Abstract
Prediction of pEC50 values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC50 values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure–activity relationship (SAR) and two-dimensional quantitative structure–activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC50 values and 162 compounds without pEC50 values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure–activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC50 value of the compound. Moreover, two-dimensional quantitative structure–activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC50 values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl–Cl], and Neoplastic-80. In addition, the pEC50 values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alibakshi A (2018) Strategies to develop robust neural network models: prediction of flash point as a case study. Anal Chim Acta 1026:69–76. https://doi.org/10.1016/j.aca.2018.05.015
Balaban AT, Motoc I, Bonchev D, Mekenyan O (1983) Topological indexes for structure-activity correlations. Top Curr Chem 114:21–55
Birnbaum LS, Staskal DF, Diliberto JJ (2003) Health effects of polybrominated dibenzo-p-dioxins (PBDDs) and dibenzofurans (PBDFs). Environ Int 29:855–860. https://doi.org/10.1016/S0160-4120(03)00106-5
Bruzzone S, Chiappe C, Focardi SE, Pretti C, Renzi M (2011) Theoretical descriptor for the correlation of aquatic toxicity of ionic liquids by quantitative structure-toxicity relationships. Chem Eng J 175:17–23. https://doi.org/10.1016/j.cej.2011.08.073
Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular-features in structure activity studies—definition and applications. J Chem Inf Comput Sci 25:64–73. https://doi.org/10.1021/ci00046a002
Chen YC, Tsai PJ, Wang LC, Shih M, Lee WJ (2010) An integrated approach for identification of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) pollutant sources based on human blood contents. Environ Sci Pollut Res 17:759–769. https://doi.org/10.1007/s11356-009-0162-0
Cherkasov A et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010. https://doi.org/10.1021/jm4004285
Cheung EN, McKinney JD (1989) Polybrominated naphthalene and diiodobenzene interactions with specific binding sites for 2,3,7,8-tetrachlorodibenzo-p-dioxin in rat liver cytosol. Mol Toxicol 2:39–52
Chi KH, Hsu SC, Lin CY, Kao SJ, Lee TY (2011) Deposition fluxes of PCDD/Fs in a reservoir system in northern Taiwan. Chemosphere 83:745–752. https://doi.org/10.1016/j.chemosphere.2011.02.069
Dietz R, Desforges JP, Gustavson K, Riget FF, Born EW, Letcher RJ, Sonne C (2018) Immunologic, reproductive, and carcinogenic risk assessment from POP exposure in east greenland polar bears (Ursus maritimus) during 1983–2013. Environ Int 118:169–178. https://doi.org/10.1016/j.envint.2018.05.020
Ding JF et al (2018) Photocatalytic reductive dechlorination of 2-chlorodibenzo-p-dioxin by Pd modified g-C3N4 photocatalysts under UV-vis irradiation: efficacy, kinetics and mechanism. J Hazard Mater 355:74–81. https://doi.org/10.1016/j.jhazmat.2018.05.014
Estrada E, Gutman I (1996) A topological index based on distances of edges of molecular graphs. J Chem Inf Comput Sci 36:850–853. https://doi.org/10.1021/ci9600115
Fernandez-Gonzalez R, Yebra-Pimentel I, Martinez-Carballo E, Simal-Gandara J (2015) A critical review about human exposure to polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and polychlorinated biphenyls (PCBs) through foods. Crit Rev Food Sci Nutr 55:1590–1617. https://doi.org/10.1080/10408398.2012.710279
Fu HL, Wang L, Wang JJ, Bennett BD, Li JL, Zhao B, Hu G (2019) Dioxin and AHR impairs mesoderm gene expression and cardiac differentiation in human embryonic stem cells. Sci Total Environ 651:1038–1046. https://doi.org/10.1016/j.scitotenv.2018.09.247
Gooch A, Sizochenko N, Rasulev B, Gorb L, Leszczynski J (2017) In vivo toxicity of nitroaromatics: a comprehensive quantitative structure-activity relationship study. Environ Toxicol Chem 36:2227–2233. https://doi.org/10.1002/etc.3761
Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701. https://doi.org/10.1002/qsar.200610151
Hemmer MC, Steinhauer V, Gasteiger J (1999) Deriving the 3D structure of organic molecules from their infrared spectra. Vib Spectrosc 19:151–164. https://doi.org/10.1016/s0924-2031(99)00014-4
Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set in descriptor space: a review. ATLA-Altern Lab Anim 33:445–459
Khan K et al (2019) QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors. Chemosphere 229:8–17. https://doi.org/10.1016/j.chemosphere.2019.04.204
Li F et al (2016) High performance solid-phase extraction cleanup method coupled with gas chromatography-triple quadrupole mass spectrometry for analysis of polychlorinated naphthalenes and dioxin-like polychlorinated biphenyls in complex samples. J Chromatogr A 1448:1–8. https://doi.org/10.1016/j.chroma.2016.04.037
Li HF, Liu WB, Tang C, Lei RR, Wu XL, Gao LR, Su GJ (2019) Emissions of 2,3,7,8-substituted and non-2,3,7,8-substituted polychlorinated dibenzo-p-dioxins and dibenzofurans from secondary aluminum smelters. Chemosphere 215:92–100. https://doi.org/10.1016/j.chemosphere.2018.10.004
Lin LF, Shih SI, Su JW, Shih ML, Lin KC, Wang LC, Chang-Chien GP (2010) Dry and wet deposition of polychlorinated dibenzo-p-dioxins and dibenzofurans on the drinking water treatment plant. Aerosol Air Qual Res 10:231–244. https://doi.org/10.4209/aaqr.2009.09.0059
Ma B, Chen HH, Xu MM, Hayat T, He Y, Xu JM (2010) Quantitative structure-activity relationship (QSAR) models for polycyclic aromatic hydrocarbons (PAHs) dissipation in rhizosphere based on molecular structure and effect size. Environ Pollut 158:2773–2777. https://doi.org/10.1016/j.envpol.2010.04.011
Ma SY, Lv M, Deng FF, Zhang XY, Zhai HL, Lv WJ (2015) Predicting the ecotoxicity of ionic liquids towards Vibrio fischeri using genetic function approximation and least squares support vector machine. J Hazard Mater 283:591–598. https://doi.org/10.1016/j.jhazmat.2014.10.011
Mansouri K, Consonni V, Durjava MK, Kolar B, Oberg T, Todeschini R (2012) Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling. Chemosphere 89:433–444. https://doi.org/10.1016/j.chemosphere.2012.05.081
Mitra I, Saha A, Roy K (2010) Exploring quantitative structure-activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol Simul 36:1067–1079. https://doi.org/10.1080/08927022.2010.503326
Nolte TM, Peijnenburg W, Hendriks AJ, van de Meent D (2017) Quantitative structure-activity relationships for green algae growth inhibition by polymer particles. Chemosphere 179:49–56. https://doi.org/10.1016/j.chemosphere.2017.01.067
Palmer D, Pou JO, Gonzalez-Sabate L, Diaz-Ferrero J (2018) Multiple linear regression based congener profile correlation to estimate the toxicity (TEQ) and dioxin concentration in atmospheric emissions. Sci Total Environ 622:510–516. https://doi.org/10.1016/j.scitotenv.2017.11.344
Peric B, Sierra J, Marti E, Cruanas R, Garau MA (2015) Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids. Ecotox Environ Safe 115:257–262. https://doi.org/10.1016/j.ecoenv.2015.02.027
Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) Some case studies on application of “rm2” metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J Comput Chem 34:1071–1082. https://doi.org/10.1002/jcc.23231
Roy K, Kar S, Ambure P (2015) On a simple approach for determining applicability domain of QSAR models. Chemom Intell Lab Syst 145:22–29. https://doi.org/10.1016/j.chemolab.2015.04.013
Roy K, Das RN, Ambure P, Aher RB (2016) Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom Intell Lab Syst 152:18–33. https://doi.org/10.1016/j.chemolab.2016.01.008
Roy K, Ambure P, Kar S (2018) How precise are our quantitative structure-activity relationship derived predictions for new query chemicals? ACS Omega 3:11392–11406. https://doi.org/10.1021/acsomega.8b01647
Sheridan RP, Miller MD, Underwood DJ, Kearsley SK (1996) Chemical similarity using geometric atom pair descriptors. J Chem Inf Comput Sci 36:128–136. https://doi.org/10.1021/ci950275b
Todeschini R, Gramatica P (1997) 3D-modelling and prediction by WHIM descriptors 5. Theory development and chemical meaning of WHIM descriptors. Quant Struct-Act Relat 16:113–119. https://doi.org/10.1002/qsar.19970160203
Todeschini R, Lasagni M (1994) New molecular descriptors for 2D and 3D structures—theory. J Chemom 8:263–272. https://doi.org/10.1002/cem.1180080405
Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29:476–488. https://doi.org/10.1002/minf.201000061
Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77. https://doi.org/10.1002/qsar.200390007
Van den Berg M et al (2006) The 2005 world health organization reevaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicol Sci 93:223–241. https://doi.org/10.1093/toxsci/kfl055
Walker JD, Jaworska J, Comber MHI, Schultz TW, Dearden JC (2003) Guidelines for developing and using quantitative structure-activity relationships. Environ Toxicol Chem 22:1653–1665. https://doi.org/10.1897/01-627
Wang L, Lu Y-l, He G-z, Wang T-y (2014) Construction of index system for early warning of persistent organic pollutants (POPs) pollution incidents in China. Huanjing Kexue 35:4009–4014
Wessel MD, Jurs PC (1994) Prediction of reduced ion mobility constants from structural information using multiple linear-regression analysis and computational neural networks. Anal Chem 66:2480–2487. https://doi.org/10.1021/ac00087a012
Wu JJ, Zhang B, Dong SJ, Zheng MH (2011) Determination of ultratrace polychlorinated dibenzo-p-dioxins and dibenzofurans by gas chromatography-triple quadrupole mass spectrometry. Chin J Anal Chem 39:1297–1301. https://doi.org/10.3724/sp.j.1096.2011.01297
Xu J, Stevenson J (2000) Drug-like index: a new approach to measure drug-like compounds and their diversity. J Chem Inf Comput Sci 40:1177–1187. https://doi.org/10.1021/ci000026+
Zhao B, Zheng MH, Jiang GB (2011) Dioxin emissions and human exposure in China: a brief history of policy and research. Environ Health Perspect 119:112–113. https://doi.org/10.1289/ehp.1103535
Acknowledgements
We appreciate much the program of The National Natural Science Foundation of China (NSFC, Nos. 21705064, 21275067) for the financial support of our work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Yang, H., Du, Z., Lv, WJ. et al. In silico toxicity evaluation of dioxins using structure–activity relationship (SAR) and two-dimensional quantitative structure–activity relationship (2D-QSAR). Arch Toxicol 93, 3207–3218 (2019). https://doi.org/10.1007/s00204-019-02580-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00204-019-02580-w