Understanding the toxic potencies of xenobiotics inducing TCDD/TCDF-like effectsFootnote^$

^$Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.

References

• U.S. Epa, PCBS: Cancer Dose-Response Assessment and Application to Environmental Mixtures, EPA/600/P-96/001F, Office of Research and Development, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, DC, 1996.
   Google Scholar
• Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC, 2006 available at http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02006R1907-20140410&from=EN.
   Google Scholar
• H. Gronemeyer, J.A. Gustafsson, and V. Laudet, Principles for modulation of the nuclear receptor superfamily, Nat. Rev. Drug Discov. 3 (2004), pp. 950–964.
   PubMed Web of Science ®Google Scholar
• M.S. Denison, A. Pandini, S.R. Nagy, E.P. Baldwin, and L. Bonati, Ligand binding and activation of the Ah receptor, Chem. Biol. Interact. 141 (2002), pp. 3–24.
   PubMed Web of Science ®Google Scholar
• A. Poland and J.C. Knutson, 2,3,7,8-Tetrachlorodibenzo-p-dioxin and related halogenated aromatic hydrocarbons: Examination of the mechanism of toxicity, Annu. Rev. Pharmacol. Toxicol. 22 (1982), pp. 517–554.
   PubMed Web of Science ®Google Scholar
• J. Chovancova, A. Kocan, and S. Jursa, PCDDs, PCDFs and dioxin-like PCBs in food of animal origin (Slovakia), Chemosphere 61 (2005), pp. 1305–1311.
   PubMed Web of Science ®Google Scholar
• J.L. Domingo and A. Bocio, Levels of PCDD/PCDFs and PCBs in edible marine species and human intake: A literature review, Environ. Int. 33 (2007), pp. 397–405.
   PubMed Web of Science ®Google Scholar
• K. Hilscherova, M. Machala, K. Kannan, A.L. Blankenship, and J.P. Giesy, Cell bioassays for detection of aryl hydrocarbon (AhR) and estrogen receptor (ER) mediated activity in environmental samples, Environ. Sci. Pollut. Res. 7 (2000), pp. 159–171
   PubMed Web of Science ®Google Scholar
• A. Ashek, L. Cheolju, P. Hyunsung, and J.C. Seung, 3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA, Chemosphere 65 (2006), pp. 521–529.
   PubMed Web of Science ®Google Scholar
• E. Lo Piparo, K. Koehler, A. Chana, and E. Benfanti, Virtual screening for aryl hydrocarbon receptor binding prediction, J. Med. Chem. 49 (2006), pp. 5702–5709.
   PubMed Web of Science ®Google Scholar
• E. Papa, S. Kovarich, and P. Gramatica, QSAR modeling and prediction of the endocrine-disrupting potencies of brominated flame retardants, Chem. Res. Toxicol. 23 (2010), pp. 946–954.
   PubMed Web of Science ®Google Scholar
• J. Diao, Y. Li, S. Shi, and Y. Sun, QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors, Bull. Environ. Contam. Toxicol. 85 (2010), pp. 109–115.
   PubMed Web of Science ®Google Scholar
• F. Li, X. Li, L. Zhang, L. You, J. Zhao, and H. Wu, Docking and 3D-QSAR studies on the Ah receptor binding affinities of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs), Environ. Toxicol. Pharmacol. 32 (2011), pp. 478–485.
   PubMed Web of Science ®Google Scholar
• C. Gu, M. Goodarzi, X. Yang, Y. Bian, C. Sun, and X. Jiang, Predictive insight into the relationship between AhR binding property and toxicity of polybrominated diphenyl ethers by PLS-derived QSAR, Toxicol. Lett. 208 (2012), pp. 269–274.
   PubMed Web of Science ®Google Scholar
• J. Yuan, Y. Pu, and P. Yin, Docking-based three-dimensional quantitative structure–activity relationship (3D-QSAR) predicts binding affinities to aryl hydrocarbon receptor for polychlorinated dibenzodioxins, dibenzofurans, and biphenyls, Environ. Toxicol. Chem. 32 (2013), pp. 1453–1458.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance document on the validation of (quantitative) structure–activity relationship [(Q)SAR] models, ENV/JM/MONO (2007)2, OECD Environment Health and Safety Publications Series of Testing and Assessment No. 69, Organisation for Economic Co-operation and Development, Paris, France, 2007.
   Google Scholar
• S. Safe, Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and related compounds: Environmental and mechanistic considerations which support the development of toxic equivalency factors (TEFs), Crit. Rev. Toxicol. 21 (1990), pp. 51–88.
   PubMed Web of Science ®Google Scholar
• C.L. Waller and J.D. McKinney, Three-dimensional quantitative structure–activity relationships of dioxins and dioxin-like compounds: Model validation and ah receptor characterization, Chem. Res. Toxicol. 8 (1995), pp. 847–858.
   PubMed Web of Science ®Google Scholar
• S. Safe, S. Bandiera, T. Sawyer, B. Zmudzka, G. Mason, M. Romkes, M.A. Denomme, J. Sparling, A.B. Okey, and T. Fujita, Effects of structure on binding to the 2,3,7,8-TCDD receptor protein and AHH induction—Halogenated biphenyls, Environ. Health Perspect. 61 (1985), pp. 21–33.
   PubMed Web of Science ®Google Scholar
• SPARTAN 10, Wavefunction Inc., Irvine, USA, 2010; software available at https://www.wavefun.com/products/windows/Spartan10/win_spartan.html.
   Google Scholar
• DRAGON for Windows 6.0, Talete srl, Mialn, Italy, 2014; software available at http://www.talete.mi.it/.
   Google Scholar
• ADMET 8.0, Simulations Plus; Lacnaster, CA, 2015 software available at http://www.simulations-plus.com/software/admet-property-prediction-qsar/.
   Google Scholar
• P. Gramatica, N. Chirico, E. Papa, S. Kovarich, and S. Cassani, QSARINS: A new software for the development, analysis, and validation of QSAR MLR models, J. Comput. Chem. 34 (2013), pp. 2121–2132.
   Web of Science ®Google Scholar
• P. Gramatica, S. Cassani, and N. Chirico. QSARINS-Chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS, J. Comput. Chem., Software news and updates 35 (2014), pp. 1036–1044.
   PubMed Web of Science ®Google Scholar
• J.G. Topliss and R.P. Edwards, Chance factors in studies of quantitative structure–activity relationships, J. Med. Chem. 22 (1979), pp. 1238–1244.
   PubMed Web of Science ®Google Scholar
• S. Wold and L. Eriksson, Statistical validation of QSAR results, in Chemometric Methods in Molecular Design, H. van de Waterbeemd, ed.; Wiley-VCH, Weinheim, 1995, pp. 309–318.
   Google Scholar
• P. Gramatica and A. Sangion, A historical excursus on the statistical validation parameters for QSAR models: A clarification concerning metrics and terminology, J. Chem. Inf. Model. 56 (2016), pp. 1127–1131.
   PubMed Web of Science ®Google Scholar
• K. Roy, R.D. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. Syst. 152 (2016), pp. 18–33.
   Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
   PubMed Web of Science ®Google Scholar
• N. Chirico and P. Gramatica, Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient, J. Chem. Inf. Model. 51 (2011), pp. 2320–2335.
   PubMed Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring r2m metrics for validation of QSPR models, Chemom. Int. Lab. Syst. 107 (2011), pp. 194–205.
   Web of Science ®Google Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701.
   Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, Vol. 1, Wiley-VCH, Weinheim, 2009.
   Google Scholar
• A.T. Balaban, Chemical graphs. XXXII. Five new topological indices for the branching of tree-like graphs, Theor. Chim. Acta 53 (1979), pp. 355–375.
   Google Scholar
• R. Todeschini, M. Lasagni, and E. Marengo, New molecular descriptors for 2D and 3D structures, J. Chemom. 8 (1994), pp. 236–272.
   Web of Science ®Google Scholar
• F. Cao, X. Li, L. Xie, Y. Wang, W. Shi, X. Qian, Y. Zhu, and H. Yu, Molecular docking, molecular dynamics simulation, and structure-based 3D-QSAR studies on the aryl hydrocarbon receptor agonistic activity of hydroxylated polychlorinated biphenyls, Environ. Toxicol. Pharmacol. 36 (2013), pp. 626–635.
   PubMed Web of Science ®Google Scholar
• G. Su, J. Xia, H. Liu, M.H.W. Lam, H. Yu, J.P. Giesy, and X. Zhang. Dioxin-like potency of HO- and MeO- analogues of PBDEs’ the potential risk through consumption of fish from Eastern China, Environ. Sci. Technol. 46 (2012), pp. 10781–10788.
   PubMed Web of Science ®Google Scholar
• J. Lindén, S. Lensu, J. Tuomisto, and R. Pohjanvirta, The Aryl hydrocarbon receptor and the central regulation of energy balance, Front. Neuroendocrinol. 31 (2010), pp. 452–478.
   PubMed Web of Science ®Google Scholar
• S. Lee, W. Shin, S. Hong, H. Kang, D. Jung, U.H. Yim, W.J. Shim, J.S. Khim, C. Seok, J.P. Giesy, and K. Choi, Measured and predicted affinities of binding and relative potencies to activate the AhR of PAHs and their alkylated analogues, Chemosphere 139 (2015), pp. 23–29.
   PubMed Web of Science ®Google Scholar