Nereis. Interdisciplinary Ibero-American Journal of Methods, Modelling and Simulation.

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Environmental toxicity prediction using computational tools: prediction of potential hazardous effects of chemicals in Lactuca sativa seed germination

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Abstract

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of phytotoxicity effects of chemical compounds on the Lactuca sativa seeds germination. A database of 73 compounds, assayed against L. sativa and Dragon’s molecular descriptors are used to obtain a QSAR model for the prediction of the phytotoxicity. The model is carried out with QSARINS software and validated according to OECD principles. The best model showed good value for the determination coefficient (R2 = 0.917) and others parameters appropriate for fitting (s = 0.256 and RMSEtr= 0.236). The validation results confirmed that the model has good robustness and stability (Q2LOO = 0.874 and Q2LMO= 0.875), an excellent predictive power (R2ext = 0.896) and was product of a non-random correlation (R2Y-scr = 0.130 and Q2Y-scr = -0.265). Finally, we can say that this model is a good predictor tool to predict the toxicity over L. sativa of chemical compounds.

Keywords: ecotoxicity, Lactuca sativa, phytotoxicity, QSARINS software.

References

Radisky DC, Levy DD, Littlepage LE, Liu H, Nelson CM, Fata JE, et al. Rac1b and reactive oxygen species mediate MMP-3-induced EMT and genomic instability. Nature. 2005;436:123.

D’Abrosca B, Fiorentino A, Izzo A, Cefarelli G, Pascarella MT, Uzzo P, et al. Phytotoxicity evaluation of five pharmaceutical pollutants detected in surface water on germination and growth of cultivated and spontaneous plants. J Environ Sci Health A. 2008;43(3):285-94.

Deng M, Zhang Y, Quan X, Na C, Chen S, Liu W, et al. Acute toxicity reduction and toxicity identification in pigment-contaminated wastewater during anaerobic-anoxic-oxic (A/A/O) treatment process. Chemosphere. 2017;168:1285-92.

Sánchez-Morales M, Sabater F, Muñoz I. Effects of urban wastewater on hyporheic habitat and invertebrates in Mediterranean streams. Sci Total Environ. 2018;642:937-45.

Ziajahromi S, Neale PA, Leusch FDL. Wastewater treatment plant effluent as a source of microplastics: review of the fate, chemical interactions and potential risks to aquatic organisms. Wat Sci Tech. 2016;74(10):2253-69.

Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R. A novel approach to predict aquatic toxicity from molecular structure. Chemosphere. 2008;73:415-27.

Dieguez-Santana K, Pham-The H, Villegas-Aguilar PJ, Le-Thi-Thu H, Castillo-Garit JA, Casañola-Martin GM. Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database. Chemosphere. 2016;165:434-41.

Salahinejad M, Ghasemi JB. 3D-QSAR studies on the toxicity of substituted benzenes to Tetrahymena pyriformis: CoMFA, CoMSIA and VolSurf approaches. Ecotoxicol Environ Safety. 2014;105:128-34.

Netzeva TI, Schultz TW. QSARs for the aquatic toxicity of aromatic aldehydes from Tetrahymena data. Chemosphere. 2005;61(11):1632-43.

Nicolau A, Mota M, Lima N. Effect of different toxic compounds on ATP content and acid phosphatase activity in axenic cultures of Tetrahymena pyriformis. Ecotox Environ Safe. 2004;57(2):129-35.

Wang W. Root elongation method for toxicity testing of organic and inorganic pollutants. Environ Toxicol Chem. 1987;6(5):409-14.

Adema DMM, Henzen L. A comparison of plant toxicities of some industrial chemicals in soil culture and soilless culture. Ecotox Environ Safe. 1989;18(2):219-29.

Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R, Rodríguez-Borges JE. Applications of Bond-Based 3D-Chiral Quadratic Indices in QSAR Studies Related to Central Chirality Codification. QSAR & Comb Sci. 2009;28:1465-77.

Brito-Sánchez Y, Castillo-Garit JA, Le-Thi-Thu H, González-Madariaga Y, Torrens F, Marrero-Ponce Y, et al. Comparative study to predict toxic modes of action of phenols from molecular structures. SAR QSAR Environ Res. 2013;24(3):235-51.

Castillo-Garit JA, del Toro-Cortés O, Vega MC, Rolón M, Rojas de Arias A, Casañola-Martin GM, et al. Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening. Eur J Med Chem. 2015;96:238-44.

Cañizares-Carmenate Y, Mena-Ulecia K, Perera-Sardiña Y, Torrens F, Castillo-Garit JA. An approach to identify new antihypertensive agents using Thermolysin as model: In silico study based on QSARINS and docking. Arab J Chem. 2016. doi: http://dx.doi.org/10.1016/j.arabjc.2016.10.003

Le-Thi-Thu H, Cañizares-Carmenate Y, Marrero-Ponce Y, Torrens F, Castillo-Garit JA. Prediction of Caco-2 Cell Permeability Using Bilinear Indices and Multiple Linear Regression. Lett Drug Des Discov. 2016;13(2):161-9.

Castillo-Garit JA, Abad C, Casañola-Martin GM, Barigye SJ, Torrens F, Torreblanca A. Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles. Curr Pharm Des. 2016;22(33):5085-94.

Castillo-Garit JA, Casañola-Martin GM, Le-Thi-Thu H, Pham-The H, Barigye SJ. A Simple Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Classification Trees. Med Chem. 2017;13(7):664-9.

Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S. QSARINS, software for QSAR MLR model development and validation: QSAR Res. Unit in Environ. Chem. and Ecotox. Varese (Italy): University of Insubria; 2013. Available at: http://www.qsar.it

Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S. QSARINS: A new software for the development, analysis and validation of QSAR MLR models. J Comput Chem (Software News and Updates). 2013;34(24):2121-32.

Gramatica P, Cassani S, Chirico N. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J Comput Chem (Software News and Updates). 2014;35(13):1036-44.

Hulzebos EM, Adema DMM, Dirven-van Breemen EM, Henzen L, van Gestel CAM. QSARs in phytotoxicity. Sci Total Environ. 1991;109-110:493-7.

Pan M, Chu LM. Phytotoxicity of veterinary antibiotics to seed germination and root elongation of crops. Ecotox Environ Safe. 2016;126:228-37.

Kode SRL. Dragon (software for molecular descriptor calculation) version 7.0.10; 2017. Available at: https://chm.kode-solutions.net

Worth AP, Bassan A, De Bruijn J, Gallegos Saliner A, Netzeva T, Patlewicz G, et al. The role of the European Chemicals Bureau in promoting the regulatory use of (Q)SAR methods. SAR and QSAR in Environmental Research. 2007;18(1-2):111-25.

Cañizares-Carmenate Y, Hernandez-Morfa M, Torrens F, Castellano G, Castillo Garit JA. Larvicidal activity prediction against Aedes aegypti mosquito using computational tools. J Vector Borne Dis. 2017;54(2):164-71.

Castillo Morales G, editor. Ensayos toxicológicos y métodos de evaluación de calidad de aguas: Estandarización, intercalibración, resultados y aplicaciones. Canadá: IDRC; 2004.

OECD. Test No. 208: Terrestrial Plant Test: Seedling Emergence and Seedling Growth Test2006.

Greene J, Bartels C, Warren-Hicks W, Parkhurst B. G. L. Protocols for short term toxicity screening of hazardous waste sites. In: Agency USEP, editor. Washington D. C.; 1988.

Friedman JH. Multivariate Adaptive Regression Splines. Ann Stat. 1991;19(1):1-67.

Friedman JH. Rejoinder: Multivariate Adaptive Regression Splines. Ann Stat. 1991;19(1):123-41.

Todeschini R, Consonni V, Maiocchi A. The K correlation index: theory development and its application in chemometrics. Chemom Intell Lab Syst. 1999;46(1):13-29.

Chirico N, Gramatica P. 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. 2011;51(9):2320.

Chirico N, Gramatica P. 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. 2012;52(8):2044.

Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255-68.

Golbraikh A, Tropsha A. Beware of q 2! J Mol Graphics Model. 2002;20(4):269-76.

Shi LM, Fang H, Tong W, Wu J, Perkins R, Blair RM, et al. QSAR models using a large diverse set of estrogens. J Chem Inf Comput Sci. 2001;41(1):186-95.

Schüürmann G, Ebert R, Chen J, Wang B, Kühne R. External Validation and Prediction Employing the Predictive Squared Correlation Coefficient - Test Set Activity Mean vs Training Set Activity Mean. J Chem Inf Model. 2008;48(11):2140-5.

Consonni V, Ballabio D, Todeschini R. Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model. 2009;49(7):1669-78.

Ojha PK, Mitra I, Das RN, Roy K. Further exploring rm 2 metrics for validation of QSPR models. Chemom Intell Lab Syst. 2011;107(1):194-205.

Atkinson AC. Plots, Transformations, and Regression. An Introduction to Graphical Methods of Diagnostic Regression Analysis. Clarendon Press; 1985.

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