243
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Modelling quantitative structure activity–activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

, &
Pages 785-801 | Received 30 Jun 2020, Accepted 12 Aug 2020, Published online: 03 Sep 2020

References

  • Registration, evaluation, authorisation and restriction of chemicals. Available at http://ec.europa.eu/environment/chemicals/reach/reach_intro.htm.
  • M.T. Cronin, A.O. Aptula, J.C. Duffy, T.I. Netzeva, P.H. Rowe, I.V. Valkova, and T.W. Schultz, Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis, Chemosphere 49 (2002), pp. 1201–1221. doi:10.1016/S0045-6535(02)00508-8.
  • J.A.H. Schwöbel, Y.K. Koleva, S.J. Enoch, F. Bajot, M. Hewitt, J.C. Madden, D.W. Roberts, T.W. Schultz, and M.T.D. Cronin, Measurement and estimation of electrophilic reactivity for predictive toxicology, Chem. Rev. 111 (2011), pp. 2562–2596. doi:10.1021/cr100098n.
  • G. Schüürmann, A.O. Aptula, R. Kühne, and R.-U. Ebert, Stepwise discrimination between four modes of toxic action of phenols in the Tetrahymena pyriformis assay, Chem. Res. Toxicol. 16 (2003), pp. 974–987. doi:10.1021/tx0340504.
  • T.I. Netzeva, A.P. Worth, T. Aldenberg, R. Benigni, M.T.D. Cronin, P. Gramatica, J.S. Jaworska, S. Kahn, G. Klopman, C.A. Marchant, G. Myatt, N. Nikolova-Jeliazkova, G.Y. Patlewicz, R. Perkins, D. Roberts, T. Schultz, D.W. Stanton, J.J.M. van de Sandt, W. Tong, G. Veith, and C. Yang, Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships, Altern. Lab. Anim. 33 (2005), pp. 155–173. doi:10.1177/026119290503300209.
  • P.C. von der Ohe, R. Kühne, R.-U. Ebert, R. Altenburger, M. Liess, and G. Schüürmann, Structural alerts. A new classification model to discriminate excess toxicity from narcotic effect levels of organic compounds in the acute daphnid assay, Chem. Res. Toxicol. 18 (2005), pp. 536–555. doi:10.1021/tx0497954.
  • A. Kienzler, M.G. Barron, S.E. Belanger, A. Beasley, and M.R. Embry, Mode of action (MOA) assignment classifications for ecotoxicology: An evaluation of approaches, Environ. Sci. Technol. 51 (2017), pp. 10203–10211. doi:10.1021/acs.est.7b02337.
  • H.J.M. Verhaar, C.J. van Leeuwen, and J.L.M. Hermens, Classifying environmental pollutants, Chemosphere 25 (1992), pp. 471–491. doi:10.1016/0045-6535(92)90280-5.
  • M.G. Barron, C.R. Lilavois, and T.M. Martin, MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development, Aquat. Toxicol. 161 (2015), pp. 102–107. doi:10.1016/j.aquatox.2015.02.001.
  • C.M. Ellison, P. Piechota, J.C. Madden, S.J. Enoch, and M.T.D. Cronin, Adverse outcome pathway (AOP) informed modelling of aquatic toxicology: Qsars, read-across, and interspecies verification of modes of action, Environ. Sci. Technol. 50 (2016), pp. 3995–4007. doi:10.1021/acs.est.5b05918.
  • A.O. Aptula, T.I. Netzeva, I.V. Valkova, M.T.D. Cronin, T.W. Schultz, R. Kühne, and G. Schüürmann, Multivariate discrimination between modes of toxic action of phenols, Quant. Struct. Relat. 21 (2002), pp. 12–22. doi:10.1002/1521-3838(200205)21:1<12::AID-QSAR12>3.0.CO;2-M.
  • G. Patlewicz, N. Jeliazkova, R.J. Safford, A.P. Worth, and B. Aleksiev, An evaluation of the implementation of the Cramer classification scheme in the toxtree software, SAR QSAR Environ. Res 19 (2008), pp. 495–524. doi:10.1080/10629360802083871.
  • E. Benfenati and A. Lombardo, VEGAHUB for ecotoxicological QSAR modelling, in Ecotoxicological QSARs, K. Roy, ed., Humana, New York, NY, 2020, pp. 759–787.
  • L.Y. Fan, D. Zhu, Y. Yang, Y. Huang, S.N. Zhang, L.C. Yan, S. Wang, and H.Y. Zhao, Comparison of modes of action among different trophic levels of aquatic organisms for pesticides and medications based on interspecies correlations and excess toxicity: Theoretical consideration, Ecotoxicol. Environ. Saf. 177 (2019), pp. 25–31. doi:10.1016/j.ecoenv.2019.03.111.
  • S. Cassani, S. Kovarich, E. Papa, P.P. Roy, L. van der Wal, and P. Gramatica, Daphnia and fish toxicity of (benzo)triazoles: Validated QSAR models, and interspecies quantitative activity-activity modelling, J. Hazard. Mater. 258–259 (2013), pp. 50–60. doi:10.1016/j.jhazmat.2013.04.025.
  • X. Wang, B. Fan, M. Fan, S. Belanger, J. Li, J. Chen, X. Gao, and Z. Liu, Development and use of interspecies correlation estimation models in China for potential application in water quality criteria, Chemosphere 240 (2020). doi:10.1016/j.chemosphere.2019.124848.
  • X.J. Zhang, H.W. Qin, L.M. Su, W.C. Qin, M.Y. Zou, L.X. Sheng, Y.H. Zhao, and M.H. Abraham, Interspecies correlations of toxicity to eight aquatic organisms: Theoretical considerations, Sci. Total Environ. 408 (2010), pp. 4549–4555. doi:10.1016/j.scitotenv.2010.07.022.
  • G. Tugcu, M.D. Ertürk, and M.T. Saçan, On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models, J. Hazard. Mater. 339 (2017), pp. 122–130. doi:10.1016/j.jhazmat.2017.06.027.
  • X. Wang, C. Sun, Y. Wang, and L. Wang, Quantitative structure-activity relationships for the inhibition toxicity to root elongation of Cucumis sativus of selected phenols and interspecies correlation with Tetrahymena pyriformis, Chemosphere 46 (2002), pp. 153–161. doi:10.1016/S0045-6535(01)00133-3.
  • V. Aruoja, M. Sihtmäe, H.C. Dubourguier, and A. Kahru, Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: Comparison with published data and QSARs, Chemosphere 84 (2011), pp. 1310–1320. doi:10.1016/j.chemosphere.2011.05.023.
  • S. Raimondo, D.N. Vivian, and M.G. Barron, Web-based Interspecies Correlation Estimation (Web-ICE) for acute toxicity: User manual version 3.3, EPA/600/R-15/192, US Environmental Protection Agency, Office of Research and Development, Gulf Ecology Division, Gulf Breeze, Florida, 2015.
  • M.T.D. Cronin, J.C. Dearden, and A.J. Dobbs, QSAR studies of comparative toxicity in aquatic organisms, Sci. Total Environ. 109110 (1991), pp. 431–439. doi:10.1016/0048-9697(91)90198-N.
  • J. Devillers and H. Devillers, Prediction of acute mammalian toxicity from QSARs and interspecies correlations, SAR QSAR Environ. Res. 20 (2009), pp. 467–500. doi:10.1080/10629360903278651.
  • S. Kar and K. Roy, First report on interspecies quantitative correlation of ecotoxicity of pharmaceuticals, Chemosphere 81 (2010), pp. 738–747. doi:10.1016/j.chemosphere.2010.07.019.
  • S. Raimondo, P. Mineau, and M.G. Barron, Estimation of chemical toxicity to wildlife species using interspecies correlation models, Environ. Sci. Technol. 41 (2007), pp. 5888–5894. doi:10.1021/es070359o.
  • J.J. Li, X.J. Zhang, Y. Yang, T. Huang, C. Li, L. Su, Y.H. Zhao, and M.T.D. Cronin, Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action, Sci. Total Environ. 616–617 (2018), pp. 491–499. doi:10.1016/j.scitotenv.2017.10.308.
  • J.J. Li, X.H. Wang, Y. Wang, Y. Wen, W.C. Qin, L.M. Su, and Y.H. Zhao, Discrimination of excess toxicity from narcotic effect: Influence of species sensitivity and bioconcentration on the classification of modes of action, Chemosphere 120 (2015), pp. 660–673. doi:10.1016/j.chemosphere.2014.10.013.
  • I. Kahn, U. Maran, E. Benfenati, T.I. Netzeva, T.W. Schultz, and M.T.D. Cronin, Comparative quantitative structure-activity-activity relationships for toxicity to Tetrahymena pyriformis and Pimephales prometas, ATLA 35 (2007), pp. 15–24. doi:10.1177/026119290703500112.
  • A. Furuhama, T.I. Hayashi, and H. Yamamoto, Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals, SAR QSAR Environ. Res. 30 (2019), pp. 825–846. doi:10.1080/1062936X.2019.1669707.
  • A. Sangion and P. Gramatica, Ecotoxicity interspecies QAAR models from Daphnia toxicity of pharmaceuticals and personal care products, SAR QSAR Environ. Res. 27 (2016), pp. 781–798. doi:10.1080/1062936X.2016.1233139.
  • 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.
  • J.A. Castillo-Garit, Y. Marrero-Ponce, J. Escobar, F. Torrens, and R. Rotondo, A novel approach to predict aquatic toxicity from molecular structure, Chemosphere 73 (2008), pp. 415–427. doi:10.1016/j.chemosphere.2008.05.024.
  • M. Pérez González, H. González Díaz, M.A. Cabrera, and R. Molina Ruiz, A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis, Bioorg. Med. Chem. 12 (2004), pp. 735–744. doi:10.1016/j.bmc.2003.11.028.
  • M.T.D. Cronin and T.W. Schultz, Structure-toxicity relationships for three mechanisms of action of toxicity to Vibrio fischeri, Ecotoxicol. Environ. Saf. 39 (1998), pp. 65–69. doi:10.1006/eesa.1997.1618.
  • S. Dimitrov, Y. Koleva, T.W. Schultz, J.D. Walker, and O. Mekenyan, Interspecies quantitative structure-activity relationship model for aldehydes: Aquatic toxicity, Environ. Toxicol. Chem. 23 (2004), pp. 463–470. doi:10.1897/02-579.
  • A.R. Katritzky, P. Oliferenko, A. Oliferenko, A. Lomaka, and M. Karelson, Nitrobenzene toxicity: QSAR correlations and mechanistic interpretations, J. Phys. Org. Chem. 16 (2003), pp. 811–817. doi:10.1002/poc.643.
  • F. Schramm, A. Müller, H. Hammer, A. Paschke, and G. Schüürmann, Epoxide and thiirane toxicity in vitro with the ciliates Tetrahymena pyriformis: Structural alerts indicating excess toxicity, Environ. Sci. Technol. 45 (2011), pp. 5812–5819. doi:10.1021/es200081n.
  • T.W. Schultz, Tetratox: Tetrahymena pyriformis population growth impairment endpoint - A surrogate for fish lethality, Toxicol. Methods 7 (1997), pp. 289–309. doi:10.1080/105172397243079.
  • T.W. Schultz, T.I. Netzeva, D.W. Roberts, and M.T.D. Cronin, Structure-toxicity relationships for the effects to Tetrahymena pyriformis of aliphatic, carbonyl-containing, α,β-unsaturated chemicals, Chem. Res. Toxicol. 18 (2005), pp. 330–341. doi:10.1021/tx049833j.
  • E. Benfenati, A. Manganaro, and G. Gini, VEGA-QSAR: AI inside a platform for predictive toxicology, CEUR Workshop Proc. 1107 (2013), pp. 21–28.
  • US EPA, ECOTOX, 2017. USA. Available at http://cfpub.epa.gov/ecotox/.
  • Results of eco-toxicity tests of chemicals conducted by Ministry of the Environment in Japan, 2016. Available at http://www.env.go.jp/chemi/sesaku/02e.pdf.
  • L.M. Su, X. Liu, Y. Wang, J.J. Li, X.H. Wang, L.X. Sheng, and Y.H. Zhao, The discrimination of excess toxicity from baseline effect: Effect of bioconcentration, Sci. Total Environ. 484 (2014), pp. 137–145. doi:10.1016/j.scitotenv.2014.03.040.
  • S. Gómez-Ganau, M. Marzo, R. Gozalbes, and E. Benfenati, Computational Approaches to Evaluate Ecotoxicity of Biocides: Cases from the Project COMBASE, In Ecotoxicological QSARs. Methods in Pharmacology and Toxicology, K. Roy, (eds.), Humana, New York, 2020, pp. 387–404.
  • R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Methods and Principles in Medicinal Chemistry Wiley, Weinheim, Germany, 2000.
  • M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, and T. Meinl, KNIME - The Konstanz information miner: Version 2.0 and beyond, SIGKDD Explor. Newsl. 11 (2009), pp. 26–31. doi:10.1145/1656274.1656280.
  • D.D. Varsou, S. Nikolakopoulos, A. Tsoumanis, G. Melagraki, and A. Afantitis, ENALOS+ KNIME nodes: New cheminformatics tools for drug discovery, Methods Mol. Biol. 1824 (2018), pp. 113–138.
  • P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701. doi:10.1002/qsar.200610151.
  • P. Gramatica, S. Cassani, P.P. Roy, S. Kovarich, C.W. Yap, and E. Papa, QSAR modelling is not “push a button and find a correlation”: A case study of toxicity of (benzo-)triazoles on algae, Mol. Inform. 31 (2012), pp. 817–835. doi:10.1002/minf.201200075.
  • G. Melagraki, A. Afantitis, H. Sarimveis, O. Igglessi-Markopoulou, P.A. Koutentis, and G. Kollias, In silico exploration for identifying structure-activity relationship of MEK inhibition and oral bioavailability for isothiazole derivatives, Chem. Biol. Drug Des. 76 (2010), pp. 397–406. doi:10.1111/j.1747-0285.2010.01029.x.
  • A. Afantitis, G. Melagraki, P.A. Koutentis, H. Sarimveis, and G. Kollias, Ligand - based virtual screening procedure for the prediction and the identification of novel  β-amyloid aggregation inhibitors using Kohonen maps and counterpropagation artificial neural networks, Eur. J. Med. Chem. 46 (2011), pp. 497–508. doi:10.1016/j.ejmech.2010.11.029.
  • A. Afantitis, G. Melagraki, H. Sarimveis, P.A. Koutentis, O. Igglessi-Markopoulou, and G. Kollias, A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs, Mol. Divers. 14 (2010), pp. 225–235. doi:10.1007/s11030-009-9163-7.
  • A. Tropsha and A. Golbraikh, Predictive QSAR modelling workflow, model applicability domains, and virtual screening, Curr. Pharm. Des. 13 (2007), pp. 3494–3504. doi:10.2174/138161207782794257.
  • A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:10.1016/S1093-3263(01)00123-1.
  • K. Bouhedjar, A.K. Nacereddine, H. Ghorab, and A. Djerourou, QSPR modelling for critical temperatures of organic compounds using hybrid optimal descriptors, Int. J. Quant. Struct. Relat. 4 (2019), pp. 15–26.
  • P.P. Roy, S. Paul, I. Mitra, and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009), pp. 1660–1701. doi:10.3390/molecules14051660.
  • P.P. Roy and K. Roy, On some aspects of variable selection for partial least squares regression models, QSAR Comb. Sci. 27 (2008), pp. 302–313. doi:10.1002/qsar.200710043.
  • K. Roy, On some aspects of validation of predictive quantitative structure-activity relationship models, Expert Opin. Drug Discov. 2 (2007), pp. 1567–1577. doi:10.1517/17460441.2.12.1567.
  • J.T. Leonard and K. Roy, On selection of training and test sets for the development of predictive QSAR models, QSAR Comb. Sci. 25 (2006), pp. 235–251. doi:10.1002/qsar.200510161.
  • P.P. Roy, J.T. Leonard, and K. Roy, Exploring the impact of size of training sets for the development of predictive QSAR models, Chemom. Intell. Lab. Syst. 90 (2008), pp. 31–42. doi:10.1016/j.chemolab.2007.07.004.
  • P. Gramatica, N. Chirico, E. Papa, S. Cassani, and S. Kovarich, QSARINS: A new software for the development, analysis, and validation of QSAR MLR models, J. Comput. Chem. 34 (2013), pp. 2121–2132. doi:10.1002/jcc.23361.
  • P. Gramatica, S. Cassani, and N. Chirico, QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS, J. Comput. Chem. 35 (2014), pp. 1036–1044. doi:10.1002/jcc.23576.
  • R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, Detecting “bad” regression models: Multicriteria fitness functions in regression analysis, Anal. Chim. Acta 515 (2004), pp. 199–208. doi:10.1016/j.aca.2003.12.010.
  • R. Todeschini, V. Consonni, and A. Maiocchi, The K correlation index: Theory development and its application in chemometrics, Chemom. Intell. Lab. Syst. 46 (1999), pp. 13–29. doi:10.1016/S0169-7439(98)00124-5.
  • L.M. Shi, H. Fang, W. Tong, J. Wu, R. Perkins, R.M. Blair, W.S. Branham, S.L. Dial, C.L. Moland, and D.M. Sheehan, QSAR models using a large diverse set of estrogens, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 186–195. doi:10.1021/ci000066d.
  • G. Schüürmann, R.-U. Ebert, J. Chen, B. Wang, and R. Kühne, External validation and prediction employing the predictive squared correlation coefficient — Test set activity mean vs training set activity mean, J. Chem. Inf. Model. 48 (2008), pp. 2140–2145. doi:10.1021/ci800253u.
  • V. Consonni, D. Ballabio, and R. Todeschini, Comments on the definition of the Q2 parameter for QSAR validation, J. Chem. Inf. Model. 49 (2009), pp. 1669–1678. doi:10.1021/ci900115y.
  • N. Chirico and P. Gramatica, Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection, J. Chem. Inf. Model. 52 (2012), pp. 2044–2058. doi:10.1021/ci300084j.
  • N. Chirico and P. Gramatica, Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient, J. Chem. Inf. Model. 51 (2011), pp. 2320–2335. doi:10.1021/ci200211n.
  • A. Tropsha, P. Gramatica, and V. 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/qsar.200390007.
  • P.K. Ojha and K. Roy, Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection, Chemom. Intell. Lab. Syst. 109 (2011), pp. 146–161. doi:10.1016/j.chemolab.2011.08.007.
  • I. Lessigiarska, A.P. Worth, B. Sokull-Klüttgen, S. Jeram, J.C. Dearden, T.I. Netzeva, and M.T.D. Cronin, QSAR investigation of a large data set for fish, algae and Daphnia toxicity, SAR QSAR Environ. Res 15 (2004), pp. 413–431. doi:10.1080/10629360412331297416.
  • C. Hansch and T. Fujita, p -σ-π analysis. A method for the correlation of biological activity and chemical structure, J. Am. Chem. Soc. 86 (1964), pp. 1616–1626. doi:10.1021/ja01062a035.
  • A. Furuhama, T.I. Hayashi, and N. Tatarazako, Acute to chronic estimation of Daphnia magna toxicity within the QSAAR framework, SAR QSAR Environ. Res. 27 (2016), pp. 833–850. doi:10.1080/1062936X.2016.1243151

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.