Polar non-specific toxicants

On the other hand, compounds corresponding to rather general, unspecific inodes of toxic action are distributed over a broad area in the respective layer, as shown for polar non-specific toxicants in Figure 10.1-14. 

Figure 10.1-14. Distributfon of compounds in the layer of polar non-specife toxicants.

These structural requirements are not exhaustive other compounds may also fall into the group of the non-polar non-specific toxicants. However, compounds complying with these rules may be much more toxic than baseline (Verhaar, Leeuen and Hermens, 1992). 

Figure 5.4 Comparison of selected QSAR models for non-polar non-specific toxicants relating log I/LC50 to log Pqw experimental data on fish lethality (Brooke et al., 1984 Geiger et a/., 1985, 1986, 1988,1990) for identification of the QSAR functions see Table 5.3.

Similar relationships have also been derived with other descriptors that are generally collinear with log for non-polar non-specific toxicants for example, water solubility (Konemann, 1981b Zaroogian et al., 1985), topological indices (Basak and Magnuson, 1983 Koch, 1983 Sabljic, 1983), or with substructure indicators (Hall, Maynard and Kier, 1989), which may be applied if the log P of the test compounds cannot be estimated, and also to cross-check the predictions obtained, especially when there is reasonable doubt about the correctness of the respective log P values. 

Table 5,3 Examples of QSAR models for estimating toxicity to fish of non-polar non-specific toxicants (e.g. alkanes, alkenes, saturated and unsaturated halogenated aliphatic hydrocarbons, basic ethers, cyclic ethers, ketones, amides, secondary and tertiary aliphatic and aromatic amines, alkylbenzenes, halogenated benzenes, piperazines, pyrimidines, polychlorinated hydrocarbon pesticides) log LC50 correlations with various parameters. 

The elevated specific toxicity results in increased intercepts (approximately 0) again, whereas the slopes are congruent with QSAR models for polar non-specific toxicants, indicating that the amount of the uncouplers excess toxicity is essentially independent of their log (Figure 5.6). 

Table 5.6 Examples of QSAR models for estimating toxicity to Daphnia of non-polar, non-specific toxicants log EC50 correlations with various parameters. 

Figure 8.3 Comparison of baseline QSAR models for polar non-specific toxicants indicating the differences in sensitivity of various aquatic species and test systems The tests with fish (FHM = fathead TR = trout) and crustaceans (DA = Daphnia) are generally more sensitive than the low-complexity assays with protozoa (THY = Tetrahymena), algae (ALG = Selenastrum capriconutum), bacteria (EC = Escherichia coli) and cell cultures (NR = neutralred assay).

The first point especially addresses ecological considerations about the abundance and the distribution of sensitive species in the environmental compartment of concern the second aspect relates to the correct operation of QSAR methods. In the cases of the model contaminant phenol, which was chosen because of the availability of ample experimental data for the comparative exercise, sufficient knowledge is at hand to guide the retrieval of suitable QSARs. For toxicity to fish, the available models vary regarding (a) the endpoints measured - different fish species, different effects (NOEC, LC50, LCioo, ) different durations of the experiments (4 h-30 d), and (b) the various classes of chemicals studied. Because phenol is known to be a polar non-specific toxicant, suitable QSARs for fish (section 5.1) can be selected in a straightforward manner. These models are generally log P -dependent,... 

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