In-silico ADME models: A general assessment of their utility in drug discovery applications

Current Topics in Medicinal Chemistry

Volumn 11, Issue 4, 2011, Pages 358-381

  Gleeson, M Paul ^a   Hersey, Anne ^b   Hannongbua, Supa ^a  

^a KASETSART UNIVERSITY   (Thailand)

^b EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

Author keywords

ADME; In silico modeling; Lead generation; QSAR; Rules of thumb lead optimization; SAR

Indexed keywords

AMIDE; AMINE; BENZENE; CARBOXYLIC ACID; CYTOCHROME P450; DIETHYLAMINE; DIMETHYL ETHER; PROPANE; THIOPHENE;

ARTICLE; CHEMICAL ANALYSIS; CHEMISTRY; CLINICAL ASSESSMENT; COMPUTER MODEL; DRUG ABSORPTION; DRUG BIOAVAILABILITY; DRUG CLEARANCE; DRUG DEVELOPMENT; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG PENETRATION; DRUG SOLUBILITY; DRUG SYNTHESIS; IC 50; IN VITRO STUDY; IN VIVO STUDY; MEDICAL LITERATURE; PHYSICOCHEMICAL MODEL; PREDICTION; PROTEIN BINDING; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANIMALS; DRUG DISCOVERY; HUMANS; METABOLIC CLEARANCE RATE; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS; PHARMACOKINETICS; PREDICTIVE VALUE OF TESTS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 79551667504     PISSN: 15680266     EISSN: None     Source Type: Journal    
DOI: 10.2174/156802611794480927     Document Type: Article

References (199)

1. McGinnity, D. F.; Collington, J.; Austin, R. P.; Riley, R. J. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr. Drug Metabol., 2007, 8, 463-479.
2. 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.
3. Kwon, Y. Handbook of Essential Pharmacokinetics, Pharmacodynamics and Drug Metabolism for Industrial Scientists. Kluwer Academic/Plenum Publishers: New York, NY, 2001.
4. Hollenberg, P. F.; Kent, U. M.; Bumpus, N. N. Mechanism-based inactivation of human cytochromes p450s: experimental characterization, reactive intermediates, and clinical implications. Chem. Res. Toxicol., 2008, 21,189-205.
5. Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 2004, 3, 711-715.
6. Eddershaw, P. J.; Beresford, A. P.; Bayliss, M. K. ADME/PK as part of a rational approach to drug discovery. Drug Discov. Today, 2000, 5, 409-414.
7. Poggesi I. Predicting human pharmacokinetics from preclinical data. Curr. Opin. Drug Discov. Devel., 2004, 7, 100-111.
8. Whitebread, S.; Hamon, J.; Bojanic, D.; Urban, L. In vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today, 2005, 10, 1421-1433.
9. Kerns, E. H.; Di, L. Physicochemical profiling: overview of the screens. Drug Discov. Today, 2004, 1, 343-348.
10. Li, D.; Kerns, E. H. Application of pharmaceutical profiling assays for optimization of druglike properties. Curr. Opin. Drug Discov. Devel., 2005, 8, 495-504.
11. Davis, A. M.; Keeling, D. J.; Steele, J.; Tomkinson, N. P.; Tinker, A.C. Components of successful lead generation. Curr. Top. Med. Chem., 2005, 5, 421-439.
12. Norinder, U.; Bergstrom, C. A. S. Prediction of ADME properties. Chem. Med. Chem., 2006, 1, 920-937.
13. 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.
14. Chohan, K. K.; Paine, S. W.; Waters, N. J. Quantitative structure activity relationships in drug metabolism. Curr. Top. Med. Chem., 2006, 6, 1569-1578.
15. De Groot, M. Designing better drugs: predicting cytochrome P450 metabolism. Drug Discov. Today, 2006, 11, 601-606.
16. Hansch, D.; Leo, A.; Mekapati, S. B.; Kurup, A. QSAR and ADME. Bioorg. Med. Chem., 2004, 12, 3391-3400.
17. Lombardo, F.; Gifford, F.; Shalaeva, M. Y. In silico ADME prediction: Data, models, facts and myths. Mini Rev. Med. Chem., 2003, 3, 861-875.
18. Clark, D. E.; Grootenhuis, P. D. J. Predicting passive transport in silico -history, hype, hope. Curr. Top. Med. Chem., 2003, 3, 1193-1203.
19. Gola, J.; Obrezanova, O.; Champness, E.; Segall, M. ADME property prediction: the state of the art and current challenges. QSAR Comb. Sci., 2006, 25, 1172-1180.
20. Hewitt, M.; Cronin, M. T. D.; Enoch, S. J.; Madden, J. C.; Roberts, D. W.; Dearden, C. In silico prediction of aqueous solubility: The solubility challenge. J. Chem. Inf. Model., 2009, 49, 2572-2587.
21. Gleeson, M. P.; Davis, A. M.; Chohan, K. M.; Paine, S. W.; Boyer, S.; Gavaghan, C. L.; Hasselgren Arnby, C.; Kankkonen, C.; Albertson, N. Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models. J. Comput. Aided. Mol. Des., 2007, 21, 559-573.
22. Bruneau, P. Search for predictive generic model of aqueous solubility using bayesian neural nets. J. Chem. Inf. Comput. Sci., 2001, 41, 1605-1616.
23. Hewitt, H.; Cronin, M. T. D.; Madden, J. C.; Rowe, P. H.; Johnson, C.; Obi, A.; Enoch, S. J. Consensus QSAR Models: do the benefits outweigh the complexity? J. Chem. Inf. Model., 2007, 47, 1460-1468.
24. Kubinyi, H. Drug research:myths, hype and reality. Drug Discov. Today, 2003, 2, 665-668.
25. Hou, T. J.; Zhang, W.; Xia, K.; Qiao, X. B.; Xu, X. J. ADME evaluation in drug discovery. 5. correlation of Caco-2 permeation with simple molecular properties. J. Chem. Inf. Comput. Sci., 2004, 44, 1585-1600.
26. Topliss, J. G.; Edwards, R. P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem., 1979, 22, 1238-1244.
27. Stouch, T. R.; Kenyon, J. R.; Johnson, S. R.; Chen, X. Q.; Doweyko, A.; Li, Y. J. In silico ADME/Tox: why models fail. Comp-Aided Mol. Des., 2003, 17, 83-92.
28. Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graphics Model., 2002, 20, 269-276.
29. Johnson, S. R. The trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy). J. Chem. Inf. Model., 2008, 48, 25-26.
30. Hu, Y; Gao, H. Dimension related distance and its application in QSAR/QSPR model error estimation QSAR Comb. Sci., 2003 22,422-429.
31. Tetko, I.V.; Bruneau, P.; Mewes, H.; Rohrer, D. C.; Poda, G. I. Can we estimate the accuracy of ADME-Tox predictions? Drug Discov. Today, 2006 11, 700-707.
32. Weaver, S.; Gleeson, M. P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model., 2008, 26,1315-1326.
33. 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 Deliv. Rev., 1997, 23, 3-25.
34. Hamalainen, M. D.; Frostell-Karlsson, A. Predicting the intestinal absorption potential of hits and leads. Drug Discov. Today, 2004, 1, 397-405.
35. 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.
36. 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.
37. 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.
38. Dressman, J. B.; Vertzoni, M; Goumas, K.; Reppas, C. Estimating drug solubility in the gastrointestinal tract. Adv. Drug Deliv. Rev., 2007, 59, 591-602.
39. Riley, R. J.; Martin, I. J.; Cooper, A. E. The influence of DMPK as an integrated partner in modern drug discovery. Curr. Drug Metabol., 2002, 3,527-550.
40. 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.
41. Oprea, T. I.; Allu, T.; Fara, D.; Rad, R.; Ostopovici, L.; Bologa, C. Lead-like, drug-like or "Pub-like": how different are they? J. Comput. Aided. Mol. Des., 2007, 21, 113-119.
42. Oprea, T. I.; Davis, A. M.; Teague, S. J.; Leeson, P. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci., 2001, 41, 1308-1315.
43. Wessel, M.D.; Jurs, P. C.; Tolan, J. W.; Muskal, S. M. Prediction of human intestinal absorption of drug compounds from molecular structure. J. Chem. Inf. Comput. Sci., 1998, 38, 726-773.
44. Klopman, G.; Stefan, L. R.; Saiakhov, R. D. ADME evaluation 2. A computer model for the prediction of intestinal absorption in humans. Eur. J. Pharm. Sci., 2002, 17, 253-263.
45. Hou, T.; Wang, J.; Zhang, W.; Xu, X. ADME evaluation in drug discovery. 7. prediction of oral absorption by correlation and classification. J. Chem. Inf. Model., 2007, 47, 208-218.
46. Gleeson, M. P. Generation of a set of simple, interpretable ADME rules of thumb. J. Med. Chem., 2008, 51, 817-834.
47. Perez, P. A. C.; Sanz, M. B.; Torres, L. R.; Avalos, R. C.; Gonzalez, M. P.; Diaz, H. G. A Topological sub-structural approach for predicting human intestinal absorption of drugs. Eur. J. Med. Chem., 2004, 39, 905-916.
48. Martin, Y. A bioavailability score. J. Med. Chem., 2005, 48, 3164-3170.
49. Veber, D. F.; Johnson, S. R.; Cheng, H. Y.; 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.
50. Palm, K.; Stenberg, P.; Luthmann, K.; Artursson, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm. Res., 1997, 14, 568-571.
51. Lu, J. J.; Crimin, K.; Goodwin, J. T.; Crivori, P.; Orrenius, C.; Xing, L.; Tandler, P. J.; Vidmar, T. J.; Amore, B. M.; Wilson, A. G. E.; Stouten, P. F. W.; Burton, P. S. Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. J. Med. Chem., 2004, 47, 6104-6107.
52. Hou, T. J.; Wang, J. M.; Zhang, W.; Xu, X. J. ADME evaluation in drug discovery. 6. if the oral bioavailability in human can be effectively predicted by simple molecular properties? J. Chem. Inf. Comput. Sci., 2007, 47, 460-463.
53. Alsenz, J.; Manfred Kansy, M. High throughput solubility measurement in drug discovery and development. Adv. Drug Deliv. Rev., 2007, 59, 546-567.
54. Di, L.; Kerns, E. H. Biological assay challenges from compound solubility: strategies for bioassay optimization. Drug Discov. Today, 2006, 11, 447-451.
55. Takagi, T.; Ramachandran, C.; Bermejo, M.; Yamashita, S.; Yu, L. X.; Amidon, G. L. A Provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan. Mol. Pharm., 2006, 6, 631-643.
56. 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.
57. Delaney, J. S. ESOL: Estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci., 2004, 44, 1000-1005.
58. Kuhne, R.; Ebert, R. U.; Kleint, F.; Schmidt, G.; Schuurmann, G. Group contribution methods to estimate water solubility of organic chemicals. Chemosphere, 1995, 30, 2061-2077.
59. Klopman, G.; Wang, S.; Balthasar, D. M. Estimation of aqueous solubility of organic compounds by the group contribution approach. application to the study of biodegradation. J. Chem. Inf. Comput. Sci., 1992, 32, 474-482.
60. 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. Divers., 2004, 8, 379-391.
61. Huuskonen, J. Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J. Chem. Inf. Comput. Sci., 2003, 43, 429-434.
62. 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. Mod., 2006, 46, 2601-2609.
63. Llinàs, A.; Glen, R. C.; Goodman, J. M. Solubility challenge: can you predict solubilities of 32 molecules using a database of 100 measurements? J. Chem. Inf. Model., 2008, 48, 1289-1303.
64. Hopfinger, A. J.; Esposito, E. X.; Llina, A.; Glen, R. C.; Goodman, J. M. Findings of the challenge to predict aqueous solubility. J. Chem. Inf. Model., 2009, 49, 1-5.
65. Hansch, C.; Quinlan, J. E.; Lawrence, G. L. The linear free energy relationship between partition coefficients and the aqueous solubility of organic liquids. J. Org. Chem., 1968, 33, 347-350.
66. Jain, N.; Yalkowsky, S. H. Estimation of the aqueous solubility I: Application to organic nonelectrolytes. J. Pharm. Sci., 2001, 90, 234-252.
67. Gleeson, M. P.; Bravi, G.; Modi, S.; Lowe, L. ADME rules of thumb II: A comparison of the effects of common substituents on a range of ADME parameters. Bioorg. Med. Chem. Lett., 2009, 17, 5906-5919.
68. Leach, A. G.; Jones, H. D.; Cosgrove, D. A.; Kenny, P. W.; Ruston, L.; MacFaul, P.; Wood, J. M.; Colclough, N.; Law, B. Matchedmolecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein bindingand oral exposure. J. Med. Chem., 2006, 49, 6672-6682.
69. Fujikawa, M.; Ano, R.; Nakao, K.; Shimizu, R.; Akamatsu, M. Relationships between structure and high-throughput screening permeability of diverse drugs with artificial membranes: Application to prediction of Caco-2 cell permeability. Bioorg. Med. Chem., 2005, 13, 4721-4732.
70. Camenisch, G; Alsenz, J.; van de Waterbeemd, H. and 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.
71. Nordqvist, A.; Nilsson, J.; Lindmark, T.; Eriksson, A.; Garberg, P. Kihlen, M. A General model for prediction of Caco-2 cell permeability. QSAR Comb. Sci., 2004, 23, 303-310.
72. Verma, R. P.; Hansch, C.; Selassie, C. D. Comparative QSAR studies on PAMPA/modified PAMPA for high throughput profiling of drug absorption potential with respect to Caco-2 cells and human intestinal absorption. J. Comput. Aided Mol. Des., 2007, 21, 3-22.
73. Fichert, T.; Yazdanian, M.; Proudfoot, J. R. A Structurepermeability study of small drug-like molecules. Bioorg. Med. Chem. Lett., 2003, 13, 719-722.
74. Woosley, R. L. Cardiac actions of antihistamines. Annu. Rev. Pharmacol. Toxicol., 1996, 36, 233-252.
75. Aboagye, E. O.; Price, P. M.; Jones, T. In vivo pharmacokinetics and pharmacodynamics in drug development using positronemission tomography. Drug Discov. Today, 2001, 6, 293-302.
76. Coe, R.A.J. Quantitative whole-body autoradiography. Reg. Toxicol. Pharmacol., 2000, 31, 1-3.
77. Lombardo, F.; Obach, R. S.; Shalaeva, M. Y.; Gao, F. Prediction of human volume of distribution values for neutral and basic drugs. 2. extended data set and leave-class-out statistics. J. Med. Chem., 2004, 47, 1242-1250.
78. 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.
79. Lombardo, F.; Obach, R. S.; DiCapua, F. M.; Bakken, G. A.; Lu, J.; Potter, D. M.; Gao, F.; Miller, M. D.; Zhang, Y. A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J. Med. Chem., 2006, 49, 2262-2267.
80. Berellini, G.; Springer, C.; Waters, N. J.; Lombardo, F. In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set. J. Med. Chem., 2009, 52, 4488-4495.
81. Obach, R. S.; Lombardo, F.; Waters, N. J. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 compounds. Drug Metab. Dispos., 2008, 36, 1385-1405.
82. Gleeson, M. P.; Waters, N. J.; Paine, S. W.; Davis, A. M. In silico human and rat vss quantitative structure-activity relationship models. J. Med. Chem., 2006, 49, 1953-1963.
83. 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.
84. Ghafourian, T.; Barzegar-Jalali, M.; Dastmalchi, S.; Khavari-Khorasani, T.; Hakimiha, N. Nokhodchi, A. QSPR models for the prediction of apparent volume of distribution. Int. J. Pharm., 2006, 319, 82-97.
85. Hollosy, F.; Valko, K.; Hersey, H.; Nunhuck, S.; Keri, G.; Bevan, C. Estimation of volume of distribution in humans from high throughput HPLC-based measurements of human serum albumin binding and immobilized artificial membrane partitioning J. Med. Chem., 2006, 49, 6958-6971.
86. 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. Pharmacol., 1982, 31, 2545-2548.
87. Oie S.; Tozer T. N. Effect of altered plasma protein binding on apparent volume of distribution. J. Pharm. Sci., 1979, 68, 1203-1205.
88. Colmenarejo, G.; Alvarez-Pedraglio, A.; Lavandera, J. L. Cheminformatic models to predict binding affinities to human serum albumin. J. Med. Chem., 2001, 44, 4370-4378.
89. 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.
90. Liu, J.; Yang, L.; Li, Y.; Panb, D.; Hopfinger, A. J. Constructing plasma protein binding model based on a combination of clusteranalysis and 4D-fingerprint molecular similarity analyses. Bioorg, Med. Chem., 2006, 14, 611-621.
91. Estrada, E.; Uriarte, E; Molina, E; Simón-Manso, Y; Milne, G. W. A. An integrated in silico analysis of drug-binding to human serum albumin. J. Chem. Inf. Model., 2006, 46, 2709-2724.
92. Valko, K.; Nunhuck, S.; Bevan, C.; Abraham, M. H.; Reynolds, D. P. Fast gradient HPLC method to determine compounds binding to human serum albumin. Relationships with octanol/water and immobilized artificial membrane lipophilicity. J. Pharm. Sci., 2003, 92, 2236-2248.
93. Yamakazi, K.; Kanaoka, M. Computational Prediction of plasma protein binding Percent of diverse pharmaceutical compounds. J. Pharm. Sci., 2004, 93, 1480-1494.
94. Hardman, J. G.; Limbird, L. E.; Gillman, A. G. Eds.; Goodman & Gillman's Ihe Pharmacological Basis of Therapeutics, 10th ed.; McGraw-Hill: New York, 2001.
95. Gleeson, M. P. Plasma protein binding affinity and its relationship to molecular structure: an in-silico analysis. J. Med. Chem., 2007, 50, 101-112.
96. Rodgers, A. L.; Davis, A. M.; van de Waterbeemd, Han. Timeseries QSAR analysis of human plasma protein binding data. QSAR Comb. Sci., 2007, 26, 511-521.
97. Gao, H.; Yao, L.; Mathieu, H. W.; Zhang, W.; Maurer, T. S.; Troutman, M. D.; Scott, D. O.; Ruggeri, R. B.; Lin, J. In silico modeling of nonspecific binding to human liver microsomes. Drug Metabol. Dispos., 2008, 36, 2130-2135.
98. 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, 33, 419-425.
99. Wan, H.; Rehngren, M.; Giordanetto, F.; Bergstrom, F.; Tunek, A. High-throughput screening of drug-brain tissue binding and in silico prediction for assessment of central nervous system drug delivery. J. Med. Chem., 2007, 50, 4606-4615.
100. Summerfield, S.G.; Stevens, A. J.; Cutler, L.; de Carmen Osuna, M.; 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. Therap., 2006, 316, 1282-1290.
101. Hitchcock, A. A.; Pennington, L. D. Structure-brain exposure relationships. J. Med. Chem., 2006, 49, 7559-7583.
102. Clark, D. E. In silico prediction of blood-brain barrier permeation, Drug Discov. Today, 2003, 8, 927-933.
103. Abraham, M. H.; Hersey A. In: In Silico Models to Predict Brain Uptake. Comprehensive Medicinal Chemistry II; Elsevier Ltd. 2007: Chapter 5.31, pp. 745-766.
104. Watson, J.; Wright, S.; Lucas, A.; Clarke, K. L.; Viggers, J.; Cheetham, S.; Jeffrey, P.; Porter R.; Read, K. D. Receptor occupancy and brain free fraction. Drug Metab. Dispos., 2009, 37, 753-760.
105. Abraham, M. H.; Chadha, H. S.; Mitchell, R. C. Hydrogen bonding. 33. Factors that influence the distribution of solutes between blood and brain. J. Pharm. Sci., 1994, 83, 1257-1268.
106. 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.
107. Winkler, D. A.; Burden, F. R. Modelling blood-brain barrier partitioning using Bayesian neural nets. J. Mol. Graph. Model., 2004, 22, 499-505.
108. Hemmateenejad, B.; Miri, R.; Safarpour, M. A.; Mehdipour, A. R. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. J. Comput. Chem., 2006, 27, 1125-1135.
109. Bemis, G. W.; Murcko, M. A. Designing libraries with CNS activity. J. Med. Chem., 1999, 42, 4942-4951
110. Crivori, P.; Cruciani, G.; Carrupt, P-A.; Testa, B. Predicting bloodbrain barrier permeation from three-dimensional molecular structure. J. Med. Chem., 2000, 43, 2204-2216.
111. 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.
112. Hansch, C.; Björkroth, J. P.; Leo, A. Hydrophobicity and central nervous system agents: On the principle of minimal hydrophobicity in drug design. J. Pharm. Sci., 1987, 76, 663-687.
113. Kelder, J.; Grootenhuis, P. D. J.; Bayada, D. M.; Delbressine, L. P. C.; Ploeme, J. P. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm, Res., 2004, 10, 1514-1519.
114. Ertl, P.; Rohde, B.; Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem., 2000, 43, 3714-3717.
115. Norinder, U.; Haeberlein, M. Computational approaches to the prediction of the blood-brain distribution. Adv. Drug. Deliv. Rev., 2002, 54, 291-313.
116. Ayrton, A.; Morgan, P.; Role of transport proteins in drug absorption, distribution and excretion. Xenobiotica, 2001, 31, 469-497.
117. Shitara, Y.; Sato, H.; Sugiyama, Y. Evaluation of drug-drug interaction in the hepatobiliary and renal transport of drugs. Annu. Rev. Pharmacol. Toxicol., 2005, 45, 689-723.
118. Dobson, P. D.; Kell, D. B. Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule? Nat. Rev. Drug Discov., 2008, 7, 205-220.
119. Polli, J. W.; Jarrett, J. L.; Studenberg, S. D.; Humphreys, J. E.; Dennis, S. W.; Brouwer, K. R.; Woolley, J. L. Role of Pglycoprotein on the CNS disposition of amprenavir (141W94), an HIV protease inhibitor. Pharm. Res., 1999, 16, 1206-1212.
120. Shitara, Y.; Itoh, T.; Sato, H.; Li, A. P.; Sugiyama, Y. Inhibition of transporter-mediated hepatic uptake as a mechanism for drug-drug interaction between cerivastatin and cyclosporin A. J. Pharmacol. Expt. Ther., 2003, 304, 610-616.
121. Noe, J.; Portmann R.; Brun, M. E.; Funk, C. Substrate-dependent drug-drug interactions between gemfibrozil, fluvastatin and otherorganic anion-transporting Peptide (OATP) substrates on OATP1B1, OATP2B1, and OATP1B3. Drug Metab. Disp., 2007, 35, 1308-1314.
122. de Cerqueira Lima, P.; Golbraikh, A.; Oloff, S.; Xiao, Y. Tropsha, A. Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model., 2006, 46, 1245-1254.
123. Penzotti, J. E.; Lamb, M. L.; Evensen, E.; Grootenhuis, P. D. J. A Computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J. Med. Chem., 2002, 45, 37-1740.
124. Hersey, A.; Luscombe, C.; Modi, S. In-silico transporter modelling and its application in drug discovery in pharmaceutical profiling in drug discovery for lead Selection. Borchardt, R.T.; Kerns, E. H.; Lipinski, C. A.; Thakker, D.; Wang, B. Eds. Pub. AAPS Press: 2004,
125. Crivori, P.; Reinach, B.; Pezzetta, D.; Poggesi, I. Computational models for identifying potential p-glycoprotein substrates and inhibitors. Mol. Pharm., 2006, 3, 33-44.
126. Chang, C.; Ray, A.; Swaan, P. In silico strategies for modelling membrane transport function. Drug Discov. Today, 2005, 10, 663-671.
127. Chang, C.; Swaan, P. W. Computational approaches to modelling drug transporters. Eur. J. Pharm. Sci., 2006, 27, 411-424.
128. Ekins, S.; Swaan, P. W. Computational models for enzymes, transporters, channels and receptors relevant to absorption, distribution, metabolism, excretion and toxicology. Rev. Comp. Chem., 2004, 20, 333-415.
129. Ha, S. N.; Hochman, J.; Sheridan, R. P. Mini review on molecular modeling of p-glycoprotein (Pgp). Curr. Top. Med. Chem., 2007, 7, 1525-1529.
130. Cramer, R. D. III.; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA) 1. effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967.
131. Seelig, A. A General pattern for substrate recognition by pglycoprotein. Eur. J. Biochem., 1998, 251, 252-261.
132. Didziapetris, R.; Japertas, P.; Avdeef, A.; Petrauskas, A. Classification of p-glycoprotein substrate specificity. J. Drug Target., 2003, 11, 391-406.
133. Aller, S. G.; Yu, J.; Ward, A.; Weng, Y.; Chittaboina, S.; Zhuo, R.; Harrell, P. M.; Trinh, Y. T.; Zhang, Q.; Urbatsch, I. L.; Chang, G. Structure of p-glycoprotein reveals a molecular basis for polyspecific drug binding. Science, 2009, 323, 1718-1722.
134. Suhre, W. M.; Ekins, S.; Chang, C.; Swaan, P. W.; Wright, S. H. Molecular determinants of substrate/inhibitor binding to the human and rabbit renal organic cation transporters hOCT2 and rbOCT2. Mol. Pharmacol., 2005, 67, 1067-1077.
135. Shitara, Y.; Sato, H.; Sugiyama, Y. Evaluation of drug-drug interaction in the hepatobiliary and renal transport of drugs. Annu. Rev. Pharmacol. Toxicol., 2005, 45, 689-723.
136. Chang, C.; Pang, K. S.; Swaan, P.W.; Ekins, S. Comparative pharmacophore modeling of organic anion transporting polypeptides: a meta analysis of rat Oatp1a1 and human OATP1B1. J. Pharmacol. Exp. Ther., 2005, 314, 533-541.
137. Smith, D. A.; Jones B. C. Speculations on the substrate structureactivity relationship (SSAR) of cytochrome P450 enzymes. Biochem. Pharmacol., 1992, 44, 2089-2098.
138. 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.
139. Testa, B.; Balmata, A.-L.; Long, A.; Judson, P. Predicting drug metabolism an evaluation of the expert system meteor. Chem. Biodivers., 2005, 2 872-885.
140. Zamora, I.; Afzelius, L.; Cruciani, G. Predicting drug metabolism: A site of metabolism prediction tool applied to the cytochrome P450 2C9. J. Med. Chem., 2003, 46, 2313-2324.
141. Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D. D.; Psychogios, N.; Dong, E.; Bouatra, S.; Mandal, R.; Sinelnikov, I.; Xia, J.; Jia, L.; Cruz, J. A.; Lim, E.; Sobsey, C. A.; Shrivastava, S.; Huang, P.; Liu, P.; Fang, L.; Peng, J.; Fradette, R.; Cheng, D.; Tzur, D.; Clements, M.; Lewis, A.; De Souza, A.; Zuniga, A.; Dawe, M.; Xiong, Y.; Clive, D.; Greiner, R.; Nazyrova, A.; Shaykhutdinov, R.; Li L.; Vogel, H. J.; Forsythe, I. HMDB: a knowledgebase for the human metabolome. Nucl. Acids Res., 2009 37, D603-610.
142. Accelrys Inc., 9685 Scranton Road, San Diego, CA 92121-3752, U.S.A. http://www.accelrys.com (Accessed March, 2010).
143. Sansen, S.; Yano, J. K.; Reynald, R. L.; Schoch, G. A.; Griffin, K. J.; Stout, C. 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, 14348-14355.
144. 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.
145. 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.
146. Williams, P. A.; Cosme, J.; Vinkovic, D. M.; Ward, A.; Angove, H. C.; Day, P. J.; Vonrhein, C.; Tickle, I. J. and Jhoti, H. Crystal structures of human cytochrome P450 3A4 bound to metyrapone andprogesterone. Science, 2004, 30, 683-686.
147. Madden, J. C.; Cronin, M. T. D. Structure based methods for the prediction of metabolism. Expert Opin. Drug Metab. Toxicol., 2006, 2, 545-557.
148. Langowski, J. Long, A. Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Del. Rev., 2002, 54, 407-415.
149. Sun, H.; Scott, D. O. Structure-based drug metabolism predictions for drug design. Chem. Biol. Drug Des., 2010, 75, 3-17.
150. de Groot, M. J. Designing better drugs: predicting cytochrome P450 metabolism. Drug Discov. Today, 2006, 11, 601-606.
151. Crivori, P.; Poggesi, I. Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs Eur. J. Med. Chem., 2006, 41, 795-808.
152. Sun, H.; Scott, D. O. Structure-based drug metabolism predictions for drug design. Chem. Biol. Drug. Des., 2010 75, 3-17.
153. de Groot, M. J.; Kirton, S. B.; Sutcliffe, M. J. In silico methods for predicting ligand binding determinants of cytochromes P450. Curr. Top. Med. Chem., 2004, 4, 1803-1824.
154. Ekins, S.; de Groot, M. J.; Jones, J. P. Pharmacophore and threedimensional quantitative structure activity relationship methods for modeling cytochrome P450 active Sites. Drug Metab. Dispos., 2001, 29, 936-944.
155. Warren, G. L.; Andrews, C. A.; Capelli, A.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A Critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49, 5912-5931.
156. Leach, A. R.; Shoichet, B. K.; Peishoff, C. E. Prediction of proteinligand interactions. Docking and scoring: successes and gaps. J.Med. Chem., 2006, 49, 5851-5855.
157. Goodford, P. J. A. computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28, 849-857.
158. Zhou, D.; Afzelius, L.; Grimm, S. W.; Andersson, T. B.; Zauhar, R. J.; Zamora, I. Comparison of methods for the prediction of themetabolic sites for Cyp3a4-mediated metabolic reactions. Drug Metab. Dispos., 2006, 34, 976-983.
159. Ahlstrom, M. M.; Ridderstrom, M.; Zamora, I. CYP2C9 structuremetabolism relationships: substrates, inhibitors, and metabolites. J. Med. Chem., 2007, 50, 5382-5391.
160. Kulkarni, S. A.; Zhu, J; Blechinger, S. In-silico techniques for the study and prediction of xenobiotic metabolism: a review. Xenobiotica, 2005, 35, 955-973.
161. Visser, S. P.; Ogliaro, F.; Sharma, P. K.; Shaik, S. What factors affect the regioselectivity of oxidation by cytochrome P450? A DFT study of allylic hydroxylation and double bond epoxidation ina model reaction. J. Am. Chem. Soc., 2002, 124, 11809-11826.
162. de Visser, S. P.; Ogliaro, F.; Sharma, P. K.; Shaik, S. What factors affect the regioselectivity of oxidation by cytochrome p450? a dftstudy of allylic hydroxylation and double bond epoxidation in a model reaction. J. Am. Chem. Soc., 2002, 124, 11809-11826.
163. Bathelt, C. M.; Mulholland, A. J.; Harvey, J. N. QM/MM studies of the electronic structure of the compound i intermediate in cytochrome c peroxidase and ascorbate peroxidase. Dalton Trans., 2005 21, 3470-3476.
164. Bathelt, C. M.; Mulholland, A. J.; Harvey, J. N. QM/MM modeling of benzene hydroxylation in human cytochrome P450 2C9. J. Phys. Chem. A, 2008, 112, 13149-13156.
165. Shaik, S.; Cohen, S.; Wang, Y.; Chen, H.; Kumar, D.; Thiel, W. P450 enzymes: their structure, reactivity, and selectivity-Modeled by QM/MM calculations. Chem. Rev., 2010, 10, 949-1017.
166. Lewis D. F. V.; Modi S.; Dickins M. Structure-activity relationship for human cytochrome P450 substrates and inhibitors. Drug Metab. Rev., 2002, 34, 69-82.
167. Lewis, D. F. V. Quantitative structure-activity relationships (QSARs) within the cytochrome P450 system: QSARs describing substrate binding, inhibition and induction of P450s. Inflammopharmacology, 2003, 11, 43-73.
168. Koymans, L.; Vermeulen, N. P. E.; van Acker, S. A. B. E.; Koppele, J. M.; Heykants, J. J. P.; Lavrijsen, K.; Meuldermans, W. Kelder, G. M. A predictive model for substrates of cytochrome P450-debrisoquine (2D6). Chem. Res. Toxicol., 2002, 5, 211-219.
169. Smith, D. A.; Ackland, M. J.; Jones, B. C. Properties of cytochrome P450 isoenzymes and their substrates Part 2: properties of cytochrome P450 substrates. Drug Discov. Today 1997, 2, 479-486.
170. Kalgutkar, A. S.; Soglia, J. R. Minimising the potential for metabolic activation in drug discovery. Expert. Opin. Drug Metab. Toxicol., 2005, 1, 91-142.
171. Kalgutkar, A. S.; Dalvie, D. K.; O'Donnell, J. P.; Taylor, T. J.; Sahakian, D. C. On the diversity of oxidative bioactivation reactions on nitrogen-containing xenobiotics. Curr. Drug Metab., 2002, 3, 379-424.
172. Fontana, E.; Dansette P. M.; Poli, S. M. Cytochrome P450 enzymes mechanism based inhibitors: common sub-structures and reactivity. Curr. Drug Metab., 2005, 6, 413-454.
173. Dalvie, D. K.; Kalgutkar, A. S.; Khojasteh-Bakht, S. C.; Obach, R. S.; O'Donnell, J. P. Biotransformation reactions of five-membered aromatic heterocyclic rings. Chem. Res. Toxicol., 2002, 15, 269-299.
174. Rendic, S.; Di Carlo, F. J. D. Human cytochrome P450 enzymes: A status report summarizing their reactions, substrates, inducers, andinhibitors. Drug Metab. Rev., 1997, 29, 413-580.
175. Guengerich F. P. Update information of human P450s. Drug Metab. Rev., 2002, 4, 7-15.
176. Lin, J. H.; Lu, A. Y. H. Inhibition and induction of cytochrome p450 and the clinical implications. Clin. Pharmacokinet., 1998, 35, 361-390.
177. 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 Throug. Screen., 2001, 4, 245-263.
178. Jensen, B. F.; Vind, C.; Padkjær, S. B.; Brockhoff, P. B.; Refsgaard, H. H. F. In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. J. Med. Chem., 2007, 50, 501-511.
179. Byvatov, E.; Baringhaus, K.; Schneider, G.; Matter, H. A VIRTUAL SCREENING FILTER FOR IDENTIFICATION of cytochromeP450 2C9 (CYP2C9) inhibitors. QSAR Comb. Sci., 2007, 26, 618-628.
180. Burton, J.; Ijjaali, I.; Barberan, O.; Petitet, F.; Vercauteren, D. P.; Michel, A. Recursive Partitioning for the Prediction of CytochromesP450 2D6 and 1A2 Inhibition: Importance of the Quality of the Dataset. J. Med. Chem., 2006, 49, 6231-6240.
181. Chohan, K. K.; Paine, S. W.; Mistry, J.; Barton, P.; Davis, A. M. A rapid computational filter for cytochrome p450 1a2 inhibition potential of compound libraries. J. Med. Chem. 2005, 48, 5154-5161.
182. Ekins, S.; Bravi, G.; Binkley, S.; Gillespie, J. S.; Ring, B. J.; Wikel, J. H.; Wrighton, S. A. Three-and four-dimensional-quantitativestructure activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab. Dispos., 2000, 28, 994-1002.
183. McMasters, D. R.; Torres, R. A.; Crathern, S. J.; Dooney, D. L.; Nachbar, R. B.; Sheridan, R. P.; Korzekwa, K. R. Inhibition of recombinantcytochrome p450 isoforms 2D6 and 2C9 by diverse drug-like molecules. J. Med. Chem., 2007, 50, 3205-3213.
184. Ahlström, M. M.; Zamora, I. Characterization of Type II LIgands in CYP2C9 and CYP3A4. J. Med. Chem., 2008, 51, 1755-1763.
185. 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 Metabol., 2006, 7, 251-264.
186. Madden, J. C.; Cronin, T. D. Structure-based methods for the prediction of drug metabolism. Expert Opin. Drug Metab. Toxicol., 2006 2, 545-557.
187. Yap, C. W.; Li, Z. R.; Chen, Y. Z. Quantitative structure pharmacokinetic relationships for drug clearance by using statistical learning methods. J. Mol. Graph. Model., 2006, 24, 383-395.
188. Lee, P. H.; Cucurull-Sanchez, L.; Lu, J.; Du, Y. J. Development of in silico models for human liver microsomal stability. J. Comput. Aided. Mol. Des., 2007 21, 665-673.
189. Hu, Y.; Unwalla, R.; Denny, R. A.; Bikker, J. Di, L.; Humblet, C. Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability. J. Comput. Aided Mol. Des., 2010, 24, 23-35.
190. Masimirembwa, C. M.; Bredberg, U.; Andersson, T. B. Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges. Clin. Pharmacokinet., 2003 42, 515-528.
191. Jeffrey, P.; Clarke, S. E. Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica, 2001, 31, 591-598.
192. Lewis, M. L.; Cucurull-Sanchez, L. Structural pairwise comparisons of HLM stability of phenyl derivatives: Introduction of the Pfizer metabolism index (PMI) and metabolism-lipophilicity efficiency (MLE). J. Comput. Aided Mol. Des., 2009, 23, 97-103.
193. Ghibellini, G.; Leslie, E. M.; Brouwer, K. L. R. Methods to evaluate biliary excretion of drugs in humans: an updated review. Mol. Pharm., 2006, 3, 198-211.
194. Chen, Y.; Cameron, K.; Guzman-Perez, A.; Perry, D.; Li, D.; Gao, H. Structure-pharmacokinetic relationship of in vivo rat biliary excretion. Biopharm. Drug Dispos., 2010, 31, 82-90.
195. Yang, X.; Gandhi, Y. A.; Duignan, D. B.; Morris1, M. E. Prediction of biliary excretion in rats and humans using molecular weight and quantitative structure-pharmacokinetic relationships. AAPS J., 2009, 11, 511-525.
196. Manga, N.; Duffy J. C.; Rowe, P. H.; Cronin, M. T. D. A hierarchical QSAR model for urinary excretion of drugs in humans as a predictive tool for biotransformation. QSAR Comb. Sci., 2003, 22, 263-273.
197. Millburn P. Factors in the Biliary Excretion of Organic Compounds. In: Metabolic conjugation and metabolic hydrolysis; Fishmen, W. Ed.; New York: Academic Press; 1970, pp. 1-74.
198. Millburn, P.; Smith, R. L.; Williams, R. T. Biliary excretion of foreign compounds: biphenyl, stilboestrol and phenolphthalein in the rat: molecular weight, polarity and metabolism as factors in biliary excretion. Biochem. J., 1967, 105, 1275-1281.
199. Hughes, R. D.; Millburn, P.; Williams, R. T. Biliary excretion of some diquaternary ammonium cations in the rat, guinea pig andrabbit. Biochem. J., 1973, 136, 979-984.