Conformation-independent quantitative structure-property relationships study on water solubility of pesticides
Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 °C of 1211 approve...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , , , |
| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Academic Press
2019
|
| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| Sumario: | Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 °C of 1211 approved heterogeneous pesticide compounds, collected from the online Pesticides Properties Data Base (PPDB). Validated and generally applicable Multivariable Linear Regression (MLR) models were established, including molecular descriptors carrying constitutional and topological aspects of the analyzed compounds. The most representative descriptors were selected from the exploration of a large number of about 18,000 structural variables. A hybrid approach that involves a molecular descriptor, a fingerprint, and a flexible descriptor showed the best predictive performance. © 2018 Elsevier Inc. |
|---|---|
| Bibliografía: | (2017), Agency, USEP Finalization of Guidance on Incorporation of Water Treatment Effects on Pesticide Removal and Transformations in Drinking Water Exposure Assessments; Ali, J., Camilleri, P., Brown, M.B., Hutt, A.J., Kirton, S.B., In silico prediction of aqueous solubility using simple QSPR models: the importance of phenol and phenol-like moieties (2012) J. Chem. Inf. Model., 52, pp. 2950-2957 Ali, J., Camilleri, P., Brown, M.B., Hutt, A.J., Kirton, S.B., Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area (2012) J. Chem. Inf. Model., 52, pp. 420-428 Bhhatarai, B., Gramatica, P., Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals (2010) Environ. Sci. Technol., 45, pp. 8120-8128 Cappelli, C.I., Manganelli, S., Lombardo, A., Gissi, A., Benfenati, E., Validation of quantitative structure–activity relationship models to predict water-solubility of organic compounds (2013) Sci. Total Environ., 463, pp. 781-789 Clarke, E.D., Delaney, J.S., Physical and molecular properties of agrochemicals: an analysis of screen inputs, hits, leads, and products (2003) CHIMIA Int. J. Chem., 57, pp. 731-734 Cronin, M.T.D., Livingstone, D.J., Predicting Chemical Toxicity and Fate (2004), CRC press Boca Raton; Das, R.N., Roy, K., QSPR with extended topochemical atom (ETA) indices. 4. Modeling aqueous solubility of drug like molecules and agrochemicals following OECD guidelines (2013) Struct. Chem., 24, pp. 303-331 Delaney, J.S., ESOL: estimating aqueous solubility directly from molecular structure (2004) J. Chem. Inf. Comput. Sci., 44, pp. 1000-1005 Duchowicz, P.R., Castro, E.A., Fernández, F.M., Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies (2006) MATCH Commun. Math. Comput. Chem., 55, pp. 179-192 Duchowicz, P.R., Comelli, N.C., Ortiz, E.V., Castro, E.A., QSAR study for carcinogenicity in a large set of organic compounds (2012) Curr. Drug Saf., 7, pp. 282-288 Duchowicz, P.R., Fioressi, S.E., Bacelo, D.E., Saavedra, L.M., Toropova, A.P., Toropov, A.A., QSPR studies on refractive indices of structurally heterogeneous polymers (2015) Chemom. Intell. Lab. Syst., 140, pp. 86-91 Duchowicz, P.R., Fioressi, S.E., Castro, E., Wróbel, K., Ibezim, N.E., Bacelo, D.E., Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors (2017) ChemistrySelect, 2, pp. 3725-3731 Eriksson, L., Jaworska, J., Worth, A.P., Cronin, M.T., McDowell, R.M., Gramatica, P., Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs (2003) Environ. Health Perspect., 111, p. 1361 Gadaleta, D., Mangiatordi, G.F., Catto, M., Carotti, A., Nicolotti, O., Applicability domain for QSAR models: where theory meets reality (2016) Int. J. Quant. Struct.-Prop. Relatsh., 1, pp. 45-63 Golbraikh, A., Tropsha, A., Beware of q2! (2002) J. Mol. Graph Model, 20, pp. 269-276 Gramatica, P., Principles of QSAR models validation: internal and external (2007) QSAR Comb. Sci., 26, pp. 694-701 Hamadache, M., Amrane, A., Benkortbi, O., Hanini, S., Khaouane, L., Moussa, C.S., Environmental Toxicity of Pesticides, and its Modeling by Qsar Approaches, Advances in QSAR Modeling (2017), pp. 471-501. , Springer Cham; Hamilton, D., Ambrus, A., Dieterle, R., Felsot, A., Harris, C., Holland, P., Katayama, A., Unsworth, J., Regulatory limits for pesticide residues in water (IUPAC Technical Report) (2003) Pure Appl. Chem., 75, pp. 1123-1155 Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Tong, W., Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics (2008) J. Chem. Inf. Model., 48, pp. 1337-1344 Kim, M., Li, L.Y., Grace, J.R., Predictability of physicochemical properties of polychlorinated dibenzo-p-dioxins (PCDDs) based on single-molecular descriptor models (2016) Environ. Pollut., 213, pp. 99-111 Lewis, K., Green, A., Tzilivakis, J., Warner, D., The pesticide properties database (ppdb) developed by the agriculture & environment research unit (AERU) (2018) Univ. Herts., pp. 2006-2018 Mas, S., de Juan, A., Tauler, R., Olivieri, A.C., Escandar, G.M., Application of chemometric methods to environmental analysis of organic pollutants: a review (2010) Talanta, 80, pp. 1052-1067 (2007), OECD Guidance Document On The Validation of (Quantitative) Structure-Activity Relationship [(Q)Sar] Models, Environment Health and Safety Publications Series on Testing and Assesment No. 69; Ran, Y., He, Y., Yang, G., Johnson, J.L., Yalkowsky, S.H., Estimation of aqueous solubility of organic compounds by using the general solubility equation (2002) Chemosphere, 48, pp. 487-509 Rojas, C., Duchowicz, P.R., Tripaldi, P., Diez, R.P., QSPR analysis for the retention index of flavors and fragrances on a OV-101 column (2015) Chemom. Intell. Lab. Syst., 140, pp. 126-132 Roy, K., On some aspects of validation of predictive quantitative structure–activity relationship models (2007) Expert Opin. Drug Discov., 2, pp. 1567-1577 Talevi, A., Bellera, C.L., Di Ianni, M., Duchowicz, P.R., Bruno-Blanch, L.E., Castro, E.A., An integrated drug development approach applying topological descriptors (2012) Curr. Comput. Aided Drug Des., 8, pp. 172-181 Tebes-Stevens, C., Patel, J.M., Koopmans, M., Olmstead, J., Hilal, S.H., Pope, N., Weber, E.J., Wolfe, K., Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments (2018) Chemosphere, 194, pp. 94-106 Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., CORAL: QSPR model of water solubility based on local and global SMILES attributes (2013) Chemosphere, 90, pp. 877-880 Toropov, A.A., Toropova, A.P., Benfenati, E., Nicolotti, O., Carotti, A., Nesmerak, K., Veselinović, A.M., Bacelo, D., QSPR/QSAR analyses by means of the CORAL software: results, challenges, perspectives, pharmaceutical sciences: breakthroughs in research and practice (2017) IGI Glob., pp. 929-955 Toropova, A., Toropov, A., Martyanov, S., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna (2012) Chemom. Intell. Lab. Syst., 110, pp. 177-181 Verma, R.P., Hansch, C., An approach toward the problem of outliers in QSAR (2005) Bioorg. Med. Chem., 13, pp. 4597-4621 Villaverde, J.J., Sevilla-Morán, B., López-Goti, C., Alonso-Prados, J.L., Sandín-España, P., Computational methodologies for the risk assessment of pesticides in the European Union (2017) J. Agric. Food Chem., 65, pp. 2017-2018 Villaverde, J.J., Sevilla-Morán, B., López-Goti, C., Alonso-Prados, J.L., Sandín-España, P., Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework (2018) Sci. Total Environ., 634, pp. 1530-1539 Wilczyńska-Piliszek, A.J., Piliszek, S., Falandysz, J., QSAR and ANN for the estimation of water solubility of 209 polychlorinated trans-azobenzenes (2012) J. Environ. Sci. Health, Part A, 47, pp. 155-166 Wishart, D.S., Feunang, Y.D., Marcu, A., Guo, A.C., Liang, K., Vázquez-Fresno, R., Sajed, T., Karu, N., HMDB 4.0: the human metabolome database for 2018 (2017) Nucleic Acids Res., 46, pp. D608-D617 Wold, S., Eriksson, L., Clementi, S., Statistical validation of QSAR results (1995) Chemom. Methods Mol. Des., pp. 309-338 Yap, C.W., PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints (2011) J. Comput. Chem., 32, pp. 1466-1474 Zeng, X.-L., Wang, H.-J., Wang, Y., QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method (2012) Chemosphere, 86, pp. 619-625 |
| ISSN: | 01476513 |
| DOI: | 10.1016/j.ecoenv.2018.12.056 |