Desenvolvimento de Modelos Computacionais para Avaliação Preditiva de Ecotoxicidade em Abelhas: Desafios Atuais
DOI:
https://doi.org/10.21664/2238-8869.2019v8i2.p132-146Palabras clave:
Polinizadores, Toxicologia Computacional, Inteligência Artificial, Modelagem PreditivaResumen
Os produtos químicos perigosos, especialmente os agrotóxicos e produtos químicos industriais, vem sendo considerados responsáveis por uma queda dramática no número das abelhas. Consequentemente, a avaliação de risco ecológico de novos produtos químicos é vital e necessária. Uma vez que os ensaios experimentais em abelhas são caros, demorados e apresentam um problema ético, existe uma necessidade muito urgente de desenvolver métodos alternativos para avaliar a ecotoxicidade em abelhas. Na presente revisão, nós apresentamos os principais avanços tecnológicos utilizados para identificar e filtrar com segurança, compostos potencialmente tóxicos para abelhas. Em seguida, nós destacamos os principais esforços e armadilhas na integração, preparo e padronização de dados necessários para construção de modelos computacionais e sugerimos roteiros que possam contribuir para otimizar os resultados da pesquisa e conduzir a construção de modelos computacionais mais eficientes e preditivos.
Citas
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McKinney W 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, 1697900:51-56.
Millman KJ, Aivazis M 2011. Python for Scientists and Engineers. Computing in Science & Engineering, 13(2):9-12. doi:10.1109/MCSE.2011.36.
Ministério do Meio Ambiente. 2017. Manual de avaliação de risco ambiental de agrotóxicos para abelhas.
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Braga RC, Alves VM, Muratov EN, Strickland J, Kleinstreuer N, Trospsha A, Andrade CH 2017. Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals. Journal of Chemical Information and Modeling, 57(5):1013-1017. doi:10.1021/acs.jcim.7b00194.
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Cardoso FH, Moraes MVP, Serra J, Sarney Filho J 2002. Decreto no 4.074. Brasil.
Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, et al. 2014. QSAR modeling: where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12):4977-5010. doi:10.1021/jm4004285.
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Desneux N, Decourtye A, Delpuech JM 2007. The Sublethal Effects of Pesticides on Beneficial Arthropods. Annual Review of Entomology, 52(1):81-106. doi:10.1146/annurev.ento.52.110405.091440.
Devillers J, Pham-Delègue MH, Decourtye A, Budzinski H, Cluzeau S, Maurin G 2002. Structure-toxicity modeling of pesticides to honey bees. SAR and QSAR in Environmental Research, 13(7-8):641-648. doi:10.1080/1062936021000043391.
Eiri DM, Nieh JC 2016. A nicotinic acetylcholine receptor agonist affects honey bee sucrose responsiveness and decreases waggle dancing. The Journal of Experimental Biology, 219(13):2081-2081. doi:10.1242/jeb.143727.
Fairbrother A, Purdy J, Anderson T, Fell R 2014. Risks of neonicotinoid insecticides to honeybees. Environmental Toxicology and Chemistry, 33(4):719-731. doi:10.1002/etc.2527.
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, et al. 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(Database issue):D1100-D1107. doi:10.1093/nar/gkr777.
Goh GB, Hodas NO, Vishnu A 2017. Deep learning for computational chemistry. Journal of Computational Chemistry, 38(16):1291-1307. doi:10.1002/jcc.24764.
Golbraikh A, Tropsha A 2002. Beware of q2! Journal of molecular graphics & modelling, 20(4):269-276.
Gregorc A, Evans JD, Scharf M, Ellis JD 2012. Gene expression in honey bee (Apis mellifera) larvae exposed to pesticides and Varroa mites (Varroa destructor). Journal of Insect Physiology, 58(8):1042-1049. doi:10.1016/j.jinsphys.2012.03.015.
Hunter JD 2007. Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3):90-95. doi:10.1109/MCSE.2007.55.
Johansen CA, Mayer DF 2013. Pollinator Protection: A Bee & Pesticide Handbook. JC Lawrence (org.). Wicwas Press, Cheshire, CT.
Klein AM, Vaissiere BE, Cane JH, Steffan-Dewenter I, Cunningham SA, Kremen C, Tscharntke T 2007. Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society B: Biological Sciences, 274(1608):303-313. doi:10.1098/rspb.2006.3721.
Kockar H, Alper M, Sahin T, Haciismailoglu MS 2010. Co-Fe films: effect of Fe content on their properties. Journal of nanoscience and nanotechnology, 10(11): 7639-7642. doi:10.1007/978-1-4020-9783-6.
Korotcov A, Tkachenko V, Russo DP, Ekins S 2017. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. Molecular Pharmaceutics, 14(12):4462-4475. doi:10.1021/acs.molpharmaceut.7b00578.
Landrum 2014. RDKit: Open-Source Cheminformatics Software.
Li X, Zhang Y, Chen H, Li H, Zhao Y 2017. Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee. Journal of Chemical Information and Modeling, 57(12):2948-2957. doi:10.1021/acs.jcim.7b00476.
Li X, Bao C, Yang D, Zheng M, Li X, Tao S 2010. Toxicities of fipronil enantiomers to the honeybee Apis mellifera L. and enantiomeric compositions of fipronil in honey plant flowers. Environmental Toxicology and Chemistry, 29(1):127-132. doi:10.1002/etc.17.
Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V 2015. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. Journal of Chemical Information and Modeling, 55(2):263-274. doi:10.1021/ci500747n.
Martins ES 1996. Portaria Normativa Ibama no 84. Ibama, Brasil.
McKinney W 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, 1697900:51-56.
Millman KJ, Aivazis M 2011. Python for Scientists and Engineers. Computing in Science & Engineering, 13(2):9-12. doi:10.1109/MCSE.2011.36.
Ministério do Meio Ambiente. 2017. Manual de avaliação de risco ambiental de agrotóxicos para abelhas.
Morgan HL 1965. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. Journal of Chemical Documentation, 5(2):107-113. doi:10.1021/c160017a018.
OECD 1998. Test No. 214: Honeybees, Acute Contact Toxicity Test. doi:10.1787/9789264070189-en.
OECD 2004. OECD principles for the validation, for regulatory purposes, of (Quantitative) Structure-Activity Relationship models.
Oliphant TE 2007. Python for Scientific Computing. Computing in Science & Engineering, 9(3):10-20. doi:10.1109/MCSE.2007.58.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. 2012. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825-2830. doi:10.1007/s13398-014-0173-7.2.
Pettis JS, Lichtenberg EM, Andree M, Stitzinger J, Rose R, VanEngelsdorp D 2013. Crop Pollination Exposes Honey Bees to Pesticides Which Alters Their Susceptibility to the Gut Pathogen Nosema ceranae. Nascimento FS (org.). PLoS ONE, 8(7):e70182. doi:10.1371/journal.pone.0070182.
Pettis JS, VanEngelsdorp D, Johnson J, Dively G 2012. Pesticide exposure in honey bees results in increased levels of the gut pathogen Nosema. Naturwissenschaften, 99(2):153-158. doi:10.1007/s00114-011-0881-1.
Pires CSS, Pereira FM, Lopes MTR, Nocelli RCF, Malaspina O, Pettis JS, Teixeira EW 2016. Enfraquecimento e perda de colônias de abelhas no Brasil: há casos de CCD? Pesquisa Agropecuária Brasileira, 51(5):422-442. doi:10.1590/S0100-204X2016000500003.
Riniker S, Landrum GA 2013. Similarity maps - A visualization strategy for molecular fingerprints and machine-learning methods. Journal of Cheminformatics, 5(9):1-7. doi:10.1186/1758-2946-5-43.
Rogers D, Hahn M 2010. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 50(5):742-754. doi:10.1021/ci100050t.
Sarney J, Machado IR, Alves Filho J, Denys RB 1989. Lei no 7.802. Brasil.
Singh KP, Gupta S, Basant N, Mohan D 2014. QSTR Modeling for Qualitative and Quantitative Toxicity Predictions of Diverse Chemical Pesticides in Honey Bee for Regulatory Purposes. Chemical Research in Toxicology, 27(9):1504-1515. doi:10.1021/tx500100m.
Thompson HM, Fryday SL, Harkin S, Milner S 2014. Potential impacts of synergism in honeybees (Apis mellifera) of exposure to neonicotinoids and sprayed fungicides in crops. Apidologie, 45(5):545-553. doi:10.1007/s13592-014-0273-6.
Todeschini R, Consonni V 2000. Handbook of Molecular Descriptors. R Todeschini, V Consonni (orgs.). Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH, Weinheim, Germany, Germany. doi:10.1002/9783527613106.
Todeschini R, Consonni V 2009. Molecular Descriptors for Chemoinformatics. Wiley-VCH Verlag GmbH, Weinheim, Germany.
Tropsha A 2010. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, 29(6-7):476-488. doi:10.1002/minf.201000061.
Tylianakis JM 2013. The Global Plight of Pollinators. Science, 339(6127):1532-1533. doi:10.1126/science.1235464.
U.S. Environmental Protection Agency 1993. Pesticide Ecotoxicity Database (Formerly: Environmental Effects Database (EEDB)).
van der Walt S, Colbert SC, Varoquaux G 2011. The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13(2):22-30. doi:10.1109/MCSE.2011.37.
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2019-05-01
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LEMES, Josiel Araújo; SOUSA, José Elias Flosino; PEREIRA, Kamila Siqueira; LACERDA, Bruno Francisco Cardoso; ARAÚJO, Kamyla Cristina Barbosa; PEIXOTO, Josana de Castro; PINHEIRO ROSSETO, Lucimar; NAPOLITANO, Hamilton Barbosa; NEVES, Bruno Junior. Desenvolvimento de Modelos Computacionais para Avaliação Preditiva de Ecotoxicidade em Abelhas: Desafios Atuais. Fronteira: Journal of Social, Technological and Environmental Science, [S. l.], v. 8, n. 2, p. 132–146, 2019. DOI: 10.21664/2238-8869.2019v8i2.p132-146. Disponível em: https://revistas2.unievangelica.edu.br/index.php/fronteiras/article/view/3060. Acesso em: 22 nov. 2024.
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Dossiê - Tecnologias, Inovação e Sustentabilidade
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