Prediction of soil ecotoxicity against Folsomia candida using acute and chronic endpoints

References

• R. Costanza, R. d’Arge, R. De Groot, S. Farber, M. Grasso, B. Hannon, K. Limburg, S. Naeem, R.V. O’neill, J. Paruelo, and R.G. Raskin, The value of the world’s ecosystem services and natural capital, Nature 387 (1997), pp. 253–260. doi:10.1038/387253a0.
   Web of Science ®Google Scholar
• UN (United Nations), Draft resolution submitted by the vice-chair of the committee, Ms. Farrah Brown (Jamaica), on the basis of informal consultations on draft resolution A/C.2/68/L.21, United Nations General Assembly, Washington, DC, USA, 2013. Available at http://www.fao.org/fileadmin/user_upload/GSP/docs/iys/World_Soil_Day_and_International_Year_of_Soils__UNGA_Resolution_Dec._2013.pdf. accessed 08 April 2023.
   Google Scholar
• T. Gomiero, Soil degradation, land scarcity and food security: Reviewing a complex challenge, Sustainability 8 (2016), pp. 281. doi:10.3390/su8030281.
   Web of Science ®Google Scholar
• J. Maximillian, M.L. Brusseau, E.P. Glenn, and A.D. Matthias, Pollution and environmental perturbations in the global system, in Environmental and Pollution Science, 3rded, M.L. Brusseau, I.L. Pepper, and C.P. Gerba, eds., Academic Press, New York, 2019, pp. 457–476.
   Google Scholar
• C. Arsenault, Only 60 years of farming left if soil. Scientific American, 2014. Available at https://www.scientificamerican.com/article/only-60-years-of-farming-left-if-soil-degradation-continues.
   Google Scholar
• C. Nellemann, The Environmental Food Crisis: The Environment’s Role in Averting Future Food Crises: A UNEP Rapid Response Assessment, UNEP/Earthprint, New York, 2009.
   Google Scholar
• K. Roy, S. Kar, and R.N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic press, New York, 2015.
   Google Scholar
• A.J. García-Fernández, Ecotoxicological risk assessment in the context of different EU regulations, in Ecotoxicological QSARs, K. Roy, ed., Humana, New York, NY, 2020, pp. 3–25.
   Google Scholar
• R. Paul, M. Chatterjee, and K. Roy, First report on soil ecotoxicity prediction against Folsomia candida using intelligent consensus predictions and chemical read-across, Environ. Sci. Pollut. Res. 29 (2022), pp. 88302–88317. doi:10.1007/s11356-022-21937-w.
   PubMed Web of Science ®Google Scholar
• S. Kovarich, L. Ceriani, M. Fuart Gatnik, A. Bassan, and M. Pavan, Filling data gaps by read‐across: A mini review on its application, developments and challenges, Molec. Inform. 38 (2019), pp. 1800121. doi:10.1002/minf.201800121.
   Web of Science ®Google Scholar
• M.T. Fountain and S.P. Hopkin, Folsomia candida (Collembola): A “standard” soil arthropod, Annu. Rev. Entomol. 50 (2005), pp. 201–222. doi:10.1146/annurev.ento.50.071803.130331.
   PubMed Web of Science ®Google Scholar
• D. Giesen and C.A. van Gestel, QSAR development and bioavailability determination: The toxicity of chloroanilines to the soil dwelling springtail Folsomia candida, Chemosphere 90 (2013), pp. 2667–2673. doi:10.1016/j.chemosphere.2012.11.045.
   PubMed Web of Science ®Google Scholar
• S. Ghosh, P.K. Ojha, E. Carnesecchi, A. Lombardo, K. Roy, and E. Benfenati, Exploring QSAR modeling of toxicity of chemicals on earthworm, Ecotoxicol. Environ. Saf. 190 (2020), pp. 110067. doi:10.1016/j.ecoenv.2019.110067.
   PubMed Web of Science ®Google Scholar
• D.W. Sparling, Ecotoxicology Essentials: Environmental Contaminants and Their Biological Effects on Animals and Plants, Academic Press, London, 2016.
   Google Scholar
• R.N. Das, K. Roy, and P.L. Popelier, Exploring simple, transparent, interpretable and predictive QSAR models for classification and quantitative prediction of rat toxicity of ionic liquids using OECD recommended guidelines, Chemosphere 139 (2015), pp. 163–173. doi:10.1016/j.chemosphere.2015.06.022.
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and R.N. Das, A Primer on QSAR/QSPR Modeling: Fundamental Concepts, Springer, New York, 2015.
   Google Scholar
• G.J. Lavado, D. Baderna, E. Carnesecchi, A.P. Toropova, A.A. Toropov, J.L.C. Dorne, and E. Benfenati, QSAR models for soil ecotoxicity: Development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida, J. Hazard. Mater. 423 (2022), pp. 127236. doi:10.1016/j.jhazmat.2021.127236.
   PubMed Web of Science ®Google Scholar
• N. Smirnov, Table for estimating the goodness of fit of empirical distributions, Ann. Math. Stat. 19 (1948), pp. 279–281. doi:10.1214/aoms/1177730256.
   Google Scholar
• M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, and B. Wiswedel, KNIME‑the konstanz information miner: Version 2.0 and beyond, SIG-KDD Explor. Newsl 11 (2009), pp. 26–31. doi:10.1145/1656274.1656280.
   Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, John Wiley & Sons, New York, 2009.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• R. Todeschini, M. Lasagni, and E. Marengo, New molecular descriptors for 2D and 3D structures. Theory, J. Chemom. 8 (1994), pp. 263–272. doi:10.1002/cem.1180080405.
   Web of Science ®Google Scholar
• A. Mauri, alvaDesc: A tool to calculate and analyze molecular descriptors and fingerprints, in Ecotoxicological QSARs, K. Roy, ed., Humana, New York, NY, 2020, pp. 801–820.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. Syst. 152 (2016), pp. 18–33. doi:10.1016/j.chemolab.2016.01.008.
   Web of Science ®Google Scholar
• 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 Screen. 14 (2011), pp. 450–474. doi:10.2174/138620711795767893.
   PubMed Web of Science ®Google Scholar
• P.K. Ojha and K. Roy, Chemometric modeling of odor threshold property of diverse aroma components of wine, RSC Adv. 8 (2018), pp. 4750–4760. doi:10.1039/C7RA12295K.
   PubMed Web of Science ®Google Scholar
• R. Leardi, Genetic algorithms in chemometrics and chemistry: A review, J. Chemom. 15 (2001), pp. 559–569. doi:10.1002/cem.651.
   Web of Science ®Google Scholar
• S. Wold, M. Sjöström, and L. Eriksson, PLS-regression: A basic tool of chemometrics, Chemom. Intell. Lab. Syst. 58 (2001), pp. 109–130. doi:10.1016/S0169-7439(01)00155-1.
   Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring rm2 metrics for validation of QSPR models, Chemom. Intell. Lab. Syst. 107 (2011), pp. 194–205. doi:10.1016/j.chemolab.2011.03.011.
   Web of Science ®Google Scholar
• N. Akarachantachote, S. Chadcham, and K. Saithanu, Cutoff threshold of variable importance in projection for variable selection, Int. J. Pure Appl. Math. 94 (2014), pp. 307–322. doi:10.12732/ijpam.v94i3.2.
   Google Scholar
• J.E. Jackson and A. Edward, User’s Guide to Principal Components, John Willey Sons. Inc, New York, 1991.
   Google Scholar
• 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, Springer Berlin, Heidelberg, 2010, pp. 757–774.
   Google Scholar
• J.G. Topliss and R.P. Edwards, Chance factors in studies of quantitative structure-activity relationships, J. Med. Chem. 22 (1979), pp. 1238–1244. doi:10.1021/jm00196a017.
   PubMed Web of Science ®Google Scholar
• C. Rücker, G. Rücker, and M. Meringer, y-Randomization and its variants in QSPR/QSAR, J. Chem. Inf. Model. 47 (2007), pp. 2345–2357. doi:10.1021/ci700157b.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• E. Lo Piparo, M. Smiesko, P. Mazzatorta, E. Benfenati, J. Idinger, and S. Blümel, Preliminary analysis of toxicity of benzoxazinones and their metabolites for Folsomia candida, J. Agric. Food Chem. 54 (2006), pp. 1099–1104. doi:10.1021/jf050916v.
   PubMed Web of Science ®Google Scholar
• L.B. Kier and L.H. Hall, An electrotopological-state index for atoms in molecules, Pharm. Res. 78 (1990), pp. 801–807. doi:10.1023/A:1015952613760.
   Google Scholar
• D. Giesen, M.T. Jonker, and C.A. van Gestel, Development of QSARs for the toxicity of chlorobenzenes to the soil dwelling springtail Folsomia candida, Environ. Toxicol. Chem. 31 (2012), pp. 1136–1142. doi:10.1002/etc.1805.
   PubMed Web of Science ®Google Scholar