Skip to main content
Log in

Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis

  • Original Paper
  • Published:
Monatshefte für Chemie - Chemical Monthly Aims and scope Submit manuscript

Abstract

The chemokine receptor CXCR2 plays an important role in recruiting granulocytes to sites of inflammation and has been proposed as an important therapeutic target. A linear quantitative structure–activity relationship model is presented for modeling and predicting biological activities of CXCR2 antagonists. The model was produced by using the multiple linear regression technique on a database that consists of 55 nonpeptide antagonists of CXCR2. Stepwise regression as a variable selection method was used to develop a regression equation based on 43 training compounds, and predictive ability was tested on 12 compounds reserved for that purpose. Appropriate models with low standard errors and high correlation coefficients were obtained. The mean effect of descriptors and standardized coefficients shows that the mean atomic van der Waals volume is the most important property affecting the biological activities of the molecules. The square regression coefficient of prediction set for the multiple linear regression method was 0.912.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Walters I, Austin C, Austin R, Bonnert R, Cage P, Christie M, Ebden M, Gardiner S, Grahames C, Hill S, Hunt F, Jewell R, Lewis S, Martin I, Nicholls D, Robinson D (2008) Bioorg Med Chem Lett 18:798

    Article  CAS  Google Scholar 

  2. Murphy PM, Baggiolini M, Charo IF, Hebert CA, Horuk R, Matsushima K, Miller LH, Oppenheim JJ, Power CA (2000) Pharmacol Rev 52:145

    CAS  Google Scholar 

  3. Hay D, Sarau H (2001) Curr Opin Pharmacol 1:242

    Article  CAS  Google Scholar 

  4. Ahuja S, Murphy P (1996) J Biol Chem 271:20545

    Article  CAS  Google Scholar 

  5. Baggiolini M (2001) J Intern Med 250:91

    Article  CAS  Google Scholar 

  6. Lax P, Limatola C, Fucile S, Trettel F, Di BS, Renzi M, Ragozzino D, Eusebi F (2002) J Neuroimmunol 129:66

    Article  CAS  Google Scholar 

  7. Liehn EA, Schober A, Weber C (2004) Arterioscler Thromb Vasc Biol 24:1891

    Article  CAS  Google Scholar 

  8. Cacalano G, Lee J, Kikly K, Ryan A, Pitts-Meek S, Hultgren B, Wood I, Moore W (1994) Science 265:682

    Article  CAS  Google Scholar 

  9. Bizzarri C, Allegretti M, Di Bitondo R, Neve Cervellera M, Colotta F, Bertini R (2003) Curr Med Chem 2:67

    CAS  Google Scholar 

  10. Busch-Petersen J (2006) Curr Top Med Chem 6:1345

    CAS  Google Scholar 

  11. Ghasemi J, Saaidpour S, Brown SD (2007) Theochem 805:27

    Article  CAS  Google Scholar 

  12. Katritzky AR, Petrukhin R, Tatham D (2001) J Chem Inf Comput Sci 41:679

    CAS  Google Scholar 

  13. Consonni V, Todeschini R, Pavan M, Gramatica P (2002) J Chem Inf Comput Sci 42:693

    CAS  Google Scholar 

  14. Krenkel G, Castro EA, Toropov AA (2001) J Mol Struct (Theochem) 542:107

    Article  CAS  Google Scholar 

  15. Godden JW, Stahura FL, Bajorath J (2004) J Med Chem 47:5608

    Article  CAS  Google Scholar 

  16. Moitessier N, Henry C, Maigret B, Chapleur Y (2004) J Med Chem 47:4178

    Article  CAS  Google Scholar 

  17. Carlsen L, Sørensen PB, Thomsen M (2001) Chemosphere 43:295

    Article  CAS  Google Scholar 

  18. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, Weinheim

    Book  Google Scholar 

  19. Chatterjee S, Hadi A, Price B (2000) Regression analysis by examples, 3rd edn. Wiley-VCH, New York

    Google Scholar 

  20. Shapiro S, Guggenheim B (1998) Quant Struct Act Relat 17:327

    Article  CAS  Google Scholar 

  21. Cho DH, Lee SK, Kim BT, No KT (2001) Bull Korean Chem Soc 22:388

    CAS  Google Scholar 

  22. Jalali-Heravi M, Konuze E (2002) Internet Electron J Mol Des 1:410

    Google Scholar 

  23. Consonni V, Todeschini R, Pavan M (2002) J Chem Inf Comput Sci 42:682

    CAS  Google Scholar 

  24. Moreau G, Broto P (1980) Nouv J Chim 4:359

    CAS  Google Scholar 

  25. Moran PAP (1950) Biometrika 37:17

    CAS  Google Scholar 

  26. Geary RF (1954) Incorp Stat 5:115

    Article  Google Scholar 

  27. Agüero-Chapin G, González-Dıaz H, Molina R, Varona-Santos J, Uriarte E, González-Dıaz Y (2006) FEBS Lett 580:723

    Article  Google Scholar 

  28. González-Dıaz H, Vilar S, Santana L, Uriarte E (2007) Curr Top Med Chem 7:1025

    Google Scholar 

  29. Hemmer MC, Steinhauer V, Gasteiger J (1999) Vib Spectrosc 19:151

    Article  CAS  Google Scholar 

  30. Khlebnikov AI, Schepetkin IA, Quinn MT (2006) Bioorg Med Chem 14:352

    Article  CAS  Google Scholar 

  31. ChemOffice (2005) CambridgeSoft Corporation, http://www.cambridgesoft.com/

  32. Todeschini R, Milano Chemometrics, QSPR Group, http://www.disat.unimib.it/chm

  33. Massart DL, Vandeginste BGM, Buydens LMC, De Jong S, Lewi PJ, Smeyers-Verbeke J (1997) Handbook of chemometrics and qualimetrics, Part A. Elsevier, Amsterdam

    Google Scholar 

  34. Martens H, Næs T (1989) Multivariate calibration. Wiley, Chichester

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jahan B. Ghasemi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ghasemi, J.B., Zohrabi, P. & Khajehsharifi, H. Quantitative structure–activity relationship study of nonpeptide antagonists of CXCR2 using stepwise multiple linear regression analysis. Monatsh Chem 141, 111–118 (2010). https://doi.org/10.1007/s00706-009-0225-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00706-009-0225-4

Keywords

Navigation