# Predicting the Toxicities of Chemicals to

Aquatic Animal Species

Dale Hoff

4



# Wade Lehmann 1



# Anita Pease 2


# Sandy Raimondo 3



# Chris Russom 4


# Tom Steeger 2



## 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 Di vision


### 4 Office of Research and Development, Mid-Continent E cology Division

October 27, 2010Page 2 of 126

List of Figures ...................................

................................................... ........................................... 4

List of Tables ....................................

................................................... ........................................... 5 Glossary ..........................................

................................................... ............................................. 6

Abbreviations .....................................

................................................... .......................................... 7

1 Executive Summary .................................

................................................... ............................ 9

2 Introduction ......................................

................................................... .................................. 10

3 General Overview of OPP and OW Data Requirements ..

................................................... . 12

3.1 OPP Data Requirements .............................

................................................... ................... 12

3.2 OW Data Requirements ..............................

................................................... ................... 13

4 General Concept and Background of Predictive Method s ................................................. ... 14

4.1 Consideration of Mode of Action in Predictive Toxic ology ............................................ 14

4.1.1 Mode of Action vs. Mechanism of Action ............

................................................... ... 15

4.1.2 Adverse Outcome Pathway ...........................

................................................... ........... 15

4.1.3 Methods of Classifying Substances for Use in AOPs . ................................................ 17

4.2 (Quantitative) Structure-Activity Relationships ...

................................................... ......... 21

4.2.1 Chemical-Structure Methods ........................

................................................... ........... 22

4.2.2 Chemical Category Approach ........................

................................................... .......... 23

4.2.3 Analog Approaches .................................

................................................... ................. 27

4.2.3.1 Structural Analogs ................................

................................................... 28

4.2.3.2 Similarity Indices ................................

................................................... .. 29

4.2.4 QSAR Model Development ............................

................................................... ......... 30

4.3 Interspecies Correlation Estimation (ICE) Models ..

................................................... ...... 32

4.3.1 Background ........................................

................................................... ...................... 32

4.3.2 Model Development and Validation ..................

................................................... ...... 33

4.3.3 Influence of MOA on ICE Model Predictions .........

................................................... 35

4.4 Acute-Chronic Ratios (ACRs) .......................

................................................... ................ 37

4.4.1 OPP Approach to Derive ACRs .......................

................................................... ........ 38

4.4.2 OW Approach to Derive ACRs ........................

................................................... ....... 41

4.4.3 Similarities and Differences in OPP and OW Approach es to Derive ACRs .............. 43

4.4.4 Deriving ACRs Using Predicted and Empirical Data .. ............................................... 43

4.4.5 Default ACRs ......................................

................................................... ..................... 44

4.4.6 Fixed ACRs ........................................

................................................... ...................... 45

4.5 Time-Concentration Effect (TCE) Models ............

................................................... ........ 45

4.5.1 Background ........................................

................................................... ...................... 45

5 Examples of Predictive Methods ....................

................................................... ................... 47

6 Interpretation of (Q)SAR, Read-Across/Bridging, ICE , and TCE Predictions .................... 47

6.1 (Q)SARs ...........................................

................................................... .............................. 47

6.2 ICE Models ........................................

................................................... ............................ 49

6.3 TCE Models ........................................

................................................... ........................... 50

7 Considerations for Using Toxicity Predictions .....

................................................... ............. 50

7.1 WOE and Best Professional Judgment ................

................................................... .......... 50

7.2 Selection of Descriptors in (Q)SAR models .........

................................................... ......... 52

7.3 Empirical Test Data ...............................

................................................... ........................ 55

7.4 Selecting Chemical Category and/or AOP ............

................................................... ........ 56

7.5 ICE Models ........................................

................................................... ............................ 56

8 Example Applications and Proposed Analyses of Predi ctive Methods ................................ 57Page 3 of 126

8.1 Empirical Data and Important Values from Existing A LWQC Documents (Box 1 and 2 of Figure 11) .....................................

................................................... ...................................... 57

8.2 Methods for Deriving Acute ALSVs ..................

................................................... ........... 59

8.2.1 Supplementing Empirical Data with Predicted Values for Missing MDRs (Box 4 of Figure 11). .......................................

................................................... ................................... 59

8.2.1.1 Web-ICE ...........................................

................................................... .... 60

8.2.1.2 Read-Across .......................................

................................................... ... 62

8.2.2 Only Use Results of Toxicity Tests for Chemical and Taxa of Interest ..................... 69

8.2.2.1 Calculating an ALSV using a modified SSD Methodolog y .................... 69

8.3 Additional Applications of Predictive Tools .......

................................................... .......... 71

8.3.1 EFED QSAR Guidance.................................

................................................... ........... 71

8.3.2 Comparison of endosulfan and its degradate, endosul fan sulfate, toxicities using WebICE 71

9 References ........................................

................................................... .................................. 75

Appendix A: EFED Guidance on Use of Structure-Activity Relation ships Appendix B: Overview of Modes of Action, Associated Chemical Cl asses, and Pest Organisms Appendix C: Predictive Methods available via government, open a ccess, or commercial sources

that are available to risk assessors in OW and OPP Appendix D: Predictive methods for determining MOA and physical chemical propertiesPage 4 of 126



## List of Figures

Figure 1. Abbreviated Toxicity Pathway (from NRC, 2 007). ............................................. ......... 16 Figure 2. Adverse Outcome Pathway (modified from An kley et al., 2010). ............................... . 17 Figure 3. Plot of water solubility vs. baseline tox icity (Russom unpublished data). .................. .. 19 Figure 4. Number of chemicals by the logTe values ( modified from Russom et al., 1997). ........ 20 Figure 5. A chemical category is created to describ e 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). ..........

................................................... ...................... 25 Figure 6. Example of structural analogs involving s ynthetic pyrethroids. ............................. ...... 29 Figure 7. Schematic of similarity index measures. .

................................................... ................... 30 Figure 8. Example Interspecies Correlation Estimati on Model. ......................................... .......... 34 Figure 9. Models developed from chemicals of divers e modes of action compared to those with MOA-specific data. ................................

................................................... .................................... 37 Figure 10. Log P predictions for Scilliroside usin g KowWin and Clog P predictive methods. .. 54 Figure 11. A Conceptual Framework for Deriving the Acute ALSV. ....................................... ... 57 Figure 12. Start-up screen of the OECD toolbox. ...

................................................... ................... 63 Figure 13. “Profiling” Diazinon in the OECD Toolbox

. ................................................. ............. 64 Figure 14. Identified databases and available data for the target chemical diazinon in the OECD

toolbox. ..........................................

................................................... ............................................ 65 Figure 15. Data identified from the Category defini tion screen for Diazinon in the OECD

toolbox. ..........................................

................................................... ............................................ 66 Figure 16. Read-across evaluation screen for Diazin on in the OECD toolbox. ........................... 67 Figure 17. Analogue structure similarity indexs in the OECD toolbox. ................................. ...... 67Page 5 of 126



## List of Tables

Table 1. An example read-across for pyrethroids. Or dered by the physical chemical property LogP, which describes the partitioning aspect of th e toxicity. ....................................... .............. 26 Table 2. Target (pest) organism mode of action and associated chemical class for a select group of insecticides (adapted from IARC http://eclassifi cation.irac-online.org/) .......................... ....... 28 Table 3. Comparison of LC50 data for structural ana logs, permethrin and bifenthrin. ................ 2 9 Table 4. Number of toxicity records and chemicals i ncluded in ICE model development .......... 35

Table 5. Acute and chronic values and ACRs for fres hwater nonylphenol. ............................... .. 42

Table 6. Differences in OPP and OW ACR derivation m ethodology. ....................................... .. 43 Table 7. MOA-specific ACRs reported by Raimondo et al. 2007. ......................................... ...... 44 Table 8. Important values found in ALWQC documents for Acrolein, Diazinon, Chlorpyrifos

and TBT ...........................................

................................................... .......................................... 58 Table 9. Species mean acute and chronic values foun d in criteria documents. .......................... .. 58 Table 10. Meeting minimum data requirements of the 1985 Guidelines using Web-ICE (acute values, 95% confidence limits are in parentheses).

................................................... ................... 61 Table 11. Summary of model statistics used to pred ict acute values................................... ........ 62 Table 12. Web-ICE ALSV estimates compared to FAV in criteria documents. .......................... 62 Table 13. Meeting minimum data requirements of the 1985 Guidelines using Read-across from

OECD toolbox. .....................................

................................................... ..................................... 68 Table 14. Comparison of the acute toxicity predicti ons for endosulfan and endosulfan sulfate using Web-ICE and measured data for aquatic species

.... ................................................ ........... 72Page 6 of 126



## Glossary

Adverse Outcome Pathway : A conceptual construct that portrays existing kno wledge concerning the linkage between a direct molecular initiating e vent and an adverse outcome at a biological level of organization relevant to risk assessment ( Ankley et al , 2010).

Estimation, point : The statistical derivation of a best value of a paramet er from an existing set of empirical data that are subject to random variation using a model of the relationship between the data and the parameter.

Prediction, point : The statistical derivation of an expected value of a ra ndom variable using a model of the relationship between an existing set of empiric al data and a new value of the unknown random

variable.

Mechanism of action : A complete and detailed understanding of each an d every step in the sequence of events that leads to a toxic outcome, w hich includes detailed knowledge of the causal and temporal relationships among all the ste ps leading to a specific effect (Ankley et al

, 2010).

Mode of action : A common set of biochemical, physiological, or be havioral responses that characterize an adverse biological response where m ajor, but not necessarily all, linkages between a direct initiating event and an adverse ou tcome are understood (Ankley et al , 2010).

Non-Target Species : Organisms other than those that the pesticide is intended to kill / effect ( e.g. , for an insecticides all species other than insects are considered non-target species).

Pest organism : The organism a pesticide is intended to kill or e ffect ( e.g. , for an insecticide the target species are insects).

Predictive methods : Methods that can be used to obtain a predicted to xicity value when an experimentally determined toxicity value is not ava ilable for a taxon.

Structure-Activity Relationship : Methods that relate structural features of molec ules to either biological or physico-chemical activity.

Qualitative Structure-Activity Relationship (SAR) : Methods that relate structural features of molecules to an activity in a qualitative manner. Q ualitative SARs are derived from noncontinuous data (e.g., “yes” or “no” data such as s tructure is similar or not). They do not provide a point estimate.

Quantitative Structure-Activity Relationship (QSAR) : Methods that relate structural features of molecules to an activity in a quantitative manner. QSARs are derived from continuous data (e.g., test results on toxic potency), providing a point e stimate.

Tools : A computational application capable of calculati ng toxicity by applying model algorithms.Page 7 of 126



## Abbreviations

ACE Acute-to-Chronic Estimation

ACR Acute-Chronic Ratio ALT Accelerated Life Testing ALSV Aquatic Life Screening Value AOP Adverse Outcome Pathway

ASTER Assessment Tools for the Evaluation of Risk ASTM American Society for Testing and Materials ALWQC Aquatic Life Water Quality Criteria

CBI Confidential Business Information

CMC Criterion Continuous Concentration

CCC Criterion Continuous Concentration

CWA Clean Water Act ECOSAR Ecological Structure-Activity Relationships EU European Union FA Factor Analysis

FACR Final Acute Chronic Ratio

FAV Final Acute Value

FCV Final Chronic Value FDDCA Federal Food, Drug, and Cosmetics Act FIFRA Federal Insecticide, Fungicide and Rodenticid e Act

GEOMEAN Geometric Mean HPV High Production Volume ICE Interspecies Correlation Estimation

INCHI IUPAC International Chemical Identifier IPSC International Program on Chemical Safety

LOEC Lowest Observable Effect Concentration Log P Log of the octanol/water partition coefficien t LRA Linear Regression Analysis LSER Linear Solvation Free Energy Relationship MATC Maximum Allowable Test Concentration; (geometric mean of NOEC and LOEC) MDRs Minimum Data Requirements

MOA Mode of Action MPA Multi-factor Probit Analysis MSE Mean square error NAWQC National Ambient Water Quality Criteria

NOEC No Observable Effect Concentration

NRC National Research Council OCSPP Office of Chemical Safety and Pollution Preve ntion (formerly Office of

Prevention, Pesticides, and Toxic Substances [OPPTS ]) OECD Office of Economic Cooperation and Development

OW Office of Water

OPP Office of PesticidesPage 8 of 126

OPPT Office of Pollution Prevention and Toxics ORD Office of Research and Development PCA Principle Component Analysis PMN Pre-Manufacturing Notification

PNN Probabilistic Neural Network QSAR Quantitative Structure-Activity Relationship (Q)SAR Denotes either qualitative or quantitative S tructure-Activity Relationship QSPR Quantitative Structure Property Relationship

RA Risk Assessment Tools: Software and Users Guide REACH Registration, Evaluation, Authorisation, and Restriction of Chemicals SAR Structure-Activity Relationship (qualitative) SMAV Species Mean Acute Value SMILES Simplified Molecular Input Line Entry System SSD Species Sensitivity Distribution

TCE Time-Concentration Event Te Toxicity Effect Ratio

TSCA Toxic Substances Control Act USEPA U.S. Environmental Protection Agency UVCB Unknown or Variable composition, Complex react ion products or Biological WOE Weight-of-evidencePage 9 of 126



## 1 Executive Summary

The purpose of this white paper is to present an ov erview of predictive methods that may be useful to U.S. Environmental Protection Agency (USE PA) risk assessors along with States, Regional and Tribal risk assessors in extrapolating data for estimating a level of adverse effect (toxicity) of pesticide active ingredients and degr adates to aquatic animals. The predictive methods discussed in this document may be used to d erive surrogate values that can then be incorporated into approaches for developing aquatic life screening values (ALSVs) for chemicals that have small data sets; see white paper “ Exploration of Methods for Characterizing Effects o f Chemical Stressors to Aquatic Animals .” These predictive methods will be integrated int o a suite of tools for use by both Office of Water (OW) and the Office of Pesticide Programs (OPP) when insufficient acceptable chemical-specific data are not available from submitted data and the open literature to meet effects assessment data req uirements established by the offices. These predicted values are intended for use in deriving A LSVs and supplementing data submitted under FIFRA 40 CFR Part 158 Subpart G (CFR 2010), o r when additional data are required to reduce uncertainty in OPP's ecological effects asse ssments. Although the focus of this white paper is on predictive methods and/or tools that pr edict toxicity data specifically for pesticides, the predictive methods described in this paper may have broader applicability to other chemicals that are not pesticides.

Although many of the predictive methods (e.g. QSAR) discussed in this paper have been used by USEPA to predict the potential toxicity of chemical s where measured data are not routinely available, their use has been inconsistently applie d. Even where predictive methods have served as a source of information on a compound, their uti lity in providing data that can be used qualitatively versus quantitatively is limited to t he predictive capacity of the models in a weightof-evidence (WOE) approach. This approach consider s the predictive capacity of the model for chemicals with known physical/biological characteri stics as compared to similarly structured compounds where such characteristics are unknown.

The predictive methods discussed in this white pape r include quantitative/qualitative structureactivity relationships ((Q)SARs), read-across/bridg ing, Office of Economic Cooperation and Development (OECD)/USEPA chemical categories and/or mode of action, interspecies correlation estimation (ICE) models, acute-chronic ratios (ACR), and time-concentration effect (TCE) models. Documentation needed to substantiate the use of these predictive methods will be provided in both screening-level assessments int ended to identify data gaps and prioritize testing needs as well as more refined assessments.Page 10 of 126



## 2 Introduction

In April 2009, EPA developed a document entitled “ Toward a Common Effects Characterization Methodology Scoping Document ” (hereafter referred to as the Scoping Document). The Scoping Document provided background for a proposal to deve lop a common effects characterization methodology for use in ecological assessments of ch emicals ( e.g ., pesticides) by EPA to meet the mandates of the Clean Water Act (CWA) and the Feder al Insecticide, Fungicide, and Rodenticide Act (FIFRA). The resulting framework w ould integrate the Agency’s aquatic effects characterization methods and provide a comm on basis for achieving the water quality protection goals established under the CWA and FIFR A statutes. The effort focuses on datalimited situations where methods have not been clea rly articulated for using limited taxa-specific data to characterize effects to aquatic communities composed of multiple taxa (vertebrate and invertebrate aquatic animals and vascular and nonva scular aquatic plants). Once sufficiently validated and vetted, the methods could then be use d by state, local and Tribal water management agencies to interpret aquatic ecological risks associated with chemical exposure information, e.g ., monitoring data.

The proposed approaches articulated in the Scoping Document were subsequently discussed at stakeholder meetings in various regions throughout the country in January 2010. Since that time, three white papers, including this paper on “ Predicting the Toxicity of Chemicals to Aquatic Animal Species ”, have been developed. The second white paper,“ Exploration of Methods for Characterizing Effects of Chemical Stressors to Aqu atic Animals, ” explores possible means through which limited data sets could be either enh anced using predictive tools or accommodated using adjustment factors to characteri ze the lower range of sensitivities to specific chemicals that may exist in an aquatic com munity. Additionally, a third white paper,

“ Deriving Plant Aquatic Life Screening Values for Pe sticide Effects, ” has been developed exploring the uncertainties associated with and met hods for characterizing effects to the aquatic vascular and nonvascular plant community from chemi cal exposure. . The tools and methods discussed in these papers are intended to compensat e for limited data in describing potential effects of specific chemicals on aquatic animal and plant communities and would provide regulators with a means of deriving advisory values that will ensure the protection of the aquatic

environment.

The three white papers are intended to discuss unce rtainties associated with extrapolating limited taxa-specific data to predict effects on aquatic c ommunities and to provide recommendations on methods for either enhancing and/or accommodating l imiting data to enhance their ability to better represent species sensitivities across anima l and plant taxa. This paper on predictive tools provides a broad overview of available tools that m ay be used to enhance and/or compliment limited data sets. Outputs (predictions) from the tools discussed in this paper are then used in the paper on methods for characterizing effects to expl ore how, either through assessment factors or species sensitivity distributions, values may be de rived that can be considered representative of potentially sensitive species within the aquatic en vironment.

Under FIFRA, the Office of Pesticide Programs (OPP) receives test data for use in their risk assessments based on the guideline requirements spe cified in 40 CFR Part 158; however, thePage 11 of 126

submitted data may not cover the aquatic taxonomic diversity as defined by the 1985 Guidelines minimum data requirements (MDRs). Although both OPP and the Office of Water (OW) consider data from the open literature, many pestic ides have a smaller data set than that required to develop 304(a) aquatic life water quality criter ia (ALWQC) using the established approach (Stephan et al . 1985, herein referred to as “1985 Guidelines”). OPP and OW are examining the extent to which they will leverage modeling approac hes to predict the hazard of pesticides to aquatic organisms for which data is lacking, thus b uilding upon existing methodologies for conducting risk assessments (Stephan et al., 1985; USEPA, 2007). The goal is to develop a well grounded, science-based, and informed process to de velop surrogate values that will be used in an effects assessment methodology that provides con sistency between OPP and OW approaches. In addition, these predictive techniques could also be used to characterize uncertainties in registrant-submitted data, thereby identifying pote ntial gaps for future work; or possibly to reduce data requirements, where there is sufficient concordance of information, thereby reducing animal testing and associated costs. This effort wo uld make greater use of existing data and available predictive methods in the decision making process. This is consistent with

recommendations from the National Research Council (NRC) of the National Academies of Science recommendations in their report entitled "T oxicity Testing in the Twenty-first Century: A Vision and a Strategy” (NRC, 2007).

The use of modeling techniques to predict the poten tial toxicity of chemicals where empirical data are not available is not unprecedented in the field of risk assessment. USEPA has a long history of using structure-activity approaches unde r the Toxic Substances Control Act (TSCA) (Auer et al ., 1990; Comber et al. , 2003; van Leeuwen et al. , 2009). Under Section 5 of TSCA (15 U.S.C. 2601–2692), USEPA’s Office of Pollution Prevention and Toxics (OPPT) must provide a risk assessment of new chemicals within 9 0 days of receipt of a pre-manufacturing notification (PMN) (Nabholz et al ., 1997). The PMN includes chemical structure, int ended use, anticipated production volume and release informati on, and any available exposure or effects data (Nabholz et al ., 1997). Although OPPT receives up to 2,000 PMNs annually, 65% include no test data (van Leeuwen et al ., 2009), and only about 5% include ecotoxicity dat a (Zeeman et
al ., 1995). Because of these constraints, OPPT has l everaged both qualitative (SAR) and quantitative structure-activity relationships (QSAR ) (Auer et al. , 1990; Nabholz et al ., 1997; van

Leeuwen et al ., 2009) to address various risk assessment issues. Independent assessments of predictive methods such as EcoSAR and ASTER attest to the reliability of these models for predicting toxicity for non-specific modes of actio n (MOAs), and, with limited success, predicting toxicity for more specific MOAs such as reactive mechanisms (Moore et al., 2003;

Reuschenbach et al ., 2008). QSAR models have also been used throughou t OPP, although infrequently. Use of QSAR models within OPP, specif ically in ecological effects assessments, is typically based on the WOE approach where the predi ctive capacity of the models is a critical factor for determining whether the output may be us ed quantitatively or qualitatively. Examples of how various predictive methods have been used in OPP to predict toxicity to pesticide degradates, is provided in Appendix A . Additionally, OPP has identified positive attrib utes and desired qualities for models used in regulatory dec ision making, such as peer review from the scientific community, transparency, and model avail ability to the public ( http://www.epa.gov/oppefed1/models/water/model_attr ibutes.htm .)Page 12 of 126

This white paper describes various predictive metho ds, their history and background associated with development and use, and the importance of cat egory approaches and mode of action in the application of these models. In addition, this wh ite paper addresses criteria to be used in assessing the appropriateness of various modeling a pproaches, the state-of-the-art of existing applications / predictive methods, and how these pr edictive methods can be used to fill data gaps

and address risk assessment uncertainties relevant to OPP and OW.



## 3 General Overview of OPP and OW Data Requirements

USEPA’s OPP and OW are undertaking a paradigm shift related to toxicity testing that moves

towards a more efficient and refined risk assessmen t. The goal is to reduce the use of whole organism testing and the overall cost of the risk a ssessment process while maintaining the breadth and depth of data coverage (NRC, 2007; CFR 2008.). This paradigm shift makes greater use of available data where predictive relationship s are sufficiently robust to support a risk assessment, moving from a guidelines-driven risk as sessment, to one that targets whole organism testing requirements based on a WOE approach.



### 3.1 OPP Data Requirements

Within OPP, this process starts with problem formul ation to identify what is known about the environmental fate and effects of a particular chem ical and it’s degradates and associated data gaps and uncertainties. Risk assessors either make conservative assumptions when data are not available or attempt to draw inferences from simila rly structured compounds. While guideline data are typically submitted for parent compounds u ndergoing registration, a full complement of guideline studies are rarely available for degradat es of potential concern; predictive modeling can enable degradates to be considered qualitativel y. Further, while guideline studies provide

data on freshwater and estuarine/marine fish and in vertebrates, the number of studies for these

taxa is limited. Most baseline datasets for chemic als submitted by a registrant are comprised of

two freshwater fish and one freshwater invertebrate toxicity studies. Data for estuarine/marine species are typically confined to one estuarine/mar ine fish and two estuarine/marine invertebrates. Internationally, requirements for p esticide registration data are similar. For higher profile chemicals, a broader range of data may be a vailable from both the registrant-submitted studies and open literature. However, for newer ch emicals, open literature studies are oftentimes limited. Registration of pesticides in other juris dictions often offers additional data.

OPP’s aquatic risk assessments evaluate individual taxa and are based on the most sensitive vertebrate and invertebrate endpoints for freshwate r and estuarine/marine species. Reliance on the most sensitive species is intended to be conser vative and does not reflect intra- and interspecies variability. When sufficient data exist, as sessments are refined using species sensitivity distributions (SSDs) to provide a qualitative descr iption of the range of sensitivities within and between taxa. As such, OPP’s risk conclusions have not typically relied on SSDs to quantitatively characterize the potential effects o f pesticides (and degradates), but rather to qualitatively characterize the conservatism of the most sensitive endpoint. With respect toPage 13 of 126

degradate toxicity, OPP uses the best available inf ormation to determine whether degradates are more, less, or equally toxic as compared to the par ent compound. In situations where degradates are more than or equally as toxic as the parent and /or toxicity data are not available for the degradates, OPP derives aquatic exposure estimates by using a total toxic residue approach. These approaches will be used to supplement the cur rently required test data from registrants, adopting a more integrated approach where validated models will be used along with empirical data in a WOE approach in the overall risk assessme nt process.



### 3.2 OW Data Requirements

OW does not have the statutory authority under the CWA to request specific toxicity tests and relies on open literature and data submitted by reg istrants in support of pesticide registrations under FIFRA to develop SSDs to support the developm ent of ALWQC, as per the 1985 Guidelines. Similar to the challenges confronting OPP, OW may not have sufficient data to meet the 1985 Guidelines MDRs. However, in such circums tances, OW is currently constrained from moving forward to develop ALWQC. The 1985 Guidelin es and the Aquatic Life Screening Values white paper describe the MDRs in detail, inc luding a description of how the MDRs are used to generate either an ALWQC or an ALSV. In sho rt, a freshwater Criterion Maximum Concentration (CMC) is derived from acute ( e.g . LC 50 ) toxicity values using the following taxonomic groups:

• family Salmonidae

• family in Osteichthyes

• family in phylum Chordata

• planktonic crustacean

• benthic crustacean

• an insect

• family in phylum that does not include arthropods o r chordata

• family in any order of insect or phylum not represe nted

A saltwater CMC may be derived when data are availa ble for the following groups:

• two families in phylum Chordata

• family in not in Chordata or Arthropoda

• either Mysidae or Penaeidae family

• three other families not in the phylum Chordata

• any other family

The chronic ALWQC, or Criterion Continuous Concentr ation (CCC), may be derived when the following information is provided:

• eight chronic studies from the same taxa as describ ed for the CMC; OR

• chronic studies from three families of aquatic anim als for which acceptable acute data are available and one being an acutely sensitive specie s (this approach utilizes acute toPage 14 of 126

chronic ratios (ACRs) discussed in Section 4.4 of t his white paper).

OW will explore the use of the techniques described in this white paper to fill gaps in taxonomic data to facilitate derivation of ALWQC. A critical component in migrating towards the use of predictive methods will be to identify those that p rovide reliable and relevant endpoint data and result in transparent and scientifically defensible effects assessments.



## 4 General Concept and Background of Predictive Method s

This section provides an overview of approaches, hi story of use, and technical basis for the predictive methods that are the focus of this paper ; namely quantitative and/or qualitative structure-activity relationships ((Q)SARs), intersp ecies correlation estimation (ICE) models, acute-chronic ratios (ACRs), and time-concentration s event (TCE) models. Section 5 provides general descriptions of the applications through wh ich these models are available. An important consideration in the selection of appropriate model s is the mechanistic-basis of the toxic response. QSAR models can be developed using global models which cover a number of different mechanisms of action within a single equa tion or local models which predict for a more limited set of structurally-similar chemicals, with the assumption that they are all acting via a similar MOA. The linkage of the mechanism of actio n to the adverse outcome pathway (AOP), with a reasonable level of confidence, is an import ant step in developing causal linkages of chemical exposure to the observed apical endpoint r esponse (OECD, 2007d). Therefore this section begins with an overview of terminology and historical background on approaches related

to MOA.



### 4.1 Consideration of Mode of Action in Predictive Toxic ology

Determining mode of action (MOA) for most substance s is typically based on information related to potency, structural features, in vitro data, responses related to duration of exposure, and/or mixture effects, although approaches have be en developed to help ‘bin’ chemicals into acute MOA (Verhaar et al ., 1992; Russom et al ., 1997). Pesticides are, by definition, substance s designed specifically to affect an organism’s biolo gical processes, and generally there is a known molecular site of action within the pest organism. Gathering information related to the specific MOA to the pest organism, and how conserved the mol ecular site of action is across non-target species will be a critical step in leveraging (Q)SA Rs to fill data gaps. Therefore, an important requirement for use of mechanistic approaches is ha ving an understanding of the biological processes for relevant species. Another important c onsideration is that non-target species may include many species, which taxonomically, would be considered targets, but by intended use are actually non-target species. For instance, an inse cticide with a registered use on golf courses to reduce certain insect pests may appear in streams v ia run-off events, and thereby cause effects on aquatic insects that are not the intended target sp ecies. For the purposes of this document, aquatic organisms will be considered non-target spe cies, with the exception of aquatic plants when herbicides (e.g., algaecides) are the pesticid e discussed and fish when piscicides are

discussed.Page 15 of 126

The understanding of a chemical’s mechanistic profi le is insightful for any risk assessment process. Information regarding the key events that lead to an adverse outcome, as well as toxicokinetics, toxicodynamics, and metabolism prov ide a biological basis to support assumptions used within the WOE approach supporting a risk assessment. Chemical agents can be characterized by their mode or mechanism of acti on, and, if used with SAR/QSAR approaches, this pathway information can infer grea ter confidence in predicted adverse

outcomes.

These mode of action-based techniques have permitte d the development of mechanisticallybased toxicity models such as those included in the ASTER and EcoSAR systems, and QSAR models published in the open literature (see Könema nn, 1981; Veith et al, 1983; Russom et al., 1988; Schultz 1987). The development of expert sys tems that can assess the adverse effects of large numbers of chemicals in a computationally-eff icient manner, and do so with an understanding of toxic mechanisms is essential cons idering the large number of chemicals in commerce for which no ecotoxicological data are ava ilable. Through the use of these modeling techniques, the acute toxicity of chemicals with no n-specific MOAs, which are estimated to be approximately 70% of the discrete organic chemicals released into the environment, can be predicted with a high level of confidence (Bradbury et al., 2003).

4.1.1 Mode of Action vs. Mechanism of Action

The terms mode of action (MOA) and mechanism of act ion have been used interchangeably in the past, and have had conflicting definitions (Guy ton et al. , 2008). For the purpose of this white paper, the terminology cited in the National Resear ch Council report “Toxicity Testing in the 21 st
Century” is used. The term “mode of action” is def ined as an understanding of selected key events and/or processes, starting with interaction of an agent with a cell, proceeding through operational and anatomical changes, and resulting i n a disease state or other adverse effect (NRC, 2007). A “key event” is an empirically obser vable precursor step that is itself a necessary element of the MOA or is a biologically based marke r for such an element (NRC, 2007). A

“mechanism of action” differs from MOA in that it c ontains a more detailed and complete understanding and description of each of the key ev ents, often at the molecular level (NRC, 2007). The elucidation of the key events along the toxicity pathway for a particular toxic response in a biological system is a data rich dete rmination. Significant information should be developed to ensure that a scientifically justifiab le MOA underlies the process leading to the

adverse outcome.

4.1.2 Adverse Outcome Pathway

The NRC report released in 2007 outlined a vision a nd strategic plan for toxicity testing in the conte xt of context of emerging predictive methods for use in s upplementing or replacing existing animal testing testing procedures (NRC, 2007). As part of this rep ort, a toxicity pathway framework (Figure 1Figure 1

. Abbreviated Toxicity Pathway (from NRC, 2007).Page 16 of 126

) was developed that forms the basis of discussions within the document. A toxicity pathway is defined as a cellular response that when sufficient ly perturbed, and when the organism is unable to adapt, would lead to toxicity and/or disease (NR C, 2007). In order to ensure consideration of the “toxicity pathway” to identify typical apical e ndpoints observed in the whole organism, and, ultimately, to population responses, this concept w as expanded to an Adverse Outcome Pathway (AOP) framework (see Figure 2. Adverse Outcome Pathway (modified from Ankley et a l., 2010).

) (Ankley et al ., 2010). The AOP provides a structure to organize collected data/information, with anchors at the molecular initiating event and the adverse outcome, describing interactions at the molecular target site, and the cascade of respo nses at the cellular, tissue, whole organism, and population levels. Based on the definitions provid ed in Section 4.1.1, a fully developed, mechanistically-based AOP describes a mechanism of action, while an AOP with information gaps and /or uncertainties could be described as a mode of action. In either case, the AOPs can serve to build the WOE required for a comprehensive risk assessment, and avoid confusion that may be related to use of the mode vs. mechanism of action terms. As discussed in Sections 4.1.3

and 7.4 the determination of the AOP involves a rev iew of all aspects of the exposure and experimental design, since the selected AOP could d iffer depending on organism life stage, length of exposure, concentrations used, taxonomy, etc . Therefore the identification of alternate AOPs should be part of the process.

Figure 1. Abbreviated Toxicity Pathway (from NRC, 2 007). Exposure

Tissue Dose

Biologic Interaction

Perturbation

Biological Inputs Normal Biological

Function

Early Cellular Changes

Cell Injury

Morbidity &

Mortality Adaptive Stress ResponsePage 17 of 126 Chemical Properties Receptor/Li gand Interaction

DNA Binding

Protein Oxida tion Gene Activ ation

Protein Producti on

Altered Signaling

Protein Depleti on Altered Physiology

Disrupted Homeostasis

Altered Tissue Dev elopment or Functi on Lethality

Impaired Dev elopment

Impaired Reproduc tion

Cancer Toxicant Macro-Molecular

Interactions Cellular Responses Organ Responses Organism Responses

Structure

Recruitment

Extinction Population Responses

Toxicity Pathwa y Adverse Outcome Pathway

Modified rom Ankley et al., 2010 Mode of Ac tion

Figure 2. Adverse Outcome Pathway (modified from An kley et al., 2010).

4.1.3 Methods of Classifying Substances for Use in AOPs

As mentioned in Section 4.1.1, the determination of an AOP for a substance is a data intensive process, which requires the building of WOE in supp ort of the final determination. The International Program on Chemical Safety (IPCS), a joint program between the World Health Organization, International Labour Organization, an d the United Nations Environmental Programme, has developed an approach based on the B radford Hill criteria (Hill, 1965) to be used in cancer and non-cancer endpoint assessments, but they also apply to ecological endpoints (Boobis et al ., 2008). The criteria for developing AOPs include the following:

1. Postulate a mode of action;

2. Identify key events and associated critical paramet ers, 3. Identify concordance of the dose-response relations hips with key event, correlate dose dependency on increase in magnitude of key event;

4. Characterize the temporal association of toxic resp onses and key events;

5. Describe the WOE linking the key events including t he strength, consistency, and specificity of association of key events and advers e outcome;

6. Describe biological plausibility and coherence of t he AOP as it relates to:

a. the molecular site of action,

b. systems biology,

c. relationship of the MOA and the observed adverse ou tcome,Page 18 of 126 d. evidence from structural analogs,

e. considerations related to organism life stage, sex and/or taxonomy, and

f. consistency/adequacy of data in determining the AOP ; 7. Discuss possible alternative AOP(s);

8. State fully and explicitly the uncertainties, incon sistencies, and data gaps associated with the determination of the AOP; and

9. Provide conclusions about the AOP.

The IPCS framework is intended as an analytical too l for organizing and judging whether the available data/information actually supports the po stulated MOA, and subsequently can be used in the development of an AOP. The goal is a flexib le and transparent process, but, as with any type of framework, the determination of whether the evidence is sufficient to make the final determination is based on the professional judgment as it relates to the context of how the

information will be used.

Although reliable QSAR models existed in the 1980s to predict toxicity for non-polar narcotic substances (Könemann, 1981, Veith et al , 1983), methods on classifying untested substances into MOA groups had not yet been developed. In the 1990 s, researchers in the Netherlands and the US developed schemes for grouping substances into g eneral MOA categories (Verhaar et al

., 1992; Russom et al ., 1997). Although developed after the IPCS framew ork document, these approaches used WOE approaches and processes simila r to those outlined in the Bradford Hill criteria (Hill, 1965).

The approach developed by Verhaar and co-workers pl aced chemicals into the following four categories (Verhaar et al ., 1992):

o Inert chemicals : chemicals that are not reactive, and that do not interact with specific receptors within an organism.

o Less inert chemicals : chemicals that are not reactive, but are slightly more toxic than baseline toxicity due to hydrogen bond donor acidit y.

o Reactive chemicals : chemicals that react unselectively with biomolecu les, or substances

that are bioactivated via metabolism.

o Specifically acting chemicals : chemicals that interact with receptor biomolecule s.

These general classes were developed primarily usin g excess toxicity ratios (Veith et al ., 1983, Lipnick et al .1987, Russom et al ., 1988) and structural alerts gleaned from the lit erature, as well as empirical test results from an extensive toxicit y database using the guppy ( Poecilia reticulate ) (Könemann, 1981). The excess toxicity ratio is use d frequently to identify substances that are potentially acting differently then non-polar narco tics. As background, non-polar narcosis is also referred to as baseline toxicity; representing the minimal toxic response in a biological system. Figure 3 below demonstrates how this baseline toxicity comp ares to water solubility for the fathead minnow. As lipophilicity increases, the to xicity approaches the water solubility in an acute exposure. The narcosis MOA is driven by hydr ophobic interactions between chemicalsPage 19 of 126

and biological membranes (Yamakura et al ., 2001). Traditionally, the octanol/water partiti on coefficient (log P) has been used to represent this activity, and has been a readily calculated and fairly reliable parameter for use in predicting non -polar narcosis. An estimate of excess toxicity (Te) is calculated by using reliable QSAR models to predict the baseline acute toxicity ( e.g. , Könemann, 1981; Veith et al. , 1983) and dividing this value by empirical data f rom acute toxicity tests (see Eq. 1). Verhaar found that inert chemicals have Te va lues around 1.0; less inert substances have Te ratios ranging from 5-10, and reactive and specifically acting chemicals have Te ratios of 10-10000 (Verhaar et al., 1992). Russom and coworkers found similar ranges in Te values for nonspecific and specific modes of action (Russom et al. , 1997; See Figure 4 ).

Te = LC50 baseline narcosis prediction / LC50 experimental Eq. 1

-7 -6 -5 -4 -3 -2 -1 0

1 2 3 4 5 6

Log PLog Molar Concentration Water Solubility Baseline Toxicity

Figure 3. Plot of water solubility vs. baseline tox icity (Russom unpublished data).Page 20 of 126

Figure 4. Number of chemicals by the logTe values ( modified from Russom et al., 1997).

A workshop sponsored by USEPA in 1988 (Bradbury and Lipnick, 1990) provided a major contribution to chemical structure/property informa tion used in both approaches. The workshop brought together experts in the field who described features of chemicals as they related to nonpolar narcosis (Franks and Lieb, 1990), polar narco sis (Veith and Broderius, 1990), uncouplers of oxidative phosphorylation (Terada, 1990), electo philic, (Hermens, 1990; Carlson, 1990), carcinogenic (Kadlubar et al ., 1990), redox cycling (Mason 1990), and various p esticidal MOAs (Coats, 1990; Duke, 1990; Fukuto 1990). Expanding on this workshop, Russom and coworkers outlined eight general MOAs, and defined structural fragments associated with seven of the MOAs (Russom et al., 1997). Respiratory inhibitors /blockers substructures were not defined due

to the low number of chemicals available for assess ment. MOA determination was based on the dose-response interpretation, Te ratios, mortality as it related to duration, and behavioral information gleaned from the fathead minnow data se t (N=620) (Russom et al., 1997). Additionally, joint toxic action studies and toxico dynamic profiles conducted independently on a subset of the database, structural alerts identifie d from the 1988 workshop, and evidence on structural analogs found in the open literature wer e used in a WOE approach in assigning MOA (Russom et al., 1997).

It is important to note that there is no single lis t that identifies all existing MOAs and related AOPs (see Schmidt, 2009). Both the Verhaar and Ru ssom approaches focused on industrial organic chemicals; however, these approaches did no t include metals or many classes of pesticide active ingredients and pharmaceuticals. Both approaches used extensive data collections of acute lethality data for either the guppy (Könemann, 1981) or fathead minnow (Russom et al., 1997). Therefore, the assignment o f a MOA using these approaches is specific

0 10 20 30 40 50 60 70 80

-0.8 -0.5 -0.25 0 0.25 0.5 1 1.25 1.5 2 2.5 3 3.5 >4

Log TeNo. of chemicals Narcosis Polar narcosis Uncouplers AChE ElectrophilePage 21 of 126

for fish during short exposure durations. Appendix B provides an overview of MOAs identified in the literature, with a special emphasis on pesti cide active ingredients, and previous work done on industrial organics. It should be noted that th is list is not intended to be comprehensive of all potential MOAs, but rather a starting point to buil d the WOE necessary to develop an AOP. Keeping in mind the Bradford Hill Criteria, risk as sessors should be (1) identifying a potential AOP and associated key events; (2) determining whet her the AOP is conserved across taxonomic hierarchy; i.e. , would the AOP be similar for non-target species; (3) investigating the applicability of specific AOPs to chronic exposure; (4) determining MOAs and AOPs for mixtures, and (5) determining whether QSARs are ava ilable for specific AOPs.



### 4.2 (Quantitative) Structure-Activity Relationships

The basic assumption of any structure-activity appr oach is that the chemical’s structure imparts properties that relate directly to the chemical’s a ctivity, and assume that a group of chemicals that produce the same biological activity ( e.g., sodium ion modulators) have something similar about their chemistry ( e.g., pyrethroids). Structure-activity models include q ualitative (SAR) and quantitative models (QSAR), with (Q)SAR referri ng to either qualitative or quantitative approaches. SAR models are based on non-continuous data, such as identifying active vs. inactive chemicals based on the presence or absence of specific structural features or properties ( e.g. screening tools that identify whether a chemical w ill bind or not bind to a receptor). Examples of SAR approaches include prioritization a nd ranking of chemical lists ( e.g. , Russom

et al. , 2003; Schmieder et al. , 2003a,b) or data gap analysis using read-across a pproaches ( e.g., Hewitt et al. , 2010). QSAR models are based on continuous data and result in a quantitatively derived prediction of activity (e.g., EC50), relate d to a chemical, physical or structural property (e.g., the ER-BA endpoint which is a quantitative p rediction of the relative binding affinity of compound X to the endocrine receptor). These relat ionships rely on information on many chemicals to predict the activity of a single chemi cal lacking data. The goal is to quantify ‘structural similarity’ imparting biological activi ty by defining structural analogs or chemical categories that may act ‘similarly.’ In the contex t that similar structures result in similar activit y, it would then follow that an untested chemical that is similar in structure may produce the same activity.

(Q)SAR predictive methods can be grouped based on t he type of extrapolation approach used. Although the focus of this white paper is chemical- to-chemical and species-to-species extrapolation techniques, it is important to acknow ledge that other activity-to-activity extrapolations, such as predicting whole organism r esponses from in vitro test data and lab-tofield extrapolations, also exist but are not within the scope of this paper.

SAR approaches existed prior to TSCA’s promulgation in 1976, and were primarily used in the drug and pesticide discovery and development arena. These techniques became critical to TSCA risk assessments (as mentioned in Section 2) due to imposed time constraints (90 days to complete a risk assessment) and the number of PMN r eviews (up to 2,000 assessments/year) for

new chemicals. SAR methods allowed EPA to maximize both efficiency and consistency in the evaluation of potential hazard. The use of QSARs b y risk assessors in assessing potential toxic effects of organic chemicals on aquatic organisms e volved as computational efficiency andPage 22 of 126

toxicological understanding advanced, and has prove d to be a scientifically-credible tool for use in predicting toxicity for structurally similar sub stances with little or no available empirical data. The development of SMILES (Weininger, 1988) as a me ans to identify structure information in a computer readable format, and the advancement of de sktop computing, in the 1970’s made (Q)SAR methods readily accessible to risk assessors outside of OPPT (Benfenati, 2007). The European Union (EU) under its REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals) legislation will be build ing on the efforts developed under TSCA to address the approximately 9,000 chemical safety dos siers that must be completed in December of 2010 (Royal Society of Chemistry, 2010). Many of t hese substances have data gaps and the use of (Q)SAR approaches will be critical in meeting th e legislative deadlines (van Leeuwen et al.

, 2009).

4.2.1 Chemical-Structure Methods

Critical to any (Q)SAR technique is clearly definin g the chemical structure. Structures can be depicted in one-dimensional (1-D) formats ( e.g. , molecular formula), 2-D formats ( e.g. , Simplified Molecular Line Entry System (SMILES) (We iniger, 1988) or InCHITM code (Stein et
al ., 2003)), or 3-D formats ( e.g. , structures that include geometry and spatial info rmation). Since chemical structures are three-dimensional, they can have geometric isomers ( i.e. , cis and trans or E and Z), optical isomers ( i.e. , D/L, R/S, +/-) or display tautomerism, which typi cally exist in equilibrium within solutions. Chemicals can also b e flexible, allowing rotation around single bonds. When there is more than one point of rotatio n, this can lead to a large number of conformers. In biological tissues, a chemical coul d exist as multiple conformers, which is an important consideration as it relates to activity w ithin biological tissues ( i.e. , adsorption and binding to receptors) (see Serifimova et al ., 2002 for example). Many (Q)SAR approaches do

not take into account the 3-D nature of chemical st ructures because the models were built using only the 1-D or 2-D structural information. This is changing as computational power is increasingly available at the desktop, and may be c ritical in some aspects of modeling pesticide activity, which may be dependent on the overall 3-D structure of the compound as opposed to the

chemical’s substructure.

Within the context of pesticides, a chemical active ingredient has a discrete structure, while a formulated product mixture has many chemical struct ures beyond that of the active ingredient. “Inerts”, which are components of the formulated pr oduct that are not the active ingredient, include solvents, adjuvants, and other chemicals an d are designed to improve the effectiveness of the active ingredient, but are not intended to affe ct the pest organism in the same way as the active ingredient alone (Weinhold, 2010). Inert in gredients may make up a significant portion of some formulated products. Within the context of thi s paper, the use of the terms “active ingredient” or “chemical” refer to a single discret e chemical structure and not a formulated product representing mixtures of chemicals. When d eveloping (Q)SAR models, it is important to take into account whether the empirical data are based on the formulated product (eg., mixtures of structures or an active ingredient with a discrete, single structure).Page 23 of 126 4.2.2 Chemical Category Approach

The chemical category approach is an important tool in assessing chemicals with data gaps within USEPA’s OPPT and in support of EU’s REACH an d OECD risk assessment activities. An analysis done by van der Jagt and coworkers on R EACH data requirements found that large data gaps exist for substances with dossiers due in 2010 (van der Jagt et al ., 2004). They estimate that REACH legislation will require the us e of 3.9 million test animals to fulfill data requirements if alternative methods are not used, b ut that 72% of the testing needs do not have reliable or acceptable alternative methods availabl e, specifically validated QSAR models for a wide range of chemical categories / AOPs, and accep table in vitro test methods that can be used in risk assessments. An analysis of the Canadian Do mestic Substances List, where four QSAR computational tools (i.e., ASTER, EcoSAR, PNN and T OPKAT) were evaluated, found that QSAR models worked well for non-specific AOPs (e.g. , nonpolar narcosis), but were insufficient for risk assessment purposes for AOPs related to mo re specific mechanisms (Moore et al., 2003). At the same time, the EU and USEPA have a commitmen t to reduce the use of animal testing (NRC, 2007; Holmes et al ., 2010). In the absence of validated QSAR models for some AOPs, risk assessors have been exploring the use of chemi cal category approaches to help fill these data gaps, while statistically-rigorous QSAR models and in vitro test methods are developed, validated, and approved for use within a risk asses sment context (van Leeuwen et al ., 2009). As with REACH efforts, the QSAR models specific to man y pesticide active ingredients are not included in the existing (Q)SAR modeling applicatio ns, since the focus of model development was on industrial organics to meet needs under TSCA legislation. But, similar to REACH, data gaps may be filled using the chemical category appr oach, while efforts to develop validated and reliable QSAR models are underway.

A chemical category is defined as a group of substa nces with physicochemical, human health, or ecotoxicological attributes that are similar or fol low a pattern as a result of structural similarity (OECD, 2007a). The “similarity” can be based on ch emical structure/substructure ( e.g. , common functional group or chemical class), propert ies including behavior in physical or biological process ( e.g. , similar precursors or breakdown products), an inc remental or constant change in potency ( e.g. , increased carbon chain length), and/or the functi on / use of the substance ( e.g. , detergents, fragrance) (OECD, 2007a). The OECD is holding a workshop in December of 2010 to explore the development of mechanistically- based chemical categories using AOPs (see OECD, 2009a). Chemicals within a category are not required to be similar in all these properties, and a substance can belong to more than one chemical category. The chemical category approach uses a WOE approach taking inform ation from many tested chemicals and inferring information for an untested substance (se e Figure 5 ). Each substance within a category is not tested, but rather data for select chemicals are used to make an effect assessment decision. The resulting information is used in a qualitative ( e.g. , relative potency based on a read-across of empirical data) and a quantitative ( e.g. , point-estimate from a QSAR model) manner (OECD, 2007c). Through this process, the category is used to fill data gaps (e.g., populate an MDR), and prioritize/rank substances to better inform the fin al effect assessment and testing strategies.

In order to be useful, a consistent approach for de fining chemical categories has been developed (USEPA, 2002; OECD, 2007a; OECD, 2009b). The steps include grouping a series of chemicals based on a pattern or similarity within the group, gathering information on physicochemicalPage 24 of 126

properties, fate, and ecological and human health e ffects for each chemical within the proposed category, evaluating the data for reliability, rele vance and adequacy, using a read-across approach to identify potential data gaps, and final ly, evaluating the data to determine if there is sufficient evidence to support the development of t he chemical category. A critical aspect of reviewing data reliability, relevance, and adequacy is the endpoints of concern (e.g., LC50) and the associated test guidelines (e.g., OSCPP harmoni zed test guideline for fish acute toxicity). Examples of building categories can be found in Eno ch (2010), Enoch et al. , (2009), USEPA (1999), and Worth and Patlewicz (2007).

An approach for determining the utility of (Q)SARs is to construct a chemical category matrix table such as that depicted in Figure 5 . The matrix consists of category members, in this case chemicals in each of the columns, and corresponding sets of properties ( e.g. , octanol/water partition coefficient) and/or activities, ( e.g. , effects data) represented in each of the rows. T he solid dots represent properties/activities for whic h reliable data exist. The hollow dots represent acute values for untested species. As illustrated in Figure 5 , data gap filling can be done using

read-across from one tested chemical to an untested chemical. The observation of a trend (increasing, decreasing, or constant) in the experi mental data for a given endpoint across chemicals can also be used as the basis for interpo lation (when the value to be estimated is bracketed on either side by empirical data) and als o extrapolation (when the value to be estimated is bracketed on only one side by empirica l data). As depicted in Figure 5 , using a combination of predictive methods, i.e. , SARs/read-across, extrapolation and interpolation , the matrix of properties/activities for chemicals under consideration can be rendered less uncertain by making greater use of existing data. While init ially there may be many data gaps and considerable uncertainty, the existing data inform the understanding of how chemicals with little to no data may act. The extent to which the matrix of information can be used quantitatively versus qualitatively depends on the strength of the relationships being used to predict within and beyond the measured ranges. Below is an overview of approaches that can be used within these chemical category matrices.Page 25 of 126

Figure 5. A chemical category is created to describ e a series of “similar” pesticides. Data gaps with in the data

set are identified and filled based on information from data rich chemicals. (Modified from van Leeuw en et al, 2009).

Read-across / data bridging approach : Endpoint information for one chemical (the source chemical) is used to predict the same endpoi nt for another chemical (the target chemical), which is considered to be "similar" in s ome way (usually on the basis of structural similarity or on the basis of the same A OP). It may be performed in a qualitative or quantitative manner. (OECD, 2009b)

Interpolation is defined as the estimation of an intermediate te rm. The process of interpolation is depicted in Figure 5 or filling data gaps relative to Property 2 and Activity 2 for Chemical 2. Interpolation is the es timation of a value for a chemical member using measured values from other members on “both sides” of that member within a defined chemical category. As such, the a ctivity/properties of chemicals with structures that are intermediate [between] those wh ere data exist can be predicted and

bracketed.

Extrapolation is defined as the estimation of a value outside of a tabulated or observed range. Perhaps more to the point, it is a means by which to predict properties/activities, from what is known over a range of similar endpoint s using measured data, to chemicals with no measured data outside that range. As such, extrapolation refers to estimation of a value for a member that is near or at the category boundary using measured values from internal category members. The process of extrapol ation is depicted in Figure 5 for filling data gaps relative to Property 3 and Activi ty 3 for Chemicals 1 and 4. Thus, Chemical 4 Chemical 3 Chemical 2 Chemical 1

Activity 4 Activity 3 Activity 2 Activity 1 Property 4 Property 3 Property 2 Property 1 SAR / read-across

Interpolation

Extrapolation

Trend analysis / QSAR

Empirical data

Missing dataPage 26 of 126

toxicity values could be predicted for chemicals th at lie on either side of the range of

information based on measured data.

QSAR / Trend Analysis : Within a chemical category, QSARs can be develope d based on the trends in the empirical data. By plotting the activity and properties of chemicals with empirical data, a user can predict the chemicals wi th data gaps in activity based on predicted or measured property information. A key aspect to this approach is ensuring that the property is somehow related to the activit y being modeled. Justification for use of property and activity data in predicting a parti cular activity for an unknown substance could include the applicability domain of the parti cular category developed, AOP of the chemicals under consideration, and commonality in s ubstructures or break-down products.

Table 1 presents an example of a read-across for a series of pyrethroids, where the yellow boxes with “??” entries represent missing data points. D ata from chemicals on either side of deltamethrin can be used to interpolate an LC50 val ue, or data for allethrin, dimethrin, resmethrin, and permethrin can be used to extrapola te a predicted value for bifenthrin. Using simple regression techniques and log P as the indep endent variable, the predicted toxicity of deltramethrin is 27 ug/L, and the predicted toxicit y for bifenthrin is 7.9 ug/L. An important aspect of this category is that it is mechanistical ly-based, since all component chemicals modulate the sodium ion channel resulting in neurot oxicity. Another consideration is that all the test data are based on a standard test method (e.g. flow-through exposure; 96 hr duration, etc .).

For the test data in Table 1 , all studies are from either the USEPA’s fathead m innow database (i.e., resmethrin and permethrin results; see Russo m et al., 1997)) or from the US Geological Survey data set (i.e., allethrin and dimethrin; see Mayer and Ellersieck, 1986), with all following the ASTM (American Society for Testing and Material s) guidelines for fish acute toxicity testing (ASTM 2007). But differences exist in test procedur es. The LC50s for resmethrin and permethrin are based on measured concentrations of the pesticide, while the LC50s for allethrin

and dimethrin are based on nominal concentrations. Measured chemical concentrations may be critical for some extrapolations (i.e., for volatil e substances), but may not be critical for these higher log P chemicals. These types of detailed an alysis will need to be conducted when using read-across approaches.

Table 1. An example read-across for pyrethroids. Or dered by the physical chemical property LogP, which describes the partitioning aspect of the toxicity.

Parameter ALLETHRIN DIMETHRIN DELTAMETHRIN RESMETHR IN PERMETHRIN BIFENTHRIN

Fathead

minnow
LC50 values (ug/L) 53.0 62.0 ?? 6.16 16.0 ??

Log P 5.52 6.57 7.02 7.11 7.61 8.15Page 27 of 126 4.2.3 Analog Approaches

There is a long history of the use of analogue sele ction techniques in which data associated with structurally similar chemicals are used to predict risk of chemicals where no data are available ( e.g. , Lipnick, 1995b; Zeeman, 1995; Zeeman et al., 1995; Karabunarliev et al ., 1996a; Karabunarliev, 1996b; Russom et al ., 1997; Walker and Printup, 2008). The proper app lication and continued acceptance of any structural analog a pproach requires well-defined and validated methods or models that can be used to systematicall y identify potential analogs, and subsequently identify appropriate QSAR models. Imp licit in the development of these models is the need to define the group of chemicals by some l evel of similarity. The chemical categories and structural analog methods can be used to help b in chemicals for further use in development

of models.

The analog approach is a variation on the grouping process used for chemical categories such that the grouping is only based on a single similar chemical or a limited number of similar chemicals. Traditionally, analog approaches involve predicting an endpoint or property of one

chemical based on the available data for the same e ndpoint or property of a similar chemical. In this case, predictions are largely based on read-ac ross methods. As discussed in Section 4.2.2, “similarity” can be based on common structures/subs tructures, chemical properties including behavior in physical or biological process, and/or the function/use of the substance (OECD, 2007a).

As mentioned in Section 4.2, (Q)SARs are based on t he assumptions that a chemical’s structure imparts properties that relate to biological activi ty, and that a group of chemicals that produce the same activity have something similar about their ch emistry / structure. One goal of (Q)SAR approaches is to quantify ‘structural similarity’ i mparting biological activity and identify which other chemicals may be ‘similar’ with the assumptio n that an untested chemical may produce the same activity. It is important to clarify that eve n if chemicals display a high level of structural similarity, they may not be functional analogs. Co nversely, chemicals that act similarly are not always structural analogs (Saliner et al. , 2005.) Therefore, using a WOE approach, when gathering information to use in (Q)SAR approaches, is critical.

The OECD and USEPA have defined guidelines for iden tifying similar substances (USEPA, 1999; OECD 2007a) when building chemical categories based on the following commonalities:

o a common functional group or substructure ( e.g. , phenols, aldehydes);

o a common precursor or break-down product may result in structurally-similar chemicals, which can be used to examine related che micals such as acids/esters/salts. ( e.g. , short-chained alkyl-methacrylate esters which are metabolized to methacrylic acid);

o an incremental or constant change ( e.g. , increased carbon chain length; typically used for physicochemical properties such as boiling poin t); andPage 28 of 126

o common constituents or chemical class, similar carb on range numbers - used with substances of Unknown or Variable composition, Comp lex reaction products or Biological material” (UVCBs) ( e.g. , series of linear alkyl sulfonates).

Pesticides are commonly grouped based on their simi larity in pesticide activity ( e.g. , insecticides, fungicides, herbicides, etc. ) and chemical class ( e.g. , organophosphate, triazine, etc. ). Pesticides are substances designed to adversely affect organis ms, and therefore differ in the chemicals typically addressed under TSCA, which focus on subs tances used in manufacturing, and specifically exclude those substances covered under Federal Food, Drug, and Cosmetics Act (FFDCA) and FIFRA.

Since pesticide active ingredients are designed to adversely affect the pest or target organism, and information is typically available on the molec ular or tissue target site of action, this information can be used to define similarity based on a pesticide activity, common functional group approach and/or pest organism AOP information . As an example, in Table 2, a subset of the insecticide MOAs is presented. For each MOA, m ore than one chemical class is represented; therefore, ‘similarity’ is based on all three aspec ts of the substance, its pesticide classification ( e.g. , insecticide), its pest organism MOA ( e.g. , acetylcholinesterase inhibition), and structural analog /common functional group ( e.g. , carbamate, organophosphate.)

Table 2. Target (pest) organism mode of action and associated chemical class for a select group of ins ecticides (adapted from IARC http://eclassification.irac-onli ne.org/)

Pest Organism Mode of Action Chemical Class Acetylcholine esterase inhibitor Carbamate Organophosphate GABA-gated chloride channel antagonists Cyclodiene organochlorine Phenylpyrazole (Fiprole) Sodium channel modulators Pyrethroid Organochlorine Nicotinic Acetylcholine receptor agonists Neonicoti noid

Botanical Juvenile hormone mimics Juvenile hormone analog

Carbamate Pyridine insect growth regulator

4.2.3.1 Structural Analogs

Structural analogs are chemicals that have a high d egree of chemical similarity, but one or more functional groups or substructure(s) has been subst ituted (Saliner et al. , 2007). A structural analog can be defined by a substructure or similari ty based on pattern matching or signature analysis (Saliner et al ., 2007.) In Figure 6 the structures for two pesticides belonging to the synthetic pyrethroid class of insecticides, permeth rin and bifenthrin, are presented. These two chemicals can be defined as structural analogs beca use all structures share a common substructure ( i.e., a cyclopropane carboxylic acid). The difference in potency ( Table 3 ) is basedPage 29 of 126

on partitioning and electronic charge characteristi cs of each chemical within a specific matrix ( i.e. , biological or other media such as water) and rela ted to the functional groups that are not common among each structure ( e.g. , fluorine atoms on bifenthrin versus chlorine atom s on permethrin).

Figure 6. Example of structural analogs involving s ynthetic pyrethroids.

Table 3. Comparison of LC50 data for structural ana logs, permethrin and bifenthrin.

Species PERMETHRIN
96 hr LC50

based on AI (ug/L) BIFENTHRIN

96 hr LC50

based on AI (ug/L) Reference

Bluegill 6.8 0.35 US EPA 2010

Rainbow Trout 2.1 0.15 US EPA 2010

4.2.3.2 Similarity Indices

Another approach is to use algorithms that calculat e similarity or distance based on pattern matching. These predictive methods rank chemicals based on characteristics of each structure that are similar (match/overlap) and characteristic s that are dissimilar (mismatch/difference) (Monev 2004; Salinar et al ., 2007). Figure 7. Schematic of similarity index measures. provides a schematic of the measures that can be d escribed in similarity indices including attributes that are unique to each chemical (i.e., a and b), attributes common to each (i.e., c) , and, attributes absent from each substance (i.e., d ). These techniques can utilize twodimensional information such as molecular propertie s or topological indices; i.e. , the molecular graph of the structure, or three-dimensional data s uch as the conformational property of a chemical. Various approaches for calculating simil arity indices exist; these approaches include

O

O

O

CH 3

CH 3

Cl

Cl Permethrin CH 3

O O

CH 3 C H 3

F

F

F Cl

BifenthrinPage 30 of 126

correlation-type indices ( e.g. , Tanimoto Index (See Eq. 2; also known as Jaccard coefficient), Hodgkin Ricards Index, cosine-similarity index),

T = c/(a + b + c) Eq. 2

dissimilarity measures ( e.g. , Euclidean distance index (Eq. 3), Hamming distanc e) which measures mismatches between compounds, and

D = b a + Eq. 3

composite measures which measures both similarity a nd dissimilarity ( e.g. , Hamann measure (Eq. 4), Yule measure.)

S = (c + d – a - b) / (a + b + c + d) Eq. 4

For an overview of these approaches see Monev (2004 ), Salinar et al . (2007)), and UrbanoCuadrado et al . (2008).

Figure 7. Schematic of similarity index measures.

4.2.4 QSAR Model Development

The development and use of QSAR models has a long a nd rich history, dating back to the initial work done by Meyer (see overview by Lipnick 1995a) and Overton (1901) in the early 1900s. These works resulted in the development of the Meye r-Overton rule, or general lipid theory of

a b d

c Comparing Chemicals A and B

• a = number of features present in A and absent in B

• b = number of features present in B and absent in A

• c = number of features common to both A and B

• d = number of features absent from both A and B

Chemical A Chemical BPage 31 of 126

narcosis, which states that the potency of anesthet ics, or narcotics, is directly related to the lipophilicity of the chemical (see review by Lipnic k 1995b.) The Meyer-Overton rule, with subsequent refinements by Ferguson (1939), provides the foundation for QSARs used today to predict the narcotic toxicity of industrial organic chemicals (See Könemann, 1981; Veith, et al

., 1983).

Another critical step in the development of QSAR mo deling was the use of the log of the octanol/water partition coefficient (log P or log K

ow ) as a modeling parameter to represent the partitioning of chemicals into biological membranes (Hansch and Fujita, 1964; Hansch and Dunn 1972). As represented in Eq . 5, the interactions with bio-membranes is explain ed by the partitioning (P) parameter, with steric properties (S) and/or electronic factors (E l ) used to explain more specific toxicity ( e.g. , proelectrophilic/electrophilic interactions). Em pirically derived constants (a, b, c, x) are obtained by fitting the empirical toxicity data to the equation.

Log(C) = x + a(El) + b(P) + c(S)+.....x(X) ( Eq. 5)

As QSAR models have advanced, the approaches have t aken on a more mechanistic-approach to model development, linking QSAR model descriptors t o knowledge related to mechanism of action of the substance at the molecular target ( e.g. , Bradbury 1994; Russom et al ., 1997).

In general, the development of QSAR models follows a logical and step-wise process. First, a

set of chemicals with reliable data are collected f or a particular biological / chemical activity (see

Perkins et al ., 2003; Tong et al. , 2003; Bradbury et al. , 2003; Walker et al ., 2003 for general reviews on approaches related to QSAR development). These test data are used to develop a model to predict values for chemicals which are sim ilar, but lack data. Typically the original data are randomly separated into a test set and a v alidation set, with the test set used to develop a

model and the validation set used to test the assum ptions that the model works for chemicals not included in the original model’ training set (Leona rd and Roy, 2006).

A critical component in this process is reliability and integrity of data (Bradbury et al. , 2003; Leonard and Roy 2006). The empirical data used to d evelop the model must have a well-defined endpoint, and the model should ensure that the data meet quality controls ( i.e. , were chemical concentrations measured, were standard test protoco ls followed, etc .) To ensure transparency, reliability, and consistency in QSAR models used in risk assessments, the OECD developed a set of guidelines, known as the “Principles for (Q)SAR Validation” (Jaworska et al ., 2003; http://www.oecd.org/dataoecd/33/37/37849783.pdf) The five OECD principles for (Q)SAR validation are as follows, and will be discussed in more detail in Section 6.1:

o A defined biological endpoint - clearly define the test guidelines/methods used, and the specific biological endpoint being predicted ( e.g. , LC 50 );

o An unambiguous algorithm – purpose is to ensure a transparent model, prefer ably with access to the test set of empirical data and descri ptors used in the development of the model, assuring reproducibility of the model predic tions;

o A defined domain of applicability – define the limitations / bounds of the model inc luding chemical structure, physico-chemical properties, an d/or modes of action relevant to the model;Page 32 of 126

o Appropriate measures of goodness-of-fit, robustness , and predictivity – to ensure reliability of predictions and internal performance of the model; and

o A mechanistic interpretation – although not always possible, attempts should be made to explain the biological response as it relates to th e molecular site of action, and why various descriptors were used in the QSAR model.

Although many people observed that, for narcotic ch emicals, the slope of log (Kow) vs. log (acute toxicity) was close to 1 for many aquatic sp ecies, Di Toro et al. (2000) were apparently the first to determine a pooled slope using data fo r many aquatic species. Herein this is called the multi-species QSAR approach to differentiate it from the single-species QSAR approach in which data for individual aquatic species are consi dered separately. This first multi-species QSAR model, which is referred to herein as the “Kow Target Lipid Model”, was applied in several situations (Di Toro and McGrath 2000; McGra th et al. 2004, 2005; Redman et al. 2007; McGrath and Di Toro 2009). Later, Kipta and Di Tor o (2009) developed the “polyparameter Target Lipid Model” for narcotic chemicals that use s the Abraham partitioning constants instead

of Kow. Acree and coworkers have also used the Abr aham partitioning constants (Hoover et al. 2005,2007; Bowen et al. 2006a,b). The polyparamete r Target Lipid Model merged polar and non-polar narcotics, as did the use of Kmw instead of Kow by Escher and Hermens (2002). The development of multi-species QSARs for narcotics su ggests that it might be possible to develop multi-species QSARs for other modes of action.

The availability of a multi-species QSAR for a mode of action (MOA) would be very useful because it would allow the measure of toxicity to b e extrapolated from one species to another over the range of species for which the validity of the multi-species QSAR had been validated. This would provide a rationale for saying, for exam ple, that the rainbow trout is a factor of 1.5

more sensitive than the fathead minnow to all chemi cals that have that MOA. In addition, a multi-species QSAR can also be used in the evaluati on of the acceptability of results of toxicity tests on all chemicals with that MOA, for species f or which the multi-species QSAR has been validated. Because a multi-species QSAR can be use d to extrapolate each toxicity value for any

chemical with that MOA to all other chemicals that have that MOA, the composite dataset can be

used in the derivation of ALSVs for all chemicals t hat have that MOA.



### 4.3 Interspecies Correlation Estimation (ICE) Models

4.3.1 Background

Interspecies toxicity extrapolation using regressio n analysis of acute sensitivity has been explored in ecotoxicology for decades (Kenaga, 1978 ; Doherty, 1983; LeBlanc, 1984; Thurston

et al ., 1985; Slooff et al ., 1986; Mayer et al ., 1987). Although earlier studies were based on limited species, chemicals, and chemical MOAs, they provided evidence that regression models could be used to predict acute toxicity (Kenaga, 19 78) and were more robust for closely related species (Slooff et al ., 1986) and within chemical categories (LeBlanc, 1 984). Raimondo et al

. (2010b) developed interspecies correlation estimati on (ICE) models with the most diverse species and chemical database to date and performed quantitative uncertainty analyses in relationPage 33 of 126

to taxonomic distance, chemical MOA, and model para meters. These analyses provided guidance on model selection and use in ecological risk asses sment (Raimondo et al ., 2010).

In 2003, USEPA first compiled ICE models into a cd- based modeling application that may be used in ecological risk assessment (ICE v1.0; Asfaw et al ., 2003). High quality databases were expanded and models were updated in the Web-based I nterspecies Correlation Estimation application in 2007 (Web-ICE v1.0; Raimondo et al ., 2007a; http://www.epa.gov/ceampubl/fchain/webice/). Improv ed standardization criteria were applied to the ICE database along with the addition of expande d data sources and the models were again updated in 2010 (Web-ICE v3.0; Raimondo et al ., 2010). The ICE model database will continue to be updated in future versions as toxicity data b ecome available and are reviewed for quality assurance. Web-ICE contains modules that develop Sp ecies Sensitivity Distributions and predict toxicity to threatened and endangered species from multiple surrogates. Future Web-ICE modules will include models that predict acute toxi city to algal species.

4.3.2 Model Development and Validation

Interspecies Correlation Estimation (ICE) models ar e log-linear least squares regressions of the acute toxicity of chemicals measured in two species in which the measured toxicity of the surrogate species can be used to predict the toxici ty to the target taxon. In the example in depicted in Figure 8. Example Interspecies Correlation Estimation Model.

, measured toxicity for the surrogate species, rain bow trout, can be used to predict the toxicity to the target species, razorback sucker. ICE models have been developed and validated for aq uatic

vertebrates and invertebrates to include freshwater and saltwater species (Raimondo et al

., 2010b) and wildlife species (Raimondo et al ., 2007b), and their use in developing SSDs has bee n demonstrated (Awkerman et al ., 2008, 2009; Dyer et al . ., 2008). ICE models can predict to a species, a genus, or a family. Toxicity data are po oled within genus and family to develop models for these higher taxa. ICE models for both a quatic organisms and wildlife are available on the USEPA Web-based Interspecies Correlation Est imation application (Web-ICE; http://www.epa.gov/ceampubl/fchain/webice/; Raimond o et al . 2010). For aquatic organisms, Web-ICE contains 780 species-level models (77 speci es to 77 species), 289 genus-level models (comparing 62 species to 28 genera), and 374 family -level models (comparing 69 species to 27 families). For wildlife, Web-ICE contains 560 speci es-level models (comparing 49 species to 49 species) and 292 family-level models (comparing 49 species to 16 families).Page 34 of 126

Figure 8. Example Interspecies Correlation Estimati on Model.

ICE models are developed from an extensive database of acute toxicity for a diversity of species and a wide range of chemicals. The Web-ICE aquatic toxicity database was compiled from data sources within the USEPA and open literature and is comprised of 5,487 acute EC/LC 50 values for 180 species and 1,258 chemicals. All data adher ed to standard acute toxicity test requirements outlined by the American Society for T esting and Materials (ASTM, 2007 and earlier editions) and the USEPA Office of Preventio n, Pesticides, and Toxic Substances (USEPA 1996). Rigorous quality assurance and standardizati on guidelines were applied to the database and ensured model relationships were reflective of intrinsic species sensitivity and contained minimal extraneous variation (Raimondo et al., 2010 b). Toxicity records for metals, pentachlorophenol, and ammonia were normalized acco rding to Ambient Water Quality Criteria methodology (the 1985 Guidelines). Records were exc luded if they did not contain the water quality parameters necessary for normalization ( e.g. , hardness, pH, temperature), an assessment of test quality, or were reported as non-definitive toxicity values ( e.g. , > 100 μg/L, < 100 μg/L). A detailed description of the Web-ICE database may be found at Raimondo et al . (2010b).

Although data standardization can reduce the amount of data available for model development, it is necessary to reduce extraneous sources of variat ion and ensure that the model reflects species sensitivity relationships with greater certainty. R aimondo et al . (2009) explored the variability of acute test type (static, flow-through), concentrati on reporting (measured, nominal), and organism life stage on ICE models. In general, the results i ndicated that standardizing test data by acute test type or reported concentration type may not be critical for developing ecotoxicological models using large datasets of log-transformed valu es.

The ICE models were developed by pairing all specie s within the database by chemical and fitting log-transformed toxicity to a least squares regression. Each model with a sample size of four or greater was validated using leave-one-out c ross-validation. In this approach, each pair of acute values for surrogate and target species is re moved from the original model to build a submodel from the remaining data. The submodel is u sed to predict the toxicity value of the removed target species from the removed surrogate s pecies toxicity value. The differences between the measured and predicted values of all re moved datapoints for each model were usedPage 35 of 126

to assess model accuracy. In general, ICE models di d not have a tendency for under or overestimation, based on this assessment of model accur acy

To provide a measure of cross-validation performanc e of each model, the cross-validation success rate was calculated as the percentage of re moved datapoints predicted within 5-fold of

the measured value. Cross-validation success rate i s provided for each model on Web-ICE. Accuracy of model prediction was strongly related t o taxonomic distance of the surrogate and predicted taxa; models built for two species in the same family predicted within 5 and 10-fold of the actual value for 91% and 96% of datapoints, res pectively.

Recursive partitioning analysis identified species taxonomic distance, model mean square error, and the distance of the model input value relative to the range of surrogate values from which the model was developed as the most important variables for obtaining robust predictions. Results from these analyses provide user guidelines on mode l selection (see Section 6.2).

4.3.3 Influence of MOA on ICE Model Predictions

To determine how ICE models are influenced by MOA, each chemical used in models was assigned a broad and specific MOA ( Table 4 ). MOA-specific ICE models were built for all possible species pairs and broad and specific MOAs and were developed for 7 broad MOAs (494 models, 46 species) and 15 specific MOAs (424 model s, 44 species). MOA-specific models were cross-validated to determine their prediction accur acy and compared to respective models developed from all chemical data.

Table 4. Number of toxicity records and chemicals i ncluded in ICE model development

Mode of Action Broad Specific # records # chemicals

AChE inhibition 1120 71

OP AChE inhibition 784 54

Carbamate AChE inhibition 336 17

Anticoagulation Anticoagulation (ND) 1 9 3

Cellular toxicity Cellular toxicity (ND) 37 6

Corrosive/irritant Corrosive/irritant (ND) 23 5

Metallic stress 337 14
Iono-regulatory toxicity 281 7 Respiratory toxicity 9 2
Metallic stress (ND) 47 5

Neurotoxicity 668 55 Pyrethroid neurotoxicity 216 23 OC neurotoxicity 392 22 Neurotoxicity (ND) 60 10Page 36 of 126

Narcosis 1770 376 Nonpolar narcosis 1326 273

Polar narcosis 158 40

Ester narcosis 190 36

Diester narcosis 32 5
Narcosis (ND) 64 22

Reactivity Reactivity (ND) 222 48

Respiratory toxicity 30 8 Iono-regulatory toxicity 3 1 Respiratory toxicity (ND) 27 7

Uncoupler/Inhibitor of oxidative phosphorylation 21 8 19 Uncoupling oxidative phosphorylation 112 12 Inhibiting electron transport/ATP synthase 106 7

Uncertain/Undetermined 387 102

1 ND – specific mode of action not assigned.

MOA-specific models had lower model mean square err or (MSE) than respective models developed using multiple and variable MOAs (with th e exceptions of the broad MOA metallic stress and two specific MOAs, including io n-regulatory toxicity and organophosphate AChE inhibitors) ( Figure 9. Models developed from chemicals of diverse modes of action compared to those with MOA-specific data.

). The improvement in prediction accuracy of MOA-ba sed models was dependent on taxonomic relatedness and the type of MOA. Overall, the prediction accuracy of MOAspecific models did not improve for models develope d for two species within the same phylum ( e.g. fish to fish, invertebrate to invertebrate). For m odels developed for two species within the same kingdom ( e.g. fish to invertebrate), toxicity predictions from M OA-based models were a significantly improved compared to mo dels developed using all data ( Figure

9. Models developed from chemicals of diverse modes o f action compared to those with MOA-specific data.

).Page 37 of 126

Figure 9. Models developed from chemicals of divers e modes of action compared to those with MOA-specif ic

data.



### 4.4 Acute-Chronic Ratios (ACRs)Page 38 of 126

When measured chronic toxicity data are limited or unavailable for aquatic species, it is often necessary to predict chronic data for those species based on their observed acute responses. Acute-Chronic ratios (ACRs) are used to predict chr onic toxicity in aquatic organisms when the acute toxicity is known but chronic data are absent or limited/indefinite. ACRs are typically derived for specific species and chemicals in which both acute and chronic data are available and are in turn used to predict chronic toxicity to a d ifferent species exposed to the same chemical. As shown in Equation 7, the ACR is the ratio of the acute value to chronic value; where acute value is defined as the lethal concentration (LC 50 ) or effect concentration (EC 50 ) to 50% of the organisms tested under acute durations, and the chr onic value is the No Observed Concentration (NOEC) or the Maximum Acceptable Toxicity Concentra tion (MATC is the geometric mean of

the chronic NOEC and the Lowest Observed Effect Con centration [LOEC]):

ACR = Acute Value / Chronic Value Eq. 7

An ACR has a higher level of confidence if the data for the acute and chronic values are from the same study or concurrent studies done by the same i nvestigator, with the same species, using the same batch of chemical, and under similar test cond itions. Paired acute and chronic values from the same laboratory are usually not available; ther efore ACRs reported in the literature can vary considerably.

Alternatively, ACRs may be standard or fixed values rather than quotients derived from chemical and species specific data. Both OPP and OW current ly use ACRs in effects assessments, although slightly differently. A description of th e approaches used by OPP and OW to derive ACRs is provided below along with a summary of simi larities and differences regarding derivation. In addition, various approaches for es timating ACRs when acute and/or chronic toxicity data are not available for the same specie s are described in Sections 4.4.4 and 4.4.5.

Error! Reference source not found. 45 4.4.6 contains information and uncertainties ass ociated

with the use of standard ACRs.

4.4.1 OPP Approach to Derive ACRs

OPP routinely uses ACRs in ecological risk assessme nts for pesticides to predict chronic toxicity for aquatic animals when the acute toxicity profile indicates that the most sensitive surrogate aquatic species was not tested in a chronic study o r when a data gap exists ( i.e. , no data were

submitted or submitted data were classified as “inv alid” ( e.g. , when the NOEC is less than the lowest concentration tested)). OPP derives ACRs fo r pesticides based on the ratio of the acute toxicity of a chemical (expressed as an LC 50 or EC 50 value from a valid acute study) to its chronic toxicity (expressed as a NOEC value from a valid chronic study). Ideally, the acute and chronic toxicity data used to derive the ACR should be from the same test species. This ACR is then applied to a species within a similar group (e .g., freshwater fish).

As an example of how OPP regularly makes use of ACR s, acute and chronic toxicity data are routinely submitted for the cladoceran, Daphnia magna or D. pulex , (daphnids) based on the ecological effect data requirements for pesticides specified in 40 CFR Part 158 Subpart G (CFR,Page 39 of 126

2010). Acute and chronic data from guideline tests with daphnids are normally used as a surrogate for the freshwater invertebrate taxa; the refore, these data are typically available. In situations where the available toxicity data from r egistrant-submitted or open literature studies indicate that another tested species from the same taxonomic group, e.g. , the amphipod,

Gammarus sp., is more sensitive than daphnids on an acute e xposure basis, the acute value for that species (for the Gammarus ) are used to predict acute risk to freshwater inve rtebrates. If chronic toxicity data are not available for the Gammarus , an ACR would be calculated based on the ratio of the acute to chronic toxicity data for daphnids and the ACR would be applied to the

acute value for Gammarus to yield a corresponding chronic toxicity value. In the following example, an ACR of 10 is derived for freshwater inv ertebrates based on the ratio of the acute and chronic toxicity data for daphnids (48-hr EC 50 of 20 μg/L ÷ 21-day NOEC of 2 μg/L = 10); the ACR of 10 is then applied to the acute toxicity val ue for Gammarus (96-hr LC 50 = 1 μg/L) to yield a predicted chronic NOEC of 0.1 μg/L ( Gammarus LC 50 of 1 μg/L ÷ ACR of 10 = 0.1 μg/L).

• Daphnid 48-hr EC 50 = 20 μg/L

• Gammarus 96-hr LC 50 = 1 μg/L

• Daphnid 21-day NOEC = 2 μg/L

• ACR = Daphnid EC 50 (20 μg/L) ÷ Daphnid NOEC (2 μg/L) = 10

• Predicted chronic NOEC for Gammarus = Gammarus EC 50 (1 μg/L) ÷ ACR = 0.1 μg/L

In this example, the predicted chronic toxicity val ue for Gammarus of 0.1 μg/L would be quantitatively used in OPP’s ecological risk assess ment to derive chronic risk estimates for

freshwater invertebrates as it is the most sensitiv e value for that taxonomic group. Use of the ACR to predict chronic toxicity data for use in ris k assessments considers the most acutely sensitive species and provides a means of addressin g the uncertainty regarding the potential chronic toxicity of the untested species.

In this example, the predicted chronic toxicity val ue for Gammarus of 0.1 μg/L would be quantitatively used in OPP’s ecological risk assess ment to derive chronic risk estimates for

freshwater invertebrates as it is the most sensitiv e value for that taxonomic group. Use of the ACR to predict chronic toxicity data for use in ris k assessments considers the most acutely sensitive species and provides a means of addressin g the uncertainty regarding the potential chronic toxicity of the untested species. Typicall y, ACRs determined with one species are applied to a different species, as discussed below for freshwater and estuarine/marine fish. . Typically, ACRs determined with one species are app lied to a different species, as discussed

below for freshwater and estuarine/marine fish.

When considering whether to use an ACR to predict c hronic toxicity for aquatic animals, OPP evaluates the available data based on the following factors:

• LC/EC 50 and chronic NOEC data validity : Acute and chronic data used to derive ACRs must be from scientifically valid studies ( i.e ., submitted guideline studies that are classified by OPP as either “acceptable” or “supple mental” or studies from the open literature that are classified by OPP as “quantitat ive”).Page 40 of 126

• Preference for data from the same species : Acute and chronic toxicity values used to calculate the ACR should be from the same species w hen possible. If data from acute and chronic studies for same species are not availa ble, other prediction methods (e.g., QSAR) may be more appropriate. However, deriving a n ACR from acute and chronic data for different species within the same taxonomi c group, particularly those that are in the same genus or family ( i.e. , bluegill sunfish ( Lepomis macrochirus ) and largemouth bass ( Micropterus salmoides )) may provide a reasonable prediction of the ACR i f the acute toxicity database is robust and suggests that the toxicity of the pesticide being assessed is relatively consistent across all specie s tested within a taxonomic group.

• Studies conducted under similar conditions : When possible, acute and chronic toxicity

values used to derive the ACR should be from studie s conducted under similar conditions ( e.g. , flow conditions, dilution water quality, temperat ure, hardness, pH, etc .). When test

conditions from the available acute and chronic tox icity studies vary such that the difference is expected to significantly affect the bioavailability and/or toxicity of the pesticide, data from these studies should not be us ed to derive an ACR. For example, in situations where water hardness or pH is known to a ffect the toxicity of the pesticide, it would be important that the acute and chronic studi es have comparable or similar hardness or pH conditions to allow for comparison o f acute and chronic toxicities.

• Use of mollusk shell deposition data : Aquatic invertebrate data submitted to OPP in support of pesticide registration most commonly inc lude studies in two crustaceans (one freshwater and one estuarine/marine) and a marine m ollusk. Relating crustacean toxicity data to mollusk shell deposition data is not recomm ended for ACR derivation based on the variability in responses associated with the me asured endpoints ( i.e. , mortality and growth of crustaceans versus shell growth of mollus ks). However, some mollusk studies involve larvae and are not shell deposition studies . In those cases, and depending on the study conditions, these data may be appropriate for deriving ACR values.

• Use of endpoints with “>” values : Three values are necessary for calculation and application of ACRs (two acute and chronic values f rom the same species to allow for

calculation of the ACR and one acute value to which the ACR is applied in order to predict the chronic value). If the available exper imental acute toxicity value is expressed as a non-definitive value, such as an LC 50 >100 mg/L, which is common in limit tests, an ACR should not be calculated using these data. Tox icity values expressed as a nondefinitive value often represent the maximum concen tration tested and not the inherent toxicity of the chemical. Similarly, indefinite NO EC values where chronic effects were

seen at all of the test concentrations should not b e used.

• Use of ACRs < 1 : ACRs < 1, which occur when the LC/EC 50 value is lower than the chronic NOEC should not be used; these types of val ues indicate that a measured 50% effect occurred after short-term exposures at pesti cide concentrations that did not produce any effect following chronic exposure. ACRs < 1 ma y occur if the acute and chronic values are from the following types of studies: (1) those conducted in different species with different sensitivities to the pesticide; (2) those that tested different forms of the pesticide ( i.e. , technical grade versus formulated product) where one form is more or less toxic than the other; (3) those that used different protocols or test conditions that may have affected the sensitivity of the toxicity study

.

• Consideration of mode of action : Mode of action should be considered in deciding whether to apply an ACR from one species in a broad taxonomic group to anotherPage 41 of 126

organism within that broad group. For example, it would be inappropriate to apply an ACR for a chitin synthesis inhibitor universally ac ross all aquatic invertebrates. If the mode of toxicity in the surrogate organism being ev aluated is not known, it should be discussed as uncertainty in the risk assessment.

4.4.2 OW Approach to Derive ACRs

In deriving National Ambient Water Quality Criteria (NAWQC), OW aims to calculate both an acute value (CMC), and a chronic value (CCC). The Final Chronic Value (FCV) may be considered as one of the values used to approximate the CCC. As noted in the 1985 Guidelines, the CCC is based on aquatic animal chronic exposure data ( i.e. , the FCV) or calculated based on

acute-chronic ratios derived as described herein. OW uses ACRs when insufficient empirical data are available to calculate a FCV containing 8 taxa as defined by the 1985 Guidelines. When chronic data sets do not meet MDRs, a Final Acute-C hronic Ratio (FACR) is derived, when possible, based on the geometric mean of at least t hree ACRs including (1) at least one fish; (2) at least one invertebrate and (c) at least one acut ely sensitive freshwater species (the other two may be saltwater). The chronic value used in the A CR calculation is obtained by calculating the MATC as the geometric mean of the NOEC and LOEC. Th e FACR then serves as the denominator in deriving the FCV (or CCC) as follows :

FAV ÷ FACR = FCV (Eq. 8)

where:

FAV = Final Acute Value

FACR = Final Acute Chronic Ratio

FCV = Final Chronic Value

CCC = lowest of FCV animal , FCV residue

According to the 1985 Guidelines, OW considers the following four different methods for deriving the FACR, based on trends in variance in t he Species Mean Acute Value (SMAV):

1. If the species mean ACR increases or decreases as t he SMAV increases, the FACR should be calculated as the geometric mean of the A CRs for only those species who’s

SMAVs are close to the FAV.

2. If no major trend is apparent and the ACRs for a nu mber of species are within a factor of 10, the FACR should be calculated as the geometric mean of all the species mean ACRs available for both freshwater and saltwater species

. 3. If the most appropriate species mean ACR values are less than 2, it is assumed that acclimation has probably occurred during the chroni c test. Because chronic toxicity

values derived for animals which have acclimated to chronic test conditions are not likely to provide adequate protection to aquatic animals u nder field conditions, the FCV is equal to the CMC (where the CMC equals the Final Acute Va lue [FAV] ÷ 2).Page 42 of 126

The OW criteria development process uses ACRs as in the following example ( Table 5 ). Paired acute and chronic test data are available for 3 spe cies: two tests for the Cladoceran ( Daphnia

magna ), one for rainbow trout ( Onchorhynchus mykiss ), and a third for a marine mysid ( Americamysis bahia ). The geometric mean of the two available daphnid chronic values is used as the daphnid species specific ACR. Chronic values are the MATC or geometric mean of NOEC

and LOEC.

Table 5. Acute and chronic values and ACRs for fres hwater contaminant. Species Acute Value ( μ g/L) Chronic Value ( μ g/L) ACR
Cladoceran, ( Daphnia magna ) 84.8 157.9 2 Cladoceran, ( Daphnia magna ) 190 30.59 6.211 Mysid, ( Americamysis bahia ) 43 5.112 8.412 Rainbow trout, ( Oncorhynchus mykiss ) 121 7.861 15.392
The first D. magna ACR value is 0.537, therefore it is brought up to 2 for reasons explained above.

The specific calculations involved are as follows: Final Acute Value = 33.02μg/L ± Criterion Maximum Concentration = 33.02/2 = 16.5 μg /L

Final Acute-Chronic Ratio = 6.33 Final Chronic Value = (55.71 μg/L)/6.33 = 8.80 μg/L

± Calculated based on 1985 Guidelines, data not prese nted.

When considering if data are available to derive an FACR for use in calculating the FCV or CCC, OW considers the following factors:

• Validity of the chronic data : Chronic test data used in deriving the ACR must be from acceptable studies as specified in the 1985 Guideli nes.

• Preference of paired acute and chronic data : OW gives preference to studies where the acute test was conducted as part of the same study as the chronic test. If acute tests were not conducted as part of the same study, acute test s conducted in the same laboratory and dilution water, but in a different study may be use d. If no such acute tests are available, results from different laboratories may be used pro vided that the test conditions are

similar.

• Exposure regime of chronic data : Chronic data must be from flow-through tests (or static renewal for daphnids) and the duration of the test must be appropriate to the species.

• Control performance : Control tests must have acceptable survival, gro wth, and reproduction.

• Dilution water quality : Results of chronic tests conducted in unusual di lution water ( i.e

., dissolved organic carbon [DOC] > 5 mg/L, etc .) should not be used unless data show that DOC, etc . do not affect toxicity. In addition, water quali ty characteristics that have been shown to be related to toxicity (e.g., hardness, pH

, etc .) should be accounted for.Page 43 of 126

• Endpoints and exposure duration : Chronic values should be based on endpoints and lengths of exposure appropriate to the species as s pecified in the 1985 Guidelines.

• Preference for data from same species : Acute and chronic data must be from the same species to generate an ACR. In addition, paired ac ute and chronic toxicity tests are preferred.

4.4.3 Similarities and Differences in OPP and OW Ap proaches to Derive
ACRs

There are a number of similarities and differences regarding the approaches that OPP and OW use to calculate ACRs for use in pesticide ecologic al risk assessments and NAWQC derivation, respectively. Similarities in approaches and the t ypes of data that are considered in deriving ACRs within OPP and OW include the following:

• Use of all available reliable, scientifically valid , aquatic toxicity data including data from the public literature;

• Preference for acute and chronic data from same spe cies;

• Use of same assessment endpoints (survival, growth, and reproduction);

• Consideration of control performance;

• Consideration of dilution water quality and potenti al impacts on toxicity; and

• Consideration of chronic data based on similar expo sure duration and type of exposure.

A summary of the major differences in OPP and OW AC R derivation is provided in Table 6.

Table 6. Differences in OPP and OW ACR derivation m ethodology.
OPP OW
ACRs developed when chronic data are not available for most acutely sensitive species or if data gaps exist ACRs developed when insufficient data are available to calculate a FCV meeting MDRs ACRs are taxa-specific 1 and based on most acutely sensitive species; one ACR is derived for each taxo n, depending on the availability of data One FACR derived based on the geometric mean of 3 ACRs; 3 separate ACRs are required for FACR

derivation Trends in acute values are not considered; ACR appl ied to the most acutely sensitive tested ACRs are compared to SMAVs to review trends among the parameters No provision for special consideration of chronic d ata on commercially/recreationally important species Defaults to commercially/recreationally important species chronic data if less than the calculated FC V ACR is based on ratio of acute value (LC/EC 50 ) to the

chronic NOEC value 2 ACR is based on ratio of acute LC/EC 50 to the chronic MATC value or regression-derived EC 20 value

1 OPP may derive separate taxa-specific ACRs for fres hwater and estuarine/marine fish and invertebrates, depending

on the available data.

2 OPP acute value ≠ OW FAV.

4.4.4 Deriving ACRs Using Predicted and Empirical D ata

In cases where chronic toxicity data are available for a species, but no acute toxicity data are available, it may be possible to use methods descri bed in this white paper to predict acutePage 44 of 126

toxicity data for that species ( e.g., Web-ICE, read-across). The predicted acute and em pirical chronic toxicity data may then be used to derive a chemical-specific ACR.

In other cases, acute LC 50 data may be available for species for which no chr onic toxicity data are available, requiring that a chronic toxicity te st result be predicted. Fewer QSAR methods are available for predicting chronic toxicity than exist for acute toxicity and most of what is available is focused on non-polar organic chemicals and narcosis. Predicting chronic values might be obtained using read-across and time-concen tration effect (TCE, Section 4.5) models.

Another scenario would be where empirical data for both the acute and chronic values are not available and (Q)SAR models for both acute and chro nic toxicity are used to predict the ACR. Once again, with limited QSAR models for chronic to xicity, read-across methods based on chemicals with the same adverse outcome pathway may have the most potential for predicting the chronic toxicity of pesticides.

4.4.5 Default ACRs

As part of analyses underlying the Great Lakes Wate r Quality Initiative (Host et al . 1995), distributions of ACR values from existing AWQC were evaluated to derive a default ACR value of 18. This value, representing the 80 th percentile of the available data, was derived inde pendent

of the chemical or AOP. TenBrook et al. (2010) took a similar approach, but limited the an alysis to data for pesticides, and calculated an 80 th percentile ACR of 12.4.

This lack of reliable acute and chronic data has ma de it difficult to calculate chemical class or AOP specific ACRs with a high level of confidence, although some research has been done in this area. Call and coworkers, comparing acute LC5 0 values to the MATC from an early life stage test (~32d)) using fathead minnows, found the average ACR (range of ACRs in parenthesis) of 4.7 (1.7-8.3) for uncouplers of oxi dative phosphorylation, 10.4 (4.5-27.9) for organophosphates, 7.3 (4.6-9.1) for carbamates (Cal l et al., 1989), and 9.8 (3.3-24.0) for nonpolar narcotic chemicals (Call et al., 1985).

Based on theoretical considerations, one would expe ct that the magnitude of an ACR for a particular species, or closely related species, wou ld show greater similarity within rather than between adverse outcome pathways. The extent to wh ich this has been demonstrated through analysis of actual data is mixed. Raimondo et al. (2007c) compiled a large database of ACR

values and evaluated their distribution relative to AOP ( Table 7 ); this analysis did not clearly indicate that classifying chemicals by AOP did much to parse the overall variability reported in the literature. However, other evaluations with mo re narrowly defined data sets (by species and/or chemical group) have suggested lower degrees of variability in ACRs, at least for some adverse outcome pathways ( e.g ., Call et al . 1989; Di Toro et al . 2000; Roex et al . 2000).

Table 7. MOA-specific ACRs reported by Raimondo et al. 2007.

MOA/class Median ACR 90 th percentile ACR

AChE inhibitors - carbamates 8.9 28.0
AChE inhibitors - organophosphates 6.2 77.8Page 45 of 126

Cell function/division 7.4 63.0
Narcosis (non-specific) 10.4 148.9

Ester narcosis 5.6 169.7
Nonpolar narcosis 9.9 148.9

Polar narcosis 14.1 188.5
Neurotoxicity (non-specific) 5.6 109.1
Neurotoxicity – cyclodiene-type 7.8 109.1
Neurotoxicity – DDT-type 3.6 5.1
Neurotoxicity – Pyrethroid 4.7 25.1

Metals 9.1 88.0
Reactivity (non-specific) 7.9 33.7
Reactivity – alkylation/arylation based 17.2 150.8
Reactivity – dinitroaromatic group 14.0 76.1
Respiratory blocker 8.3 68.3
Uncoupler of oxidative phosphorylation 6.2 30.4

4.4.6 Fixed ACRs

Certain regulatory programs, such as the European U nion (EU) and the Office of Chemical Safety and Pollution Prevention (OCSPP), use fixed ACRs of 10 for fish and 4 for green algae to provide protection from chronic toxicity to species for which there are no chronic data. However, there are concerns regarding the use of fi xed ACRs for the extrapolation of acute to chronic toxicity because of large variation associa ted with ACR differences in species, chemical class, MOA, and test conditions (Kenaga, 1982; Ahle rs et al ., 2006; Raimondo et al., 2007c).

Based on the work of Ahlers et al . (2006), ACRs of up to 100 are not protective for all chemicals and trophic levels.

Use of fixed ACRs assumes that the relationship bet ween acute and chronic toxicity is independent of the test species and the test compou nd including differing mechanisms of biological action from species to species. The rat io of acute and chronic effect levels for different species with different life histories is assumed to be the same. These assumptions may result in under- or over-estimation of toxicity in the field when using fixed ACRs to predict chronic toxicity of pesticides.



### 4.5 Time-Concentration Effect (TCE) Models

4.5.1 Background

TCE models use time-course to mortality data from a cute toxicity tests to extrapolate to a prediction of chronic lethality for a chemical to a particular species. TCEs are derived from generic time-to-event models which employ nonparame tric, semi-parametric, and parametric approaches and are not restricted to toxicity-speci fic mechanisms or applications. Rather, timeto-event models simply assume that an event (e.g. d eath, birth, disease onset) occurs at some point in time (Newman and Crane 2002), and some mod els (e.g., accelerated life testing [ALT])Page 46 of 126

have theory originally routed in industrial reliabi lity studies (Sun et al. 1995). Since conventional toxicity metrics such as LC/EC 50 and NOECs focus on exposure intensity during a con stant duration of exposure and time course distinguishes acute and chronic toxicity, TCE models have the potential to provide an understanding of toxic effects over time (Sun et al. 1995; Newman and Crane, 2002).

A common assumption of generic TCE models is that a chronic endpoint can be predicted by the time course of that endpoint in acute tests. While the endpoint measured in acute toxicity tests is typically mortality, except where mobility or shell growth are the endpoints of effect concentrations (e.g., EC 50 ), the endpoint of chronic tests, as determined by the LOEC or ECx, may be mortality, reproduction, or growth. Validati on of Accelerated Life Testing (ALT) and Linear Regression Analysis (LRA) has shown relative ly good accuracy of models to predict chronic mortality (Mayer et al., 1994; Sun et al., 1995; Barron et al., 2008); however, prediction of non-lethal chronic endpoints is less accurate us ing these models (Barron et al. 2008). Mayer et al. (1986) recommends applying additional safety fa ctors to lethality-based NOEC to include other biologically significant effects.

An additional assumption of TCEs is that time-cours e to mortality is independent of chemical MOA. Since many TCEs have non-chemical origins (e.g ., ALT; Ellersieck et al., 2003), a toxicological mechanism is lacking. A change in MOA between acute toxicity and chronic toxicity could result in poor accuracy of chronic t oxicity prediction due to a shift in the doseresponse curve (Barron et al., 2008).

TCE models require the input of time-course acute d ata such as the number of live organisms during each time step of an acute test ( e.g., 24-h, 48-h, 72-h and 96-hr). Barron et al., (2008) demonstrated that ALT and LRA had the highest accur acy when the majority of mortality occurred early in the acute test. This same study f ound that the inclusion of additional time steps (e.g., 12-hr, 36-hr) did not improve the accuracy o f chronic toxicity prediction.

The TCE models that have received the most attentio n and application in predicting chronic toxicity from acute time-course data are the ALT (S un et al., 1995; Mayer et al., 2002), linear regression analysis (LRA; Mayer et al., 1986; Mayer et al. 1994; Mayer et al. 2002), and the Multi-factor Probit analysis (MPA; Lee et al., 1995; Mayer et al., 2002). The ALT model employs a survival analysis assuming a Weibulll dis tribution and accelerated life testing theory. This method was originally used to predict “time to failure” for mechanical and electrical devices placed under short-term stress (Ellersieck et al., 2003). The ALT model should be used when there are at least 3 “partial responses” (mortality between 0 and 100% at a given time step) in the acute toxicity test. The LRA model is a two-step li near regression analysis that combines regressions that predict the low lethal concentrati on at each observation time period and regresses those concentrations against the reciproc al of time. The intercept of this second regression is the chronic NOEC. The LRA model may b e used when there are no partial responses during the acute toxicity test. The MPA m odels are multiple regressions that describe the relationship among exposure concentration, time and probit percent mortality. The model was developed to predict chronic toxicity when acut e tests contain varying conditions, such as varying exposure scenarios of effluent tests. The MPA model should be used when there are at least 5 partial responses during the acute test.Page 47 of 126



## 5 Examples of Predictive Methods

This paper is not intended to provide an exhaustive overview of computational tools available via government, open access, or commercial sources, but rather an overview of the types of predictive methods that are available to scientists in OW and OPP, focusing on those that are frequently used in environmental risk assessments, and those predictive methods that will directly assist in derivation of aquatic life crite ria. Appendix C provides information on these methods for predicting toxicity using Expert System s, Similarity Tools, ICE models, and TCE models. (Q)SAR models also exist for other endpoin ts such as bioaccumulation (Arnot and Gobas, 2004), and physical chemical properties (Eur opean Commission (EC) 1995a,b; Deardon and Worth, 2007). The physical chemical property d ata will typically be a required input variable for (Q)SAR models. For most of the comput ational expert systems described below, the data are populated when the structure is identified , but Appendix D provides a list of physical chemical property (Q)SAR tools that may be useful w hen data are not available. (Q)SAR predictive methods used for screening, prioritizati on and ranking of large chemical inventories

will not be covered but are available to risk asses sors (See Schmieder et al ., 2004, Walker et al

., 2004; Brown and Wania 2008, Jensen et al ., 2008; Pavan and Worth 2008; Daginnus et al ., 2009 for examples). Several reviews have been written o n the types of predictive methods available (see EC, 1995a,b; Pavan et al ., 2005a,b; Jensen et al. , 2008); however, it should be kept in mind that the inventory of available predictive methods is constantly changing with emerging research

in the area



## 6 Interpretation of (Q)SAR, Read-Across/Bridging, ICE , and

TCE Predictions

6.1 (Q)SARs

(Q)SAR approaches are based on assumptions that the biological activity of a chemical is related to the chemical’s structure, and therefore have mor e uncertainty surrounding the predicted value than empirical toxicity tests which follow acceptab le test methodologies. Selection of (Q)SAR predictive methods is dependent upon the assessment context ( e.g ., limited documentation for screening/data identification, more complex documen tation for effects/risk assessment decisions). Recognizing that these (Q)SAR approach es could assist in filling data gap needs within risk assessments, the OECD developed validat ion principles for (Q)SARs to help identify scientifically valid models that are reliable and a lso acceptable for use in the risk assessment process. Five validation principles were identifie d that addressed both statistical and nonstatistical aspects of (Q)SAR models (OECD, 2004).

1. The (Q)SAR model must be associated with a well def ined endpoint : The purpose of this criterion is to ensure clarity in the predicted end point . The model should clearly define the experimental protocols and conditions employed in the development of the model database. For instance, the USEPA’s fathead minnow databasePage 48 of 126

( http://www.epa.gov/med/Prods_Pubs/fathead_minnow.ht m ) was a data source for many (Q)SAR models (Veith et al ., 1983; 1989; Russom et al ., 1988; Karabunarliev et al

., 1996a,b.) The database was developed using an ASTM protocol for acute fish toxicity, endpoints were based on the measured chemical conce ntrations in water, all fish were from the same culture unit, and the dilution water was from the same source, etc

. (Russom et al ., 1997.) Therefore variance associated with chang es in experimental procedures / conditions was controlled. It is also important to consider the relevance of the predicted endpoint as it relates to its use in answering a particular hazard assessment question. For instance, the prediction of a no obs ervable effect concentration (NOEC) may be relevant for some risk assessments, but may not be suitable where hypothesis testing does not provide a clear understanding of t he uncertainty or precision of the endpoint.

2. The model must be an unambiguous algorithm : The purpose of this criterion is to ensure transparency of the model. Under this rule, inform ation on the test set used in development of the model including the effects data points, and all parameters used as input variables must be provided. This information allows for independent assessment of the reproducibility and performance of the model. I t should be noted that unambiguous algorithms are not always possible, especially when models are based on CBI data, as in

the case of commercial models and the OPPT EcoSAR a pplication.

3. The model must have a defined domain of applicabili ty : The purpose of this criterion is to ensure the reliability of the model. All models are developed using a training set of chemicals and an associated list of input variables to predict the endpoint in question.

These variables have limits that define the chemica l domain that the model describes. For instance, the domain of applicability may be re lated to certain chemical analogs ( e.g. , model developed using a test set of organophosphate pesticides), and/or a range of physical/chemical parameters ( e.g. , chemical in test set had log P values ranging fro m 0 to 6.) Predictions outside these domains are quest ionable (not the intent of the model), and are therefore, not reliable for use in a risk a ssessment.

4. The model must have appropriate measures of goodnes s-of-fit, robustness and predictivity : The purpose of this criterion is to ensure intern al performance and predictivity of the model. The internal performanc e of a model is represented by goodness-of-fit, or how well the model predicted va lues agrees with observed values, and model robustness, which ensures the model is not un duly affected by outliers or small departures from model assumptions. Measures of goo dness of fit for regressions include the coefficient of determination ( i.e. , R-squared (R 2 ), and the sum of square. The predictive power of the model is determined by test ing data not used in the development of the model, but within the model’s domain of appl icability, thereby conducting an

external validation of the model.

5. The model should have a mechanistic interpretation : At times, the mechanism of action may not be fully understood, but if possible, model s with a mechanistic understanding

should be used. A critical consideration is whethe r the descriptors used in the models make mechanistic sense. Development of an AOP can assist in this assessment. ForPage 49 of 126

instance, log P is used in most QSAR models to pred ict toxicity for chemicals acting via a narcosis MOA, which makes mechanistic sense, beca use log P is used as a surrogate for the biological membrane which is the proposed molec ular site of action for narcotic chemicals (Ankley et al ., 2010).

Under REACH, models that meet the OECD validation p rinciples and are proposed for use in filling data gaps are currently being gathered. A searchable catalog of all models including background information required to validate the mod els, authors/source of model, related publications, predicted endpoints and related exper imental protocols, algorithms with training and validation sets, including all input variables for the models, can be found at the website: http://qsardb.jrc.ec.europa.eu/qmrf/index.jsp

.

When using these predictive methods, users should d ocument any interpretations, and /or restrictions related to use of the predicted value. The strengths and weakness of the data prediction method used should be clearly described by the user when such predictions are included in assessments. When any (Q)SAR model has been used to predict toxicity values, the output of the model should be included as an append ix to the assessment to enhance

transparency.



### 6.2 ICE Models

ICE toxicity predictions can be made from a suite o f models available for a diversity of surrogate species predicting to the species, genus, and famil y level. As such, multiple models are frequently available to predict toxicity to a desir ed species. When comparing and selecting ICE models, the following guidelines should be used (Ra imondo et al. 2010b):

1. Relatively low mean square error (MSE) (< approxima tely 0.22)

2. Large sample sizes (df > 3)

3. Close taxonomic relatedness (Genus > Family >>Class )

4. Use MOA-specific models where available

5. Narrow confidence intervals surrounding prediction

6. Input values occur within the data boundaries of th e model.

MSE error is the recommend measure of model robustn ess. ICE models are based on the acute toxicity available for two species, so available mo dels usually contain different sample sizes. Because MSE is derived using model degrees of freed om, it is an unbiased estimated of model error and variance. In regression models, such as I CE models, R 2 captures the percentage of variability in the data explained by the model and is not adjusted for sample size. Model sample

sizes should also be considered in model selection. Models developed with only three data points may have extremely low MSE, which may be an artefac t of limited chemical diversity and small domain of applicability.

While these criteria provide the user with guidance , for selecting and evaluating various ICE models, it should be noted that professional judgme nt is ultimately required to evaluate model predictions. For example, models with MSE error < 0 .22 were identified through uncertaintyPage 50 of 126

analyses as having an average accuracy of model pre dictions within 4 fold of the actual value. Models with MSE > 0.22 will not necessarily yield p oor predictions, however the average fold difference between predicted and measured values wa s 10 (stdev = 37, N = 2542). Additionally, models between closely related species were shown t o yield more accurate predictions than models for less related species. However, some exce ptions to this trend may apply depending on the availability and variance of the data available between species. In cases where multiple acceptable models exist, documentation of decision making/professional judgement should be provided/maintained.

Prediction confidence intervals should be used in c onjunction with MSE and taxonomic

relatedness. The size of the confidence interval is inversely relative to the robustness of the prediction. Large confidence intervals may be produ ced by models with large MSEs as well as other models where the input value (measured surrog ate toxicity) is outside the range of surrogate date used to parameterize the model.

ICE estimated confidence intervals represent the r ange in which the actual toxicity is likely to fall with a certain level of confidence (e.g. 95% c onfidence). Conservative predictions of toxicity may use the lower confidence limit as the predicted value of toxicity for untested species.



### 6.3 TCE Models

Chronic toxicity predictions obtained from TCEs are NOECs for survival corresponding to the species, chemical, and test conditions of the enter ed acute toxicity data. TCE models do not provide a prediction for non-lethal chronic toxicit y and may not represent standard chronic test NOECs, which are typically based on the most sensit ive endpoint measured (e.g. growth, reproduction). In a dataset comprised of 138 chroni c toxicity tests, survival represented the most sensitive endpoint measured in approximately 43% of all tests (Barron et al. 2008). Additional safety factors should be applied to chronic NOECs d erived from TCEs to be protective of nonlethal endpoints. A safety factor of 0.2 is reco mmended based on frequency analysis demonstrating NOECs for survival being 5 times or l ess than the NOECs for most other endpoints at least 95% of the time (Mayer et al. 19 86).



## 7 Considerations for Using Toxicity Predictions

7.1 WOE and Best Professional Judgment

Predictive methods are not intended to be used as t he sole indicator of a chemical’s toxicity but

rather are intended to be used in concert with othe r sources of information. As such, they represent only one element in a WOE approach. Outp ut from these predictive methods should be weighted (qualitative/quantitative) according to re liability, availability of specific data types ( e.g ., in vivo study results), and assessment context ( e.g ., identification of data requirements vs. hazard assessment decision). Under ideal condition s multiple predictions are available fromPage 51 of 126

multiple computational tools, although this may not always be possible. Users should also consider predictive performance when use (Q)SARs in a WOE approach.

Keeping in mind the OECD Validation Principles for use of QSARs, and the Bradford Hill criteria for identification of AOPs, users should r ecognize that these are prediction methods and they have associated limitations. With every model used, the rules, restrictions, and limitations associated with the model should be considered, and the following questions should be asked: (1) are there particular groups of chemicals identified as potential issues as they relate to the specific models, and (2) what are the bounds of the model an d is the prediction exceeding the limits of

the model?

When using read-across approaches, it is important that the data sets being used to interpolate/extrapolate the unknown value are struc turally similar. It is also important to remember that extrapolating out from a data set whe re the chemical to be estimated is very

‘distant’ from other chemicals identified as simila r may have unknown errors and should be

avoided. For any (Q)SAR predictive methods, the user should determine whether model outputs for selected parameters ( i.e. , fate, effects) are consistent with what is known for the chemical under evaluation. In some cases, some toxicity data on e ither fish and/or invertebrates may exist and the reviewer should evaluate whether the model outp uts are consistent with what has been measured. The ability of the (Q)SARs to reliably p redict the toxicity of other chemicals depends on the extent to which structurally similar chemica ls with similar MOAs are represented in the training set of data used to populate the model. A s such, (Q)SAR model results must be put into the context of any existing data and what is known about the chemical. Additionally, for each of the (Q)SAR models, the structural moieties and/or M OA on which the model predicts toxicity are provided by the model. The reviewer should be aware of this output and make an effort to

determine whether these moieties and/or MOA are con sistent with their understanding of the

chemical.

With any model, as with measured toxicity data, the re are uncertainties regarding the variability and relevancy of predicted values. (Q)SAR estimate s should only be considered when actual measured chemical-specific data are not available. The decision to use such estimates should be weighed against other sources of data that may be a vailable. When used, SAR model output should be properly identified and the uncertainties associated with the values must be discussed in the assessment. Ultimately, the use of SARs is dependent on the best professional judgment

of the user.

The use of (Q)SARs is dependent on multiple lines o f evidence and must be viewed in the context of the model’s domain of applicability; the se multiple lines of evidence must be integrated. Some QSAR models like ASTER warn users that certain chemical structures are not

in their domain. Users are cautioned to examine m odel outputs carefully to determine whether predictions are consistent with what may be known a bout the chemical however limited.Page 52 of 126



### 7.2 Selection of Descriptors in (Q)SAR models

There are a large number of descriptor variables th at can be used in the development of a QSAR to predict the toxicity of a substance. Many of th ese descriptors are calculated using first order principles, i.e. , calculated based on theory as it relates to chemi cal structure and not experimentation. In theory, these have less error because they would not have error related to experimentation; however, selection of these parame ters should be based on how they relate and help explain the mechanistic interpretation of the AOP. For instance, log P is a logical parameter to explain partitioning into biological membranes s ince it is by definition a measure of lipophilicity and membranes are by nature lipophili c. But it is less clear what parameters should be used in defining inhibition of an acetylcholines terase inhibitor. Due to limited data, many QSAR models combine data from various mechanisms an d use a global QSAR approach (Netzeva et al ., 2007) which incorporates parameters associated w ith hydrophobicity, electronic, and possibly steric information regarding each comp ound in the test set ( i.e. , Hansch approach.) Ensuring an understanding of what each parameter in an equation explains as it relates to toxicity becomes more complicated. In addition, some global QSARs include a disproportionately large number of descriptor variables in comparison to the number of chemicals used in developing the model, and users should be wary of overfitting the data For instance, even though small standard errors are related to greater predictivity of a model, an overfitted model could result where the standard error is actually smaller than t he experimental error associated with the biological test data, which is not recommended (Wo ld et al., 1984). To this end, techniques such as genetic algorithms, Principle Component Analysis (PCA) or Factor Analysis (FA) can be used to identify appropriate descriptor variables (OECD, 2007d).

Many (Q)SAR models do not address the three-dimensi onal nature of the chemical which may be critical in its interactions with biological mat rices. Log P has been used historically to describe the partitioning of chemicals into biologi cal membranes, and works well in QSARs developed for baseline narcosis (see Table 8 ). However, regressions using log P for mechanisms such as polar narcosis, uncouplers of oxidative pho sphorylation, etc. show lower goodness-of-fit measures, presumably because steric and electronic effects are not addressed by log P. Table 8 provides QSAR models developed for non-polar and po lar narcotic MOAs for three different species. A slope approaching unity (1) would mean that the descriptor variable, in this case log P, adequately describes the toxicity that is being modeled. The models for guppy, fathead minnow, and the ciliate, Tetrahymena , all have slopes approaching unity ranging from 0. 871- 0.929 for the non-polar narcotic MOA implying that log P is an adequate descriptor of toxicity. It is a different case for the chemicals acting via a polar narcosis MOA, where the slopes range from 0.46-0.65, implying that log P is NOT describi ng all aspects of the toxicity. This makes sense when you consider that log P captures only th e partitioning aspect and not the hydrophilic or polar nature of the chemical in question.

Table 8. Examples of baseline narcosis and polar na rcosis models using only log P as a means of predic ting toxicity.Page 53 of 126

Organism Endpoint Slope Intercept R 2 N Source

Guppy Baseline

narcosis LC 50 0.871 1.13 0.976 50 Könemann 1981

Guppy Polar narcosis LC 50 0.46 3.04 0.824 11 Könemann and Musch 1981

Fathead minnow Baseline

narcosis LC 50 0.94 1.25 0.94 60 Russom et al ., 1997

Fathead minnow Polar narcosis LC 50 0.65 2.29 0.90 39 Veith and Broderius, 1987

Ciliate Baseline

narcosis IGC 50 0.929 2.639 0.986 20 Schultz 1996

Ciliate Polar narcosis IGC 50 0.574 0.8652 0.756 30 Schultz 1987

It is important to remember that, as with any QSAR model, the prediction of log P has error associated with it, and the reliability, predictivi ty, and appropriateness of the model is related to the knowledgebase behind the predictive method. Fo r instance, Benfenati and coworkers, predicted the log P for a series of pesticides usin g four log P models, and found that performance varied depending on the chemical class (Benfanati et al ., 2003.) In Figure 10, the property screen from ASTER (http://cfint.rtpnc.epa.gov/aster / ) for the rodenticide Scilliroside is provided. Note that the two predictions of the oct anol/water partition coefficient; the USEPA Episuite’s KowWin model (-0.98) and BioByte’s CLOG P (1.36) are more than 2 log units different, which could greatly impact toxicity pred ictions using QSARs.Page 54 of 126

Figure 10. Log P predictions for Scilliroside usin g KowWin and Clog P predictive methods.

Another reason that log P calculation methods may b e a poor representation of the actual physical/chemistry properties of a substance is tha t the calculation is based on fragments of the chemical, typically added up with correction factor s applied, thereby losing any information about intra-molecular interactions (Benfenatti et al ., 2003.) For these reasons, researchers have explored other models for predicting how a chemical partitions into tissues. One such method is the use of empirical measures in liposomes vs. meas urements of octanol/water partitioning. Liposomes, which are an artificial phospholipid bil ayer, are better mimics of a biological membrane, having regions with hydrophobic, hydrophi lic, charged or neutral properties (Smejtek and Wang, 1993; Escher and Schwarzenbach, 1996).

Although the partitioning coefficients developed wi th liposomes are better at describing activity in biological membranes, there is not a large datab ase of measured values for use in model development, such as the database that exists for o ctanol/water partition coefficient measurements. Additionally, models are not as read ily available for predicting the membrane / water partition coefficient; however, this is chang ing with the advancement in QSPR (quantitative structure-property relationship) pred iction methods using linear solvation free energy relationships (LSERs). LSER approaches to p redicting lipophilicity have been around since the early 1980s (Abraham et al ., 1983; Abraham et al ., 1985; Abraham 1993), and differ from fragment contribution methods such as log P in that they use quantum chemical calculations to predict solute/solvent interactions by accounting for the dipolarity / polarizabilityPage 55 of 126

of the chemical, electrostatic interactions, hydrog en bonding interactions, and steric measures such as molar volume to predict lipophilicity. Pre viously, these models were not readily available due to computational requirements, but re cent advances have allowed for availability of these calculations via personal computers. Once ag ain, these models must be taken in the context of the associated knowledgebase. Tetko and coworkers found issues related to predictivity when comparing 96,000 substances from Pfizer and Nycomed company in-house data sets of measured lipophilicity, with 30 predic tion methods including those utilizing LSER approaches (Mannhold et al ., 2009; Tetko et al. , 2010). These models performed well on publicly available measures of lipophilicity, but n ot as well on structures not previously made available (i.e., not part of the models domain of a pplicability).



### 7.3 Empirical Test Data

In keeping with the first and fourth OECD validatio n principles, some QSAR models use training sets of test data generated by single rese arch laboratories specifically developed for use in model development. These single laboratory data sets may reduce experimental variability due to genetic variability of test species, animal husbandry, variation in testing protocols, etc. Examples of species having these types of data sets in ecotoxicology, include the guppy (Könemann, 1981), the fathead minnow (Russom et al. , 1997), the ciliate, Tetrahymena (Schultz, 1996), and the bacterium, Vibrio fischeri (Kaiser and Palabrica 1991). Some QSAR models may be based on training sets gleaned from the open lit erature. These models may have more variability due to inconsistent test methodologies and data analysis. Therefore, users of QSAR models should be knowledgeable of the training set used to develop the model. Another limitation in the development of QSAR models for a specific test species, are logistical constraints related to aquaculture, husbandry and t est methodologies.

Pesticides are complicated by the fact that they ex ist as formulated products which may include ‘inert’ ingredients that may not be completely iden tified, but may affect the ultimate toxicity of the substance. Therefore, when selecting test data for use in (Q)SAR approaches, such as readacross, ICE, etc. , it is important to consider only test results bas ed on the active ingredient.

It is presumed that for the OW/OPP common assessmen t efforts, most data gathering will be on the parent compound, but some active ingredients be come more toxic upon degradation. Having a clear understanding of degradate exposure and tox icity is necessary, and predictive methods such as the Metabolism Simulator under development by USEPA (see Jones et al ., 2009) may be useful in identifying degradates of interest. It s hould be kept in mind though that a degradate may not have the same MOA or even fall within the s ame chemical category as the parent compound. As such, toxicity predictions for the de gradate may be based on a different working QSAR model than the parent compound. It is also r ecommended that when deciding which (Q)SAR model to use, consideration be given to the extent to which various models yield predictions that best approximate measured values.Page 56 of 126



### 7.4 Selecting Chemical Category and/or AOP

One of the largest sources of uncertainty regarding the use of QSARs is that the models are typically developed for relatively generic structur es and AOPs; however, conventional pesticides have been engineered to be toxic, oftentimes to spe cific taxa, through relatively specific MOAs. Thus, a chemical may have a number of substructures (moieties) that have associated toxicities based on what may be dissimilar MOAs, while the ent ire chemical structure may impart a specific AOP and toxicity. For example, the insect icide carbaryl, is classified in EcoSAR as a neutral organic and it predicts the toxicity of car baryl based on an ester moiety contained within the compound. However, the overall MOA of this N-m ethyl carbamate is to inhibit acetylcholine esterase. The extent to which the com ponent toxicities contribute to the overall activity of the compound is an important considerat ion and can be a challenge to decipher. As compounds are transformed/degraded to smaller compo nents, the importance/relevance of substructure-activity relationships increases as th e overall activity of the parent compound may no longer dominate. Consideration should be given to the extent to which the compound retains structural similarities to the parent and/or chemic als with known toxicities and are accurately predicted by the QSAR model.

As outlined in the IPCS framework document (Boobis et al ., 2008), all aspects of the experimental design should be considered when deter mining the AOP, since differences in the exposure duration (acute vs. chronic), life-stage, and concentrations could result in very different adverse outcomes in the whole organism. Most of th e methods for determining MOA are based on acute toxicity data, and may not be reflective o f chronic exposure.



### 7.5 ICE Models

While the taxonomic relatedness of surrogate and pr edicted species within ICE models is an important consideration for model selection, evalua tion of ICE models should be a holistic

evaluation of all available models and their attrib utes. Models are available to predict to a species, genus, and family, and models predicting t o all three taxonomic levels may be available for a species of interest. Additionally, available measured toxicity values for multiple surrogate species may also increase the number of models from which to choose. Model uncertainty analyses have identified guidelines that may be use d in model selection. However, the recommended rules of thumb are intended as guidance and should not be interpreted as requirements. For example, while selecting a model with the most taxonomically related species is recommended, model mean square error and predict ed toxicity confidence intervals may indicate otherwise. Additionally, MOA-specific mode ls may provide more accurate predictions for two species with greater taxonomic distances. A dditionally, it is important that the input value of the surrogate species is within or close t o the range of toxicity values used to develop

the model.Page 57 of 126



## 8 Example Applications and Proposed Analyses of Predi ctive

Methods

In order to begin assessing the feasibility of usin g the predictive methods described in this white paper, an effort was undertaken to work through the mechanics of some of the tools and the logic displayed in Figure 11 (from “ Exploration of Methods for Characterizing Effects o f Chemical Stressors to Aquatic Animals ”), and to perform a preliminary comparison of acut e and chronic ALSVs with actual FAVs from published criteria docu ments. These brief comparisons do not constitute a critical evaluation of any one method, but are used to illustrate the proposed analyses

that will be done to evaluate the various tools.

Compile empirical toxicity data

Determine

adverse

outcome pathway Can empirical toxicity data be supplemented with predicted data? If yes, use appropriate tools to obtain predicted toxicity

data

If no, acute toxicity database contains only empirical data. Acute toxicity database contains empirical and predicted data.

Use extrapolation factor or sensitivity

distribution to derive ALSV. (1)

(2) (3)

(5) (4)

(6)

(7)

Figure 11. A Conceptual Framework for Deriving the Acute ALSV.



### 8.1 Empirical Data and Important Values from Existing A LWQC Documents (Box 1 and 2 of Figure 11)

For these examples, pesticides were sought that met the following criteria: 1) have an established ALWQC, 2) the ALWQC had enough toxicity data to meet minimum data requirements as described in the 1985 Guidelines, 3 ) important values such as the FAV and the FCV were calculated by formulae consistent with the 1985 Guidelines, and 4) represent different toxicological MOAs. Four pesticides were identifi ed that met these criteria: Acrolein, Diazinon, Chlorpyrifos and Tributyltin (TBT). The acute MOAs for these chemicals were listed in Web-ICE as reactive (Acrolein), anticholinestera se –OP (Diazinon and Chlorpyrifos) and uncoupler/inhibitor of oxidative phosphorylation (T BT). Important values ( Table 8 ) for these chemicals for the example applications were obtaine d from the criteria documents.Page 58 of 126

Table 8. Important values found in ALWQC documents for Acrolein, Diazinon, Chlorpyrifos and TBT

Chemical FAV FCV CMC CCC FACR
(ug/L) (ug/L) (ug/L) (ug/L)

Acrolein (2009) 5.92 3 3 3 1.906 = 2.0 1

Diazinon (2005) 0.3397 0.1699 0.1699 0.1699 1.328 = 2.0 1

Chlorpyrifos (1986) 0.0485 3 0.012 4 0.024 4 0.012 4 4.046

TBT (2003) 0.9177 0.0723 0.4589 0.0074 2 12.69
1 - Per 1985 guidelines, if geomean of ACRs are< 2, FACR = 2

2 - CCC is less than FCV to account for concerns re garding imposex in female snails.

3 - FAV recalculated to reflect the addition of sen sitive cladoceran, Critera FAV =0.1669 ug/L
4 - Values are not from criteria document, see text for further description.

When a new pesticide is registered, a minimum of 3 acute values (a cold water fish, a warm water fish and an invertebrate) and 2 chronic value s (either a cold water or warm water fish and an invertebrate) are used to develop benchmarks for assessing potential aquatic risks (US EPA 2007). The three acute values (48/96 hr EC/LC50) a re typically from toxicity tests using rainbow trout ( O. mykiss ), bluegill ( L. macrochiris ) and a cladoceron species (e.g., D. magna ). The two chronic values are typically represented by toxicity tests using fathead minnow ( P. promelas , early life stage or full life-cycle) and a cladoc eron species (eg. 21 day life-cycle test, D. magna ). Therefore, criteria documents were used as a so urce of toxicity data that could populate this minimum data set for each of the four pesticides ( Table 9 ). There is one important exception. The 1986 criteria document for chlorpyr ifos did not have acute or chronic values for a cladoceran species. Therefore, a data search was completed in ECOTOX which yielded two acceptable acute tests (Harmon et al. 2003 & El-Mer hibi et al. 2003) and two acceptable chronic tests (Rose et al. 2001 and Rose et al. 2002). Mea n acute and chronic values (the geometric mean (geomean) of the NOEC and LOECs ) were calcula ted from the studies for each species.

C.dubia was the most sensitive species tested for chlorpyr ifos and would have been ranked first in species sensitivity within the criteria document . Therefore, for the purposes of this analysis only, a new chlorpyrifos FAV was calculated, along with the commensurate CMC and CCC ( Table 8 ).

These data ( Table 9 ) were subsequently used as a baseline of minimum d ata available to develop ALSVs using the various predictive methods, assumin g no other toxicity data were available. Resultant ALSVs were then compared to criteria FAVs ( Table 8. Important values found in ALWQC documents for Acrolein, Diazinon, Chlorpyrifo s and TBT

12-14 ).

Table 9. Species mean acute and chronic values foun d in criteria documents.

Chemical Acute Values (ug/L) Chronic Values (ug/L) Oncorhynchus mykiss Lepomis
macrochiris Cladoceran

spp. Pimephales

promelas Cladoceran

spp.

Acrolein (2009) 16 27.19 <39.76 1 11.4 2 23.83 1,2

Diazinon (2005) 425.8 459.6 0.3773 3, 4 40.73 5 0.3382 2,3Page 59 of 126

Chlorpyrifos (1986) 8.485 10 0.053 3,8 2.263 6 0.05 8

TBT (2003) 4.571 8.3 4.3 1 0.2598 6 0.1896 2,7

1 - Daphnia magna

2 - Life-cycle test

3 - Ceriodaphnia dubia

4 – Diazinon did have a D.magna A.V., but no corres ponding C.V., therefore C. dubia was used.

5 - Geomean of two ELS tests (Norberg King 1989, Ja rvinen and Tanner 1982)

6 – ELS test

7 - Geomean of two life-cycle tests ( Brook et al. 1986, ABC Labs, Inc. 1990.)

8 - SMAVs not available in 1986 criteria document. Values are from studies found in literature that w ould be acceptable for

inclusion in criteria development consistent with the 1985 Guidelines. AVs from Harmon et al. 2 003 & El-Merhibi et al. 2003

CVs are from Rose et al. 2001 and Rose et al. 2002.



### 8.2 Methods for Deriving Acute ALSVs

Following the conceptual framework from Figure 11

, Table 9 represents the actions described in

the Box 1 of Figure 11 ; “Compile empirical toxicity data”. Assuming no a dditional toxicity data are available and that additional data can be predi cted using tools described in this paper, the decision from Box 3 would be, “Yes”. Moving to Bo x 4 , the next step is to use the appropriate tools to then predict toxicity data. The rest of S ection 8.2 is divided into providing examples where 1) empirical data are supplemented with predi cted values from Web-ICE and the OECD Toolbox to populate a species sensitivity distribut ion of 8 taxa when possible (box 6); and 2) development of ALSVs (box 7). ALSVs were developed in two ways. First, predictive methods were used to populate MDRs as described in the curr ently accepted paradigm of the 1985

Guidelines and an ALSV was calculated in an identic al manner as an FAV. Second, the ALSV was calculated as the HC5 concentration using examp les of SSD approaches independent of the

1985 Guidelines.

8.2.1 Supplementing Empirical Data with Predicted V alues for Missing MDRs (Box 4 of Figure 11).

The FIFRA registration process for pesticides resul ts in a minimum of toxicity data for three of the eight acute FW MDRs required by the 1985 Guidel ines. The following sections provide examples of two methods used to populate the remain ing acute freshwater MDRs with predicted acute values and the resultant ALSVs are then compa red to FAVs derived from experimentally determined acute values. The MDRs, as outlined in the 1985 Guidelines, require that data be available for at least eight genera with a specifie d taxonomic diversity, in order to address a wide variety of the taxa constituting an aquatic animal community. For freshwater criteria, the MDRs

are:

MDR #1. a salmonid fish

MDR #2. a nonsalmonid fishPage 60 of 126

MDR #3. a species from a third chordate family

MDR #4. a planktonic crustacean

MDR #5. a benthic crustacean

MDR #6. an insect

MDR #7. a species from a family in a phylum other than Chor data or Arthropoda

MDR #8. a species from a family in another order of insect or in a fourth phylum

8.2.1.1 Web-ICE

Web-ICE models were strategically used to predict a cute values for additional species that would meet MDRs ( Table 10 ). Acute values were predicted for fathead minnow (MDR #3), an amphipod ( Gammarus fasciatus , MDR #5), a midge ( Chironomus plumosus , MDR #6), a stonefly ( Claassenia sabulosa ), MDR #8), and the eastern oyster ( Crassostrea virginica , MDR #7. It is recognized that the eastern oyster is a saltwater species, and use herein does not imply that acute values for saltwater species can be used to satisfy freshwater species requirements in Agency risk assessments or water quality criteria, However, given the assumption in this scenario that there are no acute toxicity data beyo nd rainbow trout (MDR #1), bluegill (MDR #2) and cladoceron species (MDR #4), there are no predi cted species available that meet the 7 th
MDR and correlate with any of those 3 surrogate spe cies. In choosing species that met MDRs, care was taken to choose the most appropriate surro gate species for each of these predictions. Although it may be intuitive to use an invertebrate surrogate for a prediction of an invertebrate acute, mathematical constraints in the model may re duce the robustness of the relationship. In this example, rainbow trout and bluegills were used as surrogates. Model predictions are most robust when there is a high number of data points ( i.e., toxicity tests) correlating the two species. When a low number of tests ( e.g., <5) are used in the ICE model, the range of exposu re becomes limited and, therefore, predictions may fall outsid e of the domain of the model. Also, with a low number of tests, the confidence intervals for each predicted acute value often become larger. Therefore, this exercise sought taxa combinations t hat met minimum data requirements and had the highest number of toxicity test results correla ting the two species. Model statistics varied among the species combinations ( Table 11 ). The number of tests correlating the taxa ranged from 7 (Bluegill → Stonefly) to 95 (Bluegill → Eastern oyster) while R 2 ranged from 0.311 (Rainbow trout → the midge) to 0.825 (Rainbow trout → fathead minnow). After predicted values were obtained, acute ALSVs were calculated f or each chemical using formulae from the 1985 Guidelines (n=8, the four most sensitive speci es are bolded in Table 10 ).Page 61 of 126Table 10. Meeting minimum data requirements of the1985 Guidelines using Web-ICE (acute values, 95% confidence limits are in parentheses).Chemical Species Mean Acute Values (ug/L)Predicted Acute Values (LC/EC50ug/L)Acute ALSVOncorhynchusmykiss1Lepomis macrochiris2Ceriodaphnia sp.4Pimephalespromelas3Gammarusfasciatus5Chironomusplumosus6Crassostrea virginica7,11Claasseniasabulosa8(ug/L)RBT9RBT9RBT9Bluegill10Bluegill10Acrolein (2009)1627.19 <39.7654.916.714.936.81.60.8886(33.7 - 89.3)(3.6 - 76.7)(2.6 - 84.5)(20 - 67.6)(0.6 - 4.5)Diazinon (2005) 425.8 459.60.37731011.6153.1185.3299.94.540.0368(736.7 -1389.2)(53.3 - 439.6)(61.2 - 561)(203.5 - 442.1)(1.0 - 21.2)Chlorpyrifos(1986)8.485100.05331.210.99.217.51.120.0113(18.3 - 53.2)(2.0 - 58.7)(1.4 - 62.2)(8.5 - 36.0)(0.4 - 3.1)TBT (2003)4.5718.34.3187.65.715.310.6566(10.1 - 32.2)(1.1 - 45.7)(0.7 - 46.6)(7.3 - 32.0)(0.4 - 3.0)Note: Bolded values were used to calculate ALSV with an n=81 - The family Salmonidae in the class Osteichthyes;2 - One other family (preferably a commercially orrecreationally important, warmwater species) in theclass Osteichthyes (e.g.,bluegill, channel catfish);3 - A third family in the phylum Chordata (e.g.,fish, amphibian);4 - A planktonic crustacean (e.g.,cladoceran, copepod);5 - A benthic crustacean (e.g.,ostracod, isopod, amphipod, crayfish);6 - An insect (e.g.,mayfly, dragonfly, damselfly, stonefly, caddisfly,mosquito, midge);7 - A family in a phylum other than Arthropoda or Chordata (e.g.,Rotifera, Annelida,Mollusca);8 - A family in any order of insect or any phylum not already represented.9 - Rainbow Trout (O. mykiss) was used as a surrogate for these species10 - Bluegill (L. macrochiris) was used as a surrogate for these species11-No predicted freshwater species were available that met MDR #7 using SMAVs from available freshwater surrogates (i.e..RBT, blugill and cladoceran)Page 62 of 126

Table 11. Summary of model statistics used to pred ict acute values. Surrogate species Predicted species N DF R 2

Rainbow trout Pimephales

promelas 81 79 0.825

Rainbow trout Gammarus fasciatus 36 34 0.311

Rainbow trout Chironomus

plumosus 20 18 0.531

Bluegill Crassostrea

virginica 95 93 0.549

Bluegill Claassenia sabulosa 7 5 0.585

The performance of Web-ICE in accurately predicting acute values for use in ALSV derivation varied by chemical ( Table 12 ). For TBT, the models performed reasonably well i n that the

Web-ICE derived ALSV was 1.4 fold less than the cri teria FAV, thus achieving the goal of having the ALSV being a somewhat conservative estim ate of the FAV. For acrolein, diazinon and chlorpyrifos, the Web-ICE ALSVs were 6.6, 9.2 a nd 4.3 fold respectively lower than the criteria FAV ( Table 12 ). The Web-ICE ALSV for diazinon is particularly t roublesome in that it is 9 fold lower the most sensitive species in the r estricted data set and even if one considers all the data in the criteria document, this observation remains the same.

Table 12. Web-ICE ALSV estimates compared to FAV in criteria documents.

Criteria Web-ICE

Chemical FAV ALSV (ug/L) (ug/L)

Acrolein (2009) 5.92 0.8886

X Fold Difference -6.6

Diazinon (2005) 0.3397 0.0368

X Fold Difference -9.2

Chlorpyrifos (1986) 0.0485 0.0113

X Fold Difference -4.3

TBT (2003) 0.9177 0.6566

X Fold Difference -1.4

Proposed analyses will determine when Web-ICE is a useful tool and when it is not. Types of analyses proposed will : 1) comparing individual sp ecies predictions to experimentally determined values; 2) comparing the shapes of predi cted species sensitivity distributions to experimentally determined distributions; 3) explore species sensitivity distributions developed using different predicted taxa meeting the 1985 Gui delines MDRs; 4) consideration of biases in surrogate species that may, in-turn, bias the predi cted values; 5) compare the accuracy of ICE predictions among types of MOA; 6) determine if the re are characteristics of surrogate data sets that limit, or enhance, the predictability of regre ssion models used in ICE.

8.2.1.2 Read-AcrossPage 63 of 126

As another example of a means to populate MDRs with predicted acute values, the OECD (Q)SAR application toolbox (V. 1.1.02, Nov. 2009) w as used to fill data gaps using read-across techniques for single chemicals. The toolbox softw are, as well as guidance documents, can be downloaded free of charge from the following URL: http://www.oecd.org/document/54/0,3343,en_2649_3437 9_42923638_1_1_1_1,00.html#Downl oad_qsar_application_toolbox

.

Step by step instruction is available in the guidan ce document, but some description here is necessary to document various steps that influence the predicted acute values in Table 13 . From the start up screen ( Figure 12 ) of the QSAR application toolbox, the “Flexible Tr ack” was used to identify the target chemical, gather analogue da ta, and fill data gaps.

Figure 12. Start-up screen of the OECD toolbox.

After identifying the target chemical, users are pr ompted to “Profile” the chemical. In profiling, the software can affiliate the target chemical with previously defined categories of chemical structures, or mode of action. For acute values fo r aquatic species, the toolbox guidance recommends using the OASIS Acute toxicity MOA, Verh aar and ECOSAR classifications ( Figure 13 ). Figure 13 demonstrates an example using diazinon. Note that ECOSAR classifies diazinon as an ester, phosphate ester, and as a pes ticide. OASIS classifies it simply as a “Reactive Unspecified”, while the Verhaar scheme cl assifies diazinon as Class 5 (Not possible to classify this chemical).Page 64 of 126

Figure 13. “Profiling” Diazinon in the OECD Toolbox

.

Next, users are asked to identify endpoints of inte rest. The first step is to identify what databases will be queried. Depending on the user’s purpose, all or few of the databases can be selected. For the purposes of this example, 5 databases were chosen to focus on acute values for aquatic species: The ECETOC Aquatic toxicity, EPISUITE_OBS _data, ISSCAN UPDATE 3, Japan Aquatic, OASIS Aquatic and USEPA ECOTOX. After ide ntifying the databases, the software gathers the requested data ( Figure 14 ).Page 65 of 126

Figure 14. Identified databases and available data for the target chemical diazinon in the OECD toolbo x.

After gathering the toxicity data for the target ch emical, the next step is to define the category from which analogue data will be gathered. In this example, the ECOSAR classification was used which yielded 170 analogue structures. The ot her two categories (Verhaar scheme and OASIS Acute toxicity MOA) were not helpful in this example. Since the Verhaar scheme classified diazinon as “unclassifiable”, there were no other analogues for comparison. Since the

OASIS classification labeled diazinon as “reactive unspecified”, it was lumped with 14,156 other analogues. After choosing the ECOSAR classification , the user is prompted to identify what data should be gathered. Through a series of check boxe s on pop-up windows, only acute values for Amphibians, Crustaceans, Echinoderms, Fish, Insects , Invertebrates, Molluscs and Worms were selected for either 48 or 96 hr exposures ( Figure 15 ).Page 66 of 126

Figure 15. Data identified from the Category defini tion screen for Diazinon in the OECD toolbox.

The final step is to move onto the “Filling data ga p” module of the toolbox. The first step is to identify the species of interest (e.g. P. promelas ) for which the toolbox will gather analogue data. The data are gathered and subsequently graphed acco rding to a “descriptor”. The default descriptor is Log_Kow_EPISUITE. In the diazinon ex ample, there were 18 analogues with 48 hr LC50 data for fathead minnow ( Figure 16 ). The toolbox then identified 5 (may be more or l ess) of the closest analogues (as defined by the descrip tor) and calculated an average LC50 value for those 5 points. In Figure 16 , the 5 analogues used to calculate the average LC5 0 are identified as the maroon colored points, the target value is i n red and all others are in blue. At this point, users can further refine the points by subcategoriz ation. For this example, a refinement of structure similarity using the Dice index ( Figure 17 ) was used and all data points with structures having similarity index of 70% or less were removed . Applying that filter to diazinon fathead minnow acute values, resulted in having data from 7 analogues from which an average LC50 was

calculated.

These steps were repeated for all four of the pesti cides and for multiple species to meet MDRs. Unfortunately, it was not possible for acrolein and TBT because too few analogues are available

in the database. Predicted acute values and calcul ated ASLVs are illustrated in Table 13

.Page 67 of 126

Figure 16. Read-across evaluation screen for Diazin on in the OECD toolbox.

Figure 17. Analogue structure similarity indexs in the OECD toolbox.Page 68 of 126Table 13. Meeting minimum data requirements of the1985 Guidelines using Read-across from OECD toolbox.(LC/EC50s, 95% confidence limits are in parentheses)Chemical Species Mean Acute Values (ug/L)Predicted Acute Values (ug/L)ALSVCriteriaFAVOncorhynchusmykiss1Lepomis macrochiris2Cladoceransp.4Pimephalespromelas3Gammarusfasciatus5Pteronarcys californicus6Crassostrea virginica7,9Dugesiatigrina8(ug/L) (ug/L)Diazinon (2005)425.8459.60.377313503.186.814373060 0.02060.3397(31 - 58.2)(0.014 - 700)(0.67-690)(96.5 - 1970)(0.979 - 9.55)X FoldDifference-16.5Chlorpyrifos(1986)8.485 10 0.053131041.79.991403420 0.01410.0485(56.4 - 30300)(0.29 -5820)(0.35 -286)(14.3 - 1360)(1090 - 10700)X FoldDifference-3.5Note: Bolded values were used to calculate ALSV with n = 81 - The family Salmonidae in the class Osteichthyes;2 - One other family (preferably a commercially orrecreationally important, warmwater species) in theclass Osteichthyes (e.g.,bluegill, channel catfish);3 - A third family in the phylum Chordata (e.g.,fish, amphibian);4 - A planktonic crustacean (e.g.,cladoceran, copepod);5 - A benthic crustacean (e.g.,ostracod, isopod, amphipod, crayfish);6 - An insect (e.g.,mayfly, dragonfly, damselfly, stonefly, caddisfly,mosquito, midge);7 - A family in a phylum other than Arthropoda or Chordata (e.g.,Rotifera, Annelida,Mollusca);8 - A family in any order of insect or any phylum not already represented.9 - Saltwater species retained for read-across comparison to Web-ICELike WEB-ICE, the OECD toolbox using the read-acros s application met the overall goal of

ALSV derivation in that it provided predicted acute values that led to a conservative estimate of the FAV for both diazinon (-16.5 fold) and chlorpyr ifos (-3.5 fold). And, like Web-ICE, diazinon had the largest discrepancy between the AL SV and the criteria-derived FAV.

Proposed analyses will further explore the use of t he OECD toolbox with its read-across, trend analysis and (Q)SAR model applications. In this ex ample, some observations and points of investigation are apparent. Obviously, the more da ta that are available from similarly structured chemicals, the more useful this tool will be. In t he case of acrolein and TBT, sufficient data are not available to begin using any of these predictiv e methods as employed currently. In the case of the two organophosphates, sufficient data were a vailable for a number of different analogues

with similar structures to consider their use. Add itionally, more work needs to be completed to identify profiles more closely linked to adverse ou tcome pathways. For example, using the default recommended aquatic acute toxicity classifi cations (ECOSAR classification, OASIS Acute Toxicity and Verhaar Classification) all four pesticides fell into the same classification schemes closely linked to the 3 discreet MOAs they represent. Other classification schemes such as Protein Binding may yield a more refined in ference to MOA as compared to the OASIS Acute Toxicity MOA (e.g. reactive unspecific). In our example, the default descriptor that identifies the 5 closest analogues was log K

ow . The toolbox has dozens of other physical and chemical descriptors that would likely be more appr opriate for anticholinesterase pesticides such as diazinon or chlorpyrifos. Proposed analyses wil l attempt to identify the most appropriate descriptors for various pesticide MOAs. Also in ou r examples, the Dice similarity index for chemical structure was used with a 70% similarity c ut off. It is not known at this point, what similarity index is most appropriate for each type of chemical structure. Proposed analyses will be completed to identify the most appropriate simil arity indexes with MOA or structure type. Finally, this was a very quantitative example of re ad-across. Proposed analysis will also describe how the technique can be used in a qualitative mann er to rank relative potencies of chemical

structures among taxa types.

8.2.2 Only Use Results of Toxicity Tests for Chemic al and Taxa of

Interest

The previous two predictive methods were examples o f how one may be able to supplement data sets to meet MDRs and subsequently calculate an ALS V using an SSD approach. The following section provides examples of how one may generate a n ASLV by calculating an ALSV using the available toxicity data (Box 7 in Figure 11 ). These calculations can be accomplished by: 1) Calculating the ALSV using extrapolation factors, o r 2) using an SSD methodology. The companion paper (“ Exploration of Methods for Characterizing Effects o f Chemical Stressors to Aquatic Animals ”) illustrates an example of using GLI extrapolatio n factors and therefore will not be duplicated here. Therefore, this section foc uses on two examples of SSD approaches.

8.2.2.1 Calculating an ALSV using a modified SSD Methodolo gy (Box 7, Figure 11)70

Two different methods are readily available for est imating an ALSV using a SSD methodology. The first example described here is also within the Web-ICE tool. Within Web-ICE there is an option for calculating an SSD with as few as 1 spec ies. In this example, the 3 acute values ( Figure 18 ) from Table 9. Species mean acute and chronic values found in c riteria documents.

9 were used to calculate a HC5 for each of the 4 pes ticides ( Table 14 ).

Figure 18. Estimating an ASLV using the SSD functi on of Web-ICE Data here are for

Acrolein.

The HC5 estimates using the Web-ICE log-logistic as sumed distribution were relatively accurate

and conservative for 2 of the chemicals: acrolein (-4.3 fold) and TBT (-2.4 fold). However, diazinon (62.3 fold) and chlorpyrifos (22.1 fold) w ere both severely overestimated. As described earlier when Web-ICE was being used to extrapolate single species, the proposed analyses will describe the reasons behind these apparent biases t o assess characteristics of datasets that are

most amenable to the use of this tool.

A second example of how minimum toxicity data sets can be used to estimate HC5s from
an assumed distribution was completed. Rainbow tr out, bluegill and cladoceran acute

values from Table 9. Species mean acute and chronic values found in c riteria documents.

9 were used with an assumed Log-logistic SSD accordi ng to methods proposed by de Zwart (2002). The log-logistic equation from the Species Sensitivities Distribution section (Appendix71

B) of the companion white paper “ Exploration of Methods for Characterizing Effects o f Chemical Stressors to Aquatic Animals ” was used to calculate HC5s ( Table 14 ). Two parameters ( α and β ) were determined for each of the four pesticides ( Table 14 ). The α parameter was the average log 10 of the 3 acute values for each chemical, while β was taken from de Zwart (2002) specific to the chemicals MOA as li sted in Web ICE

In this example, HC5s were very similar to the FAV. In the case of diazinon, the two values

were identical. The maximum difference between the FAVs and the HC5s was -3.62 fold and all had a negative bias, meaning that they were conserv ative estimates.

Table 14. Predicted HC5 concentrations assuming Log -logistic shape of SSD specific to MOA Experimentally derived Acute Values (ug/L)

Chemical ASLV Criteria

FAV Oncorhynchus

mykiss Lepomis
macrochiris Cladoceran

sp. Avg

Log β

deZwart 1 HC5

Α β Μ g/L μ g/L

Acrolein (2009) 2 16 27.19 39.76 1.41 0.28 3.876 5.92

Log 1.20 1.43 1.60 x
- fold difference

- 1.53

Diazinon (2005) 3 425.8 459.6 0.3773 1.62 0.71 0.341 0.3397

Log 2.63 2.66 -0.42 x
- fold difference 1.00

Chlorpyrifos (1986) 3 8.485 10 0.053 0.22 0.71 0.013 0.0485

Log 0.93 1.00 -1.28 x
- fold difference

- 3.62

TBT (2003) 4 4.571 8.3 4.3 0.74 0.38 0.416 0.9177

Log 0.66 0.92 0.63 x
- fold difference

- 2.21

1 - β values are those from de Zwart, 2002 and are depen dent of MOA designation

2 – The β value for Acrolein was that of Reaction with carbo nyl compounds

3 - The β value for Diazinon and Chlorpyrifos was that of Ac etylcholinesterase inhibitors: organophosphates

4 - The β value for TBT was that of uncouplers of oxidative phosphorylation

One obvious and perhaps important difference in the se two SSD examples is de Zwart’s incorporation of MOA in the HC5 estimate as paramet ers that help shape the Log-logistic curve.



### 8.3 Additional Applications of Predictive Tools

8.3.1 EFED QSAR Guidance

Guidance and use of (Q)SAR models to predict enviro nmental fate and toxicity values when chemical-specific data are not available for EFED r isk assessment is provided in Appendix A

. (Q)SAR may be used in effects assessments for all t ypes of risk assessments conducted by OPP.

8.3.2 Comparison of endosulfan and its degradate, endosulfan sulfate, toxicities using Web-ICE72

Section 8.2.1.1 discussed the application of Web-IC E models for predicting acute toxicity values to meet MDRs. This section demonstrates another pot ential application of Web-ICE models that compares toxicities of a chemical and it’s degradat e, which may have limited test data. The organochlorine insecticide, endosulfan, and it’s de gradate endosulfan sulfate appear to be of relatively equal toxicity, with endosulfan sulfate generally being slightly less toxic than the parent compound. Limited toxicity data for endosulf an sulfate allow direct comparison of toxicities for bluegill ( Lepomis macrochirus ), carp ( Cyprinus carpo ), sheepshead minnow ( Cyrpinodon variegates ), daphnid ( Daphnia magna ), and the mysid shrimp ( Americamysis

bahia ), and show measured toxicity within an order of ma gnitude of each other (Error! Reference source not found.). Since direct comparis ons of endosulfan and endosulfan sulfate were limited to only five aquatic species, the Web- ICE (Web-based Interspecies Correlation Estimation v 3.0; http://www.epa.gov/ceampubl/fchai n/webice/) application was used to predict toxicity to endosulfan sulfate for additional speci es.

Endosulfan sulfate toxicity was predicted for speci es for which a measured endosulfan toxicity value existed, but the degradate toxicity was lacki ng. Predicted toxicity values could only be

obtained where models exist for both the taxa of in terest (the predicted taxa) and the surrogate for which measured toxicity is available. Therefore , the data presented in Table 14 are for species for which predictions could be made from th e measured data and where models were

available.

For aquatic species, surrogate selection and assess ment of model predictions for aquatic species followed Web-ICE user guidance (Raimondo et al. 201 0). To predict toxicity of endosulfan sulfate to the untested species listed in Table 14 ( e.g., rainbow trout, fathead minnow), all species, genus, and family models were extracted fo r each potential surrogate (bluegill, carp, sheepshead minnow, Daphnia magna , and the mysid shrimp) and predicted species. Wher e multiple models existed for a predicted species (mu ltiple surrogates, multiple taxonomic levels) models were sorted by MSE in ascending order. For a ll predicted species except the scud,

Gammarus lacustris , low MSE correlated with high R 2 , close taxonomic distance, and high cross-validation success rate. For predicted specie s with multiple models, all degrees of freedom (df) were greater than 10 and p-values less than 0. 01. For all species with multiple models except for the scud, toxicity predictions and confidence l imits were calculated using the top 2 or 3 models based on the criteria above (low MSE). Confi dence intervals were used to confirm that the models with the lowest MSE, highest R 2 , highest cross-validation success rate, and closes taxonomic distances yielded the most robust predict ions. All models for the scud contained relatively equal and large MSE (>1.48), low R 2 (<0.53) and low cross-validation success rates (< 51%), whi ch indicate high model uncertainty. Since these model criteria were generally the same for al l scud models and confidence intervals of all model predictions were generally equal, taxonomic r elatedness was used to select the best surrogate (Mysid shrimp). Results of the extrapolat ions support the measured data in demonstrating similar toxicity of endosulfan and en dosulfan sulfate, with the degradate being slightly less toxic than the parent compound.73

Table 14. Comparison of the acute toxicity predicti ons for endosulfan and endosulfan sulfate using Web -ICE and measured data for aquatic species. The upper t able includes species with toxicity values for both

endosulfan and endosulfan sulfate. The lower table contains species for which endosulfan sulfate toxic ity was predicted from Web-ICE.

Species Endosulfan Endosulfan Sulfate

Measured Acute Toxicity (ug/L) MRID or Ecotox

reference Measured Acute

Toxicity (ug/L) MRID or

Ecotox

reference Bluegill 1.7 3.8 Carp

0.1 Sunderam et al. 1992; ( ECOTOX 5850) a 2.2 45421402 a Daphnia magna

166 5008271 300 45421403 a Sheepshead minnow

1.3 b Shimmel 1981; (Ecotox 3740) 3.1 46382603 Mysid shrimp

0.83 b Shimmel 1981; (Ecotox 3740) 7.9 46406401
Species Endosulfan Endosulfan Sulfate

Measured Acute Toxicity (ug/L) MRID or Ecotox

reference Web-ICE

Predicted Toxicity and

95% c.i. c (ug/L) Surrogate (input

value) - Model

level

Rainbow trout ( Oncorhynchus mykiss ; Salmonidae) 0.83 136999 4.23 (3.32-5.39) Bluegill (3.8) - Species

Channel catfish ( Ictalurus

punctatus ; Ictaluridae) 1.5 40094602 1.95 (0.77 – 4.92) Common carp (2.2) - Family

Fathead minnow ( Pimephales promelas ; Cyprinidae) 1.5 40094602 2.89 (1.35 – 6.21) Common carp (2.2) - Species

Scud ( Gammarus lacustris ; Gammaridae) 5.8 40094602 30.15 (8.27 – 109.93) Mysid (7.9) -

Genus

Stonefly ( Pteronarcys californica ; Pteronarcyidae) 2.3 40094602 2.23 (0.49 – 10.08) Bluegill (3.8) -

Family

Striped mullet ( Mugil cephalus ; Mugilidae) 0.38 40098001 3.06 (0.66-14.25) Bluegill (3.8) - Family

Pink shrimp (Penaeus dourarum, Farfantepenaeus

duorarum ; Penaeidae) 0.04 5005824 1.03 (0.28 – 3.82) Bluegill (3.8) - Family

Grass shrimp ( Palaemonetes pugio ; Palaemonidae) 1.31 5005824 156.2 (90.76 – 268.71) Daphnid (300)

Genus

Eastern oyster ( Crassostrea virginica ; Ostreidae) 0.45 128688 8.55 (3.71 – 19.71) Bluegill (3.8) -

Species

a study classified as supplemental74

b geometric means

c confidence interval75



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Appendix A: EFED Guidance on Use of Structure-Acti vity Relationships

Executive Summary

This document is intended as an example of how quan titative structure-activity relationships ([Q]SARs can be used for estimating chemical/physic al and toxicity characteristics of pesticides. Although QSARS have been used in other areas of EPA to estimate the potential toxicity of chemicals where measured data are not available, th e use of these models in the Environmental Fate and Effects Division (EFED) has been sporadic. Even where QSARs have served as a source of information on a compound, their utility to risk assessment is typically based on a weight-of-evidence approach where the predictive ca pacity of the models among other lines of evidence is a critical factor for determining wheth er the information can be used qualitatively versus quantitatively. This weight-of-evidence app roach considers the predictive capacity of the model for chemicals with known physical/biological characteristics compared to similarly structured compounds where such characteristics are unknown.

Available QSAR Tools

Two SAR tools most readily available to ecological risk assessors in OPP include Eco logical

S tructure A ctivity R elationships (ECOSAR) 1 and the As sessment T ools for the E valuation of

R isk 2 (ASTER). Both tools rely on the chemical name, CA S number, and the chemical’s structure as represented by a S implified M olecular I nput L ine E ntry S ystem (SMILES) string. Both have undergone Agency peer review and are post ed to the Agency’s intranet. Information on chemical-specific SMILES strings can be found us ing some freely accessible tools on the internet. ChemiSpider 3 maintained by the Royal Society of Chemistry and the Toxicology Data Network (ToxNet) 4 maintained by the National Library of Medicine bot h provide SMILES string information along with chemical/physical properties of a chemical.

The user manual for ECOSAR 5 can be found at http://www.epa.gov/oppt/newchems/tools/ecosarusersg uide.pdf . An example of the ECOSAR data entry window is depicted in Figure A1 ; Figure A2 depicts the ECOSAR results window with chemical class assignments and toxicity estima tes across various organisms.

The ASTER model allows the user to modify chemical/ physical properties of the pesticide to more accurately define these parameters if measured values are available. ASTER requires the

1 USEPA 2009 a . Ecological Structure Activity Relationships (ECOS AR). http://www.epa.gov/oppt/newchems/tools/21ecosar.htm

2 USEPA 2009 b . Assessment Tools for the Evaluation of Risk (AST ER). U.S. EPA, National Health and Environmental Effects Research Laboratory (NHEERL), Mid-Continent Ecology Division (MED), http://www.epa.gov/med/Prods_Pubs/aster.htm

3 Royal Society of Chemistry. 2009. ChemSpider. h ttp://www.chemspider.com/

4 National Library of Medicine. 2009. Toxicology D ata Network (TOXNET). http://toxnet.nlm.nih.gov/

5 USEPA. 2009 d . User’s Guide for the ECOSAR Class Program, MS-Wi ndows Version 0.99d November 1998. Prepared by W. Meylan and P. H. Howard, Syracuse Re search Corporation Environmental Sciences Center, 6 225 Running ridge Road, North Syracuse, NY 13210. http://www.epa.gov/oppt/newchems/tools/ecosarusersg uide.pdf91

chemical CAS Number, chemical name and SMILES strin g as inputs. The model allows the use to modify the mode of action leading to the most to xic response to ensure protection. The user should review the model’s output to determine wheth er its estimates of chemical/physical properties are consistent with available measured v alues and adjust the input where necessary.

The user manual for ASTER can be found at http://www.epa.gov/med/Prods_Pubs/ASTER_Quick_User_ Guide.pdf . An example of the ASTER data entry window is depicted in Figure A3 ; Figures A4 and A5 depict the ASTER output windows for estimated chemical properties an d ecotoxicological hazard, respectively. The toxicity values depicted in Figure A5 represent the geometric means of each of the taxa represented for all of the data passing the ASTER f ilter. For carbaryl, the mean 96-hr LC 50 value for fish is 3.253 mg/L.

SAR programs typically used by ecological risk asse ssors in OPP estimate toxicity to aquatic organisms and do not currently estimate the toxicit y of chemicals to nonmammalian terrestrial organisms. The SARs in ECOSAR express correlations between a compound's physicochemical properties and its aquatic toxicity within specific chemical classes; whereas, the SARs in ASTER express correlations between a compound's physioche mical properties (K

ow ) and its aquatic toxicity within specific chemical modes of action. QSARs within ASTER use the K

ow as the chemical attribute (descriptor) for estimating toxi city and ASTER will not provide estimates for

chemicals with K

ow values outside the range of its training set.

Other useful tools include the Distributed Structur e-Searchable Toxicity (DSSTox) 6 public database network. DSSTox is a project of EPA's Off ice of Research and Development National Center for Computational Toxicology that is helping to build a public data foundation for improved structure-activity and predictive toxicolo gy capabilities. The DSSTox website ( http://www.epa.gov/ncct/dsstox/ ) provides a public forum for publishing downloadab le, structure-searchable, standardized chemical structu re files associated with toxicity data (USEPA 2009). An example of the DSSTox data entry window is depicted in Figure A6 using carbaryl as an example; Figure A7 depicts the DSSTox search results summary and the number of files containing data which exactly match and partially m atch for carbaryl. Figure A8 depicts where there are exact matches on data for carbaryl; in th e far lower right column of the output, acronyms are listed for files with data on carbaryl . If the acronym EPAFHM (EPA Fathead Minnow) dataset is selected, DSSTox displays the ac ute toxicity data and mode of action of carbaryl ( Figure A9 ). The output indicates that carbaryl has a mean L C 50 of 8.75 mg/L representing the geometric mean of 4 experiments an d that carbaryl’s mode of action is through acetylchlolinesterase inhibition.

An important use of QSARs is in the identification of potential degradates of concern An additional tool for determining potential degradate s of concern is MetaPath 7 . MetaPath is a

6 USEPA 2009f. Distributed Structure-Searchable Tox icity (DSSTox) Database Network. http://www.epa.gov/ncct/dsstox/index.html

7 Jones, W. J., P. K. Schmieder, R. C. Kolanczyk, and O. Mekenyan. Development of a Searchable Metabolit e Database and Simulator of Xenobiotic Metabolism. Pr esented at BOSC Computational Toxicology Research Program Review, Research Triangle Park, NC, Septemb er 29 - 30, 2009.92

computational tool developed for the storage and an alysis of metabolic pathways and associated metadata. The tool is capable of text and chemical structure/substructure searching as well as comparing metabolites formed across chemicals, spec ies, and/or experimental conditions. The database has been constructed primarily from in viv o rat metabolism studies of pesticides. This system also serves as a foundation of an expert sys tem to predict metabolite formation. Figure

A10 depicts the MetaPath window for selecting chemical s of interest and the associate degradation pathway reported in rat studies.

For ECOSAR and ASTER, the user should determine whe ther model outputs for both fate and effects parameters are consistent with what is know n for the chemical under evaluation. In some cases, some toxicity data on either fish and/or inv ertebrates may exist and the reviewer should evaluate whether the model outputs are consistent w ith what has been measured. The ability of the SARs to reliably estimate the toxicity of other chemicals depends on the extent to which similarly structure chemicals with similar modes of action are represented in the training set of data used to populate the model and estimate relati onships. As such, SAR model results must be put into the context of any existing data and what the reviewer may already know about their chemical. Additionally, for each of the SAR models , the structural moieties and/or mode of action on which the model estimates toxicity are pr ovided by the model. The reviewer should be aware of this output and make an effort to determin e whether these moieties and/or mode of action are consistent with their understanding of t he chemical.

The use of SARs is dependent on multiple lines of e vidence and must be viewed in the context of the model’s domain of applicability; these multiple lines of evidence must be integrated. Some

SAR models like ASTER warn users that certain chemi cal structures are not in their domain. Users are cautioned to examine model outputs carefu lly to determine whether estimates are consistent with what may be known about the chemica l however limited.

With any model as with measured toxicity data, ther e are uncertainties regarding the variability and relevancy of estimates. The decision to use su ch estimates should be weighed against other sources of data that may be available. When used, SAR model output should be properly

identified and the uncertainties associated with th e values must be discussed in the assessment. Ultimately, the use of SARs is dependent on the bes t professional judgment of the user and are part of a weight-of-evidence approach toward charac terizing potential chemical hazards.

One of the largest sources of uncertainty regarding the use of SARs is that the models are typically developed for relatively generic structur es and modes of action; however, conventional pesticides have been engineered to be toxic, oftent imes to specific taxa, through relatively specific modes of action. Thus, a chemical may hav e a number of substructures (moieties) that have associated toxicities based on what may be dis similar modes of action while the entire chemical structure may impart a specific mode of ac tion and toxicity. For example, the insecticide carbaryl, is classified in ECOSAR as a neutral organic and the tool estimates the toxicity of carbaryl based on an ester moiety conta ined within the compound. However, the overall mode of action of this N-methyl carbamate i s to inhibit acetylcholine esterase. The93

extent to which the component toxicities contribute to the overall activity of the compound is an important consideration and challenge to decipher. As compounds are transformed/degraded to smaller components, the importance/relevance of sub structure activity relationships increases as the overall activity of the parent compound may no longer dominate. Consideration should be given to the extent to which the compound retains s tructural similarities to the parent and/or chemicals with known toxicities and are accurately estimated by the QSAR model.

As mentioned previously, some SARs are based on the structure and mode of action of chemicals. Again though, the mode of action may be relatively generic even though the specific mode of action for a pesticide may not be reported and/or evaluated. ECOSAR may group chemicals as acting through nonpolar narcosis; howe ver, this relatively broad mode of action may include chemicals with widely divergent modes o f action. Chemical mode of action may also differ between target and non-target species; however, by the same token, for most of the SAR estimates, while the species may be different t he mode of action across those species is the

same.

Criteria for Use of [Q]SARs

Using OECD validation principles as template/guidan ce for the use of QSAR models, 8 the following attributes should be examined for complet eness. The model must estimate a defined endpoint, e.g ., a 96-hr lethal concentration to 50% of the test organisms (LC 50 ), a no-observed adverse effect concentration (NOAEC), or a maximum acceptable toxic concentration (MATC). As such, the model must be transparent as to the ty pes of studies included in model database.

The user must in turn determine whether these defin ed endpoints are relevant to the effects

assessment.

The model must be based on an unambiguous algorithm . That is to say that the mathematical relationship on which the model is based, must be c learly identified. If the model is regressionbased and uses K

ow , then the exact mathematic expression (function) s hould be identified and its derivation transparent. Known limitations of the m odel should be identified. For example, in the output for the ASTER model ( Figure A4 ), estimates for environmental fate properties like vapor pressure are based on averages. The fifth column o f the output table indicates the method used to calculate the vapor pressure and the percent err or (47%) associated with the estimate.

As with any mathematical relationship it is constru cted over a specified domain that is dictated by the range of chemical data contained within its training set. Attempting to estimate the toxicity of chemicals that fall outside of the esti mation capabilities of the model could lead to erroneous values. As such, the user should be cogn izant of the domain of applicability for the model and should not extend analyses beyond that ra nge. For example, in Figure A2 depicting output from ECOSAR, the user is warned that chemica l solubility may limit the model's ability

to estimate certain effects. It also notes that fo r fish and daphnids the model does estimate

8 OECD. 1007. Report on the Regulatory Uses and Ap plications in OECD Member Countries of (Quantitativ e Structure-Activity Relationship [(Q)SAR] Models in the Assessment of New and Existing Chemicals. ENV/JM/MONO(2006)25. http://www.olis.oecd.org/olis/2006doc.nsf/LinkTo/NT 00003B0A/$FILE/JT03221927.PDF94

toxicity for compounds with log octanol-water parti tion coefficient (log K

ow ) values of 5 and that for green algae, the cutoff is a log K

ow of 6.4. The molecular weight cutoff for the model is also provided ( i.e ., 1000 g/mole).

Again, since QSARs are typically based on mathemati cal relationships and are regression-based, these models can be evaluated for their ability to estimate the training values used to derive them. Models should contain an analysis of their g oodness of fit, robustness and predictivity. Typically, the mean square error term and/or regres sion coefficient (r 2 ) of the model serve as measures of the ability of the model to represent t he data in its training set. Robustness of the model can be evaluated by examining how toxicity es timates vary with relatively minor changes in structure. Such evaluations may be accomplished through examining toxicity estimates across several degradates. For example, Figure A4 depicts ASTER-estimated fate properties for the carbamate insecticide carbaryl. ASTER reports a lo g octanol-water partition coefficient (log P) of 2.38, vapor pressure of 1.9 x 10 -4 , and solubility of 7.68 x 10 -4 moles/L (154 mg/L) for carbaryl. Measured values reported in the environm ental fate and ecological risk assessment written in support of the reregistration eligibilit y decision (RED) on carbaryl are near identical for log P; however, the ASTER estimate for vapor pr essure differs from the measured value by 3 orders of magnitude and the ASTER-estimated solubil ity is roughly 5X greater than what is reported in the RED. In cases where the model allo ws the user to modify environmental fate characteristics to reflect measured values, the use r should consider doing so to determine the extent to which the toxicity values are affected.

Ideally, the toxicity estimated for a chemical shou ld be mechanistically plausible and be

consistent with what has been demonstrated for simi larly structured chemicals, e.g. , analogs. Additional guidance on the use of QSAR models may a lso be available from the tool developers as well and should ideally contain defined criteria for determining whether the model is likely to be biased either positively or negatively. For exa mple, in Figure A2 depicting output from the ECOSAR model for carbaryl, the toxicity estimates a re based on a neutral organic SAR for chemicals containing esters. It is clear from the structure of carbaryl depicted in Figure 3 that compound has a well defined ester linkage. Howeve r, as stated previously, it is also known that carbaryl's primary mode of action is to inhibit ace tyl cholinesterase. As such, the toxicity estimates based on the ester moiety by itself may o r may not be reflective of the toxicity

associated with the entire chemical.

Finally, the strengths and weakness of the data est imation method used should be clearly described by the user when such estimates are inclu ded in assessments. When any SAR model has been used to estimate toxicity values, the outp ut of the model should be included as an appendix to the assessment to enhance transparency.

As an example of how these tools can be used, consi der carbaryl again. Registrant-submitted and open literature studies indicate that carbaryl can readily degrade. One of its primary degradates is 1-naphthol. Figure A10 depicting the MetaPath output also indicates that 1- naphthol is a primary degradate and the window in t he left of the output provides the SMILES string for the compound. Figure A11 depicts chemical information on 1-naphthol entered into95

ECOSAR and Figure A12 depicts the ECOSAR output for the chemical with a fish 96-hr LC 50 of 7.959 mg/L. The ASTER outputs for 1-naphthol ar e depicted in Figures A13 and A14 and

indicate the mean acute LC 50 value for freshwater fish is 1.78 mg/L. When naph thol is entered into DSSTox ( Figure A15 ), the tool indicates that there are no exact match es but 9 partial matches for the compound. If 1-naphtol is selected from the chemicals depicted among the partial matches and the EPA fathead minnow database is then selected, DSSTox provides the

measured LC 50 values (4.63 mg/L) and the indicates that the mode of action is through polar narcosis ( Figure A16 ). Table 1 summarizes the measured and estimated toxicity val ues for carbaryl and its primary degradate 1-naphthol; the most sensitive measured values used in the carbaryl risk assessment are also provided for comp arison. Although QSAR model estimates for carbaryl appear to be considerably higher than the most sensitive toxicity value (Atlantic salmon

96-hr LC 50 =0.22 mg/L) used in the ecological risk assessment of carbaryl, the mean measured

96-hr LC 50 value across all freshwater fish species is roughl y 2.6 mg/L and is relatively consistent with QSAR estimates. Similarly for 1-na pthol, the most sensitive measured toxicity value is 0.75 mg/L; however, values ranged as high at 1.6 mg/L. Therefore, QSAR-estimated values for both carbaryl and its 1-naphthol degrada te are not substantially different than measured toxicity values. .

Table 1. Summary of acute toxicity estimates for c arbaryl and its primary degradate 1 naphthol from t hree predictive tools for freshwater fish.

Method Carbaryl Toxicity Estimate for Fish (mg/L) Naphthol Toxicity Estimate for Fish (mg/L) ECOSAR (estimated) 19.79 7.95 ASTER (estimated) 3.25* 1.78* DSSTox (estimated) 8.75 4.63 Measured (most sensitive) 0.22** 0.75

*Geometric mean of all freshwater fish acute toxici ty data passing the ASTER filter.

** Most sensitive freshwater fish 96-hr LC 5096

Figure A1. ECOSAR chemical input screen. Informat ion depicted is for the carbamate insecticide carba ryl.97

Figure A2. ECOSAR output window screen shot. Scre en is depicting estimates for the carbamate insecti cide carbaryl.98

Figure A3. ASTER chemical data entry screen.99

Figure A4. ASTER chemical property screen. Screen is depicting estimates for the carbamate insectici de carbaryl (1-naphthalenol methylcarbamate; CAS 63252 ).100

Figure A5. ASTER ecptoxicological hazard assessmen t. Screen is depicting estimates for the carbamate insecticide carbaryl (1-naphthalenol methylcarbamat e; CAS 63252).101

Figure A6. DSSTox chemical data entry screen where carbaryl has been entered. The drop down menu in the upper box has been set to Chemical Name and car baryl has been entered as the search text. Options in

the Choose search window also include CAS number.

Figure A7. DSSTox search results using carbaryl as an example. In this example the upper box of DSST ox search window indicates that for carbaryl, there is one exact match and no partial matches. The lower box102

indicates there are records (data) for carbaryl in 7 of the databases in which it searched. Double cl icking on the box labeled "Exact matches" will provide more d etail on those matches.103

Figure A8. DSSTox search details screen using carb aryl as an example. In this example, the uppermost box

entitled "Exact matches" has been double clicked an d the lowermost box now displays the similarity sco re, structure, CAS number, and hot links to each of the databases containing chemical-specific information on carbaryl. Double clicking on the hotlinks will dis play the chemical-specific data.104

Figure A9. DSSTox substance results for EPA fathea d minnow (EPAFHM) acute toxicity data using carbary l as an example. In this example, the EPAFHM chemica l/physcial and biological data are displayed for carbaryl.105

Figure A10. Metabolism Pathway (MetaPath) chemical selection window depicting carbaryl and it primary degradates. Header over the metabolism pathway (ma p) in lower right is the open literature citation f rom which the metabolism data were derived. In this ex ample, the upper left pull down menu (Desc) under chemical description quick search has been set to " PCCode" and the PC Code of carbaryl (56801) has bee n entered as the Value. The metabolic pathway for ca rbaryl is displayed in the window to the right show ing the parent compound along with three of its degradates. Above the metabolic pathway is the full reference for the study used to depict the metabolites.106

Figure A11. ECOSAR chemical input screen. Informa tion depicted is for the carbaryl degradate naphtho l. In this example, the SMILES string, Name and CAS Nu mber have been entered. Alternatively, the tab "CA S Input" could be selected and by entering the CAS Nu mber (1321671), ECOSAR will automatically populate the window. Afterward, the user would double-click the tab "Calculate".107

Figure A12. ECOSAR output window screen shot. Scr een is depicting estimates for the carbaryl degradatenaphthol. Toxicity estimates are calculat ed for both neutral organics and phenols. Both acu te (LC50 and EC50) and chronic (ChV) toxicity values f or aquatic animals and nonvascular plants are provided.108

Figure A13. ASTER chemical output screen for carbaryl degradate 1-naphthol (aka 1-naphthalenol).109

Figure A14. ASTER ecotoxicological hazard output s creen for carbaryl degradate 1-naphthol. The uppermost window contains acute and chronic toxicit y estimates based on behavior (BEH), growth (GRO), mortality (MOR) and physiology (PHY) across both aq uatic and terrestrial animals; these estimates represent the geometric mean (plus minimum and maxi mum values) of all the data passing the ASTER filte r by species group. The lowermost window depicts med ian acute toxicity values for specific species.110111

Figure A15. DSSTox search details for naphthol.112

Figure A16. DSSTox substance results for 1-naphtho l using the EPA Fathead Minnow (EPAFHM) database.113 

### Appendix B. Overview of modes of action identified, associated chemical classes, and pest organisms.

Modes of Action Chemical Class Example chemicals Pest Organism Refe rence
Acetylcholinesterase Inhibition - Carbamate Carbama tes Carbaryl Insect IRAC 2010

Acetylcholinesterase Inhibition - Organophosphates Organophosphates Chlorpyrifos Insect IRAC 2010

Acylation based reactivity Ketenes, Acid Halides, Dialkyl carbonyl chlorides, Carboxylic acid anhydrides Ethenone Fish Russom et al

, 1997

Alkylation and Arylation based reactivity Epoxides, Aziridines, Sulphonic/Sulphuric/Phosphoric acid esters, Halogenated acids/amides/ethers/sulphides/amines; Diazo compounds, Mustard compounds, Some alkyl/aryl halides, Propargylic alcohols, unsaturated aldehydes, acrylates, quinones, allylic compounds Ethylene oxide Fish Russom et al

, 1997

Anticoagulant: Vitamin-K antagonist Coumarins, 4-Hydroxycoumarins, 1,3-Indandione Warfarin Rodent Pelz 2005

Base-line / Non-polar narcosis Various 1-Octanol Fi sh Russom et al

, 1997

B-Halogenated Alcohol Based Reactivity Beta-halogen ated alcohols 1,2-Dichloropropanol Fish Russom et al

, 1997

Bleaching: Inhibition of 4-hydroxyphenyl-pyruvatedioxygenase (4-HPPD) Triketone, Isoxazole, Pyrazoles,

Unclassified Mesotrione Plant HRAC 2010

Bleaching: Inhibition of carotenoid biosynthesis at the phytoene desaturase step (PDS) Pyridazinones, Pyridinecarboxamides,

Unclassified Norflurazon Plant HRAC 2010

Block lipid synthesis by inhibiting enoyl-acy carri er protein reductase Chlorinated phenoxyphenol Irgasan (Triclosan) Fungi , Bacteria Russell 2004

Blocks potassium ion channels in nerve fibers Amino pyridine 4-Aminopyridine Bird Choquet 1992

Carbonyl based reactivity Lactones (alpha, beta, and unsaturated), Aldehydes Acetaldehyde Fish Russom et al

, 1997

Chloride channel activators Glycosides Abemectin In sect IRAC 2010

Ecdysone agonists / moulting disruptors Diacylhydra zines Chromafenozide Insect IRAC 2010

Ester narcosis Esters Methyl acetate Fish Russom et al

, 1997

GABA-gated chloride channel antagonists - Cyclodien e organochlorines Cyclodiene organochlorines Endosulfan Insect IRAC 2 010 GABA-gated chloride channel antagonists - Phenylpyrazoles Phenylpyrazoles (Fiproles) Fipronil Insect IRAC 201 0

Host defense inducer: salicylic pathway Benzothiadi azole Probenazole Fungi FRAC 2010

Hydrazine Based Reactivity Hydrazines 1,2-Diethylhy drazine Fish Russom et al

, 1997

Hypercalcemia resulting in calcification of soft ti ssue Vitamin D analogs Cholecalciferol Rodent Mars hall 1984 Inhibition of AA and Protein Synthesis: methionin biosynthesis Anilinopyrimidines Cyprodinil Fungi FRAC 2010

Inhibition of AA and Protein Synthesis: Protein syn thesis Hexopyranosyl antibiotic, Glucopyranosyl antibiotic, Tetracycline antibiotic Blasticidin-S Fungi FRAC 20 10

Inhibition of acetyl CoA carboxylase (ACCase) Aryloxyphenoxyproprionates; Cyclohexanediones Diclofop-methyl Plant HRAC 2010

Inhibition of ALS/AHAS (acetolactate/actohydroxy ac id synthase) Sulfonylureas; Imidazolinones; Triazolopyrimidines; Pyrimidinyl(thio)benzoates; Sulfonylaminocarbonyltriazolinones Imazapyr Plant H RAC 2010

Inhibition of ATP production Thiophenecarboximides Silthiofam Fungi FRAC 2010114 Inhibition of auxin transport Phthalamates; Semicar bazones Naptalam Plant HRAC 2010

Inhibition of cell division; Inhibition of very lon g chain fatty acids (VLCFA) Chloroacetamides; Acetamides; Oxyacetamides; Tetrazolinones,

Unclassified Metolachlor Plant HRAC 2010

Inhibition of cell membrane permiability, fatty aci ds Carbamates Propamocarb Fungi FRAC 2010

Inhibition of cell wall (cellulose) synthesis Nitriles; Benzamides;

Triazolocarboxamides Dichlobenil Plant HRAC 2010

Inhibition of Complex I - NADH oxidoreductase Pyrim idinamines Diflumetorim Fungi FRAC 2010

Inhibition of Complex II: succinate-dehydrogenase Phenylbenzamides, Pyridinyl-ethylbenzamides, furan-carboxamides,Oxathin-carboximides, Thiazolecarboximides, PyrazoleCarboximides, Pyridine-carboximides Carboxin Fungi FRAC 2010

Inhibition of Complex III: cycochrome (ubiquinone oxidase at Q0 site Methoxyacrylates, Methoxycarbamates, Oximinoacetates, Oximinoacetamides, Oxazolidinediones, Dihydrodioxazines, Imidazolinones, benzylcarbamates Fluoxastrobin Fungi FRAC 2010

Inhibition of Complex III: cycochrome bc1 (ubiquino ne reductase) at Q1 site Cyanoimidazole, Sulfamoyltriazole Amisulbrom Fungi FRAC 2010

Inhibition of DHP (dihydropteroate) synthase Carbam ates Asulam Plant HRAC 2010 Inhibition of EPSP (5-enolpyruvylshikimate-3-phosph ate ) synthase Glycines Glyphosate Plant HRAC 2010

Inhibition of Glutamine synthetase Phosphinic acid Glufosinate-ammonium Plant HRAC 2010

Inhibition of lipid peroxidation Aromatic hydrocarbons, 1,2,4- Thhiadiazoles Dicloran (DCNA) Fungi FRAC 2010

Inhibition of lipid synthesis - not ACCase inhibiti on Thiocarbamates; Phosphorodithioates; Benzofuranes; Chloro-carbonic acids EPTC Plant HRAC 2010

Inhibition of Lycopene cyclase Triazole Amitrol Pla nt HRAC 2010 Inhibition of mitosis / microtubule polymerization

inhibitor Carbamates Chlorpropham Plant HRAC 2010

Inhibition of Mitosis/Cell Division: Beta-tubulin Benzimidazoles, Thiophanates, Nphenyl carbamates, Toluamides Benomyl Fungi FRAC 20 10

Inhibition of Mitosis/Cell Division: Cell division Phenylureas Pencycuron Fungi FRAC 2010

Inhibition of Mitosis/Cell Division: delocalisation of spectrin-like proteins Pyridinylmethylbenzamides Fl uopicolide Fungi FRAC 2010 Inhibition of nucleic acid synthesis: DNA topoisome rase Type II Carboxylic acids Oxolinic acid Fungi FRAC 2010

Inhibition of nucleic acid synthesis: DNA/RNA synth esis Isoxazoles, Isothiazolones Octhilinone Fungi F RAC 2010

Inhibition of nucleic acid synthesis: Purine metabo lism (adenosin-demainase) Hydroxy-(2-amino)pyrimidines E thirimol Fungi FRAC 2010

Inhibition of nucleic acid synthesis: RNA polymeras e I Acylalanines, Oxazolidinones, Butyrolactones Metalaxyl Fungi FRAC 2010

Inhibition of phospholid biosynthesis: methyltransf erase Phosphorothiolates; Dithiolanes Iprobenfos Fu ngi FRAC 2010

Inhibition of phospholipid biosynthesis and cell wa ll deposition Cinnamic acid amides, Valinamide carbamates, Mandelic acid amides Dimethomorph Fungi FRAC 2010

Inhibition of photosynthesis at photosystem II, Si te A Triazine, Triazinones, Triazolinone,Uracils, Pyridazinones, Phenylcarbamates Atrazine Plant HRAC 2010

Inhibition of photosynthesis at photosystem II, Si te A but different binding behavior Ureas; Amides Linuron Pl ant HRAC 2010

Inhibition of photosynthesis at photosystem II, Sit e B Nitriles; Benzothiadiazinone, Phenylpyridazines Benzothiadiazole Plant HRAC 2010

Inhibition of protoporphyrinogen oxidase (PPO) Diphenyl ethers; Phenylpyrazoles; Nphenylphthalimides; Thiadiazoles; Oxadiazoles; Triazolinones; Oxazolidinediones; Pyrimidindiones; Fomesafen Plant HRAC 2010115 Unclassified

Inhibition of signal transduction: G-proteins Quino lines, Quinazolinones Quinoxyfen Fungi FRAC 2010 Inhibition of signal transduction: Osmotic (MAP/his tidine kinase) Phenylpyrroles; Dicarboximides Iprodione Fungi FRAC 2010

Inhibition of sterol biosynthesis class I (demethyl ation): c14-demethylase Piperazines, Pyridines, Imidazoles, Triazoles Fenarimol Fungi FRAC 2010 Inhibition of sterol biosynthesis class II (amines) : ergosterol Morpholines, Piperidines, Spiroketalamines Piperalin Fungi FRAC 2010

Inhibition of sterol biosynthesis class III: 3-keto reductase Hydroxyanilides Fenhexamid Fungi FRAC 20 10 Inhibition of sterol biosynthesis class IV: Squalen e epoxidase Thiocarbamates, Allyamines Terbinafine Fungi FRAC 2 010

Inhibitors of acetyl CoA carboxylase - Lipid synthe sis, growth regulation Tetronic acid Spiromesifen Insect IRAC 2010

Inhibitors of cell wall synthesis: chitin synthase Peptidyl pyrimidine nucleoside Polyoxin B Fungi FRA C 2010 Inhibitors of cell wall synthesis: trehalase and in ositol biosynthesis Glucopyranosyl antibiotic Validamycin Fungi FRAC 20 10

Inhibitors of chitin biosynthesis, type 0, Lepidopt eran Benzoylureas Diflubenzuron Insect IRAC 2010

Inhibitors of chitin biosynthesis, type 1 Homoptera n Unclassified Buprofezin Insect IRAC 2010

Inhibitors of melanin synthesis in cell wall: dehyd ratase Cyclopropanecarboxamide, Carboxamide, Propionamide Fenoxanil Fungi FRAC 2010

Inhibitors of melanin synthesis in cell wall: reduc tase Isobenzofuranone, Pyrroloquinolinone, Triazolobenzothiazole Tricyclazole Fungi FRAC 2010

Inhibitors of mitochondrial ATP synthase Thioureas, Organotins, Sulfite esters, Bridged diphenyl Fenbutatin oxide Insect IRAC 2010

Inhibitors of oxidative phosphorylation: ATP syntha se Triphenyl tin compounds Fentin acetate Fungi FRA C 2010

Isocyanate based reactivity Isocyanates, Isothiocya nates Butyl isocyanate Fish Russom et al

, 1997

Juvenile hormone mimics Juvenile hormone analog, Carbamates, Pyridine insect growth regulator Methoprene Insect IRAC 2010

Microbial disruptors of insect midgut membranes (includes transgenic crops expressing B.t. toxins) Biopesticides Bacillus thuringiensis Insect IRAC 2 010

Microbial disruptors of pathogen cell membranes Bacillus subtilius and fungicidal lipopeptides they produce Bacillus sp. Fungi FRAC 2 010

Microtubule assembly inhibition Dinitroanilines, Phosphoroamidates, Pyridines, Benzamides, Benzenedicarboxylic acids Trifluralin Plant HRAC 20 10

Miscellaneous nonspecific (multi-site) inhibitors Alkyl halides, Unclassified, Inorganic compounds Chloropicrin Insect IRAC 2010

Mitochondrial complex I electron transport inhibito rs Unclassified, Phenoxypyrazole, Pyridazinones, Pyrazole, Biopesticides Rotenone Insect IRAC 2010

Mitochondrial complex II electron transport inhibit ors Pyrazole Cyenopyrafen Insect IRAC 2010

Mitochondrial complex III electron transport inhibi tors (Coupling site II) Unclassified, Naphthoquinone derivative, Strobilurin Hydramethylnon, Antimycin Insect, Fish IRAC 2010

Mitochondrial complex IV electron transport inhibit ors (cytochrome oxidase) Inorganic compounds, Unclassif ied Zinc phosphide, Sodium

azide Insect, Rodent,

Bacteria IRAC 2010

Mitochondrial complex V electron transport inhibito r - ATP synthase Sulfite ester Propargite Insect IRAC 2010

Moulting disruptor, Dipteran Triazine Cyromazine In sect IRAC 2010

Multi-site contact inhibitor activity Inorganic compounds, Dithiocarbamates and relatives, Phthalimides, Chloronitriles, Sulfamides, Guanidines, Triazines, Quinones Captafol Fungi FRAC 2010116 N-Halogenated Acetophenone Based Reactivity Beta-ha logenated acetophenones alpha-Bromo-pnitroacetophenone Fish Russom et al

, 1997

Nicotinic Acetylcholine receptor agonists Neonicoti noids, Botanical Dinotefuran Insect IRAC 2010

Nicotinic Acetylcholine receptor allosteric activat ors Spinosyns Spinosad Insect IRAC 2010

Nicotinic acetylcholine receptor channel blockers N ereistoxin analogues Thiosultap-sodium Insect IRAC 2010

Nitroso Based Reactivity Nitroso compounds N-Nitros omethylamine Fish Russom et al

, 1997

Octopaminergic receptor agonists Amidine Amitraz In sect IRAC 2010

Oxime Based Reactivity Oximes Nifuroxime Fish Russom et al

, 1997

Pheromone: Attract thru odor Pheromone analogs Calc ium lactate Insect IRAC 2010

Photosystem I electron diversion Bipyridilium Paraq uat Plant HRAC 2010

Polar narcosis Anilines, Phenols, Pyridines Phenol Fish Russom et al

, 1997

Reactive nitriles Allylic/Propargylic nitriles; alpha halogenated nitriles Malononitrile Fish Russom et al

, 1997

Ryanodine receptor modulators Diamide Chlorantranil iprole Insect IRAC 2010

Selective homopteran feeding blockers Pyridine azomethines, Pyridine

carboxamides Flonicamid Insect IRAC 2010

Sodium channel modulators - organochlorines Organoc hlorine Organochlorines Insect IRAC 2010

Sodium channel modulators - pyrethoids Pyrethroid P yrethroid Insect IRAC 2010

Sulfhydryl Based Reactivity Disulfides, Sulfenyl halides, Peroxides, Thiocyanates Dimethyl disulfide Fish Russom et al

, 1997

Synthetic Auxins Phenoxycarboxylic acids; benzoic acids; pyridine carboxylic acids; Quinoline carboxylic acids;

Unclassified Dicamba Plant HRAC 2010

Uncertain mode of action Various Azadirachtin, Dazomet, Fosetyl-Al All groups IRAC 2010,

HRAC 2010

Uncoupler of oxidative phosphorylation Arylpyrroles, Dinitrophenol For Plants: Dinitrophenylcrotonates,2,6-Dinitroanilines, Pyrimidinonehydrazones Meptyl dinocap, Dinoseb, Bromethalin Fungi, Plant,

Rodent FRAC 2010,

HRAC 2010, van

Lier 1988

Voltage-dependent sodium channel blockers Oxadiazin e, Semicarbazone Metaflumizone Insect IRAC 2010117



### Appendix C. Computational tools available via government, open access, or commercial sources that are available to risk asses sors in OW and OPP

Expert Systems for Predicting Toxicity

ASTER (ASsessment Tools for the Evaluation of Risk)

URL: http://cfint.rtpnc.epa.gov/aster/

Owner: USEPA ORD

Overview: Freely-available within USEPA firewall, or via Ave ntail AAA access. System is windows-based. ASTER is designed to provide high qu ality data for discrete chemicals, when available in the associated databases (i.e., ECOTOX and EcoChem), and QSAR-based predictions when data are lacking. Toxicity QSAR m odels are MOA-based. System can be run in either a batch mode or interactive / single chem ical mode. Outputs to Excel format or HTML. Coverage: Version 2.00 links to the most recent version of E COTOX and EcoChem databases for empirical data. Includes 9 acute QSARs models for 7 MOAs (nonpolar narcosis, polar narcosis, ester narcosis, uncouplers of oxidative p hosphorylation, reactive diesters, reactive carbonyls (3 equations), and reactive acrylates), a nd 3 chronic QSAR models for three MOA (nonpolar narcosis, polar narcosis, uncouplers of o xidative phosphorylation) for fathead minnow. Predicts toxicity for rainbow trout, blueg ill, daphnid, and catfish for 2 MOA (nonpolar narcosis and polar narcosis) based on species extra polation from fathead minnow LC50. When QSAR models do not exist, the system provides an es timated acute MOA. Models are primarily based on the fathead minnow database (Russom et al. , 1997). Input requirements: Log P and chemical structure via SMILES string, but if structure is in the supporting database, you only need enter the CAS nu mber and both values will be populated.

Known limitations : Models perform well within certain log P ranges, with a domain of applicability of log P ranging from 0 to 6.0. With in the domain of applicability, MOA assignments default to nonpolar narcosis, if substr ucture fragments associated with other MOA

are not identified. Models not available for metal s or organometallics. QSAR models do not function outside of the domain of applicability. MO A assessments will function for organic structures within the domain of applicability. Mod els were built for use under TSCA and therefore do not include QSAR models for MOA relate d to many pesticide activities. MOA SAR includes substructures related to insecticides, but do not include substructures associated with fungicides, rodenticides, and herbicides.

DEMETRA (Development of Environmental Modules for E valuation of Toxicity of pesticide Residues in Agriculture)

URL: http://www.demetra-tox.net/index.php?option=com_fro ntpage&Itemid=1

Owner: European Union Funded

Overview: Freely-available; online and PC-based application. Models are a hybrid of regression types (e.g., partial least squares (PLS), multi-lin ear regression (MLR), and classification algorithms (e.g., adaptive fuzzy partition). The m ain goal is to derive models that integrate the best algorithms obtained, and this forms the basis for a hybrid system software to be used for predictive purposes.118

Coverage: Includes QSAR models to predict the acute toxicity for rainbow trout (96 hr LC50), and daphnid (48 hr LC50.) QSAR models were specifi cally developed using a training set including 20 pesticide classes including: organotin s, organochlorines, organophosphates, carbamates, formamidines, terpenes, pyrethroids, ph enols, spinosyns, pyrroles, pyridazinones, benzoylureas, etc

. Input requirements: Chemical structure and electronic descriptors which must be calculated using external computational tools (not included in DEMETRA)

Known limitations : Models have been published, but the level of ind ependent validation of

models is unknown. Main limitation is the need to input molecular descriptors that must be calculated using third-party applications, based on recommendations by DEMETRA site. Recommended computational tools are commercially av ailable for cost. It is unknown if these parameters can be predicted using the OECD Toolbox.

EcoSAR: (Ecological Structure-Activity Relationship )

URL: http://www.epa.gov/oppt/newchems/tools/21ecosar.htm

Owner: USEPA OPPT

Overview: Freely-available, PC-based system. Models based o n data submitted by manufacturers following OPPT guidelines, much of wh ich is confidential business information (CBI), and data collections developed for use in QS AR modeling such as the USEPA fathead minnow database (Russom et al., 1997). System can be run in either a batch mode or interactive / single chemical mode. Outputs are delimited file formats and in ‘summary’ or ‘full’ reports. Coverage: Version 1.00 includes over 600 QSAR models; models are based on 120 chemical classes. EcoSAR includes acute predictions of LC/E C50 for fish (saltwater and freshwater), mysid shrimp (96 hr) and algae (72 and 96 hr), and daphnid (48 hr); chronic toxicity predictions of chronic value (MATC) for fish (saltwater and fre shwater), mysid shrimp, daphnid, and algae. Most acute models are based on a QSAR model, but so me estimations are based on ACRs. Input requirements: Log P and chemical structure via SMILES string, but if structure is in the supporting database, you only need enter the CAS nu mber and both values will be populated.

Known limitations : Models perform well within certain log P ranges, with a domain of applicability of log P ranging from -3 to 8.0. Mod els were built for use under TSCA and

therefore do not include models for MOA related to many pesticide activities. QSARs for some species are limited depending on the chemical class . Most models exist for neutral organics (narcotic-like compounds).

MCASETM

URL: http://www.multicase.com

Owner: MultiCASE, Inc.

Overview: Commercially available; MCASE is a knowledge-based system using fragment methodology to develop QSAR models for non-congener ic databases. MCASE (and MC4PC)

evaluate the structural features of a set of non-co ngeneric molecules and identify the substructural fragments, referred to as biophores within MCASETM, that may be responsible for the observed activity (i.e. chemical functionalities). The chemicals containing the same biophore are grouped into subsets for which independent QSAR mod els are developed. The descriptors of119

these models are called modulators and consist of f ragments found within the individual sets as well as calculated transport and partition properti es and quantum mechanical indices. The result of this operation is a set of QSAR models built for the congeneric sets of molecules containing

the same biophore (identified as the “chemical functionality” respons ible for the observed property. The domain of validity of the methodology is linked (and assessed) as a function of the probability that the corresponding biophore is related to activity and the determination that e very three bonded non-hydrogen atom groups has been seen and therefore evaluated by the model builder or not seen and therefore of questionable e ffect on the prediction results. Batch mode is

available. Coverage: Includes QSAR models for acute toxicity (LC50) for bluegill (96 hr), fathead minnow (96 hr), guppy (14 d), rainbow trout (48 hr) , and red killifish (48 hr). Also includes an NR50 (50% inhibition of netural red uptake) for pre dicting cytotoxicity in goldfish. Input requirements: Chemical structure; via SMILES, drawing, etc ., but if structure is in the supporting database, you only need enter the CAS nu mber and chemical information will be populated.

Known limitations : The data used in development of the aquatic toxi city models is unknown at this time, therefore it is unknown how the models w ould perform with pesticides. Fathead minnow has the largest number of compounds with 683 (most likely USEPA fathead minnow database).

OECD (Q)SAR Application Toolbox

URL: http://www.oecd.org/document/54/0,3343,en_2649_3437 9_42923638_1_1_1_1,00.html

Owner: OECD

Overview: . Freely –available; PC-Based application. Coverage: Includes ECOTOX database, and other large collectio ns for empirical data; includes EcoCHEM, EcoSAR, and many other chemical informatio n databases, includes models used in EcoSAR, ASTER, and other large computational tools. OECD is currently in version 1.1.02;

with version 2.0 to be released at the end of 2010. The OECD Toolbox is designed to be a decision support system, where users identify their chemical of concern, pull in empirical data from associated databases, predict data if models a vailable, provide predictive methods to build chemical categories and read-across tables, include s structural similarity methods, and QSAR

model builders. Input requirements: Chemical structure; via SMILES, drawing, etc ., but if structure is in the supporting database, you only need enter the CAS nu mber and chemical information will be populated.

Known limitations : System is currently in beta-testing, and limitat ions have not been fully documented, but limitations associated with models mentioned in this document that have been included in the OECD Toolbox, would apply.

TerraQSARTM

URL: http://www.terrabase-inc.com/

Owner: TerraBase, Inc.120

Overview: Commercially available; Stand-alone Windows-Based application. QSARs are based on a proprietary probabalistic neural network model

. Coverage: Includes a module to predict acute LC50 to Daphnia magna , and a model for 96 hr

LC50 to the fathead minnow. Input requirements: Chemical structure; via SMILES, drawing, etc ., but if structure is in the supporting database, you only need enter the CAS nu mber and chemical information will be populated.

Known limitations : The data used in development of the daphnid and fathead models is unknown at this time; therefore, it is unknown how the models would perform with pesticides.

TOPKAT

URL: http://accelrys.com/products/discovery-studio/pred ictive-toxicology.html

Owner: Accelrys, Inc.

Overview: Commercially available; TOPKAT runs on PCs under W indows 95/98/NT. TOPKAT is a toxicity prediction program, which uses electrotopological states (Kier and Hall, 1999) as well as shape, symmetry, MW, and logP as d escriptors to build statistically robust Quantitative Structure Toxicity Relationship (QSTR) models for over 18 endpoints. TOPKAT

will validate its assessments via a univariate anal ysis of the descriptors, a multivariate analysis of the fit of the query structure in Optimum Predictio n Space (OPS), and by similarity searching in descriptor space. QSAR models are preselected by th e software based on the chemical class. The program can be executed in batch mode and the resul t is available in a format that can be imported into Microsoft Excel for Windows. TOPKAT m akes visible experimental test data if

available for the chemicals of interested Coverage: DS TOPKAT includes models for fathead minnow LC50 and daphnid EC50. Input requirements: Chemical structure; via SMILES, drawing, etc ., but if structure is in the supporting database, you only need enter the CAS nu mber and chemical information will be populated.

Known limitations : TOPKAT produces information for the (Q)SAR appli cability domain at several levels: 1) the prediction is within the OPS of the model; 2) the model is within the limits of OPS; 3) all fragments identified in a molecule a re known to the model. Users need to be aware that predictions may be outside the domain of applicability; OPS is the preferred mode. The data used in development of the daphnid and fat head models is unknown at this time; therefore it is unknown how the models would perfor m with pesticides.



### Similarity Tools

AIM: (Analog Identification Methodology)

URL: https://aim.epa.gov

Owner: USEPA OPPT

Overview: AIM was designed to identify structurally analogou s compounds based on a three pass atom-fragment matching algorithm. The substru cture library contains 645 atom-fragment definitions along with super fragments for identify ing important ring systems. The library focuses mostly on molecular features of neutral org anic chemicals, but the predictive method will also provide results for salts and organometal lic compounds. However, AIM does not121

consider oxidation state which is often an importan t consideration when performing a toxicity assessment on metal containing compound and compoun ds with complex organic cations and anions may give unexpected result. Analog identifi cation is based on structures within the currently linked databases. Current data sets cove r 31,031 compounds, and therefore are limited to these structure sets. The empirical data are we ighted more towards human health endpoints and do not include as much ecotoxicology data, alth ough ECOTOX data set is to be included by 2011. The current AIM methodology requires exact m atching with respect to rings in the candidate compound. No substitutions are allowed (e .g. phenyl ring for a pyridine ring). The same number of rings is also required (e.g. dichlor odiphenylsilane will not be identified as an analog for trichlorophenylsilane). Methodology to r emove this limitation is under investigation. The number of analogs included in the analog list - If Pass 1 locates seven or more analogs, Pass 2 and Pass 3 are not currently implemented; therefo re, some additional good analogs may not appear in the results.. Coverage: AIM accesses 10 data sources: TSCATS (USEPA TSCA t est submission), HSDB (National Library of Medicine’s Toxicology Data Net work, Hazardous Substances Data Bank), IRIS (USEPA Integrated Risk Information System), Na tional Institute of Health’s NTP (National Toxicology Program), Centers for Disease Control an d Prevention’s ATSDR (Agency for Toxic Substances and Disease Registry), US EPA’s HPV (Hig h Production Volume) Challenge Program, US EPA’s DSSTox website (see below), NIOSH ’s RTECS (Registry of Toxic Effects of Chemical Substances), OECD’s IUCLID (Internation al Uniform Chemical Information Database) of HPV chemical data reported by European industry, and the National Advisory Committee for the Development of Acute Exposure Gui deline Levels (AEGL) for Hazardous Substances, AEGL rank.

Known limitations: AIM was designed to identify analogs only for neut ral organic compounds. Other chemical classes should not be run through AI M. Analog identification is based on structures within the currently linked databases. Current data sets cover 31,031 compounds, and

therefore are limited to these structure sets. The empirical data are weighted more towards human health endpoints and do not include as much e cotoxicology data, although ECOTOX data set is to be included by 2011. The current AIM met hodology requires exact matching with respect to rings in the candidate compound. No subs titutions are allowed (e.g. phenyl ring for a pyridine ring). The same number of rings is also re quired (e.g. dichlorodiphenylsilane will not be identified as an analog for trichlorophenylsilane). Methodology to remove this limitation is under investigation. The number of analogs included in th e analog list - If Pass 1 locates seven or more analogs, Pass 2 and Pass 3 are not currently implem ented; therefore, some additional good analogs may not appear in the results.

DSSTox (Distributed Structure-Searchable Toxicity) Structure-Browser

URL: http://www.epa.gov/ncct/dsstox/index.html

Owner: USEPA National Computational Toxicology Center (NC CT)

Overview: The USEPA DSSTox Structure-Browser, developed from available structure-viewing freeware and open-source programming methods, deliv ers a simple, easy-to-use structuresearching capability through the chemical inventory of published DSSTox Data Files. Search is initiated by entering either a chemical name, SMILE S string, CAS Registry number, InChITM (International Chemical Identifier) code, drawing t he chemical, or structural formula (e.g.,122

C4H5Cl). Users can also select how they want to se arch for similarity; i.e., exact match, substructure search, or similarity with defined thr eshold percentage. Coverage: System searches all the DSSTox databases including the European Bioinformatics Institute (EBI) ArrayExpress Repository for Gene Ex pression Experiments, NTP bioassay data, NTP high-throughput screening data, National Center for Biotechnology Information (NCBI ) Gene Expression Omnibus (GEO) Series Experiments , USEPA Estrogen Receptor Ki binding study, the University of California, Berkeley Carci nogenic Potency data set, USEPA fathead minnow acute toxicity data, USEPA water disinfectio n by-products carcinogenicity predictions, FDA’s Maximum Daily Dose data set, USEPA HPV data, IRIS, FDA estrogen receptor binding data, USEPA’s ToxCastTM, and the Istituto Superiore di Sanita chemical carcinogen data set.

Users can select to search on all data sets or limi t searches to specific data sets.

Known limitations: Current data sets cover 7,410 compounds, and theref ore are limited to these structure sets. The empirical data are weighted mo re towards human health endpoints and do not include as much ecotoxicology data.

OECD (Q)SAR Application Toolbox

URL: http://www.oecd.org/document/54/0,3343,en_2649_3437 9_42923638_1_1_1_1,00.html

Owner: OECD

Overview: . Freely –available; PC-Based application. Coverage: Version 1.1.02 includes 17 databases and 8 chemical inventories with a total of 323,403 chemicals included in similarity searches. System includes predictive methods to build chemical categories and read-across tables based on structural similarity or mechanistic information. System provides estimates of similari ty based on Tanimoto coefficient, Dice, Ochial, and Kulcynski-2 methods. Users can set simi larity threshold limits (% similarity) and molecular features (e.g., atom pairs, functional gr oups). Input requirements: Chemical structure; via SMILES, drawing, etc ., but if structure is in the supporting database, you only need enter the CAS nu mber and chemical information will be populated.

Known limitations : System is currently in beta-testing, and limitat ions have not been fully

documented.



### ICE Models

Web-ICE

URL : http://www.epa.gov/ceampubl/fchain/webice/

Owner : USEPA ORD

Overview : Freely-available; web-based application Coverage: Web-ICE version 3.1 includes 1443 models predictin g toxicity to aquatic species, genera, and families and 852 models predicting to w ildlife species and families. Aquatic models are based on 5501 EC/LC50 values of 180 species and 1266 chemicals. Wildlife models are developed from 4329 acute LD50 value for 156 specie s and 951 chemicals. Models described in

Section 4.3 are available on Web-ICE . All models w ithin Web-ICE are statistically significant123

and those with at least four datapoints have been c ross-validated, the results of which are provided to the user to assist with model selection

.

Web-ICE also contains modules that develop Species Sensitivity Distributions from ICEpredicted toxicity and entered measured data. The e ndangered species module of this application predicts toxicity to selected listed species using all available models and multiple surrogates. The MOA-specific models, as well as full documentation of the data used to develop the models will be available in an update of the application to be posted in late 2010. ICE models for algal species will be posted to Web-ICE in 2011. User gui dance provides assistance with model selection and results interpretation. Input requirements: Acute EC/LC50 value in ug/L for aquatic species, ac ute LD50 in mg/kg body weight for wildlife.

Known limitations : Models are restricted to species-pairs available from the database. Variability in underlying databases influences mode l uncertainty.

Risk Assessment Tools (RA)

URL: http://www.setac.org/node/97

Owner : SETAC press (Mayer et al. 2010)

Overview : CD-based application available for purchase ($75) Coverage: ICE models contained in RA are developed from 6 da tasets: 1) acute aquatic organism (4890 EC/LC50 tests), 2) aquatic plant (14 39 EC/LC50 tests), 3) wildlife acute (997 LD50 tests), 4) wildlife subacute (490 LC50 tests), 5) aquatic chronic (214 tests), and 6) wildlife chronic (98 tests). The RA documentation does not p rovide the number of significant models developed for each dataset. Input requirements: Acute values corresponding to each dataset.

Known limitations : Models are restricted to species-pairs available from the database. Variability in underlying databases influences mode l uncertainty. Chronic tests were not

standardized for test duration or most sensitive en dpoint measured. Model cross-validation, uncertainty analyses, and influence of MOA have not been conducted for this application.



### TCE Models

Acute-to-Chronic Estimation (ACE) v 2.0

URL: http://www.epa.gov/ceampubl/fchain/ace/index.htm

Owner : USEPA ORD

Overview : freely available internet executable application (Ellersieck et al., 2003) Coverage: ACE v 2.0 contains three TCE models: ALT, LRA, and MPA. ACE software documentation indicates that the models predict to the chronic NOEC for lethality. Input requirements: time-course data for acute toxicity studies to incl ude the number of individuals alive at each time step and each concen tration.124

Known limitations : Model output is for chronic lethality. Model uncert ainty analyses have not been thoroughly conducted.

Risk Assessment Tools (RA)

URL: http://www.setac.org/node/97

Owner : SETAC press (Mayer et al. 2010)

Overview : Cd-based application available for purchase ($75) Coverage: The ACE application within RA contains two TCE mod els: ALT and LRA. RA documentation indicates these models predict to the chronic MATC for lethality. RA and ACE v 2.0 were developed by the same authors, however pot ential differences in algorithms that result in predictions to MATC and NOECs, respectively, are unclear. Input requirements: time-course data for acute toxicity studies to incl ude the number of individuals alive at each time step and each concen tration.

Known limitations : Model output is for chronic lethality. Model uncert ainty analyses have not been thoroughly conducted.125 Appendix D: Predictive methods for use in determin ing mode of action and physical chemical properties

TITLE Brief description Web address Owner

Tools and Websites for use in Determining MOA of ch emical of interest

ASTER Predicts acute mode of action based on fish model h ttp://www.epa.gov/med/Prods_Pubs/as

ter.htm

http://cfint.rtpnc.epa.gov/aster/ EPA

Classification of

Herbicides Table of herbicidal mode of action, chemical family and

associated active ingredients http://www.hracglobal.com/Publica

tions/ClassificationofHerbicideMod eofAction/tabid/222/Default.aspx HRAC

Compendium of

Pesticides Provides CAS number, chemical names, structures,

INCHI notation, pesticidal activity, molecular form ula,

etc. for more than 1100 substances. Site is indexe d by chemical name, CAS number and pesticide classificat ion http://www.alanwood.net/pesticides

/index.html Alan Wood

Fungicide

Resistance Action

Committee Includes link to fungide mode of action maps as wel l as

FRAC code list of modes of action, with chemical groups and associated active ingredients http://www .frac.info/frac/index.htm FRAC

Herbicide MOA

classification Mode of action, chemical family, active ingredient and

formulated products for herbicides http://www1.agric.gov.ab.ca/$depar tment/deptdocs.nsf/all/prm6487 Alberta

Canada

Herbicide MOA

classification Mode of action, chemical family, active ingredient, and

formulated products for herbicides along with info on half-life http://www.omafra.gov.on.ca/englis h/crops/facts/00-061.htm Ontario

Canada

Herbicide MOA

classification Provides search tool for herbicide with output of H RAC

Group, mode of action, example trade names, and company producing AI http://www.weedscience.org/summ ary/ChemFamilySum.asp Weed

Science

Insecticide

Resistance Action

Committee MOA classification poster, as well as species speci fic

information on mode of action http://irac-online.or g/ IRAC

IRAC (Insecticide

Resistance Action Committee) Lists mechanism of action of insecticide; drop down

boxes provide information on associated chemical classes and specific active ingredients http://www.iraconline.org/eClassification/ IRAC

OECD Toolbox The Toolbox is a software application intended to b e

used by governments, chemical industry and other stakeholders in filling gaps in (eco)toxicity data needed for assessing the hazards of chemicals. The Toolbo x incorporates information and predictive methods fro m various sources into a logical workflow. Crucial t o this workflow is grouping chemicals into chemical categories. Has several mechanistically-based cate gories http://www.oecd.org/document/54/ 0,3343,en_2649_34379_42923638_ 1_1_1_1,00.html OECD

Plant Hormone

information Provides detailed information on plant hormones

including function http://www.plant-hormones.info/ BBSRC

Ware and

Whitacre Overview of Insecticide classes, mode of action, an d

structures http://ipmworld.umn.edu/chapters/

ware.htm Univ MN

Wildlife Active Ingredients Provides overview of active ingredients, including mode

of action http://icwdm.org/handbook/pestche m/active.asp Internet

Center for

Wildlife Damage126 Management

World of Herbicides Map Graphic of herbicide mode of action. Chemical

structures are lumped based on activity http://www.hracglobal.com/Publica tions/WorldofHerbicidesMap/tabid/ 354/Default.aspx HRAC

Sources of Physical Chemical Property Data

ASTER Predict p-chem and fate properties to assist in env ironmental

assessment http://cfint.rtpnc.epa.gov/aster/ USEPA

EPISuite Predict p-chem and fate properties to assist in env ironmental

assessment. Following models included: KOWWINTM, AOPWINTM, HENRYWINTM, MPBPWINTM, BIOWINTM, BioHCwin, KOCWINTM, WSKOWWINTM, WATERNTTM, BCFBAFTM, HYDROWINTM, KOAWIN and AEROWINTM, and the fate models WVOLWINTM, STPWINTM and

LEV3EPITM http://www.epa.gov/oppt/expo sure/pubs/episuite.htm USEPA Sparc Performs

Automated Reasoning in Chemistry (SPARC) Calculates a large number of physical/ chemical par ameters

from pollutant molecular structure and basic inform ation about the environment (media, temperature, pressure , pH, etc.). http://ibmlc2.chem.uga.edu/sp

arc/ USEPA

OECD Toolbox The Toolbox is a software application intended to b e used by

governments, chemical industry and other stakeholde rs in filling gaps in (eco)toxicity data needed for asses sing the hazards of chemicals. The Toolbox incorporates inf ormation and predictive methods from various sources into a logical workflow. Crucial to this workflow is grouping che micals into chemical categories.

The seminal features of the Toolbox are:

1. Identification of relevant structural characteri stics and potential mechanism or mode of action of a target c hemical.

2. Identification of other chemicals that have the same structural characteristics and/or mechanism or mode of action. 3. Use of existing experimental data to fill the da ta gap(s). http://www.oecd.org/documen t/54/0,3343,en_2649_34379_4 2923638_1_1_1_1,00.html OECD

NoMiracle

Toolbox NoMiracle, an integrated European research project, will

develop novel methods and computational tools to be tter
evaluate chemical risks http://nomiracle.jrc.ec.europa. eu/Lists/Toolbox/Exposure.as px EU