Estudio de la Relación Cuantitativa Estructura-Actividad de pesticidas mediante técnicas de clasificación

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Fernando Cárdenas
Piercosimo Tripaldi
Cristian Rojas

Resumen

El objetivo de este trabajo fue la comparación entre los métodos de clasificación del vecino más cercano (κ-NN) y las redes neuronales artificiales de contrapropagación (CP-ANN) para modelar la toxicidad de un conjunto de 192 pesticidas organoclorados, organofosforados, carbamatos y piretroides, medidos como Concentración Efectiva (EC50) y que fueron divididos en tres clases, es decir, baja, intermedia y alta toxicidad. Se calcularon 4885 descriptores moleculares usando el programa DRAGON, los que fueron simultáneamente analizados mediante el método κ-NN acoplado con la técnica de selección de variables de los Algoritmos Genéticos (GA-VSS). Los modelos fueron apropiadamente validados mediante un subconjunto de predicción. Los resultados claramente sugieren que los descriptores 3D no ofrecen información relevante para modelar las clases. Por otro lado, κ-NN muestra mejores resultados que CP-ANN.

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Cómo citar
Cárdenas, F., Tripaldi, P., & Rojas, C. (2014). Estudio de la Relación Cuantitativa Estructura-Actividad de pesticidas mediante técnicas de clasificación. ACI Avances En Ciencias E Ingenierías, 6(2). https://doi.org/10.18272/aci.v6i2.169
Sección
SECCIÓN A: CIENCIAS EXACTAS Y FÍSICAS
Biografía del autor/a

Fernando Cárdenas, Universidad Politécnica Salesiana

Grupo de Investigación en Biotecnología y Ambiente (INBIAM). Escuela de Ingeniería Ambiental. Universidad Politécnica Salesiana.
Calle Vieja 12-30 y Elia Liut, Cuenca-Ecuador.

Piercosimo Tripaldi, Universidad del Azuay

Laboratorio UDALAB, Facultad de Ciencia y Tecnología, Universidad del Azuay.
Av. 24 de Mayo 7-77 y Hernán Malo, Apartado postal 01.01.981. Cuenca-Ecuador.

Cristian Rojas, Universidad Nacional de la Plata

Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), Universidad Nacional de la Plata.
Diagonal 113 y calle 64, C.C. 16, Suc. 4 (1900), La Plata-Argentina.

Citas

[1] Jaramillo, B.; Martelo, I.; Duarte, E. 2013. “Toxicidad Aguda de Pesticidas Organofosforados y Análisis de la Relación Cuantitativa de Estructura Actividad (QSAR)”. Biotecnología en el Sector Agropecuario y Agroindustrial, 11: 76-84.

[2] Castillo Morales, G. 2004. “Ensayos toxicológicos y métodos de evaluación de calidad de aguas; estandarización, intercalibración, resultados y aplicaciones”. IDRC/IMTA.

[3] Mazzatorta, P; Smiesko, M.; Lo Piparo, E.; Benfenati, E. 2005. “QSAR model for predicting pesticide aquatic toxicity”. Journal of chemical information and modeling, 45: 1767-1774.

[4] Zvinavashe, E.; Du, T.; Griff, H. T. van den Berg; Soffers, A.; Vervoort, J.; Murk, A.; Rietjens, I. 2009. “Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio”. Chemosphere, 75: 1531-1538.

[5] Toropov, A.; Benfenati, E. 2006. “QSAR models for Daphnia toxicity of pesticides based on combinations of topological parameters of molecular structures”. Bioorganic & medicinal chemistry, 14: 2779-2788.

[6] Duchowicz, P; Castro, E. 2013. “The Importance of the QSAR-QSPR Methodology to the Theoretical Sudy of Pesticides”. International Journal of Chemical Modeling, 5: 35-50.

[7] Hansch, C.; Hoekman, D.; Leo, A.; Weininger, D.; Selassie, C. 2002. “Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology”. Chemical reviews, 102: 783-812.

[8] Frank, I.; Todeschini, R. 1994. “The data analysis handbook”. Elsevier.

[9] Todeschini, R. 1998. “Introduzione alla chemiometria”. Edi SES, Napoli, Italia.

[10] Zupan, J.; Novic, M.; Gasteiger, J. 1995. “Neural networks with counter-propagation learning strategy used for modelling”. Chemometrics and intelligent laboratory systems, 27: 175-187.

[11] Zupan, J.; Novic, M.; Ruisánchez, I. 1997. “Kohonen and counterpropagation artificial neural networks in analytical chemistry”. Chemometrics and intelligent laboratory systems, 38: 1-23.

[12] Leardi, R. 2001. “Genetic algorithms in chemometrics and chemistry: a review”. Journal of Chemometrics, 15: 559-569.

[13] Leardi, R. 2003. “Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks”. Elsevier.

[14] Leardi, R. 2007. “Genetic algorithms in chemistry”. J. Chromatogr. A, 1158: 226-233.

[15] University of Hertfordshire. 2013. “PPDB: Pesticide Properties DataBase”. http://sitem.herts.ac.uk/aeru/ppdb/en/index.htm.

[16] Lewis, K.; Green, A. 2011. “The Pesticide Properties Database”. Chemistry International.

[17] Todeschini, R.; Consonni, V. 2009. “Molecular Descriptors for Chemoinformatics”. WILEY-VCH, Weinheim.

[18] Hypercube, Inc. 2014. “HyperChem”. http://www.hyper.com.

[19] TALETE. 2014. “DRAGON, Software for Molecular Descriptor Calculation”. http://www.talete.mi.it/.

[20] Cho, S.; Hermsmeier, M. 2002. “Genetic algorithm guided selection: variable selection and subset selection”. Journal of chemical information and computer sciences, 42: 927-936.

[21] Fan, Y.; Shi, L.; Kohn, K.; Pommier, Y.; Weinstein, J. 2001. “Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies”. Journal of medicinal chemistry, 44: 3254-3263.

[22] Fernandez, M.; Caballero, J.; Fernandez, L.; Sarai, A. 2011. “Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)”. Molecular diversity, 15: 269-289.

[23] Gao, H. 2001. “Application of BCUT metrics and genetic algorithm in binary QSAR analysis”. Journal of chemical information and computer sciences, 41: 402-407.

[24] Ghosh, P; Bagchi, M. 2009. “QSAR modeling for quinoxaline derivatives using genetic algorithm and simulated annealing based feature selection”. Current medicinal chemistry, 16: 4032-4048.

[25] Hemmateenejad, B. 2004. “Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm-based PCR”. Journal of Chemometrics, 18: 475-485.

[26] Hemmateenejad, B.; Akhond, M.; Miri, R.; Shamsipur, M. 2003. “Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1, 4-dihydropyridines (nifedipine analogous)”. Journal of chemical information and computer sciences, 43: 1328-1334.

[27] Hemmateenejad, B.; Miri, R.; Akhond, M.; Shamsipur, M. 2002. “QSAR study of the calcium channel antagonist activity of some recently synthesized dihydropyridine derivatives. An application of genetic algorithm for variable selection in MLR and PLS methods”. Chemometrics and intelligent laboratory systems, 64: 91-99.

[28] Hoffman, B.; Kopajtic, T.; Katz, J.; Newman, A. 2000. “2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors”. Journal of medicinal chemistry, 43: 4151-4159.

[29] Leardi, R.; Lupiáñez González, A. 1998. “Genetic algorithms applied to feature selection in PLS regression: how and when to use them”. Chemom. Intell. Lab. Syst, 41: 195-207.

[30] Leardi, R.; Seasholtz, M.; Pell, R. 2002. “Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data”. Analytica Chimica Acta, 461: 189-200.

[31] Li, T.; Mei, H.; Cong, P 1999. “Combining nonlinear PLS with the numeric genetic algorithm for QSAR”. Chemometrics and intelligent laboratory systems, 45: 177-184.

[32] Saripinar, E.; Gejen, N.; Sahin, K.; Yanmaz, E. 2010. “Pharmacophore identification and bioactivity prediction for triaminotriazine derivatives by electron conformational-genetic algorithm QSAR method”. European journal of medicinal chemistry, 45: 4157-4168.

[33] Sutherland, J.; O’brien, L.; Weaver, D. 2003. “Splinefitting with a genetic algorithm: A method for developing classification structure-activity relationships”. Journal of chemical information and computer sciences, 43: 1906-1915.

[34] Taha, M.; Qandil, A.; Zaki, D.; AlDamen, M. 2005. “Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling”. European journal of medicinal chemistry, 40: 701-727.

[35] Turner, D.; Willett, P. 2000. “Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA)”. Journal of Computer-Aided Molecular Design, 14: 1-21.

[36] Wanchana, S.; Yamashita, F.; Hashida, M. 2003. “QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method”. Pharmaceutical research, 20: 1401-1408.

[37] Weber, L. 1998. “Applications of genetic algorithms in molecular diversity”. Current Opinion in Chemical Biology, 2: 381-385.

[38] Pavan, M.; Consonni, V; Gramatica, P; Todeschini, R. 2006. “New QSAR Modelling Approach Based on Ranking Models by Genetic Algorithms - Variable Subset Selection (GA-VSS)”. in: R. Brüggemann, L. Carlsen (Eds.) Partial Order in Environmental Sciences and Chemistry, Springer Berlin Heidelberg: 181-217.

[39] Ballabio, D.; Vasighi, M.; Consonni, V.; Kompany-Zareh, M. 2011. “Genetic algorithms for architecture optimisation of counter-propagation artificial neural networks”. Chemometrics and intelligent laboratory systems, 105: 56-64.

[40] Devillers, J. 2012. “Genetic Algorithms in Molecular Modeling (Principles of QSAR and Drug Design)”. Academic Press, New York.

[41] The MathWorks, Inc. 2014. “Matlab”. Masachussetts, USA, http://www.mathworks.com.

[42] Gemperline, P 2012. “Practical guide to chemometrics”. CRC press.

[43] Ballabio, D.; Consonni, V; Todeschini, R. 2009. “The Kohonen and CP-ANN toolbox: a collection of MATLAB modules for self organizing maps and counterpropagation artificial neural networks”. Chemometrics and intelligent laboratory systems, 98: 115-122.

[44] Ballabio, D.; Vasighi, M. 2012. “A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies”. Chemometrics and intelligent laboratory systems, 118: 24-32.

[45] Ferreira, M. 2002. “Multivariate QSAR”. Journal of the Brazilian Chemical Society, 13: 742-753.

[46] Benfenati,E. 2011. “Quantitative Structure-Activity Relationships (QSAR) for pesticide regulatory purposes”. Elsevier.

[47] Gramatica, P. 2007. “Principles of QSAR models validation: internal and external”. QSAR Comb. Sci, 26: 694-701.

[48] Brandmaier, S.; Peijnenburg, W.; Durjava, M.; Kolar, B.; Gramatica, P; Papa, E.; Bhhatarai, B.; Kovarich, S.; Cassani, S.; Roy, P 2014. “The QSPR-THESAURUS: The online platform of the CADASTER project”. Alternatives to laboratory animals: ATLA, 42: 13-24.