Performance of (Q)SAR Models for Predicting Ames Mutagenicity of Aryl Azo and Benzidine Based Compounds

Abstract

Regulatory agencies worldwide are committed to the objectives of the Strategic Approach to International Chemicals Management to ensure that by 2020 chemicals are used and produced in ways that lead to the minimization of significant adverse effects on human health and the environment. Under the Government of Canada's Chemicals Management Plan, the commitment to address a large number of substances, many with limited data, has highlighted the importance of pursuing alternative hazard assessment methodologies that are able to accommodate chemicals with varying toxicological information. One such method is (Quantitative) Structure Activity Relationships ((Q)SAR) models. The current investigation into the predictivity of 20 (Q)SAR tools designed to model bacterial reverse mutation in Salmonella typhimurium is one of the first of this magnitude to be carried out using an external validation set comprised mainly of industrial chemicals which represent a diverse group of aromatic and benzidine-based azo dyes and pigments. Overall, this study highlights the value in challenging the predictivity of existing models using a small but representative subset of data-rich chemicals. Furthermore, external validation revealed that only a handful of models satisfactorily predicted for the test chemical space. The exercise also provides insight into using the Organisation for Economic Co-operation and Development (Q)SAR Toolbox as a read across tool.

Keywords:

• Predictivity
• (Q)SAR
• mutagenicity
• external validation
• aryl azo
• consensus

ACKNOWLEDGMENTS

We are extremely thankful to Christine Norman, Director, and Eeva Leinala, Senior Manager of Existing Substances Risk Assessment Bureau, Health Canada, for their encouragement, support, and critical feedback on this work. We would also like to thank Guosheng Chen for his efforts in coordinating the generation of the Ames assay data on the test chemicals used in this study. Finally, we would like to acknowledge the meaningful inputs from Matthew Gagne, Pete Robinson, Saman Alavi, and Bio Aikawa.