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Research Article

QSAR assessment of aquatic toxicity potential of diverse agrochemicals

, &
Pages 923-942 | Received 23 Aug 2023, Accepted 24 Oct 2023, Published online: 09 Nov 2023

References

  • S. Bin and H. Dowlatabadi, Consumer lifestyle approach to US energy use and the related CO2 emissions, Energy Policy 33 (2005), pp. 197–208. doi:10.1016/S0301-4215(03)00210-6.
  • A. Bharadwaj, D. Yadav, and S. Varshney, Non-biodegradable waste–its impact & safe disposal, Int. J. Adv. Technol. Eng. Sci. 3 (2015), pp. 184–191.
  • F. Corradini, P. Meza, R. Eguiluz, F. Casado, E. Huerta-Lwanga, and V. Geissen, Evidence of microplastic accumulation in agricultural soils from sewage sludge disposal, Sci. Total Environ. 671 (2019), pp. 411–420. doi:10.1016/j.scitotenv.2019.03.368.
  • D.W. Kweku, O. Bismark, A. Maxwell, K.A. Desmond, K.B. Danso, E.A. Oti-Mensah, A.T. Quachie, and B.B. Adormaa, Greenhouse effect: Greenhouse gases and their impact on global warming, J. Sci. Res. Rep. 17 (2018), pp. 1–9. doi:10.9734/JSRR/2017/39630.
  • X. Zhu, W. Liu, J. Chen, L.A. Bruijnzeel, Z. Mao, X. Yang, R. Cardinael, F.R. Meng, R.C. Sidle, S. Seitz, V.D. Nair, K. Nanko, X. Zou, C. Chen, and X.J. Jiang, Reductions in water, soil and nutrient losses and pesticide pollution in agroforestry practices: A review of evidence and processes, Plant Soil 453 (2020), pp. 45–86. doi:10.1007/s11104-019-04377-3.
  • A.O. Aptula and D.W. Roberts, Mechanistic applicability domains for nonanimal-based prediction of toxicological end points: General principles and application to reactive toxicity, Chem. Res. Toxicol. 19 (2006), pp. 1097–1105. doi:10.1021/tx0601004.
  • M.T. Cronin, J.S. Jaworska, J.D. Walker, M.H. Comber, C.D. Watts, and A.P. Worth, Use of QSARs in international decision-making frameworks to predict health effects of chemical substances, Environ. Health Perspect. 111 (2003), pp. 1391–1401. doi:10.1289/ehp.5760.
  • S.A. Saghir, M.J. Bartels, D.L. Rick, A.T. McCoy, R.J. Rasoulpour, R.G. Ellis-Hutchings, M.S. Marty, C. Terry, J.P. Bailey, R. Billington, and J.S. Bus, Assessment of diurnal systemic dose of agrochemicals in regulatory toxicity testing–an integrated approach without additional animal use, Regul. Toxicol. Pharmacol. 63 (2012), pp. 321–332. doi:10.1016/j.yrtph.2012.03.004.
  • D. Ebert, Ecology, Epidemiology, and Evolution of Parasitism in Daphnia, National Center for Biotechnology, Bethesda, MD, 2005.
  • W. Gu, X. Li, M. Du, Z. Ren, Q. Li, and Y. Li, Identification and regulation of ecotoxicity of polychlorinated naphthalenes to aquatic food Chain (green algae-Daphnia magna-fish), Aquat. Toxicol. 233 (2021), pp. 105774. doi:10.1016/j.aquatox.2021.105774.
  • A. Nath, P.K. Ojha, and K. Roy, Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across, Aquat. Toxicol. 257 (2023), pp. 106429. doi:10.1016/j.aquatox.2023.106429.
  • K.S. Sidhu, Health benefits and potential risks related to consumption of fish or fish oil, Regul. Toxicol. Pharmacol. 38 (2003), pp. 336–344. doi:10.1016/j.yrtph.2003.07.002.
  • J.H. Thorp and D.C. Rogers (Eds.), Field Guide to Freshwater Invertebrates of North America, Academic Press, London, UK, 2010.
  • S. Martinez-Morcillo, J.L. Rodríguez-Gil, J. Fernández-Rubio, S. Rodriguez-Mozaz, M.P. Míguez-Santiyán, M.E. Valdes, D. Barceló, and Y. Valcárcel, Presence of pharmaceutical compounds, levels of biochemical biomarkers in seafood tissues and risk assessment for human health: Results from a case study in North-Western Spain, Int. J. Hyg. Environ. Health 223 (2020), pp. 10–21. doi:10.1016/j.ijheh.2019.10.011.
  • A.G. Tacon, D. Lemos, and M. Metian, Fish for health: Improved nutritional quality of cultured fish for human consumption, Rev. Fish. Sci. Aquac. 28 (2020), pp. 449–458. doi:10.1080/23308249.2020.1762163.
  • A. Bownik, Physiological endpoints in daphnid acute toxicity tests, Sci. Total Environ. 700 (2020), pp. 134400. doi:10.1016/j.scitotenv.2019.134400.
  • A. Villegas-Navarro, E. Rosas-L, and J.L. Reyes, The heart of Daphnia magna: Effects of four cardioactive drugs, Comp. Biochem. Physiol. C Toxicol. Pharmacol. 136 (2003), pp. 127–134. doi:10.1016/S1532-0456(03)00172-8.
  • D. Ebert, A genome for the environment, Science 331 (2011), pp. 539–540. doi:10.1126/science.1202092.
  • OECD, Test No. 202: Daphnia sp. Acute Immobilisation Test, OECD Guidelines for the Testing of Chemicals, Section 2, OECD Publishing, Paris, 2004. doi:10.1787/9789264069947-en.
  • OECD, Test No. 211: Daphnia magna Reproduction Test, OECD Publishing, Paris, 2008. doi:10.1787/9789264070127-en.
  • A. Lombardo, A. Roncaglioni, E. Benfenati, M. Nendza, H. Segner, S. Jeram, E. Pauné, and G. Schüürmann, Optimizing the aquatic toxicity assessment under REACH through an integrated testing strategy (ITS), Environ. Res. 135 (2014), pp. 156–164. doi:10.1016/j.envres.2014.09.002.
  • European Union (EU), Directive 2010/63/EU of the European Parliament and of the Council on the Protection of Animals Used for Scientific Purposes, (2010). Available at https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32010L0063&from=EN. (accessed March, 2023).
  • H. Sanderson and K. Solomon, Contaminants of emerging concern challenge ecotoxicology, Environ. Toxicol. Chem. 28 (2009), pp. 1359. doi:10.1897/09-119.1.
  • ECHA, The Use Of Alternatives To Testing On Animals For The REACH Regulation, 2020. Available at https://echa.europa.eu/documents/10162/0/alternatives_test_animals_2020_en.pdf. (accessed March, 2023).
  • CEPA, Background Paper: Evolution Of The Existing Substances Risk Assessment Program Under The Canadian Environmental Protection Act, (1999). Available at https://www.canada.ca/en/health-canada/services/chemical-substances/chemicals-management-plan/science-committee/meeting-records-reports/background-paper-evolution-existing-substances-risk-assessment-program-canadian-environmental-protection-act-1999.html#shr-pg0. (accessed March, 2023).
  • US TSCA, Toxic Substances Control Act, 1976. Available at https://www.govinfo.gov/content/pkg/COMPS-895/pdf/COMPS-895.pdf. (accessed March, 2023).
  • P. Gramatica, S. Cassani, and A. Sangion, Aquatic ecotoxicity of personal care products: QSAR models and ranking for prioritization and safer alternatives’ design, Green Chem. 18 (2016), pp. 4393–4406. doi:10.1039/C5GC02818C.
  • O. Nicolotti, E. Benfenati, A. Carotti, D. Gadaleta, A. Gissi, G.F. Mangiatordi, and E. Novellino, REACH and in silico methods: An attractive opportunity for medicinal chemists, Drug Discov. Today 19 (2014), pp. 1757–1768. doi:10.1016/j.drudis.2014.06.027.
  • M. Zeeman, C.M. Auer, R.G. Clements, J.V. Nabholz, and R.S. Boethling, US EPA regulatory perspectives on the use of QSAR for new and existing chemical evaluations, SAR QSAR Environ. Res. 3 (1995), pp. 179–201. doi:10.1080/10629369508234003.
  • J.C. Dearden, The history and development of quantitative structure-activity relationships (QSARs), in Oncology: Breakthroughs in Research and Practice, Intern. J. Quant. Struct.-Prop. Relat. 1 (2017), pp. 1–44.
  • K. Roy (Ed.), Chemometrics and Cheminformatics in Aquatic Toxicology, John Wiley & Sons Inc, Hoboken, NJ, 2022.
  • OECD, OECD Principles For The Validation, For Regulatory Purposes, Of (Quantitative) Structure-Activity Relationship Models, 2004. Available at https://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf. (accessed March, 2023).
  • L.S. McCarty, The relationship between aquatic toxicity QSARs and bioconcentration for some organic chemicals, Environ. Toxicol. Chem. 5 (1986), pp. 1071–1080. doi:10.1002/etc.5620051207.
  • US EPA, KABAM Version 1.0 User’s Guide And Technical Documentation - Appendix F -Description Of Equations Used To Calculate The BCF, BAF, BMF, And BSAF Values, 2009. Available at https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/kabam-version-10-users-guide-and-technical-3#:~:text=Bioconcentration%20factors%20(BCFs)%20are%20calculated,which%20the%20pesticide%20was%20taken. (accessed March, 2023).
  • M. Vighi, M.M. Garlanda, and D. Calamari, QSARs for toxicity of organophosphorous pesticides to Daphnia and honeybees, Sci. Total Environ. 109 (1991), pp. 605–622. doi:10.1016/0048-9697(91)90213-X.
  • E. Zvinavashe, T. Du, T. Griff, H.H. van den Berg, A.E. Soffers, J. Vervoort, A.J. Murk, and I.M. Rietjens, Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio, Chemosphere 75 (2009), pp. 1531–1538. doi:10.1016/j.chemosphere.2009.01.081.
  • W.X. Wang, Bioaccumulation and biomonitoring, in Marine Ecotoxicology, J.Blasco (Eds.), Academic Press (Elsevier), London, UK, 2016, pp. 99–119.
  • H.J. Geyer, I. Scheunert, R. Brüggemann, C. Steinberg, F. Korte, and A. Kettrup, QSAR for organic chemical bioconcentration in Daphnia, algae, and mussels, Sci. Total Environ. 109 (1991), pp. 387–394. doi:10.1016/0048-9697(91)90193-I.
  • L.S. McCarty and D. Mackay, Enhancing ecotoxicological modeling and assessment. Body residues and modes of toxic action, Environ. Sci. Technol. 27 (1993), pp. 1718–1728. doi:10.1021/es00046a001.
  • Y. Wen, L. Su, W. Qin, Y. Zhao, J.C. Madden, F.P. Steinmetz, and M.T. Cronin, Investigation of critical body residues and modes of toxic action based on injection and aquatic exposure in fish, Wat. Air Soil Pollut. 226 (2015), pp. 1–11. doi:10.1007/s11270-015-2427-1.
  • J. Wang, Y. Yang, Y. Huang, X. Zhang, Y. Huang, W.C. Qin, Y. Wen, and Y.H. Zhao, Evaluation of modes of action of pesticides to Daphnia magna based on QSAR, excess toxicity and critical body residues, Ecotoxicol. Environ. Saf. 203 (2020), pp. 111046. doi:10.1016/j.ecoenv.2020.111046.
  • R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Vol. 11, John Wiley & Sons, Weinheim, Germany, 2008.
  • A. Mauri, alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints, in Ecotoxicological QSARs, Journal of Pharmacological and Toxicological Methods, K. Roy, ed., Humana, New York, 2020. pp. 801–820.
  • T.M. Martin, P. Harten, D.M. Young, E.N. Muratov, A. Golbraikh, H. Zhu, and A. Tropsha, Does rational selection of training and test sets improve the outcome of QSAR modeling? J. Chem. Inf. Model. 52 (2012), pp. 2570–2578. doi:10.1021/ci300338w.
  • K. Roy, S. Kar, and R.N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press (Elsevier), London, 2015.
  • A.K. Saxena and P. Prathipati, Comparison of MLR, PLS and GA-MLR in QSAR analysis, SAR QSAR Environ. Res. 14 (2003), pp. 433–445. doi:10.1080/10629360310001624015.
  • J. Cai, J. Luo, S. Wang, and S. Yang, Feature selection in machine learning: A new perspective, Neurocomputing 300 (2018), pp. 70–79. doi:10.1016/j.neucom.2017.11.077.
  • J. Devillers, Genetic Algorithms in Molecular Modeling, Academic Press, London, UK, 1996.
  • M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA, 1996.
  • K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemometr. Intell. Lab. Syst. 152 (2016), pp. 18–33. doi:10.1016/j.chemolab.2016.01.008.
  • S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: A basic tool of chemometrics, Chemometr. Intell. Lab. Syst. 58 (2001), pp. 109–130. doi:10.1016/S0169-7439(01)00155-1.
  • K. Roy and I. Mitra, On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design, Comb. Chem. High Throughput Screening. 14 (2011), pp. 450–474. doi:10.2174/138620711795767893.
  • Z. Wu, D. Li, J. Meng, and H. Wang, Introduction to SIMCA-P and its application, in Handbook of Partial Least Squares: Concepts, Methods and Applications, V. Esposito Vinzi, W. Chin, J. Henseler and H. Wang, eds., Springer Handbooks of Computational Statistics, Springer, Berlin, Heidelber, 2010, pp. 757–774.
  • A. Nath and K. Roy, Chemometric modeling of acute toxicity of diverse aromatic compounds against Rana japonica, Toxicol. Vitro 83 (2022), pp. 105427. doi:10.1016/j.tiv.2022.105427.
  • K. Khan, P.M. Khan, G. Lavado, C. Valsecchi, J. Pasqualini, D. Baderna, M. Marzo, A. Lombardo, K. Roy, and E. Benfenati, QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors, Chemosphere. 229 (2019), pp. 8–17. doi:10.1016/j.chemosphere.2019.04.204.
  • E. Estrada, Physicochemical interpretation of molecular connectivity indices, J. Phys. Chem. A. 106 (2002), pp. 9085–9091. doi:10.1021/jp026238m.
  • L.B. Kier and L.H. Hall, Molecular connectivity VII: Specific treatment of heteroatoms, J. Pharm. Sci. 65 (1976), pp. 1806–1809. doi:10.1002/jps.2600651228.
  • L.H. Hall and L.B. Kier, Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information, J. Chem. Inf. Comput. 35 (1995), pp. 1039–1045. doi:10.1021/ci00028a014.
  • L.M. Saavedra and P.R. Duchowicz, Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach, Sci. Total Environ. 796 (2021), pp. 148820. doi:10.1016/j.scitotenv.2021.148820.

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