Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach^$

^$Presented at the 17th International Conference on QSAR in Environmental and Health Sciences (QSAR 2016), 13–17 June 2016, Miami Beach, FL, USA

Abstract

One of the key challenges of Canada’s Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model’s ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model’s predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

Keywords:

• (Q)SAR
• toxicity
• confidence
• chemical space
• predictivity
• chemical risk assessment

Acknowledgments

We are thankful to Christine Norman, Director of Existing Substances Risk Assessment Bureau, Health Canada, for the encouragement, support, and critical feedback on this work. We would also like to acknowledge the meaningful inputs from Matthew Gagné, Joel Paterson and Lynn Berndt-Weis.

Notes

^$ Presented at the 17th International Conference on QSAR in Environmental and Health Sciences (QSAR 2016), 13–17 June 2016, Miami Beach, FL, USA