Properties and in silico ADME modeling in drug discovery

Medicinal Chemistry

Volumn 9, Issue 2, 2013, Pages 163-176

  Honório, Káthia M ^a,b   Moda, Tiago L ^b   Andricopulo, Adriano D ^b  

^a UNIVERSIDADE FEDERAL DO ABC   (Brazil)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Adme; Drug design; Medicinal chemistry; Pharmacokinetics; QSAR; QSPR

Indexed keywords

DRUG SCRE; MULTIDRUG RESISTANCE PROTEIN; UNCLASSIFIED DRUG; DRUG;

ALZHEIMER DISEASE; ARTICLE; BINDING AFFINITY; BLOOD BRAIN BARRIER; COMPUTER MODEL; DRUG ABSORPTION; DRUG BINDING; DRUG BIOAVAILABILITY; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG POTENCY; DRUG QUALITY; DRUG RESEARCH; DRUG SCREENING; DRUG SELECTIVITY; DRUG SOLUBILITY; DRUG SYNTHESIS; HUMAN; HYDROGEN BOND; LIPOPHILICITY; NONHUMAN; PHARMACOKINETICS; PLASMA PROTEIN BINDING; PRIORITY JOURNAL; TECHNOLOGY; ABSORPTION; ANIMAL; BIOLOGY; CHEMISTRY; COMPUTER SIMULATION; DRUG DEVELOPMENT; METABOLISM; METHODOLOGY; REVIEW;

ABSORPTION; ANIMALS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DRUG DISCOVERY; HUMANS; PHARMACEUTICAL PREPARATIONS; PHARMACOKINETICS;

EID: 84877987070     PISSN: 15734064     EISSN: 18756638     Source Type: Journal    
DOI: 10.2174/1573406411309020002     Document Type: Article

References (141)

1. Chiaradia, L.D.; Martins, P.G.A.; Cordeiro, M.N.S.; Guido, R.V.C.; Ecco, G.; Andricopulo, A.D.; Yunes, R.A.; Vernal, J.; Nunes, R.J.; Terenzi, H. Synthesis, biological evaluation and molecular modeling of chalcone derivatives as potent inhibitors of Mycobacterium tuberculosis protein tyrosine phosphatases (PtpA and PtpB). J. Med. Chem., 2012, 55, 390-402.
2. Postigo, M.P.; Guido, R.V.C.; Oliva, G.; Castilho, M.S.; Pitta, I.R.; Albuquerque, J.F.C.; Andricopulo, A.D. Discovery of new inhibitors of Schistosoma mansoni PNP by pharmacophore-based virtual screening. J. Chem. Inf. Model., 2010, 50, 1693-1705.
3. Rajman, I. PK/PD modelling and simulations: utility in drug development. Drug Discov. Today, 2008, 13, 341-346.
4. Grabowski, H. Follow-on biologics: data exclusivity and the balance between innovation and competition. Nat. Rev. Drug. Discov., 2008, 7, 479-488.
5. Hirunpanich, V.; Murakoso, K.; Sato, H. Inhibitory effect of docosahexaenoic acid (DHA) on the intestinal metabolism of midazolam: in vitro and in vivo studies in rats. Int. J. Pharm., 2008, 351, 133-143.
6. Jorgensen, W.L. The many roles of computation in drug discovery. Science, 2004, 303, 1813-1818.
7. May, M. Simplifying sample and compound management. Drug Discov. Develop., 2009, 12, 10.
8. van de Waterbeemd, H. Improving compound quality through in vivo and in silico physicochemical profiling. Chem. Biodivers., 2009, 6, 1760-1766.
9. Matsson, P.; Christel, A.S.; Bergstrom, C.A.S.; Nagahara, N.; Tavelin, S.; Norinder, U.; Artursson, P. Exploring the role of different drug transport routes in permeability screening. J. Med. Chem., 2005, 48, 604-613.
10. Sinz, M.W.; Kim, S. Stem cells, immortalized cells and primary cells in ADMET assays. Drug Discov. Today: Technol., 2006, 3, 79-85.
11. Kaplita, P.V.; Hu, H.; Liu, L.; Farrell, T.M.; Grbic, H.; Spero, D.M. Automatable formats of higher throughput ADMET profiling for drug discovery support. JALA, 2005, 10, 140-148.
12. Gleeson, M.P.; Hersey, A.; Hannongbua, S. In-Silico ADME models: a general assessment of their utility in drug discovery applications. Curr. Top. Med. Chem., 2011, 11, 358-381.
13. Ritchie, T.J.; Ertl, P.; Lewis, R. The graphical representation of ADME-related molecule properties for medicinal chemistry. Drug Discov. Today, 2011, 16, 65-72.
14. Smith, D.A. Discovery and ADMET: where are we now. Curr. Top. Med. Chem., 2010, 11, 367-481.
15. Agrafiotis, D.K.; Lobanov, V.S.; Salemme, F.R. Combinatorial informatics in the post-genomics era. Nat. Rev. Drug. Discov., 2007, 1, 337-346.
16. Ruiz-Garcia, A.; Bermejo, M.; Moss, A.; Casabo, V. G. Pharmacokinetics in drug discovery. J. Pharm. Sci., 2008, 97, 654-690.
17. Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov., 2004, 3, 711-715.
18. O'Brien, S.E.; de Groot, M.J. Greater than the sum of its parts: combining models for useful ADMET prediction. J. Med. Chem., 2005, 48, 1287-1291.
19. Wang, J.; Skolnik, S. Recent Advances in physicochemical and ADMET porfiling in drug discovery. Chem. Biodivers., 2009, 6, 1887-1899.
20. Kassel, D.B. Applications of high-throughput ADME in drug discovery. Curr. Opin. Chem. Biol., 2004, 8, 339-345.
21. Jang, G.R.; Harris, R.Z.; Lau, D.T. Pharmacokinetics and its role in small molecule drug discovery research. Med. Res. Rev., 2001, 21, 382-396.
22. van de Waterbeemd, H.; Gifford, E. ADMET in silico modeling: towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2, 92- 204.
23. Gleeson, M, P.; Hersey, A.; Montanari. D.; Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat. Rev. Drug Discov., 2011, 10, 197-208.
24. Li, A.P. Screening for human ADME/Tox drug properties in drug discovery. Drug Disc. Today, 2001, 6, 357-366.
25. Boobis, A.; Gundert-Remy, U.; Kremers, P.; Macheras, P.; Pelkonen, O. In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organized by COST B15. Eur. J. Pharm. Sci., 2002, 17, 183-193.
26. Stoll, F.; Göller, A.H.; Hillisch, A. Utility of protein structures in overcoming ADMET-related issues of drug-like compounds. Drug Discov. Today, 2011, 16, 530-538.
27. Yamashita, S.; Furubayashi, T.; Kataoka, M.; Sakane, T.; Sezaki, H.; Tokuda, H. Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells. Eur. J. Pharm. Sci., 2000, 10, 195-204.
28. Sasaki, M.; Suzuki, H.; Aoki, J.; Ito, K.; Meier, P.J.; Sugiyama, Y. Prediction of in vivo biliary clearance from the in vitro transcellular transport of organic anions across a double-transfected madindarby canine kidney II monolayer expressing both rat organic anion transporting polypeptide 4 and multidrug resistance associated protein 2. Mol. Pharmacol., 2004, 66, 450-459.
29. Irvine, J.D.; Takahashi, L.; Lockhart, K.; Cheong, J.; Tolan, J.W.; Selick, H.E.; Grove, J.R. MDCK (Madin-Darby canine kidney) cells. A tool for membrane permeability screening. J. Pharm. Sci., 1999, 88, 28-33.
30. Berellini, G.; Springer, C.; Waters, N.J.; Lombardo, F.J. In Silico predicition of volume of distribution in human using linear and nonlinear models on a 669 compound data set. J. Med. Chem., 2009, 52, 4488-4495.
31. Motl, S.; Zhuang, Y.; Waters, C.M.; Stewart, C.F. Pharmacokinetic considerations in the treatment of CNS tumours. Clin. Pharmacokinet., 2006, 45, 871-903.
32. th ed.; Lippincott Williams & Wilkins: United States of America, 2002.
33. Cimitan, S.; Lindgren, M.T.; Bertucci, C.; Danielson, U.H. Early absorption and distribution analysis of antitumor and anti-AIDS drugs: lipid membrane and plasma protein interactions. J. Med. Chem., 2005, 48, 3536-3546.
34. th ed.; McGraw- Hill: United States of America, 2001.
35. Murai, T.; Reilly, C.A.; Ward, R.M.; Yost, G.S.; The inhaled Glucocorticoid Fluticasone propionate efficiently inactivates cytochrome P450 3A5, a predominant lung P450 enzyme. Chem. Res. Toxicol., 2010, 8, 1356-1364.
36. Josephy, P.D.; Guengerich, F.P.; Miners, J.O. "Phase I and Phase II" drug metabolism: terminology that we should phase out? Drug Metab. Rev., 2005, 37, 575-580.
37. Estabrook, R.W. A passion for P450s (rememberances of the early history of research on cytochrome P450). Drug Metab.Dispos., 2003, 31, 1461-1473.
38. Guengerich, F.P. Cytochrome P450: what have we learned and what are the future issues? Drug Metab. Rev., 2004, 36, 159-197.
39. Yengi, L.G.; Leung, L.; Kao, J. The evolving role of drug metabolism in drug discovery and development. Pharm. Res., 2007, 24, 842-858.
40. th ed.; American Pharmacists Association: United States of America, 2004; pp. 131-132.
41. Chaturvedi, P.R.; Decker, C.J.; Odinecs, A. Prediction of pharmacokinetic properties using experimental approaches during early drug discovery. Curr. Op. Chem. Biol., 2001, 5, 452-463.
42. Norinder, U.; Haeberlein, M. Computational approaches to the prediction of blood brain distribution. Adv. Drug Delivery Rev., 2002, 54, 291-313.
43. Li, H.; Yap, C.W.; Ung, C.Y.; Xue, Y.; Cao, Z.W.; Chen, Y.Z. Effect of selection of molecular descriptors on the prediction of blood brain barrier penetrating and nonpenetrating agents by statistical learning methods. J. Chem. Inf. Model., 2005, 45, 1376- 1384.
44. Cecchelli, R.; Berezowski, V.; Lundquist, S.; Culot, M.; Renftel, M.; Dehouck, M.P.; Fenart, L. Modelling of the blood-brain barrier in drug discovery and development. Nature Rev. Drug Discov., 2007, 6, 650-661.
45. Reichel, A. Adressing central nervous system (CNS) penetration in drug discovery: basics and implications of the evolving new concept. Chem. Biodivers., 2009, 6, 2030-2049.
46. 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.
47. Parran, D.K.; Geraldine, M.; Li, W.; Jortner, S.B.; Ehrich, M. Chlorpyrifos alters functional integrity and structure of an in vitro BBB model: co-cultures of bovine endothelial cells and neonatal rat astrocytes. Neurotoxicology, 2005, 26, 77-88.
48. 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.
49. Lazaro, E.; Rafols, C.; Abraham, M.H.; Roses, M. Chromatographic estimation of drug disposition properties by means of immobilized artificial membranes (IAM) and C18 Columns. J. Med. Chem., 2006, 49, 4861-4870.
50. Guillot, A.; Henchoz, Y.; Moccand, C.; Guillarme, D.; Veuthey, J.L.; Carrupt, P.A.; Martel, S. Lipophilicity determination of highly lipophilic compounds by liquid chromatography. Chem. Biodivers., 2009, 6, 1828-1836.
51. Balon, K.; Riebesehl, B.U.; Muller, B.W. Drug liposome partitioning as a tool for the prediction of human passive intestinal absorption. Pharm. Res., 1999, 16, 882-888.
52. Sugano, K. Computational oral absorption simulation for lowsolubility compounds. Chem. Biodivers., 2009, 6, 2014-2029.
53. Kerns, E.H.; Di, L. Pharmaceutical profiling in drug discovery. Drug Discov. Today, 2003, 8, 316-323.
54. Saunders, K.C. Automation and robotics in ADME screening. Drug Discov. Today, 2004, 1, 373-380.
55. Lipinski, C.A. Drug-like properties and the cause of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods, 2001, 44, 235-249.
56. Bevan, C.D.; Lloyd, R.S. A high-throughput screening method for the determination of aqueous drug solubility using laser nephelometry in microtiter plates. Anal. Chem., 2000, 72, 1781-1787.
57. Barták, P.; Bednar, P.; Stransky, Z.; Bocek, P.; Vespalec, R. Determination of dissociation constants of cytokinins by capillary zone electrophoresis. J. Chromatogr., 2000, A878, 249-255.
58. st ed.; BIOS Scientific Publishers: Oxford, 2000.
59. Abraham, M.H.; Duce, P.P.; Prior, D.V.; Barratt, D.G.; Morris, J.J.; Taylor, P. J. Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design. J. Chem. Soc. Perkin Trans., 1989, 2, 1355-1375.
60. Raevsky, O.A.; Fetisov, V.I.; Trepalina, E.P.; McFarland, J.W.; Schaper, K.J. Quantitative estimation of drug absorption in humans for passively transported compounds on the basis of their physicochemical parameters. Quant. Struct.-Act. Relat., 2000, 19, 366-374.
61. 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.
62. 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.
63. Vieth, M.; Siegel, M.G.; Higgs, R.E.; Watson, I.A.; Robertson, D.H.; Savin, K.A.; Durst, G.L.; Hipskind, P.A. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem., 2004, 47, 224-232.
64. Hutter, M.C. In silico prediction of drug properties. Curr. Med. Chem., 2009, 16, 189-202.
65. Kirchmair, J.; Distinto, S.; Schuster, D.; Spitzer, G.; Langer, T, Wolber, G. Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr. Med. Chem., 2008, 15, 2040-2053.
66. Modi, S. Positioning ADMET in silico tools in drug discovery. J. Drug Discov. Today, 2004, 9, 14-15.
67. Carlson, T.J.; Fisher, M.B. Recent advances in high throughput screening for ADME properties. Comb. Chem. High Throughput Screen., 2008, 11, 258-264.
68. Segall, M.; Champness, E.; Obrezanova, O.; Leeding, C. Beyond profiling: using ADME models to guide decisions. Chem. Biodivers., 2009, 6, 2144-2151.
69. Ekins, S.; Nikolsky, Y.; Nikolskaya, T. Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol. Sci., 2005, 26, 202-209.
70. Lennernäs, H. Animal data: the contributions of the Using Chamber and perfusion systems to predicting human oral drug delivery in vivo. Adv. Drug. Deliv. Rev., 2007, 59, 1103-1120.
71. May, M. Improving preclinical testing with new animal models. Drug Discov. Develop., 2009, 2, 10.
72. Huynh, L.; Masereeuw, R,; Friedberg, T.; Sundberg, M.I.; Manivet, P. In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: beyond the reduction of animal model use. Drug Discov. Today, 2009, 14, 330-340.
73. Hansch, C.; Leo, A.; Mekapati, S.B.; Kurup, A. QSAR and ADME. Bioorg. Med. Chem., 2004, 12, 3391-3400.
74. De Benedetti, P.G.; Fanelli, F. Computational quantum chemistry and adaptative ligand modeling in mechanistic QSAR. Drug Discov. Today, 2010, 15, 859-866.
75. Andricopulo, A.D.; Yunes, R.A.; Nunes, R.J.; Savi, A.O.S.; Correa, R.; Cruz, A.B.; Cechinel, V. Synthesis and antibacterial activity of cyclic imides: 3, 4-dichloromaleimides and 3-chloro-4-substitutedmaleimides. Quim. Nova, 1998, 21, 573-577.
76. Li, H.; Sun, J.; Fan, X.; Sui, X.; Zhang, L.; Wang, Y.; He, Z. Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction. J. Comput. Aided. Mol. Des., 2008, 22, 843-855.
77. Obrezanova, O.; Gola, J.M.; Champness, E.J.; Segall, M.D. Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J. Comput. Aided Mol. Des., 2008, 22, 431-340.
78. Konovalov, D.A.; Coomans, D.; Deconinck, E.; Heyden, Y.V. Benchmarking of QSAR models for blood-brain barrier permeation. J. Chem. Inf. Model., 2007, 47, 1648-1656.
79. Goller, A.H.; Hennemann, M.; Keldenich, J.; Clark, T. In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors. J. Chem. Inf. Model., 2006, 45, 648-658.
80. Wang, J.; Krudy, G.; Hou, T.; Zhang, W.; Holland, G.; Xu, X. Development of reliable aqueous solubility models and their application in druglike analysis. J. Chem. Inf. Model., 2007, 47, 1395- 1404.
81. Hou, T.; Wang, J.; Li, Y. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. J. Chem. Inf. Model., 2007, 47, 2408-2415.
82. Obata, K.; Sugano, K.; Saitoh, R.; Higashida, A.; Nabuchi, Y.; Machida, M.; Aso, Y. Prediction of oral drug absorption in humans by theoretical passive absorption model. Int. J. Pharm., 2005, 293, 183-192.
83. Yu, L.X.; Amidon, G.L. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharm., 1999, 186, 119-125.
84. Lombardo, F.; Obach, R.S.;Di Capua, 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.
85. Grandison, M.K.; Boudinot, F.D. Age-related changes in protein binding of drugs: implications for therapy. Clin. Pharmacokinet., 2000, 38, 271-290.
86. Benet, L.Z.; Hoener, B.A. Changes in plasma protein binding have little clinical relevance. Clin. Pharmacol. Ther., 2002, 71, 115-121.
87. Gleeson, M.P. Plasma protein binding affinity and its relationship to molecular structure: an in silico analysis. J. Med. Chem., 2007, 50, 101-112.
88. Gunturi, S.B.; Narayanan, R.; Khandelwal, A. In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems. Bioorg. Med. Chem., 2006, 14, 4118-4129.
89. Votano, J.R.; Parham, M.; Hall, L.M.; Hall, L.H.; Kier, L.B.; Oloff, S.; Tropsha, A. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem., 2006, 49, 7169-7181.
90. Moda, T.L.; Montanari, C.A.; Andricopulo, A.D. In silico prediction of human plasma protein binding using hologram QSAR. Lett. Drug. Des. Discov., 2007, 4, 502-509.
91. Salum, L.B.; Polikarpov, I.; Andricopulo, A.D. Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies. J. Mol. Graphics Modell., 2007, 26, 434-442.
92. Honorio, K.M.; Garratt, R.C.; Polikarpov, I.; Andricopulo, A.D. Hologram QSAR studies on farnesoid X receptor activators. Lett. Drug Des. Discov., 2006, 3, 261-267.
93. Castilho, M.S.; Postigo, M.P.; de Paula, C.B.V.; Montanari, C.A.; Oliva, G.; Andricopulo, A.D. Classical and hologram QSAR studies on a series of tacrine derivatives as butyryleholinesterase inhibitors. Bioorg. Med. Chem., 2006, 14, 516-527.
94. Salum, L.B.; Andricopulo, A.D. Fragment-based QSAR: perspectives in drug design. Mol. Divers., 2009, 13, 277-285.
95. Bu, H.Z. A literature review of enzyme kinetic parameters for CYP3A4-mediated metabolic reactions of 113 drugs in human liver microsomes: structure-kinetics relationship assessment. Curr. Drug Metab., 2006, 7, 231-249.
96. Vedani, A.; Huhta, D.W. An algorithm for the systematic salvation of proteins based on the directionality of hydrogen-bonds. J. Am. Chem. Soc., 1991, 113, 5860-5862.
97. Lill, M.A.; Dobler, M.; Vedani, A. Prediction of small-molecule binding to cytochrome P450 3A4: flexible docking combined with multidimensional QSAR. Chem Med Chem, 2006, 1, 73-81.
98. Guengerich, F.P. Common and uncommon cytochrome P450 reactions related to metabolism and chemical toxicity. Chem. Res. Toxicol., 2001, 14, 611-650.
99. Katz, D.A.; Murray, B.; Bhathena, A.; Sahelijo, L. Defining drug disposition determinants: a pharmacogenetic-pharmacokinetic strategy. Nat. Rev. Drug. Discov., 2008, 7, 293-305.
100. Eta, J.E.; Yengi, L.G.; Kao, J.; Scatina, J.A reporter gene cellular model for evaluating induction of CYP3A4 by new chemical entities. Drug Dev. Res., 2006, 67, 470-475.
101. Keseru, G.M. A virtual high throughput screen for high affinity cytochrome P450cam substrates. Implications for in silico prediction of drug metabolism. J. Comput. Aided. Mol. Des., 2001, 15, 649-657.
102. Anari, M.R.; Baillie, A.T. Bridging cheminformatic metabolite prediction and tandem mass spectrometry. Drug Discov. Today, 2005, 10, 711-717.
103. Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28, 849-857.
104. Massimo, B.; Cruciani, G.; Costantino, G.; Riganelli, D.; Valigi, R.; Clementi, S. Generating optimal linear PLS estimations (GOLPE) - an advanced chemometric tool for handling 3D-QSAR problems. Quant. Struct.-Activity Relat., 1993, 12, 9-20.
105. Ridderstrom, M.; Zamora, I.; Fjellstrom, O.; Andersson, T.B. Analysis of selective regions in the active sites of human cytochromes P450, 2C8, 2C9, 2C18, and 2C19 homology models using GRID/CPCA. J. Med. Chem., 2001, 44, 4072-4081.
106. Keldenich, J. Prediction of human clearance (CL) and volume of distribution (VD). Drug Discov. Today: Technol., 2004, 1, 389- 395.
107. Yap, C.W.; Li, Z.R.; Chen, Y.Z. Quantitative structurepharmacokinetic relationships for drug clearance by using statistical learning methods. J. Mol. Graph. Model., 2006, 24, 383-395.
108. Doddareddy, M.R.; Cho, Y.S.; Koh, H.Y.; Kim, D.H.; Pae, A.N. In silico renal clearance model using classical Volsurf approach. J. Chem. Inf. Model., 2006, 46, 1312-1320.
109. Aureli, L.; Cruciani, G.; Mariconi, A. Predicting human serum albumin affinity of interleukin-8 (CXCL8) inhibitors by 3D-QSPR approach. J. Med. Chem., 2005, 48, 2469-2479.
110. Cruciani, G.; Crivori, P.; Carrupt, P.A.; Testa, B. Molecular fields in quantitative structure-permeation relationships: the VolSurf approach. Theochem-J. Mol. Struct., 2000, 503, 17-30.
111. st ed.; Research Studies Press Ltd/Wiley: Letchworth, 1986.
112. Wold, S. Pattern-recognition by means of disjoint principal components models. Pattern Recognit., 1976, 8, 127-139.
113. Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees, Chapman & Hall: Belmont, United States of America, 1984.
114. Reid, D.G.; Murphy, P.S. Fluorine magnetic resonance in vivo: a powerful tool in the study of drug distribution and metabolism. Drug Discov. Today, 2008, 13, 473-480.
115. 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.
116. Carrara, S.; Reali, V.; Misiano, P.; Dondio, G.; Bigogno, C. Evaluation of in vitro brain penetration: optimized PAMPA and MDCKII-MDR1 assay comparison. Int. J. Pharm., 2007, 345, 125- 133.
117. Yoshida, F.; Topliss, J.G. QSAR model for drug human oral bioavailability. J. Med. Chem., 2000, 43, 2575-2585.
118. Moda, T.L.; Montanari, C.A.; Andricopulo, A.D. Hologram QSAR model for the prediction of human oral bioavailability. Bioorg. Med. Chem., 2007, 15, 7738-7745.
119. Winkler, D.A.; Burden, F.R. Modelling blood-brain barrier partitioning using Bayesian neural nets. J. Mol. Graph. Model., 2004, 22, 499-505.
120. Clark, D.E. In silico prediction of blood-brain barrier permeation. Drug Discov. Today, 2003, 8, 927-933.
121. 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.
122. Lessigiarska, I.; Pajeva, I.; Cronin, M.T.D.; Worth, A.P. 3D QSAR investigation of the blood-brain barrier penetration of chemical compounds. SAR QSAR Environ. Res., 2005, 16, 79-91.
123. Narayanan, R.; Gunturi, S.B. In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method. Bioorg. Med. Chem., 2005, 13, 3017- 3028.
124. Chem Axon. Standardizer. http://www.chemaxon.com(Accessed Sep. 15, 2012).
125. Moda, T.L.; Torres, L.G.; Carrara, A.E.; Andricopulo, A.D. PK/DB: database for pharmacokinetic properties and predictive in silico ADME models. Bioinformatics, 2008, 24, 2270-2271.
126. Hewett, M.; Oliver, D.E.; Rubin, D.L.; Easton, K.L.; Stuart, J.M; Altman, R.B.; Klein, T.E. Pharm GKB: the pharmacogenetics knowledge base. Nucleic Acids Res., 2002, 30, 163-165.
127. Olah, M.; Bologa, C.; Oprea, T.I. An automated PLS search for biologically relevant QSAR descriptors. J. Comput. Aided. Mol. Des., 2004, 18, 437-449.
128. Li, A.C.; Alton, D.; Bryant, M.S.; Shou, W.Z. Simultaneously quantifying parent drugs and screening for metabolites in plasma pharmacokinetic samples using selected reaction monitoring information- dependent acquisition on a QTrap instrument. Rapid Commun. Mass Spectrom., 2005, 19, 1943-1950.
129. Shou, W.Z.; Magis, L.; Li, A.C.; Bryant, M.S. A novel approach to perform metabolite screening during the quantitative LC-MS/MS analyses of in vitro metabolic stability samples using a hybrid triple- quadrupole linear ion trap mass spectrometer. J. Mass Spectrom., 2005, 40, 1347-1356.
130. Tiller, P.R.; Romanyshyn, L.A. Liquid chromatography/tandem mass spectrometric quantification with metabolite screening as a strategy to enhance the early drug discovery process. Rapid Commun. Mass Spectrom., 2002, 16, 1225-1231.
131. Trunzer, M.; Faller, B.; Zimmerlin, A. Metabolic soft spot identification and compound optimization in early discovery phases using Meta Site and LC-MS/MS validation. J. Med. Chem., 2009, 52, 329-335.
132. Zhou, D.; Afzelius, L.; Grimm, S.W.; Andersson, T.B.; Zauhar, R.J.; Zamora, I. Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions. Drug Metab. Dispos., 2006, 34, 976-983.
133. Testa, B.; Balmat, A.L.; Long, A. Predicting drug metabolism: Concepts and challenges. Pure Appl. Chem., 2004, 76, 907-914.
134. Ekins, S.; Andreyev, S.; Ryabov, A.; Kirillov, E.; Rakhmatulin, E.A.; Bugrim, A.; Nikolskaya, T. Computational prediction of human drug metabolism. Expert Opin. Drug Metab. Toxicol., 2005, 2, 303-324.
135. Anzali, S.; Gasteiger, J.; Holzgrabe, U.; Polanski, J.; Sadowski, J.; Teckentrup, A.; Wagener, M. The use of self-organizing neural networks in drug design. Perspect. Drug Discov. Des., 1998, 273- 299.
136. Polanski, J.; Gasteiger, J.; Jarzembek, K. Self-organizing neural networks for screening and development of novel artificial sweetener candidates. Comb. Chem. High Throughput Screening, 2000, 3, 481-495.
137. Kaiser, D.; Terfloth, L.; Kopp, S.; Schulz, J.; de Laet, R.; Chiba, P.; Ecker, G.F,; Gasteiger, J. Self-organizing maps for identification of new inhibitors of P-glycoprotein. J. Med. Chem., 2007, 50, 1698- 1702.
138. Cai, H.; Stoner, C.; Reddy, A.; Freiwald, S.; Smith, D.; Winters, R.; Stankovic, C.; Surendran, N. Evaluation of an integrated in vitro- in silico PBPK (physiologically based pharmacokinetic) model to provide estimates of human bioavailability. Int. J. Pharm., 2006, 308, 133-139.
139. Gil, C.; Dorronsoro, I.; Castro, A.; Martinez, A. Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques. Bioorg. Med. Chem. Lett., 2005, 15, 1919-1921.
140. Martinez, A.; Castro, A.; Stud, M.; Rodríguez, J.; Cardelús, I.; Llenas, J.; Fernández, A.; Palacios, J.M. Neural networks in QSAR studies of new potassium channels openers. Med. Chem. Res., 1999, 8, 171-180.
141. Quiñones, C.; Caceres, J.; Stud, M.; Martínez, A. Prediction of drug half-life values of antihistamines based on the CODES/neural network model. Quant. Struct.-Act. Relat., 2000, 19, 448-454.