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Predicting the Toxicities of Chemicals to Aquatic Animal Species

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Predicting the Toxicities of Chemicals to Aquatic Animal Species Predicting the Toxicities of Chemicals to Aquatic Animal Species Dale Hoff4 Wade Lehmann1 Anita Pease2 Sandy Raimondo3 Chris Russom4 Tom Steeger2 U.S. Environmental Protection Agency 1 Office of Water, Washington, DC 2 Office of Pesticide Programs, Washington, DC 3 Office of Research and Development, Gulf Ecology Division 4 Office of Research and Development, Mid-Continent Ecology Division October 27, 2010 List of Figures ................................................................................................................................. 4 List of Tables .................................................................................................................................. 5 Glossary .......................................................................................................................................... 6 Abbreviations .................................................................................................................................. 8 1 Executive Summary .............................................................................................................. 10 2 Introduction ........................................................................................................................... 11 3 General Overview of OPP and OW Data Requirements ...................................................... 13 3.1 OPP Data Requirements ................................................................................................... 13 3.2 OW Data Requirements .................................................................................................... 14 4 General Concept and Background of Predictive Methods .................................................... 15 4.1 Consideration of Mode of Action in Predictive Toxicology ............................................ 15 4.1.1 Mode of Action vs. Mechanism of Action .................................................................. 16 4.1.2 Adverse Outcome Pathway ......................................................................................... 16 4.1.3 Methods of Classifying Substances for Use in AOPs ................................................. 18 4.2 (Quantitative) Structure-Activity Relationships ............................................................... 22 4.2.1 Chemical-Structure Methods ...................................................................................... 23 4.2.2 Chemical Category Approach ..................................................................................... 24 4.2.3 Analog Approaches ..................................................................................................... 28 4.2.3.1 Structural Analogs ................................................................................... 29 4.2.3.2 Similarity Indices .................................................................................... 30 4.2.4 QSAR Model Development ........................................................................................ 31 4.3 Interspecies Correlation Estimation (ICE) Models ........................................................... 33 4.3.1 Background ................................................................................................................. 33 4.3.2 Model Development and Validation ........................................................................... 34 4.3.3 Influence of MOA on ICE Model Predictions ............................................................ 36 4.4 Acute-Chronic Ratios (ACRs) .......................................................................................... 38 4.4.1 OPP Approach to Derive ACRs .................................................................................. 39 4.4.2 OW Approach to Derive ACRs .................................................................................. 42 4.4.3 Similarities and Differences in OPP and OW Approaches to Derive ACRs .............. 44 4.4.4 Deriving ACRs Using Predicted and Empirical Data ................................................. 44 4.4.5 Default ACRs .............................................................................................................. 45 4.4.6 Fixed ACRs ................................................................................................................. 46 4.5 Time-Concentration Effect (TCE) Models ....................................................................... 46 4.5.1 Background ................................................................................................................. 46 5 Examples of Predictive Methods .......................................................................................... 48 6 Interpretation of (Q)SAR, Read-Across/Bridging, ICE, and TCE Predictions .................... 48 6.1 (Q)SARs ............................................................................................................................ 48 6.2 ICE Models ....................................................................................................................... 50 6.3 TCE Models ...................................................................................................................... 51 7 Considerations for Using Toxicity Predictions ..................................................................... 51 7.1 WOE and Best Professional Judgment ............................................................................. 51 7.2 Selection of Descriptors in (Q)SAR models ..................................................................... 53 7.3 Empirical Test Data .......................................................................................................... 56 7.4 Selecting Chemical Category and/or AOP ....................................................................... 57 7.5 ICE Models ....................................................................................................................... 57 Page 2 of 127 8 Example Applications and Proposed Analyses of Predictive Methods ................................ 58 8.1 Empirical Data and Important Values from Existing ALWQC Documents (Box 1 and 2 of Figure 11) .............................................................................................................................. 58 8.2 Methods for Deriving Acute ALSVs ................................................................................ 60 8.2.1 Supplementing Empirical Data with Predicted Values for Missing MDRs (Box 4 of Figure 11). ............................................................................................................................. 60 8.2.1.1 Web-ICE .................................................................................................. 61 8.2.1.2 Read-Across ............................................................................................ 63 8.2.2 Only Use Results of Toxicity Tests for Chemical and Taxa of Interest ..................... 70 8.3 Additional Applications of Predictive Tools .................................................................... 72 8.3.1 EFED QSAR Guidance............................................................................................... 72 8.3.2 Comparison of endosulfan and its degradate, endosulfan sulfate, toxicities using Web- ICE 72 9 References ............................................................................................................................. 76 Appendix A: EFED Guidance on Use of Structure-Activity Relationships Appendix B: Overview of Modes of Action, Associated Chemical Classes, and Pest Organisms Appendix C: Predictive Methods available via government, open access, or commercial sources that are available to risk assessors in OW and OPP Appendix D: Predictive methods for determining MOA and physical chemical properties Page 3 of 127 List of Figures Figure 1. Abbreviated Toxicity Pathway (from NRC, 2007). ...................................................... 17 Figure 2. Adverse Outcome Pathway (modified from Ankley et al., 2010). ................................ 18 Figure 3. Plot of water solubility vs. baseline toxicity (Russom unpublished data). .................... 20 Figure 4. Number of chemicals by the logTe values (modified from Russom et al., 1997). ........ 21 Figure 5. A chemical category is created to describe a series of “similar” pesticides. Data gaps within the data set are identified and filled based on information from data rich chemicals. (Modified from van Leeuwen et al, 2009). ................................................................................... 26 Figure 6. Example of structural analogs involving synthetic pyrethroids. ................................... 30 Figure 7. Schematic of similarity index measures. ....................................................................... 31 Figure 8. Example Interspecies Correlation Estimation Model. ................................................... 35 Figure 9. Models developed from chemicals of diverse modes of action compared to those with MOA-specific data. ....................................................................................................................... 38 Figure 10. Log P predictions for Scilliroside using KowWin and Clog P predictive methods. .. 55 Figure 11. A Conceptual Framework for Deriving the Acute ALSV. .......................................... 58 Figure 12. Start-up screen of the OECD toolbox. ......................................................................... 64 Figure
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