QSARs for PBPK modelling of environmental contaminants

SAR and QSAR in Environmental Research

Volumn 22, Issue 1-2, 2011, Pages 129-169

  Peyret, T ^a   Krishnan, K ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

In silico; Pbpk modelling; Pharmacokinetics; Qsar pbpk

Indexed keywords

BENZENE; HEALTH RISKS; ORGANIC CHEMICALS; PHARMACOKINETICS; PHYSIOLOGICAL MODELS; TISSUE; VOLATILE ORGANIC COMPOUNDS;

BENZENE RING; CHEMICAL SPECIFIC; ENVIRONMENTAL CONTAMINANT; IN-SILICO; INPUT PARAMETER; MOLECULAR FRAGMENTS; PBPK MODELING; PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELS; QSAR-PBPK; RISKS ASSESSMENTS;

RISK ASSESSMENT;

EID: 79952493624     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2010.548351     Document Type: Article

References (132)

1. C. Hansch, A. Leo, and D. H. Hoekman, Exploring QSAR, American Chemical Society, Washington, DC, 1995.
2. J. M. Mayer and H. van de Waterbeemd, Development of quantitative structure-pharmacokinetic relationships, Environ. Health Perspect. 61 (1985), pp. 295-306.
3. J. K. Seydel and K. J. Schaper, Quantitative structure-pharmacokinetic relationships and drug design, Pharmacol. Ther. 15 (1981), pp. 131-182.
4. A. M. Davis and R. J. Riley, Predictive ADMET studies, the challenges and the opportunities, Curr. Opin. Chem. Biol. 8 (2004), pp. 378-386.
5. S. Ekins, G. Bravi, B. J. Ring, T. A. Gillespie, J. S. Gillespie, M. Vandenbranden, S. A. Wrighton, and J. H. Wikel, Three-dimensional quantitative structure activity relationship analyses of substrates for CYP2B6, J. Pharmacol. Exp. Ther. 288 (1999), pp. 21-29.
6. S. Ekins and R. S. Obach, Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance, J. Pharmacol. Exp. Ther. 295 (2000), pp. 463-473.
7. S. Ekins and J. Rose, In silico ADME/Tox: The state of the art, J. Mol. Graph. Model. 20 (2002), pp. 305-309.
8. S. Ekins, C. L. Waller, P. W. Swaan, G. Cruciani, S. A. Wrighton, and J. H. Wikel, Progress in predicting human DME parameters in silico, J. Pharmacol. Toxicol. Meth. 44 (2000), pp. 251-272.
9. C. Hansch, S. B. Mekapati, A. Kurup, and R. P. Verma, QSAR of cytochrome P450, Drug Metab. Rev. 36 (2004), pp. 105-156.
10. T. Hou and J. Wang, Structure-ADME relationship: Still a long way to go?, Expert Opinion Drug Metabol. Toxicol. 4 (2008), pp. 759-770.
11. J. Ishizaki, K. Yokogawa, E. Nakashima, and F. Ichimura, Relationships between the hepatic intrinsic clearance or blood cell-plasma partition coefficient in the rabbit and the lipophilicity of basic drugs, J. Pharm. Pharmacol. 49 (1997), pp. 768-772.
12. J. Langowski and A. Long, Computer systems for the prediction of xenobiotic metabolism, Adv. Drug Deliv. Rev. 54 (2002), pp. 407-415.
13. D. F. Lewis and M. Dickins, Baseline lipophilicity relationships in human cytochromes P450 associated with drug metabolism, Drug Metab. Rev. 35 (2003), pp. 1-18.
14. H. Li, J. Sun, X. Fan, X. Sui, L. Zhang, Y. Wang, and Z. He, Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, J. Comput. Aided Mol. Des. 22 (2008), pp. 843-855.
15. N. Manga, J. C. Duffy, P. H. Rowe, and M. T. D. Cronin, A hierarchical QSAR model for urinary excretion of drugs in humans as a predictive tool for biotransformation, QSAR Comb. Sci. 22 (2003), pp. 263-273.
16. D. E. Mager, Quantitative structure-pharmacokinetic/pharmacodynamic relationships, Avd. Drug Delivery Rev. 58 (2006), pp. 1326-1356.
17. U. Norinder, In silico modelling of ADMET - a minireview of work from 2000 to 2004, SAR QSAR Environ. Res. 16 (2004), pp. 1-11.
18. T. Wang and J. Hou, Recent advances on in silico ADME modeling, Ann. Reports Comput. Chem. 5 (2009), pp. 101-127.
19. Y. -H. Wang, Y. Li, Y. -H. Li, S. -L. Yang, and L. Yang, Modeling Km values using electrotopological state: Substrates for cytochrome P450 3A4-mediated metabolism, Bioorg. Med. Chem. Lett. 15 (2005), pp. 4076-4084.
20. C. W. Yap, Z. R. Li, and Y. Z. Chen, Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods, J. Mol. Graph. Model. 24 (2006), pp. 383-395.
21. K. Krishnan and M. E. Andersen, Physiologically based pharmacokinetic modeling in toxicology, in Principles and Methods of Toxicology, A. W. Hayes, ed., Taylor & Francis, Boca Raton, FL, 2007, pp. 231-292.
22. M. E. Andersen, H. J. Clewell, M. L. Gargas, F. A. Smith, and R. H. Reitz, Physiologically based pharmacokinetics and the risk assessment process for methylene chloride, Toxicol. Appl. Pharmacol. 87 (1987), pp. 185-205.
23. M. Reddy, R. S. Yang, M. E. Andersen, and H. J. Clewell III (eds.), Physiologically Based Pharma-cokinetic Modeling: Science and Applications, Wiley Interscience, Hoboken, N. J., 2006, pp. 1-420.
24. G. Loizou, M. Spendiff, H. A. Barton, J. Bessems, F. Y. Bois, M. B. d'Yvoire, H. Buist, H. J. Clewell, B. Meek, U. Gundert-Remy, G. Goerlitz, and W. Schmitt, Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment: The first steps, Regul. Toxicol. Pharmacol. 50 (2008), pp. 400-411.
25. A. D. Arms and C. C. Travis, Reference physiological parameters in pharmacokinetic modeling, in EPA/600/6-88/004, U. S. Environmental Protection Agency, Office of Health and Environmental Assessment, Washington, DC, 1988.
26. R. P. Brown, M. D. Delp, S. L. Lindstedt, L. R. Rhomberg, and R. P. Beliles, Physiological parameter values for physiologically based pharmacokinetic models, Toxicol. Ind. Health 13 (1997), pp. 407-484.
27. T. Lavé, P. Coassolo, and B. Reigner, Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro-in vivo correlations, Clin. Pharmacokinet. 36 (1999), pp. 211-231.
28. J. W. Nichols, I. R. Schult, and P. N. Fitzsimmons, In vitro-in vivo extrapolation of quantitative hepatic biotransformation data for fish. I. A review of methods, and strategies for incorporating intrinsic clearance estimates into chemical kinetic models, Aquat. Toxicol. 78 (2006), pp. 74-90.
29. M. Béliveau and K. Krishnan, In silico approaches for developing physiologically based pharmacokinetic (PBPK) models, in Alternative Toxicological Methods, H. Salem and S. A. Katz, eds., CRC Press, Boca Raton, FL, 2003, pp. 479-532.
30. P. Poulin, M. Béliveau, and K. Krishnan, Mechanistic animal replacement appoaches for predicting pharmacokinetics of organic chemicals, in Toxicity Assessment Alternatives: Methods, Issues, Opportunities, H. Salem and S. A. Katz, eds., Humana Press, Totowa, NJ, 1999, pp. 115-139.
31. M. Gibaldi and B. Perrier, Pharmacokinetics, Dekker, New York, 1975.
32. M. H. Abraham, M. J. Kamlet, R. W. Taft, R. M. Doherty, and P. K. Weathersby, Solubility properties in polymers and biological media. 2. The correlation and prediction of the solubilities of nonelectrolytes in biological tissues and fluids, J. Med. Chem. 28 (1985), pp. 865-870.
33. A. Falk, E. Gullstrand, A. Löf, and E. Wigaeus-Hjelm, Liquid/air partition coefficients of four terpenes, Br. J. Ind. Med. 47 (1990), pp. 62-64.
34. R. M. Featherstone, C. A. Muehlbaecher, F. L. Debon, and J. A. Forsaith, Interactions of inert anesthetic gases with proteins, Anesthesiology 22 (1961), pp. 977-981.
35. V. Fiserova-Bergerova and M. L. Diaz, Determination and prediction of tissue-gas partition coefficients, Int. Arch. Occup. Environ. Health 58 (1986), pp. 75-87.
36. V. Fiserova-Bergerova, M. Tichy, and F. J. Di Carlo, Effects of biosolubility on pulmonary uptake and disposition of gases and vapors of lipophilic chemicals, Drug Metab. Rev. 15 (1984), pp. 1033-1070.
37. M. L. Gargas, R. J. Burgess, D. E. Voisard, G. H. Cason, and M. E. Andersen, Partition coefficients of low-molecular-weight volatile chemicals in various liquids and tissues, Toxicol. Appl. Pharmacol. 98 (1989), pp. 87-99.
38. J. Järnberg and G. Johanson, Liquid/air partition coefficients of the trimethylbenzenes, Toxicol. Ind. Health 11 (1995), pp. 81-88.
39. T. Kaneko, P. Y. Wang, and A. Sato, Partition coefficients of some acetate esters and alcohols in water, blood, olive oil, and rat tissues, Occup. Environ. Med. 51 (1994), pp. 68-72.
40. C. P. J. M. D. Larson, E. I. I. M. D. Eger, and J. W. M. D. Severinghous, Ostwald solubility coefficients for anesthetic gases in various fluids and tissues, Anesthesiology 23 (1962), pp. 686-689.
41. H. J. Lowe and K. Hagler, Determination of volatile organic anaesthetic in blood, gases, tissues and lipids: Partition coefficients, in Gas Chromatography in Biology and Medicine. A Ciba-Geigy Foundation Symposium, R. Porter, ed., Churchill, New York, 1969, pp. 86-103.
42. J. W. Nichols, J. M. McKim, G. J. Lien, A. D. Hoffman, and S. L. Bertelsen, Physiologically based toxicokinetic modeling of three waterborne chloroethanes in rainbow trout (Oncorhynchus mykiss), Toxicol. Appl. Pharmacol. 110 (1991), pp. 374-389.
43. S. Paterson and D. Mackay, Correlation of tissue, blood, and air partition coefficients of volatile organic chemicals, British J. Indus. Med. 46 (1989), pp. 321-328.
44. L. Perbellini, F. Brugnone, D. Caretta, and G. Maranelli, Partition coefficients of some industrial aliphatic hydrocarbons (C5-C7) in blood and human tissues, British J. Indus. Med. 42 (1985), pp. 162-167.
45. A. Sato and T. Nakajima, A structure-activity relationship of some chlorinated hydrocarbons, Arch. Environ. Health 34 (1979), pp. 69-75.
46. A. Steward, P. R. Allott, A. L. Cowles, and W. W. Mapleson, Solubility coefficients for inhaled anaesthetics for water, oil and biological media, Br. J. Anaesth. 45 (1973), pp. 282-293.
47. M. H. Abraham, H. S. Chadha, G. S. Whiting, and R. C. Mitchell, Hydrogen bonding. 32. An analysis of water-octanol and water-alkane partitioning and the delta log P parameter of seiler, J. Pharm. Sci. 83 (1994), pp. 1085-1100.
48. M. H. Abraham and P. K. Weathersby, Hydrogen bonding. 30. Solubility of gases and vapors in biological liquids and tissues, J. Pharm. Sci. 83 (1994), pp. 1450-1456.
49. A. L. Cowles, H. H. Borgstedt, and A. J. Gillies, Solubilities of ethylene, cyclopropane, halothane and diethyl ether in human and dog blood at low concentrations, Anesthesiology 35 (1971), pp. 203-211.
50. E. I. Eger II and C. P. J. Larson, Anaesthetic solubility in blood and tissues: Values and significance, Br. J. Anaesth. 36 (1964), pp. 140-149.
51. W. Laass, Estimation of blood/air partition coefficients of organic solvents, in QSAR in Drug Design and Toxicology: Proceedings of the Sixth European Symposium on Quantitative Structure-Activity Relationships, D. Hadzi and B. Jerman-Blazic, eds., Elsevier, Amsterdam; New York, 1987, pp. 131-134.
52. R. A. Saraiva, B. A. Willis, A. Steward, J. N. Lunn, and W. W. Mapleson, Halothane solubility in human blood, Br. J. Anaesth. 49 (1977), pp. 115-119.
53. S. Batterman, L. Zhang, S. Wang, and A. Franzblau, Partition coefficients for the trihalomethanes among blood, urine, water, milk and air, Sci. Total Environ. 284 (2002), pp. 237-247.
54. J. DeJongh, H. J. Verhaar, and J. L. Hermens, A quantitative property-property relationship (QPPR) approach to estimate in vitro tissue-blood partition coefficients of organic chemicals in rats and humans, Arch. Toxicol. 72 (1997), pp. 17-25.
55. C. J. Meulenberg and H. P. Vijverberg, Empirical relations predicting human and rat tissue:air partition coefficients of volatile organic compounds, Toxicol. Appl. Pharmacol. 165 (2000), pp. 206-216.
56. C. J. Meulenberg, A. G. Wijnkerm, and H. P. Vijverberg, Relationship between olive oil:air, saline:air, and rat brain:air partition coefficients of organic solvents in vitro, J. Toxicol. Environ. Health A 66 (2003), pp. 1985-1998.
57. S. C. Basak, D. Mills, D. M. Hawkins, and H. A. El-Masri, Prediction of human blood:air partition coefficient: A comparison of structure-based and property-based methods, Risk Anal. 23 (2003), pp. 1173-1184.
58. S. C. Basak, D. Mills, and B. D. Gute, Prediction of tissue:air partition coefficients - theoretical vs. experimental methods, SAR QSAR Environ. Res. 17 (2006), pp. 515-532.
59. F. M. Parham, M. C. Kohn, H. B. Matthews, C. Derosa, and C. J. Portier, Using structural information to create physiologically based pharmacokinetic models for all polychlorinated biphenyls: I. Tissue:blood partition coefficients, Toxicol. Appl. Pharmacol. 144 (1997), pp. 340-347.
60. M. L. Gargas, P. G. Seybold, and M. E. Andersen, Modeling the tissue solubilities and metabolic rate constant (Vmax) of halogenated methanes, ethanes, and ethylenes, Toxicol. Lett. 43 (1988), pp. 235-256.
61. S. M. J. Free and J. W. Wilson, A mathematical contribution to structure-activity studies, J. Med. Chem. 7 (1964), pp. 395-399.
62. M. Béliveau, R. Tardif, and K. Krishnan, Quantitative structure-property relationships for physiologically based pharmacokinetic modeling of volatile organic chemicals in rats, Toxicol. Appl. Pharmacol. 189 (2003), pp. 221-232.
63. E. Kamgang, T. Peyret, and K. Krishnan, An integrated QSPR-PBPK modelling approach for in vitro-in vivo extrapolation of pharmacokinetics in rats, SAR QSAR Environ. Res. 19 (2008), pp. 669-680.
64. M. Béliveau and K. Krishnan, A spreadsheet program for modeling of quantitative strucure-pharmacokinetic relationships for inhaled volatile organic chemicals in humans, SAR QSAR Environ. Res. 16 (2005), pp. 63-77.
65. P. Poulin and F. P. Theil, A priori prediction of tissue:plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery, J. Pharm. Sci. 89 (2000), pp. 16-35.
66. S. Haddad, P. Poulin, and K. Krishnan, Relative lipid content as the sole mechanistic determinant of the adipose tissue:blood partition coefficients of highly lipophilic organic chemicals, Chemosphere 40 (2000), pp. 839-843.
67. M. P. Payne and L. C. Kenny, Comparison of models for the estimation of biological partition coefficients, J. Toxicol. Environ. Health A 65 (2002), pp. 897-931.
68. T. Peyret, P. Poulin, and K. Krishnan, A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals, Toxicol. Appl. Pharmacol. 249 (2010), pp. 197-207.
69. P. Poulin and K. Krishnan, An algorithm for predicting tissue: Blood partition coefficients of organic chemicals from n-octanol: Water partition coefficient data, J. Toxicol. Environ. Health 46 (1995), pp. 117-129.
70. P. Poulin and K. Krishnan, A biologically-based algorithm for predicting human tissue: Blood partition coefficients of organic chemicals, Hum. Exp. Toxicol. 14 (1995), pp. 273-280.
71. P. Poulin and K. Krishnan, A mechanistic algorithm for predicting blood:air partition coefficients of organic chemicals with the consideration of reversible binding in hemoglobin, Toxicol. Appl. Pharmacol. 136 (1996), pp. 131-137.
72. P. Poulin and K. Krishnan, A tissue composition-based algorithm for predicting tissue:air partition coefficients of organic chemicals, Toxicol. Appl. Pharmacol. 136 (1996), pp. 126-130.
73. P. Poulin and F. P. Theil, Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution, J. Pharm. Sci. 91 (2002), pp. 129-156.
74. T. Rodgers, D. Leahy, and M. Rowland, Physiologically based pharmacokinetic modeling 1: Predicting the tissue distribution of moderate-to-strong bases, J. Pharm. Sci. 94 (2005), pp. 1259-1276.
75. T. Rodgers and M. Rowland, Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions, J. Pharm. Sci. 95 (2006), pp. 1238-1257.
76. W. Schmitt, General approach for the calculation of tissue to plasma partition coefficients, Toxicol. In Vitro 22 (2008), pp. 457-46.
77. H. Zhang, A new nonlinear equation for the tissue/blood partition coefficients of neutral compounds, J. Pharm. Sci. 93 (2004), pp. 1595-1604.
78. H. Zhang, A new approach for the tissue-blood partition coefficients of neutral and ionized compounds, J. Chem. Inf. Model. 45 (2005), pp. 121-127.
79. H. Zhang and Y. Zhang, Convenient nonlinear model for predicting the tissue/blood partition coefficients of seven human tissues of neutral, acidic, and basic structurally diverse compounds, J. Med. Chem. 49 (2006), pp. 5815-5829.
80. C. -W. Lam, T. J. Galen, J. F. Boyd, and D. L. Pierson, Mechanism of transport and distribution of organic solvents in blood, Toxicol. Appl. Pharmacol. 104 (1990), pp. 117-129.
81. M. Béliveau, J. Lipscomb, R. Tardif, and K. Krishnan, Quantitative structure-property relationships for interspecies extrapolation of the inhalation pharmacokinetics of organic chemicals, Chem. Res. Toxicol. 18 (2005), pp. 475-485.
82. K. Krishnan and T. Peyret, Physiologically based toxicokinetic (PBTK) modeling in ecotoxicology, in Ecotoxicology Modeling, J. Devillers, ed., Springer, Dordrecht, 2009, pp. 145-175.
83. S. L. Bertelsen, A. D. Hoffman, C. A. Gallinat, C. M. Elonen, and J. W. Nichols, Evaluation of log Kow and tissue lipid content as predictors of chemical partitioning to fish tissues, Environ. Toxicol. Chem. 17 (1998), pp. 1447-1455.
84. C. Hansch and A. Leo, The fragment method of calculating partition coefficients, in Substituent Constants for Correlation Analysis in Chemistry and Biology, C. Hansch and A. Leo, eds., Wiley, New York, 1979, pp. 18-43.
85. J. Hine and P. K. Mookerjee, The intrinsic hydrophobic character of organic compounds: Correlations in terms of structural contributions, J. Org. Chem. 40 (1975), pp. 511-522.
86. P. Poulin and K. Krishnan, Molecular structure based prediction of partition coefficients of organic chemicals for physiological pharmacokinetic models, Toxicol. Meth. 6 (1996), pp. 117-137.
87. S. Baláž and V. Lukáčová, A model-based dependence of the human tissue/blood partition coefficients of chemicals on lipophilicity and tissue composition, Quant. Struct. -Act. Relat. 18 (1999), pp. 361-368.
88. L. M. Berezhkovskiy, Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination, J. Pharm. Sci. 93 (2004), pp. 1628-1640.
89. I. Nestorov, L. Aarons, and M. Rowland, Quantitative structure-pharmacokinetics relationships: II. A mechanistically based model to evaluate the relationship between tissue distribution parameters and compound lipophilicity, J. Pharmacokinet. Pharmacodyn. 26 (1998), pp. 521-545.
90. P. Poulin, K. Schoenlein, and F. -P. Theil, Prediction of adipose tissue: Plasma partition coefficients for structurally unrelated drugs, J. Pharm. Sci. 90 (2001), pp. 436-447.
91. K. Yokogawa, E. Nakashima, J. Ishizaki, H. Maeda, T. Nagano, and F. Ichimura, Relationships in the structure-tissue distribution of basic drugs in the rabbit, Pharm. Res. 7 (1990), pp. 691-696.
92. G. Colmenarejo, In silico prediction of drug-binding strengths to human serum albumin, Med. Res. Rev. 23 (2003), pp. 275-301.
93. G. Colmenarejo, A. Alvarez-Pedraglio, and J. L. Lavandera, Cheminformatic models to predict binding affinities to human serum albumin, J. Med. Chem. 44 (2001), pp. 4370-4378.
94. E. Estrada, E. Uriarte, E. Molina, Y. Simon-Manso, and G. W. A. Milne, An integrated in silico analysis of drug-binding to human serum albumin, J. Chem. Inf. Model. 46 (2006), pp. 2709-2724.
95. N. A. Kratochwil, W. Huber, F. Müller, M. Kansy, and P. R. Gerber, Predicting plasma protein binding of drugs: A new approach, Biochem. Pharmacol. 64 (2002), pp. 1355-1374.
96. J. R. Votano, M. Parham, L. M. Hall, L. H. Hall, L. B. Kier, S. Oloff, and A. Tropsha, QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation, J. Med. Chem. 49 (2006), pp. 7169-7181.
97. C. X. Xue, R. S. Zhang, H. X. Liu, X. J. Yao, M. C. Liu, Z. D. Hu, and B. T. Fan, QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1693-1700.
98. S. Coecke, H. Ahr, B. J. Blaauboer, S. Bremer, S. Casati, J. Castell, R. Combes, R. Corvi, C. L. Crespi, M. L. Cunningham, G. Elaut, B. Eletti, A. P. Freidig, A. Gennari, J. -F. Ghersi-Egea, A. Guillouzo, T. Hartung, P. Hoet, M. Ingelman-Sundberg, S. Munn, W. Janssens, B. Ladstetter, D. Leahy, A. Long, A. Meneguz, M. Monshouwer, S. Morath, F. Nagelkerke, O. Pelkonen, J. Ponti, P. Prieto, L. Richert, E. Sabbioni, B. Schaack, W. Steiling, E. Testai, J. -A. Vericat, and A. Worth, Metabolism: A bottleneck in in vitro toxicological test development. The report and recommendations of ECVAM workshop 54, Altern. Lab. Anim. 34 (2006), pp. 49-84.
99. J. C. Madden and M. T. Cronin, Structure-based methods for the prediction of drug metabolism, Expert Opinion Drug Metabol. Toxicol. 2 (2006), pp. 545-557.
100. R. Mackman, Z. Guo, F. P. Guengerich, and P. R. Ortiz de Montellano, Active site topology of human cytochrome P450 2E1, Chem. Res. Toxicol. 9 (1996), pp. 223-226.
101. D. F. V. Lewis, On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics: Towards the prediction of human p450 substrate specificity and metabolism, Biochem. Pharmacol. 60 (2000), pp. 293-306.
102. D. F. V. Lewis, Guide to Cytochromes P450: Structure and Function, Taylor & Francis, London; New York, 2001.
103. D. F. V. Lewis, Quantitative structure-activity relationships (QSARs) within the cytochrome P450 system: QSARs describing substrate binding, inhibition and induction of P450s, Inflammopharmacology 11 (2003), pp. 43-73.
104. D. F. V. Lewis and M. Dickins, Factors influencing rates and clearance in P450-mediated reactions: QSARs for substrates of the xenobiotic-metabolizing hepatic microsomal P450s, Toxicology 170 (2002), pp. 45-53.
105. D. F. V. Lewis, Y. Ito, and B. G. Lake, Quantitative structure-activity relationships (QSARs) for inhibitors and substrates of CYP2B enzymes: Importance of compound lipophilicity in explanation of potency differences, J. Enzyme Inhib. Med. Chem. 25 (2010), pp. 679-684.
106. H. Li, J. Sun, X. Sui, J. Liu, Z. Yan, X. Liu, Y. Sun, and Z. He, First-principle, structure-based prediction of hepatic metabolic clearance values in human, Eur. J. Med. Chem. 44 (2009), pp. 1600-1606.
107. K. Nikolic and D. Agababa, Prediction of hepatic microsomal intrinsic clearance and human clearance values for drugs, J. Mol. Graph. Model. 28 (2009), pp. 245-252.
108. R. P. Austin, P. Barton, S. L. Cockroft, M. C. Wenlock, and R. J. Riley, The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties, Drug Metab. Dispos. 30 (2002), pp. 1497-1503.
109. R. P. Austin, P. Barton, S. Mohmed, and R. J. Riley, The binding of drugs to hepatocytes and its relationship to physicochemical properties, Drug Metab. Dispos. 33 (2005), pp. 419-425.
110. H. Yin, M. W. Anders, K. R. Korzekwa, L. A. Higgins, K. T. Thummel, E. D. Kharasch, and J. P. Jones, Designing safer chemicals: Predicting the rates of metabolism of halogenated chemicals, Proc. Natl. Acad. USA 92 (1995), pp. 11076-11080.
111. G. D. Loizou, N. L. Eldirdiri, and L. J. King, Physiologically based pharmacokinetics of uptake by inhalation of a series of 1,1,1-trihaloethanes: Correlation with various physicochemical parameters, Inhal. Toxicol. 8 (1996), pp. 1-19.
112. M. L. Gargas, H. J. Clewell, and M. E. Andersen, Gas uptake inhalation techniques and the rates of metabolism of chloromethanes, chloroethanes and chloroethylenes in the rat, Inhal. Toxicol. 2 (1990), pp. 295-319.
113. B. Mortensen, I. Eide, K. Zahlsen, and O. G. Nilsen, Prediction of in vivo metabolic clearance of 25 different petroleum hydrocarbons by a rat liver head-space technique, Arch. Toxicol. 74 (2000), pp. 308-312.
114. G. Galliani, B. Rindone, G. Dagnino, and M. Salmona, Structure reactivity relationships in the microsomal oxidation of tertiary amines, Eur. J. Drug Metabol. Pharm. 9 (1984), pp. 289-293.
115. G. A. Csanady, R. J. Laid, and J. G. Filser, Metabolic transformation of halogenated and other alkenes - a theoretical approach. Estimation of metabolic reactivities for in vivo conditions, Toxicol. Lett. 75 (1995), pp. 217-223.
116. F. M. Parham and C. J. Portier, Using structural information to create physiologically based pharmacokinetic models for all polychlorinated biphenyls. II. Rates of metabolism, Toxicol. Appl. Pharmacol. 151 (1998), pp. 110-116.
117. D. F. Lewis, C. Sams, and G. D. Loizou, A quantitative structure-activity relationship analysis on a series of alkyl benzenes metabolized by human cytochrome P450 2E1, J. Biochem. Mol. Toxicol. 17 (2003), pp. 47-52.
118. J. B. Knaak, C. C. Dary, F. Power, C. Thompson, and J. N. Blancato, Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/ PD models for human risk assessment, Crit. Rev. Toxicol. 34 (2004), pp. 143-207.
119. C. L. Waller, M. V. Evans, and J. D. McKinney, Modeling the cytochrome P450-mediated metabolism of chlorinated volatile organic compounds, Drug Metab. Dispos. 24 (1996), pp. 203-210.
120. P. Poulin and K. Krishnan, Molecular structure-based prediction of the toxicokinetics of inhaled vapors in humans, Int. J. Toxicol. 18 (1999), pp. 7-18.
121. G. R. Wilkinson and D. G. Shand, Commentary: A physiological approach to hepatic drug clearance, Clin. Pharmacol. Ther. 18 (1975), pp. 377-390.
122. M. E. Andersen, A physiologically based toxicokinetic description of the metabolism of inhaled gases and vapors: Analysis at steady state, Toxicol. Appl. Pharmacol. 60 (1981), pp. 509-526.
123. G. E. Blakey, I. A. Nestorov, P. A. Arundel, L. J. Aarons, and M. Rowland, Quantitative structure-pharmacokinetics relationships: I. Development of a whole-body physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat, J. Pharmacokinet. Pharmacodyn. 25 (1997), pp. 277-312.
124. T. Yamagushi, M. Yabuki, S. Saito, T. Watanabe, H. Nishimura, N. Isobe, F. Shono, and M. Matsuo, Research to develop a predicting system of mammalian subacute toxicity (3) construction of a predictive toxicokinetic model, Chemosphere 33 (1996), pp. 2441-2468.
125. B. Agoram, W. S. Woltosz, and M. B. Bolger, Predicting the impact of physiological and biochemical processes on oral drug bioavailability, Adv. Drug Deliv. Rev. 50 (2001), pp. S41-S67.
126. P. V. Balimane, S. Chong, and R. A. Morrison, Current methodologies used for evaluation of intestinal permeability and absorption, J. Pharmacol. Toxicol. Methods 44 (2000), pp. 301-312.
127. G. M. Grass and P. J. Sinko, Physiologically-based pharmacokinetic simulation modelling, Adv. Drug Deliv. Rev. 54 (2002), pp. 433-451.
128. H. Patel, W. ten Berge, and M. T. Cronin, Quantitative structure-activity relationships (QSARs) for the prediction of skin permeation of exogenous chemicals, Chemosphere 48 (2002), pp. 603-613.
129. P. Poulin and K. Krishnan, Molecular structure-based prediction of human abdominal skin permeability coefficients for several organic compounds, J. Toxicol. Environ. Health A 62 (2001), pp. 143-159.
130. O. A. Raevsky and K. -J. Schaper, Quantitative estimation of hydrogen bond contribution to permeability and absorption processes of some chemicals and drugs, Eur. J. Med. Chem. 33 (1998), pp. 799-807.
131. O. A. Raevsky, S. V. Trepalin, H. P. Trepalina, V. A. Gerasimenko, and O. E. Raevskaja, SLIPPER-2001 - Software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity, J. Chem. Inf. Comput. Sci. 42 (2002), pp. 540-549.
132. M. Debia and K. Krishnan, Quantitative property-property relationships for computing occupational exposure limits and vapour hazard ratios of organic solvents, SAR QSAR Environ. Res. 21 (2010), pp. 583-601.