QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

References

• C. Hansch and A. Leo, Exploring QSAR. Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington, D.C., 1995.
   Google Scholar
• C. Hansch and T. Fujita, Classical and three-dimensional QSAR in agrochemistry, ACS Symposium Volume 606, American Chemical Society, Washington DC, 1995.
   Google Scholar
• E. Benfenati, Theory, guidance and applications on QSAR and REACH, Orchestra, 2012. Available at http://ebook.insilico.eu/insilico-ebook-orchestra-benfenati-ed1_rev-June2013.pdf
   Google Scholar
• M.V. Putz, QSAR and SPECTRAL-SAR in Computational Ecotoxicology, CRC Press, New York, USA, 2012.
   Google Scholar
• J. Leszczynski, Challenges and Advances in Computational Chemistry and Physics, Springer, Berlin, Germany, 2017.
   Google Scholar
• M.T.D. Cronin and D.J. Livingstone, Predicting Chemical Toxicity and Fate, CRC press, Boca Raton, 2004.
   Google Scholar
• M. Hamadache, A. Amrane, O. Benkortbi, S. Hanini, L. Khaouane, and C.S. Moussa, Environmental toxicity of pesticides, and its modeling by QSAR approaches in Advances in QSAR modeling. Challenges and Advances in Computational Chemistry and Physics, K. Roy (ed.), Springer, Cham, Switzerland (2017), pp. 471–501. doi:10.1007/978-3-319-56850-8_13.
   Google Scholar
• P.R. Duchowicz and E.A. Castro, The importance of the QSAR-QSPR methodology to the theoretical study of pesticides, Int. J. Chem. Model. 5 (2013), pp. 35–50. Available at http://www.novapublishers.org/catalog/product_info.php?products_id=41525.
   Google Scholar
• C. Tebes-Stevens, J.M. Patel, M. Koopmans, J. Olmstead, S.H. Hilal, N. Pope, E.J. Weber, and K. Wolfe, Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments, Chemosphere 194 (2018), pp. 94–106. doi:10.1016/j.chemosphere.2017.11.137.
   PubMed Web of Science ®Google Scholar
• J.J. Villaverde, B. Sevilla-Morán, C. López-Goti, J.L. Alonso-Prados, and P. Sandín-España, Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework, Sci. Total Environ. 634 (2018), pp. 1530–1539. doi:10.1016/j.scitotenv.2018.04.033.
   PubMed Web of Science ®Google Scholar
• M.V. Diudea, QSPR/QSAR Studies by Molecular Descriptors, Nova Science Publishers, New York, 2001.
   Google Scholar
• A.R. Katritzky and E.V. Goordeva, Traditional topological indices vs. electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research, J. Chem. Inf. Comput. Sci. 33 (1993), pp. 835–857. doi:10.1021/ci00016a005.
   PubMedGoogle Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics (Methods and Principles in Medicinal Chemistry), Wiley-VCH, Weinheim, 2009.
   Google Scholar
• V.H. Masand and V. Rastija, PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors, Chemom. Intell. Lab. Syst. 169 (2017), pp. 12–18. doi:10.1016/j.chemolab.2017.08.003.
   Web of Science ®Google Scholar
• N. Trinajstic, Chemical Graph Theory, CRC Press, Boca Raton (FL), 1992.
   Google Scholar
• J.C. Garro Martinez, M.F. Andrada, M.R. Estrada, E.A. Castro, G.N. Zamarbide, Z. Mucsi, and I.G. Csizmadia, An exploratory study to investigate possible simple descriptors in order to predict relative activity of antiepileptic enaminones, J. Phys. Org. Chem. 21 (2008), pp. 409–418. doi:10.1002/poc.1365.
   Web of Science ®Google Scholar
• R.D. Tosso, M. Vettorazzi, S.A. Andujar, L.J. Gutierrez, J.C. Garro Martinez, E. Angelina, R. Rodríguez, F.D. Suvire, M. Nogueras, J. Cobo, and R.D. Enriz, The electronic density obtained from a QTAIM analysis used as molecular descriptor. A study performed in a new series of DHFR inhibitors, J. Mol. Struct. 1134 (2017), pp. 464–474. doi:10.1016/j.molstruc.2016.12.060.
   Web of Science ®Google Scholar
• J.C. Dearden, P. Rotureau, and G. Fayet, QSPR prediction of physico-chemical properties for REACH, SAR QSAR Environ. Res. 24 (2013), pp. 279–318. doi:10.1080/1062936X.2013.773372.
   PubMed Web of Science ®Google Scholar
• D.M. Hawkins, The problem of overfitting, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1–12. doi:10.1021/ci0342472.
   PubMedGoogle Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701. doi:10.1002/qsar.200610151.
   Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemom. Intell. Lab. Syst. 145 (2015), pp. 22–29. doi:10.1016/j.chemolab.2015.04.013.
   Web of Science ®Google Scholar
• US EPA Agency. Finalization of guidance on incorporation of water treatment effects on pesticide removal and transformations in drinking water exposure assessments, 2017. Available at https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/finalization-guidance-incorporation-water-treatment.
   Google Scholar
• D. Hamilton, A. Ambrus, R. Dieterle, A. Felsot, C. Harris, P. Holland, A. Katayama, N. Kurihara, J. Linders, and J. Unsworth, Regulatory limits for pesticide residues in water (IUPAC technical report), Pure Appl. Chem. 75 (2003), pp. 1123–1155. doi:10.1351/pac200375081123.
   Web of Science ®Google Scholar
• J.J. Villaverde, B. Sevilla-Morán, C. López-Goti, J.L. Alonso-Prados, and P. Sandín-España, Computational methodologies for the risk assessment of pesticides in the European Union, J. Agric. Food Chem. 65 (2017), pp. 2017–2018. doi:10.1021/acs.jafc.7b00516.
   PubMed Web of Science ®Google Scholar
• Y. Ran, Y. He, G. Yang, J.L. Johnson, and S.H. Yalkowsky, Estimation of aqueous solubility of organic compounds by using the general solubility equation, Chemosphere 48 (2002), pp. 487–509. doi:10.1016/S0045-6535(02)00118-2.
   PubMed Web of Science ®Google Scholar
• E.D. Clarke and J.S. Delaney, Physical and molecular properties of agrochemicals: An analysis of screen inputs, hits, leads, and products, Chimia Int. J. Chem. 57 (2003), pp. 731–734. Available at https://www.ingentaconnect.com/content/scs/chimia/2003/00000057/00000011/art00013?crawler=true&mimetype=application/pdf.
   Web of Science ®Google Scholar
• J.S. Delaney, ESOL: Estimating aqueous solubility directly from molecular structure, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1000–1005. doi:10.1021/ci034243x.
   PubMedGoogle Scholar
• B. Bhhatarai and P. Gramatica, Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals, Environ. Sci. Technol. 45 (2010), pp. 8120–8128. doi:10.1021/es101181g.
   PubMed Web of Science ®Google Scholar
• O. Deeb and M. Goodarzi, Predicting the solubility of pesticide compounds in water using QSPR methods, Mol. Phys. 108 (2010), pp. 181–192. doi:10.1080/00268971003604575.
   Web of Science ®Google Scholar
• A.J. Wilczyńska-Piliszek, S. Piliszek, and J. Falandysz, QSAR and ANN for the estimation of water solubility of 209 polychlorinated trans-azobenzenes, J. Environ. Sci. Health, Part A 47 (2012), pp. 155–166. doi:10.1080/10934529.2012.640243.
   Web of Science ®Google Scholar
• X.L. Zeng, H.J. Wang, and Y. Wang, QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method, Chemosphere 86 (2012), pp. 619–625. doi:10.1016/j.chemosphere.2011.10.051.
   PubMed Web of Science ®Google Scholar
• R.N. Das and K. Roy, QSPR with extended topochemical atom (ETA) indices. 4. Modeling aqueous solubility of drug like molecules and agrochemicals following OECD guidelines, Struct. Chem. 24 (2013), pp. 303–331. doi: 10.1007%2Fs11224-012-0080-5.
   Web of Science ®Google Scholar
• A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, D. Leszczynska, and J. Leszczynski, CORAL: QSPR model of water solubility based on local and global SMILES attributes, Chemosphere 90 (2013), pp. 877–880. doi:10.1016/j.chemosphere.2012.07.035.
   PubMed Web of Science ®Google Scholar
• C.I. Cappelli, S. Manganelli, A. Lombardo, A. Gissi, and E. Benfenati, Validation of quantitative structure-activity relationship models to predict water-solubility of organic compounds, Sci. Total Environ. 463 (2013), pp. 781–789. doi:10.1016/j.scitotenv.2013.06.081.
   PubMed Web of Science ®Google Scholar
• M. Kim, L.Y. Li, and J.R. Grace, Predictability of physicochemical properties of polychlorinated dibenzo-p-dioxins (PCDDs) based on single-molecular descriptor models, Environ. Pollut. 213 (2016), pp. 99–111. doi:10.1016/j.envpol.2016.02.007.
   PubMed Web of Science ®Google Scholar
• A. Bouakkadia, H. Haddag, N. Bouarra, and D. Messadi, QSPR study of the water solubility of a diverse set of agrochemicals: Hybrid (GA/MLR) approach, Synth. Rev. Des. Sci. La Technol. 32 (2016), pp. 12–21. Available at https://www.ajol.info/index.php/srst/article/view/135870.
   Google Scholar
• K. Klimenko, V. Kuz’min, L. Ognichenko, L. Gorb, M. Shukla, N. Vinas, E. Perkins, P. Polishchuk, A. Artemenko, and J. Leszczynski, Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility, J. Comput. Chem. 37 (2016), pp. 2045–2051. doi:10.1002/jcc.24424.
   PubMed Web of Science ®Google Scholar
• Q. Zang, K. Mansouri, A.J. Williams, R.S. Judson, D.G. Allen, W.M. Casey, and N.C. Kleinstreuer, In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning, J. Chem. Inf. Model. 57 (2017), pp. 36–49. doi:10.1021/acs.jcim.6b00625.
   PubMed Web of Science ®Google Scholar
• S.E. Fioressi, D.E. Bacelo, C. Rojas, J.F. Aranda, and P.R. Duchowicz, Conformation-independent quantitative structure-property relationships study on water solubility of pesticides, Ecotox. Environ. Safe. 171 (2019), pp. 47–53. doi:10.1016/j.ecoenv.2018.12.056.
   PubMed Web of Science ®Google Scholar
• S. Kherouf, N. Bouarra, A. Bouakkadia, and D. Messadi, Modeling of linear and nonlinear quantitative structure property relationships of the aqueous solubility of phenol derivatives, J. Serb. Chem. Soc. 84 (2019), pp. 575–590. doi:10.2298/JSC180820016K.
   Web of Science ®Google Scholar
• D. Kempińska, T. Chmiel, A.K. Wasik, A. Mróz, Z. Mazerska, and J. Namieśnik, State of the art and prospects of methods for determination of lipophilicity of chemical compounds, TrAC Trends Anal. Chem. 113 (2019), pp. 54–73. doi:10.1016/j.trac.2019.01.011.
   Web of Science ®Google Scholar
• A. Noble, Partition coefficients (n-octanol-water) for pesticides, J. Chromatogr. 642 (1993), pp. 3–14. doi:10.1016/0021-9673(93)80072-G.
   Web of Science ®Google Scholar
• J. Devillers, Calculation of octanol/water partition coefficients for pesticides: A comparative study, SAR QSAR Environ. Res. 10 (1999), pp. 249–262. doi:10.1080/10629369908039179.
   PubMed Web of Science ®Google Scholar
• F. Wania and D. Mackay, The evolution of mass balance models of persistent organic pollutant fate in the environment, Environ. Pollut. 100 (1999), pp. 223–240. doi:10.1016/S0269-7491(99)00093-7.
   PubMed Web of Science ®Google Scholar
• M. Chessells, D.W. Hawker, and D.W. Connell, Critical evaluation of the measurement of the 1- octanol/water partition coefficient of hydrophobic compounds, Chemosphere 22 (1991), pp. 1175–1190. doi:10.1016/0045-6535(91)90213-W.
   Web of Science ®Google Scholar
• A. Paschke, P.L. Neitzel, W. Walther, and G. Schuurmann, Octanol/water partition coefficient of selected herbicides: Determination using shake-flask method and reversed-phase high-performance liquid chromatography, J. Chem. Eng. Data 49 (2004), pp. 1639–1642. doi:10.1021/je049947x.
   Web of Science ®Google Scholar
• R. Mannhold, G.I. Poda, C. Ostermann, and I.V. Tetko, Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds, J. Pharm. Sci. 98 (2009), pp. 861–893. doi:10.1002/jps.21494.
   PubMed Web of Science ®Google Scholar
• X. Hui-Ying, Z. Jian-Wei, H. Gui-Xiang, and W. Wei, QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs), Chemosphere 80 (2010), pp. 665–670. doi:10.1016/j.chemosphere.2010.04.050.
   PubMed Web of Science ®Google Scholar
• R.A. Manish and G. Madhu, QSAR prediction of n-octanol/water partition coefficient for polychlorinated biphenils, Int. J. Adv. Technol. Eng. Sci. 2 (2014), pp. 781–792. Available at http://www.ijates.com/images/short_pdf/1412369528_357_IJATES.pdf.
   Google Scholar
• A. Bouakkadia, L. Lourici, and D. Messadi, Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods, Manag. Environ. Qual. Int. J. 28 (2017), pp. 579–592. doi:10.1108/meq-08-2015-0162.
   Google Scholar
• T. Ferrari, A. Lombardo, and E. Benfenati, QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log kow case study, Sci. Total Environ. 637–638 (2018), pp. 1158–1165. doi:10.1016/j.scitotenv.2018.05.072.
   PubMed Web of Science ®Google Scholar
• R. Amiri, D. Messadi, and A. Bouakkadia, QSAR study of octanol/water partition coefficient of organophosphorous compounds: Hybrid (GA/MLR) approach and hybrid (GA/ANN), J. Serb. Chem. Soc. (2019), in press. doi:10.2298/JSC190610090A.
   Google Scholar
• B. Souyei, A.H. Seyd, F. Zaiz, and A. Rebiai, Application of inverse QSAR/QSPR analysis for pesticides structures generation, Acta Chim. Slov. 66 (2019), pp. 315–325. doi:10.17344/acsi.2018.4793.
   Web of Science ®Google Scholar
• K.B. Hanson, D.J. Hoff, T.J. Lahren, D.R. Mount, A.J. Squillace, and L.P. Burkhard, Estimating n-octanol-water partition coefficients for neutral highly hydrophobic chemicals using measured n-butanol-water partition coefficients, Chemosphere 218 (2019), pp. 616–623. doi:10.1016/j.chemosphere.2018.11.141.
   PubMed Web of Science ®Google Scholar
• E.N. Papadakis, Z. Vryzas, A. Kotopoulou, K. Kintzikoglou, K.C. Makris, and E. Papadopoulou-Mourkidou, A pesticide monitoring survey in rivers and lakes of northern Greece and its human and ecotoxicological risk assessment, Ecotoxicol. Environ. Safe. 116 (2015), pp. 1–9. doi:10.1016/j.ecoenv.2015.02.033.
   PubMed Web of Science ®Google Scholar
• P. Otieno, P.O. Owuor, J.O. Lalah, G. Pfister, and K.W. Schramm, Monitoring the occurrence and distribution of selected organophosphates and carbamate pesticide residues in the ecosystem of Lake Naivasha, Kenya, Toxicol. Environ. Chem. 97 (2015), pp. 51–61. doi:10.1080/02772248.2014.942309.
   Web of Science ®Google Scholar
• S.I. Sandler, S.-T. Lin, and A.K. Sum, The use of quantum chemistry to predict phase behavior for environmental and process engineering, Fluid Phase Equilib. 194 (2002), pp. 61–75. doi:10.1016/S0378-3812(01)00643-4.
   Web of Science ®Google Scholar
• E.P. Meulenberg, W.H. Mulder, and P.G. Stoks, Immunoassays for pesticides, Environ. Sci. Technol. 29 (1995), pp. 553–561. doi:10.1021/es00003a001.
   PubMed Web of Science ®Google Scholar
• L. Mamy, D. Patureau, E. Barriuso, C. Bedos, F. Bessac, X. Louchart, F. Martin-Iaurent, C. Miege, and P. Benoit, Prediction of the fate of organic compounds in the environment from their molecular properties: A review, Crit. Rev. Environ. Sci. Technol. 45 (2015), pp. 1277–1377. doi:10.1080/10643389.2014.955627.
   PubMed Web of Science ®Google Scholar
• M. Staikova, F. Wania, and D.J. Donaldson, Molecular polarizability as a single parameter predictor of vapour pressures and octanol-air partitioning coefficients of non-polar compounds: A priori approach and results, Atmos. Environ. 38 (2004), pp. 213–225. doi:10.1016/j.atmosenv.2003.09.055.
   Web of Science ®Google Scholar
• K. Nakajoh, M. Grabda, S. Oleszek-Kudlak, E. Shibata, F. Eckert, and T. Nakamura, Prediction of vapour pressures of chlorobenzenes and selected polychlorinated biphenyls using the COSMO-RS model, J. Mol. Struct.: Theochem 895 (2009), pp. 9–17. doi:10.1016/j.theochem.2008.10.015.
   Web of Science ®Google Scholar
• J.A. Lazzús, Prediction of solid vapor pressures for organic and inorganic compounds using a neural network, Thermochim. Acta 489 (2009), pp. 53–62. doi:10.1016/j.tca.2009.02.001.
   Web of Science ®Google Scholar
• N. Goudarzi and M. Goodarzi, Prediction of the vapor pressure of some halogenated methyl-phenylether (anisole) compounds using linear and nonlinear QSPR methods, Mol. Phys. 107 (2009), pp. 1615–1620. doi:10.1080/00268970902994293.
   Web of Science ®Google Scholar
• S.C. Basak and D. Mills, Predicting the vapour pressure of chemicals from structure: A comparison of graph theoretic versus quantum chemical descriptors, SAR QSAR Environ. Res. 20 (2009), pp. 119–132. doi:10.1080/10629360902726007.
   PubMed Web of Science ®Google Scholar
• A.J. Wilczyńska-Piliszek, S. Piliszek, and J. Falandysz, QSPR for prediction of subcooled vapor pressures (logPL) of polychlorinated trans-azobenzenes, J. Environ. Sci. Health B. 47 (2012), pp. 660–669. doi:10.1080/03601234.2012.669206.
   Google Scholar
• X.L. Zeng, X.L. Zhang, and Y. Wang, QSPR modeling of n-octanol/air partition coefficients and liquid vapor pressures of polychlorinated dibenzo-p-dioxins, Chemosphere 91 (2013), pp. 229–232. doi:10.1016/j.chemosphere.2012.12.060.
   PubMed Web of Science ®Google Scholar
• Vikas and Chayawan, Externally predictive quantitative modeling of supercooled liquid vapor pressure of polychlorinated-naphthalenes through electron-correlation based quantum-mechanical descriptors, Chemosphere 95 (2014), pp. 448–454. doi:10.1016/j.chemosphere.2013.09.093.
   PubMed Web of Science ®Google Scholar
• M. Honarmand, E. Sanjari, and H. Badihi, Prediction of saturated vapor pressures using non-linear equations and artificial neural network approach, J. Math. Comput. Sci. 8 (2014), pp. 343–358. doi:10.22436/jmcs.08.04.03.
   Google Scholar
• A. Tarjomannejad, Prediction of the liquid vapor pressure using the artificial neural network-group contribution method, Iran. J. Chem. Chem. Eng. 34 (2015), pp. 97–111. Available at http://www.ijcce.ac.ir/article_15461_af78a0f1cf78790f947437f91243d373.pdf.
   Google Scholar
• M. Goodarzi, L.D.S. Coelho, B. Honarparvar, E.V. Ortiz, and P.R. Duchowicz, Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides, Ecotox. Environ. Safe. 128 (2016), pp. 52–60. doi:10.1016/j.ecoenv.2016.01.020.
   PubMed Web of Science ®Google Scholar