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

S. A. Kulkarni, E. Benfenati, T. S. Barton-Maclaren

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalSAR and QSAR in Environmental Research
DOIs
Publication statusAccepted/In press - Oct 21 2016

Keywords

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

ASJC Scopus subject areas

  • Bioengineering
  • Molecular Medicine
  • Drug Discovery

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