Quantitative structure-activity relationships for the toxicity of nitrobenzenes to Tetrahymena thermophila

Effects of 2,4-dinitrotoluene exposure on enzyme activity, energy reserves and condition factors in common carp (Cyprinus carpio)

In this study relative condition factor (RCF) and hepatosomatic index (HSI) as well as the available energy reserves of common carp (Cyprinus carpio) by 2,4-DNT semi-static bioassay were determined and linked to effects of enzymes in liver tissues. Fish were exposed at sublethal concentrations of 2,4-DNT (0.13 μg/L, 0.1, 0.5, 1.0mg/L) for 7 and 15 d. Based on the results, there was no significant change in all parameters measured in fish exposed to 2,4-DNT at environmental related concentration, but 2,4-DNT stress in fish exposed to higher concentrations reflected the significant changes of physiological and biochemical responses. 2,4-DNT stress resulted in EROD activity induction in the liver, and the levels of EROD activity ranged from 0.39- to 1.83-fold higher than control. For GK, Na(+)/K(+)-ATPase, and GST, these enzyme activity continued to decline after exposure to 0.1, 0.5 and 1.0mg/L 2,4-DNT, whereas the trend on GK and Na(+)/K(+)-ATPase was more obvious than GST. Through principal component analysis, effects by 2,4-DNT-stress in each test group were distinguished. Additionally, indications of a trade-off between metabolic cost of toxicant exposure and processes vital to the survival of the organism were seen at the enzyme activity level as well as on higher levels of biological organization.

QSPR analysis of the toxicity of aromatic compounds to the algae (Scenedesmus obliquus)

The quantitative structure-property relationship (QSPR) model was developed for the 50% effective inhibition concentration (48h - EC(50)) of 36 selected substituted benzenes for the algae Scenedesmus obliquus by the application of the Characteristic Root Index (CRI) model. To increase the predictive power of the CRI-based model, the following semi-empirical molecular descriptors calculated by the quantum chemical PM3 method were included: the energy of the highest occupied molecular orbital (E(HOMO)), the energy of the lowest unoccupied molecular orbital (E(LUMO)), and the dipole moment (mu). A two-descriptor model with a correlation coefficient of r=0.926 was developed without the outliers from multiple regression analysis [-logEC(50)=0.494 (+/-0.072) CRI-0.798 (+/-0.063) E(LUMO)+1.985 (+/-0.169)]. E(LUMO) was the most important parameter, followed by the CRI. E(LUMO) reflects electronic properties, whereas the CRI reflects hydrophobicity, molecular size, and branching. The statistical robustness of the developed model was validated by the modified jackknife test. The predictive accuracy of the proposed model was compared with the recently published study in which a toxicity model was developed for the same algae. Because of its high statistical significance, the validated model has been used to predict -logEC(50) values of compounds for which there are no experimental measurements.

A study on prediction of the bio-toxicity of substituted benzene based on artificial neural network

Quantitative Structure-Activity Relationship (QSAR) between the bio-toxicity of seventy-eight kinds of substituted benzene chemicals to yeast Saccharomyces cerevisiae (1g(1/Cmiz)) and the components of vertex degree autocorrelation vectors (values of A, B, C and D) was studied by using the software of Artificial Neural Network (ANN). The key factors of the autocorrelation descriptors for the 1g (1/Cmiz) value of yeast Saccharomyces cerevisiae, A [0], A [1], C [3], C [5] and D [3] were selected from twenty-four descriptors, and were explained theoretically in this paper. The QSAR-ANN model has been used to predict the bio-toxicity of twenty-three substituted benzene chemicals. The correlation between Cmiz and LC50 was also discussed, and the liner correlation equation between them was established.