Generation of a set of simple, interpretable ADMET rules of thumb

Journal of Medicinal Chemistry

Volumn 51, Issue 4, 2008, Pages 817-834

  Gleeson, M Paul ^a  

^a GLAXOSMITHKLINE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ASSAY; DRUG ABSORPTION; DRUG CLEARANCE; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG PENETRATION; ENZYME INHIBITION; HUMAN; IONIZATION; MOLECULAR WEIGHT; NONHUMAN; PARAMETER; PHYSICAL CHEMISTRY; PLASMA PROTEIN BINDING; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS;

ANIMALS; BIOLOGICAL AVAILABILITY; BLOOD PROTEINS; BRAIN; CELL MEMBRANE PERMEABILITY; CHEMISTRY, PHARMACEUTICAL; CYTOCHROME P-450 ENZYME SYSTEM; DRUG DESIGN; DRUG INDUSTRY; ETHER-A-GO-GO POTASSIUM CHANNELS; MOLECULAR WEIGHT; P-GLYCOPROTEIN; PHARMACEUTICAL PREPARATIONS; PHARMACOKINETICS; PRIVATE SECTOR; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY; TISSUE DISTRIBUTION;

EID: 39749181550     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm701122q     Document Type: Article

References (123)

1. Grandison, M. K.; Boudinot, F. D. Age-related changes in protein binding of drugs. Implications for therapy. Clin. Pharmacokinet. 2000, 38, 271-290.
2. Benet, L. Z.; Hoener, B. A. Changes in plasma protein binding have little clinical relevance. Clin. Pharmacol. Ther. 2002, 71, 115-121.
3. Viskin, S. Long QT syndromes and torsade de pointes. Lancet 1999, 354, 1625-1633.
4. Rettie, A. E.; Korzekwa, K. R.; Kunze, K.; Lawrence, R. F.; Eddy, A. C.; Aoyama, T.; Gelboin, H. V.; Gonzalez, F. J.; Trager, W. F. Hydroxylation of warfarin by human cDNA-expressed cytochrome P450: a role for P-4502C9 in the etiology of (S)-warfarin-drug interactions. Chem. Res. Toxicol. 1992, 5, 54-59.
5. Norinder, U.; Bergstrom, C. A. S. Prediction of ADMET properties. ChemMedChem 2006, 1, 920-937.
6. Hou, T.; Wang, J.; Zhang, W.; Wang, W.; Xu, X. Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr. Med. Chem. 2006, 13, 2653-2667.
7. Chohan, K. K.; Paine, S. W.; Waters, N. J. Quantitative structure-activity relationships in drug metabolism. Curr. Top. Med. Chem. 2006, 6, 1569-1578.
8. De Groot, M. Designing better drugs: Predicting cytochrome P450 metabolism. Drug Discovery Today 2006, 11, 601-606.
9. Hansch, D.; Leo, A.; Mekapati, S. B.; Kurup, A. QSAR and ADME. Bioorg. Med. Chem. 2004, 12, 3391-3400.
10. Lombardo, F.; Gifford, F.; Shalaeva, M. Y. in-silico ADME prediction: Data, models, facts, and myths. Mini-Rev. Med. Chem. 2003, 3, 861-875.
11. Hall, L. H.; Kier, L. B. Electrotopological state indices for atoms types: A Novel combination of electronic, topological, and valence state information. J. Chem. Inf. Comput. Sci. 1995, 35, 1039-1045.
12. Burden, F. R. Molecular identification number for substructure searches. J. Chem. Inf. Comput. Sci. 1989, 29, 225-227.
13. Pearlman, R. S.; Smith, K. M. Novel software tools for chemical diversity. Perspect. Drug Discovery Des. 1998, 9, 339-353.
14. deGroot, M.; Ekins, S. Pharmacophore modeling of cytochromes P450. Adv. Drug Delivery Rev. 2002, 54, 367-383.
15. Cavalli, A.; Poluzzi, E.; De Ponti, F.; Recanatini, M. Toward a pharmacophore for drugs inducing the long QT syndrome: Insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem. 2002, 45, 3844-3853.
16. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3-25.
17. Van de Waterbeemd, H.; Camenisch, G.; Folkers, G.; Chretien, J. R.; Raevsky, O. A. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J. Drug Targeting 1998, 6, 151-165.
18. Höskuldsson, A. Prediction Methods in Science and Technology; Thor Publishing: Copenhagen, Denmark, 1996.
19. Wold, S.; Geladi, P.; Esbensen, K.; Öhman, J. J. Multi-way principal components-and PLS-analysis. J. Chemom. 1987, 1, 41-46.
20. Wold, H. Path models with latent variables: The NIPALS approach. In Quantitative Sociology: International perspectives on mathematical and statistical model building; Academic Press: New York, 1975; pp 307-357.
21. Wold, S.; Albano, C.; Dunn, W. J.; Edlund, U., Esbensen, K.; Geladi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjöström, M. Multivariate data analysis in chemistry. In Chemometrics: Mathematics and statistics in chemistry; Kowalski, B. R. Ed.; D. Reidel Publishing Company: Dordrecht, Holland, 1984.
22. Wold, S.; Eriksson, L.; Sjöström, M. PLS in Chemistry, Encyclopedia of Computational Chemistry, Wiley: New York, 2000.
23. Gleeson, M. P. Plasma protein binding affinity and its relationship to molecular structure: An in-silico analysis. J. Med. Chem. 2007, 50, 101-112.
24. An advantage of the ANOVA method used here is that one can directly compare results across all the assays studied in a simple manner, which would be difficult if different descriptors, cut-offs, or modelling methods were employed in each case. However, it should be noted that the independent effect of descriptors, such as clogP and MWT, may not be picked up due to the crude way they are used in this study (small numbers of binned values rather than continuous descriptors). In cases where the effect of each variable is different, this might not be seen at the 99.9% confidence level due to the limitation of the descriptors but also due to low numbers of observation.
25. Kerns, E. H.; Di, L. Physicochemical profiling: overview of the screens. Drug Discovery Today 2004, 1, 343-348.
26. Abraham, M. H.; Le, J. The correlation and prediction of the solubility of molecules in water using an amended solvation energy relationship. J. Pharm. Sci. 1999, 88, 868-880.
27. Votano, J. R.; Parham, M.; Hall, L. H.; Kier, L. B. New predictors for several ADME/Tox properties: Aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors. Mol. Diversity 2004, 8, 379-391.
28. Hansen, N. T.; Kouskoumvekaki, I.; Jørgensen, F. S.; Brunak, S.; Jonsdottir, S. O. Prediction of pH-dependent aqueous solubility of druglike molecules. J. Chem. Inf. Model. 2006, 46, 2601-2609.
29. Delaney, J. S. ESOL: Estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci. 2004, 44, 1000-1005.
30. Ran, Y.; Yalkowsky, S. H. Prediction of drug solubility by the general solubility equation (GSE). J. Chem. Inf. Comput. Sci. 2001, 41, 354-357.
31. Kansy, M.; Senner, F.; Gubernator, K. Physicochemical high throughput screening: Parallel artificial membrane permeation assay in the description of passive absorption processes. J. Med. Chem. 1998, 41, 1007-1010.
32. Cho, M. J.; Thompson, D. P.; Cramer, C. T.; Vidmar, T. J.; Scieszka, J. F. The Madin-Darby canine kidney (MDCK) epithelial cell monolayer as a model cellular transport barrier. Pharm. Res. 1989, 6, 71-77.
33. Yee, S. In vitro permeability across Caco-2 cells (colonic) can predict in vivo (small intestinal) absorption in man-fact or myth. Pharm. Res. 1997, 14, 763-766.
34. Camenisch, G.; Alsenz, J.; van de Waterbeemd, H.; Folkers, J. Estimation of permeability by passive diffusion through Caco-2 cell monolayers using the drugs' lipophilicity and molecular weight. Eur. J. Pharm. Sci. 1998, 6, 313-319.
35. van de Waterbeemd, H. G.; Camenisch, G.; Folkers, O. A.; Raevsky. Estimation of Caco-2 cell permeability using calculated molecular descriptors. Quant. Struct.-Act. Relat. 1996, 15, 480-490.
36. Bergström, C. A. S.; Strafford, M.; Lazorova, L.; Avdeef, A.; Luthman, K.; Artursson, P. Absorption classification of oral drugs based on molecular surface properties. J. Med. Chem. 2003, 46, 558-570.
37. Tantishaiyakul, V. Prediction of Caco-2 cell permeability using partial least square multivariate analysis. Pharmazie 2001, 56, 407-411.
38. Riley, R. J.; Martin, I. J.; Cooper, A. E. The influence of DMPK as an integrated partner in modern drug discovery. Curr. Drug Metab. 2002, 3, 527-550.
39. Martinez, M. N.; Amidon, G. L. A mechanistic approach to understanding the factors affecting drug absorption: A review of fundamentals. J. Clin. Pharmacol. 2002, 42, 620-643.
40. Turner, J. V.; Glass, B. D.; Agatonovic-Kustrin, S. Prediction of drug bioavailability based on molecular structure. Anal. Chim. Acta 2003, 485, 89-102.
41. Yoshida, F.; Topliss, J. G. QSAR Model for Drug Human Oral Bioavailability. J. Med. Chem. 2000, 43, 2575-2585.
42. Martin, Y. C. A Bioavailability Score. J. Med. Chem. 2005, 48, 3164-3170.
43. Veber, D. F.; Johnson, S. R.; Cheng, H.; Smith, B. R.; Ward, K. W.; Kopple, K. D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615-2623.
44. ss quantitative structure-activity relationship models. J. Med. Chem. 2006, 49, 1953-1963.
45. Lombardo, F.; Obach, R. S.; Shalaeva, M. Y.†; Gao, F. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J. Med. Chem. 2002, 45, 2867-2876.
46. Ghafourian, T.; Barzegar-Jalali, M.; Hakimiha, N.; Cronin, M. T. D. Quantitative structure-pharmacokinetic relationship modelling: Apparent volume of distribution. J. Pharm. Pharmacol. 2004, 56, 339-350.
47. Hitchcock, S. A.; Pennington, L. D. Structure-brain exposure relationships. J. Med. Chem. 2006, 49, 7559-7583.
48. Gerebtzoff, G.; Seelig, A. In silico prediction of blood-brain barrier permeation using the calculated molecular cross-sectional area as main parameter. J. Chem. Inf. Model. 2006, 46, 2638-2650.
49. Platts, J. A.; Abraham, M. H.; Zhao, Y. H.; Hersey, A.; Ijaz, L.; Butina, D. Correlation and prediction of a large blood-brain distribution data set - an LFER study. Eur. J. Med. Chem. 2001, 36, 719-730.
50. Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration. J. Pharm. Sci. 1999, 88, 815-821.
51. Seelig, A. A general pattern for substrate recognition by P-glycoprotein. Eur. J. Biochem. 1998, 251, 252-261.
52. Zhang, E. Y.; Phelps, M. A.; Cheng, C.; Ekins, S.; Swaan, P. W. Modeling of active transport systems. Adv. Drug Delivery Rev. 2002, 54, 329-354.
53. Gombar, V. K.; Polli, J. W.; Humphreys, J. E.; Wring, S. A.; Serabjit-Singh, C. S. Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model. J. Pharm. Sci. 2004, 93, 957-968.
54. Bain, L. J.; LeBlanc, G. A. Interaction of structurally diverse pesticides with the human MDR1 gene product P-glycoprotein. Toxicol. Appl. Pharmacol. 1996, 141, 288-298.
55. Litman, T.; Zeuthen, T.; Skovsgaard; T.; Stein, W. D. Structure-activity relationships of P-glycoprotein interacting drugs: Kinetic characterization of their effects on ATPase activity. Biochim. Biophys. Acta 1997, 1361, 159-168.
56. Klippert, P.; Borm, P.; Noordhoek, J. Prediction of intestinal firstpass effect of phenacetin in the rat from enzyme kinetic data-correlation with in vivo data using mucosal blood flow. Biochem. Pharmacol. 1982, 31, 2545-2548.
57. Oie, S.; Tozer, T. N. Effect of altered plasma protein binding on apparent volume of distribution. J. Pharm. Sci. 1979, 68, 1203-1205.
58. Abraham, M. H. Scales of solute hydrogen bonding: Their construction and application to physicochemical and biological processes. Chem. Soc. Rev. 1993, 22, 73-83.
59. Davis, A. M.; Riley, R. Impact of physical organic chemistry on the control of drug-like properties. In Drug design cutting edge; Flower D. R., Ed.; Royal Society of Chemistry: Cambridge, U.K., 2002; pp 106-123.
60. Valko, K.; Nunhuck, S.; Bevan, C.; Abraham, M. H.; Reynolds, D. P. Fast gradient HPLC method to determine molecules binding to human serum albumin. Relationships with octanol/water and immobilized artificial membrane lipophilicity. J. Pharm. Sci. 2003, 92 (11), 2236-2248.
61. Kratochwil, N. A.; Huber, W.; Muller, F.; Kansy, M.; Gerber, P. R. Plasma protein binding of drugs. A new approach. Biochem. Pharmacol. 2002, 64, 1355-1374.
62. Colmenarejo, G.; Alvarez-Pedraglio, A.; Lavandera, J. L. Chemin-formatic models to predict binding affinities to human serum albumin. J. Med. Chem. 2001, 44, 4370-4378.
63. Lobell, M.; Sivarajah, V. In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of molecules from calculated pKa and AlogP98 values. Mol. Diversity 2003, 7, 69-87.
64. Yamakazi, K.; Kanaoka, M. Computational prediction of plasma protein binding percent of diverse pharmaceutical molecules. J. Pharm. Sci. 2004, 93, 1480-1494.
65. Saiakhov, R. D.; Stefan, L. R.; Klopman, G. Multiple computer-automated structure evaluation model of plasma protein binding affinity of diverse drugs. Perspect. Drug Discovery Des. 2000, 19, 133-155.
66. Kalvass, J. C.; Maurer, T. S. Influence of nonspecific brain and plasma binding on CNS exposure: implications for rational drug discovery. Biopharm. Drug Dispos. 2002, 23, 327-338.
67. Austin, R. P.; Barton, P.; Mohmed, S.; Riley, R. J. The binding of drugs to hepatocytes and its relationship to physicochemical properties. Drug Metab. Dispos. 2005, 333, 419-425.
68. Yokogawa, K. K.; Ishizaki, J.; Ohkumal, S.; Miyamoto, K. Influence of lipophilicity and lysosomal accumulation on tissue distribution kinetics of basic drugs: A physiologically based pharmacokinetic model. Methods Find. Exp. Clin. Pharmacol. 2002, 24, 81-93.
69. Madden, J. C.; Cronin, T. D. Structure-based methods for the prediction of drug metabolism. Expert Opin. Drug Metab. Toxicol. 2006, 2, 545-557.
70. Kalgutkar, A. S.; Gardner, I.; Obach, R. S.; Shaffer, C. L.; Callegari, E.; Henne, K. R.; Mutlib, A. E.; Dalvie, D. K.; Lee, J. S.; Nakai, Y.; O'Donnell, J. P.; Boer, J.; Harriman, S. P. A comprehensive listing of bioactivation pathways of organic functional groups. Curr. Drug Metabol. 2005, 6, 161-225.
71. Klippert, P.; Borm, P.; Noordhoek, J. Prediction of intestinal first-pass effect of phenacetin in the rat from enzyme kinetic data - Correlation with in vivo data using mucosal blood flow. Biochem. Pharmacol. 1982, 31, 2545-2548.
72. Grime, K.; Riley, R. J. The impact of in vitro binding on in vitro-in vivo extrapolations, projections of metabolic clearance, and clinical drug-drug interactions. Curr. Drug Metab. 2006, 7, 251-264.
73. Viskin, S.; Long, Q. T. Syndromes and torsade de pointes. Lancet 1999, 354, 1625-1633.
74. Witchel, H. J.; Milnes, J. T.; Mitcheson, J. S.; Hancox, J. C. Troubleshooting problems with in vitro screening of drugs for QT interval prolongation using HERG K+ channels expressed in mammalian cell lines and Xenopus oocytes. J. Pharmacol. Toxicol. Methods 2002, 48, 65-80.
75. Aptula, A. O.; Cronin, M. T. D. Prediction of hERG K+ blocking potency: Application of structural knowledge. SAR QSAR Environ. Res. 2004, 15, 399-411.
76. Ekins, S.; Crumb, W. J.; Sarazan, R. D.; Wikel, J. H.; Wrighton, S. A. Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel. J. Pharmacol. Exp. Ther. 2002, 301, 427-434.
77. Keseru, G. M. Prediction of hERG potassium channel affinity by traditional and hologram QSAR methods. Bioorg. Med. Chem. Lett. 2003, 13, 2773-2775.
78. Vaz, R. J.; Li, Y.; Rampe, D. Human ether-a-go-go related gene (HERG): A chemist's perspective. Prog. Med. Chem. 2005, 43, 1-18.
79. Zhu, B.; Jia, Z. J.; Zhang, P.; Su, T.; Huang, W.; Goldman, E.; Tumas, D.; Kadambi, V.; Eddy, P.; Sinha, U.; Scarborough, R. M.; Song, Y. Inhibitory effect of carboxylic acid group on hERG binding. Bioorg. Med. Chem. Lett. 2006, 16, 5507-5512.
80. Price, D. A.; Armour, D.; de Groot, M.; Leishman, D.; Napier, C.; Perros, M.; Stammen, B. L.; Anthony Wood, A. Overcoming HERG affinity in the discovery of the CCR5 antagonist maraviroc. Bioorg. Med. Chem. Lett. 2006, 16, 4633-4637.
81. Rendic, S.; Di Carlo, F. J. D. Human cytochrome P450 enzymes: A status report summarizing their reactions, substrates, inducers, and inhibitors. Drug Metab. Rev. 1997, 29, 413-580.
82. Guengerich, F. P. Update information of human P450s. Drug Metab. Rev. 2002, 4, 7-15.
83. Lin, J. H.; Lu, A. Y. H. Inhibition and induction of cytochrome P450 and the clinical implications. Clin. Pharmacokinet. 1998, 35, 361-390.
84. Bertz, R. J.; Granneman, G. R. Use of in vitro and in vivo data to estimate the likelihood of metabolic pharmacokinetic interactions. Clin. Pharmacokinet. 1997, 32, 210-258.
85. Rendic, S.; Di Carlo, F. J. Human cytochrome P450 enzymes: A status report summarizing their reactions, substrates, inducers, and Inhibitors. Drug Metab. Rev. 1997, 29, 413-580.
86. Shimada, T.; Yamazaki, H.; Mimura, M.; Inui, Y.; Guengerich, F. P. Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J. Pharmacol. Exp. Ther. 1994, 270, 414-423.
87. Masimirembwa, C. M.; Thompson, R.; Andersson, T. B. In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery. Comb. Chem. High Throughput Screening 2001, 4, 245-263.
88. Lewis, D. F. V. Human cytochromes P450 associated with the phase 1 metabolism of drugs and other xenobiotics: A compilation of substrates and inhibitors of the CYP1, CYP2, and CYP3 families. Curr. Med. Chem. 2003, 10, 1955-1972.
89. Lewis, D. F. V. Structural characteristics of human P450s involved in drug metabolism: QSARs and lipophilicity profiles. Toxicology 2000, 144, 197-203.
90. Sansen, S.; Yano, J. K.; Reynald, R. L.; Schoch, G. A.; Griffin, K. J.; Stout, D. D.; Johnson, E. F. Adaptations for the oxidation of polycyclic aromatic hydrocarbons exhibited by the structure of human P450 1A2. J. Biol. Chem. 2007, 282 (19), 14348-14355.
91. Burton, J.; Ijjaali, I.; Barberan, O.; Petitet, F.; Vercauteren, D. P.; Michel, A. Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: Importance of the quality of the dataset. J. Med. Chem. 2006, 49, 6231-6240.
92. Chohan, K. K.; Paine, S. W.; Mistry, J.; Barton, P.; Davis, A. M. A rapid computational filter for cytochrome P450 1A2 inhibition potential of molecule libraries. J. Med. Chem. 2005, 48, 5154-5161.
93. Korhonen, L. E.; Rahnasto, M.; Maehoenen, N. J.; Wittekindt, C.; Poso, A.; Juvonen, R. O.; Raunio, H. Predictive three-dimensional quantitative structure-activity relationship of cytochrome P450 1A2 inhibitors. J. Med. Chem. 2005, 48, 3808-3815.
94. Lewis, F. V.; Modi, S.; Dickins, M. Quantitative structure-activity relationships (QSARs) within substrates of human cytochrome P450 involved in drug metabolism. Drug Metab. Drug Rev. 2001, 18, 221-242.
95. Williams, P. A.; Cosme, J.; Ward, A.; Angove, H. C.; Vinkovic, D. M.; Jhoti, H. Crystal structure of humancytochrome P450 2C9 with bound warfarin. Nature 2003, 424, 464-468.
96. Wester, M. R.; Yano, J. K.; Schoch, G. A.; Yang, C.; Griffin, K. J.; Stout, D. D.; Johnson, E. F. The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-Å resolution. J. Biol. Chem. 2004, 279, 563035637.
97. Niwa, N.; Kageyama, A.; Kishimoto, K.; Yabusaki, Y.; Ishibashi, F.; Katagiri, M. Amino acid residues affecting the activities of human cytochrome P450 2C9 and 2C19. Drug Metab. Dispos. 2002, 30, 931-936.
98. Lewis, D. F. On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics towards the prediction of human P450 substrate specificity and metabolism. Biochem. Pharmacol. 2000, 60, 293-306.
99. Snyder, R.; Sangar, R.; Wang, J.; Ekins, S. Three-dimensional quantitative structure-activity relationship for Cyp2D6 substrates. Quant. Struct.-Act. Relat. 2002, 21, 357-368.
100. Ekins, S.; Bravi, G.; Binkley, S.; Gillespie, J. S.; Ring, B. J.; Wikel, J. H.; Wrighton, S. A. Three and four dimensional-quantitative structure-activity relationship (3D/4D-QSAR) analyses of CYP2D6 inhibitors. Pharmacogenetics 1999, 9, 477-489.
101. Rowland, P.; Blaney, F. E.; Smyth, M. G.; Jones, J. J.; Leydon, V. R.; Oxbrow, A. K.; Lewis, C. J.; Tennant, M. G.; Modi, S.; Eggleston, D. S.; Chenery, R. J.; Bridges, A. M. Crystal structure of human cytochrome P450 2D6. J. Biol. Chem. 2006, 281, 7614-7622.
102. Rendic, S.; Di Carlo, F. J. Human cytochrome P450 enzymes: A status report summarizing their reactions, substrates, inducers, and inhibitors. Drug Metab. Rev. 1997, 29, 413-580.
103. Williams, P. A.; Cosme, J.; Vinkovic, D. M.; Ward, A.; Angove, H. C.; Day, P. J.; Vonrhein, C.; Tickle, I. J.; Jhoti, H. Crystal structures of human cytochrome P450 3A4 bound to metyrapone and progesterone. Science 2004, 305, 683-686.
104. Yano, J. K.; Wester, M. R.; Schoch, G. A.; Griffin, K. J.; Stout, C. D.; Johnson, E. F. The structure of human microsomal cytochrome P450 3A4 determined by X-ray crystallography to 2.05 Å resolution. J. Biol. Chem. 2004, 279, 38091-38094.
105. Ekroos, M.; Sjogren, T. Structural basis for ligand promiscuity in cytochrome P450 3A4. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 13682-13687.
106. Ekins, S.; Stresser, D. M. ; Williams, A. J. In vitro and pharmacophore insights into CYP3A enzymes. Trends Pharmacol. Sci. 2003, 24, 161-166.
107. Hadjuk, P. J. Fragment-based drug design: How big is too big. J. Med. Chem. 2006, 49, 6972-6976.
108. SIMCA-P 10, Umetrics: Tvistevägen 48, Box 7960, SE-907 19 Umeå, Sweden.
109. Advanced Chemistry Development, Inc., 110 Yonge Street, 14th floor, Toronto, Ontario, M5C 1T4, Canada, www.acdlabs.com.
110. Statistica System Reference, Statsoft Inc., 2300 East Tulsa, OK 74104, www.statsoft.com.
111. Pan, L.; Ho, G.; Tsutsui, K.; Takahashi, L. Comparison of chromatographic and spectroscopic methods used to rank compounds for aqueous solubility. J. Pharm. Sci. 2001, 90, 521-529.
112. All animal studies were approved by an internal ethical review committee and performed in accordance with the UK Animals (Scientific Procedures) Act 1986 and "Principles of Laboratory Animal Care" (NIH Publication #85-23, rev. 1985). Standard methodologies were used to determine basic pharmacokinetic parameters following oral or intravenous dosing (e.g., clearance (blood or plasma), volume of distribution, terminal half-life, and oral bioavailability). Serial blood (or plasma) samples were obtained and analysed for parent compound concentrations using LC-MS/MS methodologies. Doses and formulations were selected on the basis of compound solubility and pharmacological activity. To minimize the impact of the individual study design, such as dosing vehicle, amount, and so on, only discrete results where the oral dose was <10 mg/kg in a standard formulation of polyethylene glycol (PEG) were used. For the purpose of illustration, a number of PK studies by GSK scientists are referenced below (refs 113118).
113. Watson, N. S.; Brown, D.; Campbell, M.; Chan, C.; Chaudry, L.; Convery, M. A.; Fenwick, R.; Hamblin, J. N.; Haslam, C.; Kelly, H. A.; King, N. P.; Kurtis, C. L.; Leach, A. R.; Manchee, G. R.; Mason, A. M.; Mitchell, C.; Patel, C.; Patel, V. K.; Senger, S.; Shah, G. P.; Weston, H. E.; Whitworth, C.; Young, R. J. Design and synthesis of orally active pyrrolidin-2-one-based factor Xa inhibitors. Bioorg. Med. Chem. Lett. 2006, 16, 3784-3788.
114. Austin, N. E.; Baldwin, S. J.; Cutler, L.; Deeks, N.; Kelly, P. J.; Nash, M.; Shardlow, C. E.; Stemp, G.; Thewlis, K.; Ayrton, A.; Jeffrey, P. Pharmacokinetics of the novel, high-affinity, and selective dopamine D3 receptor antagonist SB-277011 in rat, dog, and monkey: In vitro/in vivo correlation and the role of aldehyde oxidase. Xenobiotica 2001, 31, 677-686.
115. Forbes, I. T.; Douglas, S.; Gribble, A. D.; Ife, R. J.; Lightfoot, A. P.; Garner, A. E.; Riley, G. J.; Jeffrey, P.; Stevens, A. J.; Stean, T. O.; Thomas, D. R. SB-656104-A: A novel 5-HT7 receptor antagonist with improved In vivo properties. Bioorg. Med. Chem. Lett. 2002, 12, 3341-3344.
116. Hagan, J. J.; Price, G. W.; Jeffrey, P.; Deeks, N. J.; Stean, T.; Piper, D.; Smith, M. I.; Upton, N.; Medhurst, A. D.; Middlemiss, D. N.; Riley, G. J.; Lovell, P. J.; Bromidge, S. M.; Thomas, D. R. Characterization of SB-269970-A, a selective 5-HT7 receptor antagonist. Br. J. Pharmacol. 2000, 130, 539-548.
117. Plasma protein binding data was measured in the same experimental assay as that described in ref 118. The key difference was that the blood sample was centrifuged and the analysis was performed on the plasma sample only.
118. Summerfield, S. G.; Stevens, A. J.; Cutler, L.; Carmen-Osuna, M. D.; Hammond, B.; Tang, S.; Hersey, A.; Spalding, D. J.; Jeffrey, P. Improving the in vitro prediction of in vivo central nervous system penetration: Integrating permeability, P-glycoprotein efflux, and free fractions in blood and brain. J. Pharmacol. Exp. Ther. 2006, 316, 1282-1290.
119. 3H response was determined using a Wallac 1430 ViewLux microplate imager. Dose-response curves were fitted using Grafit.
120. 50 curve. Dose-response curves were subsequently fitted using Grafit.
121. Crespi, C. L.; Miller, V. P.; Penman, B. W. Microtiter plate assays for inhibition of human drug-metabolizing cytochromes P450. Anal. Biochem. 1997, 248, 188-190.
122. Advanced Chemistry Development, Inc., 110 Yonge Street, 14th Floor, Toronto, Ontario, M5C 1T4, Canada, http://www.acdlabs.com.
123. Daylight Chemical Information Systems, Inc., 120 Vantis, Suite 550, Aliso Viejo, CA 92656, U.S.A. http://www.daylight.com.