Identification of Some DPP-4 Inhibitors Using QSAR Modeling Based Drug Repurposing Approach
Abstract
Post-prandial hyperglycemia still remains a problem in the management of type II diabetes mellitus. Of all available anti-diabetic drugs, DPP-4 inhibitors seem to be one of the most effective in reducing post-prandial hyperglycemia. In present study, QSAR modeling based drug repurposing approach has been implemented to identify some repurposed DPP-4 inhibitors with established safety profile. For this QSAR modeling based analysis, initially a (S)-1-((S)-2-amino-3-phenylpropanoyl) pyrrolidine-2-carbonitrile having two different types of substitutions i.e. R1 on phenyl and R2 on pyrrolidine as well as proper variation in the biological activity was selected thereafter models were developed using various conventional QSAR approaches including Free Wilson, Hansch, and Mixed modeling by utilizing PaDEL descriptor calculator and DTC lab software. Hansch type 2D QSAR model, which was derived using some PaDEL descriptor, showed acceptable internal as well as external consistencies. Some repurposed DPP-4 inhibitors were successfully identified. These identified approved drugs may be further explored as new anti-diabetics for type II diabetes patient especially for the management of post-prandial hyperglycemia which is a major issue in these patients
Keywords: QSAR, Hyperglycemia, Substitutions, Diabetes mellitus, PaDEL descriptor
Keywords:
QSAR, Hyperglycemia, Substitutions, Diabetes mellitus, PaDEL descriptorDOI
https://doi.org/10.22270/jddt.v15i3.7030References
1. Pizzi RA. Defying diabetes: The discovery of insulin. Modern Drug Discovery 2000; 3(6); 77-80.
2. APh A Special Report. New approaches to insulin therapy for diabetes. American Pharmaceutical Association, Washington DC 2001.
3. Derosa G, Maffioli P. α-Glucosidase inhibitors and their use in clinical practice, Arch Med Sci 2012; 5:899-906 https://doi.org/10.5114/aoms.2012.31621 PMid:23185202 PMCid:PMC3506243
4. Wehmeier U, Piepersberg W. Biotechnology and molecular biology of the α - glucosidase inhibitor acarbose, Appl. Microbiol. Biot. 2004; 63:613-625. https://doi.org/10.1007/s00253-003-1477-2 PMid:14669056
5. Narita T, Yokoyama H, Yamashita R, Sato T, Hosoba M, Morii T., et al., Comparisons of the effects of 12-week administration of miglitol and voglibose on the responses of plasma incretins after a mixed meal in Japanese type 2 diabetic patients, Diabetes. Obes. Metab. 2011; 14:283-287. https://doi.org/10.1111/j.1463-1326.2011.01526.x PMid:22051162
6. Derosa G, Mereu R, D'Angelo A, Salvadeo S, Ferrari I, Fogari E, et al., Effect of pioglitazone and acarbose on endothelial inflammation biomarkers during oral glucose tolerance test in diabetic patients treated with sulphonylureas and metformin, J. Clin. Pharm. Ther. 2010; 35:565-579. https://doi.org/10.1111/j.1365-2710.2009.01132.x PMid:20831680
7. Derosa G, Maffioli P. Mini-Special Issue paper Management of diabetic patients with hypoglycemic agents α-Glucosidase inhibitors and their use in clinical practice, Arch. Med. Sci. 2012; 5:899-906. https://doi.org/10.5114/aoms.2012.31621 PMid:23185202 PMCid:PMC3506243
8. Holt R, Lambert K. The use of oral hypoglycaemic agents in pregnancy, Diabet. Med. 2014; 31:282-291. https://doi.org/10.1111/dme.12376 PMid:24528229
9. Syahrul I. et al. Synthesis of novel flavone hydrazones: In-vitro evaluation of α-glucosidase inhibition, QSAR analysis and docking studies Eur. J. Med. Chem., 2015; 105:156-170. https://doi.org/10.1016/j.ejmech.2015.10.017 PMid:26491979
10. Muhammad T. et al. Synthesis of novel inhibitors of α-glucosidase based on the benzothiazole skeleton containing benzohydrazide moiety and their molecular docking studies, Eur. J. Med. Chem, 2015; 92:387-400. https://doi.org/10.1016/j.ejmech.2015.01.009 PMid:25585009
11. Farman A, et al. Hydrazinyl arylthiazole based pyridine scaffolds: Synthesis, structural characterization, in vitro α-glucosidase inhibitory activity, and in silico studies, Eur. J. Med. Chem., 2017; 138:255-272 https://doi.org/10.1016/j.ejmech.2017.06.041 PMid:28672278
12. Flynn GL. Substituent constants for correlation analysis in chemistry and biology. By Corwin Hansch and Albert Leo. Wiley, 605 Third Ave., New York, NY 10016. 1979.
13. Golbraikh A, Tropsha A., Beware of Q2, J Mol Graph Model, 2002; 20:269-76. https://doi.org/10.1016/S1093-3263(01)00123-1 PMid:11858635
14. Krzywinski M, Altman N. Classification and regression trees. Nat Methods. 2017; 14(8):757. https://doi.org/10.1038/nmeth.4370
15. Costa VG, Pedreira CE. Recent advances in decision trees: an updated survey. Artif Intell Rev. 2023; 56:4765-4800. https://doi.org/10.1007/s10462-022-10275-5
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