Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
권호 표지
DOI QR코드

DOI QR Code

CoMFA vs. Topomer CoMFA, which One is better a Case Study with 5-Lipoxygenase Inhibitors

  • Gadhe, Changdev G. (Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research)
  • Received : 2011.05.24
  • Accepted : 2011.06.20
  • Published : 2011.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Quantitative structure-activity relationships (QSAR) have been applied for two decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the QSAR is that the structural difference is responsible for the variations in biological activities of the compounds. In this work, we developed 3D-QSAR model for a series of 5-Lipoxygenase inhibitors, utilizing comparative molecular field analysis (CoMFA) and Topomer CoMFA methodologies. Our developed models addressed superiority of Topomer CoMFA over CoMFA. The CoMFA model was obtained with $q^2$=0.593, $r^2$=0.939, $Q^2$=0.334 with 6 optimum number of components (ONC). Higher statistical results were obtained with the Topomer CoMFA model ($q^2$=0.819, $r^2$=0.947, ONC=5). Further robustness of developed models was checked with the ANOVA test and it shows F=113 for CoMFA and F=162.4 for Topomer CoMFA model. Contour map analysis indicated that the more requirement of electrostatic parameter for improved potency.

Keywords

References

  1. C. Hansch, P. P. Maloney, T. Fujita and R. M. Muir, "Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients", Nature, Vol. 194, pp. 178, 1962.
  2. T. Fujita, J. Iwasa and C. Hansch, "A new substituent constant, , derived from partition coefficients", J. Am. Chem. Soc., Vol. 86, pp. 5175-5180, 1964. https://doi.org/10.1021/ja01077a028
  3. C. Hansch, and A. Leo, Exploring QSAR, Fundamentals and Applications in Chemistry and Biology, ACS Professional Reference Book, American Chemical Society, Washington, DC, 1995.
  4. R. D. Cramer III, D. E. Patterson and J. D. Bunce, "Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins", J. Am. Chem. Soc., Vol. 110, pp. 5959-5967, 1988. https://doi.org/10.1021/ja00226a005
  5. P. Goodford, "Multivariate characterization of molecules for QSAR analysis", J. Chemometr., Vol. 10, pp. 107-117, 1996. https://doi.org/10.1002/(SICI)1099-128X(199603)10:2<107::AID-CEM408>3.0.CO;2-E
  6. G. Cruciani and K. A. Watson, "Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b", J. Med. Chem., Vol. 37, pp. 2589-2601, 1994. https://doi.org/10.1021/jm00042a012
  7. GOLPE 4.0, Multivariate Infometric Analysis, Perugia, Italy, 1998.
  8. G. Klebe, U. Abraham and T. Mietzner, "Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity", J. Med. Chem., Vol. 37, pp. 4130-4146, 1994. https://doi.org/10.1021/jm00050a010
  9. W. J. Dunn, S. Wold, V. Edlund, S. Hellherg and J. Gasteiger, "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., Vol. 3, pp. 131-137, 1984. https://doi.org/10.1002/qsar.19840030402
  10. S. Wold, M. Sjostrom, and L. Eriksson, "PLS-regression: a basic tool of chemometrics", Chemom. Intell. Lab. Syst., Vol. 58, pp. 109-130, 2001. https://doi.org/10.1016/S0169-7439(01)00155-1
  11. R. D. Cramer III, "Partial least squares (PLS): its strengths and limitations", Perspect. Drug Discovery Des., Vol. 1, pp. 269-278, 1993. https://doi.org/10.1007/BF02174528
  12. S. Sogawa, Y. Nihro, H. Ueda, A. Izumi, T. Miki, H. Matsumoto and T. Satoh, "3, 4-Dihydroxychalcones as potent 5-lipoxygenase and cyclooxygenase inhibitors", J. Med. Chem., Vol. 36, pp. 3904-3909, 1993. https://doi.org/10.1021/jm00076a019
  13. S. H. R. SYBYL8.1; Tripos Inc. St. Louis, MO 63144 USA.
  14. Stewart, J. J. P. MOPAC2009, Stewart Computational Chemistry, Colorado Springs, CO, USA, HTTP://OpenMOPAC.net
  15. R. D. Cramer III, "Topomer CoMFA: a design methodology for rapid lead optimization", J. Med. Chem., Vol. 46, pp. 374-388, 2003. https://doi.org/10.1021/jm020194o
  16. R. J. Jilek and R. D. Cramer III, "Topomers: a validated protocol for their self-consistent generation", J. Chem. Inf. Comput. Sci., Vol. 44, pp. 1221-1227, 2004. https://doi.org/10.1021/ci049961d
  17. R. D. Cramer, R. D. Clark, D. E. Patterson and A. M. Ferguson, "Bioisosterism as a molecular diversity descriptor: steric fields of single "topomeric" conformers", J. Med. Chem., Vol. 39, pp. 3060-3069, 1996. https://doi.org/10.1021/jm960291f

Cited by

  1. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors vol.2017, pp.None, 2011, https://doi.org/10.1155/2017/4649191