Journal of Computer-Aided Molecular Design

, Volume 21, Issue 10–11, pp 559–573 | Cite as

Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models

  • M. Paul Gleeson
  • Andrew M. Davis
  • Kamaldeep K. Chohan
  • Stuart W. Paine
  • Scott Boyer
  • Claire L. Gavaghan
  • Catrin Hasselgren Arnby
  • Cecilia Kankkonen
  • Nan Albertson


In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC50 is at least one root mean square error (RMSE) from the high/low pIC50 cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.


Cytochrome P450 1A2 2C9 2C19 2D6 3A4 In-silico QSAR ADMET model Partial least squares regression PLS Regression trees 


  1. 1.
    Rendic S, Di Carlo FJD (1997) Drug Metab Rev 29:413Google Scholar
  2. 2.
    Guengerich FP (2002) Drug Metab Rev 4:7CrossRefGoogle Scholar
  3. 3.
    Lin JH, Lu AYH (1998) Clin Pharmacokinet 35:361CrossRefGoogle Scholar
  4. 4.
    Bertz RJ, Granneman GR (1997) Clin Pharmacokinet 32:210Google Scholar
  5. 5.
    Rendic S, Di Carlo J (1997) Drug Metab Rev 29:413CrossRefGoogle Scholar
  6. 6.
    Shimada T, Yamazaki H, Mimura M, Inui Y, Guengerich FPJ (1994) Pharmacol Exp Ther 270:414Google Scholar
  7. 7.
    Masimirembwa CM, Thompson R, Andersson TB (2001) Comb Chem High Throughput Screen 4:245Google Scholar
  8. 8.
    Soars MG, Gelboin HV, Krausz KW, Riley RJ (2003) Br J Clin Pharmacol 35:175CrossRefGoogle Scholar
  9. 9.
    McGinnity DF, Griffin SJ, Moody GC, Voice M, Hanlon S, Friedberg T, Riley RJ (1999) Drug Metab Dispos 27:1017Google Scholar
  10. 10.
    Riley RJ, Martin IJ, Cooper AE (2002) Curr Drug Metab 3:527CrossRefGoogle Scholar
  11. 11.
    Hanch C, Fugita TJ (1964) J Am Chem Soc 86:1616CrossRefGoogle Scholar
  12. 12.
    Abraham MH, Le J (1999) J Pharm Sci 88:868CrossRefGoogle Scholar
  13. 13.
    Platts JA, Abraham MH, Zhao YH, Hersey A, Ijaz L, Butina D (2001) Eur J Med Chem 36:719CrossRefGoogle Scholar
  14. 14.
    Gleeson MP, Waters, Paine SW, Davis AM (2006) J Med Chem 49:1953CrossRefGoogle Scholar
  15. 15.
    Gleeson MP (2007) J Med Chem 50:101CrossRefGoogle Scholar
  16. 16.
    Bruneau P (2001) J Chem Inf Comput Sci 41:1605CrossRefGoogle Scholar
  17. 17.
    Riley RJ, Parker AJ, Trigg S, Manners CN (2001) Pharm Res 18:652CrossRefGoogle Scholar
  18. 18.
    Lewis DFV (2004) In Vitro 18:89CrossRefGoogle Scholar
  19. 19.
    Lewis DFV, Modi S, Dickins M (2002) Drug Metab Rev 34:69CrossRefGoogle Scholar
  20. 20.
    Susnow RG, Dixon SL (2003) J Chem Inf Comput Sci 43:1308CrossRefGoogle Scholar
  21. 21.
    Ekins S, De Groot MJ, Jones JP (2001) Drug Metab Dispos 29:936Google Scholar
  22. 22.
    Vaid TP, Lewis NS (2000) Bioorg Med Chem 8:795CrossRefGoogle Scholar
  23. 23.
    Rao S, Aoyama R, Schrag M, Trager WF, Rettie A, Jones JP (2000) J Med Chem 43:2789CrossRefGoogle Scholar
  24. 24.
    Smith DA, Ackland MJ, Jones BC (1997) DDT 2:479Google Scholar
  25. 25.
    Lewis DFV, Jacobs MN, Dickins M (2004) DDT 9:530Google Scholar
  26. 26.
    Hatch FT, Lightstone FC, Colvin ME (2000) Environ Mol Mutagen 35:279CrossRefGoogle Scholar
  27. 27.
    Lesigiarska I, Pajeva I, Yanev S (2002) Xenobiotica 32:1063CrossRefGoogle Scholar
  28. 28.
    Jones JP, He M, Trager WF, Rettie AE (1996) Drug Metab Dispos 24:1Google Scholar
  29. 29.
    De Groot MJ, Ackland MJ, Horne VA, Alex AA, Jones BCJ (1999) Med Chem 42:4062CrossRefGoogle Scholar
  30. 30.
    Wold H (1975) Quantitative sociology: international perspectives on mathematical and statistical model building. Academic Press, New York, pp 307–357Google Scholar
  31. 31.
    Japertas P, Didziapetris R, Petrauskas A (2002) QSAR 21:23Google Scholar
  32. 32.
    Afzelius L, Zamora I., Masimirembwa CM, Karlen A, Andersson TB, Mecucci S, Baroni M, Cruciani GJ (2004) Med Chem 47:907CrossRefGoogle Scholar
  33. 33.
    Roberts G, Myatt GJ, Johnson WP, Cross KP, Blower PE Jr (2000) J Chem Inf Comput Sci 40:1302CrossRefGoogle Scholar
  34. 34.
    Höskuldsson A (1996) Prediction methods in science and technology. Thor Publishing, Copenhagen, DenmarkGoogle Scholar
  35. 35.
    Wold S, Albano C, Dunn WJ, Edlund U, Esbensen K, Geladi P, Hellberg S, Johansson E, Lindberg W, Sjöström M (1984) In: BR Kowalski BR, Chemometrics: mathematics and statistics in chemistry. D. Reidel Publishing Company, Dordrecht, HollandGoogle Scholar
  36. 36.
    Wold S, Eriksson L, Sjöström M (2000) PLS in chemistry, encyclopedia of computational chemistry. Wiley, New YorkGoogle Scholar
  37. 37.
    Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman and Hall, New YorkGoogle Scholar
  38. 38.
    Chohan KK, Paine SW, Mistry J, Barton P, Davis AM (2005) J Med Chem 48:5154CrossRefGoogle Scholar
  39. 39.
    Asikainen AH, Ruuskanen J, Tuppurainen K (2004) SAR QSAR in Environ Res 15:19CrossRefGoogle Scholar
  40. 40.
    Baurin N, Mozziconacci JC, Arnoult E, Chavatte P, Marot C, Morin-Allory L (2004) J Chem Inf Comput Sci 44:276CrossRefGoogle Scholar
  41. 41.
    Jackson JE (1991) A user’s guide to principal components. John Wiley, New YorkGoogle Scholar
  42. 42.
    Wold S, Geladi P, Esbensen K, Öhman J (1987) J Chemom 1:41CrossRefGoogle Scholar
  43. 43.
    GOLPE: Multivariate Infometric Analysis Srl., Viale dei Castagni 16, Perugia, Italy.
  44. 44.
    CART 4.0. Salford Systems, 8880 Rio San Diego Dr., STE. 1045, San Diego, California, 92108.
  45. 45.
    Rodgers SL, Davis AM, van de Waterbeemd H (2007) QSAR Comb. Sci 26:511CrossRefGoogle Scholar
  46. 46.
    Gleeson MP, Waters NJ, Paine SW, Davis AM (2006) J Med Chem 49:1953CrossRefGoogle Scholar
  47. 47.
    Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer SJ (2007) Comp Aided Mol Des 21:189CrossRefGoogle Scholar
  48. 48.
    Lewis FV, Modi S, Dickins M (2001) Drug Metabol Drug Rev 18:18, 221Google Scholar
  49. 49.
    Williams PA, Cosme J, Ward A, Angove HC, Vinkovic DM, Jhoti H (2003) Nature 424:464CrossRefGoogle Scholar
  50. 50.
    Rowland P, Blaney FE, Smyth MG, Jones JJ, Leydon VR, Oxbrow AK, Lewis CJ, Tennant MG, Modi S, Eggleston DS, Chenery RJ, Bridges AM (2006) J Biol Chem 281:7614CrossRefGoogle Scholar
  51. 51.
    Williams PA, Cosme J, Vinkovic DM, Ward A, Angove HC, Day PJ, Vonrhein C, Tickle IJ, Jhoti H (2004) Science 30:683CrossRefGoogle Scholar
  52. 52.
    Yano JK, Wester MR, Schoch GA, Griffin KJ, Stout CD, Johnson EF (2004) J Biol Chem 279:38091CrossRefGoogle Scholar
  53. 53.
    Leach AG, Jones HD, Cosgrove DA, Kenny PW, Ruston L, MacFaul P, Wood JM, Colclough N, Law B (2006) J Med Chem 49:6672CrossRefGoogle Scholar
  54. 54.
    Golbraikh A, Tropsha A (2002) J Mol Graph Model 20:269CrossRefGoogle Scholar
  55. 55.
    Byvatov E, Baringhaus K, Schneider G, Matter H (2006) QSAR Comb Sci 26:618CrossRefGoogle Scholar
  56. 56.
    O Brien SE, de Groot MJ (2005) J Med Chem 48:1287CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • M. Paul Gleeson
    • 1
    • 2
  • Andrew M. Davis
    • 1
  • Kamaldeep K. Chohan
    • 1
  • Stuart W. Paine
    • 1
  • Scott Boyer
    • 3
  • Claire L. Gavaghan
    • 3
  • Catrin Hasselgren Arnby
    • 3
  • Cecilia Kankkonen
    • 4
  • Nan Albertson
    • 5
  1. 1.Department of Physical & Metabolic SciencesAstraZeneca R&D CharnwoodLoughboroughUK
  2. 2.Computational and Structural ChemistryGlaxoSmithKline Medicines Research CentreStevenageUK
  3. 3.Computational Toxicology, Safety AssessmentAstraZeneca R&DMolndalSweden
  4. 4.HTS ScreeningAstraZeneca R&D MölndalMolndalSweden
  5. 5.Discovery DMPK & BACAstraZeneca R&D MölndalMolndalSweden

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