Quantitative StructureâActivity Relationships for the Aquatic Toxicity of

J. Chem. Inf. Comput. Sci. 1998, 38, 845-852

845

Quantitative Structure-Activity Relationships for the Aquatic Toxicity of Polar and Nonpolar Narcotic Pollutants En˜aut Urrestarazu Ramos, Wouter H. J. Vaes, Henk J. M. Verhaar, and Joop L. M. Hermens* Research Institute of Toxicology (RITOX), Environmental Toxicology and Chemistry Section, Utrecht University, P.O. Box 80.176, NL-3508 TD Utrecht, The Netherlands

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Received March 20, 1998

QSARs were developed for the acute toxicity of narcotic pollutants (nonpolar and polar) to the water flea (Daphnia magna), the guppy (Poecilia reticulata), and the pond snail (Lymnaea stagnalis) using hydrophobicity (log KOW) and hydrogen bonding capacity descriptors (Q-, Q+, HOMO, LUMO). Toxicity increases with increasing hydrophobicity and to a minor extent with decreasing LUMO energies and increasing absolute charges in the molecule. The models are rationalized by taking into account the composition of biomembranes, into which chemicals must partition for displaying narcosis. The similarity of these results with models for the membrane/water partition coefficients supports the hypothesis that the toxicity of narcotics is directly related to the accumulation in biological membranes. The results indicate that baseline toxicity based on log KOW should be redefined for chemicals for which log KOW is not a good surrogate for partitioning into biological membranes. INTRODUCTION

Narcotic pollutants are believed to act by nonspecifically disrupting the functioning of cell membranes,1,2 and the toxic potency of the chemical is therefore related to its propensity to accumulate in the cell membranes. Octanol is often used as a surrogate for membrane phospholipids, and consequently the octanol/water partition coefficient (log KOW) is used to describe the affinity of the chemicals for the membrane phospholipids compared to water. Hence, the aquatic toxicity of narcotic chemicals to a given species is inversely related to their log KOW;3 this has been confirmed for several aquatic species.4 This relation is commonly referred to as “baseline toxicity”, because any chemical will be at least as toxic as its log KOW indicates. For baseline toxicity, or class 1 chemicals, no-effect-levels are well defined for aquatic ecosystems.4,5 As described by Schultz et al.,6 Veith and Broderius,7,8 and Verhaar et al.9 a group of polar narcotics (anilines, phenols, nitrobenzenes, pyridines), so-called class 2 chemicals, show hydrophobicity dependent toxicity. However, these chemicals are slightly more toxic than baseline toxicity models would predict. In addition, these chemicals present toxicological differences with respect to nonpolar narcotics: different fish acute toxicity syndromes,10 lower lethal body burdens (i.e., concentration in individual organisms at the time of death),11 and nonadditive toxicity with nonpolar narcotic compounds (for chemicals with log KOW < 2.7).7 It has been suggested that the differences are due to the ability of the class 2 chemicals to form hydrogen bonds.8,12 Recently, Vaes et al.13 have indicated that the partitioning behavior of these chemicals to phospholipids is higher than to octanol, whereas no difference is observed for class 1 compounds. The differences in partitioning behavior can * Corresponding author: Tel: + 31-30-253 54 00. Fax: +31-30-253 50 77. E-mail: [email protected].

explain some of the toxicological differences mentioned above.14 In addition, the modeling of the membrane/water partition coefficients of several chemicals, including nonpolar and polar narcotics, shows the influence of both hydrophobicity and hydrogen bonding capabilities of the chemicals.15 Abraham et al.16,17 have already studied the role of hydrogen bonding in general anesthesia to tadpoles. They found that anesthetic potency of chemicals mainly increased with hydrophobicity and decreased with the ability to accept hydrogen bonds and, to a minor extent, increased with increasing ability to donate hydrogen bonds. In addition, they compared linear free energy relationships (LFER) for anesthetic potency and partition coefficients between water and several organic solvents. The results showed that log KOW does not properly mimic the anesthetic potency of the chemicals. Therefore the inclusion of hydrogen bonding capabilities in models for narcosis seems to be justified. As indicated by Cramer et al.,18 hydrogen bonding capabilities can be divided into acceptor and donor capabilities. In addition, these two capabilities can, again, be subdivided into ionic and covalent contributions to the bond. The ionic contributions to hydrogen bonding are represented by Q- (the most negative partial charge on any non-hydrogen atom of the molecule) and Q+ (the most positive partial charge on a hydrogen atom), for the hydrogen bonding acceptor and donor, respectively. The covalent contribution to the hydrogen bond is given by the energy gap between the HOMO of the hydrogen bond acceptor and the LUMO of hydrogen bonding donor. Therefore the covalent contribution of acceptor pollutants is related to the energy gap between the HOMO of the chemical and the LUMO of the target, whereas the contribution of donor pollutants is related to the energy gap of their LUMO and the HOMO of the target. Since the target is the same for all chemicals, the covalent contributions are related to the energy of the frontier orbitals of the

S0095-2338(98)00027-4 CCC: $15.00 © 1998 American Chemical Society Published on Web 07/25/1998

846 J. Chem. Inf. Comput. Sci., Vol. 38, No. 5, 1998 Table 1. Effects of the Hydrogen Bonding Descriptors in Hydrogen Bonding, Toxicity, and Partitioning to Biomembranesa


a

The arrows represent an increase for each of the three properties.

pollutants. The covalent contribution is represented by the energy of the frontier orbitals, HOMO (the energy of the highest occupied molecular orbital) and LUMO (the energy of the lowest unoccupied molecular orbital), representing the hydrogen bonding acceptor and donor characteristics of the hydrogen bonding, respectively. The influence of these parameters on hydrogen bonding is shown in Table 1. The more negative the value of Qthe stronger the attraction to a hydrogen, whereas the ability to donate a hydrogen increases with increasing Q+. If the hydrogen of the molecule forms a hydrogen bond, the electrons donated to the hydrogen will be preferentialy accepted in the LUMO, and this will be more favorable at low LUMO values. On the other hand, if an external hydrogen bonds to the molecule, the electrons donated by the molecule will be released from the HOMO. Consequently, the higher the energy of the HOMO, the more favorable the bond will be. In short, good hydrogen bond donors will have high Q+ and low LUMO, whereas good hydrogen acceptors will be molecules with low (large absolute value) Q- and high HOMO. In this study we will present quantitative structure-activity relationship (QSAR) models for the acute toxicity of narcotic pollutant (nonpolar and polar), represented as median effect or lethal concentrations (EC50 or LC50). Three aquatic species have been used for this purpose: the water flea Daphnia magna, the fish Poecilia reticulata, and the snail Lymnaea stagnalis. The descriptors used in developing the models are log KOW (describing the hydrophobicity of the chemicals) and the hydrogen bonding capabilities (Q-, Q+, HOMO, and LUMO). METHODS

Data. To obtain a balanced set of chemicals representing the whole chemical domain to be modeled, the training set of chemicals selected by Vaes et al.19 for class 1 and the ones selected by Urrestarazu Ramos et al.20 for class 2 were used. The acute toxicity of these polar narcotics had been previously determined in our own laboratories (24h-EC50 for D. magna and 96h-LC50 for P. reticulata and L. stagnalis).21 The toxicity data of class 1 chemicals were obtained from literature. For the snail, data reported by several authors23-25 were used. For the water flea, data summarized by Verhaar et al.22 and reported by Ku¨hn et al.26 were selected. For the fish, the data summarized by Verhaar et al.22 for two fish species with comparable sensitivity12,20 (Poecilia reticulata and Pimephales promelas) were used. The selected set of chemicals was only available for fish,

URRESTARAZU RAMOS

ET AL.

Table 2. Selected Class 1 and 2 Chemicals for the Training Set P. reticulata

L. stagnalis

D. magna

1-butanol 1-hexanol 1,3,5-trichlorobenzene 2-butoxyethanol 2,4,5-trichlorotoluene 3-pentanol 4-xylene chlorobenzene

Class 1 1-propanol 1-heptanol hexachlorobutadiene lindane trichloroethene benzene ethylacetate 2-propanone

1-butanol cyclohexane 1,2,3-trichlorobenzene 2-ethoxyethanol 2,4,5-trichlorotoluene 1-pentanol 4-xylene chlorobenzene

2-nitrotoluene nitrobenzene 2-allylphenol 2-phenylphenol 4-n-pentylphenol 4-chloro-3-methylphenol quinoline aniline N,N-dimethylaniline 2,4,5-trichloroaniline 3-nitroaniline

Class 2 2-nitrotoluene nitrobenzene 2-allylphenol 2-phenylphenol 4-n-pentylphenol 4-chloro-3-methylphenol quinoline aniline N,N-dimethylaniline 2,4,5-trichloroaniline 3-nitroaniline

2-nitrotoluene nitrobenzene 2-allylphenol 2-phenylphenol 4-n-pentylphenol 4-chloro-3-methylphenol quinoline

and therefore some alternative chemicals were selected for the other two species. The final training set of class 1 and 2 chemicals for all three species is presented in Table 2. The predictive power of the models were validated externally with literature data. The external validation sets were formed with data taken from the same sources used for the class 1 chemicals for the training set. The original toxicity data, together with the values for the descriptors, are given in Table 5. Finally, although anilines are classified as polar narcotics, there are indications to believe that they act by a different mode of action to daphnids.21,27 Therefore anilines were not included in either the training set or the external validation set for the model for D. magna. Descriptors. As explained in the Introduction, parameters describing the hydrophobicity and hydrogen bonding capacity of the chemicals were used for QSAR analyses. Experimental or calculated values of log KOW were collected from ClogP Macintosh version 1.0.0 (Biobyte Corp., Claremont, CA). The semiempirical descriptors HOMO, LUMO, Q- and Q+ were calculated, as reported previously,12 using Spartan software (Wavefuction Inc., Irvine, CA) for AM1 semiempirical calculations. Models. QSAR models were established between the mentioned descriptors and the log-transformed molar effect concentrations. The models were constructed by means of partial least squares (PLS) regression,28 using the SCAN 1.1 chemometric software package (Minitab Inc., State College, PA). The optimum number of latent variables was determined by the leave-one-out cross-validated Q2. RESULTS AND DISCUSSION

The results of the PLS regression analysis are shown in Table 3. The optimum number of latent variables in the models is between 4 and 5. In the latter cases, the models are therefore the same as a multiple linear regression. The models for the three species are of good quality (R2 > 0.95 and Q2 > 0.83). The external validations (Figure 1) show that the predictive capability of the models is excellent (R2val > 0.88). Note that the lowest value is found for the snail,

AQUATIC TOXICITY

OF

NARCOTIC POLLUTANTS

J. Chem. Inf. Comput. Sci., Vol. 38, No. 5, 1998 847

Table 3. Results of the QSAR Models, Given as Pseudoregression Coefficients species

P. reticulata

L. stagnalis

D. magna

D. magna* a

Nb LVc R2 Q2 R2val(N)b intercept log KOW HOMO QLUMO Q+

19 4 0.97 0.93 0.93 (143) -2.100 -0.776 -0.097 0.295 0.189 -1.934

19 5 0.97 0.94 0.88 (4) -0.047 -0.955 0.084 0.722 0.189 -0.047

15 4 0.95 0.83 0.96 (52) -1.156 -0.933 -0.0002 0.712 0.164 -0.332

14 5 0.99 0.95 0.85 (53) -0.645 -0.780 0.074 -0.036 0.416 -3.072


a Model without cyclohexane in the training set. b N: number of compounds. c LV: number of latent variables.

Figure 1. Predictions of the QSAR model for the three species under study. (a) L. stagnalis, (b) P. reticulata, (c) D. magna (original training set), and (d) D. magna (including cyclohexane). Table 4. Residuals of the Predictions for Different Sets of Chemicalsa set training validation class 1 class 2 anilines a

P. reticulata

L. stagnalis

D. magna

D. magna*

0.00 ( 0.19 0.00 ( 0.23 0.00 ( 0.29 0.00 ( 0.15 -0.13 ( 0.33 -0.12 ( 0.31 -0.11 ( 0.32 -0.27 ( 0.58 -0.12 ( 0.33 0.01 ( 0.19 -0.06 ( 0.30 -0.10 ( 0.30 -0.02 ( 0.27 -0.17 ( 0.34 -0.01 ( 0.33 -0.87 ( 0.66

The average and the standard deviation are given.

which has a validation set of four chemicals. Although the predictions for these four chemicals are good (see Figure 1a), the R2 is low due to the narrow toxicity range of the set. Figure 1c shows that the model for D. magna underestimates the toxicity of certain compounds. This leads to a high and biased residuals, as shown in Table 4. These outliers are all saturated hydrocarbons with no electronegative atoms (i.e., Cl, N, O). Therefore a new model was developed in which cyclohexane was included in the training set. The internal cross validation of the new model resulted in a lower Q2 (0.83 instead of 0.95), but its actual predictive capability

is higher (R2val ) 0.96 instead of 0.85). The residuals of the new model are also lower and unbiased (see Table 4). The lower Q2 is due to the bad prediction of the toxicity of cyclohexane when it is not present in the model (during cross-validation), because it is the only chemical of its chemical class. Figure 1d shows that the new model correctly predicts the toxicity of hydrocarbons with no electronegative atoms. The set of the other two species do not contain simple hydrocarbons. As shown in Table 4, the standard deviations of the residuals for the three species (considering the one with cyclohexane for D. magna) are relatively low: 0.19-0.29 for the training sets and 0.31-0.33 for the validation sets. In addition, no difference is observed between the residuals for class 1 and class 2, showing that the models predict the toxicity of both classes as a single group as in Verhaar et al.12 In order to confirm that the anilines do not act by polar narcosis to the water flea, the predictions of the toxicity of anilines by the QSAR models were compared for different species. Table 4 shows that the toxicity of anilines to the water flea is clearly underestimated by the model, whereas the toxicity to fish is very well predicted. In order to discard biased results due to the presence of anilines in the training set of the fish model (anilines are not included in the water flea model), a new model without anilines was established for the fish. The prediction of the toxicity of anilines by the new model did not differ from those of the original model (0.06 ( 0.36). Therefore the underestimation of the toxicity of anilines to D. magna supports that these chemicals do not induce polar narcosis on water fleas, but they act by a more specific mode of toxic action. Figure 2 shows the importances of the descriptors in the models. The higher the absolute value the higher the importance of the variable in the model. The sign (positive or negative) of the importances shows whether the toxicity increases or decreases when increasing the value of the parameter. A schematic representation of the target site is depicted in Figure 3 for a better comprehension of the interpretation of the models. It can be seen that the toxicity for narcotics is mainly determined by the hydrophobicity of the chemical: toxicity increases with increasing log KOW. However, hydrophobicity alone is not sufficient to describe the toxicity of narcotics and parameters describing the hydrogen bonding capacity of the compound appear necessary. The effects of these descriptors in the toxicity are compared to hydrogen bonding in Table 1. The second parameter in importance is the energy of the LUMO, i.e., the energy of the molecular orbital to which the accepted electrons will be located. The lower the LUMO the higher the toxicity. This could be interpreted as the importance of a donation of electrons in the process governing narcosis. The difference between nonpolar and polar narcotics in their LUMO energies (average LUMO is 1.30 ( 1.58 and -0.10 ( 0.68 eV, respectively) explains the higher toxicity of the latter group. The relevance of Q- and Q+ varies depending on the training set used. When relevant, toxicity increases when increasing the absolute charges (low Q- and high Q+). The effect of the training set on the influence of Q- and Q+ is clearly seen in the case of D. magna. Inclusion of cyclohexane in the model switches the relevance of Q+ to Q-.


848 J. Chem. Inf. Comput. Sci., Vol. 38, No. 5, 1998

Figure 2. Influence of the descriptors in the QSAR models. (a) The models for L. stagnalis and D. magna (D. magna* corresponds to the original model, without cyclohexane). (b) The model for toxicity to fish and for partitioning to biomembranes.

Figure 3. Scheme of interactions at the target site.

Summarizing, the highly toxic pollutants are those with high log KOW, low LUMO, and high absolute charges, i.e., electronegative hydrophobic chemicals with charged atoms. These results are in accordance with the models developed by Abraham et al.16,17 Using a similar training set and descriptors, the same findings were achieved when modeling the partition coefficients of several chemicals to biological membranes.15 Since we have seen that the training set affects the importances of some of the descriptors in the model, a better comparison of the toxicity and membrane/water partition coefficients (log Kmw) could be derived if the same training sets were used.

URRESTARAZU RAMOS

ET AL.

Data on partition coefficients were available for the training set used for the fish. A new model was developed for log Kmw for these 19 chemicals. The analysis resulted in a good model (R2 ) 0.98 and Q2 ) 0.97). The importances of the descriptors in both models (toxicity to fish and partitioning to biomembranes) are shown in Figure 2b. It is seen that the importances of the descriptors in both models is identical (the sign is opposite because higher partitioning leads to higher toxicity, i.e., lower LC50). The effect of the descriptors in the affinity to biomembranes is compared to hydrogen bonding and toxicity in Table 1. The similarity between these models suggests that both groups of narcotics act at the same target site (biological membranes). The differences in toxicity observed between nonpolar and polar narcotics seems to be due to differences in partitioning behavior from water to octanol or biomembranes. The relevance of hydrogen bonding descriptors can be considered as correction factors to improve the surrogate character of log KOW. From the analysis of the models, it seems that a negative charge is present at the target site. For the accumulation in phospholipids, the negative charge was tentatively identified as the phosphate group in the hydrophilic head of the membrane phospholipids.15 The high influence of the phosphate group of the biomembrane phospholipids can be understood from by the mechanistic explanation given by Cantor2 for the general anesthesia phenomenon. According to this theory, the ratio between open/closed ion channels in cell membranes is affected when xenobiotic molecules are present in the outer (i.e., hydrophilic) part of the bilayer. Therefore it is not surprising that higher affinity for phosphate groups, which make up the outer layer of the biomembranes, increase the narcotic potency of aquatic pollutants. The quantum-chemical descriptors have been presented here as hydrogen bonding parameters. From the results of the models, however, it is not clear that a hydrogen bond is actually involved. The effects of LUMO indicate that the chemical interacts with an electron donating orbital. The influence of Q- and Q+ in the toxicity models is highly dependent on the training set used and therefore is not consistent in all models. These results suggest that it is not necessarily hydrogen bond what is involved here, because enhanced toxicity is also observed for chemicals that do not have hydrogens to be donated, as long as their LUMO energy is low. It seems that the interaction of the pollutant with the phosphate group is not an attraction between opposite charges but a donation of electrons from the phosphate group to the chemical. Therefore an interaction with a covalent character is indicated, rather than hydrogen bonding. This study, together with the studies carried out by Vaes et al.,14,15 indicates that more chemicals than recognized act by the same mode of action as class 1 chemicals. This suggests that the baseline toxicity, which is nowadays defined based on log KOW, should be redefined based on actual membrane/water partition coefficients. CONCLUSIONS

The aquatic toxicity can be modeled for both classes of narcotics together, using hydrophobicity and hydrogen bonding descriptors as shown also by Verhaar et al.12 The models are of high statistical quality and predictive capability. High

AQUATIC TOXICITY

OF

NARCOTIC POLLUTANTS


Table 5. Original Raw Data Used for Developing/Validating the QSAR Modelsa


chemical methanol ethanol 1-propanol 2-propanol 1-butanol 2-butanol isobutanol tert-butyl alcohol 1-pentanol 3-pentanol 1-hexanol 1-heptanol 1-octanol 1-nonanol 1-decanol 1-undecanol 1-dodecanol 1,2-ethanediol 1,3-propenediol 2-methyl-2,4-pentanediol 3-furanmethanol cyclohexanol 2,2,2-trichloroethanol butyldigol diethyleneglycol triethyleneglycol 2-methoxyethanol 2-ethoxyethanol 2-isopropoxyethanol 2-butoxyethanol 2-(2-ethoxyethoxy)ethanol 2-phenoxyethanol acetone 2-propanone 2-butanone 3-pentanone 2-octanone 5-nonanone 2-decanone 3-methyl-2-butanone 6-methyl-5-hepten-2-one 2,3,4-trimethoxyacetophenone acetophenone 3,3-dimethyl-2-butanone 4-methyl-2-pentanone benzophenone 2,4-dichloroacetophenone cyclohexanone ethyl acetate diethyl ether diiso-propyl ether dibutyl ether dipentyl ether diphenyl ether tert-butylmethyl ether furan tetrahydrofuran 2,6-dimethoxytoluene 1,4-dimethoxybenzene 2-hydroxy-4-methoxyacetophenone dichloromethane chloroform tetrachloromethane 1,1-dichloroethane 1,2-dichloroethane 1,1,1-trichloroethane 1,1,2-trichloroethane 1,1,2,2-tetrachloroethane pentachloroethane hexachloroethane 1,2-dichloropropane

log KOW -0.77 -0.31 0.25 0.05 0.88 0.61 0.76 0.35 1.35 1.21 2.03 2.72 3.00 4.26 4.57 4.53 5.13 -1.36 -1.04 -0.68 0.30 1.23 1.42 0.56 -1.30 -1.24 -0.77 -0.10 0.05 0.83 -0.54 1.16 -0.24 -0.24 0.29 0.85 2.37 2.97 3.73 0.84 1.82 1.63 1.58 1.20 1.31 3.18 2.73 0.81 0.73 0.87 1.52 3.21 4.04 4.21 0.94 1.34 0.47 2.87 2.03 1.98 1.25 1.97 2.83 1.79 1.48 2.49 1.89 2.39 3.62 4.14 1.98

HOMO (eV) -11.135 -11.050 -10.940 -10.895 -10.940 -10.952 -10.858 -10.991 -10.940 -10.805 -10.930 -10.924 -10.917 -10.912 -10.907 -10.903 -10.900 -10.946 -9.493 -10.677 -9.176 -10.304 -11.578 -10.523 -10.982 -10.281 -10.807 -10.687 -10.670 -10.650 -10.584 -8.973 -10.668 -10.646 -10.541 -10.420 -10.512 -10.392 -10.509 -10.409 -9.445 -9.581 -9.936 -10.337 -10.493 -9.875 -9.890 -10.616 -11.006 -10.393 -10.383 -10.388 -10.389 -8.955 -10.431 -9.317 -10.180 -9.424 -8.568 -9.119 -11.390 -11.771 -12.379 -11.422 -11.417 -11.992 -11.513 -11.655 -11.870 -12.182 -11.290

Q(au) -0.5353 -0.5360 -0.5317 -0.5469 -0.5422 -0.5456 -0.5476 -0.5517 -0.5422 -0.5394 -0.5506 -0.5517 -0.5526 -0.5539 -0.5539 -0.5524 -0.5506 -0.5293 -0.5567 -0.5777 -0.5465 -0.4832 -0.5113 -0.5258 -0.5148 -0.5460 -0.5114 -0.5150 -0.5233 -0.5209 -0.5514 -0.5669 -0.4700 -0.4779 -0.4659 -0.4578 -0.4751 -0.4763 -0.4726 -0.4635 -0.4760 -0.4887 -0.4591 -0.4722 -0.4713 -0.4512 -0.4423 -0.5584 -0.5045 -0.4057 -0.5014 -0.4487 -0.4523 -0.4029 -0.4234 -0.2135 -0.3943 -0.3773 -0.3696 -0.4636 -0.1854 -0.2708 -0.2974 -0.1724 -0.1151 -0.1807 -0.1659 -0.2785 -0.2966 -0.2913 -0.2122

LUMO (eV) Class 1 3.7775 3.6513 3.6324 3.4925 3.5041 3.5536 3.5052 3.4384 3.5041 3.4884 3.4642 3.4370 3.4174 3.4031 3.3928 3.3851 3.3793 3.2671 1.0283 3.1360 0.7497 0.9217 -0.4003 2.4765 2.4265 2.3815 2.8028 2.6958 2.6498 2.6755 2.3600 0.5669 0.8443 0.8489 0.8772 0.9096 0.8723 0.9090 0.8715 0.9131 0.8556 -0.4590 -0.3606 0.9430 0.8962 -0.4759 -0.5146 3.3960 1.1370 2.9807 2.8648 2.8852 2.8700 0.1708 2.9892 0.7228 3.1103 0.2306 0.3924 -0.0249 0.5946 -0.3035 -1.1170 0.5822 0.6838 -0.2658 0.3239 -0.0738 -0.6817 -0.9677 1.1169

Q+ (au) 0.3182 0.3107 0.3122 0.3166 0.3141 0.3093 0.3149 0.3144 0.3141 0.3197 0.3129 0.3134 0.3165 0.3140 0.3156 0.3151 0.3145 0.3173 0.3342 0.3386 0.3312 0.0839 0.3378 0.3309 0.3221 0.3629 0.3202 0.3201 0.3242 0.3233 0.3123 0.3339 0.0897 0.1238 0.0978 0.0557 0.0890 0.0776 0.0853 0.0923 0.1321 0.1393 0.1079 0.0906 0.0878 0.1196 0.1518 0.3123 0.1113 0.0762 0.0775 0.0722 0.0777 0.1439 0.0809 0.1296 0.0549 0.1263 0.1430 0.3301 0.1618 0.2426 0.0000 0.1722 0.1152 0.0824 0.1900 0.2506 0.2486 0.0000 0.1432

L. stagnalis

P. reticulata

obs

obs

pred

-0.06 -0.56

-0.48 -0.86

-0.84 -1.63

-1.20 -1.83

-1.71 -1.32

-1.75 -1.43

-1.95 -3.02

-2.11 -2.73

-3.98 -4.40 -4.81 -5.21 -5.26 -0.10

-3.50 -4.47 -4.72 -4.69 -5.15 -0.14

-1.04 -2.28 -2.15 -2.69 -2.15 -0.24 -0.35 -0.64 -0.74 -1.28 -2.08 -0.70 -2.60 -0.90

-0.77 -2.10 -2.19 -2.96 -1.84 -0.34 -0.55 -0.70 -1.25 -1.38 -1.98 -0.98 -2.84 -1.03

-1.35 -1.74 -3.55 -3.66 -4.43 -1.99 -3.16 -3.08 -2.87 -3.06 -2.29 -4.07 -4.20 -2.27

-1.46 -1.82 -3.07 -3.51 -4.11 -1.88 -2.83 -2.94 -2.77 -2.16 -2.24 -4.06 -3.78 -1.83

-1.54 -3.04 -3.60 -4.69 -4.62 -2.09 -3.04 -1.52 -3.87 -3.07 -3.48 -2.46 -3.07 -3.36 -2.69 -2.94 -3.00 -3.18 -3.77 -4.26 -5.19 -2.99

-1.47 -2.03 -3.31 -3.97 -4.86 -1.53 -2.41 -1.11 -3.72 -3.16 -3.53 -2.22 -3.09 -3.39 -2.65 -2.27 -3.13 -2.80 -3.40 -4.45 -4.40 -2.67

-0.97

-3.46

-0.92

-1.90

pred

-0.92

D. magna obs

pred

-0.93 -1.22

-0.75 -1.27

-1.57 -1.24 -1.71 -1.13 -2.09

-1.83 -1.63 -1.80 -1.41 -2.33

-0.09 -1.00

0.17 -0.52

-2.88

-3.02

-1.07

-1.09

-0.98

-1.16

-1.73

-1.79

-2.79 -3.17

-2.41 -3.31

-2.49 -3.36

-2.54 -3.68

-3.40

-3.02

-3.33

-0.91

-1.83

850 J. Chem. Inf. Comput. Sci., Vol. 38, No. 5, 1998

URRESTARAZU RAMOS

ET AL.

Table 5 (Continued)


chemical

log KOW

HOMO (eV)

Q(au)

LUMO (eV)

1,3-dichloropropane 1,2,3-trichloropropane 1-chlorobutane trichloroethene tetrachloroethene hexachlorobutadiene lindane chlorobenzene 1,2-dichlorobenzene 1,3-dichlorobenzene 1,4-dichlorobenzene 1,2,3-trichlorobenzene 1,2,4-trichlorobenzene 1,3,5-trichlorobenzene 1,2,3,4-tetrachlorobenzene 1,2,3,5-tetrachlorobenzene 1,2,4,5-tetrachlorobenzene 3-chlorotoluene 4-chlorotoluene 2,4-dichlorotoluene 2,4,5-trichlorotoluene 3,4-dichlorotoluene pentachlorobenzene 2-chloronaphthalene hexane octane decane benzene toluene 2-xylene 3-xylene 4-xylene 1,2,4-trimethylbenzene 1,3,5-trimethylbenzene 1,2,4,5-tetramethylbenzene ethylbenzene cumene 1-methylnaphthalene 2-methylnaphthalene biphenyl cyclopentane cyclohexane methylcyclohexane

2.00 1.98 2.64 2.61 3.40 4.78 3.72 2.89 3.43 3.52 3.44 4.13 4.05 4.18 4.64 4.65 4.60 3.28 3.33 4.24 4.78 4.06 5.18 4.14 3.90 5.18 5.98 2.13 2.78 3.12 3.20 3.15 3.63 3.42 4.00 3.15 3.66 3.87 3.86 4.01 3.00 3.44 3.61

-11.372 -11.442 -11.133 -9.956 -9.902 -9.542 -11.475 -9.561 -9.602 -9.682 -9.523 -9.784 -9.623 -9.921 -9.735 -9.763 -9.655 -9.444 -9.299 -9.447 -9.475 -9.407 -9.786 -8.868 -11.084 -11.066 -11.063 -9.653 -9.330 -9.183 -9.186 -9.062 -8.972 -9.165 -8.832 -9.381 -9.383 -8.584 -8.620 -8.952 -10.970 -10.937 -10.822

-0.1625 -0.2074 -0.1880 -0.0901 -0.0372 -0.1091 -0.1923 -0.1262 -0.1028 -0.1298 -0.7993 -0.1345 -0.1004 -0.1888 -0.0587 -0.1772 -0.0512 -0.2176 -0.2161 -0.2153 -0.2593 -0.2519 -0.0571 -0.1939 -0.1641 -0.1330 -0.1293 -0.0921 -0.1922 -0.1838 -0.1782 -0.1846 -0.2105 -0.2229 -0.2022 -0.1464 -0.1710 -0.1574 -0.1959 -0.1264 -0.1258 -0.0753 -0.2031

Class 1 1.0193 0.7594 1.5109 -0.0608 -0.4367 -1.3444 0.2284 0.1545 -0.1425 -0.1580 -0.2162 -0.3646 -0.4691 -0.4022 -0.6518 -0.6841 -0.7308 0.1844 0.1351 -0.1489 -0.4355 -0.1363 -0.8904 -0.5063 3.7357 3.6386 3.5774 0.5551 0.5204 0.5231 0.5250 0.4871 0.5030 0.5756 0.4947 0.5281 0.5417 -0.2668 -0.2459 -0.0680 3.6228 3.6562 3.6095

nitrobenzene 2-nitrotoluene 3-nitrotoluene 4-nitrotoluene 2,3-dimethylnitrobenzene 3,4-dimethylnitrobenzene 2-chloronitrobenzene 3-chloronitrobenzene 4-chloronitrobenzene 2,3-dichloronitrobenzene 2,4-dichloronitrobenzene 2,5-dichloronitrobenzene 3,5-dichloronitrobenzene 2-chloro-6-nitrotoluene 4-chloro-2-nitrotoluene 4-chloro-3-nitrotoluene phenol 2-methylphenol 3-methylphenol 4-methylphenol 2,4-dimethylphenol 2,6-dimethylphenol 3,4-dimethylphenol 2,3,6-trimethylphenol 2,4,6-trimethylphenol 4-ethylphenol 4-propylphenol

1.85 2.30 2.42 2.37 2.83 2.91 2.24 2.46 2.39 3.05 3.09 2.90 3.13 3.09 3.05 2.90 1.46 1.95 1.96 1.94 2.30 2.36 2.23 2.92 2.97 2.58 3.20

-10.562 -10.171 -10.197 -10.305 -9.941 -10.077 -10.332 -10.367 -10.475 -10.283 -10.470 -10.218 -10.416 -10.146 -10.324 -10.036 -9.114 -8.960 -9.052 -8.880 -8.784 -8.885 -8.803 -8.833 -8.691 -8.912 -8.903

-0.4939 -0.5043 -0.4984 -0.5017 -0.5097 -0.5050 -0.4984 -0.4842 -0.4911 -0.4900 -0.4938 -0.4879 -0.4772 -0.4966 -0.4952 -0.5006 -0.4958 -0.4813 -0.4963 -0.4927 -0.4980 -0.4751 -0.4982 -0.4751 -0.4750 -0.4931 -0.4964

Class 2 -1.0679 -1.0109 -1.0138 -1.0449 -0.9491 -0.9975 -1.0722 -1.2855 -1.3436 -1.2297 -1.3555 -1.2921 -1.4880 -0.8587 -1.2798 -1.0159 0.3976 0.4093 0.3732 0.4317 0.3979 0.3940 0.4360 0.3648 0.4322 0.4334 0.4383

Q+ (au) 0.1073 0.1419 0.1146 0.1238 0.0000 0.0000 0.2210 0.1054 0.1053 0.1082 0.0887 0.1149 0.0966 0.0582 0.0995 0.0608 0.0741 0.1054 0.1165 0.1238 0.0994 0.1156 0.0718 0.1241 0.0505 0.0484 0.0499 0.0921 0.0989 0.1107 0.1146 0.1047 0.1274 0.1241 0.1328 0.1103 0.1154 0.1153 0.1234 0.1032 0.0575 0.0393 0.0562 0.1107 0.1163 0.1199 0.1154 0.1208 0.1269 0.1290 0.1217 0.1247 0.1279 0.1425 0.1080 0.0938 0.1157 0.1250 0.1323 0.3382 0.3292 0.3361 0.3359 0.3436 0.3368 0.3370 0.3364 0.3327 0.3362 0.3371

L. stagnalis obs

pred

-3.37

-3.46

-6.09 -4.60

-5.75 -4.67

P. reticulata obs

pred

-3.13 -3.55 -2.98 -3.42 -4.02

-2.61 -2.72 -3.06 -3.44 -3.87

-3.77 -4.40 -4.28 -4.56 -4.89 -4.83 -4.74 -5.35 -5.43 -5.85 -3.84 -4.33 -4.54 -5.06 -4.60 -6.15

-3.63 -4.09 -4.17 -4.29 -4.69 -4.61 -4.62 -5.09 -5.06 -5.03 -3.96 -4.04 -4.80 -5.24 -4.66 -5.49

-2.53

-2.86

-3.09 -3.13 -3.48 -3.45 -3.48

-2.92 -3.50 -3.80 -3.87 -3.83

-3.28 -3.51

-3.27 -3.66

-2.97 -3.59 -3.65 -3.67 -4.39 -4.21 -3.72 -4.01 -4.42 -4.66 -4.46 -4.59 -4.58 -4.52 -4.44

-3.07 -3.46 -3.56 -3.51 -3.89 -3.96 -3.43 -3.62 -3.58 -4.09 -4.16 -3.96 -4.12 -4.05 -4.09

-3.45 -3.77 -3.48 -3.74 -3.86 -3.75 -3.92 -4.21

-3.07 -3.45 -3.47 -3.46 -3.77 -3.79 -3.69 -4.23

-4.07 -4.09

-3.95 -4.43

-2.83

-2.95

D. magna obs

pred

-3.62

-3.07

-4.04

-3.70

-3.85 -4.68

-3.95 -4.49

-5.10 -4.83

-5.20 -5.11

-5.60 -5.40

-5.66 -5.75

-4.00 -5.42 -5.55

-4.43 -5.33 -5.90

-6.07 -5.50 -4.34 -5.47 -6.69 -3.28 -3.87 -4.50 -3.98 -4.06 -4.53 -4.30 -5.44 -4.76 -5.34 -5.00 -4.98 -5.15 -3.81 -4.34 -4.81

-6.20 -5.28 -4.31 -5.50 -6.26 -3.15 -3.83 -4.15 -4.22 -4.18 -4.65 -4.45 -4.99 -4.15 -4.64 -4.96 -4.98 -5.03 -3.47 -3.83 -4.09

-3.55 -4.19

-3.44 -3.86

-4.12

-3.82

-4.02

-3.99

-4.16 -4.25 -3.66

-4.60 -4.43 -2.92

-4.12

-3.36

-4.49 -4.09

-4.30 -3.95

AQUATIC TOXICITY

OF

NARCOTIC POLLUTANTS


Table 5 (Continued)


chemical

log KOW

HOMO (eV)

Q(au)

LUMO (eV)

Q+ (au) 0.3363 0.3364 0.3109 0.3362 0.3382 0.3338 0.3303 0.3371 0.3397 0.3356 0.3400 0.3410 0.3268 0.3382 0.1512 0.1537 0.3801 0.3902 0.3756 0.3773 0.3896 0.3776 0.14693 0.3834 0.3790 0.3816 0.3790 0.3728 0.3123 0.3190 0.3832 0.3174 0.3165 0.3201 0.3155 0.3225 0.3195 0.3279 0.3071 0.3071 0.3951 0.3923 0.3834 0.3797 0.3510 0.3922 0.3134 0.3183 0.3969

4-n-butylphenol 4-tert-butylphenol 2-tert-butyl-4-methylphenol 4-n-pentylphenol 4-tert-pentylphenol 2-allylphenol 2-phenylphenol 1-naphthol 4-chlorophenol 4-chloro-3-methylphenol 4-chloro-3,5-dimethylphenol 3-methoxyphenol 4-methoxyphenol 4-phenoxyphenol pyridine quinoline

3.56 3.31 3.80 4.09 3.83 2.55 3.09 2.84 2.39 3.10 3.45 1.58 1.34 3.35 0.65 2.03

-8.903 -8.894 -8.761 -8.902 -8.885 -9.016 -8.731 -8.455 -9.125 -9.051 -8.977 -8.941 -8.636 -8.806 -9.932 -9.181

-0.4930 -0.4990 -0.4381 -0.4951 -0.4992 -0.4818 -0.4813 -0.4810 -0.4928 -0.4894 -0.4982 -0.4939 -0.4790 -0.4904 -0.6610 -0.6538

Class 2 0.4362 0.4709 0.4780 0.4370 0.4722 0.3597 -0.0489 -0.2472 0.0946 0.0930 0.1466 0.4134 0.3034 0.1133 0.1385 -0.4666

aniline 2-methylaniline 3-methylaniline 4-methylaniline 2,3-dimethylaniline 3,4-dimethylaniline N,N-dimethylaniline 2-ethylaniline 3-ethylaniline 4-ethylaniline 4-butylaniline 2,6-diisopropylaniline 2-chloroaniline 3-chloroaniline 4-chloroaniline 2,4-dichloroaniline 2,5-dichloroaniline 3,4-dichloroaniline 3,5-dichloroaniline 2,3,4-trichloroaniline 2,3,6-trichloroaniline 2,4,5-trichloroaniline 4-bromoaniline R,R,R,4-tetrafluoro-3-methylaniline R,R,R,4-tetrafluoro-2-methylaniline pentafluoroaniline 3-benzyloxyaniline 4-hexyloxyaniline 2-nitroaniline 3-nitroaniline 4-nitroaniline 2-chloro-4-nitroaniline 4-ethoxy-2-nitroaniline

0.90 1.32 1.40 1.39 1.81 1.86 2.31 1.74 1.94 1.96 3.05 3.18 1.90 1.88 1.88 2.91 2.92 2.69 2.90 3.68 3.32 3.69 2.26 2.51 2.51 1.86 2.77 3.64 1.85 1.37 1.39 2.06 2.38

-8.522 -8.435 -8.478 -8.356 -8.399 -8.314 -9.332 -8.431 -8.482 -8.379 -8.376 -8.338 -8.376 -8.458 -8.577 -8.466 -8.589 -8.499 -8.687 -8.607 -8.702 -8.630 -8.393 -8.759 -8.934 -9.272 -8.540 -8.371 -9.068 -9.254 -9.160 -9.256 -8.994

-0.8545 -0.9317 -0.9380 -0.9429 -0.9301 -0.9480 -0.6200 -0.9294 -0.9510 -0.9589 -0.9518 -0.8995 -0.6743 -0.6965 -0.9487 -0.6755 -0.6638 -0.6796 -0.6550 -0.6808 -0.6761 -0.6849 -0.6621 -0.6372 -0.8982 -0.8360 -0.9448 -0.9489 -0.6488 -0.9468 -0.6493 -0.6434 -0.8070

Anilines 0.6392 0.6007 0.6051 0.6156 0.5917 0.6089 0.4336 0.6081 0.6107 0.6219 0.6182 0.6459 0.3928 0.3781 0.2920 0.1239 0.0302 0.1307 0.0543 -0.1427 -0.2406 -0.1974 0.4109 -0.3958 -0.4233 -1.0127 0.3454 0.4853 -0.7937 -0.9503 -0.7050 -0.9066 -0.8747

a

L. stagnalis

P. reticulata

obs

obs

pred

-4.47 -4.46 -4.90 -5.12 -4.81 -3.96 -4.76 -4.50 -4.18 -4.33 -4.66 -3.22 -3.05 -4.58

-4.71 -4.51 -4.84 -5.12 -4.92 -3.92 -4.44 -4.32 -3.85 -4.40 -4.68 -3.19 -3.02 -4.62

-3.63 -2.91 -3.12 -3.47 -3.72

pred

-4.65

-5.00

-3.93 -4.58

-3.54 -4.11

-4.01

-4.12

-2.35 -3.58

-1.96 -3.33

-2.13

-2.14

-3.17

-3.41

-4.58

-4.85

-2.98

-3.02

-3.33 -3.21 -3.65 -3.52 -4.16 -4.06 -4.31 -3.98 -3.67 -4.41 -4.99 -4.39 -4.62 -5.15 -4.73 -4.92 -3.56 -3.77 -3.78 -3.69 -4.34 -4.78 -4.15 -3.24 -3.23 -3.93 -3.85

D. magna obs

pred

-4.55

-4.63

-5.10

-5.36

-4.13 -4.80

-3.93 -4.50

-4.32 -4.98

-3.83 -4.49

-3.36

-3.40

-3.64

-2.84 -3.22 -3.25 -3.26

-4.31

-2.62

-4.08 -4.62 -3.95

-3.54 -3.59 -3.73

-4.33 -4.83 -4.83

-3.45 -3.45 -3.66

-4.52

-4.23

-5.01

-5.23

-5.15

-3.39

-3.37 -3.53 -3.68 -3.71 -4.55 -4.62 -3.49 -3.49 -3.69 -4.33 -4.33 -4.16 -4.30 -4.97 -4.69 -5.00 -3.75 -4.05 -4.29 -3.84 -4.38 -5.03 -3.68 -3.48 -3.22 -3.78 -4.24

The toxicity value of the chemicals used in the training sets are highlighted. See text for the source of the toxicity data.

toxicity is found for hydrophobic chemicals with low LUMO energies and high absolute charges. The influence of LUMO can tentatively be explained by the presence of negatively charged phosphate heads in the outer layer of the cell membranes, which are believed to be the target site. Comparison of the toxicity models with similar models for membrane/water partition coefficients suggest that the toxicity of nonpolar and polar narcotics are caused by the same mode of toxic action. This strongly suggests that the perceived differences in toxicity between these groups are due to the use of octanol to mimic the membrane phospholipids. Because the baseline toxicity is based on log KOW, it seems that the baseline toxicity should be reconsidered by taking the real membrane/water partitioning into account.

ACKNOWLEDGMENT

The authors are grateful to the Department of Education, Universities and Research of the Basque Government, and to the Dutch Ministry of Housing, Spatial Planning and Environment for their financial support. This work was partially carried out within the framework of the EC project Fate and Activity Modeling of Environmental Pollutants Using Structureactivity Relationships (FAME) under contract ENV4-CT96-0221. REFERENCES AND NOTES (1) van Wezel, A. P.; Opperhuizen, A. Narcosis due to Environmental Pollutants in Aquatic Organisms: Residue-Based Toxicity, Mechanisms and Membrane Burdens. Crit. ReV. Toxicol. 1995, 25, 255279.


852 J. Chem. Inf. Comput. Sci., Vol. 38, No. 5, 1998 (2) Cantor, R. S. The Lateral Pressure Profile in Membranes: a Physical Mechanism of General Anesthesia. Biochemistry 1997, 36, 23392344. (3) Hermens, J. L. M. In Handbook of EnVironmental Chemistry; Hutzinger, O., Ed.; Springer-Verlag: Berlin, 1989; Vol. 2E, p 111. (4) Van Leeuwen, C. J.; van der Zandt, P. T. J.; Aldenberg, T.; Verhaar, H. J. M.; Hermens, J. L. M. Application of QSARs, Extrapolation and Equilibrium Partitioning in Aquatic Effects Assessment. I. Pollutants that Act by Narcosis. EnViron. Toxicol. Chem. 1992, 11, 267-282. (5) Verhaar, H. J. M.; Van Leeuwen, C. J.; Bol, J.; Hermens, J. L. M. Application of QSARs in Risk Management of Existing Chemicals. SAR & QSAR EnViron. Res. 1994, 2, 39-58. (6) Schultz, T. W.; Holcombe, G. W.; Phipps, G. L. Relationships of Quantitative Structure-Activity to Comparative Toxicity of Selected Phenols in the Pimephales promelas and Tetrahymena pyriformis Test Systems. Ecotoxicol. EnViron. Safety 1986, 12, 146-153. (7) Veith, G. D.; Broderius, S. J. In QSAR in EnVironmental Toxicology II; Kaiser, K. L. E., Ed.; D. Reidel Publishing Company: Dordrecht, 1987; p 385. (8) Veith, G. D.; Broderius, S. J. Rules for Distinguishing Toxicants that Cause Type I and Type II Narcosis Syndromes. EnViron. Health Persp. 1990, 87, 207-211. (9) Verhaar, H. J. M.; van Leeuwen, C. J.; Hermens, J. L. M. Classifying Environmental Pollutants. 1: Structure-Activity Relationships for Prediction of Aquatic Toxicity. Chemosphere 1992, 25, 471-491. (10) Bradbury, S. P.; Henry, T. R.; Niemi, G. J.; Cralson, R. W.; Snarski, V. M. Use of Respiratory-Cardiovascular Responses of Rainbow Trout (Salmo gardianeri) in Identifying Acute Toxicity Syndromes in Fish: Part 3. Polar Narcotics. EnViron. Toxicol. Chem. 1989, 8, 247-261. (11) McCarty, L. S.; Mackay, D. Enhancing Ecotoxicological Modeling and Assessment. EnViron. Sci. Technol. 1993, 27, 1719-1728. (12) Verhaar, H. J. M.; Urrestarazu Ramos, E.; Hermens; J. L. M. Classifying Environmental Pollutants. 2: Separation of Class 1 (Baseline Toxicity) and Class 2 (‘Polar Narcosis’) Type Compounds Based on Chemical Descriptors. J. Chemometr. 1996, 10, 149-162. (13) Vaes, W. H. J.; Urrestarazu Ramos, E.; Hamwijk, C.; van Holsteijn, I.; Blaauboer, B. J.; Seinen, W.; Verhaar, H. J. M.; Hermens; J. L. M. Solid-Phase Microextraction as a Tool to Determine Membrane/Water Partition Coefficients and Bioavailable Concentrations in in Vitro Systems. Chem. Res. Toxicol. 1997, 10, 1067-1072. (14) Vaes, W. H. J.; Urrestarazu Ramos, E.; Verhaar, H. J. M.; Hermens; J. L. M. Acute Toxicity of Nonpolar Versus Polar Narcosis: Is There a Difference? EnViron. Toxicol. Chem. 1998, 17, 1380-1384. (15) Vaes, W. H. J.; Urrestarazu Ramos, E.; Verhaar, H. J. M.; Hermens; J. L. M. Understanding and Estimating Membrane/Water Partition Coefficients: Approaches to Derive Quantitative Structure Property Relationships (QSPR). Chem. Res. Toxicol. In press. (16) Abraham, M. H.; Lieb, W. R.; Franks, N. P. Role of Hydrogen Bonding in General Anesthesia. J. Pharmaceutical Sci. 1991, 80, 719-724.

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(17) Abraham, M. H.; Rafols, C. Factors Influencing Tadpole Narcosis. An LFER Analysis. J. Chem. Soc., Perkin Trans. 2, 1995, 18431851. (18) Cramer, C. J.; Famini, G. R.; Lowrey, A. H. Use of Calculated Quantum Chemical Properties as Surrogates for Solvatochromic Parameters in Structure-Activity Relationships. Acc. Chem. Res. 1993, 26, 599-605. (19) Vaes W. H. J.; Hamwijk, C.; Urrestarazu Ramos, E.; Verhaar, H. J. M.; Hermens, J. L. M. Partitioning of Organic Chemicals to Polyacrylate-Coated Solid-Phase Microextraction Fibers: Kinetic Behavior and Quantitative Structure-Property Relationships. Anal. Chem. 1996, 68, 4458-4462. (20) Urrestarazu Ramos, E.; Vaes, W. H. J.; Verhaar, H. J. M.; Hermens, J. L. M. Polar Narcosis: Designing a Suitable Training Set for QSAR Studies. EnViron. Sci., & Pollut. Res. 1997, 4, 83-90. (21) Urrestarazu Ramos, E.; Vermeer, C.; Vaes, W. H. J.; Hermens, J. L. M. Acute Toxicity of Polar Narcotics to Three Aquatic Species (Daphnia magna, Poecilia reticulata and Lymnaea stagnalis) and its Relation to Hydrophobicity. Chemosphere in press. (22) Verhaar, H. J. M.; Mulder, W.; Hermens, J. L. M.; Rorije, E.; Langenberg, J. H.; Peijnenburg, W. J. G. M.; Sabljic, A.; Gu¨sten, H.; Eriksson, L.; Sjo¨stro¨m, M.; Mu¨ller, M.; Hansen, B.; Nouwen, J.; Karcher, W. Overview of Structure-Activity Relationships for Environmental Endpoints. Part 1: General Outline and Procedure. Report of the EU-DG-XII Project QSAR for Predicting Fate and Effects of Chemicals in the Environment. (Contract # EV5V-CT920211) 12 July 1995. (23) Slooff, W.; Canton, J. H.; Hermens, J. L. M. Comparison of the Susceptibility of 22 Freshwater Species to 15 Chemical Compounds. I.(Sub)acute Toxicity Tests. Aquat. Toxicol. 1983, 4, 113-128. (24) Leeuwangh, P.; Bult, H.; Schneiders, L. In Sublethal Effects of Toxic Chemicals on Aquatic Animals; Koneman, J. H., Strik, J. J. T. W. A., Eds.; Elsevier Publ.: Amsterdam, 1975; pp 167-176. (25) Bluzat, R.; Seuge, J. Effects of Three Insecticides (Lindane, Fenthion, and Carbaryl) on the Acute Toxicity to Four Aquatic Invertebrate Species. EnViron. Pollut. 1979, 18, 51-70. (26) Ku¨hn, R.; Pattard, M.; Pernak, K.; Winter, A. Results of the Harmful Effects of Selected Water Pollutants (Anilines, Phenols, Aliphatic Compounds) to Daphnia magna. Water Res. 1989, 23, 495-499. (27) Urrestarazu Ramos, E.; Vaal, M. A.; Hermens, J. L. M. Interspecies Sensitivity to the Aquatic Toxicity of Aromatic Amines. Research Institute of Toxicology (RITOX). October 1996. 31 p. (28) Eriksson, L.; Hermens, J. L. M.; Johansson, E.; Verhaar, H. J. M.; Wold, S. Multivariate Analysis of Aquatic Toxicity Data with PLS. Aquat. Sci. 1995, 57, 217-241.

CI980027Q

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