Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction. Issue 1 (March 2013)

• Record Type: Journal Article
• Authors: Cassani, Stefano; Kovarich, Simona; Papa, Ester; Roy, Partha Pratim; Rahmberg, Magnus; Nilsson, Sara; Sahlin, Ullrika; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor V.; Brandmaier, Stefan; Durjava, Mojca Kos; Kolar, Boris; Peijnenburg, Willie; Gramatica, Paola
• Is Part Of: ATLA. Volume 41:Issue 1(2013)
• Page Start: 49
• Page End: 64
• Publication Date: 2013-03
• Subjects: aquatic toxicity -- applicability domain -- (benzo)triazoles -- consensus -- QSAR -- REACH -- validation; Laboratory animals -- Periodicals; 616.0273
• DOI: 10.1177/026119291304100107
• Languages: English
• ISSNs: 0261-1929

Abstract

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 QSPR-THESAURUS 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.