Discrepancies in the acute versus chronic toxicity of compounds with a designated narcotic mechanism
University of Antwerp, Department of Biology, Laboratory for Ecophysiology, Biochemistry and Toxicology, Groenenborgerlaan 171, 2020 Antwerp, Belgium.Chemosphere (Impact Factor: 3.34). 01/2012; 87(7):742-9. DOI: 10.1016/j.chemosphere.2011.12.069
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.
[Show abstract] [Hide abstract]
- "These HC 5 values can be used to derive predicted no effect concentrations (PNEC). Species sensitivity distributions have been constructed for different chemicals including organic compounds (Dom et al., 2012) and metals (Xu et al., 2015). To our knowledge only one study has constructed species sensitivity distributions for ZnO nanoparticles (Gottschalk et al., 2013), while these distributions are still absent for CuO nanoparticles. "
ABSTRACT: Metal oxide nanoparticles are increasingly being produced and will inevitably end up in the aquatic environment. Up till now, most papers have studied individual nanoparticle effects. However, the implementation of these data into a risk assessment tool, needed to characterise their risk to the aquatic environment, is still largely lacking. Therefore, aquatic species sensitivity distributions (SSDs) were constructed for ZnO and CuO nanoparticles and 5% hazard concentrations (HC5) were calculated in this study. The effect of individual nanoparticles on these SSDs was estimated by comparison with bulk SSDs. Additionally, the effect of nanoparticle dynamics (aggregation and dissolution) was considered by evaluating the effect of aggregate size on the toxicity, by estimation of the dissolved fraction and comparison with SSDs for ZnCl2 and CuCl2 inorganic salt. Bacteria, protozoa, yeast, rotifera, algae, nematoda, crustacea, hexapoda, fish and amphibia species were included in the analysis. The results show that algae (Zn) and crustacea (Zn, Cu) are the most sensitive species when exposed to the chemicals. Similar acute sensitivity distributions were obtained for ZnO nanoparticles (HC5: 0.06 with 90% confidence interval: 0.03-0.15mg Zn/l; 43 data points), bulk ZnO (HC5: 0.06 with CI: 0.03-0.20mg Zn/l; 23dps) and ZnCl2 (HC5: 0.03 with CI: 0.02-0.05mg Zn/l; 261dps). CuO nanoparticles (HC5: 0.15 with CI: 0.05-0.47mg Cu/l; 43dps) are more toxic than the bulk materials (HC5: 6.19 with CI: 2.15-38.11mg Cu/l; 12dps) but less toxic than CuCl2 (HC5: 0.009 with CI: 0.007-0.012mg Cu/l; 594dps) to aquatic species. However, the combined dissolution and SSD results indicate that the toxicity of these nanoparticles is mainly caused by dissolved metal ions. Based on the available information, no current risk of these nanoparticles to the aquatic environment is expected. Copyright © 2015 Elsevier B.V. All rights reserved.
- [Show abstract] [Hide abstract]
ABSTRACT: As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure–activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R 2) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.
- [Show abstract] [Hide abstract]
ABSTRACT: The flood of chemical substances in the environment result in the complexity of chemical mixtures, and one of the reasons for complexity is that their individual chemicals bind to different binding sites on different (or same) target proteins within the organism. A general approaches therefore are proposed in this study to predict the toxicity of chemical mixtures with different binding sites by using molecular docking-based binding energy (Ebinding). Aldehydes and cyanogenic toxicants were selected as the example of chemical mixtures with same binding site. Triazines and urea herbicide were selected as the example of chemical mixtures with different binding sites but on same target protein. Sulfonamides and trimethoprim toxicants were selected as the example of chemical mixtures with different target proteins. Although these chemical mixtures bind to their binding sites by different ways, there is a general relationship between their binary mixture toxicity (EC50M) and their corresponding Ebinding of individual chemicals and logKow(mix). By using the Ebinding to describe how the individual chemicals work in the different binding sites, the approach may provide a general and simply model to predict mixture toxicity to microorganism.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.