Abstract
A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds.
Abbreviations: ADME – absorption, distribution, metabolism and excretion; CYP – cytochrome P450; MIFs – molecular interaction fields; HTS – high throughput screening; DDI – drug-drug interactions; 3D – three-dimensional; PCA – principal components analysis; CPCA – consensus principal components analysis; PLS – partial least squares; PLSD – partial least squares discriminant; GRIND – grid independent descriptors; GRID – software originally created and developed by Professor Peter Goodford.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Clark, D.E. and Pickett, S.D., Drug Discov. Today, 5 (2000) 49.
van de Waterbeemd, H., Smith, D.A., Beaumont, K. and Walker, D.K., J. Med. Chem., 44 (2001) 1313.
van de Waterbeemd, H., Curr. Opin. Drug Discov. Dev., 5 (2002) 33.
van de Waterbeemd, H. and Gifford, E., Nat. Rev. Drug Discov., 2 (2003)192.
Boobis, A., Gundert-Remy, U., Kremers, P., Macheras, P. and Pelkonen, O., Eur. J. Pharm. Sci., 17 (2002) 183.
Guengerich, F.P., Chem. Res. Toxicol., 14 (2001) 611.
Testa, B., In Wolff, M. (Ed) Drug Metabolism, in Burger's Medicinal Chemistry and Drug Discovery. Principles and Practice, Vol. 1. John Wiley & Son, New York, 1994, pp. 129–180.
Bertrand, M., Jackson, P. and Walther, P., Eur. J. Pharm. Sci. 11 (2 Suppl) (2000) S61.
Lewis, D.F.V., Xenobiotica, 28 (1998) 617.
Wrighton, S.A., Schuetz, E.G., Thummel, K.E., Shen, D.D., Korzekwa, K.R. and Watkins, P.B., Drug Metab. Rev., 32 (2000) 339.
Thummel, K.E. and Shen, D.D., In Hardman, J.G., Limbird, L.E., Molinoff, P.B. and Gilman, A.G. (Eds), Appendix II. Design and Optimization of Dosage Regimens: Pharmacokinetic Data. In Goodman and Gilman's. The Pharmacological Basis of Therapeutics, 10th Edition, McGraw-Hill, New York, 2001, pp. 1917–2023.
Cruciani, G., Crivori, P., Carrupt, P-A. and Testa, B., Theochem,503 (2000) 17.
Pastor, M., Cruciani, G., McLay, I., Pickett, S. and Clementi, S., J. Med. Chem., 43(2000)3233.
Singh, S.B., Shen, L.Q., Walker, M.J. and Sheridan, R.P., J. Med. Chem., 46 (2003) 1330.
Zamora, I., Afzelius, L. and Cruciani, G., J. Med. Chem., 46 (2003) 2313.
CORINA. Molecular Networks, GmbH, Computerchemie, Erlangen, Germany, 1998.
VolSurf v.3.0, Molecular Discovery Ltd., Oxford, UK, 2002.
Almond v.3.0, Multivariate Infometric Analysis S.r.l., Perugia, Italy, 2002.
Wold, S., Esbensen, K. and Geladi, P., Chemom. Intell. Lab. Syst., 2 (1987) 37.
Kastenholz, M.A., Pastor, M., Cruciani, G., Haaksma, E.E.J. and Fox, T., J. Med. Chem., 43 (2000) 3033.
Dunn, W.J. and Wold, S., Pattern Recognition Techniques in drug design. In Hansch, C., Sammes, P.G. and Taylor, J.B. (Eds), Comprehensive Medicinal Chemistry, Vol. 4, Pergamon Press, Oxford, UK, 1990, pp. 691–714.
GOLPE 4.5, Multivariate Infometric Analysis S.r.l., Perugia, Italy, 2002.
GRID v. 20, Molecular Discovery Ltd., Oxford, UK, 2002.
Goodford, P.J., J. Med. Chem., 28 (1985) 849.
Wold, S., Technometrics, 20 (1979) 379.
Wold, S., Albano, C., Dunn, W. J. III, Edlund, U., Esbensen, K., Geladi, P., Helberg, S., Johansson, E., Lindberg, W. and Sjostrom, M., Multivariate data analysis in chemistry. In Kowalsky, B.R. (Ed), Chemometrics Mathematics and Statistics in Chemistry. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1983, pp. 17–96.
Cruciani, G., Pastor, M. and Guba, W., Eur. J. Pharm. Sci., 11(Suppl. 2) (2000) S29.
Crivori, P., Cruciani, G., Carrupt, P.-A. and Testa, B., J. Med. Chem., 43 (2000) 2204.
Alifrangis, L.H., Christensen, I.T., Berglund, A., Sandberg, M., Hovgaard, L. and Frokjaer, S., J. Med. Chem., 43 (2000) 103.
Ajay, W., Walters, P. and Murck, M.A., J. Med. Chem., 41 (1998) 3314.
Sadowski, J. and Kubinyi, H., J. Med. Chem., 41 (1998) 3325.
Guengerich, F.P., Annu. Rev. Pharmaco. Toxicol., 39 (1999)1.
Lewis, D.F.V., Eddershaw, P.J., Goldfarb, P.S. and Tarbit, M.H., Xenobiotica, 26 (1996) 1067.
Smith, D.A., Jones, B.C. and Walker, D., Med. Res. Rev., 16(3) (1996) 243.
Smith, D.A., Ackland, M.J. and Jones, B.C., Drug Discov. Today, 2(10) (1997) 406.
Smith, D.A., Ackland, M.J. and Jones, B.C., Drug Discov. Today, 2(11) (1997) 479.
Shen, M., Xiao, Y., Golbraikh, A., Gombar, V.K. and Tropsha, A., J. Med. Chem.,46 (2003) 3013.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Crivori, P., Zamora, I., Speed, B. et al. Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates. J Comput Aided Mol Des 18, 155–166 (2004). https://doi.org/10.1023/B:JCAM.0000035184.11906.c2
Issue Date:
DOI: https://doi.org/10.1023/B:JCAM.0000035184.11906.c2