Design of Novel Benzimidazole Derivatives as Potential α-amylase Inhibitors by 3D-QSAR Modeling and Molecular Docking Studies

Yıl 2020, Cilt: 7 Sayı: 2, 471 - 480, 23.06.2020

Khalil El Khatabi , İlham Aanouz , Reda El-mernissi , Ayoub Khaldan , Mohammed Aziz Ajana , Mohammed Bouachrıne , Tahar Lakhlıfı

https://doi.org/10.18596/jotcsa.703026

Cited By: 6

Öz

The α-amylase is an enzyme of a highly conserved glycoside hydrolase family, α-amylase inhibitors can be used as clinical agents for the treatment of Diabetes Mellitus (DM). A 3D-QSAR study was performed on 45 2-aryl benzimidazole derivatives, which have been identified as insulin-independent antidiabetic agents. The 3D-QSAR technique includes CoMFA with Q2 of 0.696 and R2 of 0.860 and CoMSIA with Q2 of 0.514 and R2 of 0.852. Both models were derived from a training set of 37 compounds based on an appropriate method of alignment, while the predictive ability was approved by a test set containing 8 compounds with rext2 values of 0.990 and 0.987, respectively. Moreover, contour maps generated from CoMFA and CoMSIA models provided much helpful information to figure out the features requirements that have control over the activity. To further reinforce the 3D-QSAR results, the molecular docking method was implemented which led to design new potent insulin-independent antidiabetic compounds with high predicted activity values.

Anahtar Kelimeler

3D-QSAR, Molecular modeling, Computational study, Benzimidazole, α-amylase inhibitors

Teşekkür

We are grateful to the “Association Marocaine des Chimistes Théoriciens” (AMCT) for its pertinent help concerning the software programs.

Kaynakça

• 1. Orbak R, Simsek S, Orbak Z, Kavrut F, Colak M. The Influence of Type-1 Diabetes Mellitus on Dentition and Oral Health in Children and Adolescents. Yonsei Med J. 2008;49(3):357-365.
• 2. Latti BR, Kalburge JV, Birajdar SB, Latti RG. Evaluation of relationship between dental caries, diabetes mellitus and oral microbiota in diabetics. J Oral Maxillofac Pathol JOMFP. 2018;22(2):282.
• 3. Gonick HC, Kramer HJ, Schapiro AE. Urinary ß-Glucuronidase Activity in Renal Disease. Arch Intern Med. 1973;132(1):63‑69.
• 4. Kim D-H, Lee S-W, Han MJ. Biotransformation of Glycyrrhizin to 18β-Glycyrrhetinic Acid-3-O-β-D-glucuronide by Streptococcus LJ-22, a Human Intestinal Bacterium. Biol Pharm Bull. 1999;22(3):320‑322.
• 5. Abou-Haila A, Orgebin-Crist M-C, Skudlarek MD, Tulsiani DRP. Identification and androgen regulation of egasyn in the mouse epididymis. Biochim Biophys Acta BBA - Mol Cell Res. 1998;1401(2):177‑186.
• 6. Pereira W, Cruz A, Albuquerque E, Santos E, Oliveira A, Sales M, et al. Purification and Characterization of a ß-Glucuronidase Present During Embryogenesis of the Mollusk Pomacea sp. Protein Pept Lett. 2005;12(7):695‑700.
• 7. Zhang C, Zhang Y, Chen J, Liang X. Purification and characterization of baicalin-β-d-glucuronidase hydrolyzing baicalin to baicalein from fresh roots of Scutellaria viscidula Bge. Process Biochem. 2005;40(5):1911‑1915.
• 8. Wang X, Liu Y, Wang C, Feng X, Li C. Properties and structures of β-glucuronidases with different transformation types of glycyrrhizin. RSC Adv. 2015;5(84):68345‑68350.
• 9. Küçükbay H. PART I: MICROWAVE-ASSISTED SYNTHESIS OF BENZIMIDAZOLES: AN OVERVIEW (UNTIL 2013). J Turk Chem Soc Sect Chem 2017; 4(1):1–22.
• 10. Küçükbay H, Mumcu A, Teki̇n S, Sandal S. Synthesis and evaluation of novel N, N’-disubstituted benzimidazolium bromides salts as antitumor agents. Turk J Chem. 2016;40(3):393‑401.
• 11. Khabnadideh S, Rezaei Z, Pakshir K, Zomorodian K, Ghafari N. Synthesis and antifungal activity of benzimidazole, benzotriazole and aminothiazole derivatives. 2012;7(2):65-72.
• 12. Sondhi SM, Singh N, Kumar A, Lozach O, Meijer L. Synthesis, anti-inflammatory, analgesic and kinase (CDK-1, CDK-5 and GSK-3) inhibition activity evaluation of benzimidazole/benzoxazole derivatives and some Schiff’s bases. Bioorg Med Chem. 2006;14(11):3758‑65.
• 13. Salahuddin, Shaharyar M, Mazumder A. Benzimidazoles: A biologically active compounds. Arab J Chem. 2017;10:157‑73.
• 14. Carcanague D, Shue Y-K, Wuonola MA, Uria-Nickelsen M, Joubran C, Abedi JK, et al. Novel Structures Derived from 2-[[(2-Pyridyl)methyl]thio]-1 H -benzimidazole as Anti- Helicobacter p ylori Agents, Part 2. J Med Chem. 2002;45(19):4300‑9.
• 15. Küçükbay H, Yilmaz Ü, Şi̇reci̇ N, Güvenç AN. Synthesis and antimicrobial activities of some bridged bis-benzimidazole derivatives. Turk J Chem. 2011;35(4):561‑71.
• 16. Ou-Yang S, Lu J, Kong X, Liang Z, Luo C, Jiang H. Computational drug discovery. Acta Pharmacol Sin. 2012;33(9):1131‑40.
• 17. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment 1, Kunal Roy, Supratik Kar, Rudra Narayan Das - Amazon.com [Internet]. available on: https://www.amazon.com/Understanding-Applications-Pharmaceutical-Sciences-Assessment-ebook/dp/B00URFMD7Q
• 18. Adegboye AA, Khan KM, Salar U, Aboaba SA, Kanwal, Chigurupati S, et al. 2-Aryl benzimidazoles: Synthesis, In vitro α-amylase inhibitory activity, and molecular docking study. Eur J Med Chem. 2018;150:248‑60.
• 19. Sybyl-X [Internet]. omicX. available on: https://omictools.com/sybyl-x-tool
• 20. Clark M, Cramer RD, Van Opdenbosch N. Validation of the general purpose tripos 5.2 force field. J Comput Chem. 1989;10(8):982‑1012.
• 21. Purcell WP, Singer JA. A brief review and table of semiempirical parameters used in the Hueckel molecular orbital method. J Chem Eng Data. 1967;12(2):235‑46.
• 22. SCIGRESS - a unique desktop molecular modeling software package [Internet]. available on:https://fqs.pl/en/chemistry/products/scigress?gclid=EAIaIQobChMIsrORqqe95wIVw7HtCh0bGAueEAAYASAAEgJ0M_D_BwE
• 23. Cramer RD, 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.
• 24. Klebe G, Abraham U, Mietzner T. Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity. J Med Chem. 1994;37(24):4130‑46.
• 25. Ståhle L, Wold S. 6 Multivariate Data Analysis and Experimental Design in Biomedical Research. In: Progress in Medicinal Chemistry [Internet]. Elsevier; 1988:291‑338. available on: https://linkinghub.elsevier.com/retrieve/pii/S0079646808702819
• 26. Bush BL, Nachbar RB. Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des. 1993;7(5):587‑619.
• 27. Baroni M, Clementi S, Cruciani G, Costantino G, Riganelli D, Oberrauch E. Predictive ability of regression models. Part II: Selection of the best predictive PLS model. J Chemom. 1992;6(6):347‑56.
• 28. DeLano WL. Pymol: An open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr. 2002;40(1):82–92.
• 29. Discovery Studio Predictive Science Application | Dassault Systèmes BIOVIA [Internet]. available on: https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/
• 30. Rücker C, Rücker G, Meringer M. y-Randomization and Its Variants in QSPR/QSAR. J Chem Inf Model. 2007;47(6):2345‑57.