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Development & Evaluation Development & Evaluation of Ecotoxicity Predictive of Ecotoxicity Predictive

ToolsTools

EPA Development TeamEPA Development Team

Regional Stakeholder Meetings Regional Stakeholder Meetings January 11-22, 2010January 11-22, 2010

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OutlineOutline Purpose and ScopePurpose and Scope

ToolsTools

Application of ToolsApplication of Tools

Interpretation of PredictionsInterpretation of Predictions

Other ConsiderationsOther Considerations

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Purpose and ScopePurpose and Scope

Present an overview of 21Present an overview of 21stst century tools for EPA to century tools for EPA to use in generating estimated toxicity data for use in generating estimated toxicity data for pesticidespesticides

Tools will be used by both OW and OPP when:Tools will be used by both OW and OPP when: Acceptable chemical-specific data are not available in the open Acceptable chemical-specific data are not available in the open

literature literature Data requirements are not met according to the 1985 Guidelines or Data requirements are not met according to the 1985 Guidelines or

FIFRA Subdivision G, Parts 158.630 and 158.660; and/orFIFRA Subdivision G, Parts 158.630 and 158.660; and/or Additional data are required to reduce uncertainties in OPP Additional data are required to reduce uncertainties in OPP

assessmentsassessments Tools include, but not limited to:Tools include, but not limited to:

Quantitative/Qualitative Structure-Activity Relationships ([Q]SAR)Quantitative/Qualitative Structure-Activity Relationships ([Q]SAR) Read-across / data bridgingRead-across / data bridging OECD / EPA chemical categories and/or mode of action inferenceOECD / EPA chemical categories and/or mode of action inference Acute-to-chronic ratios (ACR)Acute-to-chronic ratios (ACR) Interspecies correlation models (ICE)Interspecies correlation models (ICE)

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Role of Tools for Predicting Role of Tools for Predicting Species Sensitivity – How Species Sensitivity – How

do they fit in?do they fit in? Key Role – potential to reduce uncertaintyKey Role – potential to reduce uncertainty Reduce reliance on “safety factors”Reduce reliance on “safety factors”

Frequency of useFrequency of use Magnitude of “safety” factorMagnitude of “safety” factor

Ability to derive estimated dataAbility to derive estimated data May provide rationale for inclusion or May provide rationale for inclusion or

exclusion of minimum acceptable data exclusion of minimum acceptable data requirementsrequirements Based on pesticide class/categoryBased on pesticide class/category Based on mode of actionBased on mode of action Based on taxonomic sensitivityBased on taxonomic sensitivity

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[Quantitative] Structure [Quantitative] Structure Activity Relationship ([Q]SAR)Activity Relationship ([Q]SAR)

CChemical structure hemical structure is [quantitatively] correlated is [quantitatively] correlated with a well defined action, with a well defined action, e.ge.g., biological activity ., biological activity or chemical reactivity.or chemical reactivity.

Assumptions:Assumptions: Chemical’s structure imparts properties that relate to Chemical’s structure imparts properties that relate to

biological activitybiological activity Chemicals that produce the same biological activity Chemicals that produce the same biological activity

(toxicity; adverse effect) have something similar about (toxicity; adverse effect) have something similar about their structuretheir structure

Goal is to quantify ‘structural similarity’ imparting activityGoal is to quantify ‘structural similarity’ imparting activity Example: EPA ECOSAR (Example: EPA ECOSAR (EcoEcological logical SStructure tructure

AActivity ctivity RRelationships)elationships) ECOSAR is a library of [Q]SARs for predicting aquatic ECOSAR is a library of [Q]SARs for predicting aquatic

toxicity based on chemical structuretoxicity based on chemical structure ECOSAR expert system for selecting the appropriateECOSAR expert system for selecting the appropriate QSAR QSAR

valuevalue (http://www.epa.gov/oppt/newchems/tools/manual.pdf)(http://www.epa.gov/oppt/newchems/tools/manual.pdf)

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Read-Across/Data Read-Across/Data BridgingBridging

Endpoint information for one chemical is Endpoint information for one chemical is used to predict the same endpoint for used to predict the same endpoint for another chemical, which is considered to be another chemical, which is considered to be “similar” based on:“similar” based on: Structural similarityStructural similarity Similar mode of action (MOA)Similar mode of action (MOA)

Read-across process involves:Read-across process involves: The identification of a chemical substructure or MOA The identification of a chemical substructure or MOA

that is common to two substances (analogues); andthat is common to two substances (analogues); and The assumption that toxicological effects of each The assumption that toxicological effects of each

analogous substance in the set will show common analogous substance in the set will show common behavior (behavior (i. ei. e., organophosphate pesticides)., organophosphate pesticides)

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OECD-EPA Chemical OECD-EPA Chemical CategoriesCategories

A chemical category is a group of chemicals whose A chemical category is a group of chemicals whose physicochemical and human health and/or physicochemical and human health and/or ecotoxicological properties and/or environmental ecotoxicological properties and/or environmental fate and properties are likely to be similar or follow fate and properties are likely to be similar or follow a regular pattern, usually as a result of structural a regular pattern, usually as a result of structural similaritysimilarity

The similarities may be based on:The similarities may be based on: a common chemical functional groupa common chemical functional group similar carbon range numbers – similar carbon range numbers – i.ei.e., C6 or C8., C6 or C8 Physicochemical properties – Physicochemical properties – i.e.,i.e., boiling point boiling point Common precursors and/or degradatesCommon precursors and/or degradates

Data gap filling in a chemical category can be Data gap filling in a chemical category can be achieved by applying one or more of the following achieved by applying one or more of the following procedures: read-across, trend analysis, and procedures: read-across, trend analysis, and (Q)SARs (Q)SARs

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Chemical Category Chemical Category Matrix TableMatrix Table

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Mode of Action (MOA)Mode of Action (MOA)

MOAs serve to describe how a particular MOAs serve to describe how a particular chemical or chemical group acts to kill or chemical or chemical group acts to kill or disable insects, noxious plants, or fungi through disable insects, noxious plants, or fungi through specific interaction (activity) with a target specific interaction (activity) with a target within the plant or animalwithin the plant or animal

Each group of insecticides and fungicides has Each group of insecticides and fungicides has associated with it a particular mode of activity associated with it a particular mode of activity or mode of action. or mode of action.

Potential Utility of MOAPotential Utility of MOA Use of MOA within [Q]SAR, read-across, SSD modelsUse of MOA within [Q]SAR, read-across, SSD models Target species MOA vs. non-target species MOATarget species MOA vs. non-target species MOA Adverse outcome pathways and how MOA ties into Adverse outcome pathways and how MOA ties into

themthem

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Acute-to-Chronic Ratios Acute-to-Chronic Ratios (ACR)(ACR)

Used to estimate chronic toxicity in aquatic Used to estimate chronic toxicity in aquatic organisms for which acute toxicity is known, but organisms for which acute toxicity is known, but chronic data are limited or absentchronic data are limited or absent

ACR = ratio of LCACR = ratio of LC5050 or EC or EC5050 to chronic NOAEC or to chronic NOAEC or MATC (geometric mean of NOEC and LOEC)MATC (geometric mean of NOEC and LOEC)

OPP uses ACRs when acute toxicity profile OPP uses ACRs when acute toxicity profile indicates that the most sensitive surrogate indicates that the most sensitive surrogate aquatic species was not tested in chronic study or aquatic species was not tested in chronic study or data gaps existdata gaps exist

OW uses ACRs routinely when insufficient data OW uses ACRs routinely when insufficient data are available to calculate a Final Chronic Value are available to calculate a Final Chronic Value (FCV) containing 8 families as defined by the 1985 (FCV) containing 8 families as defined by the 1985 Guidelines (Guidelines (≥ 3 chronic tests) – calculate FACR≥ 3 chronic tests) – calculate FACR

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Interspecies Correlation Interspecies Correlation Models (ICE)Models (ICE)

Interspecies extrapolation modelsInterspecies extrapolation models

Estimates acute toxicity (LCEstimates acute toxicity (LC5050 or EC or EC5050) for a ) for a species, genus or family from a surrogate species, genus or family from a surrogate speciesspecies

Uses of ICE in ecological effects assessmentUses of ICE in ecological effects assessment Populates toxicity databasePopulates toxicity database Allows for species sensitivity comparisons Allows for species sensitivity comparisons Direct toxicity estimation for endangered speciesDirect toxicity estimation for endangered species Quantifiable model confidenceQuantifiable model confidence

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Interspecies Correlation Interspecies Correlation ModelsModels

ICE Models are Log-linear models of the relationship between the acute toxicity (LC50 or LD50) of chemicals tested in two species

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Interpretation Framework For Interpretation Framework For Predictive ToolsPredictive Tools

Using OECD validation principles as a Using OECD validation principles as a framework for guidance ([Q]SAR)framework for guidance ([Q]SAR) Defined endpointDefined endpoint An unambiguous algorithmAn unambiguous algorithm Defined domain of applicabilityDefined domain of applicability Appropriate measures of goodness of fit, robustness, Appropriate measures of goodness of fit, robustness,

and predictive capacityand predictive capacity Mechanistic interpretation if possibleMechanistic interpretation if possible

Using guidance provided by tool Using guidance provided by tool developersdevelopers Defined criteria: Positive vs negative vs Defined criteria: Positive vs negative vs

inconclusive associations and/or correlationsinconclusive associations and/or correlations Strengths and weaknesses of existing data Strengths and weaknesses of existing data

estimation techniques for pesticide active estimation techniques for pesticide active ingredientsingredients

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Considerations for Use of Considerations for Use of Predictive ToolsPredictive Tools

Only one element in a multiple lines-of-Only one element in a multiple lines-of-evidence approach evidence approach Considered according to reliability, data Considered according to reliability, data

availability/reliability for tool interpretation, and availability/reliability for tool interpretation, and assessment contextassessment context

Ideally will have multiple predictions from Ideally will have multiple predictions from multiple toolsmultiple tools Evaluate strengths and limitations of concordance Evaluate strengths and limitations of concordance

approach approach ReliabilityReliability Predictive performancePredictive performance Domain of applicabilityDomain of applicability

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Considerations for Use of Considerations for Use of Predictive ToolsPredictive Tools

Obtain predictions for test compound Obtain predictions for test compound and similar (chemical category or and similar (chemical category or class/MOA) data rich compounds, parent class/MOA) data rich compounds, parent compound, and possibly metabolitescompound, and possibly metabolites

Documentation of predictions and Documentation of predictions and interpretationsinterpretations Dependent on assessment context:Dependent on assessment context:

Screening – limited documentationScreening – limited documentation Criteria development – comprehensive Criteria development – comprehensive

documentationdocumentation

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SummarySummary

Through this White Paper, the Agency Through this White Paper, the Agency (OW, OPP, ORD) will present an overview (OW, OPP, ORD) will present an overview of predictive tools that may be useful in of predictive tools that may be useful in generating data for use in effects generating data for use in effects assessment and derivation of aquatic assessment and derivation of aquatic community-level benchmarkscommunity-level benchmarks

Goals:Goals: More consistent and uniform use of predictive More consistent and uniform use of predictive

tools toward estimating and accounting for tools toward estimating and accounting for sensitivity of non-target organisms to pesticidessensitivity of non-target organisms to pesticides

Increased transparency and consistency in Increased transparency and consistency in effects characterization between OPP and OWeffects characterization between OPP and OW