In silico Assessment of Potential Leads Identified from Bauhinia rufescens Lam. as α-Glucosidase and α-Amylase Inhibitors
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Published
May 5, 2020
Page:
27-38
Main Article Content
Wadah Osman
Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Esraa M. O. A. Ismail
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Shaza W. Shantier
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Mona S. Mohammed
Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Ramzi A. Mothana
Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
Abdelkhalig Muddathir
Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Hassan S. Khalid
Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Khartoum, P.O.Box 1996, Sudan.
Abstract
Aim: Natural products play a pivotal role in innovative drug discovery by providing structural leads for the development of new therapeutic agents against various diseases. The present study aims to focus on the in silico assessment of the therapeutic potential of antidiabetic phytoconstituents which were identified and isolated from the extracts of Bauhinia rufescens Lam., a medicinal plant traditionally used for various pharmacotherapeutic purposes.
Methods: The physicochemical and pharmacokinetic parameters of the isolated thirty eight compounds were predicted using Swiss ADME web tool whereas OSIRIS Property Explorer was used for toxicity risk assessment and drug- likeliness. Twelve compounds were selected for docking on human α-glucosidase and α-amylase enzymes using Autodock 4.0 software.
Results and Discussion: Eriodictyol was found to have the highest potential as an inhibitor against α-amylase with binding energy of -9.92 kcal/mol. Rutin was the most potent against α-glucosidase with binding energy of-9.15 kcal/mol. A considerable number of hydrogen bonds and hydrophobic interactions were computed between the compounds and the enzymes thereby making them energetically favorable and suggesting inhibition of these two enzymes as a plausible molecular mechanism for their antidiabetic effect.
Conclusion: These two flavonoids could therefore be used as potential leads for structure- based design of new effective hypoglycemic agents.
Keywords:
Bauhinia rufescens Lam, phytoconstituents, ADME, toxicity risk, molecular docking, α-glucosidase, α-amylase
Article Details
How to Cite
Osman, W., Ismail, E. M. O. A., Shantier, S. W., Mohammed, M. S., Mothana, R. A., Muddathir, A., & Khalid, H. S. (2020). In silico Assessment of Potential Leads Identified from Bauhinia rufescens Lam. as α-Glucosidase and α-Amylase Inhibitors. Journal of Pharmaceutical Research International, 32(6), 27-38. https://doi.org/10.9734/jpri/2020/v32i630444
Section
Original Research Article
References
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Patridge E, Gareiss P, Kinch MS, Hoyer D. An analysis of FDA-approved drugs: Natural products and their derivatives. Drug Discov. Today. 2016;21:204–207.
Blass B. Basic principles of drug discovery and development. 1st Edition, Academic Press, Amsterdam; 2015.
Yuan H, Ma Q, Ye L, Piao G. Molecules. 2016;21:559.
Hefti FF. Requirements for a lead compound to become a clinical candidate. BMC Neurosci. 2008;9(Suppl 3):S7.
(Published; 2008)
DOI: 10.1186/1471-2202-9-S3-S7
Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci. 2010;11(8):3016-3034.
Petrovska BB. Historical review of medicinal plants' usage. Pharmacogn Rev. 2012;6:1–5.
Carvalho JC, Santos LS, Viana EP, De Almeida SS, Marconato E, Rodrigues M, Ferreira LR, Van de Kamp A. Anti-inflammatory and analgesic activities of the crude extracts from stem bark of Bauhinia guianensis. Pharm. Bio. 1999;37(4):281-4.
Cipak L, Grausova L, Miadokova E, Novotny L, Rauko P. Dual activity of triterpenoids: Apoptotic versus anti-differentiation effects. Arc of Toxi. 2006;80(7):429-35.
Deepak M, Handa SS. Antiinflammatory activity and chemical composition of extracts of Verbena officinalis. Phyto-therapy Research. 2000;14(6):463-5.
Kittakoop P, Kirtikara K, Tanticharoen M, Thebtaranonth Y. Antimalarial preracemosols A and B, possible biogenetic precursors of racemosol from Bauhinia malabarica Roxb. Phytochem. 2000;55(4):349-52.
Rao AV, Gurfinkel DM. The bioactivity of saponins: Triterpenoid and steroidal glycosides. Drug Metabol. Drug Interact. 2000;17:211-235.
Songarsa S, Rajviroongit S, Sae‐Tang D, Hannongbua S, Kirtikara K, Kittakoop P. New racemosol derivatives as potent cyclooxygenase (COX) inhibitors. Chemistry & Biodiversity. 2005;1635-47.
Osman W, Khalid H, Muddathir A, Shantier S, Mohammed M, Osman B, Abdoon I. HPTLC fingerprints profile and identification of antidiabetic and antioxidant leads from Bauhinia rufescens (L.). Advan. Pharmacol. Pharma. Sci. Hindawi Publishers; 2020.
Daina A, Michielin O, Zoete V. Swiss ADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017;7:42717.
Organic Chemistry Portal; 2019.
Available:http://www.organicchemistry.org/prog/peo/
(Accessed 13 March 2019)
Guex N, Peitsch MC. SWISS-MODEL and the swiss-Pdb viewer: An environment for comparative protein modeling. Electrophoresis. 1997;18:2714–23.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. Autodock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–91.
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, et al. Automated docking using a Lamarckian geneticalgorithm and empirical binding free energy function. J Comput Chem. 1998;19:1639–62.
Goddard TD, Huang CC, Ferrin TE. Software extensions to UCSF chimera for interactive visualization of large molecular assemblies. J Structure. 2005;13(3):473-82.
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 2001;46:3–26.
Montanari F, Ecker GF. Prediction of drug-ABC-transporter interaction–Recent advances and future challenges. Adv. Drug Deliv. Rev. 2015;86:17–26.
Testa B, Kraemer SD. The biochemistry of drug metabolism – an introduction - Testa - 2007 - Chemistry & Biodiversity - Wiley Online Library. Chem. Biodivers; 2007.
Hollenberg PF. Characteristics and common properties of inhibitors, inducers, and activators of CYP enzymes. Drug Metab. Rev. 2002;34:17–35.
Huang SM, et al. New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters and the guidance process. J. Clin. Pharmacol. 2008;48:662–670.
Hermans MMP, Kroos MA, van Beeumen J, Oostra BA, Reuser AJJ. Human lysosomal alpha-glucosidase. Characterization of the catalytic site. J. Biol. Chem. 1991;266:13507-13512.
Roig-Zamboni V, Cobucci-Ponzano B, Iacono R, Ferrara MC, Germany S, Bourne Y, Parenti G, Moracci M, Sulzenbacher G. Structure of human lysosomal acid alpha-glucosidase-a guide for the treatment of Pompe disease. Nature Communications 8. 2017;1111.
Williams John A. Amylase. Pancreapedia: Exocrine Pancreas Knowledge Base; 2017.
Brayer GD, Luo Y, Withers SG. The structure of human pancreatic alpha-amylase at 1.8 A resolution and comparisons with related enzymes. Protein Sci. 1995;4(9):1730–1742.
Brayer GD, Sidhu G, Maurus R, Rydberg EH, Braun C, Wang Y, Nguyen NT, Overall CM, Withers SG. Subsite mapping of the human pancreatic alpha-amylase active site through structural, kinetic and mutagenesis techniques. Biochemistry. 2000;25(16): 4778-91.
Rohan Patil, Suranjana Da, Ashley Stanley, Lumbani Yadav, Akulapalli Sudhakar Ashok K. Varma. Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLoS ONE. 2010;5(8).