Design and prediction of favorable substitution site in trifluorophenyl-substituted homopiperazine, pyrazoline, triazepane derivatives as dipeptidyl peptidase IV Inhibitors: HQSAR and docking studies

  • M. C. SharmaEmail author
  • S. JainEmail author
Original Article


In this study, hologram quantitative structure–activity relationship (HQSAR) and molecular docking studies were performed on a dataset of 108 trifluorophenyl homopiperazine, pyrazoline, and triazepane derivatives as dipeptidyl peptidase IV inhibitors. HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameters with fragment size (4–7) using components (q2 = 0.738, r2 = 0.962). Molecular docking study was performed to identify novel potent inhibitors and the important amino acid residues, which formed an interaction with compound 105, were Ser-631, His-741, Tyr-663, Glu-204, Arg-123 and Ala-655 with receptor. These models were used to design new compounds for homopiperazine, pyrazoline, triazepane analogs and the results obtained from this study could be useful for further investigations.


HQSAR Fragment size Docking Homopiperazine Pyrazoline Triazepane DPP-IV inhibitors 



The authors are thankful to the Head, School of Pharmacy, Devi Ahilya Vishwavidyalaya, Indore for providing facilities.

Compliance with ethical standards

Conflict of interest

There are no conflicts of interest.


  1. Ahn JH, Park WS, Jun MA, Shin MS, Kang SK, Kim KY, Rhee SD, Bae MA, Kim KR, Kim SG, Kim SY, Sohn SK, Kang NS, Lee JO, Lee DH, Cheon HG, Kim SS (2008) Synthesis and biological evaluation of homopiperazine derivatives with beta-aminoacyl group as dipeptidyl peptidase IV inhibitors. Bioorg Med Chem Lett 18:6525–6529CrossRefGoogle Scholar
  2. Ahrén B, Holst JJ, Mårtensson H, Balkan B (2000) Improved glucose tolerance and insulin secretion by inhibition of dipeptidyl peptidase IV in mice. Eur J Pharmacol 404(1–2):239–245CrossRefGoogle Scholar
  3. Al-Najjar BO, Wahab HA, Tengku Muhammad TS, Shu-Chien AC, Ahmad Noruddin NA, Taha MO (2011) Discovery of new nanomolar peroxisome proliferator-activated receptor gamma activators via elaborate ligand-based modeling. Eur J Med Chem 46(6):2513–2529CrossRefGoogle Scholar
  4. Augustyns K, Van Der Veken P, Senten K et al (2003) Dipeptidyl peptidase IV inhibitors as new therapeutic agents for the treatment of type 2 diabetes. Expert Opin Ther Pat 13(4):499–510CrossRefGoogle Scholar
  5. Bush BL, Nachbar RB (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des 7(5):587–619CrossRefGoogle Scholar
  6. Conarello SL, Li Z, Ronan J, Roy RS, Zhu L, Jiang G, Liu F, Woods J, Zycband E, Moller DE, Thornberry NA, Zhang BB (2003) Mice lacking dipeptidyl peptidase IV are protected against obesity and insulin resistance. Proc Natl Acad Sci 100(11):6825–6830CrossRefGoogle Scholar
  7. Cramer RD, Patterson DE, Bunce JD (1988) Comparative Molecular-Field Analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  8. Demuth H, Heins J (1995) Catalytic mechanism of dipeptidyl peptidase-IV. In: dipeptidyl peptidase-IV (CD26) in metabolism and the immune response. Fleischer B, Landes RG (eds) Austin, TX, vol. 3, pp 1–35Google Scholar
  9. Flower DR (1998) On the properties of bit string-based measures of chemical similarities. J Chem Inf Comput Sci 38:379–386CrossRefGoogle Scholar
  10. Heritage TW, Lowis DR (1999) Molecular hologram QSAR. In: Rational drug design: novel methodology and practical applications. Oxford University Press, New YorkGoogle Scholar
  11. Jun MA, Park WS, Kang SK, Kim KY, Kim KR, Rhee SD, Bae MA, Kang NS, Sohn SK, Kim SG, Lee JO, Lee DH, Cheon HG, Kim SS, Ahn JH (2008) Synthesis and biological evaluation of pyrazoline analogues with beta-amino acyl group as dipeptidyl peptidase IV inhibitors. Eur J Med Chem 43:1889–1902CrossRefGoogle Scholar
  12. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indexes in a comparative-analysis (comsia) of drug molecules to correlate and predict their biological-activity. J Med Chem 37:4130–4146CrossRefGoogle Scholar
  13. Lowis DR (1997) HQSAR: a new, highly predictive QSAR technique. Tripos Tech Notes 1(5):17Google Scholar
  14. Marguet D (2000) Enhanced insulin secretion and improved glucose tolerance in mice lacking CD26. Proc Natl Acad Sci 97(12):6874–6879CrossRefGoogle Scholar
  15. Mentlein R (1999) Dipeptidyl-peptidase IV (CD26)-role in the inactivation of regulatory peptides. Regul Pept 85:9–24CrossRefGoogle Scholar
  16. Mest HJ, Mentlein R (2005) Dipeptidyl peptidase inhibitors as new drugs for the treatment of type 2 diabetes. Diabetologia 48(4):616–620CrossRefGoogle Scholar
  17. Nordhoff S, López-Canet M, Hoffmann-Enger B, Bulat S, Cerezo-Gálvez S, Hill O, Rosenbaum C, Rummey C, Thiemann M, Matassa VG, Edwards PJ, Feurer A (2009) From lead to preclinical candidate optimization of beta-homophenylalanine based inhibitors of dipeptidyl peptidase IV. Bioorg Med Chem Lett 19:4818–4823CrossRefGoogle Scholar
  18. Park WS, Jun MA, Shin MS, Kwon SW, Kang SK, Kim KY, Dal Rhee S, Bae MA, Narsaiah B, Lee DH, Cheon HG, Ahn JH, Kim SS (2009) Synthesis and biological evaluation of triazepane derivatives as DPP-IV inhibitors. J Fluor Chem 130:1001–1010CrossRefGoogle Scholar
  19. Ping L, Chen WN, Chen WM (2011) Molecular modeling studies on imidazo[4,5- b]pyridine derivatives as Aurora A kinase inhibitors using 3D-QSAR and docking approaches. Eur J Med Chem 46:77–94CrossRefGoogle Scholar
  20. Pospisilik JA, Stafford SG, Demuth HU, Brownsey R, Parkhouse W, Finegood DT, McIntosh CH, Pederson RA (2002) Long-term treatment with the dipeptidyl peptidase IV inhibitor P32/98 causes sustained improvements in glucose tolerance, insulin sensitivity, hyperinsulinemia, and beta-cell glucose responsiveness in VDF (fa/fa) Zucker rats. Diabetes 51(4):943–950CrossRefGoogle Scholar
  21. Saqib U, Siddiqi MI (2009) 3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents SAR QSAR. Environ Res 20:519–535Google Scholar
  22. Sebokova E, Christ AD, Boehringer M, Mizrahi J (2007) Dipeptidyl peptidase IV inhibitors: the next generation of new promising therapies for the management of type 2 diabetes. Curr TopMed Chem 7:547–555CrossRefGoogle Scholar
  23. Sharma MC, Jain S, Sharma R (2018) Trifluorophenyl -based inhibitors of dipeptidyl peptidase-IV as antidiabetic agents: 3D-QSAR COMFA, CoMSIA methodologies. Netw Model Anal Health Inform Bioinform 7:1. CrossRefGoogle Scholar
  24. Sonawane L, Bari S (2011) Ligand-based in silico 3D-QSAR study of PPAR-γ agonists. Med Chem Res 20(7):1005–1014CrossRefGoogle Scholar
  25. Tong W, Lowis DR, Perkins R, Chen Y, Welsh WJ, Goddette DW, Heritage TW, Sheehan DM (1998) Evaluation of quantitative structure–activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sci 38:669–677CrossRefGoogle Scholar
  26. Yang XL, Zhou Y, Liu XL (2014) Hologram quantitative structure–activity relationship studies on 1-(5-carboxyindol-1-yl) propan-2-one inhibitors of human cytosolic phospholipase A2α. Med Chem Res 23(3):1512–1518CrossRefGoogle Scholar
  27. Yaron A, Nadier F (1993) Proline-Dependent Structural and Biological Properties of Peptides and Proteins. Crit Rev Biochem Mol Biol 28:31–81CrossRefGoogle Scholar
  28. Yasuda N, Nagakura T, Yamazaki K et al (2002) Improvement of high fat-diet-induced insulin resistance in dipeptidyl peptidase IV-deficient Fischer rats. Life Sci 71(2):227–238CrossRefGoogle Scholar
  29. Zeng J, Liu G, Tang Y, Jiang H (2007) 3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA. J Mol Model 13:993–1000CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of PharmacyDevi Ahilya VishwavidyalayaIndoreIndia

Personalised recommendations