229
Views
11
CrossRef citations to date
0
Altmetric
18th International Conference on QSAR in Environmental and Health Sciences (QSAR 2018)

Classification models for identifying substances exhibiting acute contact toxicity in honeybees (Apis mellifera)$

, &
Pages 743-754 | Received 11 Jul 2018, Published online: 17 Sep 2018

References

  • EFSA, Scientific Opinion on the science behind the development of a risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees), EFSA J. 10 (5) (2012), p. 266.
  • EFSA, Harmonisation of human and ecological risk assessment of combined exposure to multiple chemicals, EFSA Supporting Publication 2015: EN-784, (2015), p. 39.
  • European regulation (EU) No 283/2013, Setting Out the Data Requirements for Active Substances, in Accordance with Regulation (EC) No. 1107/2009 of the European Parliament and of the Council Concerning the Placing of Plant Protection Products on the Market, 2013.
  • EFSA, Towards holistic approaches to the risk assessment of multiple stressors in bees. EFSA Supporting Publication, 2013: EN-509, (2013), p. 76.
  • F. Sanchez-Bayo and K. Goka, Pesticide residues and bees - a risk assessment. PLoS one 9/4 (2014), e94482. Available at http://dx.doi.org/10.1371/journal.pone.0094482.
  • EFSA, Modern methodologies and tools for human hazard assessment of chemicals, EFSA J. 12/4 (2014), 13. Available at http://dx.doi.org/10.2903/j.efsa.2014.3638, 3638.
  • E. Benfenati, E. Boriani, M. Craciun, L. Malazizi, D. Neagu, and A. Roncagioni, Databases for pesticide ecotoxicity, in Quantitative Structure–activity Relationships (QSAR) for Pesticide Regulatory Purposes, E. Benfenati, ed., Elsevier, Amsterdam, The Netherlands, 2007, pp. 59–81.
  • J. Devillers and J. Flatin, A general QSAR model for predicting the acute toxicity of pesticides to Oncorhynchus mykiss, SAR QSAR Environ. Res. 11 (2000), pp. 25–43.
  • A.A. Toropov, A.P. Toropova, M. Marzo, J.L. Dorne, N. Georgiadis, and E. Benfenati, QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database, Environ. Toxicol. Pharmacol. 53 (2017), pp. 158–163.
  • R. Croce, F. Cinà, A. Lombardo, G. Crispeyn, C.I. Cappelli, M. Vian, S. Maiorana, E. Benfenati, and D. Baderna, Aquatic toxicity of several textile dye formulations: Acute and chronic assays with Daphnia magna and Raphidocelis subcapitata, Ecotoxicol. Environ. Safety 144 (2017), pp. 79–87.
  • V. Drgan, Š. Župerl, M. Vračko, F. Como, and M. Novič, Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm, SAR QSAR in Environ. Res. 27 (2016), pp. 501–519.
  • F. Como, E. Carnesecchi, S. Volani, J.L. Dorne, J. Richardson, A. Bassan, M. Pavan, and E. Benfenati, Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model, Chemosphere 166 (2017), pp. 438–444.
  • A.A. Toropov, A.P. Toropova, F. Como, and E. Benfenati, Quantitative structure–activity relationship models for bee toxicity, Toxicol. Environ. Chem. 99 (2016), pp. 1117–1128.
  • K.P. Singh, S. Gupta, N. Basant, and D. Mohan, QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honeybee for regulatory purposes, Chem. Res. Toxicol. 27 (2014), pp. 1504–1515.
  • J. Devillers, M.H. Pham-Delègue, A. Decourtye, H. Budzinski, S. Cluzeau, and G. Maurin, Structure-toxicity modeling of pesticides to honeybees, SAR QSAR Environ. Res. 13 (2002), pp. 641−648.
  • J. Devillers, M.H. Pham-Delègue, A. Decourtye, H. Budzinski, and S. Cluzeau, Modeling the acute toxicity of pesticides to Apis mellifera, Bull. Insectol. 56 (2003), pp. 103−109.
  • J. Devillers, A. Decourtye, H. Budzinski, M.H. Pham-Delegue, S. Cluzeau, and G. Maurin, Comparative toxicity and hazards of pesticides to Apis and non-Apis bees. A chemometrical study, SAR QSAR Environ. Res. 14 (2003), pp. 389–403.
  • A.A. Toropov and E. Benfenati, SMILES as an alternative to the graph in QSAR modelling of bee toxicity, Comput. Biol. Chem. 31 (2007), pp. 57−60.
  • F.-X. Cheng, J. Shen, W.-H. Li, P.W. Lee, and T. Yun, In silico prediction of terrestrial and aquatic toxicities for organic chemicals, Chin. J. Pestic. Sci. 12 (2010), pp. 477−488.
  • OECD, Test No. 214: Honeybees, Acute Contact Toxicity Test, OECD Guidelines for the Testing of Chemicals, Section 2. OECD Publishing, Paris, 1998.
  • D. Weininger, SMILES, a chemical language and information system. Introduction to methodology and encoding rules, J. Chem. Inf. Comput. Sci. 28 (1998), pp. 26–31.
  • PPDB - Pesticide Properties Database. Available at http://sitem.herts.ac.uk/aeru/iupac/docs/Background_and_Support.pdf.
  • R. Martinčič, K. Venko, Š. Župerl, and M. Novič, Chemometrics approach for the prediction of structure–activity relationship for membrane transporter bilitranslocase, SAR QSAR Environ. Res. 25 (2014), pp. 853–872.
  • Dragon 7.0 – Software for molecular descriptors calculation, 2016; software available at https://chm.kode-solutions.net/products_dragon.php.
  • V. Drgan, Š. Župerl, M. Vračko, C.I. Cappelli, and M. Novič, CPANNatNIC software for counter-propagation neural network to assist in read-across, J. Cheminf. 9 (2017), e30.
  • N. Minovski, Š. Župerl, V. Drgan, and M. Novič, Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study, Anal. Chim. Acta 759 (2013), pp. 28–42.
  • D.M.W. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation, J. Machin. Learn. Technol. 2 (2011), pp. 37–63.
  • OECD, Guidance on the Principle of Measure of Goodness-of-Fit, Robustness and Predictivity, Guideline no. ENV/JM/MONO 2, Chapter 5, Paris, France, 2007, pp. 42−65.
  • N. Fjodorova, M. Vračko, M. Novič, A. Roncaglioni, and E. Benfenati, New public QSAR models for carcinogenicity, Chem. Cent. J. 4 (2010), pp. 1−15.
  • M. Marzo, S. Kulkarni, A. Manganaro, A. Roncaglioni, S. Wu, T.S. Barton-Mclaren, C. Lester, and E. Benfenati, Integrating in silico models to enhance predictivity for developmental toxicity, Toxicology 370 (2016), pp. 127–137.
  • OPP Pesticide Ecotoxicity Database, US Environmental Protection Agency. Available at http://www.ipmcenters.org/ecotox/DataAccess.cfm.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.