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Computational Approaches to Evaluate Ecotoxicity of Biocides: Cases from the Project COMBASE

  • Sergi Gómez-Ganau
  • Marco Marzo
  • Rafael GozalbesEmail author
  • Emilio Benfenati
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

The evaluation of the ecotoxicological profile of chemicals is of high relevance when a substance can have an impact on the environment, such as the case of biocides. Due to the high number of animal tests conducted each year for regulatory purposes and the ethical considerations that this entails, the requirement of alternative methods by companies and regulatory agencies is increasing. Within these, in silico tools are useful to minimize time, costs, and resources, and they can be applied as alternatives to traditional laboratory assays.

In this chapter, we present some computational models developed in the context of the EU LIFE+ project entitled “Computational tool for the assessment and substitution of biocidal active substances of ecotoxicological concern (COMBASE)” (http://www.life-combase.com). The main objective of the project was the development of a tool based on computational toxicology, integrating predictive models of the toxic effects associated with biocidal substances at different trophic levels. Here, different quantitative structure-activity relationship (QSAR) models for the estimation of ecotoxicity of biocides in microorganisms and fish are presented. First, an integrated model to predict the respiratory inhibition in activated sludge was developed, by combining sequentially a qualitative and a quantitative QSAR model. Previously to the development of the model, a set of 94 chemicals with known EC50 values was selected to this study, based on their “biocide-like” structural features. Second, a model to predict LC50 on rainbow trout was developed on a dataset made by collection data from OpenFoodTox database of the European Food Safety Authority (EFSA) and Pesticide Ecotoxicity Database of Office of Pesticide Programs (OPP) (https://ecotox.ipmcenters.org/).

Both models showed good performances and robustness and have been integrated in the VEGA last release (version 1.1.5; https://www.vegahub.eu/) as well as the specific COMBASE tool (http://webtool.life-combase.com).

Key words

Biocides QSAR Biocidal Products Regulation (BPR) Activated sludge Rainbow trout VEGA COMBASE 

References

  1. 1.
    Gheorghe S, Stoica C et al (2019) Ecotoxicity of biocides (chemical disinfectants) – lethal and sublethal effects on non-target organisms. Revista de Chimie (Bucharest) 70(1):307–312Google Scholar
  2. 2.
    ECHA (2014) Transitional Guidance on Regulation (EU) No 528/ 2012 of the European Parliament and of the Council of 22 May 2012 concerning the making available on the market and use of biocidal products (Biocidal Products Regulation, the BPR). European Chemicals Agency, Helsinki, Finland 2014Google Scholar
  3. 3.
    Guidance on the Biocidal Products Regulation Volume IV Environment – Assessment and Evaluation (Parts B + C) Version 2.0, October 2017Google Scholar
  4. 4.
    Myatt GJ, Ahlberg E et al (2018) In silico toxicology protocols. Regul Toxicol Pharmacol 96:1–17CrossRefGoogle Scholar
  5. 5.
    Gómez-Ganau S, De Julián-Ortiz JV, Gozalbes R (2018) Recent advances in computational approaches for designing potential anti-alzheimer’s agents. Springer Book “Computational modeling of drugs against Alzheimer’s disease”. Chapter 2, Pages 25–59 (Series: Neuromethods, Kunal Roy (ed.), Vol. 132, ISBN 978-1-4939-7404-7)Google Scholar
  6. 6.
    Gozalbes R, de Julián Ortiz JV (2018) Applications of chemoinformatics in predictive toxicology for regulatory purposes, especially in the context of the EU REACH legislation. Int J Quantitat Struct-Prop Relat 3(1):1–24CrossRefGoogle Scholar
  7. 7.
    Valerio LG Jr (2011) In silico toxicology models and databases as FDA critical path initiative toolkits. Hum Genomics 5(3):200–207.  https://doi.org/10.1186/1479-7364-5-3-200CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    The Organisation for Economic Co-operation and Development (OECD) (2007) Guidance document on the validation of (Quantitative)Structure-Activity Relationships [(Q)SAR] models. OECD Environment Health and Safety Publications. Retrieved from www.oecd.org/ehs/
  9. 9.
    Organization for Economic Cooperation and Development, Activated Sludge, Respiration Inhibition Test, OECD Chemicals Programme, Ecotoxicological Testing (1981)Google Scholar
  10. 10.
    Willighagen EL, Mayfield JW et al (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9(1):33CrossRefGoogle Scholar
  11. 11.
    Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO (2008) FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC Bioinformatics 9:396CrossRefGoogle Scholar
  12. 12.
    Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474CrossRefGoogle Scholar
  13. 13.
    Cherkasov A, Muratov EN et al (2014 Jun 26) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010CrossRefGoogle Scholar
  14. 14.
    SANCO/10597/2003 –rev. 10.1 (2012)Google Scholar
  15. 15.
    Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics. Wiley-VCH Verlag GmbH & Co. KGaAGoogle Scholar
  16. 16.
    StatSoft, Inc. (2007) STATISTICA (data analysis software system), version 8.0. http://www.statsoft.com
  17. 17.
    Cheng H, Garrick DJ, Fernando RL (2017) Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction. J Anim Sci Biotechnol 8:38.  https://doi.org/10.1186/s40104-017-0164-6. eCollection 2017. PubMed PMID: 28469846; PubMed Central PMCID: PMC5414316CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Dorne JL et al (2017) EFSA (European Food Safety Authority), 2017. OpenFoodTox: EFSA’s open source toxicological database on chemical hazards in food and feed. EFSA J 15(1):e15011. [3 pp.].  https://doi.org/10.2903/j.efsa.2017.e15011CrossRefGoogle Scholar
  19. 19.
    OECD 203. OECD (1992) Test no. 203: fish, acute toxicity test, OECD guidelines for the testing of chemicals, section 2. OECD Publishing, Paris.  https://doi.org/10.1787/9789264069961-enCrossRefGoogle Scholar
  20. 20.
    Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2007) KNIME: The Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds.) Data Analysis, Machine Learning and Applications – Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V (GfKL 2007), Studies in Classification, Data Analysis, and Knowledge Organization, Berlin, Germany, pp 319–326Google Scholar
  21. 21.
    Kode (2016) Kode srl, Dragon (software for molecular descriptor calculation) version 7.0.4. 2016, software available at: https://chm.kode-solutions.net

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Sergi Gómez-Ganau
    • 1
  • Marco Marzo
    • 2
  • Rafael Gozalbes
    • 1
    • 3
    Email author
  • Emilio Benfenati
    • 2
  1. 1.ProtoQSAR SL (www.protoqsar.com), Centro Europeo de Empresas Innovadoras (CEEI), Parque Tecnológico de ValenciaValenciaSpain
  2. 2.Istituto di Ricerche Farmacologiche Mario Negri IRCCSMilanoItaly
  3. 3.MolDrug AI Systems SLValenciaSpain

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