Ecotoxicological QSAR modelling of organic chemicals against Pseudokirchneriella subcapitata using consensus predictions approach

References

• 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
• C. Plagellat, T. Kupper, R. Furrer, L.F. De Alencastro, D. Grandjean, and J. Tarradellas, Concentrations and specific loads of UV filters in sewage sludge originating from a monitoring network in Switzerland, Chemosphere 62 (2006), pp. 915–925. doi:10.1016/j.chemosphere.2005.05.024.
   PubMed Web of Science ®Google Scholar
• W. Peysson and E. Vulliet, Determination of 136 pharmaceuticals and hormones in sewage sludge using quick, easy, cheap, effective, rugged and safe extraction followed by analysis with liquid chromatography–time--of-flight-mass spectrometry, J. Chromatogr. 1290 (2013), pp. 46–61. doi:10.1016/j.chroma.2013.03.057.
   PubMed Web of Science ®Google Scholar
• C. Plagellat, T. Kupper, L.F. De Alencastro, D. Grandjean, and J. Tarradellas, Biocides in sewage sludge: Quantitative determination in some Swiss wastewater treatment plants, Bull Environ. Contam. Toxicol. 73 (2004), pp. 794–801. doi:10.1007/s00128-004-0497-7.
   PubMed Web of Science ®Google Scholar
• M. Petrovic and D. Barcelo, Determination of anionic and nonionic surfactants, their degradation products, and endocrine-disrupting compounds in sewage sludge by liquid chromatography/mass spectrometry, Anal. Chem. 72 (2000), pp. 4560–4567. doi:10.1021/ac000306o.
   PubMed Web of Science ®Google Scholar
• H. Sanderson, D.J. Johnson, C.J. Wilson, R.A. Brain, and K.R. Solomon, Probabilistic hazard assessment of environmentally occurring pharmaceuticals toxicity to fish, daphnids and algae by ECOSAR screening, Toxicol. Lett. 144 (2003), pp. 383–395.
   PubMed Web of Science ®Google Scholar
• D. Pearce, G. Atkinson, and S. Mourato, Organisation for Economic Co-operation and Development, 2006, Cost-benefit analysis and the environment: Recent developments, Organisation for Economic Co-operation and Development, Paris, 2006.
   Google Scholar
• L. Musset, Current Approaches in the Statistical Analysis of Ecotoxicity Data: A Guidance to Application, OECD Series on testing and assessment, Paris, 2006, pp. 54.
   Google Scholar
• S. Kooijman, An alternative for NOEC exists, but the standard model has to be abandoned first, oikos, 75 (1996), pp. 310–316.
   Google Scholar
• A. Beasley, S.E. Belanger, J.L. Brill, and R.R. Otter, Evaluation and comparison of the relationship between NOEC and EC10 or EC20 values in chronic Daphnia toxicity testing, Environ. Toxicol. Chem. 34 (2015), pp. 2378–2384. doi:10.1002/etc.3086.
   PubMed Web of Science ®Google Scholar
• Y. Iwasaki, K. Kotani, S. Kashiwada, and S. Masunaga, Does the choice of NOEC or EC10 affect the hazardous concentration for 5% of the species? Environ. Sci. Technol. 49 (2015), pp. 9326–9330. doi:10.1021/acs.est.5b02069.
   PubMed Web of Science ®Google Scholar
• J.N. Shieh, M.R. Chao, and C.Y. Chen, Statistical comparisons of the no-observed-effect concentration and the effective concentration at 10% inhibition (EC10) in algal toxicity tests, Wat. Sci. Tech. 43 (2001), pp. 141–146. doi:10.2166/wst.2001.0083.
   PubMed Web of Science ®Google Scholar
• C.I. Weber, W. Peltier, T. Norberg-King, W. Horning, and F. Kessler, Short-term Methods for Estimating the Chronic Toxicity of Effluents and Receiving Waters to Freshwater Organisms, Environmental Protection Agency, Cincinnati, OH (USA), 1989.
   Google Scholar
• A.J. Bailer, J.T. Oris, K. See, M.R. Hughes, and R. Schaefer, Defining and evaluating impact in environmental toxicology, Environmetrics 14 (2003), pp. 235–243. doi:10.1002/(ISSN)1099-095X.
   Web of Science ®Google Scholar
• P.A. Lewis, D.J. Klemm, J.M. Lazorchak, T.J. Norberg-King, W.H. Peltier, and M.A. Heber, Short-term Methods for Estimating the Chronic Toxicity of Effluents and Receiving Waters to Freshwater Organisms, US Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Cincinnati, 1994.
   Google Scholar
• E. Papa and P. Gramatica, QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure, Green Chem. 12 (2010), pp. 836–843. doi:10.1039/b923843c.
   Web of Science ®Google Scholar
• E. 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/EC 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/EC and 2000/21/EC, Off, J. Eur. Union. 396 (2006), pp. 374–375.
   Google Scholar
• K. Khan, S. Kar, H. Sanderson, K. Roy, and J. Leszczynski, Ecotoxicological assessment of pharmaceuticals using computational toxicology approaches: QSTR and interspecies QTTR modeling, MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition, Basque Country, Multidisciplinary Digital Publishing Institute, 2018.
   Google Scholar
• J.C. Dearden, The History and Development of Quantitative Structure-activity Relationships (qsars), in Oncology: Breakthroughs in Research and practice, IGI Global, Pennsylvania, 2017, pp. 67–117.
   Google Scholar
• K. Khan, D. Baderna, C. Cappelli, C. Toma, A. Lombardo, K. Roy, and E. Benfenati, Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis, Aquat. Toxicol. 212 (2019), pp. 162–174. doi:10.1016/j.aquatox.2019.05.011.
   PubMed Web of Science ®Google Scholar
• K. Khan, E. Benfenati, and K. Roy, Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: Ranking and prioritization of the DrugBank database compounds, Ecotoxicol. Environ. Saf. 168 (2019), pp. 287–297. doi:10.1016/j.ecoenv.2018.10.060.
   PubMed 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.
   PubMed Web of Science ®Google Scholar
• K.O. Kusk, A.M. Christensen, and N. Nyholm, Algal growth inhibition test results of 425 organic chemical substances, Chemosphere 204 (2018), pp. 405–412. doi:10.1016/j.chemosphere.2018.04.047.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, S. Cassani, and A. Sangion, Aquatic ecotoxicity of personal care products: QSAR models and ranking for prioritization and safer alternatives design, Green Chem. 18 (2016), pp. 4393–4406. doi:10.1039/C5GC02818C.
   Web of Science ®Google Scholar
• C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
   PubMed Web of Science ®Google Scholar
• A. Mauri, V. Consonni, M. Pavan, and R. Todeschini, Dragon software: An easy approach to molecular descriptor calculations, Match 56 (2006), pp. 237–248.
   Google Scholar
• K. Khan and K. Roy, Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach, SAR QSAR Environ. Res. 28 (2017), pp. 567–594. doi:10.1080/1062936X.2017.1352621.
   PubMed Web of Science ®Google Scholar
• H. Golmohammadi, Z. Dashtbozorgi, and W.E. Acree Jr, Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine, Eur. J. Pharm. Sci. 47 (2012), pp. 421–429. doi:10.1016/j.ejps.2012.06.021.
   PubMed Web of Science ®Google Scholar
• R.W. Kennard and L.A. Stone, Computer aided design of experiments, Technometrics 11 (1969), pp. 137–148. doi:10.1080/00401706.1969.10490666.
   Web of Science ®Google Scholar
• P.M. Khan and K. Roy, Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR), Expert Opin. Drug Discov. 13 (2018), pp. 1075–1089. doi:10.1080/17460441.2018.1542428.
   PubMed Web of Science ®Google Scholar
• S. Wold, M. Sjostrom, 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
• K. Roy, I. Mitra, P.K. Ojha, S. Kar, R.N. Das, and H. Kabir, Introduction of rm2 (rank) metric incorporating rank-order predictions as an additional tool for validation of QSAR/QSPR models, Chemom. Intell. Lab. Syst. 118 (2012), pp. 200–210. doi:10.1016/j.chemolab.2012.06.004.
   Web of Science ®Google Scholar
• K. Roy, P. Ambure, S. Kar, and P.K. Ojha, Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J. Chemom. 32 (2018), pp. e2992. doi:10.1002/cem.2992.
   Web of Science ®Google Scholar
• R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, DRAGON-Software for the Calculation of Molecular Descriptors, Web version 3, Milan, Italy, 2004.
   Google Scholar
• K. Khan, K. Roy, and E. Benfenati, Ecotoxicological QSAR modeling of endocrine disruptor chemicals, J. Hazard. Mater. 369 (2019), pp. 707–718. doi:10.1016/j.jhazmat.2019.02.019.
   PubMed Web of Science ®Google Scholar
• J.A. Padron, R. Carrasco, and R.F. Pellon, Molecular descriptor based on a molar refractivity partition using Randic-type graph-theoretical invariant, J. Pharm. Pharmaceut. Sci. 5 (2002), pp. 258–266.
   PubMed Web of Science ®Google Scholar
• P. Sharma, N. Rane, and V.K. Gurram, Synthesis and QSAR studies of pyrimido [4, 5-d] pyrimidine-2, 5-dione derivatives as potential antimicrobial agents, Bioorg. Med. Chem. Lett. 14 (2004), pp. 4185–4190. doi:10.1016/j.bmcl.2004.06.014.
   PubMed Web of Science ®Google Scholar
• K. Khan, S. Kar, H. Sanderson, K. Roy, and J. Leszczynski, Ecotoxicological modeling, ranking and prioritization of pharmaceuticals using QSTR and i‐QSTTR approaches: Application of 2D and fragment based descriptors, Mol. Inform. 25 (2018). doi:10.1002/minf.201800078.
   Google Scholar
• K. Roy, P. Ambure, and S. Kar, How precise are our quantitative structure–activity relationship derived predictions for new query chemicals? ACS Omega 3 (2018), pp. 11392–11406. doi:10.1021/acsomega.8b01647.
   PubMed Web of Science ®Google Scholar
• A. Furuhama, K. Hasunuma, T.I. Hayashi, and N. Tatarazako, Predicting algal growth inhibition toxicity: Three-step strategy using structural and physicochemical properties, SAR QSAR Environ. Res. 27 (2016), pp. 343–362. doi:10.1080/1062936X.2016.1174151.
   PubMed Web of Science ®Google Scholar
• A.A. Lagunin, A.V. Zakharov, D.A. Filimonov, and V.V. Poroikov, A new approach to QSAR modelling of acute toxicity, SAR QSAR Environ. Res. 18 (2007), pp. 285–298. doi:10.1080/10629360701304253.
   PubMed Web of Science ®Google Scholar
• F. Ding, Z. Wang, X. Yang, L. Shi, J. Liu, and G. Chen, Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata, SAR QSAR Environ. Res. 30 (2019), pp. 39–50. doi:10.1080/1062936X.2018.1545694.
   PubMed Web of Science ®Google Scholar