 2002;2(12):1381–1394.

[53] G DamaleM, N Harke S, A Kalam KhanF, B ShindeD, N Sangshetti J. Recent advances in multidimensional QSAR (4D-6D): a critical review. Mini Rev Med Chem. 2014;14(1):35–55.

[54] Er-rajyM, MujwarS, ImtaraH, et al. Design of novel anti-cancer agents targeting COX-2 inhibitors based on computational studies. Arab J Chem. 2023;16(10):105193.

[55] XieH, ChenL, ZhangJ, Xie X, QiuK, FuJ. A combined pharmacophore modeling, 3D QSAR and virtual screening studies on imidazopyridines as B-Raf inhibitors. Int J Mol Sci. 2015;16(6):12307–12323.

[56] PoleboyinaPK, NaikU, PashaA, et al. Virtual screening, molecular docking, and dynamic simulations revealed TGF-β1 potential inhibitors to curtail cervical cancer progression. Appl Biochem Biotechnol. 2023:1–34.

[57] AbdullahiM, UzairuA, ShallangwaGA, et al. Unveiling 1, 3-thiazine derivative as a potential neuraminidase inhibitor: molecular docking, molecular dynamics, ADMET and DFT studies. Chem Afr. 2023:1–11.

[58] AdedirinO, UzairuA, ShallangwaGA, AbechiSE. Computational studies on α-aminoacetamide derivatives with anticonvulsant activities. Beni-Suef Univ J Basic Appl Sci. 2018;7(4):709–718.

[59] TodeschiniR, Consonni V. Molecular descriptors. Recent Adv QSAR Stud. 2010:29–102.

[60] DepizzolDB, PaivaMHM, Dos SantosTO, GaudioAC. MoCalc: a new graphical user interface for molecular calculations. J Comput Chem. 2005;26(2):142–144.

[61] KhanK, KumarV, ColomboE, Lombardo A, BenfenatiE, RoyK. Intelligent consensus predictions of bioconcentration factor of pharmaceuticals using 2D and fragmentbased descriptors. Environ Int. 2022;170:107625.

[62] ThompsonCG, KimRS, AloeAM, Becker BJ. Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl Soc Psychol. 2017;39(2):81–90.

[63] KumarS, SinghV, TiwariM. QSAR modeling of the inhibition of reverse transcriptase enzyme with benzimidazolone analogs. Med Chem Res. 2011;20:1530–1541.

[64] SadeghiF, Afkhami A, MadrakianT, GhavamiR. QSAR analysis on a large and diverse set of potent phosphoinositide 3-kinase gamma (Pi3Kγ) inhibitors using MLR and ANN methods. Sci Rep. 2022;12(1):6090.

[65] BanerjeeA, Chatterjee M, DeP, RoyK. Quantitative predictions from chemical readacross and their confidence measures. Chemometr Intell Lab Syst. 2022;227:104613.

[66] JiangY, YangW, WangF, Zhou B. In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations. RSC Adv. 2023;13(22):14808–14824.

[67] BanerjeeS, BaidyaSK, GhoshB, Adhikari N, JhaT. The first report on predictive comparative ligand-based multi-QSAR modeling analysis of 4-pyrimidinone and 2-pyridinone based APJ inhibitors. New J Chem. 2022;46(24):11591–11607.

[68] KoubiY, Moukhliss Y, HajjiH, et al. A computational study of Di-substituted 1, 2, 3-triazole derivatives as potential drug candidates against Mycobacterium tuberculosis:3D-QSAR, molecular docking, molecular dynamics, and ADMETox. New J Chem. 2023;47(25):11832–11841.

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