Using HPV Chemical Data to Develop QSARs for Non‐HPV Chemicals: Opportunities to Promote More Efficient Use of Chemical Testing Resources

There are opportunities to use data developed for High Production Volume (HPV) chemicals to save chemical testing resources for non-HPV chemicals. First, data being developed for HPV chemicals could reduce chemical testing resources for non-HPV chemicals, if the data can be used to develop and validate Quantitative Structure Activity Relationships (QSARs) that can be used to make predictions for non-HPV chemicals. Second, strategies need to be developed to identify chemicals for which QSARs may not make reliable predictions and appropriate tests need to be conducted so that these and related chemicals can be included in more robust QSAR predictions. This paper illustrates the chemical testing resources that could be saved by using QSAR predictions and discusses guidelines and issues that need to be addressed before using data from HPV chemicals to develop QSARs for non-HPV chemicals.

[1]  J. Hermens,et al.  Classifying environmental pollutants. 2: Separation of class 1 (baseline toxicity) and class 2 (‘polar narcosis’) type compounds based on chemical descriptors , 1996 .

[2]  John D. Walker,et al.  Structure Activity Relationships For Predicting Ecological Effects Of Chemicals , 2002 .

[3]  Jd Walker,et al.  Testing Decisions of The TSCA Interagency Testing Committee for Chemicals on The Canadian Environmental Protection Act Domestic Substances List and Priority Substances List: Di- tert -butylphenol, Ethyl Benzene, Brominated Flame Retardants, Phthalate Esters, Chloroparaffins, Chlorinated Benzenes, an , 1996 .

[4]  John D. Walker,et al.  New Cost-Effective, Computerized Approachesto Selecting Chemicals for Priority Testing Consideration , 1988 .

[5]  John D. Walker,et al.  Chemical selection by the Interagency Testing Committee: use of computerized substructure searching to identify chemical groups for health effects, chemical fate and ecological effects testing. , 1991, The Science of the total environment.

[6]  H. Guard,et al.  Predicting toxicity using computed molecular topologies: The example of triorganotin compounds , 1984 .

[7]  J. Hermens,et al.  Classifying environmental pollutants: Part 3. External validation of the classification system. , 2000, Chemosphere.

[8]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[9]  J. D. Walker,et al.  The TSCA interagency testing committee`s approaches to screening and scoring chemicals and chemical groups: 1977-1983 , 1990 .

[10]  R. Laughlin.  Quantitative Structure-Activity Studies of Di-and Triorganotin Compounds , 1987 .

[11]  Jd Walker,et al.  The TSCA Interagency Testing Committee, 1977 to 1992: Creation, Structure, Functions and Contributions , 1993 .

[12]  H S Rosenkranz,et al.  Estimating the extent of the health hazard posed by high-production volume chemicals. , 2001, Environmental health perspectives.

[13]  H. Guard,et al.  Structure‐activity relationships for organotin compounds , 1985 .

[14]  John D. Walker,et al.  Global Government applications of analogues, SAR s and QSAR s to predict aquatic toxicity, chemical or physical properties, environmental fate parameters and health effects of organic chemicals , 2002, SAR and QSAR in environmental research.

[15]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

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