Advertisement

Predicting the cytotoxicity of disinfection by-products to Chinese hamster ovary by using linear quantitative structure–activity relationship models

  • Li-Tang Qin
  • Xin Zhang
  • Yu-Han Chen
  • Ling-Yun MoEmail author
  • Hong-Hu Zeng
  • Yan-Peng Liang
  • Hua LinEmail author
  • Dun-Qiu Wang
Research Article
  • 55 Downloads

Abstract

A suitable model to predict the toxicity of current and continuously emerging disinfection by-products (DBPs) is needed. This study aims to establish a reliable model for predicting the cytotoxicity of DBPs to Chinese hamster ovary (CHO) cells. We collected the CHO cytotoxicity data of 74 DBPs as the endpoint to build linear quantitative structure–activity relationship (QSAR) models. The linear models were developed by using multiple linear regression (MLR). The MLR models showed high performance in both internal (leave-one-out cross-validation, leave-many-out cross-validation, and bootstrapping) and external validation, indicating their satisfactory goodness of fit (R2 = 0.763–0.799), robustness (Q2LOO = 0.718–0.745), and predictive ability (CCC = 0.806–0.848). The generated QSAR models showed comparable quality on both the training and validation levels. Williams plot verified that the obtained models had wide application domains and covered the 74 structurally diverse DBPs. The molecular descriptors used in the models provided comparable information that influences the CHO cytotoxicity of DBPs. In conclusion, the linear QSAR models can be used to predict the CHO cytotoxicity of DBPs.

Keywords

Disinfection by-products Quantitative structure-activity relationship Cytotoxicity Chinese hamster ovary 

Notes

Acknowledgements

Thank you for Professor Paola Gramatica (University of Insubria) for providing the QSARINS software (website www.qsar.it).

Funding information

This work was supported by Provincial Natural Science Foundation of Guangxi (2017GXNSFAA198346), the National Natural Science Foundation of China (21866010, 21667013, and 51638006), Special Funding for Guangxi ‘BaGui Scholar’ Construction Projects, and Guangxi Science and Technology Planning Project (GuiKe-AD18126018).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11356_2019_4947_MOESM1_ESM.docx (34 kb)
ESM 1 (DOCX 34 kb)

References

  1. Bang SJ, Cho SJ (2004) Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study of mutagen X. Bull Kor Chem Soc 25:1525–1530CrossRefGoogle Scholar
  2. Bellar TA, Lichtenberg JJ, Kroner RC (1974) The occurrence of organohalides in chlorinated drinking waters. J Am Water Works Assoc 66:703–706CrossRefGoogle Scholar
  3. Chen BY, Zhang T, Bond T, Gan YQ (2015) Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: a review of methods and resources. J Hazard Mater 299:260–279CrossRefGoogle Scholar
  4. Chirico N, Gramatica P (2011) 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 51:2320–2335CrossRefGoogle Scholar
  5. Concu R, Kleandrova VV, Speck-Planche A, Mnds C (2017) Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology 11:891–906CrossRefGoogle Scholar
  6. Consonni V, Ballabio D, Todeschini R (2010) Evaluation of model predictive ability by external validation techniques. J Chemom 24:194–201CrossRefGoogle Scholar
  7. Devinyak O, Havrylyuk D, Lesyk R (2014) 3D-MoRSE descriptors explained. J Mol Graph Model 54:194–203CrossRefGoogle Scholar
  8. Dong Y, Li F, Shen H, Lu R, Yin S, Yang Q, Li Z, Wang S (2018) Evaluation of the water disinfection by-product dichloroacetonitrile-induced biochemical, oxidative, histopathological, and mitochondrial functional alterations: subacute oral toxicity in rats. Toxicol Ind Health 34:158–168CrossRefGoogle Scholar
  9. Eriksson L, Jaworska J, Worth AP, Cronin MTD, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361–1375CrossRefGoogle Scholar
  10. Golbraikh A, Tropsha A (2002) Beware of q 2! J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  11. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26:694–701CrossRefGoogle Scholar
  12. Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34:2121–2132CrossRefGoogle Scholar
  13. Gramatica P, Cassani S, Chirico N (2014) QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem 35:1036–1044CrossRefGoogle Scholar
  14. Jeong CH, Postigo C, Richardson SD, Simmons JE, Kimura SY, Marinas BJ, Barcelo D, Liang P, Wagner ED, Plewa MJ (2015) Occurrence and comparative toxicity of haloacetaldehyde disinfection byproducts in drinking water. Environ Sci Technol 49:13749–13759CrossRefGoogle Scholar
  15. Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787CrossRefGoogle Scholar
  16. Kiralj R, Ferreira MMC (2010) Is your QSAR/QSPR descriptor real or trash? J Chemom 24:681–693CrossRefGoogle Scholar
  17. Kleandrova VV, Luan F, Speckplanche A, Cordeiro MN (2015) In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect. Mini-Rev Med Chem 15:677–686CrossRefGoogle Scholar
  18. Krasner SW, Weinberg HS, Richardson SD, Pastor SJ, Chinn R, Sclimenti MJ, Onstad GD, Thruston AD (2006) Occurrence of a new generation of disinfection byproducts. Environ Sci Technol 40:7175–7185CrossRefGoogle Scholar
  19. Labute P (2000) A widely applicable set of descriptors. J Mol Graph Model 18:464–477CrossRefGoogle Scholar
  20. Lee WN, Huang CH, Zhu GX (2018) Analysis of 40 conventional and emerging disinfection by-products in fresh-cut produce wash water by modified EPA methods. Food Chem 256:319–326CrossRefGoogle Scholar
  21. Liu J, Zhang X (2014) Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: Halophenolic DBPs are generally more toxic than haloaliphatic ones. Water Res 65:64–72CrossRefGoogle Scholar
  22. Muellner MG, Wagner ED, McCalla K, Richardson SD, Woo YT, Plewa MJ (2007) Haloacetonitriles vs. regulated haloacetic acids: are nitrogen-containing DBPs more toxic? Environ Sci Technol 41:645–651CrossRefGoogle Scholar
  23. Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring r 2m metrics for validation of QSPR models. Chemometr Intell Lab 107:194–205CrossRefGoogle Scholar
  24. Pals JA (2009) Comparative cytotoxicity, genotoxicity, and DNA repair kinetics of drinking water disinfection by-products, Crop Sciences. University of Illinois, Urbana, ILGoogle Scholar
  25. Pals JA, Ang JK, Wagner ED, Plewa MJ (2011) Biological mechanism for the toxicity of haloacetic acid drinking water disinfection byproducts. Environ Sci Technol 45:5791–5797CrossRefGoogle Scholar
  26. Pals JA, Wagner ED, Plewa MJ (2016) Energy of the lowest unoccupied molecular orbital, thiol reactivity, and toxicity of three monobrominated water disinfection byproducts. Environ Sci Technol 50:3215–3221CrossRefGoogle Scholar
  27. Plewa M, Wagner E (2009) Mammalian cell cytotoxicity and genotoxicity of disinfection by-products. Water Research Foundation, DenverGoogle Scholar
  28. Plewa MJ, Kargalioglu Y, Vankerk D, Minear RA, Wagner ED (2002) Mammalian cell cytotoxicity and genotoxicity analysis of drinking water disinfection by-products. Environ Mol Mutagen 40:134–142CrossRefGoogle Scholar
  29. Plewa MJ, Wagner ED, Jazwierska P, Richardson SD, Chen PH, McKague AB (2004) Halonitromethane drinking water disinfection byproducts: chemical characterization and mammalian cell cytotoxicity and genotoxicity. Environ Sci Technol 38:62–68CrossRefGoogle Scholar
  30. Plewa MJ, Muellner MG, Richardson SD, Fasanot F, Buettner KM, Woo Y-T, McKague AB, Wagner ED (2008) Occurrence, synthesis, and mammalian cell cytotoxicity and genotoxicity of haloacetamides: an emerging class of nitrogenous drinking water disinfection byproducts. Environ Sci Technol 42:955–961CrossRefGoogle Scholar
  31. Plewa MJ, Simmons JE, Richardson SD, Wagner ED (2010) Mammalian cell cytotoxicity and genotoxicity of the haloacetic acids, a major class of drinking water disinfection by-products. Environ Mol Mutagen 51:871–878CrossRefGoogle Scholar
  32. Qin LT, Liu SS, Chen F, Xiao QF, Wu QS (2013) Chemometric model for predicting retention indices of constituents of essential oils. Chemosphere 90:300–305CrossRefGoogle Scholar
  33. Qin LT, Zhang X, Chen YH, Mo LY, Zeng HH, Liang YP (2017) Predictive QSAR models for the toxicity of disinfection byproducts. Molecules 22Google Scholar
  34. Richard AM, Hunter ES (1996) Quantitative structure-activity relationships for the developmental toxicity of haloacetic acids in mammalian whole embryo culture. Teratology 53:352–360CrossRefGoogle Scholar
  35. Richardson SD, Plewa MJ, Wagner ED, Schoeny R, Demarini DM (2007) Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-products in drinking water: a review and roadmap for research. Mutat Res Rev Mutat Res 636:178–242CrossRefGoogle Scholar
  36. Richardson SD, Fasano F, Ellington JJ, Crumley FG, Buettner KM, Evans JJ, Blount BC, Silva LK, Waite TJ, Luther GW, McKague AB, Miltner RJ, Wagner ED, Plewa MJ (2008) Occurrence and mammalian cell toxicity of iodinated disinfection byproducts in drinking water. Environ Sci Technol 42:8330–8338CrossRefGoogle Scholar
  37. Sangion A, Gramatica P (2016) Hazard of pharmaceuticals for aquatic environment: prioritization by structural approaches and prediction of ecotoxicity. Environ Int 95:131–143CrossRefGoogle Scholar
  38. Schüürmann G, Ebert RU, Chen JW, Wang B, Kuhne R (2008) External validation and prediction employing the predictive squared correlation coefficient - test set activity mean vs training set activity mean. J Chem Inf Model 48:2140–2145CrossRefGoogle Scholar
  39. Shi LM, Fang H, Tong W, Wu J, Perkins R, Blair RM, Branham WS, Dial SL, Moland CL, Sheehan DM (2001) QSAR models using a large diverse set of estrogens. J Chem Inf Comput Sci 41:186–195CrossRefGoogle Scholar
  40. Shi W, Wang L, Chen B (2017) Kinetics, mechanisms, and influencing factors on the treatment of haloacetonitriles (HANs) in water by two household heating devices. Chemosphere 172:278–285CrossRefGoogle Scholar
  41. Stalter D, Dutt M, Escher BI (2013) Headspace-free setup of in vitro bioassays for the evaluation of volatile disinfection by-products. Chem Res Toxicol 26:1605–1614CrossRefGoogle Scholar
  42. Tenorioborroto E, Ramirez FR, Speckplanche A, Cordeiro MN, Luan F, Gonzalezdiaz H (2014) QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Curr Drug Metab 15:414–428CrossRefGoogle Scholar
  43. Todeschini R, Lasagni M (1994) New molecular descriptors for 2d and 3d structures - theory. J Chemom 8:263–272CrossRefGoogle Scholar
  44. 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 22:69–77CrossRefGoogle Scholar
  45. Tuppurainen K (1994) QSAR approach to molecular mutagenicity: a survey and a case study: MX compounds. THEOCHEM J Mol Struct 112:49–56CrossRefGoogle Scholar
  46. Tuppurainen K, Lötjönen S, Laatikainen R, Vartiainen T, Maran U, Strandberg M, Tamm T (1991) About the mutagenicity of chlorine-substituted furanones and halopropenals. A QSAR study using molecular orbital indices. Mutat Res Fundam Mol Mech Mutagen 247:97–102CrossRefGoogle Scholar
  47. Wagner ED, Plewa MJ (2017) CHO cell cytotoxicity and genotoxicity analyses of disinfection by-products: an updated review. J Environ Sci 58:64–76CrossRefGoogle Scholar
  48. Wagner ED, Osiol J, Mitch WA, Plewa MJ (2014) Comparative in vitro toxicity of nitrosamines and nitramines associated with amine-based carbon capture and storage. Environ Sci Technol 48:8203–8211CrossRefGoogle Scholar
  49. Wang W, Qian Y, Li J, Moe B, Huang R, Zhang H, Hrudey SE, Li X-F (2014) Analytical and toxicity characterization of halo-hydroxyl-benzoquinones as stable halobenzoquinone disinfection byproducts in treated water. Anal Chem 86:4982–4988CrossRefGoogle Scholar
  50. Yang M, Zhang X (2013) Comparative developmental toxicity of new aromatic halogenated DBPs in a chlorinated saline sewage effluent to the marine Polychaete Platynereis dumerilii. Environ Sci Technol 47:10868–10876CrossRefGoogle Scholar
  51. Zazouli MA, Kalankesh LR (2017) Removal of precursors and disinfection by-products (DBPs) by membrane filtration from water; a review. J Environ Health Sci Eng 15:10CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Li-Tang Qin
    • 1
    • 2
    • 3
  • Xin Zhang
    • 1
  • Yu-Han Chen
    • 1
  • Ling-Yun Mo
    • 1
    • 2
    • 3
    Email author
  • Hong-Hu Zeng
    • 1
    • 2
    • 3
  • Yan-Peng Liang
    • 1
    • 2
    • 3
  • Hua Lin
    • 1
    • 2
    • 3
    Email author
  • Dun-Qiu Wang
    • 1
    • 2
    • 3
  1. 1.College of Environmental Science and EngineeringGuilin University of TechnologyGuilinChina
  2. 2.Guangxi Key Laboratory of Environmental Pollution Control Theory and TechnologyGuilin University of TechnologyGuilinChina
  3. 3.Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst AreaGuilin University of TechnologyGuilinChina

Personalised recommendations