Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction

QSAR regression models of the toxicity of triazoles and benzotriazoles ([B]TAZs) to an alga
(Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by
five partners in the FP7-EU Project, CADASTER. The models were developed by different methods —
Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative
Neural Network (ASNN) — by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPRTHESAURUS
web). In addition, different procedures were used for variable selection, validation and applicability
domain inspection. The predictions of the models developed, as well as those obtained in a
consensus approach by averaging the data predicted from each model, were compared with the results of
experimental tests that were performed by two CADASTER partners. The individual and consensus models
were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming
the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity
of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental
data. This highlights the importance of QSAR models for the screening and prioritisation of
untested chemicals, in order to reduce and focus experimental testing.