QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?

References

• H.J. Geyer, G.G. Rimkus, I. Scheunert, A. Kaune, K.-W. Schramm, A. Kettrup, M. Zeeman, D.C. Muir, L.G. Hansen, and D. Mackay, Bioaccumulation and occurrence of endocrine-disrupting chemicals (EDCs), persistent organic pollutants (POPs), and other organic compounds in fish and other organisms including humans, in Bioaccumulation–New Aspects and Developments, B. Beek, ed., Springer Publisher, Berlin, 2000, pp. 1–166.
   Google Scholar
• European Commission, Technical guidance document in support of commission directive 93/67/EEC on risk assessment for new notified substances and commission regulation (EC) No 1488/94 on risk assessment for existing substances, Tech. Rep. EUR 20418 EN/2, Institute for Health and Consumer Protection, Joint Research Centre, Ispra, IT, 2003.
   Google Scholar
• OECD, Test No. 305: Bioaccumulation in fish: Aqueous and dietary exposure, Tech Rep. 9264185291, Organisation for Economic Co-operation Development, Paris, FR, 2012.
   Google Scholar
• ECHA, Guidance on information requirements and chemical safety assessment, r.11: PBT/vPvB assessment, Tech. Rep. ED-01-17-294, European Chemicals Agency, Helsinki, FI, 2017.
   Google Scholar
• European Commission, Regulation (EC) no 1907/2006 of the european parliament and of the council of 18 december 2006 concerning the registration, evaluation, authorisation and restriction of chemicals (REACH), establishing a european chemicals agency, amending directive 1999/45/ECC and repealing council regulation (EEC) No 793/93 and commission regulation (EC) No 1488/94 as well as council directive 76/769/EEC and commission directives 91/155/EEC, 93/67/EEC, 93/105/EEC and 2000/21/EC, Off. J. Eur. Union. 50 (2007), pp. 1–281.
   Google Scholar
• J.A. Arnot and F.A.P.C. Gobas, A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms, Environ. Rev. 14 (2006), pp. 257–297. doi:10.1139/a06-005.
   Google Scholar
• M. Pavan, T.I. Netzeva, and W. Andrew, Review of literature-based quantitative structure - activity relationship models for bioconcentration, QSAR Comb. Sci. 27 (2008), pp. 21–31. doi:10.1002/qsar.200710102.
   Google Scholar
• J.C. Dearden and M. Hewitt, QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors, SAR QSAR Environ. Res. 21 (2010), pp. 671–680. doi:10.1080/1062936X.2010.528235.
   PubMed Web of Science ®Google Scholar
• M. Nendza and M. Müller, Screening for low aquatic bioaccumulation (1): Lipinski’s ‘rule of 5ʹ and molecular size, SAR QSAR Environ. Res. 21 (2010), pp. 495–512. doi:10.1080/1062936X.2010.502295.
   PubMed Web of Science ®Google Scholar
• J.F. Aranda, D.E. Bacelo, M.S. Leguizamón Aparicio, M.A. Ocsachoque, E.A. Castro, and P.R. Duchowicz, Predicting the bioconcentration factor through a conformation-independent QSPR study, SAR QSAR Environ. Res. 28 (2017), pp. 749–763. doi:10.1080/1062936X.2017.1377765.
   PubMed Web of Science ®Google Scholar
• K. Mansouri, C.M. Grulke, R.S. Judson, and A.J. Williams, OPERA models for predicting physicochemical properties and environmental fate endpoints, J. Cheminformatics 10 (2018), pp. 1–19. doi:10.1186/s13321-018-0263-1.
   PubMed Web of Science ®Google Scholar
• E. Benfenati, A. Manganaro, and G. Gini, VEGA-QSAR: AI inside a platform for predictive toxicology, Proceedings of the workshop ‘Popularize Artificial Intelligence 2013ʹ, December 5th 2013, Turin, Italy, 2013, published in CEUR Workshop Proceedings Vol 1107, pp. 21–28.
   Google Scholar
• T. Martin, P. Harten, and D. Young, TEST (Toxicity Estimation Software Tool) V 4.1, US Environmental Protection Agency, Washington DC, USA, 2012; software available at https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test.
   Google Scholar
• US EPA, Estimation Programs Interface Suite™ for Microsoft® Windows V 4.11, US Environmental Protection Agency, Washington DC, USA, 2012; software available at https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface.
   Google Scholar
• UFZ, ChemProp V 6.7, Helmholtz Centre for Environmental Research-UFZ, Leipzig, DE, 2018; software available at http://www.ufz.de/ecochem/chemprop.
   Google Scholar
• A.P. Toropova, A.A. Toropov, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, Coral: Quantitative models for estimating bioconcentration factor of organic compounds, Chemometr. Intell. Lab. 118 (2012), pp. 70–73. doi:10.1016/j.chemolab.2012.08.002.
   Web of Science ®Google Scholar
• ACD/Labs, ACD/logD V 2018.1, Advanced Chemistry Development, Inc. (ACD/Labs), Toronto, CA, 2000; software available at https://www.acdlabs.com.
   Google Scholar
• Catalogic: Environmental Fate and Ecotoxicity Models V 5. 11.13, OASIS Laboratory of Mathematical Chemistry, Burgas, BG, 2013; software available at http://oasis-lmc.org/products/software/catalogic.aspx.
   Google Scholar
• M.I. Petoumenou, F. Pizzo, J. Cester, A. Fernández, and E. Benfenati, Comparison between bioconcentration factor (BCF) data provided by industry to the european chemicals agency (ECHA) and data derived from QSAR models, Environ. Res. 142 (2015), pp. 529–534. doi:10.1016/j.envres.2015.08.008.
   PubMed Web of Science ®Google Scholar
• ECHA Homepage, European Chemicals Agency, Helsinki, FI, 2019. Available at https://echa.europa.eu/.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• F. Grisoni, V. Consonni, S. Villa, M. Vighi, and R. Todeschini, QSAR models for bioconcentration: Is the increase in the complexity justified by more accurate predictions? Chemosphere 127 (2015), pp. 171–179. doi:10.1016/j.chemosphere.2015.01.047.
   PubMed Web of Science ®Google Scholar
• F. Grisoni, V. Consonni, M. Vighi, S. Villa, and R. Todeschini, Expert QSAR system for predicting the bioconcentration factor under the reach regulation, Environ. Res. 148 (2016), pp. 507–512. doi:10.1016/j.envres.2016.04.032.
   PubMed Web of Science ®Google Scholar
• C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, The chemistry development kit (CDK): An open-source java library for chemo- and bioinformatics, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 493–500. doi:10.1021/ci025584y.
   PubMedGoogle Scholar
• Y.C. Wei, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comp. Chem. 32 (2010), pp. 1466–1474.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance Document on the Validation of (Quantitative) Structure Activity Relationship [(Q)SAR] Models, ENV/JM/MONO(2007)2, OECD Series on Testing and Assessment No. 69. Organisation for Economic Cooperation and Development, Paris, FR, 2007.
   Google Scholar
• G. Marcou, D. Horvath, F. Bonachera, and A. Varnek, Laboratoire De Chémoinformatique UMR 7140 CNRS, University of Strasbourg, Strasbourg, FR, 2019. Available at http://infochim.u-strasbg.fr/.
   Google Scholar
• NITE, Data from: Biodegradation and bioconcentration data under CSCL National Institute of Technology and Evaluation, 2007; dataset available at https://www.nite.go.jp/en/.
   Google Scholar
• CEFIC, Data from: BCF Bioconcentration factor database, European Chemical Industry Council Long range research initiative, 2007; dataset available at http://cefic-lri.org/.
   Google Scholar
• Government of Canada, Data from: Canadian domestic substances list (DSL), Environment and Climate Change Canada, 1999; dataset available at https://www.canada.ca/en/environment-climate-change/services/canadian-environmental-protection-act-registry/substances-list/domestic.html.
   Google Scholar
• US EPA, Data from: ECOTOX Knowledgebase, US Environmental Protection Agency, 2017; dataset available at https://cfpub.epa.gov/ecotox/.
   Google Scholar
• QSAR Toolbox v 4.1, OASIS Laboratory of mathematical chemistry, Burgas, BG, 2017; software available at http://oasis-lmc.org/products/software/toolbox.aspx.
   Google Scholar
• OECD, Data from: EChemPortal: Global portal to information on chemical substances, Organisation for Economic Co-operation Development, 2017; dataset available at https://www.echemportal.org/echemportal/index.action.
   Google Scholar
• S. Dimitrov,  N. Dimitrova, T. Parkerton, M. Comber, M. Bonnell, and O. Mekenyan, Base-line model for identifying the bioaccumulation potential of chemicals, SAR QSAR Environ. Res. 16 (2005), pp. 531–554. doi:10.1080/10659360500474623.
   PubMed Web of Science ®Google Scholar
• W. Fu, A. Franco, and S. Trapp, Methods for estimating the bioconcentration factor of ionizable organic chemicals, Environ. Tox. Chem. 28 (2009), pp. 1372–1379. doi:10.1897/08-233.1.
   PubMed Web of Science ®Google Scholar
• R.S. Boethling and J. Costanza, Domain of EPISuite biotransformation models, SAR QSAR Environ. Res. 21 (2010), pp. 415–443. doi:10.1080/1062936X.2010.501816.
   PubMed Web of Science ®Google Scholar
• A. Gissi, D. Gadaleta, M. Floris, S. Olla, A. Carotti, E. Novellino, E. Benfenati, and O. Nicolotti, An alternative QSAR-based approach for predicting the bioconcentration factor for regulatory purposes, Altex 31 (2014), pp. 23–36. doi:10.14573/altex.1305221.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, S. Cassani, and A. Sangion, PBT assessment and prioritization by PBT Index and consensus modeling: Comparison of screening results from structural models, Environ. Int. 77 (2015), pp. 25–34. doi:10.1016/j.envint.2014.12.012.
   PubMed Web of Science ®Google Scholar
• N. Kireeva, I.I. Baskin, H.A. Gaspar, D. Horvath, G. Marcou, and A. Varnek, Generative topographic mapping (GTM): Universal tool for data visualization, structure-activity modeling and dataset comparison, Mol. Inf. 31 (2012), pp. 301–312. doi:10.1002/minf.v31.3/4.
   PubMed Web of Science ®Google Scholar
• C.M. Bishop, M. Svensén, C.K.I. Williams, and M. Svens, The generative topographic mapping, Neural Comput. 10 (1998), pp. 215–234. doi:10.1162/089976698300017953.
   Web of Science ®Google Scholar
• H.A. Gaspar, I.I. Baskin, G. Marcou, D. Horvath, and A. Varnek, Chemical data visualization and analysis with incremental generative topographic mapping: Big data challenge, J. Chem. Inf. Model. 55 (2015), pp. 84–94. doi:10.1021/ci500575y.
   PubMed Web of Science ®Google Scholar
• D. Horvath, I. Baskin, G. Marcou, and A. Varnek, Generative topographic mapping of conformational space, Mol. Inf. 36 (2017), pp. 24–36. doi:10.1002/minf.201700036.
   Web of Science ®Google Scholar
• V.P. Solov’ev, A. Varnek, and G. Wipff, Modeling of ion complexation and extraction using substructural molecular fragments, J. Chem. Inf. Comput. Sci. 40 (2000), pp. 847–858.
   PubMedGoogle Scholar
• R.E. Carhart, D.H. Smith, and R. Venkataraghavan, Atom pairs as molecular features in structure-activity studies: Definition and applications, J. Chem. Inf. Comput. Sci. 25 (1985), pp. 64–73. doi:10.1021/ci00046a002.
   Google Scholar
• F. Ruggiu, G. Marcou, A. Varnek, and D. Horvath, ISIDA property-labelled fragment descriptors, Mol. Inf. 29 (2010), pp. 855–868. doi:10.1002/minf.v29.12.
   PubMed Web of Science ®Google Scholar
• C. Chih-Chung and L. Chih-Jen, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011), pp. 1–27. doi:10.1145/1961189.1961199.
   Web of Science ®Google Scholar
• M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten, The WEKA machine learning workbench, in Data Mining: Practical Machine Learning Tools and Techniques, I.H. Witten and E. Frank, eds., Morgan Kaufmann Publishers, San Fransisco, 2005, pp. 363–449.
   Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77. doi:10.1002/(ISSN)1611-0218.
   Google Scholar
• G. Marcou, D. Horvath, V. Solov’Ev, A. Arrault, P. Vayer, and A. Varnek, Interpretability of SAR/QSAR models of any complexity by atomic contributions, Mol. Inf. 31 (2012), pp. 639–642. doi:10.1002/minf.201100136.
   PubMed Web of Science ®Google Scholar
• M.S. McLachlana, Can the stockholm convention address the spectrum of chemicals currently under regulatory scrutiny? Advocating a more prominent role for modeling in POP screening assessment, Environ. Sci. Proces. Impacts 20 (2018), pp. 32–37. doi:10.1039/C7EM00473G.
   PubMed Web of Science ®Google Scholar