Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative, and inherently toxic to non-human organisms

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999) , Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.

[1]  Ş. Niculescu,et al.  Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): a study based on 865 compounds. , 1999, Chemosphere.

[2]  Gerald T. Ankley,et al.  A Computationally-Based Hazard Identification Algorithm That Incorporates Ligand Flexibility. 1. Identification of Potential Androgen Receptor Ligands , 1997 .

[3]  J. Hermens,et al.  Classifying environmental pollutants , 1992 .

[4]  M. W. Williams,et al.  Comments on softness parameters and metal ion toxicity , 1981 .

[5]  M. W. Williams,et al.  MULTIPARAMETER CORRELATIONS BETWEEN PROPERTIES OF METAL IONS AND THEIR ACUTE TOXICITY IN MICE , 1987 .

[6]  T. Hayden,et al.  Correlations between pairs of simple physicochemical parameters of metal ions and acute toxicity in mice. , 1988, The Science of the total environment.

[7]  P. Jurs,et al.  Prediction of fathead minnow acute toxicity of organic compounds from molecular structure. , 1999, Chemical research in toxicology.

[8]  Gerrit Schüürmann,et al.  Feed Forward Backpropagation Neural Networks and their Use in Predicting the Acute Toxicity of Chemicals to the Fathead Minnow , 1997 .

[9]  Phillip L. Williams,et al.  Use of ion characteristics to predict relative toxicity of mono-, di- and trivalent metal ions: Caenorhabditis elegans LC50 , 1998 .

[10]  M. C. Newman,et al.  Predicting relative toxicity and interactions of divalent metal ions: Microtox® bioluminescence assay , 1996 .

[11]  J. Turner,et al.  Inorganic concepts relevant to metal binding, activity, and toxicity in a biological system. , 1991, The Science of the total environment.

[12]  C L Russom,et al.  ASTER: an integration of the AQUIRE data base and the QSAR system for use in ecological risk assessments. , 1991, The Science of the total environment.

[13]  Klaus L.E. Kaiser,et al.  Influence of Data Preprocessing and Kernel Selection on Probabilistic Neural Network Modeling of the Acute Toxicity of Chemicals to the Fathead Minnow and Vibrio fischeri Bacteria , 1998 .

[14]  C. Russom,et al.  Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas) , 1997 .

[15]  Chris Park,et al.  The Environment , 2010 .

[16]  D. Lewis,et al.  Metal toxicity in two rodent species and redox potential: Evaluation of quantitative structure—activity relationships , 1999, Environmental toxicology and chemistry.

[17]  A. Lucas.  The Canadian Environmental Protection Act , 1987 .

[18]  Robert S. Boethling,et al.  Group contribution method for predicting probability and rate of aerobic biodegradation. , 1994, Environmental science & technology.