构建基于GHS标准的黑头呆鱼(Pimephales promelas)急性毒性二元分类模型Development of GHS-based Binary Classification Models for Predicting Acute Toxicity of Fathead Minnow(Pimephales promelas)
王雅琪;刘会会;杨先海;
摘要(Abstract):
鱼类急性毒性参数是进行化学品生态风险评估、分类标签等工作不可或缺的毒性指标。本文选取634个有机化学品对黑头呆鱼(Pimephales promelas)的急性毒性数据,并依据"全球化学品统一分类和标签制度"(GHS)中推荐的分类标准,将急性毒性值小于和大于100 mg·L-1的物质分别划分为有毒物质和无毒物质。以分类结果为建模指标,构建了基于欧几里德距离的K最近邻(k NN)二元分类模型。评估结果表明,模型训练集和验证集的预测准确度(Q)、敏感性(Sn)和特异性(Sp)参数均大于0.7,说明模型具有较好的预测能力。因而,在化学品分类标签工作中,可使用该模型预测缺失的鱼类急性毒性类别。
关键词(KeyWords): 黑头呆鱼;急性毒性;kNN;欧几里德距离;全球化学品统一分类和标签制度
基金项目(Foundation): 国家自然科学基金(No.41671489,21507038,21507061)
作者(Author): 王雅琪;刘会会;杨先海;
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参考文献(References):
- [1] Schwarzenbach R P,Escher B I,Fenner K,et al. The challenge of micropollutants in aquatic systems[J]. Science,2006,313(5790):1072-1077
- [2] Rappaport S M,Smith M T. Epidemiology. Environment and disease risks[J]. Science,2010,330(6003):460-461
- [3] Matthiessen P,Wheeler J R,Weltje L. A review of the evidence for endocrine disrupting effects of current-use chemicals on wildlife populations[J]. Critical Reviews in Toxicology,2018,48(3):195-216
- [4] Tang W,Chen J W,Wang Z Y,et al. Deep learning for predicting toxicity of chemicals:A mini review[J].Journal of Environmental Science and Health. Part C,2018,36(4):252-271
- [5] van Leeuwen C J,Vermeire T G. Risk Assessment of Chemicals:An Introduction. 2nd edition[M]. Dordrecht:Springer,2007:2-5
- [6]周红,郭琳琳,卢玲,等.中国化学品环境管理对本土模式生物的需求和应用[J].生态毒理学报,2017,12(2):11-19Zhou H,Guo L L,Lu L,et al. Needs and application of native model organisms for Chinese chemical management[J]. Asian Journal of Ecotoxicology,2017,12(2):11-19(in Chinese)
- [7] Collins F S,Gray G M,Bucher J R. Toxicology. Transforming environmental health protection[J]. Science,2008,319(5865):906-907
- [8]王中钰,陈景文,乔显亮,等.面向化学品风险评价的计算(预测)毒理学[J].中国科学:化学,2016,46(2):222-240Wang Z Y,Chen J W,Qiao X L,et al. Computational toxicology:Oriented for chemicals risk assessment[J].Scientia Sinica Chimica,2016,46(2):222-240(in Chinese)
- [9]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB 30000.28—2013,化学品分类和标签规范第28部分:对水生环境的危害[S].北京:中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会,2013General Administration of Quality Supervision,Inspection and Quarantine of the People’s Republic of China,Standardization Administration of the People’s Republic of China. GB 30000. 28-2013,Rules for classification and labelling of chemicals-Part 28:Hazardous to the aquatic environment[S]. Beijing:General Administration of Quality Supervision,Inspection and Quarantine of the People’s Republic of China and Standardization Administration of the People’s Republic of China,2013(in Chinese)
- [10] United Nations. Globally Harmonized System of Classification and Labelling of Chemicals(GHS),Seventh revised edition GHS(Rev. 7)[R]. New York:United Nations,2017
- [11]刘羽晨,乔显亮.水生生物急性毒性QSAR模型研究进展[J].生态毒理学报,2015,10(2):26-35Liu Y C,Qiao X L. Progress in quantitative structure-activity relationship models for acute aquatic toxicity[J].Asian Journal of Ecotoxicology,2015,10(2):26-35(in Chinese)
- [12] Ding F,Wang Z,Yang X H,et al. Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata[J]. SAR and QSAR in Environmental Research,2019,30(1):39-50
- [13] Lyakurwa F S,Yang X H,Li X H,et al. Development and validation of theoretical linear solvation energy relationship models for toxicity prediction to fathead minnow(Pimephales promelas)[J]. Chemosphere,2014,96:188-194
- [14] James J P. Stewart Computational Chemistry[CP]. Colorado Springs,CO:James Stewart,2016
- [15] Talete S R L. Dragon(Software for Molecular Descriptor Calculation)Version 6.0[CP]. Milano:Talete,2012
- [16] Liu H H,Yang X H,Lu R. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin[J]. Chemosphere,2016,156:1-7
- [17] U. S. Environmental Protection Agency(US EPA). Estimation Programs Interface SuiteTMfor Microsoft Windows,v 4.10[CP]. Washington DC:US EPA,2012
- [18] He J Y,Peng T,Yang X H,et al. Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor[J]. Ecotoxicology and Environmental Safety,2018,148:211-219
- [19] Lin S Y,Yang X H,Liu H H. Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals[J]. Ecotoxicology and Environmental Safety,2019,179:40-49
- [20] Kovarich S,Papa E,Gramatica P. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants[J]. Journal of Hazardous Materials,2011,190:106-112
- [21] Sun L,Zhang C,Chen Y J,et al. In silico prediction of chemical aquatic toxicity with chemical category approaches and structural alerts[J]. Toxicology Research,2015,4:452-463
- [22] Fawcett T. An introduction to ROC analysis[J].Pattern Recognition Letters,2006,27:861-874
- [23] Todeschini R,Consonni V. Molecular Descriptors for Chemoinformatics, 2nd ed[M]. Weinheim:WileyVCH,2009
- [24] Fassihi A,Abedi D,Saghaie L,et al. Synthesis,antimicrobial evaluation and QSAR study of some 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives[J]. European Journal of Medicinal Chemistry,2009,44(5):2145-2157