Advertisement

Journal of Molecular Modeling

, Volume 13, Issue 4, pp 465–476 | Cite as

QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties

  • Michael Fernández
  • Julio CaballeroEmail author
Original Paper

Abstract

Multiple linear regression (MLR) combined with genetic algorithm (GA) and Bayesian-regularized Genetic Neural Networks (BRGNNs) were used to model the binding affinity (pKI) of 38 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A for the Human Luteinizing Hormone-Releasing Hormone (LHRH) receptor using quantum chemical descriptors. A multiparametric MLR equation with good statistical quality was obtained that describes the features relevant for antagonistic activity when the substituent at the position 3 of the erythronolide core was varied. In addition, four-descriptor linear and nonlinear models were established for the whole dataset. Such models showed high statistical quality. However, the BRGNN model was better than the linear model according to the external validation process. In general, our linear and nonlinear models reveal that the binding affinity of the compounds studied for the LHRH receptor is modulated by electron-related terms.

Figure 1

General structure and numbering of the 11,12-cyclic carbamate derivatives of 6-O-methylerythromycin A used in this study

Keywords

QSAR analysis Bayesian-regularized Genetic Neural Network Quantum chemical descriptors Macrolide LHRH antagonists 

Supplementary material

894_2006_163_MOESM1_ESM.doc (50 kb)
ESM 1 Supporting Information Available (DOC 50kb)

References

  1. 1.
    Huirne JAF, Lambalk CB (2001) Lancet 358:1793–1803CrossRefGoogle Scholar
  2. 2.
    Filicori M, Flamigni C (1988) Drugs 35:63–82CrossRefGoogle Scholar
  3. 3.
    Kutscher B, Bernd M, Beckers T, Polymeropoulos EE, Engel J (1997) Angew Chem Int Ed 36:2148–2161CrossRefGoogle Scholar
  4. 4.
    Karten MJ (1992) An overview of GnRH antagonist development: Two decades of progress. In: Crowley Jr WF, Conn PM (eds) Modes of Action of GnRH and GnRH Analog. Elsevier, New York, pp 277–297Google Scholar
  5. 5.
    Cho N, Harada M, Imaeda T, Imada T, Matsumoto H, Hayase Y, Sasaki S, Furuya S, Suzuki N, Okubo S, Ogi K, Endo S, Onda H, Fujino M (1998) J Med Chem 41:4190–4195CrossRefGoogle Scholar
  6. 6.
    De Vita RJ, Hollings DD, Goulet MT, Wyvratt MJ, Fisher MH, Lo JL, Yang YT, Cheng K, Smith RG (1999) Bioorg Med Chem Lett 9:2615–2620CrossRefGoogle Scholar
  7. 7.
    Chu L, Hutchins JE, Weber AE, Lo JL, Yang YT, Cheng K, Smith RG, Fisher MH, Wyvratt MJ, Goulet MT (2001) Bioorg Med Chem Lett 11:509–513CrossRefGoogle Scholar
  8. 8.
    Zhu YF, Struthers RS, Connors Jr PJ, Gao Y, Gross TD, Saunders J, Wilcoxen K, Reinhart GJ, Ling N, Chen C (2002) Bioorg Med Chem Lett 12:399–402CrossRefGoogle Scholar
  9. 9.
    Flanagan CA, Becker II, Davidson JS, Wakefield IK, Zhou W, Sealfon SC, Millar RP (1994) J Biol Chem 269:22636–22641Google Scholar
  10. 10.
    Gasteiger J (2006) Anal Bioanal Chem 384:57–64CrossRefGoogle Scholar
  11. 11.
    Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley, WeinheimGoogle Scholar
  12. 12.
    Karelson M, Lobanov VS, Katritzky AR (1996) Chem Rev 96:1027–1043CrossRefGoogle Scholar
  13. 13.
    Fernández M, Caballero J (2006) J Mol Graph Modell 25:410–422CrossRefGoogle Scholar
  14. 14.
    Randolph JT, Waid P, Nichols C, Sauer D, Haviv F, Diaz G, Bammert G (2004) J Med Chem 47:1085–1097CrossRefGoogle Scholar
  15. 15.
    Randolph JT, Sauer DR, Haviv F, Nilius AM, Greer J (2004) Bioorg Med Chem Lett 14:1599–1602CrossRefGoogle Scholar
  16. 16.
    HyperChem 7.0 (2002) Hypercube, GainesvilleGoogle Scholar
  17. 17.
    Stewart JJP (1989) J Comput Chem 10:210–220Google Scholar
  18. 18.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Zarkzewski VG, Montgomery JA, Stratmann RE, Burant JC, Dapprich S, Millam JM, Daniels AD, Kudin KN, Strain MC, Farkas O, Tomasi J, Baone V, Cossi M, Cammi R, Mennucci B, Pomelli C, Adamo C, Clifford S, Ochterski J, Peterson GA, Ayala PY, Cui Q, Morokuma K, Makick DK, Rabuck AD, Raghavachari K, Foresman JB, Cioslowski J, Ortiz JV, Baboul AG, Stefanov BB, Liu G, Kiashenko A, Piskorz P, Komaromi I, Gomperts R, Martin RL, Fox DJ, Keith T, Al-laham MA, Peng CY, Nanayakkara A, González C, Challacombe M, Gill PMW, Johnson BG, Chen W, Wong MW, Andres JL, Head-Gordon M, Repogle ES, Pople JA (1998) Gaussian 98, Revision A1. Gaussian, Pittsburgh, PAGoogle Scholar
  19. 19.
    Thanikaivelan P, Subramanian V, Rao JR, Nair BU (2000) Chem Phys Lett 323:59–70CrossRefGoogle Scholar
  20. 20.
    Hawkins DM (2004) J Chem Inf Comput Sci 44:1–12CrossRefGoogle Scholar
  21. 21.
    Topliss JG, Costello RJ (1972) J Med Chem 15:1066–1068CrossRefGoogle Scholar
  22. 22.
    Topliss JG, Edwards RP (1979) J Med Chem 22:1238–1244CrossRefGoogle Scholar
  23. 23.
    Caballero J, Fernández M (2006) J Mol Model 12:168–181CrossRefGoogle Scholar
  24. 24.
    So SS, Karplus M (1996) J Med Chem 39:1521–1530CrossRefGoogle Scholar
  25. 25.
    Mackay DJC (1992) Neural Comput 4:415–447Google Scholar
  26. 26.
    Mackay DJC (1992) Neural Comput 4:448–472Google Scholar
  27. 27.
    Burden FR, Winkler DA (1999) J Med Chem 42:3183–3187CrossRefGoogle Scholar
  28. 28.
    Winkler DA, Burden FR (2004) Biosilico 2:104–111Google Scholar
  29. 29.
    Fernández M, Tundidor-Camba A, Caballero J (2005) J Chem Inf Model 45:1884–1895CrossRefGoogle Scholar
  30. 30.
    Caballero J, Garriga M, Fernández M (2005) J Comput-Aided Mol Des 19:771–789CrossRefGoogle Scholar
  31. 31.
    MATLAB 7.0 (2004) The Mathworks Inc, 3 Apple Hill Drive, Natick, MA 01760–2098, USAGoogle Scholar
  32. 32.
    Foresee FD, Hagan MT (1997) Gauss-Newton approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks. IEEE, Houston, pp 1930–1935Google Scholar
  33. 33.
    Golbraikh A, Tropsha A (2002) J Mol Graph Modell 20:269–276CrossRefGoogle Scholar
  34. 34.
    Aptula AO, Jeliazkova NG, Schultz TW, Cronin MTD (2005) QSAR Comb Sci 24:385–396CrossRefGoogle Scholar
  35. 35.
    Doweyko AM (2004) J Comput-Aided Mol Des 18:587–596CrossRefGoogle Scholar
  36. 36.
    Guha R, Stanton DT, Jurs PC (2005) J Chem Inf Model 45:1109–1121CrossRefGoogle Scholar
  37. 37.
    Stanton DT (2003) J Chem Inf Comput Sci 43:1423–1433CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Molecular Modeling Group, Center for Biotechnological StudiesUniversity of MatanzasMatanzasCuba

Personalised recommendations