QSAR analysis of the toxicity of aromatic compounds to Chlorella vulgaris in a novel short-term assay

The use of alternative toxicity tests and computational prediction models is widely accepted to fill experimental data gaps and to prioritize chemicals for more expensive and time-consuming assessment. A novel short-term toxicity test using the alga Chlorella vulgaris was utilized in this study to produce acute aquatic toxicity data for 65 aromatic compounds. The compounds tested included phenols, anilines, nitrobenzenes, benzaldehydes and other poly-substituted benzenes. The toxicity data were employed in the development of quantitative structure-activity relationships (QSARs). Using multiple regression (MLR) and partial least squares (PLS) analyses, statistically significant, transparent and interpretable QSARs were developed using a small number of physicochemical descriptors. A two-descriptor model was developed using MLR (log(l/EC50) = 0.73 log K-ow - 0.59 E-lumo - 1.91; n = 65, r(2) = 0.84, r(CV)(2) = 0.82, s = 0.43) and a four-descriptor model using PLS (log(l/EC50) = 0.40 log K-ow - 0.23 E-lumo + 9.84 A(max) + 0.20 (0)chi(v) - 5.40; n = 65, r(2) = 0.86, q(2) = 0.84, RMSEE = 0.40). The latter model was obtained by stepwise elimination of variables from a set of 102 calculated descriptors. Both models were validated successfully by simulating external prediction through the use of complementary subsets. The two factors, which were identified as being critical for the acute algal toxicity of this set of compounds were hydrophobicity and electrophilicity.