Desenvolvimento de Modelos Computacionais para Avaliação Preditiva de Ecotoxicidade em Abelhas: Desafios Atuais
DOI:
https://doi.org/10.21664/2238-8869.2019v8i2.p132-146Palavras-chave:
Polinizadores, Toxicologia Computacional, Inteligência Artificial, Modelagem PreditivaResumo
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.
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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.
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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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Cajá DF, Silva RA, Santos AS, Souza FS, Silva SS, Silva VLS, Andrade ABA 2015. Frequência de visitas de abelhas africanizadas Apis melífera L) em flores de chanana (Turnera ulmifolia L.). ACSA - Agropecuária Científica no Semiárido, 11(1):164-169.
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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: 26 dez. 2024.
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Dossiê - Tecnologias, Inovação e Sustentabilidade
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