There are often many chemicals coexisting in aquatic ecosystems, and few information on the joint toxicity of a mixture of organic pollutants is available at present. The 48-h toxicity of substituted phenols and anili...There are often many chemicals coexisting in aquatic ecosystems, and few information on the joint toxicity of a mixture of organic pollutants is available at present. The 48-h toxicity of substituted phenols and anilines and their binary mixtures to Scenedesmus obliquus was determined by the algae inhibition test. The median effective inhibition concentration EC50 values for single compounds and EC50mix values for coexistent compounds were obtained. The n-octanol/water partition coefficient （logPmlx） and the frontier orbital energy gap （AEmlx） for mixtures were calculated. The following two-descriptor quantitative structure-activity relationships （QSARs） models were developed to predict single toxicity and joint toxicity respectively： log（1/ECs0） = 0.445logP - 0.801AE ＋ 9.501 （r2 = 0.876） and log （1/EC50mix） = 0.338logPmix- 0.492AEmix ＋ 6.928 （r^2 = 0.831）. The two equations were found to fit well. In addition, the model derived from the structural parameters of single components in binary mixtures log（1/EC50mix） = 0.2221ogP - 0.277AE ＋ 5.250 （r^2 = 0.879） can be used successfully to predict the toxicity of a mixture.展开更多
Objective To measure the toxicity of phenol, aniline, and their derivatives to algae and to assess, model, and predict the toxicity using quantitative structure-activity relationship （QSAR） method. Methods Oxygen pr...Objective To measure the toxicity of phenol, aniline, and their derivatives to algae and to assess, model, and predict the toxicity using quantitative structure-activity relationship （QSAR） method. Methods Oxygen production was used as the response endpoint for assessing the toxic effects of chemicals on algal photosynthesis. The energy of the lowest unoccupied molecular orbital （ELUMO） and the energy of the highest occupied molecular orbital （EHOMO） were obtained from the ChemOffice 2004 program using the quantum chemical method MOPAC, and the frontier orbital energy gap （△E） was obtained. Results The compounds exhibited a reasonably wide range of algal toxicity. The most toxic compound was α-naphthol, whereas the least toxic one was aniline. A two-descriptor model was derived from the algal toxicity and structural parameters： logl/EC50=0.2681ogKow-1.006△E＋11.769 （n=20, r^2=0.946）. This model was stable and satisfactory for predicting toxicity. Conclusion Phenol, aniline, and their derivatives are polar narcotics. Their toxicity is greater than estimated by hydrophobicity only, and addition of the frontier orbital energy gap AE can significantly improve the prediction of logKow-dependent models.展开更多
Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values c...Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.展开更多
Parent and alkylated polycyclic aromatic hydrocarbons(alkyl-PAHs),which are a class of important toxic components of crude oil especially in the marine environment,exhibit adverse effects on aquatic life and potential...Parent and alkylated polycyclic aromatic hydrocarbons(alkyl-PAHs),which are a class of important toxic components of crude oil especially in the marine environment,exhibit adverse effects on aquatic life and potentially pose a human health risk.However,the lack of chronic toxicity data is one of the hindrances for alkylPAHs when assessing their ecological risks.In this study,predicted no-effect concentrations(PNECs)in seawater and marine sediment for ten parent-and alkyl-PAHs were derived by applying species sensitivity distributions(SSDs)and quantitative structure-activity relationships(QSARs).The local area,Dalian Bay,where an oil-spilled accident happened in 2010,was chosen as a case site to assess ecological risks for ten PAHs in surface seawaters and marine sediments.Their PNECs in seawater and sediment for protecting aquatic organisms in marine ecosystems were calculated and recommended in the range of 0.012-2.79μg/L and 48.2-1337 ng/g(dry weight),respectively.Overall,the derived PNECs for the studied PAHs in seawater and marine sediment were comparable to those obtained by classical methods.Risk quotient results indicate low ecological risks to ecosystems for ten parent-and alkyl-PAHs in surface seawaters and surface sediments from the Dalian Bay.These findings provide a first insight into the PNECs and ecological risks of alkyl-PAHs,emphasizing the role of the computational toxicology in ecological risk assessments.The use of QSARs has been identified as a valuable tool for preliminarily assessing ecological risks of emerging pollutants,being more predictable of real exposure scenarios for risk assessment purposes.展开更多
Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the p...Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.展开更多
文摘There are often many chemicals coexisting in aquatic ecosystems, and few information on the joint toxicity of a mixture of organic pollutants is available at present. The 48-h toxicity of substituted phenols and anilines and their binary mixtures to Scenedesmus obliquus was determined by the algae inhibition test. The median effective inhibition concentration EC50 values for single compounds and EC50mix values for coexistent compounds were obtained. The n-octanol/water partition coefficient （logPmlx） and the frontier orbital energy gap （AEmlx） for mixtures were calculated. The following two-descriptor quantitative structure-activity relationships （QSARs） models were developed to predict single toxicity and joint toxicity respectively： log（1/ECs0） = 0.445logP - 0.801AE ＋ 9.501 （r2 = 0.876） and log （1/EC50mix） = 0.338logPmix- 0.492AEmix ＋ 6.928 （r^2 = 0.831）. The two equations were found to fit well. In addition, the model derived from the structural parameters of single components in binary mixtures log（1/EC50mix） = 0.2221ogP - 0.277AE ＋ 5.250 （r^2 = 0.879） can be used successfully to predict the toxicity of a mixture.
基金This work was supported by the Program for New Century Excellent Talents in University (No. 05-0481)National "973" Great Foundation Research Items of China (No. 2002CB412303)
文摘Objective To measure the toxicity of phenol, aniline, and their derivatives to algae and to assess, model, and predict the toxicity using quantitative structure-activity relationship （QSAR） method. Methods Oxygen production was used as the response endpoint for assessing the toxic effects of chemicals on algal photosynthesis. The energy of the lowest unoccupied molecular orbital （ELUMO） and the energy of the highest occupied molecular orbital （EHOMO） were obtained from the ChemOffice 2004 program using the quantum chemical method MOPAC, and the frontier orbital energy gap （△E） was obtained. Results The compounds exhibited a reasonably wide range of algal toxicity. The most toxic compound was α-naphthol, whereas the least toxic one was aniline. A two-descriptor model was derived from the algal toxicity and structural parameters： logl/EC50=0.2681ogKow-1.006△E＋11.769 （n=20, r^2=0.946）. This model was stable and satisfactory for predicting toxicity. Conclusion Phenol, aniline, and their derivatives are polar narcotics. Their toxicity is greater than estimated by hydrophobicity only, and addition of the frontier orbital energy gap AE can significantly improve the prediction of logKow-dependent models.
基金This work was supported by the National 973 Great Foundation Research Item of China (2002CB412303) and the National Natural Science Foundation of Jiangsu Province (BK2004118).
文摘Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.
基金The National Key Research and Development Program of China under contract No.2016YFC1402305the Postdoctoral Research Foundation of China under contract No.2016M601148the Scientific Research Special Fund of Marine Public Welfare Industry under contract No.201305002
文摘Parent and alkylated polycyclic aromatic hydrocarbons(alkyl-PAHs),which are a class of important toxic components of crude oil especially in the marine environment,exhibit adverse effects on aquatic life and potentially pose a human health risk.However,the lack of chronic toxicity data is one of the hindrances for alkylPAHs when assessing their ecological risks.In this study,predicted no-effect concentrations(PNECs)in seawater and marine sediment for ten parent-and alkyl-PAHs were derived by applying species sensitivity distributions(SSDs)and quantitative structure-activity relationships(QSARs).The local area,Dalian Bay,where an oil-spilled accident happened in 2010,was chosen as a case site to assess ecological risks for ten PAHs in surface seawaters and marine sediments.Their PNECs in seawater and sediment for protecting aquatic organisms in marine ecosystems were calculated and recommended in the range of 0.012-2.79μg/L and 48.2-1337 ng/g(dry weight),respectively.Overall,the derived PNECs for the studied PAHs in seawater and marine sediment were comparable to those obtained by classical methods.Risk quotient results indicate low ecological risks to ecosystems for ten parent-and alkyl-PAHs in surface seawaters and surface sediments from the Dalian Bay.These findings provide a first insight into the PNECs and ecological risks of alkyl-PAHs,emphasizing the role of the computational toxicology in ecological risk assessments.The use of QSARs has been identified as a valuable tool for preliminarily assessing ecological risks of emerging pollutants,being more predictable of real exposure scenarios for risk assessment purposes.
基金the National Natural Science Foundation of China (Grant No. 20507008)the National Natural Science Foundation Key Project of China (Grant No. 20737001)+1 种基金the Natural Science Foundation of Jiangsu Province,China (Grant No. BK200418)the National Basic Research Program of China (973 Program) (Grant No. 2003CB415002)
文摘Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
