Calculating the aquatic toxicity of hydrocarbon mixtures

For acute toxicity to aquatic organisms, individual hydrocarbons are equally toxic on the basis of their internal molar concentration within the organism. The differences in measured toxicities among hydrocarbons lies with differences in their equilibrium partitioning behavior between water and the organism. For complex hydrocarbon mixtures, an additional complication of partitioning between the bulk hydrocarbon and the water is encountered. Equations are developed for calculating the water concentration of components of complex hydrocarbon mixtures. Using gasoline as an example, a method is presented for first calculating the concentration of gasoline components in water after equilibration with different gasoline volumes and then, the component toxicities are used to estimate the gasoline volume causing 50% mortality to aquatic organisms.     

Influence of hydroxypropylcyclodextrins on the toxicity of mixtures

We studied the influence of hydroxypropylcyclodextrins (HPCDs) on the toxicity of some mixtures. Using the Photobacterium phosphoreum toxicity test, the joint toxicological effect for Mixture I (containing p-nitrobenzaldehyde and 1-nitronaphthalene) and Mixture II (containing p-nitrobenzaldehyde and malononitrile) were determined in water and in aqueous solutions of HPCDs. The results indicate that, although the toxicological joint effect for Mixture I (simple addition) differs from that of Mixture II (synergism), alpha- and beta-HPCD can significantly reduce the toxicity of the test compounds, whereas gamma-HPCD has only a slight effect. Explanations for these observations are given that invoke the molecular structure of the individual chemicals as well as the structures of HPCDs. This provides information to assist the application of HPCDs in remediation of environmental pollution.     

Properties of the 2,3,7,8-TCDD receptor- a QSAR approach

The Ah or 2,3,7,8-TCDD receptor protein plays an important role in mediating the biologic, toxic and genotoxic effects of aryl and halogenated aryl hydrocarbons. An assessment of the physicochemical parameters which facilitate ligand: receptor interactions was determined by multiple parameter linear regression analysis of the relative receptor binding affinities of a series of 7-substituted-2,3-dichlorodibenzo-p-dixoins (Eq 1), 8-substituted-2,3-di (Eq. 2) and 2,3,4-trichlorodibenzofurans (Eq 3). The results demonstrate that substituent lipophilicity (π) and molecular volumes were

(Eq.1)

log (I/EC₅₀) = 1.24 π + 6.10

(Eq.2)

log (I/EC₅₀) = 1.10 π + 5.19

(Eq.3)

log (I/EC₅₀) = 1.09 π + 5.77

the major determinant factors governing interactions between the rat hepatic cytosolic receptor protein and the diverse substituted ligands. It has been shown that there are marked differences in species sensitivity to 2,3,7,8-TCDD (i.e. guinea pig > rat maice > hamster) although hepatic receptor levels in these species are comparable. QSAR analysis of the receptor binding EC₅₀ data for the 7-substituted-2,3-dichlorodibenzo-p-dioxins using rat (Eq. 1), mouse (Eq. 4), guinea pig (Eq. 5) and hamster (Eq. 6) hepatic cytosol demonstrated that there were significant differences in these equations. However it was also noted that

(Eq.4)

log (I/EC₅₀) = 0.95 + 0.93 E_s + 5.28

(Eq.5)

log (I/EC₅₀) = 0.94 + 0.579 σ_p + 6.13

(Eq.6)

log (I/EC₅₀) = 0.70 + 1.227 σ_p + 6.38

physicochemical factors which are important for ligand-receptor interactions were identical for the most sensitive (guinea pig) and least sensitive (hamster) species. These results indicate that events which follow the initial ligand-receptor interaction must be the major factors which determine species sensitivity to 2,3,7,8-TCDD.     

Discrepancies in the acute versus chronic toxicity of compounds with a designated narcotic mechanism

In this study, it was illustrated that even for certain simple organic compounds with a designated mode of action (MOA) (i.e. narcotic toxicity) unexpected differences in acute and chronic toxicity can be observed. In a first part of the study, species sensitivity distributions (SSDs) based on either acute or chronic toxicity data of three narcotic test compounds (methanol, ethanol and 2-propanol) were constructed. The results of the acute SSDs were as expected for narcotic compounds: rather similar sensitivity and small differences in toxicity were observed among different species. On the contrary, the chronic SSDs of methanol and ethanol indicated larger interspecies variation in sensitivity. Furthermore, the chronic toxicity trend (ethanol > methanol > 2-propanol) was unexpectedly different from the acute toxicity trend (2-propanol > ethanol > methanol) and acute versus chronic extrapolation could not be successfully described for methanol and ethanol using an ACR of 10 (as suggested for narcotic compounds). In contrast to the interspecies approach in the first part of this study, the second part of the study was focused on the assessment of acute and chronic toxicity of the three test compounds in Daphnia magna, which was identified as one of the most sensitive organisms to methanol and ethanol. Here, the differences in acute and chronic toxicity trend were in accordance to the results of the SSDs. The enhancement of membrane penetration due to the small molecular size of methanol and ethanol, in combination with the higher toxicity of their respective biotransformation products were suggested as potential causes of the increased chronic toxicity. Furthermore, it was stressed that larger awareness of these irregularities in acute to chronic extrapolations of narcotic compounds is required and should receive additional attention in further environmental risk assessment procedure.     

Assessment of the toxicity of mixtures of halogenated dibenzo-p-dioxins and dibenzofurans by use of toxicity equivalency factors (TEF)

In March 1988 the authors completed a report on the risk assessment of mixtures of halogenated dibenzo-p-dioxins and dibenzofurans at the request of the National Institute of Public Health and Environmental Protection in the Netherlands. Based on a study of the current knowledge of these compounds, a set of toxicity equivalency factors was proposed for use in such risk assessments. This paper presents the section of this report that deals with the literature on the effects of these compounds and the choice of toxicity equivalency factors. The uncertanties in the selection and application of toxicity equivalency factors are discussed.     

Mechanism of concentration addition toxicity: they are different for nonpolar narcotic chemicals, polar narcotic chemicals and reactive chemicals

According to the toxicity mechanism of the individual chemicals, the concentration addition toxicity mechanism is revealed for nonpolar-narcotic-chemical mixtures, polar-narcotic-chemical mixtures and reactive-chemical mixtures, respectively. For nonpolar-narcotic-chemical mixtures, the partitioning of individual chemicals from water to biophase was determined, and the result shows that their concentration additive effect results from no competitive partitioning among individual chemicals. For polar-narcotic-chemical mixtures, their toxicity are contributed by two factors (the total baseline toxicity and the hydrogen bond donor activity of individual chemicals), and it is the concentration additive effect for either of these two factors that leads to their concentration addition toxicity. In addition, the interactions between the reactive chemicals and the biological macromolecules are discussed thoroughly. The results suggest that the net effect of these interactions is zero, and it is this zero net effect that leads to the concentration addition toxicity mechanism for reactive-chemical mixtures.     

QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals

A quantitative structure-activity relationship model, based on the atom-type electrotopological state (E-state) indices, for the prediction of toxicity to fathead minnow for a diverse set of 140 organic chemicals is presented. Multiple linear regression and artificial neural network techniques were employed in the modeling of experimental toxicity (-logLC(50)) values ranging from 0.85 to 6.09. For the training set of 130 organic compounds a linear regression model with r(2)=0.84 and s=0.36 was obtained with 14 atom-type E-state indices. For the test set of 10 compounds, the corresponding statistics were r(2)=0.83 and s=0.47, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.31 for the training set and s=0.30 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the prediction of toxicity for a diverse set of organic chemicals using easily calculated parameters.     

QSAR study on the toxicity of substituted benzenes to the algae (Scenedesmus obliquus).

50% effective inhibition concentration 48h-EC50 of 40 substituted benzenes to the algae (Scenedesmus obliquus) was determined. The energy of the lowest unoccupied molecular orbital (E(LUMO)) was calculated by the quantum chemical method MOPAC6.0-AM1. By using E(LUMO) and the hydrophobicity parameter log K(OW) the quantitative structure-activity relationship model (QSAR) was developed: log1/EC50=0.272 logK(OW) - 0.659E(LUMO) + 2.54, R2 = 0.793, S.E. = 0.316, F = 71.07, n = 40. A series of equations were obtained about the measured EC50 values of different subclasses of compounds. For those compounds containing double -NO2, their toxicity may be related chiefly to the intracellular reduction of -NO2 obtaining electron, while for anilines and phenols, K(OW) contributes most to the QSAR and E(LUMO) very little.     

大孔非极性树脂处理TAIC生产废水

TAIC(三烯丙基异氰脲酸酯)作为过氧化物交联或自由基反应交联的助交联剂被广泛使用.由于TAIC性质稳定,难以降解,常规的物化方法对TAIC废水处理效果普遍较差.为了寻找一种新的废水处理工艺,本研究采用了大孔吸附树脂处理TAIC生产废水,并考察了流速、pH、反应时间对处理效果的影响以及反应动力学过程.结果表明,影响该工艺的因素主次关系为:流速 >反应时间 >pH;在最佳进水条件pH为5,流速为4 BV/h,反应时间为120 min时,COD的去除率达到62%以上,TAIC的去除率达到80%以上.反应动力学分析表明,大孔非极性树脂处理TAIC生产废水的过程基本符合二级动力学规律.     

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

     

Perspectives of the chemical fate and toxicity of pesticides

The wide-spread use of pesticides in modern agriculture has created a need to investigate the chemical transformation of pesticides in plants and animals. This paper reviews the chemical and biochemical fate of various pesticides and other xenobiotics. Photochemical mechanisms appear to be the most common pathways for the abiotic transformation of these chemicals. Biotic transformation includes a large group of biochemical reactions which may result in either deactivation (detoxication) or activation (toxication) of bioactive compounds. The need for quality control in the production of pesticides is also discussed.     

Perspectives of the chemical fate and toxicity of pesticides

Abstract

The wide‐spread use of pesticides in modern agriculture has created a need to investigate the chemical transformation of pesticides in plants and animals. This paper reviews the chemical and biochemical fate of various pesticides and other xenobiotics. Photochemical mechanisms appear to be the most common pathways for the abiotic transformation of these chemicals. Biotic transformation includes a large group of biochemical reactions which may result in either deactivation (detoxication) or activation (toxication) of bioactive compounds. The need for quality control in the production of pesticides is also discussed.     

Acute toxicity of benzene derivatives to the tadpoles (Rana japonica) and QSAR analyses

Acute lethal toxicity (the negative logarithm of molar concentrations of 12 h acute median lethal, expressed as 12 h-log1/LC50) of 46 benzene derivatives to Rana japonica tadpoles was determined. 1-octanol/water partition coefficient (logKow)-dependent models were developed to study the toxicity of different categories chemicals. In an effort to model all chemicals, response surface analyses and stepwise multiple regression analyses were performed and successful models were obtained. A general and robust QSAR model was achieved with the combined application of variables reflecting hydrophobicity, electric property, and molecular size respectively (12h-log1/LC50 = 0.393logKow - 0.428Elumo + 0.0110Vol. + 1.362 n = 51, r2 = 0.834) using stepwise multiple regression analyses. Because of strong dissociation of carboxyl group greatly decreasing their observed toxicity, using logDow in instead of logKow the quality of the models is greatly improved. The conventional r2 and cross-validation r2(CV) were 0.914 and 0.785, respectively, indicating that QSAR was both internally consistent and highly predictive.     

An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis

This study presents an analysis of the ability of a two-parameter response surface, a multiple linear regression and a neural network model to produce global quantitative structure-activity relationships (QSARs) to predict the toxic potency of phenols to Tetrahymena pyriformis. The phenolic toxicity data set analysed is characterised by multiple mechanisms of toxic action. The study aimed to evaluate the confidence that can be applied to the modelling of the differing mechanisms of action. Assessment of confidence was decided in terms of whether the statistics for the global models reflect the ability of the QSARs to model the individual mechanisms of toxic action present in the data set. The results showed that the global statistics only reflected the ability of models to predict the two non-covalent mechanisms (polar narcosis and respiratory uncoupling), with the metabolically transformed and electrophilic mechanism (pre-electrophiles and soft electrophiles) being modelled poorly by all three model building methods. The results confirm the difficulty in modelling electrophilic mechanisms of toxic action. The results also highlight the fact that this poor predictivity is often 'hidden' in good statistical fit of some global models. In particular these results emphasise that for practical predictive purposes the mechanistic applicability domain is required to give confidence to estimated toxicity values.     

MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri

Environmental Science and Pollution Research - Risk assessment of pollutants to humans and ecosystems requires much toxicological data. However, experimental testing of compounds expends a large...