Skip to main content

Advertisement

SpringerLink
Log in

Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

In the present study, a quantitative relationship between the biological inhibitory activity of alpha-amylase and molecular structures of novel benzimidazole derivatives is analyzed in silico. The best QSAR model screened via MLR technique indicated that the exact mass, topological diameter and numerical rotational bonding structural properties of benzimidazole derivatives highly affect the bioactivity of these compounds against α-amylase. Based on the structural properties identified via linear QSAR model favorable for improving pIC50 of benzimidazole derivatives, fourteen new molecules bearing benzimidazole radicals were designed and their biological inhibitory activity against α-amylase was improved. QSAR model predictions showed that the designed molecules exhibited a higher potential biological level activity IC50 than acarbose used in positive control (IC50= 1.46 μM). Screening of drug-like properties, pharmacokinetics and toxicity of the proposed molecules led to select three molecules as candidates for use as drug aid to ingest starch and glycogen. As a result, using molecular docking simulations, the docking poses of the three molecules inside the α-amylase receptor pocket (PDB code: 1HNY) were predicted. Also, the most important potential interactions between the active amino acid sites in α-amylase protein pocket and the proposed drug molecules were described. The obtained hypotheses regarding the stability of the proposed molecules inside α-amylase pocket were validated by carrying out molecular dynamic simulations in aqueous background similar to the ones of proteins. The DM results confirmed the optimal stability of the α-amylase backbone with the drug molecules proposed in this computational investigation.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

Not applicable.

Code availability

Not applicable.

References

  1. Danaei G, Lawes CM, Vander Hoorn S, Murray CJ, Ezzati M (2006) Global and regional mortality from ischaemic heart disease and stroke attributable to higher-than-optimum blood glucose concentration: comparative risk assessment. Lancet 368:1651–1659

    Article  Google Scholar 

  2. The effects of diabetes on your body, Healthline (2014) https://www.healthline.com/health/diabetes/effects-on-body. Accessed 26 Mar 2022

  3. Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M (2010) Emerging risk factors collaboration diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet 375:2215–2222

    Article  CAS  Google Scholar 

  4. Chun BY, Rizzo JF III (2016) Dominant optic atrophy: updates on the pathophysiology and clinical manifestations of the optic atrophy 1 mutation. Curr Opin Ophthalmol 27:475–480

    Article  Google Scholar 

  5. Saran R, Robinson B, Abbott KC, Agodoa LY, Albertus P, Ayanian J, Balkrishnan R, Bragg-Gresham J, Cao J, Chen JL (2017) US renal data system 2016 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis 69:A7–A8

    Article  Google Scholar 

  6. Diabetes mellitus guide: causes, symptoms and treatment options (n.d.) https://www.drugs.com/health-guide/diabetes-mellitus.html. Accessed 26 Mar 2022

  7. Funke I, Melzig MF (2006) Plantas tradicionalmente utilizadas na terapia da diabetes: fitomedicamentos como inibidores da atividade alfa-amilase, Revista Brasileira de. Farmacognosia 16:1–5

    Article  Google Scholar 

  8. Sales PM, Souza PM, Simeoni LA, Magalhães PO, Silveira D (2012) α-Amylase inhibitors: a review of raw material and isolated compounds from plant source. J Pharm Pharm Sci 15:141–183

    Article  Google Scholar 

  9. Van De Laar FA, Lucassen PL, Akkermans RP, Van De Lisdonk EH, Rutten GE, Van Weel C (2005) α-Glucosidase inhibitors for patients with type 2 diabetes: results from a Cochrane systematic review and meta-analysis. Diabetes Care 28:154–163

    Article  Google Scholar 

  10. Arshad T, Khan KM, Rasool N, Salar U, Hussain S, Tahir T, Ashraf M, Waddod A, Riaz M, Perveen S, Taha M, Ismail NH (2016) Syntheses, in vitro evaluation and molecular docking studies of 5-bromo-2-aryl benzimidazoles as α-glucosidase inhibitors. Med Chem Res 25:2058–2069. https://doi.org/10.1007/s00044-016-1614-y

    Article  CAS  Google Scholar 

  11. Aminpour M, Montemagno C, Tuszynski JA (2019) An overview of molecular modeling for drug discovery with specific illustrative examples of applications. Molecules 24:1693

    Article  CAS  Google Scholar 

  12. Chalkha M, Akhazzane M, Moussaid FZ, Daoui O, Nakkabi A, Bakhouch M, Chtita S, Elkhattabi S, Housseini AI, Yazidi ME (2022) Design, synthesis, characterization, in vitro screening, molecular docking, 3D-QSAR, and ADME-Tox investigations of novel pyrazole derivatives as antimicrobial agents. New J Chem. 46:2747–2760 https://doi.org/10.1039/D1NJ05621B

    Article  Google Scholar 

  13. Adegboye AA, Khan KM, Salar U, Aboaba SA, Chigurupati S, Fatima I, Taha M, Wadood A, Mohammad JI, Khan H (2018) 2-Aryl benzimidazoles: synthesis, in vitro α-amylase inhibitory activity, and molecular docking study. Eur J Med Chem 150:248–260

    Article  CAS  Google Scholar 

  14. ChemOffice, PerkinElmer Informatics.http://www.cambridgesoft.com, 2016. Accessed 26 Mar 2022

  15. MarvinSketch 5.11.4., Chem Axon 2012. https://chemaxon.com/products/marvin. Accessed 26 Mar 2022

  16. ACDLABS 10. Advanced Chemistry Development. Inc. Toronto. ON. Canada 2015. Available:(http://www.acdlabs.com/ressources/freeware/chemsketch/). Accessed 26 Mar 2022

  17. XLSTAT, Software, XLSTAT company.www.xlstat.com, 2013. Accessed 26 Mar 2022

  18. Chtita S, Ghamali M, Ousaa A, Aouidate A, Belhassan A, Taourati AI, Masand VH, 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

    Article  Google Scholar 

  19. Chtita S, Aouidate A, Belhassan A, Ousaa A, Taourati AI, 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 J Chem 44:1747–1760

    Article  CAS  Google Scholar 

  20. Elidrissi B, Ousaa A, Ghamali M, Chtita S, Ajana MA, Bouachrine M, Lakhlifi T (2014) Toxicity in vivo of nitro-aromatic compounds: DFT and QSAR results. J Comput Aided Mol Des 4:28–37

    Google Scholar 

  21. Daoui O, Elkhattabi S, Chtita S, Elkhalabi R, Zgou H, Benjelloun AT (2021) QSAR, Molecular Docking and ADMET properties in silico studies of Novel 4, 5, 6, 7-Tetrahydrobenzo [D]-Thiazol-2-Yl Derivatives Derived from Dimedone as potent Anti-Tumor agents through inhibition of C-Met receptor Tyrosine Kinase. Heliyon 7(7):E07463. https://doi.org/10.1016/j.heliyon.2021.e07463

    Article  Google Scholar 

  22. Ouassaf M, Belaidi S, Benbrahim I, Belaidi H, Chtita S (2020) Quantitative structure-activity relationships of 1.2. 3 triazole derivatives as aromatase inhibition activity, Turkish Computational and Theoretical Chemistry 4(1):1–11. https://doi.org/10.33435/tcandtc.545369

  23. 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 e5137289. https://doi.org/10.1155/2016/5137289

  24. Diana G, Tommasi C (2002) Cross-validation methods in principal component analysis: a comparison. Stat Methods Appl 11:71–82

    Article  Google Scholar 

  25. Chatterjee S, Hadi AS, Regression analysis by example, John Wiley & Sons, 2013

  26. Rücker C, Rücker G, Meringer M (2007) Y-randomization–a useful tool in QSAR validation, or folklore. J Chem Inf Model 47:2345–2357

    Article  Google Scholar 

  27. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Perspect 111:1361–1375

    Article  CAS  Google Scholar 

  28. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships: the report and recommendations of ECVAM workshop 52. Altern Lab Anim 33:155–173

    Article  CAS  Google Scholar 

  29. Sabando MV, Ponzoni I, Soto AJ (2019) Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Appl Soft Comput 85:105777

    Article  Google Scholar 

  30. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set in descriptor space: a review. Altern Lab Anim 33:445–459

    Article  CAS  Google Scholar 

  31. Zoete V, Daina A, Bovigny C, Michielin O (2016) SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening

  32. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:1–13

    Article  Google Scholar 

  33. U.N.E.C. for E. Secretariat, United Nations. Economic Commission for Europe. Secretariat. Globally Harmonized System of Classification and Labelling of Chemicals (GHS)., United Nations Publications, 2013.

  34. Daoui O, Mazoir N, Bakhouch M, et al (2022) 3D-QSAR, ADME-Tox, and molecular docking of semisynthetic triterpene derivatives as antibacterial and insecticide agents. Struct Chem. https://doi.org/10.1007/s11224-022-01912-4

    Article  CAS  Google Scholar 

  35. Shou WZ (2020) Current status and future directions of high-throughput ADME screening in drug discovery. Journal of Pharmaceutical Analysis 10:201–208

    Article  Google Scholar 

  36. 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. Comb Chem High Throughput Screening 24:441–454

    Article  CAS  Google Scholar 

  37. Ouassaf M, Belaidi S, Mogren Al Morgen M, Chtita S, Ullah Khan S, ThetHtar T (2021) Combined docking methods and molecular dynamics to identify effective antiviral 2, 5-diaminobenzophenonederivatives against SARS-CoV-2. J King Saud Univ Sci 33:101352. https://doi.org/10.1016/j.jksus.2021.101352

    Article  PubMed  PubMed Central  Google Scholar 

  38. Brayer GD, Luo Y, Withers SG (1995) The structure of human pancreatic α-amylase at 1.8 Å resolution and comparisons with related enzymes. Protein Sci 4:1730–1742

    Article  CAS  Google Scholar 

  39. Sybyl-X 2.0, Tripos International, St. Louis, Missouri, 63144, USA., Software Informer. (n.d.). https://sybyl-x.software.informer.com/2.0/. Accessed 26 Mar 2022

  40. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  Google Scholar 

  41. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Dassault Systèmes BIOVIA Discovery Studio Modeling Environment. Release 2017 Dassault Systèmes 2016., (n.d.). https://discover.3ds.com/discovery-studio-visualizer-download . Accessed 26 Mar 2022

  43. H.J.C. Berendsen, B. Hess, E. Lindahl, D. Van Der Spoel, A.E. Mark, G. Groenhof, van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. (2005) GROMACS: fast, flexible, and free. J of Comput Chem 26(16):1701–1718. https://doi.org/10.1002/jcc.20291

  44. Liao S-Y, Mo G-Q, Chen J-C, Zheng K-C (2014) Exploration of the binding mode between (-)-zampanolide and tubulin using docking and molecular dynamics simulation. J Mol Model 20:1–9

    Article  CAS  Google Scholar 

  45. Sousa da Silva AW, Vranken WF (2012) ACPYPE-Antechamber python parser interface. BMC Res Notes 5:1–8

    Article  Google Scholar 

  46. Brand W, Oosterhuis B, Krajcsi P, Barron D, Dionisi F, Van Bladeren PJ, Rietjens IM, Williamson G (2011) Interaction of hesperetin glucuronide conjugates with human BCRP, MRP2 and MRP3 as detected in membrane vesicles of overexpressing baculovirus-infected Sf9 cells. Biopharm Drug Dispos 32:530–535

    Article  CAS  Google Scholar 

  47. Parrinello M, Rahman A, (2002) Polymorphic transítions in single crystals: a new molecular dynamics method polymorphic transítions in single crystals: a new molecular dynamics method

  48. Kumari R., Kumar C, (2014) Open source drug discovery and A Lynn J Chem Inf Model 54:10–1021

  49. Dahmani R, Manachou M, Belaidi S, Chtita S, Boughdiri S (2021) Structural characterization and QSAR modeling of 1,2,4-triazole derivatives as α-glucosidase inhibitors. New J Chem 45:1253–1261. https://doi.org/10.1039/D0NJ05298A

    Article  CAS  Google Scholar 

  50. Chtita S, Belhassan A, Bakhouch M, Taourati AI, 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. Chemom Intell Lab Syst 210:104266

    Article  CAS  Google Scholar 

  51. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  52. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623

    Article  CAS  Google Scholar 

  53. Ghose AK, Viswanadhan VN, Wendoloski JJ (1999) 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 1:55–68

    Article  CAS  Google Scholar 

  54. Szakács G, Váradi A, Özvegy-Laczka C, Sarkadi B (2008) The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME–Tox). Drug Discovery Today 13:379–393

    Article  Google Scholar 

  55. Hollenberg PF (2002) Characteristics and common properties of inhibitors, inducers, and activators of CYP enzymes. Drug Metab Rev 34:17–35

    Article  CAS  Google Scholar 

  56. Qais FA, Sarwar T, Ahmad I, Khan RA, Shahzad SA, Husain FM (2021) Glyburide inhibits non-enzymatic glycation of HSA: an approach for the management of AGEs associated diabetic complications. Int J Biol Macromol 169:143–152

    Article  CAS  Google Scholar 

  57. Fouedjou RT, Chtita S, Bakhouch M, Belaidi S, Ouassaf M, Djoumbissie LA, Tapondjou LA, Qais FA (2021) Cameroonian medicinal plants as potential candidates of SARS-CoV-2 inhibitors. J Biomol Struct Dyn 0:1–15. https://doi.org/10.1080/07391102.2021.1914170

    Article  CAS  Google Scholar 

  58. Rath B, Qais FA, Patro R, Mohapatra S, Sharma T (2021) Design, synthesis and molecular modeling studies of novel mesalamine linked coumarin for treatment of inflammatory bowel disease. Bioorganic Med Chem Lett 41:128029

    Article  CAS  Google Scholar 

  59. Siddiqui S, Ameen F, Jahan I, Nayeem SM, Tabish M (2019) A comprehensive spectroscopic and computational investigation on the binding of the anti-asthmatic drug triamcinolone with serum albumin. New J Chem 43:4137–4151

    Article  CAS  Google Scholar 

  60. Siddiqui S, Ameen F, Kausar T, Nayeem SM, Rehman SU, Tabish M (2021) Biophysical insight into the binding mechanism of doxofylline to bovine serum albumin: an in vitro and in silico approach. Spectrochim Acta Part A: Mol Biomol Spectrosc 249:119296

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Physical Chemistry of Materials, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Casablanca, Morocco

    Oussama Abchir & Said Belaaouad

  2. Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, BP 72, Fez, Morocco

    Ossama Daoui & Souad ElKhattabi

  3. Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, BP 145 , 707000, Biskra, Algeria

    Salah Belaidi & Mebarka Ouassaf

  4. Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, UP-202002, India

    Faizan Abul Qais

  5. Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, Sidi Othman, Box 7955, Casablanca, Morocco

    Samir Chtita

Authors
  1. Oussama Abchir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ossama Daoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salah Belaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mebarka Ouassaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Faizan Abul Qais
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Souad ElKhattabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Said Belaaouad
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Samir Chtita
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Oussama Abchir: conceived and designed the experiments; performed the experiments; contributed reagents, materials, analysis tools, or data; and wrote the paper. Mebarka Ouassaf, and Faizan Abul Qais: conceived and designed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools, or data; and wrote the paper. Salah Belaidi and Said Belaaouad: conceived and designed the experiments. Ossama Daoui and Souad Elkhattabi: analyzed and interpreted the data and revised the paper. Samir Chtita: conceived and designed the experiments; analyzed and intereffects of diabetes on your bodpreted the data; contributed reagents, materials, analysis tools, or data; wrote the paper and supervision.

Corresponding author

Correspondence to Samir Chtita.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 20.9 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abchir, O., Daoui, O., Belaidi, S. et al. Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies. J Mol Model 28, 106 (2022). https://doi.org/10.1007/s00894-022-05097-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-022-05097-9

Keywords

Search

Navigation