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Research Article

Fish early life stage toxicity prediction from acute daphnid toxicity and quantum chemistry

ORCID Icon, ORCID Icon, &
Pages 151-174 | Received 06 Nov 2020, Accepted 07 Jan 2021, Published online: 02 Feb 2021

References

  • Test No. 210: Fish, early-life stage toxicity test. OECD Guidelines for the Testing of Chemicals, Section 2, OECD Publishing, Paris, 2013.
  • N. Choudhury, Ecotoxicology of aquatic system: A review on fungicide induced toxicity in fishes, Pro. Aqua Farm. Marine Biol. 1 (2018), pp. 180001–180005.
  • K.R. Solomon, K. Dalhoff, D. Volz, and G. van der Kraak, Effects of herbicides on fish, in Fish Physiology, K.B. Tierney, A.P. Farrell, and C.J. Brauner, eds., Academic Press, London, 2013, pp. 369–409.
  • M.H. Fulton, P.B. Key, and M.E. DeLorenzo, Insecticide toxicity in fish, in Fish Physiology, K.B. Tierney, A.P. Farrell, and C.J. Brauner, eds., Academic Press, London, 2013, pp. 309–368.
  • L. Gunnarsson, J.R. Snape, B. Verbruggen, S.F. Owen, E. Kristiansson, L. Margiotta-Casaluci, T. Österlund, K. Hutchinson, D. Leverett, B. Marks, and C. Tyler, Pharmacology beyond the patient – The environmental risks of human drugs, Environ. Int. 129 (2019), pp. 320–332. doi:10.1016/j.envint.2019.04.075.
  • S. Ullah and J. Zorriehzahra, Ecotoxicology: A review of pesticides induced toxicity in fish, Adv. Anim. Vet. Sci. 3 (2014), pp. 40–57. doi:10.14737/journal.aavs/2015/3.1.40.57.
  • M. Halder, A. Kienzler, M. Whelan, and A. Worth, EURL ECVAM Strategy to Replace, Reduce and Refine the Use of Fish in Aquatic Toxicity and Bioaccumulation Testing, Publications Office of the European Union, Luxembourg, 2014.
  • M. May, W. Drost, S. Germer, T. Juffernholz, and S. Hahn, Evaluation of acute-to-chronic ratios of fish and Daphnia to predict acceptable no-effect levels, Environ. Sci. Eur. 28 (2016), pp. 16. doi:10.1186/s12302-016-0084-7.
  • A. Kienzler, M. Halder, and A. Worth, Waiving chronic fish tests: Possible use of acute-to-chronic relationships and interspecies correlations, Toxicol. Environ. Chem. 99 (2017), pp. 1129–1151. doi:10.1080/02772248.2016.1246663.
  • S. Scholz, R. Schreiber, J. Armitage, P. Mayer, B.I. Escher, A. Lidzba, M. Léonard, and R. Altenburger, Meta-analysis of fish early life stage tests—Association of toxic ratios and acute-to-chronic ratios with modes of action, Environ. Toxicol. Chem. 37 (2018), pp. 955–969. doi:10.1002/etc.4090.
  • K. Mayo-Bean, K. Moran, B. Meylan, and P. Ranslow, Ecological Structure - Activity Relationships Program (ECOSAR) Methodology Document V2.0, US Environmental Protection Agency, Washington, DC, USA, 2017.
  • E.M. de Haas, T. Eikelboom, and T. Bouwman, Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSARTM, SAR QSAR Environ. Res. 22 (2011), pp. 545–559. doi:10.1080/1062936X.2011.569949.
  • L. Claeys, F. Iaccino, C.R. Janssen, P. van Sprang, and F. Verdonck, Development and validation of a quantitative structure–activity relationship for chronic narcosis to fish, Environ. Toxicol. Chem. 32 (2013), pp. 2217–2225. doi:10.1002/etc.2301.
  • T.J. Austin and C.V. Eadsforth, Development of a chronic fish toxicity model for predicting sub-lethal NOEC values for non-polar narcotics, SAR QSAR Environ. Res. 25 (2014), pp. 147–160. doi:10.1080/1062936X.2013.871577.
  • A. Furuhama, T.I. Hayashi, and H. Yamamoto, Development of models to predict fish early-life stage toxicity from acute Daphnia magna toxicity, SAR QSAR Environ. Res. 29 (2018), pp. 725–742. doi:10.1080/1062936X.2018.1513423.
  • I. Halleux, N. Bornatowicz, B. Grillitsch, K. Delbeke, C. Janssen, G. Atkinson, P. Delorme, D. Moore, C. Hansen, H. Holst, G. Akkerhuis, N. Nyholm, H. Braunschweiler, J. Férard, E. Vindimian, A. Lange, S. Martin, T. Ratte, M. Streloke, S. Marchini, J. Badeaux, R. Bogers, C. Leeuwen, E. Spikkerud, E. Moliner, B. Dahl, L. Lindqvist, R. Fisch, M. Crane, J. Fenlon, A. Riddle, T. Sparks, R. Clements, D. Farrar, M. Newman, M. Harras, R. Maisch, J. Jaworska, R. Egmond, K. Steward, L. Tattersfield, K. Romijn, P. Isnard, G. Joermann, B. Kooijman, J.T. Meister, L. Touart, P. Chapman, S. Park, N. Grandy, and M. Huet, Report of the OECD Workshop on Statistical Analysis of Aquatic Toxicity Data, 1998.
  • D.R. Fox, E. Billoir, S. Charles, M.L. Delignette-Muller, and C. Lopes, What to do with NOECS/NOELS—prohibition or innovation? Integr. Environ. Assess. Manag. 8 (2012), pp. 764–766. doi:10.1002/ieam.1350.
  • EFSA - European Food Safety Authority, Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge‐of‐field surface waters, Efsa J. 11 (2013), pp. 1–268.
  • ECHA - European Chemicals Agency, Guidance on information requirements and chemical safety assessment (Chapter R.11: PBT/vPvB assessment), 2017.
  • A. Rácz, D. Bajusz, and K. Héberger, Modelling methods and cross-validation variants in QSAR: A multi-level analysis, SAR QSAR Environ. Res. 29 (2018), pp. 661–674. doi:10.1080/1062936X.2018.1505778.
  • J.A. Wegelin, A survey of partial least squares (PLS) methods, with emphasis on the two-block case, Technical Report No. 371, University of Washington, USA, 2000.
  • M. Fernández-Delgado, M.S. Sirsat, E. Cernadas, S. Alawadi, S. Barro, and M. Febrero-Bande, An extensive experimental survey of regression methods, Neural Netw. 111 (2019), pp. 11–34. doi:10.1016/j.neunet.2018.12.010.
  • A. Bassan, L. Ceriani, J. Richardson, A. Livaniou, A. Ciacci, R. Baldin, S. Kovarich, E. Fioravanzo, M. Pavan, D. Gibin, G. Di Piazza, L. Pasinato, S. Cappé, H. Verhagen, T. Robinson, and J. Dorne, OpenFoodTox: EFSA’s chemical hazards database, Version 2, 2018. doi:10.5281/zenodo.1252752.
  • S.D. Dimitrov, R. Diderich, T. Sobanski, T.S. Pavlov, G.V. Chankov, A.S. Chapkanov, Y.H. Karakolev, S.G. Temelkov, R.A. Vasilev, K.D. Gerova, C.D. Kuseva, N.D. Todorova, A.M. Mehmed, M. Rasenberg, and O.G. Mekenyan, QSAR Toolbox – Workflow and major functionalities, SAR QSAR Environ. Res. 27 (2016), pp. 203–219. doi:10.1080/1062936X.2015.1136680.
  • S.E. Belanger, J.M. Rawlings, and G.J. Carr, Use of fish embryo toxicity tests for the prediction of acute fish toxicity to chemicals, Environ. Toxicol. Chem. 32 (2013), pp. 1768–1783. doi:10.1002/etc.2244.
  • RDKit: Open-source cheminformatics; http://www.rdkit.org. 2020.
  • Schrödinger Release 2019-1: LigPrep and MacroModel, Schrödinger, LLC, New York, NY, 2019.
  • TURBOMOLE V7.0.1. A development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989-2007, TURBOMOLE GmbH, since 2007, 2016. available from: http://www.turbomole.com.
  • F. Eckert and A. Klamt, Fast solvent screening via quantum chemistry: COSMO-RS approach, AIChE J. 48 (2002), pp. 369–385. doi:10.1002/aic.690480220.
  • F. Eckert and A. Klamt, COSMOtherm, Version C3.0, Release 16.01, COSMOlogic GmbH & Co KG, Leverkusen, Germany, 2015.
  • J. Mayfield, E. Willighagen, R. Guha, G. Torrance, K. Ujihara, S.A. Rahman, J. Alvarsson, M.B. Vine, S. Grazulis, T. Pluskal, Y.C. Wei, D. Szisz, M.J. Williamson, N. Kochev, N. Jeliazkova, E. Bach, A. Berg, A. Clark, R. Stephan, M. Wenk ficolas2, O. Stueker, K. Dole, K. Jönsson kaibioinfo, L. Burgoon, D. Katsubo, A. Howlett, U. Köhler, and C. Harmon, CDK 2.3, 2019.
  • M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, and B. Wiswedel, KNIME: The konstanz information miner, in Studies in Classification, Data Analysis, and Knowledge Organization (GfKL), 2007.
  • Sybyl Version X 2.1 – Discovery Software for Computational Chemistry and Molecular Modelling, including UNITY fingerprint tools. distributed by Tripos Inc., St. Louis, MO, USA, 2004.
  • Dragon v7.0: Software for molecular descriptor calculation. Kode Chemoinformatics, 2017.
  • C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
  • A. Mauri, alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints, in Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, K. Roy, ed., Humana, New York, NY, 2020, pp. 801–820.
  • M. Ashton, J. Barnard, F. Casset, M. Charlton, G. Downs, D. Gorse, J. Holliday, R. Lahana, and P. Willett, Identification of diverse database subsets using property-based and fragment-based molecular descriptions, Quant. Struct-Act. Rel. 21 (2002), pp. 598–604. doi:10.1002/qsar.200290002.
  • S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: A basic tool of chemometrics, Chemom. Intell. Lab. Syst. 58 (2001), pp. 109–130. doi:10.1016/S0169-7439(01)00155-1.
  • SIMCA 16, Sartorius Stedim Data Analytics AB, Umeå, Sweden, 2020. www.sartorius.com/umetrics.
  • K.-A. Lê Cao, D. Rossouw, C. Robert-Granié, and P. Besse, A sparse PLS for variable selection when integrating omics data, Stat. Appl. Genet. Mol. Biol. 7 (2008), pp. 1–29. doi:10.2202/1544-6115.1390.
  • K.-A. Lê Cao, F. Rohart, I. Gonzalez, S. Dejean, B. Gautier, F. Bartolo, P. Monget, J. Coquery, F. Yao, and B. Liquet, MixOmics: Omics data integration project. R Package Version 6.1.1., 2016. https://CRAN.R-project.org/package=mixOmics.
  • A.K. Ghose and G.M. Crippen, Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity, J. Comput. Chem. 7 (1986), pp. 565–577. doi:10.1002/jcc.540070419.
  • V.N. Viswanadhan, A.K. Ghose, G.R. Revankar, and R.K. Robins, Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics, J. Chem. Inform. Comput. Sci. 29 (1989), pp. 163–172.
  • A. Gissi, A. Lombardo, A. Roncaglioni, D. Gadaleta, G.F. Mangiatordi, O. Nicolotti, and E. Benfenati, Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF), Environ. Res. 137 (2015), pp. 398–409. doi:10.1016/j.envres.2014.12.019.
  • K. Wichmann, M. Diedenhofen, and A. Klamt, Prediction of blood-βrain partitioning and human serum albumin binding based on COSMO-RS σ-Moments, J. Chem. Inf. Model. 47 (2007), pp. 228–233. doi:10.1021/ci600385w.
  • W.P. Walters and M.A. Murcko, Prediction of ‘drug-likeness, Adv. Drug Deliv. Rev. 54 (2002), pp. 255–271. doi:10.1016/S0169-409X(02)00003-0.
  • N. Krämer and M. Sugiyama, The degrees of freedom of partial least squares regression, J. Am. Stat. Assoc. 106 (2011), pp. 697–705. doi:10.1198/jasa.2011.tm10107.
  • W.H. van der Schalie, The Toxicity of Nitroguanidine and Photolyzed Nitroguanidine to Freshwater Aquatic Organisms, Army Medical Bioengineering Research and Development Lab Fort Detrick, Frederick, Maryland, 1985.
  • REACH registration dossier for 1-nitroguanidine, registration number 01-2119489412-35-0002, 2020. accessed 24 May 2020. https://echa.europa.eu/registration-dossier/-/registered-dossier/13550/6/2/4/?documentUUID=2c157e51-4cba-4bcf-81f5-7b48becf2c11.
  • T.J. Thrupp, T.J. Runnalls, M. Scholze, S. Kugathas, A. Kortenkamp, and J.P. Sumpter, The consequences of exposure to mixtures of chemicals: Something from ‘nothing’ and ‘a lot from a little’ when fish are exposed to steroid hormones, Sci. Total Environ. 619–620 (2018), pp. 1482–1492. doi:10.1016/j.scitotenv.2017.11.081.
  • R.L. Clubbs and B.W. Brooks, Daphnia magna responses to a vertebrate estrogen receptor agonist and an antagonist: A multigenerational study, Ecotoxicol. Environ. Saf. 67 (2007), pp. 385–398. doi:10.1016/j.ecoenv.2007.01.009.
  • Beta-Estradiol EQS Dossier, 2011. https://circabc.europa.eu/sd/a/c5356fa7-be0e-4d3b-b199-208d6e144a91/E2%20EQS%20dossier%202011.pdf
  • L.J.P. van der Maaten and G.E. Hinton, Visualizing high-dimensional data using t-SNE, J. Mach. Learn. Res. 9 (2008), pp. 2579–2605.
  • C. Yang, A. Tarkhov, J. Marusczyk, B. Bienfait, J. Gasteiger, T. Kleinoeder, T. Magdziarz, O. Sacher, C. Schwab, J. Schwoebel, L. Terfloth, K. Arvidson, A. Richard, A. Worth, and J. Rathman, New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling, J. Chem. Inf. Model. 55 (2015), pp. 510–528. doi:10.1021/ci500667v.