On the aquatic toxicity of substituted phenols to Chlorella vulgaris: QSTR with an extended novel data set and interspecies models.

This study provides for the first time the 96-h toxicity of 16 nitro- and methyl- substituted phenols to Chlorella vulgaris. Enabling the circulation of new ecotoxicity data has expanded the previously reported toxicity data set of 30 phenols to C. vulgaris by our laboratory. In this respect, high quality, single source algal toxicity data, generated in the same laboratory according to a REACH (Registration, Evaluation, Authorization and Restriction of CHemicals) compatible endpoint, provided a sound basis to explore quantitative structure-toxicity relationship (QSTR), which can be used for regulatory purposes. Of the developed linear models on a new data set, the selected one was applied to a data set lack of toxicity values, and prediction ability of the model was discussed. Interspecies relations were sought related to Pseudokirchneriella subcapitata and Tetrahymena pyriformis. The developed models displayed decent predictivity, which can be used to predict the toxicity of untested phenols on C. vulgaris.

Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris.

This study provides for the first time the 96 h toxicity of 30 phenols to Chlorella vulgaris. Analysis of the novel data set revealed that the algal toxicity of polar narcotics and respiratory uncouplers was correlated strongly to the pH corrected hydrophobicity parameter, Log D, demonstrating the importance of ionization in the C. vulgaris test system. Compounds expected to act by more reactive mechanisms were shown to have toxicity in excess of that predicted by Log D and were successfully modelled using the activation energy index (AEI). Three global quantitative structure-activity relationships (QSARs) were constructed using the C. vulgaris data set and validated externally using a data set retrieved from literature comprising the toxicity of 58 compounds to freshwater alga Pseudokirchneriella subcapitata. Results revealed that the response-surface model was highly interpretable and provided acceptable predictions for polar narcotics and respiratory uncouplers, though it lacked the reliability to predict the toxicity of reactive phenols. In two other externally validated QSAR models, a WHIM (Weighted Holistic Invariant Molecular) descriptor, namely, Tm (T total size index/weighted by atomic masses), revealed promising results that could be used to predict algal toxicity of compounds other than phenols such as anilines.

Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols.

The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (Log P). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure-activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q(test)²=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided an excellent external predictivity (Q(test)²=0.93). The proposed algal QSARs were further tested in their predictivity using an external set comprising toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (C(ortho)) not only had high external predictivity (Q(ext)²=0.74), but it also had excellent external data set coverage (%97).

First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships.

The toxicity of phenol and 13 chlorinated phenols to the marine alga Dunaliella tertiolecta is presented for the first time. The newly generated marine algal toxicity data was found to correlate strongly with the widely used hydrophobicity parameter-the logarithm of the n-octanol-water partition coefficient (log K(OW)). Interspecies relationships using the new marine algal toxicity data of chlorophenols with the previously published data on bacterium (Vibrio fischeri), protozoan (Tetrahymena pyriformis), daphnid (Daphnia magna), freshwater alga (Pseudokirchneriella subcapitata), and fish (Pimephales promelas) revealed promising results that could be exploited in extrapolations using freshwater data to predict marine algal toxicity.