Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study

References

• Netzeva, T. I., Worth, A., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D., Schultz, T., Stanton, D. W., van de Sandt, J. J. M., Tong, W., Veith, G., & Yang, C. (2005). Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships: The report and recommendations of ECVAM workshop 52. Alternatives to Laboratory Animals: ATLA, 33(2), 155–173. https://doi.org/10.1177/026119290503300209
   PubMed Web of Science ®Google Scholar
• Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
   PubMed Web of Science ®Google Scholar
• Abchir, O., Daoui, O., Belaidi, S., Ouassaf, M., Qais, F. A., ElKhattabi, S., Belaaouad, S., & Chtita, S. (2022). Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and Molecular Dynamics Simulation Studies. Journal of Molecular Modeling, 28(4), 106. https://doi.org/10.1007/s00894-022-05097-9
   PubMed Web of Science ®Google Scholar
• Abchir, O., Daoui, O., Nour, H., Yamari, I., Elkhattabi, S., Errougui, A., & Chtita, S. (2023). Exploration of cannabis constituents as potential candidates against diabetes mellitus disease using molecular docking, dynamics simulations and Admet investigations. Scientific African, 21, e01745. https://doi.org/10.1016/j.sciaf.2023.e01745
   Google Scholar
• Abchir, O., Nour, H., Daoui, O., Yamari, I., ElKhattabi, S., El Kouali, M., Talbi, M., Errougui, A., & Chtita, S. (2023). Structure-based virtual screening, ADMET analysis, and molecular dynamics simulation of Moroccan natural compounds as candidates for the SARS-COV-2 inhibitors. Natural Product Research, 1–8. https://doi.org/10.1080/14786419.2023.2281002
   PubMed Web of Science ®Google Scholar
• Abchir, O., Yamari, I., Nour, H., Daoui, O., Elkhattabi, S., Errougui, A., & Chtita, S. (2023). Structure‐based Virtual Screening, ADMET analysis, and molecular dynamics simulation of Moroccan natural compounds as candidates α‐amylase inhibitors. ChemistrySelect, 8(26),e202301092. https://doi.org/10.1002/slct.202301092
   PubMed Web of Science ®Google Scholar
• Arzine, A., Abchir, O., Chalkha, M., Chebbac, K., Rhazi, Y., Barghady, N., Yamari, I., El Moussaoui, A., Nakkabi, A., Akhazzane, M., Bakhouch, M., Chtita, S., & El Yazidi, M. (2024). Design, synthesis, in-vitro, in-silico and DFT studies of novel functionalized isoxazoles as antibacterial and antioxidant agents. Computational Biology and Chemistry, 108, 107993. https://doi.org/10.1016/j.compbiolchem.2023.107993
   PubMed Web of Science ®Google Scholar
• Bajad, N. G., Rayala, S., Gutti, G., Sharma, A., Singh, M., Kumar, A., & Singh, S. K. (2021). Systematic review on role of structure-based drug design (SBDD) in the identification of anti-viral leads against SARS-COV-2. Current Research in Pharmacology and Drug Discovery, 2, 100026. https://doi.org/10.1016/j.crphar.2021.100026
   PubMedGoogle Scholar
• Basnet, S., Ghimire, M. P., Lamichhane, T. R., Adhikari, R., & Adhikari, A. (2023). Mar 16 Identification of potential human pancreatic α-amylase inhibitors from natural products by molecular docking, MM/GBSA calculations, MD simulations, and ADMET analysis. PloS One, 18(3), e0275765. https://doi.org/10.1371/journal.pone.0275765
   PubMed Web of Science ®Google Scholar
• Basri, R., Ullah, S., Khan, A., Mali, S. N., Abchir, O., Chtita, S., El-Gokha, A., Taslimi, P., Binsaleh, A. Y., El-Kott, A. F., Al-Harrasi, A., & Shafiq, Z. (2023). Synthesis, biological evaluation and molecular modelling of 3-formyl-6-isopropylchromone derived thiosemicarbazones as α-glucosidase inhibitors. Bioorganic Chemistry, 139, 106739. https://doi.org/10.1016/j.bioorg.2023.106739
   PubMed Web of Science ®Google Scholar
• Chatterjee, S., & Hadi, A. S. (2013). Regression analysis by example. John Wiley & Sons.
   Google Scholar
• ChemOffice. (2016). PerkinElmer Informatics. http://www.cambridgesoft.com,
   Google Scholar
• Chtita, S., Belhassan, A., Aouidate, A., Belaidi, S., Bouachrine, M., & Lakhlifi, T. (2021). Discovery of Potent SARS-CoV-2 Inhibitors from Approved Antiviral Drugs via Docking and Virtual Screening. Combinatorial Chemistry & High Throughput Screening, 24(3), 441–454. https://doi.org/10.2174/1386207323999200730205447 32748740
   PubMed Web of Science ®Google Scholar
• Chtita, S., Ghamali, M., Larif, M., Hmamouchi, R., Bouachrine, M., & Lakhlifi, T. (2017). Quantitative Structure–Activity Relationship Studies of Anticancer Activity for Isatin (1H-indole-2,3-dione) Derivatives Based on Density Functional Theory. International Journal of Quantitative Structure-Property Relationships, 2(2), 90–115. https://doi.org/10.4018/IJQSPR.2017070108
   Google Scholar
• Chtita, S., Belaidi, S., Qais, F. A., Ouassaf, M., AlMogren, M. M., Al-Zahrani, A. A., Bakhouch, M., Belhassan, A., Zaki, H., Bouachrine, M., & Lakhlifi, T. (2022). Unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitors: Molecular docking, molecular dynamics, and ADME scoring investigations. Journal of King Saud University. Science, 34(7), 102226 https://doi.org/10.1016/j.jksus.2022.102226. PMC: 35875823
   PubMed Web of Science ®Google Scholar
• Chtita, S., Aouidate, A., Belhassan, A., Ousaa, A., Taourati, A. I., Elidrissi, B., Ghamali, M., Bouachrine, M., & Lakhlifi, T. (2020). QSAR study of N-substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods. New Journal of Chemistry, 44(5), 1747–1760. https://doi.org/10.1039/C9NJ04909F
   Web of Science ®Google Scholar
• Chtita, S., Belhassan, A., Bakhouch, M., Taourati, A. I., Aouidate, A., Belaidi, S., Moutaabbid, M., Belaaouad, S., Bouachrine, M., & Lakhlifi, T. (2021). QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-cov main protease inhibitors using quantum chemical descriptors and statistical methods. Chemometrics and Intelligent Laboratory Systems: An International Journal Sponsored by the Chemometrics Society, 210, 104266. https://doi.org/10.1016/j.chemolab.2021.104266
   PubMed Web of Science ®Google Scholar
• Chtita, S., Ghamali, M., Hmamouchi, R., Elidrissi, B., Bourass, M., Larif, M., Bouachrine, M., & Lakhlifi, T. (2016). Investigation of antileishmanial activities of acridines derivatives against promastigotes and amastigotes form of parasites using quantitative structure activity relationship analysis. Advances in Physical Chemistry, 2016, 1–16. https://doi.org/10.1155/2016/5137289
   Google Scholar
• Chtita, S., Ghamali, M., Ousaa, A., Aouidate, A., Belhassan, A., Taourati, A. I., Masand, V. H., Bouachrine, M., & Lakhlifi, T. (2019). QSAR study of anti-human African trypanosomiasis activity for 2-phenylimidazopyridines derivatives using DFT and Lipinski’s descriptors. Heliyon, 5(3), e01304. https://doi.org/10.1016/j.heliyon.2019.e01304
   PubMed Web of Science ®Google Scholar
• Das, N. R., Sharma, T., Goyal, N., Singh, N., Toropov, A. A., Toropova, A. P &., & Achary, P. G. (2023). Isoprenylcysteine carboxyl methyltransferase inhibitors: QSAR, docking and molecular dynamics studies. Journal of Molecular Structure, 1291, 135966. https://doi.org/10.1016/j.molstruc.2023.135966
   Web of Science ®Google Scholar
• Das, N. R., Sharma, T., Toropov, A. A., Toropova, A. P., Tripathi, M. K., & Achary, P. G. (2023). Machine-learning technique, QSAR and molecular dynamics for herg–drug interactions. Journal of Biomolecular Structure & Dynamics, 41(23), 13766–13791. https://doi.org/10.1080/07391102.2023.2193641
   PubMed Web of Science ®Google Scholar
• Dassault Systèmes BIOVIA Discovery Studio Modeling Environment. Release. (2017). Dassault Systems 2016, https://discover.3ds.com/discovery-studio-visualizer-download
   Google Scholar
• Diabetes mellitus guide: Causes, symptoms, and treatment options. (2023). Retrieved March 10, 2023, from https://www.drugs.com/health-guide/diabetes-mellitus.html
   Google Scholar
• Diabetes. (2022). Retrieved March 10, 2023, from https://www.who.int/news-room/fact-sheets/detail/diabetes
   Google Scholar
• Doak, B. C., & Kihlberg, J. (2016). Drug discovery beyond the rule of 5 - opportunities and challenges. Expert Opinion on Drug Discovery, 12(2), 115–119. https://doi.org/10.1080/17460441.2017.1264385
   PubMed Web of Science ®Google Scholar
• Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003). Methods for reliability & uncertainty assessment & for applicability evaluations of classification & regression-based QSARs. Environmental Health Perspectives, 111(10), 1361–1375. https://doi.org/10.1289/ehp.5758
   PubMed Web of Science ®Google Scholar
• Ghamali, M., Chtita, S., Hmamouchi, R., Adad, A., Bouachrine, M., & Lakhlifi, T. (2016). The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations. Journal of Taibah University for Science, 10(4), 534–542. https://doi.org/10.1016/j.jtusci.2015.09.006
   Web of Science ®Google Scholar
• Gramatica, P. (2007). Principles of QSAR models validation: Internal & external. QSAR & Combinatorial Science, 26(5), 694–701. https://doi.org/10.1002/qsar.200610151
   Google Scholar
• Hameed, S., Khan, K. M., Salar, U., Özil, M., Baltaş, N., Saleem, F., Qureshi, U., Taha, M., & Ul-Haq, Z. (2022). Hydrazinyl thiazole linked indenoquinoxaline hybrids: Potential leads to treat hyperglycemia and oxidative stress; multistep synthesis, α-amylase, α-glucosidase inhibitory and antioxidant activities. International Journal of Biological Macromolecules, 221, 1294–1312. https://doi.org/10.1016/j.ijbiomac.2022.09.102
   PubMed Web of Science ®Google Scholar
• Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17
   PubMed Web of Science ®Google Scholar
• International Diabetes Federation IDF Diabetes Atlas. “Diabetes around the world in 2021,” (2021).
   Google Scholar
• Jaworska, J., Nikolova-Jeliazkova, N., & Aldenberg, T. (2005). QSAR applicability domain estimation by projection of the training set in descriptor space: A review. Alternatives to Laboratory Animals: ATLA, 33(5), 445–459. https://doi.org/10.1177/026119290503300508
   PubMed Web of Science ®Google Scholar
• Jin, H., Wu, B.-X., Zheng, Q., Hu, C.-H., Tang, X.-Z., Zhang, W., & Rao, G.-W. (2021). Design, synthesis, biological evaluation and docking study of novel quinazoline derivatives as EGFR-TK inhibitors. Future Medicinal Chemistry, 13(7), 601–612. https://doi.org/10.4155/fmc-2020-0015
   PubMed Web of Science ®Google Scholar
• Kaushal, T., Srivastava, G., Sharma, A., & Negi, A. S. (2019). An insight into medicinal chemistry of anticancer quinoxalines. Bioorganic & Medicinal Chemistry, 27(1), 16–35. https://doi.org/10.1016/j.bmc.2018.11.021
   PubMed Web of Science ®Google Scholar
• Khedraoui, M., Nour, H., Yamari, I., Abchir, O., Errougui, A., & Chtita, S. (2023). Design of a new potent Alzheimer’s disease inhibitor based on QSAR, molecular docking and Molecular Dynamics Investigations. Chemical Physics Impact, 7, 100361. https://doi.org/10.1016/j.chphi.2023.100361
   Google Scholar
• Krishnendu, P. R., Benny, S., Kumar, S., Jayan, J., Bhaskar, V., Pappachen, L., Aneesh, T. P., Abdelgawad, M. A., Ghoneim, M. M., Nicolotti, O., Zachariah, S. M., & Mathew, B. (2024). A structure-based approach to explore novel COX-2 inhibitors using pharmacophore modelling, 3D-QSAR analysis, virtual screening and Dynamics Simulation Study. Journal of Molecular Structure, 1295, 136634. https://doi.org/10.1016/j.molstruc.2023.136634
   Web of Science ®Google Scholar
• Lanka, G., Begum, D., Banerjee, S., Adhikari, N., P, Y., & Ghosh, B. (2023). Pharmacophore-based virtual screening, 3D QSAR, docking, ADMET, and MD Simulation Studies: An in silico perspective for the identification of new potential HDAC3 inhibitors. Computers in Biology and Medicine, 166, 107481. https://doi.org/10.1016/j.compbiomed.2023.107481
   PubMed Web of Science ®Google Scholar
• M. J., Frisch, G. W., Trucks, H. B., Schlegel, G. E., Scuseria, M. A., Robb, J. R., Cheeseman, G., Scalmani, V., Barone, G. A., Petersson, H., Nakatsuji, X., Li, M., Caricato, A. V., Marenich, J., Bloino, B. G., Janesko, R., Gomperts, B., Mennucci, H. P., Hratchian, J. V., Ortiz, … Fox. (2016). Gaussian. 09, Rev. A.02.
   Google Scholar
• Mouhsin, M., Abchir, O., El Otmani, F. S., Oumghar, A. A., Oubenali, M., Chtita, S., Mbarki, M., & Gamouh, A. (2023). Identification of novel NLRP3 inhibitors: A comprehensive approach using 2D-QSAR, molecular docking, molecular dynamics simulation and drug-likeness evaluation. Chemical Papers, https://doi.org/10.1007/s11696-023-03157-9
   Web of Science ®Google Scholar
• Mounadi, N., Nour, H., Errougui, A., Talbi, M., ElKouali, M., & Chtita, S. (2024). Discovery of eugenol-derived drug candidates for the treatment of COVID-19 by applying molecular docking, molecular dynamics, and pharmacokinetic analysis. Physical Chemistry Research, 12(2), 289–303. https://doi.org/10.22036/pcr.2023.409956.2390
   Google Scholar
• Nour, H., Abchir, O., Belaidi, S., & Chtita, S. (2022). Research of new acetylcholinesterase inhibitors based on QSAR and molecular docking studies of benzene-based carbamate derivatives. Structural Chemistry, 33(6), 1935–1946. https://doi.org/10.1007/s11224-022-01966-4
   Web of Science ®Google Scholar
• Nour, H., Abchir, O., Belaidi, S., Qais, F. A., Chtita, S., & Belaaouad, S. (2021). 2d‐QSAR and molecular docking studies of carbamate derivatives to discover novel potent anti‐butyrylcholinesterase agents for Alzheimer’s disease treatment. Bulletin of the Korean Chemical Society, 43(2), 277–292. https://doi.org/10.1002/bkcs.12449
   Web of Science ®Google Scholar
• Nour, H., Daoui, O., Abchir, O., ElKhattabi, S., Belaidi, S., & Chtita, S. (2022). Combined computational approaches for developing new anti-alzheimer drug candidates: 3D-QSAR, molecular docking and molecular dynamics studies of liquiritigenin derivatives. Heliyon, 8(12), e11991. https://doi.org/10.1016/j.heliyon.2022.e11991
   PubMed Web of Science ®Google Scholar
• Nour, H., Hashmi, M. A., Belaidi, S., Errougui, A., El Kouali, M., Talbi, M., & Chtita, S. (2023). Design of acetylcholinesterase inhibitors as promising anti‐alzheimer’s agents based on QSAR, molecular docking, and molecular dynamics studies of liquiritigenin derivatives. ChemistrySelect, 8(32), e202301466, https://doi.org/10.1002/slct.202301466
   PubMed Web of Science ®Google Scholar
• Pereira, J. A., Pessoa, A. M., Cordeiro, M. N., Fernandes, R., Prudêncio, C., Noronha, J. P., & Vieira, M. (2015). Quinoxaline, its derivatives and applications: A state of the art review. European Journal of Medicinal Chemistry, 97, 664–672. https://doi.org/10.1016/j.ejmech.2014.06.058
   PubMed Web of Science ®Google Scholar
• Protein preparation wizard. (2023). Retrieved March 10https://www.schrodinger.com/science-articles/protein-preparation-wizard.
   Google Scholar
• Rakshit, G., Murtuja, S., Kumar, B. K., Murugesan, S., & Jayaprakash, V. (2022). Structure-based drug design (SBDD). In Computer aided drug design (CADD): From ligand-based methods to structure-based approaches, Drug Discovery Update, Elsevier, (pp. 181–229). https://doi.org/10.1016/b978-0-323-90608-1.00003-4
   Google Scholar
• Rbaa, M., Haida, S., Tuzun, B., Hichar, A., El Hassane, A., Kribii, A., Lakhrissi, Y., Ben Hadda, T., Zarrouk, A., Lakhrissi, B., & Berdimurodov, E. (2022). Synthesis, characterization and bioactivity of novel 8-hydroxyquinoline derivatives: Experimental, molecular docking, DFT and POM analyses. Journal of Molecular Structure, 1258, 132688. https://doi.org/10.1016/j.molstruc.2022.132688
   Web of Science ®Google Scholar
• Rücker, C., Rücker, G., & Meringer, M. (2007). Y-randomization–a useful tool in QSAR validation, or folklore. Journal of Chemical Information and Modeling, 47(6), 2345–2357. https://doi.org/10.1021/ci700157b
   PubMed Web of Science ®Google Scholar
• Sabando, M. V., Ponzoni, I., & Soto, A. J. (2019). Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Applied Soft Computing, 85, 105777. https://doi.org/10.1016/j.asoc.2019.105777
   Web of Science ®Google Scholar
• System, Maestro-Desmond Interoperability Tools Schrödinger, New York, NY. (2021). D.E. Shaw Research, Desmond Molecular Dynamics System. Maestro-Desmond%0AInteroperability Tools., Schrödinger. 2021
   Google Scholar
• Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• Van De Laar, F. A., Lucassen, P. L., Akkermans, R. P., Van De Lisdonk, E. H., Rutten, G. E., & Van Weel, C. (2005). α-Glucosidase inhibitors forpatients with type 2 diabetes: Results from a Cochrane systematicreview and meta-analysis. Diabetes Care, 28(1), 154–163. https://doi.org/10.2337/diacare.28.1.154
   PubMed Web of Science ®Google Scholar
• Vlachou, E., Ntikoudi, A., Owens, D. A., Nikolakopoulou, M., Chalimourdas, T., & Cauli, O. (2022). Effectiveness of cognitive behavioral therapy-based interventions on psychological symptoms in adults with type 2 diabetes mellitus: An update review of randomized controlled trials. Journal of Diabetes and Its Complications, 36(5), 108185. https://doi.org/10.1016/j.jdiacomp.2022.108185
   PubMed Web of Science ®Google Scholar
• Wadavrao, S. B., Ghogare, R. S., & Narsaiah, A. V. (2013). A simple and efficient protocol for the synthesis of quinoxalines catalyzed by pyridine. Organic Communications, 6(1), 23–30.
   Google Scholar
• Yamari, I., Abchir, O., Mali, S. N., Errougui, A., Talbi, M., Kouali, M. E., & Chtita, S. (2023). The anti-SARS-cov-2 activity of novel 9, 10-dihydrophenanthrene derivatives: An insight into molecular docking, ADMET analysis, and molecular dynamics simulation. Scientific African, 21, e01754. https://doi.org/10.1016/j.sciaf.2023.e01754
   PubMedGoogle Scholar
• Yamari, I., Abchir, O., Nour, H., El Kouali, M., & Chtita, S. (2023). Identification of new dihydrophenanthrene derivatives as promising anti-SARS-cov-2 drugs through in silico investigations. Main Group Chemistry, 22(3), 469–484. https://doi.org/10.3233/MGC-220127
   Web of Science ®Google Scholar
• Zhang, H., Zhang, J., Qu, W., Xie, S., Huang, L., Chen, D., Tao, Y., Liu, Z., Pan, Y., & Yuan, Z. (2020). Design, synthesis, and biological evaluation of novel thiazolidinone-containing quinoxaline-1,4-di-n-oxides as antimycobacterial and antifungal agents. Frontiers in Chemistry, 8, 598. https://doi.org/10.3389/fchem.2020.00598
   PubMed Web of Science ®Google Scholar
• Zhang, S., Golbraikh, A., Oloff, S., Kohn, H., & Tropsha, A. (2006). A novel Automated lazy learning QSAR (ALL-QSAR) Approach: method development, Applications, and virtual screening of chemical databases using validated ALL-QSAR models. Journal of Chemical Information and Modeling, 46(5), 1984–1995. https://doi.org/10.1021/ci060132x
   PubMed Web of Science ®Google Scholar
• Zochodne, D. W. (2014). Diabetes mellitus. In Michael J. Aminoff, Robert B. Daroff (Eds.), Encyclopedia of the Neurological Sciences, (2nd Ed., pp. 992–994). ISBN 9780123851581. https://doi.org/10.1016/b978-0-12-385157-4.00080-4
   Google Scholar