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Xenobiotic Metabolism Activation as a Biomarker of Cigarette Smoke Exposure Response

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Computational Systems Toxicology

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

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Abstract

The recent advances in “omics” technologies have generated various in silico approaches for toxicity assessment. In silico-based toxicity predictions can overcome certain major drawbacks of laboratory experiments, including the limitation of conducting experiments in a chemical-by-chemical basis that can be expensive. This chapter discusses some recent applications of in silico approaches utilizing xenobiotic metabolism that can be used to assess the impact of cigarette smoke (CS). We first outline recent studies using quantum mechanics/molecular modeling and quantitative structure–activity relationships that focus on smoking-relevant cytochrome P450 (CYP) enzymes. Subsequently, we describe several network-based approaches for toxicity assessment and relevant use cases leveraging a xenobiotic metabolism network model for a quantitative assessment of CS impact.

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References

  1. Burgess-Herbert SL, Euling SY (2013) Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs. Toxicol Appl Pharmacol 271(3):372–385

    Article  CAS  PubMed  Google Scholar 

  2. Peach ML, Zakharov AV, Liu R et al (2012) Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software. Future Med Chem 4(15):1907–1932. doi:10.4155/fmc.12.150

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Courcot E, Leclerc J, Lafitte J-J et al (2012) Xenobiotic metabolism and disposition in human lung cell models: comparison with in vivo expression profiles. Drug Metab Dispos 40(10):1953–1965

    Article  CAS  PubMed  Google Scholar 

  4. Oláh J, Mulholland AJ, Harvey JN (2011) Understanding the determinants of selectivity in drug metabolism through modeling of dextromethorphan oxidation by cytochrome P450. Proc Natl Acad Sci 108(15):6050–6055

    Article  PubMed Central  PubMed  Google Scholar 

  5. Shimada T (2006) Xenobiotic-metabolizing enzymes involved in activation and detoxification of carcinogenic polycyclic aromatic hydrocarbons. Drug Metab Pharmacokinet 21(4):257–276

    Article  CAS  PubMed  Google Scholar 

  6. Omiecinski CJ, Vanden Heuvel JP, Perdew GH et al (2011) Xenobiotic metabolism, disposition, and regulation by receptors: from biochemical phenomenon to predictors of major toxicities. Toxicol Sci 120(Suppl 1):S49–S75. doi:10.1093/toxsci/kfq338

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Croom E (2012) Metabolism of xenobiotics of human environments. Prog Mol Biol Transl Sci 112:31–88. doi:10.1016/B978-0-12-415813-9.00003-9

    Article  CAS  PubMed  Google Scholar 

  8. Shaik S, Cohen S, Wang Y et al (2009) P450 enzymes: their structure, reactivity, and selectivity modeled by QM/MM calculations. Chem Rev 110(2):949–1017

    Article  CAS  Google Scholar 

  9. Groenhof G (2013) Introduction to QM/MM simulations, Biomolecular simulations. Springer, New York, NY, pp 43–66

    Google Scholar 

  10. van der Kamp MW, Mulholland AJ (2013) Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 52(16):2708–2728

    Article  PubMed  CAS  Google Scholar 

  11. Yang Y, Wong SE, Lightstone FC (2014) Understanding a substrate’s product regioselectivity in a family of enzymes: a case study of acetaminophen binding in cytochrome P450s. PLoS One 9(2), e87058. doi:10.1371/journal.pone.0087058

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  12. Banáš P, Jurečka P, Walter NG et al (2009) Theoretical studies of RNA catalysis: hybrid QM/MM methods and their comparison with MD and QM. Methods 49(2):202–216

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  13. Sponer J, Leszczynski J, Hobza P (2001) Electronic properties, hydrogen bonding, stacking, and cation binding of DNA and RNA bases. Biopolymers 61(1):3–31. doi:10.1002/1097-0282(2001)61:1<3::aid-bip10048>3.0.co;2-4

    Article  CAS  PubMed  Google Scholar 

  14. Kirchmair J, Williamson MJ, Tyzack JD et al (2012) Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 52(3):617–648

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Rydberg P, Olsen L, Ryde U (2012) Quantum-mechanical studies of reactions performed by cytochrome P450 enzymes. Curr Inorg Chem 2(3):292–315

    Article  CAS  Google Scholar 

  16. Pezeshki S, Lin H (2011) Adaptive-partitioning redistributed charge and dipole schemes for QM/MM dynamics simulations: on-the-fly relocation of boundaries that pass through covalent bonds. J Chem Theory Comput 7(11):3625–3634. doi:10.1021/ct2005209

    Article  CAS  PubMed  Google Scholar 

  17. Mishra NK (2011) Computational modeling of P450s for toxicity prediction. Expert Opin Drug Metab Toxicol 7(10):1211–1231

    Article  CAS  PubMed  Google Scholar 

  18. Castell JV, Donato MT, Gomez-Lechon MJ (2005) Metabolism and bioactivation of toxicants in the lung. The in vitro cellular approach. Exp Toxicol Pathol 57(Suppl 1):189–204

    Article  CAS  PubMed  Google Scholar 

  19. Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationships (QSAR): a review. Comb Chem High Throughput Screen 9(3):213–228

    Article  CAS  PubMed  Google Scholar 

  20. Sun H, Veith H, Xia M et al (2012) Prediction of cytochrome P450 profiles of environmental chemicals with QSAR models built from drug‐like molecules. Mol Inform 31(11–12):783–792

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Sushko I, Novotarskyi S, Korner R et al (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput Aided Mol Des 25(6):533–554. doi:10.1007/s10822-011-9440-2

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Novotarskyi S, Sushko I, Körner R et al (2011) A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J Chem Inf Model 51(6):1271–1280

    Article  CAS  PubMed  Google Scholar 

  23. Aguiar-Pulido V, Gestal M, Cruz-Monteagudo M et al (2013) Evolutionary computation and QSAR research. Curr Comput Aided Drug Des 9(2):206–225

    Article  CAS  PubMed  Google Scholar 

  24. Gertrudes JC, Maltarollo VG, Silva RA et al (2012) Machine learning techniques and drug design. Curr Med Chem 19(25):4289–4297

    Article  CAS  PubMed  Google Scholar 

  25. Andrada MF, Duchowicz PR, Castro EA (2013) QSAR applications on polycyclic aromatic hydrocarbons and some derivatives. Curr Org Chem 17(23):2872–2879

    Article  CAS  Google Scholar 

  26. Fourches D, Muratov E, Tropsha A (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 50(7):1189–1204

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Leonard JT, Roy K (2006) On selection of training and test sets for the development of predictive QSAR models. QSAR Comb Sci 25(3):235–251

    Article  CAS  Google Scholar 

  28. Roy PP, Leonard JT, Roy K (2008) Exploring the impact of size of training sets for the development of predictive QSAR models. Chemom Intell Lab Syst 90(1):31–42

    Article  CAS  Google Scholar 

  29. Csermely P, Korcsmáros T, Kiss HJ et al (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138(3):333–408

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. van Delft JH, Mathijs K, Staal YC et al (2010) Time series analysis of benzo[A]pyrene-induced transcriptome changes suggests that a network of transcription factors regulates the effects on functional gene sets. Toxicol Sci 117(2):381–392. doi:10.1093/toxsci/kfq214

    Article  PubMed  CAS  Google Scholar 

  31. Vastrik I, D’Eustachio P, Schmidt E et al (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol 8(3):R39

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. Kiyosawa N, Manabe S, Sanbuissho A et al (2010) Gene set-level network analysis using a toxicogenomics database. Genomics 96(1):39–49

    Article  CAS  PubMed  Google Scholar 

  33. Yang Y, Maxwell A, Zhang X et al (2013) Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment. BMC Bioinformatics 14(Suppl 14):S3

    Article  PubMed Central  PubMed  Google Scholar 

  34. Schlage WK, Westra JW, Gebel S et al (2011) A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC Syst Biol 5:168. doi:10.1186/1752-0509-5-168, 1752-0509-5-168 [pii]

    Article  PubMed Central  PubMed  Google Scholar 

  35. Slater T (2014) Recent advances in modeling languages for pathway maps and computable biological networks. Drug Discov Today 19(2):93–98

    Article  Google Scholar 

  36. Hoeng J, Talikka M, Martin F et al (2013) Toxicopanomics: applications of genomics, transcriptomics, proteomics and lipidomics in predictive mechanistic toxicology. In: Hayes AW (ed) Principle and methods on toxicology. Taylor & Francis, London, In press

    Google Scholar 

  37. Iskandar AR, Martin F, Talikka M et al (2013) Systems approaches evaluating the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues. Biomed Res Int 2013:512086. doi:10.1155/2013/512086

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  38. Gonzalez FJ, Fernandez-Salguero P (1998) The Aryl hydrocarbon receptor: studies using the AHR-null mice. Drug Metab Dispos 26(12):1194–1198

    CAS  PubMed  Google Scholar 

  39. Thomson TM, Sewer A, Martin F et al (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol Appl Pharmacol 272(3):863–878

    Article  CAS  PubMed  Google Scholar 

  40. Martin F, Thomson TM, Sewer A et al (2012) Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks. BMC Syst Biol 6(1):54

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Demetriou CA, Raaschou-Nielsen O, Loft S et al (2012) Biomarkers of ambient air pollution and lung cancer: a systematic review. Occup Environ Med 69(9):619–627

    Article  CAS  PubMed  Google Scholar 

  42. Peluso M, Neri M, Margarino G et al (2004) Comparison of DNA adduct levels in nasal mucosa, lymphocytes and bronchial mucosa of cigarette smokers and interaction with metabolic gene polymorphisms. Carcinogenesis 25(12):2459–2465. doi:10.1093/carcin/bgh259

    Article  CAS  PubMed  Google Scholar 

  43. Sridhar S, Schembri F, Zeskind J et al (2008) Smoking-induced gene expression changes in the bronchial airway are reflected in nasal and buccal epithelium. BMC Genomics 9(1):259

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  44. Zhang X, Sebastiani P, Liu G et al (2010) Similarities and differences between smoking-related gene expression in nasal and bronchial epithelium. Physiol Genomics 41(1):1–8. doi:10.1152/physiolgenomics.00167.2009

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  45. Bosse Y, Postma DS, Sin DD et al (2012) Molecular signature of smoking in human lung tissues. Cancer Res 72(15):3753–3763. doi:10.1158/0008-5472.CAN-12-1160, 0008-5472.CAN-12-1160 [pii]

    Article  CAS  PubMed  Google Scholar 

  46. Karp PH, Moniger T, Weber SP et al (2002) An in vitro model of differentiated human airway epithelia. Methods Mol Biol 188:115–137

    PubMed  Google Scholar 

  47. Mathis C, Poussin C, Weisensee D et al (2013) Human bronchial epithelial cells exposed in vitro to cigarette smoke at the air-liquid interface resemble bronchial epithelium from human smokers. Am J Physiol Lung Cell Mol Physiol 304(7):L489–L503

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Maunders H, Patwardhan S, Phillips J et al (2007) Human bronchial epithelial cell transcriptome: gene expression changes following acute exposure to whole cigarette smoke in vitro. Am J Physiol Lung Cell Mol Physiol 292(5):L1248–L1256. doi:10.1152/ajplung.00290.2006, 00290.2006 [pii]

    Article  CAS  PubMed  Google Scholar 

  49. Pezzulo AA, Starner TD, Scheetz TE et al (2011) The air-liquid interface and use of primary cell cultures are important to recapitulate the transcriptional profile of in vivo airway epithelia. Am J Physiol Lung Cell Mol Physiol 300(1):L25–31. doi:10.1152/ajplung.00256.2010, ajplung.00256.2010 [pii]

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Gebel S, Gerstmayer B, Kuhl P et al (2006) The kinetics of transcriptomic changes induced by cigarette smoke in rat lungs reveals a specific program of defense, inflammation, and circadian clock gene expression. Toxicol Sci 93(2):422–431

    Article  CAS  PubMed  Google Scholar 

  51. Novotarskyi S, Sushko I, Koerner R et al (2013) Chemogenomic approach to increase accuracy of QSAR modeling of inhibition activity against five major P450 isoforms. J Cheminform 5(Suppl 1):P23

    PubMed Central  Google Scholar 

  52. Bugrim A, Nikolskaya T, Nikolsky Y (2004) Early prediction of drug metabolism and toxicity: systems biology approach and modeling. Drug Discov Today 9(3):127–135. doi:10.1016/S1359-6446(03)02971-4

    Article  CAS  PubMed  Google Scholar 

  53. Roy K (2007) On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin Drug Discov 2(12):1567–1577

    Article  CAS  PubMed  Google Scholar 

  54. Buriani A, Garcia-Bermejo ML, Bosisio E et al (2012) Omic techniques in systems biology approaches to traditional Chinese medicine research: present and future. J Ethnopharmacol 140(3):535–544

    Article  PubMed  Google Scholar 

  55. Tao W, Xu X, Wang X et al (2013) Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol 145(1):1–10

    Article  CAS  PubMed  Google Scholar 

  56. Leung EL, Cao Z-W, Jiang Z-H et al (2013) Network-based drug discovery by integrating systems biology and computational technologies. Brief Bioinform 14(4):491–505

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Kell DB, Goodacre R (2014) Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 19(2):171–182

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Nam D, Kim SY (2008) Gene-set approach for expression pattern analysis. Brief Bioinform 9(3):189–197. doi:10.1093/bib/bbn001

    Article  PubMed  Google Scholar 

  59. Chagoyen M, Pazos F (2011) MBRole: enrichment analysis of metabolomic data. Bioinformatics 27(5):730–731. doi:10.1093/bioinformatics/btr001

    Article  CAS  PubMed  Google Scholar 

  60. Kamburov A, Cavill R, Ebbels TMD et al (2011) Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27(20):2917–2918. doi:10.1093/bioinformatics/btr499

    Article  CAS  PubMed  Google Scholar 

  61. Kamburov A, Pentchev K, Galicka H et al (2011) ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res 39(suppl 1):D712–D717

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  62. Kuo TC, Tian TF, Tseng YJ (2013) 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 7:64. doi:10.1186/1752-0509-7-64

    Article  PubMed Central  PubMed  Google Scholar 

  63. Thiele I, Swainston N, Fleming RM et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31(5):419–425

    Article  CAS  PubMed  Google Scholar 

  64. Li D, Huang X, Lin J et al (2013) Catalytic mechanism of cytochrome P450 for N-methylhydroxylation of nicotine: reaction pathways and regioselectivity of the enzymatic nicotine oxidation. Dalton Trans 42(11):3812–3820

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  65. Lu H, Huang X, AbdulHameed MDM et al (2014) Binding free energies for nicotine analogs inhibiting cytochrome P450 2A6 by a combined use of molecular dynamics simulations and QM/MM-PBSA calculations. Bioorg Med Chem 22(7):2149–2156

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  66. Usharani D, Zazza C, Lai W et al (2012) A Single-site mutation (F429H) converts the enzyme CYP 2B4 into a heme oxygenase: a QM/MM study. J Am Chem Soc 134(9):4053–4056

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  67. Lonsdale R, Houghton KT, Zurek J et al (2013) Quantum mechanics/molecular mechanics modeling of regioselectivity of drug metabolism in cytochrome P450 2C9. J Am Chem Soc 135(21):8001–8015

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Shi R, Li W, Liu G et al (2013) Catalytic mechanism of cytochrome P450 2D6 for 4-hydroxylation of aripiprazole: A QM/MM study. Chin J Chem 31(9):1219–1227. doi:10.1002/cjoc.201300427

    Article  CAS  Google Scholar 

  69. Lonsdale R, Olah J, Mulholland AJ et al (2011) Does compound I vary significantly between isoforms of cytochrome P450? J Am Chem Soc 133(39):15464–15474. doi:10.1021/ja203157u

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  70. Shahrokh K, Orendt A, Yost GS et al (2012) Quantum mechanically derived AMBER-compatible heme parameters for various states of the cytochrome P450 catalytic cycle. J Comput Chem 33(2):119–133. doi:10.1002/jcc.21922

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Calvaresi M, Stenta M, Garavelli M et al (2012) Computational evidence for the catalytic mechanism of human glutathione S-transferase A3-3: a QM/MM investigation. ACS Catal 2(2):280–286

    Article  CAS  Google Scholar 

  72. Parker LJ, Italiano LC, Morton CJ et al (2011) Studies of glutathione transferase P1‐1 bound to a platinum (IV)‐based anticancer compound reveal the molecular basis of its activation. Chemistry 17(28):7806–7816

    Article  CAS  PubMed  Google Scholar 

  73. Mueller RM, North MA, Yang C et al (2011) Interplay of flavin’s redox states and protein dynamics: an insight from QM/MM simulations of dihydronicotinamide riboside quinone oxidoreductase 2. J Phys Chem B 115(13):3632–3641

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  74. Pan J, Liu G-Y, Cheng J et al (2010) CoMFA and molecular docking studies of benzoxazoles and benzothiazoles as CYP450 1A1 inhibitors. Eur J Med Chem 45(3):967–972

    Article  CAS  PubMed  Google Scholar 

  75. Gonzalez J, Marchand-Geneste N, Giraudel J et al (2012) Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1. SAR QSAR Environ Res 23(1-2):87–109

    Article  CAS  PubMed  Google Scholar 

  76. Sridhar J, Ellis J, Dupart P et al (2012) Development of flavone propargyl ethers as potent and selective inhibitors of cytochrome P450 enzymes 1A1 and 1A2. Drug Metab Lett 6(4):275–284

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Sridhar J, Foroozesh M, Stevens CK (2011) QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA, CoMSIA and HQSAR. SAR QSAR Environ Res 22(7-8):681–697

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  78. Rahnasto MK, Raunio HA, Wittekindt C et al (2011) Identification of novel CYP2A6 inhibitors by virtual screening. Bioorg Med Chem 19(23):7186–7193

    Article  CAS  PubMed  Google Scholar 

  79. Gharaghani S, Khayamian T, Keshavarz F (2012) Docking, molecular dynamics simulation studies, and structure-based QSAR model on cytochrome P450 2A6 inhibitors. Struct Chem 23(2):341–350

    Article  CAS  Google Scholar 

  80. Lewis DF, Ito Y, Lake BG (2010) 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(5):679–684

    Article  CAS  PubMed  Google Scholar 

  81. Roy PP, Roy K (2010) Pharmacophore mapping, molecular docking and QSAR studies of structurally diverse compounds as CYP2B6 inhibitors. Mol Simul 36(11):887–905

    Article  CAS  Google Scholar 

  82. Taxak N, Bharatam P (2013) 2D QSAR study for gemfibrozil glucuronide as the mechanism-based inhibitor of CYP2C8. Indian J Pharm Sci 75(6)

    Google Scholar 

  83. Saraceno M, Massarelli I, Imbriani M et al (2011) Optimizing QSAR models for predicting ligand binding to the drug‐metabolizing cytochrome P450 isoenzyme CYP2D6. Chem Biol Drug Des 78(2):236–251

    Article  CAS  PubMed  Google Scholar 

  84. Jónsdóttir SÓ, Ringsted T, Nikolov NG et al (2012) Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis. Bioorg Med Chem 20(6):2042–2053

    Article  PubMed  CAS  Google Scholar 

  85. Mo S-L, Liu W-F, Li C-G et al (2012) Pharmacophore, QSAR, and binding mode studies of substrates of human cytochrome P450 2D6 (CYP2D6) using molecular docking and virtual mutations and an application to Chinese herbal medicine screening. Curr Pharm Biotechnol 13(9):1640–1704

    Article  CAS  PubMed  Google Scholar 

  86. Martikainen LE, Rahnasto-Rilla M, Neshybova S et al (2012) Interactions of inhibitor molecules with the human CYP2E1 enzyme active site. Eur J Pharm Sci 47(5):996–1005

    Article  CAS  PubMed  Google Scholar 

  87. Didziapetris R, Dapkunas J, Sazonovas A et al (2010) Trainable structure–activity relationship model for virtual screening of CYP3A4 inhibition. J Comput Aided Mol Des 24(11):891–906

    Article  CAS  PubMed  Google Scholar 

  88. Hamon V, Horvath D, Gaudin C et al (2012) QSAR modelling of CYP3A4 inhibition as a screening tool in the context of drug-drug interaction studies. Mol Inform 31(9):669–677

    Article  CAS  Google Scholar 

  89. Handa K, Nakagome I, Yamaotsu N et al (2012) Three-dimensional quantitative structure-activity relationship analysis of inhibitors of human and rat cytochrome P4503A enzymes. Drug Metab Pharmacokinet 28(4):345–355

    Article  CAS  Google Scholar 

  90. Ako R, Dong D, Wu B (2012) 3D-QSAR studies on UDP-glucuronosyltransferase 2B7 substrates using the pharmacophore and VolSurf approaches. Xenobiotica 42(9):891–900

    Article  CAS  PubMed  Google Scholar 

  91. Kobeticova K, Simek Z, Brezovsky J et al (2011) Toxic effects of nine polycyclic aromatic compounds on Enchytraeus crypticus in artificial soil in relation to their properties. Ecotoxicol Environ Saf 74(6):1727–1733. doi:10.1016/j.ecoenv.2011.04.013

    Article  CAS  PubMed  Google Scholar 

  92. Li F, Li X, Liu X et al (2011) Noncovalent interactions between hydroxylated polycyclic aromatic hydrocarbon and DNA: molecular docking and QSAR study. Environ Toxicol Pharmacol 32(3):373–381. doi:10.1016/j.etap.2011.08.001

    Article  PubMed  CAS  Google Scholar 

  93. Al-Fahemi JH (2012) The use of quantum-chemical descriptors for predicting the photoinduced toxicity of PAHs. J Mol Model 18(9):4121–4129. doi:10.1007/s00894-012-1417-0

    Article  CAS  PubMed  Google Scholar 

  94. Li F, Wu H, Li L et al (2012) Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor. Ecotoxicol Environ Saf 80:273–279. doi:10.1016/j.ecoenv.2012.03.009

    Article  CAS  PubMed  Google Scholar 

  95. Xu X, Li X-G, Sun S-W (2012) A QSAR study on the biodegradation activity of PAHs in aged contaminated sediments. Chemometr Intell Lab Syst 114:50–55, http://dx.doi.org/10.1016/j.chemolab.2012.03.002

    Article  CAS  Google Scholar 

  96. Xu HY, Zou JW, Min JQ et al (2012) A quantitative structure-property relationship analysis of soot-water partition coefficients for persistent organic pollutants. Ecotoxicol Environ Saf 80:1–5. doi:10.1016/j.ecoenv.2012.02.002

    Article  CAS  PubMed  Google Scholar 

  97. Zhang Y-F, Zhang L, Gao Z-X et al (2012) Investigating the quantitative structure-activity relationships for antibody recognition of two immunoassays for polycyclic aromatic hydrocarbons by multiple regression methods. Sensors 12(7):9363–9374

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  98. Zhang YF, Ma Y, Gao ZX et al (2010) Predicting the cross-reactivities of polycyclic aromatic hydrocarbons in ELISA by regression analysis and CoMFA methods. Anal Bioanal Chem 397(6):2551–2557. doi:10.1007/s00216-010-3785-6

    Article  CAS  PubMed  Google Scholar 

  99. Tansel B, Lee M, Tansel DZ (2013) Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR. Mar Pollut Bull 73(1):258–262

    Article  CAS  PubMed  Google Scholar 

  100. Vračko M, Bobst S (2013) Performance evaluation of CAESAR–QSAR output using PAHs as a case study. J Chemom 28(2):100–107

    Article  CAS  Google Scholar 

  101. Lim SJ, Fox P (2014) Effects of halogenated aromatics/aliphatics and nitrogen (N)-heterocyclic aromatics on estimating the persistence of future pharmaceutical compounds using a modified QSAR model. Sci Total Environ 470:348–355

    Article  PubMed  CAS  Google Scholar 

  102. Xu X, Li X (2014) QSAR for photodegradation activity of polycyclic aromatic hydrocarbons in aqueous systems. J Ocean Univ China 13(1):66–72

    Article  CAS  Google Scholar 

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Acknowledgements

The in vitro experiments used for the use cases in Sect. 2.3 were conducted at and funded by Philip Morris International R&D and has been published previously in Biomed Research International [37]. We are grateful for the valuable comments from Elyette Martin, Marja Talikka, and Pavel Pospisil during the preparation of this manuscript. We thank the support from Edanz Group Ltd. for the assistance in editing and generating tables summarizing the recent QSAR studies.

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  1. Biological Systems Research, Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, CH-2000, Switzerland

    Anita R. Iskandar

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Correspondence to Anita R. Iskandar .

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  1. Philip Morris International R&D, Neuchâtel, Switzerland

    Julia Hoeng

  2. Philip Morris International R&D, Neuchâtel, Switzerland

    Manuel C. Peitsch

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Iskandar, A.R. (2015). Xenobiotic Metabolism Activation as a Biomarker of Cigarette Smoke Exposure Response. In: Hoeng, J., Peitsch, M. (eds) Computational Systems Toxicology. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2778-4_12

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  • DOI: https://doi.org/10.1007/978-1-4939-2778-4_12

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2777-7

  • Online ISBN: 978-1-4939-2778-4

  • eBook Packages: Springer Protocols

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