A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity^$

ABSTRACT

Results from the Ames test are the first outcome considered to assess the possible mutagenicity of substances. Many QSAR models and structural alerts are available to predict this endpoint. From a regulatory point of view, the recommendation from international authorities is to consider the predictions of more than one model and to combine results in order to develop conclusions about the mutagenicity risk posed by chemicals. However, the results of those models are often conflicting, and the existing inconsistency in the predictions requires intelligent strategies to integrate them. In our study, we evaluated different strategies for combining results of models for Ames mutagenicity, starting from a set of 10 diverse individual models, each built on a dataset of around 6000 compounds. The novelty of our study is that we collected a much larger set of about 18,000 compounds and used the new data to build a family of integrated models. These integrations used probabilistic approaches, decision theory, machine learning, and voting strategies in the integration scheme. Results are discussed considering balanced or conservative perspectives, regarding the possible uses for different purposes, including screening of large collection of substances for prioritization.

KEYWORDS:

• prediction of mutagenicity
• Ames test
• ensembles of models
• integrating SAR and QSAR
• naïve Bayes
• Dempster–Shafer theory
• self-organizing neural networks
• GMDH

Acknowledgements

The research at Swetox (UN) was supported by Knut and Alice Wallenberg Foundation [2013.0253] and Swedish Research Council FORMAS [2016-02031].

The authors from Mario Negri and Kode kindly acknowledge the Projects JANUS and the LIFE VERMEER project (LIFE16 ENV/IT/000167) for financial support.

Additional information

Funding

This work was supported by the EU LIFE VERMEER project [(LIFE16 ENV/IT/000167)]; UBA, Germany [Projects JANUS]; Swedish Research Council FORMAS [[2016-02031]]; Knut and Alice Wallenberg Foundation [[2013.0253]].