Integrated approach to assess the domain of applicability of some commercial (Q)SAR models1

¹Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).

Abstract

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.

¹Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).

Keywords:

• domain of applicability
• (Q)SAR
• cluster analysis
• Casetox
• TOPKAT

Acknowledgements

This work was supported by the Canadian Environmental Protection Act (CEPA) project. We also acknowledge the award of Canadian Government Laboratory Visiting Fellowship by the government of Canada to one of the authors (S.K.). Intense discussion with and helpful knowledge of Joel Paterson, Bette Meek and Christine Norman, and internal review of this paper by Eeva Leinala and Elizabeth Verberne of Health Canada are highly appreciated.

Notes

¹Presented at CMTPI 2007: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Moscow, Russia, September 1–5, 2007).