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Sainy, Navin
- 3d Qsar Analysis of Flavones as Antidiabetic Agents
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1 School of Pharmacy, Devi Ahilya Vishwavidyalaya, Indore (M.P.) 452001,, IN
2 College of Pharmacy, IPS Academy Indore (M.P.) 452012, IN
1 School of Pharmacy, Devi Ahilya Vishwavidyalaya, Indore (M.P.) 452001,, IN
2 College of Pharmacy, IPS Academy Indore (M.P.) 452012, IN
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Research Journal of Pharmacy and Technology, Vol 15, No 4 (2022), Pagination: 1689-1695Abstract
Diabetes is the most prevailing disease worldwide and emerged as the fourth leading cause of mortality. Inhibition of intestinal α-Glucosidase enzyme is an effective approach for controlling post prandial hyperglycemia. α-Glucosidase inhibitors are known to be very effective in decreasing post-prandial hyperglycemia but the existing drugs are weak inhibitors of α-Glucosidase and also have side effects. Hence it needs for new therapeutic candidate which can effectively inhibit the activity of α-Glucosidase. Flavones recognized as the potential lead structure for many pharmacological activities. In the present research work 3D QSAR (comparative molecular field analysis and comparative molecular similarity indices analysis) was carried out on a series of flavones to identify structural requirement for effective inhibition of α-Glucosidase enzyme. The QSAR results shows that the LOO cross-validated q2 values of CoMFA and CoMSIA models are 0.742 and 0.759, respectively. The outcome of this research work could be effectively utilized for design of better α-Glucosidase inhibitors.Keywords
α-Glucosidase, Flavone, CoMFA, CoMSIA.References
- World Health Organisation, WHO 2020 World Diabetes Report. https://www.who.int/health-topics/diabetes
- Verspohl EJ. Novel Pharmacological Approaches to the Treatment of Type 2 Diabetes. Pharmacological Reviews. 2012; 64(2): 2188-2237.
- Chiba S. Molecular mechanism in alpha glucosidase and alpha amylase. Biosci. Biotechnol. Biochem. 199; 61(8): 1233-39.
- Hsieh PC, Huang G, Ho Y, Lin Y, Huang S, Chiang Y, Tseng MC, Chang YS. Activities of antioxidants, α-Glucosidase inhibitors and aldose reductase inhibitors of the aqueous extracts of four Flemingia species in Taiwan. Bot Stud. 2010; 51: 293-302.
- Ahmed N. Advanced glycation end products role in pathology of diabetic comlications. Diab. Res. Clin. Pr. 2005; 67(1): 3-21.
- Asano N. Glycosidase inhibitors: update and perspectives on practical use. Glycobiology 2003; 13(10): 93-104.
- Chougale AD, Ghadyale VA, Panaskar SN, Arvindekar AU. Alpha glucosidase inhibition by stem extract of Tinospora cordifolia. J. Enzyme Inhib. Med. Chem. 2009; 24(4): 998-1001.
- Kashtoh H, Hussain S, Khan A, Saad SM, Khan AJ, Khan KM, Perveen S, Choudhary M I. Oxadiazoles and thiadiazoles: Novel a-glucosidase inhibitors. Bioorg and Med Chem. 2014; 22(19): 5454-65.
- Kumar S, Narwal S, Kumar V, Prakash O. α-glucosidase inhibitors from plants: A natural ap α-glucosidase inhibitors from plants: A natural approach to treat diabetes. Pharmacogn Rev. 2011; 5(9): 19–29.
- Singh M, Kaur M, Silakari O. Flavones: An important scaffold for medicinal chemistry. Eur J Med Chem. 2014; 84: 206-239.
- Xu JD, Zhang LW, Liu YF. Synthesis and antioxidant activities of flavonoids derivatives, troxerutin and 30,40,7-triacetoxyethoxyquercetin Chin. Chem. Lett. 2013; 24(3):223-226.
- Zhengn JB, Zhang HF, Gao H. Investigation on electrochemical behavior and scavenging superoxide anion ability of chrysin at mercury electrode topon. J. Chem. 2005; 23(8): 1042-1046.
- Prasada Rao K., Santha Kumari K., Mohan S. Synthesis, Characterization and Antimicrobial activity of Some Flavones. Asian J. Research Chem. 2013; 6(2): 163-165.
- Shanmugapriya E, Ravichandiran V, Vijey Aainandhi M. Molecular docking studies on naturally occurring selected flavones against protease enzyme of Dengue virus. Research J. Pharm. and Tech. 2016; 9(7): 929-932.
- Kumar L, Verma R. Molecular docking-based approach for the design of Novel Flavone Analogues as inhibitor of Beta- Hydroxyacyl-ACP Dehydratase HadAB complex. Research J. Pharm. and Tech. 2017; 10(8): 2439-2445.
- Gejalakshmi S, Harikrishnan N, Mohameid AS. In-Vitro and In-Silico Alpha Glucucosidase Inhibitory activity of Oroxylum indicum. Research Journal of Pharmacognosy and Phytochemistry. 2021; 13(3): 119-5.
- Hanhineva K, Torronen R, Bondia-Pons I, Pekkinen J, Kolehmainen M, Mykkanen H, Poutanen K. Impact of dietary polyphenols on carbohydrate metabolism. Int. J. Mol. Sci. 2010; 11(4): 1365-1402.
- Sainy J, Sharma R. QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA, SAR QSAR Enviro. Res. 2015; 26(10): 873–892.
- Mandloi N, Sharma R, Sainy J, Patil S. Exploring Structural Requirement for Design and Development of compounds with Antimalarial Activity via CoMFA, CoMSIA and HQSAR. Research J. Pharm. and Tech. 2018; 11(8): 3341-3349.
- Pai A, B. Jayashree S. Computational Approach for the Design of Flavone based CDK2/CyclinA Inhibitors: A Simulation Study Employing Pharmacophore based 3D QSAR. Research J. Pharm. and Tech. 2019; 12(5): 2299-2303.
- Karthikeyan L, Hari BB, Rajasekaran A, Arivukkarasu R. Molecular Docking Studies of Flavones in Gentianaceae Family against Liver Corrective Targets. Res. J. Pharmacognosy and Phytochem. 2019; 11(2): 49-53.
- Tanveer H, Raza MG, Sayed H M, Singh PK, Baqri SSR. Normal Mode Analysis, Electronic Parameters and molecular docking study of 3, 5, 4’-Trihydroxy-6, 7-Dimethoxy-Flavone (Eupalitin) using First Principle. Asian J. Research Chem. 2017; 10(6): 789-797.
- Bhavanisha Rithiga S, Shanmugasundaram S. Virtual Screening of Pentahydroxyflavone – A Potent COVID-19 Major Protease Inhibitor. Asian J. Res. Pharm.Sci. 2021; 11(1): 7-14.
- Kumawat D, Goswami R, Pathak S, Gupta DK, Dwivedi S K, Chaturvedi SC. Molecular Modeling Study of Some β-Ketoacyl-acyl Carrier Protein Synthase III Inhibitors as Antibacterial Agents. Asian J. Res. Pharm. Sci. 2019; 9(4): 253-259.
- Stewart JJP. Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem. 1989; 10(2): 209–220.
- Cramer III RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA): I. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988; 110(18): 5959–5967.
- Klebe G, Abraham U, MietzneT. Molecular similarity indexes in a comparative-analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 1994; 37(24): 4130–4146.
- SYBYL-X 2.1, Tripos Inc., St. Louis, MO.
- Moda TL, Montanarib CA, Andricopulo AD. Hologram QSAR model for the prediction of human oral bioavailability. Bioorg. Med. Chem. 2007; 15(24): 7738–7745.
- Hong Gao and Jun Kawabata α-Glucosidase inhibition of 6-hydroxyflavones. Part 3: Synthesis and evaluation of 2,3,4- trihydroxybenzoyl-containing flavonoid analogs and 6-aminoflavones as a-glucosidse inhibitors.bmcl. 2005; 13(5): 1661-1671.
- Imran S, Taha Muhammad, Ismail Nor Hadiani, Kashif Syed Muhammad C, Rahim Fazal D, Jamil Waqas C, Hariono Maywan E, Yusuf Muhammad E, Wahab Habibah. Synthesis of novel flavone hydrazones: In-vitro evaluation of α-glucosidase inhibition, QSAR analysis and docking studies. EJMC 105(2015); 156-170.
- Cho S.J. and Tropsha A. Cross-validated R2-guided region selection for comparative molecular field analysis: A simple method to achieve consistent results. J. Med. Chem. 1995; 38(7): 1060–1066.
- Tong W, Lowis DR, Perkins R, Chen Y, Welsh WJ, Goddette DW, Heritage TW, and Sheehan DM. Evaluation of quantitative structure–activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J. Chem. Inf. Comput. Sci. 38. 1998; 22(8): 669–677.
- Waller CL. A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. J. Chem. Inf. Compu.t Sci. 2004; 44(2): 758–765.
- Clark M, Cramer III RD, Opdenbosch NV. Validation of the general purpose Tripos 5.2 forcefield. J. Comput. Chem. 1989; 10(8): 982–1012.
- Dunn WJ, Wold S, Edlund V, Hellherg S, and Gasteiger J. Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of chemical descriptors: The PLS method, Quant. Struct.-Act. Relat. 1984; 3: 131–137.
- Wold S, Sjöström M, and Eriksson L. PLS-regression: A basic tool of chemometrics, Chemom. Intell. Lab. Syst. 2001; 58(2): 109–130.
- Cramer R.D. Partial least squares (PLS): Its strengths and limitations. Perspect Drug Discov. Des.1. 1993; 1(2): 269–278.
- S. Wold and L. Ericksson, Partial least squares projections to latent structures (PLS) in chemistry, In Encyclopedia of Computational Chemistry, Ragu and P. Schleyer, eds., John Wiley and Sons, Chichester, 1998, pp. 2006–2021.
- A.K. Debnath, Combinatorial library design and evaluation, in Principles, Software, Tools and Application in Drug Discovery, K. Ghose and V.N. Viswanadhan, eds., Marcel Dekker Inc, New York, NY, 2001; pp. 73–129.
- Walker JD, Jaworska J, Comber MH, Schultz TW, Dearden JC. Guidelines for developing and using quantitative structure-activity relationships. Environ Toxicol. Chem. 2003; 22(8): 1653–1665.