Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action

Abstract

Three quantitative structure–activity relationship (QSAR) models were evaluated for their power to predict the toxicity of chemicals in two datasets: (1) EPAFHM (US Environmental Protection Agency—Fathead Minnow) and (2) derivatives having a high production volume (HPV), as compiled by the European Chemical Bureau. For all three QSAR models, the quality of the predictions was found to be highly dependent on the mode of action of the chemicals. An analysis of outliers from the three models gives some clues for improving the QSAR models. Two classification methods, Toxtree and a Bayesian approach with fingerprints as descriptors, were also analyzed. Predictions following the Toxtree classification for narcosis were good, especially for the HPV set. The learning model (Bayesian approach) produced interesting results for the EPAFHM dataset but gave lower quality predictions for the HPV set.

Keywords::

• QSAR
• mode of action
• LC₅₀
• fathead minnow
• classifications