Predicting the Toxicities of Chemicals to 

Aquatic Animal Species 

Dale Hoff4

Wade Lehmann1 

Anita Pease2

Sandy Raimondo3

Chris Russom4

Tom Steeger2

U.S. Environmental Protection Agency 

1

Office of Water, Washington, DC 

2

Office of Pesticide Programs, Washington, DC 

3

Office of Research and Development, Gulf Ecology Division

4

Office of Research and Development, Mid-Continent Ecology Division 

October 27, 2010 

 
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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 Methods .................................................... 14

4.1 Consideration of Mode of Action in Predictive Toxicology ............................................ 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 Approaches 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 Predictive Methods ................................ 57
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8.1 Empirical Data and Important Values from Existing ALWQC 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 Methodology .................... 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, endosulfan sulfate, toxicities using WebICE 71

9 References ............................................................................................................................. 75

Appendix A: EFED Guidance on Use of Structure-Activity Relationships

Appendix B: Overview of Modes of Action, Associated Chemical Classes, and Pest Organisms 

Appendix C: Predictive Methods available via government, open access, or commercial sources 

that are available to risk assessors in OW and OPP 

Appendix D: Predictive methods for determining MOA and physical chemical properties

 
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List of Figures 

Figure 1. Abbreviated Toxicity Pathway (from NRC, 2007). ...................................................... 16 

Figure 2. Adverse Outcome Pathway (modified from Ankley et al., 2010). ................................ 17 

Figure 3. Plot of water solubility vs. baseline toxicity (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 describe a series of “similar” pesticides. Data gaps 

within the data set are identified and filled based on information from data rich chemicals. 

(Modified from van Leeuwen et al, 2009). ................................................................................... 25 

Figure 6. Example of structural analogs involving synthetic pyrethroids. ................................... 29 

Figure 7. Schematic of similarity index measures. ....................................................................... 30 

Figure 8. Example Interspecies Correlation Estimation Model. ................................................... 34 

Figure 9. Models developed from chemicals of diverse modes of action compared to those with 

MOA-specific data. ....................................................................................................................... 37 

Figure 10. Log P predictions for Scilliroside using 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 definition screen for Diazinon in the OECD 

toolbox. ......................................................................................................................................... 66 

Figure 16. Read-across evaluation screen for Diazinon in the OECD toolbox. ........................... 67 

Figure 17. Analogue structure similarity indexs in the OECD toolbox. ....................................... 67 
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List of Tables 

Table 1. An example read-across for pyrethroids. Ordered by the physical chemical property 

LogP, which describes the partitioning aspect of the toxicity. ..................................................... 26 

Table 2. Target (pest) organism mode of action and associated chemical class for a select group 

of insecticides (adapted from IARC http://eclassification.irac-online.org/) ................................. 28 

Table 3. Comparison of LC50 data for structural analogs, permethrin and bifenthrin. ................ 29 

Table 4. Number of toxicity records and chemicals included in ICE model development .......... 35 

Table 5. Acute and chronic values and ACRs for freshwater nonylphenol. ................................. 42 

Table 6. Differences in OPP and OW ACR derivation methodology. ......................................... 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 found 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 predict 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 predictions for endosulfan and endosulfan sulfate 

using Web-ICE and measured data for aquatic species.... ........................................................... 72 

 
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Glossary 

Adverse Outcome Pathway: A conceptual construct that portrays existing knowledge concerning 

the linkage between a direct molecular initiating event 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 parameter 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 random variable using a model of 

the relationship between an existing set of empirical data and a new value of the unknown random 

variable.

Mechanism of action: A complete and detailed understanding of each and every step in the 

sequence of events that leads to a toxic outcome, which includes detailed knowledge of the 

causal and temporal relationships among all the steps leading to a specific effect (Ankley et al, 

2010). 

Mode of action: A common set of biochemical, physiological, or behavioral responses that 

characterize an adverse biological response where major, but not necessarily all, linkages 

between a direct initiating event and an adverse outcome 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 effect (e.g., for an insecticide the 

target species are insects). 

Predictive methods: Methods that can be used to obtain a predicted toxicity value when an 

experimentally determined toxicity value is not available for a taxon. 

Structure-Activity Relationship: Methods that relate structural features of molecules 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. Qualitative SARs are derived from noncontinuous data (e.g., “yes” or “no” data such as structure 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 estimate. 

Tools: A computational application capable of calculating toxicity by applying model 

algorithms. 
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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 Rodenticide 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 coefficient 

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 Prevention (formerly Office of 

Prevention, Pesticides, and Toxic Substances [OPPTS]) 

OECD Office of Economic Cooperation and Development

OW Office of Water 

OPP Office of Pesticides 
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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 Structure-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 reaction products or Biological 

WOE Weight-of-evidence 

 
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1 Executive Summary 

The purpose of this white paper is to present an overview of predictive methods that may be 

useful to U.S. Environmental Protection Agency (USEPA) 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 degradates to aquatic animals. The predictive 

methods discussed in this document may be used to derive 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 of 

Chemical Stressors to Aquatic Animals.” These predictive methods will be integrated into 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 requirements established by the offices. These 

predicted values are intended for use in deriving ALSVs and supplementing data submitted 

under FIFRA 40 CFR Part 158 Subpart G (CFR 2010), or when additional data are required to 

reduce uncertainty in OPP's ecological effects assessments. Although the focus of this white 

paper is on predictive methods and/or tools that predict 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 chemicals where measured data are not routinely 

available, their use has been inconsistently applied. Even where predictive methods have served 

as a source of information on a compound, their utility in providing data that can be used 

qualitatively versus quantitatively is limited to the predictive capacity of the models in a weightof-evidence (WOE) approach. This approach considers the predictive capacity of the model for 

chemicals with known physical/biological characteristics as compared to similarly structured 

compounds where such characteristics are unknown. 

The predictive methods discussed in this white paper include quantitative/qualitative structureactivity relationships ((Q)SARs), read-across/bridging, 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 intended 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 develop a common effects characterization 

methodology for use in ecological assessments of chemicals (e.g., pesticides) by EPA to meet the 

mandates of the Clean Water Act (CWA) and the Federal Insecticide, Fungicide, and 

Rodenticide Act (FIFRA). The resulting framework would integrate the Agency’s aquatic 

effects characterization methods and provide a common basis for achieving the water quality 

protection goals established under the CWA and FIFRA statutes. The effort focuses on datalimited situations where methods have not been clearly 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 nonvascular aquatic plants). Once sufficiently 

validated and vetted, the methods could then be used 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 Aquatic Animals,” explores possible means 

through which limited data sets could be either enhanced using predictive tools or 

accommodated using adjustment factors to characterize the lower range of sensitivities to 

specific chemicals that may exist in an aquatic community. Additionally, a third white paper, 

“Deriving Plant Aquatic Life Screening Values for Pesticide Effects,” has been developed 

exploring the uncertainties associated with and methods for characterizing effects to the aquatic 

vascular and nonvascular plant community from chemical exposure. . The tools and methods 

discussed in these papers are intended to compensate 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 uncertainties associated with extrapolating limited 

taxa-specific data to predict effects on aquatic communities and to provide recommendations on 

methods for either enhancing and/or accommodating limiting data to enhance their ability to 

better represent species sensitivities across animal and plant taxa. This paper on predictive tools 

provides a broad overview of available tools that may 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 explore how, either through assessment factors or 

species sensitivity distributions, values may be derived that can be considered representative of 

potentially sensitive species within the aquatic environment. 

Under FIFRA, the Office of Pesticide Programs (OPP) receives test data for use in their risk 

assessments based on the guideline requirements specified in 40 CFR Part 158; however, the 
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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 pesticides have a smaller data set than that required 

to develop 304(a) aquatic life water quality criteria (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 approaches to predict the hazard of pesticides to 

aquatic organisms for which data is lacking, thus building 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 develop surrogate values that will be used in 

an effects assessment methodology that provides consistency between OPP and OW approaches. 

In addition, these predictive techniques could also be used to characterize uncertainties in 

registrant-submitted data, thereby identifying potential 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 would 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 "Toxicity Testing in the Twenty-first Century: 

A Vision and a Strategy” (NRC, 2007). 

The use of modeling techniques to predict the potential 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 under 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 90 days of receipt of a pre-manufacturing 

notification (PMN) (Nabholz et al., 1997). The PMN includes chemical structure, intended use, 

anticipated production volume and release information, 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 data (Zeeman et 

al., 1995). Because of these constraints, OPPT has leveraged 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 action (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 throughout OPP, although 

infrequently. Use of QSAR models within OPP, specifically in ecological effects assessments, is 

typically based on the WOE approach where the predictive capacity of the models is a critical 

factor for determining whether the output may be used 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 attributes and 

desired qualities for models used in regulatory decision making, such as peer review from the 

scientific community, transparency, and model availability to the public 

(http://www.epa.gov/oppefed1/models/water/model_attributes.htm .) 
Page 12 of 126 

This white paper describes various predictive methods, their history and background associated 

with development and use, and the importance of category approaches and mode of action in the 

application of these models. In addition, this white paper addresses criteria to be used in 

assessing the appropriateness of various modeling approaches, the state-of-the-art of existing 

applications / predictive methods, and how these predictive 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 assessment. The goal is to reduce the use of whole 

organism testing and the overall cost of the risk assessment 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 relationships are sufficiently robust to support a risk 

assessment, moving from a guidelines-driven risk assessment, 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 formulation to identify what is known about the 

environmental fate and effects of a particular chemical 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 similarly structured compounds. While guideline 

data are typically submitted for parent compounds undergoing registration, a full complement of 

guideline studies are rarely available for degradates of potential concern; predictive modeling 

can enable degradates to be considered qualitatively. Further, while guideline studies provide 

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

taxa is limited. Most baseline datasets for chemicals 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/marine fish and two estuarine/marine 

invertebrates. Internationally, requirements for pesticide registration data are similar. For higher

profile chemicals, a broader range of data may be available from both the registrant-submitted 

studies and open literature. However, for newer chemicals, open literature studies are oftentimes 

limited. Registration of pesticides in other jurisdictions often offers additional data. 

OPP’s aquatic risk assessments evaluate individual taxa and are based on the most sensitive 

vertebrate and invertebrate endpoints for freshwater and estuarine/marine species. Reliance on 

the most sensitive species is intended to be conservative and does not reflect intra- and interspecies variability. When sufficient data exist, assessments are refined using species sensitivity 

distributions (SSDs) to provide a qualitative description 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 of pesticides (and degradates), but rather to 

qualitatively characterize the conservatism of the most sensitive endpoint. With respect to 
Page 13 of 126 

degradate toxicity, OPP uses the best available information to determine whether degradates are 

more, less, or equally toxic as compared to the parent 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 currently 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 assessment 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 registrants in support of pesticide registrations 

under FIFRA to develop SSDs to support the development 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 circumstances, OW is currently constrained from 

moving forward to develop ALWQC. The 1985 Guidelines and the Aquatic Life Screening 

Values white paper describe the MDRs in detail, including a description of how the MDRs are 

used to generate either an ALWQC or an ALSV. In short, a freshwater Criterion Maximum 

Concentration (CMC) is derived from acute (e.g. LC50) 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 or chordata 

• family in any order of insect or phylum not represented 

 

A saltwater CMC may be derived when data are available 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 Concentration (CCC), may be derived when the 

following information is provided: 

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

• chronic studies from three families of aquatic animals for which acceptable acute data are 

available and one being an acutely sensitive species (this approach utilizes acute to 
Page 14 of 126 

chronic ratios (ACRs) discussed in Section 4.4 of this 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 provide reliable and relevant endpoint data and 

result in transparent and scientifically defensible effects assessments. 

4 General Concept and Background of Predictive Methods 

This section provides an overview of approaches, history 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), interspecies correlation estimation (ICE) models, 

acute-chronic ratios (ACRs), and time-concentrations event (TCE) models. Section 5 provides 

general descriptions of the applications through which these models are available. An important 

consideration in the selection of appropriate models 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 equation 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 action to the adverse outcome pathway (AOP), 

with a reasonable level of confidence, is an important step in developing causal linkages of 

chemical exposure to the observed apical endpoint response (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 Toxicology 

Determining mode of action (MOA) for most substances 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 been developed to help ‘bin’ chemicals into 

acute MOA (Verhaar et al., 1992; Russom et al., 1997). Pesticides are, by definition, substances 

designed specifically to affect an organism’s biological 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 molecular site of action is across non-target 

species will be a critical step in leveraging (Q)SARs to fill data gaps. Therefore, an important 

requirement for use of mechanistic approaches is having an understanding of the biological 

processes for relevant species. Another important consideration 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 insecticide with a registered use on golf courses to 

reduce certain insect pests may appear in streams via run-off events, and thereby cause effects on 

aquatic insects that are not the intended target species. For the purposes of this document, 

aquatic organisms will be considered non-target species, with the exception of aquatic plants 

when herbicides (e.g., algaecides) are the pesticide discussed and fish when piscicides are 

discussed. 
Page 15 of 126 

The understanding of a chemical’s mechanistic profile 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 provide 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 action, and, if used with SAR/QSAR 

approaches, this pathway information can infer greater confidence in predicted adverse 

outcomes. 

These mode of action-based techniques have permitted 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 Könemann, 1981; Veith et al, 1983; Russom et al., 

1988; Schultz 1987). The development of expert systems that can assess the adverse effects of 

large numbers of chemicals in a computationally-efficient manner, and do so with an 

understanding of toxic mechanisms is essential considering the large number of chemicals in 

commerce for which no ecotoxicological data are available. Through the use of these modeling 

techniques, the acute toxicity of chemicals with non-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 action have been used interchangeably in 

the past, and have had conflicting definitions (Guyton et al., 2008). For the purpose of this white 

paper, the terminology cited in the National Research Council report “Toxicity Testing in the 21st

Century” is used. The term “mode of action” is defined 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 in a disease state or other adverse effect 

(NRC, 2007). A “key event” is an empirically observable precursor step that is itself a necessary 

element of the MOA or is a biologically based marker for such an element (NRC, 2007). A 

“mechanism of action” differs from MOA in that it contains a more detailed and complete 

understanding and description of each of the key events, 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 determination. Significant information should be 

developed to ensure that a scientifically justifiable MOA underlies the process leading to the 

adverse outcome. 

4.1.2 Adverse Outcome Pathway 

The NRC report released in 2007 outlined a vision and strategic plan for toxicity testing in the context of 

context of emerging predictive methods for use in supplementing or replacing existing animal testing 

testing procedures (NRC, 2007). As part of this report, 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 sufficiently perturbed, and when the organism is unable 

to adapt, would lead to toxicity and/or disease (NRC, 2007). In order to ensure consideration of 

the “toxicity pathway” to identify typical apical endpoints observed in the whole organism, and, 

ultimately, to population responses, this concept was expanded to an Adverse Outcome Pathway 

(AOP) framework (see Figure 2. Adverse Outcome Pathway (modified from Ankley et al., 

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 responses at the cellular, tissue, whole organism, and 

population levels. Based on the definitions provided 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 review of all aspects of the exposure and 

experimental design, since the selected AOP could differ 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, 2007). 

Exposure

Tissue Dose

Biologic Interaction

Perturbation

Biological 

Inputs

Normal 

Biological

Function

Early Cellular

Changes

Cell

Injury

Morbidity &

Mortality

Adaptive Stress 

Response
Page 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 Ankley 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 support of the final determination. The 

International Program on Chemical Safety (IPCS), a joint program between the World Health 

Organization, International Labour Organization, and the United Nations Environmental 

Programme, has developed an approach based on the Bradford 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 parameters, 

3. Identify concordance of the dose-response relationships with key event, correlate dose 

dependency on increase in magnitude of key event; 

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

5. Describe the WOE linking the key events including the strength, consistency, and 

specificity of association of key events and adverse outcome; 

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

a. the molecular site of action, 

b. systems biology, 

c. relationship of the MOA and the observed adverse outcome, 
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, inconsistencies, 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 tool for organizing and judging whether the 

available data/information actually supports the postulated MOA, and subsequently can be used 

in the development of an AOP. The goal is a flexible 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 1990s, researchers in the Netherlands and the 

US developed schemes for grouping substances into general MOA categories (Verhaar et al., 

1992; Russom et al., 1997). Although developed after the IPCS framework document, these 

approaches used WOE approaches and processes similar to those outlined in the Bradford Hill 

criteria (Hill, 1965). 

The approach developed by Verhaar and co-workers placed 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 acidity. 

o Reactive chemicals: chemicals that react unselectively with biomolecules, or substances 

that are bioactivated via metabolism. 

o Specifically acting chemicals: chemicals that interact with receptor biomolecules. 

These general classes were developed primarily using excess toxicity ratios (Veith et al., 1983, 

Lipnick et al.1987, Russom et al., 1988) and structural alerts gleaned from the literature, as well 

as empirical test results from an extensive toxicity database using the guppy (Poecilia reticulate) 

(Könemann, 1981). The excess toxicity ratio is used frequently to identify substances that are 

potentially acting differently then non-polar narcotics. 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 compares to water solubility for the 

fathead minnow. As lipophilicity increases, the toxicity approaches the water solubility in an 

acute exposure. The narcosis MOA is driven by hydrophobic interactions between chemicals 
Page 19 of 126 

and biological membranes (Yamakura et al., 2001). Traditionally, the octanol/water partition 

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 from acute 

toxicity tests (see Eq. 1). Verhaar found that inert chemicals have Te values 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 = LC50baseline narcosis prediction / LC50experimental Eq. 1

-7

-6

-5

-4

-3

-2

-1

0

1 2 3 4 5 6

Log P

Log Molar Concentration

Water Solubility Baseline Toxicity

Figure 3. Plot of water solubility vs. baseline toxicity (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 information 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 narcosis (Veith and Broderius, 1990), uncouplers 

of oxidative phosphorylation (Terada, 1990), electophilic, (Hermens, 1990; Carlson, 1990), 

carcinogenic (Kadlubar et al., 1990), redox cycling (Mason 1990), and various pesticidal 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 assessment. 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 set (N=620) (Russom et al., 1997). 

Additionally, joint toxic action studies and toxicodynamic profiles conducted independently on a 

subset of the database, structural alerts identified from the 1988 workshop, and evidence on 

structural analogs found in the open literature were used in a WOE approach in assigning MOA 

(Russom et al., 1997). 

It is important to note that there is no single list that identifies all existing MOAs and related 

AOPs (see Schmidt, 2009). Both the Verhaar and Russom approaches focused on industrial 

organic chemicals; however, these approaches did not 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 of 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 Te

No. of chemicals

Narcosis Polar narcosis Uncouplers AChE Electrophile
Page 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 pesticide active ingredients, and previous work done 

on industrial organics. It should be noted that this list is not intended to be comprehensive of all 

potential MOAs, but rather a starting point to build the WOE necessary to develop an AOP. 

Keeping in mind the Bradford Hill Criteria, risk assessors should be (1) identifying a potential 

AOP and associated key events; (2) determining whether 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 available for specific AOPs. 

4.2 (Quantitative) Structure-Activity Relationships 

The basic assumption of any structure-activity approach is that the chemical’s structure imparts 

properties that relate directly to the chemical’s activity, 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 qualitative (SAR) 

and quantitative models (QSAR), with (Q)SAR referring 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 will bind or not bind to a receptor). 

Examples of SAR approaches include prioritization and ranking of chemical lists (e.g., Russom 

et al., 2003; Schmieder et al., 2003a,b) or data gap analysis using read-across approaches (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), related to a chemical, physical or structural property 

(e.g., the ER-BA endpoint which is a quantitative prediction of the relative binding affinity of 

compound X to the endocrine receptor). These relationships rely on information on many 

chemicals to predict the activity of a single chemical lacking data. The goal is to quantify 

‘structural similarity’ imparting biological activity by defining structural analogs or chemical 

categories that may act ‘similarly.’ In the context that similar structures result in similar activity, 

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 the 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 acknowledge that other activity-to-activity 

extrapolations, such as predicting whole organism responses 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 reviews (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 by risk assessors in assessing potential toxic 

effects of organic chemicals on aquatic organisms evolved as computational efficiency and 
Page 22 of 126 

toxicological understanding advanced, and has proved to be a scientifically-credible tool for use 

in predicting toxicity for structurally similar substances with little or no available empirical data. 

The development of SMILES (Weininger, 1988) as a means to identify structure information in a 

computer readable format, and the advancement of desktop 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 building on the efforts developed under TSCA to 

address the approximately 9,000 chemical safety dossiers that must be completed in December of 

2010 (Royal Society of Chemistry, 2010). Many of these substances have data gaps and the use 

of (Q)SAR approaches will be critical in meeting the legislative deadlines (van Leeuwen et al., 

2009). 

4.2.1 Chemical-Structure Methods

Critical to any (Q)SAR technique is clearly defining 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) (Weiniger, 1988) or InCHITM code (Stein et 

al., 2003)), or 3-D formats (e.g., structures that include geometry and spatial information). 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 typically exist in 

equilibrium within solutions. Chemicals can also be flexible, allowing rotation around single 

bonds. When there is more than one point of rotation, this can lead to a large number of 

conformers. In biological tissues, a chemical could exist as multiple conformers, which is an 

important consideration as it relates to activity within 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 structures 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 critical 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 structures beyond that of the active ingredient. 

“Inerts”, which are components of the formulated product that are not the active ingredient, 

include solvents, adjuvants, and other chemicals and are designed to improve the effectiveness of 

the active ingredient, but are not intended to affect the pest organism in the same way as the 

active ingredient alone (Weinhold, 2010). Inert ingredients may make up a significant portion of 

some formulated products. Within the context of this paper, the use of the terms “active 

ingredient” or “chemical” refer to a single discrete chemical structure and not a formulated 

product representing mixtures of chemicals. When developing (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 and OECD risk assessment activities. 

An analysis done by van der Jagt and coworkers on REACH 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 use of 3.9 million test animals to fulfill data 

requirements if alternative methods are not used, but that 72% of the testing needs do not have 

reliable or acceptable alternative methods available, specifically validated QSAR models for a 

wide range of chemical categories / AOPs, and acceptable in vitro test methods that can be used 

in risk assessments. An analysis of the Canadian Domestic Substances List, where four QSAR 

computational tools (i.e., ASTER, EcoSAR, PNN and TOPKAT) 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 more specific mechanisms (Moore et al., 2003). 

At the same time, the EU and USEPA have a commitment 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 chemical 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 assessment context (van Leeuwen et al., 2009). As 

with REACH efforts, the QSAR models specific to many pesticide active ingredients are not 

included in the existing (Q)SAR modeling applications, 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 approach, while efforts to develop validated and 

reliable QSAR models are underway. 

A chemical category is defined as a group of substances with physicochemical, human health, or 

ecotoxicological attributes that are similar or follow a pattern as a result of structural similarity 

(OECD, 2007a). The “similarity” can be based on chemical structure/substructure (e.g., 

common functional group or chemical class), properties including behavior in physical or 

biological process (e.g., similar precursors or breakdown products), an incremental or constant 

change in potency (e.g., increased carbon chain length), and/or the function / 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 information from many tested chemicals and 

inferring information for an untested substance (see 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 final effect assessment and testing strategies. 

In order to be useful, a consistent approach for defining 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 physicochemical 
Page 24 of 126 

properties, fate, and ecological and human health effects for each chemical within the proposed 

category, evaluating the data for reliability, relevance and adequacy, using a read-across 

approach to identify potential data gaps, and finally, evaluating the data to determine if there is 

sufficient evidence to support the development of the 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 harmonized test guideline for fish acute toxicity). 

Examples of building categories can be found in Enoch (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. The 

solid dots represent properties/activities for which 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 experimental data for a given endpoint across 

chemicals can also be used as the basis for interpolation (when the value to be estimated is 

bracketed on either side by empirical data) and also extrapolation (when the value to be 

estimated is bracketed on only one side by empirical 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 initially 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 describe a series of “similar” pesticides. Data gaps within the data 

set are identified and filled based on information from data rich chemicals. (Modified from van Leeuwen et 

al, 2009). 

Read-across / data bridging approach: Endpoint information for one chemical (the 

source chemical) is used to predict the same endpoint for another chemical (the target 

chemical), which is considered to be "similar" in some way (usually on the basis of 

structural similarity or on the basis of the same AOP). It may be performed in a 

qualitative or quantitative manner. (OECD, 2009b) 

Interpolation is defined as the estimation of an intermediate term. 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 estimation 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 activity/properties of chemicals with 

structures that are intermediate [between] those where 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 endpoints 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 extrapolation is depicted in Figure 5 for 

filling data gaps relative to Property 3 and Activity 3 for Chemicals 1 and 4. Thus, 

Chemical 1 Chemical 2 Chemical 3 Chemical 4

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 data
Page 26 of 126 

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

information based on measured data. 

QSAR / Trend Analysis: Within a chemical category, QSARs can be developed 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 with 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 activity being modeled. Justification for use 

of property and activity data in predicting a particular activity for an unknown substance 

could include the applicability domain of the particular category developed, AOP of the 

chemicals under consideration, and commonality in substructures 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. Data from chemicals on either side of 

deltamethrin can be used to interpolate an LC50 value, or data for allethrin, dimethrin, 

resmethrin, and permethrin can be used to extrapolate a predicted value for bifenthrin. Using 

simple regression techniques and log P as the independent variable, the predicted toxicity of 

deltramethrin is 27 ug/L, and the predicted toxicity for bifenthrin is 7.9 ug/L. An important 

aspect of this category is that it is mechanistically-based, since all component chemicals 

modulate the sodium ion channel resulting in neurotoxicity. 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 minnow database 

(i.e., resmethrin and permethrin results; see Russom 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 Materials) guidelines for fish acute toxicity testing 

(ASTM 2007). But differences exist in test procedures. 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 volatile substances), but may not be critical for these 

higher log P chemicals. These types of detailed analysis will need to be conducted when using 

read-across approaches. 

 

Table 1. An example read-across for pyrethroids. Ordered by the physical chemical property LogP, which

describes the partitioning aspect of the toxicity. 

Parameter ALLETHRIN DIMETHRIN DELTAMETHRIN RESMETHRIN 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 8.15 
Page 27 of 126 

4.2.3 Analog Approaches

There is a long history of the use of analogue selection 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 application 

and continued acceptance of any structural analog approach requires well-defined and validated 

methods or models that can be used to systematically identify potential analogs, and 

subsequently identify appropriate QSAR models. Implicit in the development of these models is 

the need to define the group of chemicals by some level of similarity. The chemical categories 

and structural analog methods can be used to help bin 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 endpoint or property of a similar chemical. In 

this case, predictions are largely based on read-across methods. As discussed in Section 4.2.2, 

“similarity” can be based on common structures/substructures, 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 the assumptions that a chemical’s structure 

imparts properties that relate to biological activity, and that a group of chemicals that produce the 

same activity have something similar about their chemistry / structure. One goal of (Q)SAR 

approaches is to quantify ‘structural similarity’ imparting biological activity and identify which 

other chemicals may be ‘similar’ with the assumption that an untested chemical may produce the 

same activity. It is important to clarify that even if chemicals display a high level of structural 

similarity, they may not be functional analogs. Conversely, 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 identifying 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 chemicals 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 point); and 
Page 28 of 126 

o common constituents or chemical class, similar carbon range numbers - used with 

substances of Unknown or Variable composition, Complex reaction products or 

Biological material” (UVCBs) (e.g., series of linear alkyl sulfonates). 

Pesticides are commonly grouped based on their similarity in pesticide activity (e.g., insecticides, 

fungicides, herbicides, etc.) and chemical class (e.g., organophosphate, triazine, etc.). Pesticides 

are substances designed to adversely affect organisms, and therefore differ in the chemicals 

typically addressed under TSCA, which focus on substances 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 molecular 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, more than one chemical class is represented; 

therefore, ‘similarity’ is based on all three aspects 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 insecticides 

(adapted from IARC http://eclassification.irac-online.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 Neonicotinoid 

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 degree of chemical similarity, but one or more 

functional groups or substructure(s) has been substituted (Saliner et al., 2007). A structural 

analog can be defined by a substructure or similarity 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, permethrin and bifenthrin, are presented. These two 

chemicals can be defined as structural analogs because all structures share a common 

substructure (i.e., a cyclopropane carboxylic acid). The difference in potency (Table 3) is based 
Page 29 of 126 

on partitioning and electronic charge characteristics of each chemical within a specific matrix 

(i.e., biological or other media such as water) and related to the functional groups that are not 

common among each structure (e.g., fluorine atoms on bifenthrin versus chlorine atoms on 

permethrin). 

Figure 6. Example of structural analogs involving synthetic pyrethroids. 

Table 3. Comparison of LC50 data for structural analogs, 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 calculate similarity or distance based on pattern 

matching. These predictive methods rank chemicals based on characteristics of each structure 

that are similar (match/overlap) and characteristics 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 described 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 properties or topological indices; i.e., the molecular 

graph of the structure, or three-dimensional data such as the conformational property of a 

chemical. Various approaches for calculating similarity indices exist; these approaches include 

O

O

O

CH3

CH3

Cl

Cl Permethrin

CH3

O

O

CH3

H3C

F

F

F

Cl

Bifenthrin
Page 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 distance) which 

measures mismatches between compounds, and 

D = a + b Eq. 3

composite measures which measures both similarity and 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 and 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 Meyer-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 B
Page 31 of 126 

narcosis, which states that the potency of anesthetics, or narcotics, is directly related to the 

lipophilicity of the chemical (see review by Lipnick 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 modeling was the use of the log of the 

octanol/water partition coefficient (log P or log Kow) 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 explained by the 

partitioning (P) parameter, with steric properties (S) and/or electronic factors (El) used to explain 

more specific toxicity (e.g., proelectrophilic/electrophilic interactions). Empirically 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 taken on a more mechanistic-approach to 

model development, linking QSAR model descriptors to 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 for 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 similar, but lack data. Typically the original 

data are randomly separated into a test set and a validation set, with the test set used to develop a 

model and the validation set used to test the assumptions that the model works for chemicals not 

included in the original model’ training set (Leonard 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 develop 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 protocols 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., LC50); 

o An unambiguous algorithm – purpose is to ensure a transparent model, preferably with 

access to the test set of empirical data and descriptors used in the development of the 

model, assuring reproducibility of the model predictions; 

o A defined domain of applicability – define the limitations / bounds of the model including 

chemical structure, physico-chemical properties, and/or modes of action relevant to the 

model; 
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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 the molecular site of action, and why 

various descriptors were used in the QSAR model. 

Although many people observed that, for narcotic chemicals, the slope of log (Kow) vs. log 

(acute toxicity) was close to 1 for many aquatic species, Di Toro et al. (2000) were apparently 

the first to determine a pooled slope using data for 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 considered 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; McGrath et al. 2004, 2005; Redman et al. 2007; 

McGrath and Di Toro 2009). Later, Kipta and Di Toro (2009) developed the “polyparameter 

Target Lipid Model” for narcotic chemicals that uses the Abraham partitioning constants instead 

of Kow. Acree and coworkers have also used the Abraham partitioning constants (Hoover et al. 

2005,2007; Bowen et al. 2006a,b). The polyparameter 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 suggests 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 be 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 example, that the rainbow trout is a factor of 1.5 

more sensitive than the fathead minnow to all chemicals that have that MOA. In addition, a 

multi-species QSAR can also be used in the evaluation of the acceptability of results of toxicity 

tests on all chemicals with that MOA, for species for which the multi-species QSAR has been 

validated. Because a multi-species QSAR can be used 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 that have that MOA. 

4.3 Interspecies Correlation Estimation (ICE) Models 

4.3.1 Background

Interspecies toxicity extrapolation using regression 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, 1978) and were more robust for closely related 

species (Slooff et al., 1986) and within chemical categories (LeBlanc, 1984). Raimondo et al. 

(2010b) developed interspecies correlation estimation (ICE) models with the most diverse 

species and chemical database to date and performed quantitative uncertainty analyses in relation 
Page 33 of 126 

to taxonomic distance, chemical MOA, and model parameters. These analyses provided guidance 

on model selection and use in ecological risk assessment (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 Interspecies Correlation Estimation 

application in 2007 (Web-ICE v1.0; Raimondo et al., 2007a; 

http://www.epa.gov/ceampubl/fchain/webice/). Improved standardization criteria were applied to 

the ICE database along with the addition of expanded 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 become available and are reviewed for quality 

assurance. Web-ICE contains modules that develop Species Sensitivity Distributions and predict 

toxicity to threatened and endangered species from multiple surrogates. Future Web-ICE 

modules will include models that predict acute toxicity to algal species. 

4.3.2 Model Development and Validation

Interspecies Correlation Estimation (ICE) models are 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 toxicity to the target taxon. In the example in 

depicted in Figure 8. Example Interspecies Correlation Estimation Model.

, measured toxicity for the surrogate species, rainbow trout, can be used to predict the toxicity to 

the target species, razorback sucker. ICE models have been developed and validated for aquatic 

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 been 

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 pooled within genus and family to develop 

models for these higher taxa. ICE models for both aquatic organisms and wildlife are available 

on the USEPA Web-based Interspecies Correlation Estimation application (Web-ICE; 

http://www.epa.gov/ceampubl/fchain/webice/; Raimondo et al. 2010). For aquatic organisms, 

Web-ICE contains 780 species-level models (77 species 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 species-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 Estimation 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/LC50 values 

for 180 species and 1,258 chemicals. All data adhered to standard acute toxicity test 

requirements outlined by the American Society for Testing and Materials (ASTM, 2007 and 

earlier editions) and the USEPA Office of Prevention, Pesticides, and Toxic Substances (USEPA 

1996). Rigorous quality assurance and standardization guidelines were applied to the database 

and ensured model relationships were reflective of intrinsic species sensitivity and contained 

minimal extraneous variation (Raimondo et al., 2010b). Toxicity records for metals, 

pentachlorophenol, and ammonia were normalized according to Ambient Water Quality Criteria 

methodology (the 1985 Guidelines). Records were excluded 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 variation and ensure that the model reflects species 

sensitivity relationships with greater certainty. Raimondo et al. (2009) explored the variability of 

acute test type (static, flow-through), concentration reporting (measured, nominal), and organism 

life stage on ICE models. In general, the results indicated 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 values. 

The ICE models were developed by pairing all species 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 cross-validation. In this approach, each pair of 

acute values for surrogate and target species is removed from the original model to build a 

submodel from the remaining data. The submodel is used to predict the toxicity value of the 

removed target species from the removed surrogate species toxicity value. The differences 

between the measured and predicted values of all removed datapoints for each model were used 
Page 35 of 126 

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

To provide a measure of cross-validation performance of each model, the cross-validation 

success rate was calculated as the percentage of removed datapoints predicted within 5-fold of 

the measured value. Cross-validation success rate is provided for each model on Web-ICE. 

Accuracy of model prediction was strongly related to 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, respectively. 

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 model 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 models, 44 species). MOA-specific models were 

cross-validated to determine their prediction accuracy and compared to respective models 

developed from all chemical data. 

Table 4. Number of toxicity records and chemicals included 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 10 
Page 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 218 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 error (MSE) than respective models 

developed using multiple and variable MOAs (with the exceptions of the broad MOA 

metallic stress and two specific MOAs, including ion-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-based models was dependent on 

taxonomic relatedness and the type of MOA. Overall, the prediction accuracy of MOAspecific models did not improve for models developed for two species within the same 

phylum (e.g. fish to fish, invertebrate to invertebrate). For models developed for two species 

within the same kingdom (e.g. fish to invertebrate), toxicity predictions from MOA-based 

models were a significantly improved compared to models developed using all data (Figure 

9. Models developed from chemicals of diverse modes of action compared to those with 

MOA-specific data. 

). 
Page 37 of 126 

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

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 chronic 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 different 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 (LC50) or effect concentration (EC50) to 50% of the 

organisms tested under acute durations, and the chronic value is the No Observed Concentration 

(NOEC) or the Maximum Acceptable Toxicity Concentration (MATC is the geometric mean of 

the chronic NOEC and the Lowest Observed Effect Concentration [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 investigator, with the same species, using the 

same batch of chemical, and under similar test conditions. Paired acute and chronic values from 

the same laboratory are usually not available; therefore 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 currently use ACRs in effects assessments, 

although slightly differently. A description of the approaches used by OPP and OW to derive 

ACRs is provided below along with a summary of similarities and differences regarding 

derivation. In addition, various approaches for estimating ACRs when acute and/or chronic 

toxicity data are not available for the same species are described in Sections 4.4.4 and 4.4.5. 

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

with the use of standard ACRs. 

4.4.1 OPP Approach to Derive ACRs

OPP routinely uses ACRs in ecological risk assessments 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 or when a data gap exists (i.e., no data were 

submitted or submitted data were classified as “invalid” (e.g., when the NOEC is less than the 

lowest concentration tested)). OPP derives ACRs for pesticides based on the ratio of the acute 

toxicity of a chemical (expressed as an LC50 or EC50 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 ACRs, 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; therefore, these data are typically available. In 

situations where the available toxicity data from registrant-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 exposure basis, the acute value for 

that species (for the Gammarus) are used to predict acute risk to freshwater invertebrates. 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 invertebrates based on the ratio of the acute and 

chronic toxicity data for daphnids (48-hr EC50 of 20 μg/L ÷ 21-day NOEC of 2 μg/L = 10); the 

ACR of 10 is then applied to the acute toxicity value for Gammarus (96-hr LC50 = 1 μg/L) to 

yield a predicted chronic NOEC of 0.1 μg/L (Gammarus LC50 of 1 μg/L ÷ ACR of 10 = 0.1 

μg/L). 

• Daphnid 48-hr EC50 = 20 μg/L 

• Gammarus 96-hr LC50 = 1 μg/L 

• Daphnid 21-day NOEC = 2 μg/L 

• ACR = Daphnid EC50 (20 μg/L) ÷ Daphnid NOEC (2 μg/L) = 10 

• Predicted chronic NOEC for Gammarus = Gammarus EC50 (1 μg/L) ÷ ACR = 0.1 

μg/L 

In this example, the predicted chronic toxicity value for Gammarus of 0.1 μg/L would be 

quantitatively used in OPP’s ecological risk assessment to derive chronic risk estimates for 

freshwater invertebrates as it is the most sensitive value for that taxonomic group. Use of the 

ACR to predict chronic toxicity data for use in risk assessments considers the most acutely 

sensitive species and provides a means of addressing the uncertainty regarding the potential 

chronic toxicity of the untested species. 

In this example, the predicted chronic toxicity value for Gammarus of 0.1 μg/L would be 

quantitatively used in OPP’s ecological risk assessment to derive chronic risk estimates for 

freshwater invertebrates as it is the most sensitive value for that taxonomic group. Use of the 

ACR to predict chronic toxicity data for use in risk assessments considers the most acutely 

sensitive species and provides a means of addressing the uncertainty regarding the potential 

chronic toxicity of the untested species. Typically, 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 applied to a different species, as discussed 

below for freshwater and estuarine/marine fish. 

When considering whether to use an ACR to predict chronic toxicity for aquatic animals, OPP 

evaluates the available data based on the following factors: 

• LC/EC50 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 “supplemental” or studies from the open 

literature that are classified by OPP as “quantitative”). 
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 when possible. If data from acute 

and chronic studies for same species are not available, other prediction methods (e.g., 

QSAR) may be more appropriate. However, deriving an ACR from acute and chronic 

data for different species within the same taxonomic 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 if the 

acute toxicity database is robust and suggests that the toxicity of the pesticide being 

assessed is relatively consistent across all species 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 studies conducted under similar conditions 

(e.g., flow conditions, dilution water quality, temperature, hardness, pH, etc.). When test 

conditions from the available acute and chronic toxicity 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 used to derive an ACR. For example, in 

situations where water hardness or pH is known to affect the toxicity of the pesticide, it 

would be important that the acute and chronic studies have comparable or similar 

hardness or pH conditions to allow for comparison of acute and chronic toxicities. 

• Use of mollusk shell deposition data: Aquatic invertebrate data submitted to OPP in 

support of pesticide registration most commonly include studies in two crustaceans (one 

freshwater and one estuarine/marine) and a marine mollusk. Relating crustacean toxicity 

data to mollusk shell deposition data is not recommended for ACR derivation based on 

the variability in responses associated with the measured endpoints (i.e., mortality and 

growth of crustaceans versus shell growth of mollusks). 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 from 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 experimental acute toxicity value is expressed 

as a non-definitive value, such as an LC50>100 mg/L, which is common in limit tests, an 

ACR should not be calculated using these data. Toxicity values expressed as a nondefinitive value often represent the maximum concentration tested and not the inherent 

toxicity of the chemical. Similarly, indefinite NOEC values where chronic effects were 

seen at all of the test concentrations should not be used. 

• Use of ACRs < 1: ACRs < 1, which occur when the LC/EC50 value is lower than the 

chronic NOEC should not be used; these types of values indicate that a measured 50% 

effect occurred after short-term exposures at pesticide concentrations that did not produce 

any effect following chronic exposure. ACRs < 1 may 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 another 
Page 41 of 126 

organism within that broad group. For example, it would be inappropriate to apply an 

ACR for a chitin synthesis inhibitor universally across all aquatic invertebrates. If the 

mode of toxicity in the surrogate organism being evaluated 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-Chronic Ratio (FACR) is derived, when 

possible, based on the geometric mean of at least three ACRs including (1) at least one fish; (2) 

at least one invertebrate and (c) at least one acutely sensitive freshwater species (the other two 

may be saltwater). The chronic value used in the ACR calculation is obtained by calculating the 

MATC as the geometric mean of the NOEC and LOEC. The 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 FCVanimal, FCVresidue

According to the 1985 Guidelines, OW considers the following four different methods for 

deriving the FACR, based on trends in variance in the Species Mean Acute Value (SMAV): 

1. If the species mean ACR increases or decreases as the SMAV increases, the FACR 

should be calculated as the geometric mean of the ACRs for only those species who’s 

SMAVs are close to the FAV. 

2. If no major trend is apparent and the ACRs for a number 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 chronic 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 under field conditions, the FCV is equal 

to the CMC (where the CMC equals the Final Acute Value [FAV] ÷ 2). 
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The OW criteria development process uses ACRs as in the following example (Table 5). Paired 

acute and chronic test data are available for 3 species: 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 freshwater contaminant. 

Species Acute Value (μg/L) Chronic Value (μg/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 presented.

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 Guidelines. 

• 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 tests conducted in the same laboratory and 

dilution water, but in a different study may be used. If no such acute tests are available, 

results from different laboratories may be used provided 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, growth, and 

reproduction. 

• Dilution water quality: Results of chronic tests conducted in unusual dilution 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 quality characteristics that have been 

shown to be related to toxicity (e.g., hardness, pH, etc.) should be accounted for. 
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• Endpoints and exposure duration: Chronic values should be based on endpoints and 

lengths of exposure appropriate to the species as specified 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 acute and chronic toxicity tests are 

preferred. 

4.4.3 Similarities and Differences in OPP and OW Approaches 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 ecological risk assessments and NAWQC derivation, 

respectively. Similarities in approaches and the types 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 species; 

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

• Consideration of control performance; 

• Consideration of dilution water quality and potential impacts on toxicity; and 

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

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

Table 6. Differences in OPP and OW ACR derivation methodology.

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-specific1 and based on most acutely 

sensitive species; one ACR is derived for each taxon, 

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 applied 

to the most acutely sensitive tested 

ACRs are compared to SMAVs to review trends among 

the parameters 

No provision for special consideration of chronic data on 

commercially/recreationally important species 

Defaults to commercially/recreationally important 

species chronic data if less than the calculated FCV 

ACR is based on ratio of acute value (LC/EC50) to the 

chronic NOEC value2

ACR is based on ratio of acute LC/EC50 to the chronic 

MATC value or regression-derived EC20 value 

1 OPP may derive separate taxa-specific ACRs for freshwater 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 Data

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 described in this white paper to predict acute 
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toxicity data for that species (e.g., Web-ICE, read-across). The predicted acute and empirical 

chronic toxicity data may then be used to derive a chemical-specific ACR. 

In other cases, acute LC50 data may be available for species for which no chronic toxicity data 

are available, requiring that a chronic toxicity test 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-concentration 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 chronic toxicity are used to predict the ACR. 

Once again, with limited QSAR models for chronic toxicity, 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 Water 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 80th percentile of the available data, was derived independent 

of the chemical or AOP. TenBrook et al. (2010) took a similar approach, but limited the analysis 

to data for pesticides, and calculated an 80th percentile ACR of 12.4. 

This lack of reliable acute and chronic data has made 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 LC50 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 oxidative phosphorylation, 10.4 (4.5-27.9) for 

organophosphates, 7.3 (4.6-9.1) for carbamates (Call et al., 1989), and 9.8 (3.3-24.0) for 

nonpolar narcotic chemicals (Call et al., 1985). 

Based on theoretical considerations, one would expect that the magnitude of an ACR for a 

particular species, or closely related species, would show greater similarity within rather than 

between adverse outcome pathways. The extent to which 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 more 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 90th percentile ACR 

AChE inhibitors - carbamates 8.9 28.0 

AChE inhibitors - organophosphates 6.2 77.8 
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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 Union (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 fixed ACRs for the extrapolation of acute to 

chronic toxicity because of large variation associated with ACR differences in species, chemical 

class, MOA, and test conditions (Kenaga, 1982; Ahlers 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 between acute and chronic toxicity is 

independent of the test species and the test compound including differing mechanisms of 

biological action from species to species. The ratio 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 acute 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 nonparametric, semi-parametric, and parametric 

approaches and are not restricted to toxicity-specific mechanisms or applications. Rather, timeto-event models simply assume that an event (e.g. death, birth, disease onset) occurs at some 

point in time (Newman and Crane 2002), and some models (e.g., accelerated life testing [ALT]) 
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have theory originally routed in industrial reliability studies (Sun et al. 1995). Since conventional 

toxicity metrics such as LC/EC50 and NOECs focus on exposure intensity during a constant 

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., EC50), the endpoint of chronic tests, as determined by the LOEC or ECx, 

may be mortality, reproduction, or growth. Validation of Accelerated Life Testing (ALT) and 

Linear Regression Analysis (LRA) has shown relatively 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 using these models (Barron et al. 2008). Mayer et 

al. (1986) recommends applying additional safety factors to lethality-based NOEC to include 

other biologically significant effects. 

An additional assumption of TCEs is that time-course 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 toxicity prediction due to a shift in the doseresponse curve (Barron et al., 2008). 

TCE models require the input of time-course acute data 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 accuracy when the majority of mortality 

occurred early in the acute test. This same study found that the inclusion of additional time steps 

(e.g., 12-hr, 36-hr) did not improve the accuracy of chronic toxicity prediction. 

The TCE models that have received the most attention and application in predicting chronic 

toxicity from acute time-course data are the ALT (Sun 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 distribution 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 linear regression analysis that combines 

regressions that predict the low lethal concentration at each observation time period and 

regresses those concentrations against the reciprocal of time. The intercept of this second 

regression is the chronic NOEC. The LRA model may be used when there are no partial 

responses during the acute toxicity test. The MPA models are multiple regressions that describe 

the relationship among exposure concentration, time and probit percent mortality. The model 

was developed to predict chronic toxicity when acute 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. 
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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 criteria. Appendix C provides information on these 

methods for predicting toxicity using Expert Systems, Similarity Tools, ICE models, and TCE 

models. (Q)SAR models also exist for other endpoints such as bioaccumulation (Arnot and 

Gobas, 2004), and physical chemical properties (European Commission (EC) 1995a,b; Deardon 

and Worth, 2007). The physical chemical property data will typically be a required input 

variable for (Q)SAR models. For most of the computational 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 when data are not available. (Q)SAR 

predictive methods used for screening, prioritization and ranking of large chemical inventories 

will not be covered but are available to risk assessors (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 on 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 more uncertainty surrounding the predicted value 

than empirical toxicity tests which follow acceptable test methodologies. Selection of (Q)SAR 

predictive methods is dependent upon the assessment context (e.g., limited documentation for 

screening/data identification, more complex documentation for effects/risk assessment 

decisions). Recognizing that these (Q)SAR approaches could assist in filling data gap needs 

within risk assessments, the OECD developed validation principles for (Q)SARs to help identify 

scientifically valid models that are reliable and also acceptable for use in the risk assessment 

process. Five validation principles were identified that addressed both statistical and nonstatistical aspects of (Q)SAR models (OECD, 2004). 

1. The (Q)SAR model must be associated with a well defined endpoint: The purpose of this 

criterion is to ensure clarity in the predicted endpoint . 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 database 
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(http://www.epa.gov/med/Prods_Pubs/fathead_minnow.htm) 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 concentrations 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 changes 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 observable 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 the 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, information 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. It 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 application. 

3. The model must have a defined domain of applicability: 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 chemical domain that the model describes. 

For instance, the domain of applicability may be related 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 from 0 

to 6.) Predictions outside these domains are questionable (not the intent of the model), 

and are therefore, not reliable for use in a risk assessment. 

4. The model must have appropriate measures of goodness-of-fit, robustness and 

predictivity: The purpose of this criterion is to ensure internal performance and 

predictivity of the model. The internal performance of a model is represented by 

goodness-of-fit, or how well the model predicted values agrees with observed values, and 

model robustness, which ensures the model is not unduly affected by outliers or small 

departures from model assumptions. Measures of goodness of fit for regressions include 

the coefficient of determination (i.e., R-squared (R2

), and the sum of square. The 

predictive power of the model is determined by testing data not used in the development 

of the model, but within the model’s domain of applicability, 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, models with a mechanistic understanding 

should be used. A critical consideration is whether the descriptors used in the models 

make mechanistic sense. Development of an AOP can assist in this assessment. For 
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instance, log P is used in most QSAR models to predict toxicity for chemicals acting via 

a narcosis MOA, which makes mechanistic sense, because log P is used as a surrogate for 

the biological membrane which is the proposed molecular site of action for narcotic 

chemicals (Ankley et al., 2010). 

Under REACH, models that meet the OECD validation principles 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 models, authors/source of model, related 

publications, predicted endpoints and related experimental 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 document 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 appendix to the assessment to enhance 

transparency. 

6.2 ICE Models 

ICE toxicity predictions can be made from a suite of models available for a diversity of surrogate 

species predicting to the species, genus, and family level. As such, multiple models are 

frequently available to predict toxicity to a desired species. When comparing and selecting ICE 

models, the following guidelines should be used (Raimondo et al. 2010b): 

1. Relatively low mean square error (MSE) (< approximately 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 the model. 

MSE error is the recommend measure of model robustness. ICE models are based on the acute 

toxicity available for two species, so available models usually contain different sample sizes. 

Because MSE is derived using model degrees of freedom, it is an unbiased estimated of model 

error and variance. In regression models, such as ICE models, R2

 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 artefact 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 judgment is ultimately required to evaluate model 

predictions. For example, models with MSE error < 0.22 were identified through uncertainty 
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analyses as having an average accuracy of model predictions within 4 fold of the actual value. 

Models with MSE > 0.22 will not necessarily yield poor predictions, however the average fold 

difference between predicted and measured values was 10 (stdev = 37, N = 2542). Additionally, 

models between closely related species were shown to yield more accurate predictions than 

models for less related species. However, some exceptions 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 conjunction 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 produced by models with large MSEs as well as 

other models where the input value (measured surrogate toxicity) is outside the range of 

surrogate date used to parameterize the model. 

 ICE estimated confidence intervals represent the range in which the actual toxicity is likely to 

fall with a certain level of confidence (e.g. 95% confidence). 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 entered acute toxicity data. TCE models do not 

provide a prediction for non-lethal chronic toxicity and may not represent standard chronic test 

NOECs, which are typically based on the most sensitive endpoint measured (e.g. growth, 

reproduction). In a dataset comprised of 138 chronic 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 derived from TCEs to be protective of 

nonlethal endpoints. A safety factor of 0.2 is recommended based on frequency analysis 

demonstrating NOECs for survival being 5 times or less than the NOECs for most other 

endpoints at least 95% of the time (Mayer et al. 1986). 

7 Considerations for Using Toxicity Predictions 

7.1 WOE and Best Professional Judgment 

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

rather are intended to be used in concert with other sources of information. As such, they 

represent only one element in a WOE approach. Output from these predictive methods should be 

weighted (qualitative/quantitative) according to reliability, 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 conditions multiple predictions are available from 
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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 recognize 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 and 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 structurally similar. It is also important to 

remember that extrapolating out from a data set where the chemical to be estimated is very 

‘distant’ from other chemicals identified as similar 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 either fish and/or invertebrates may exist and 

the reviewer should evaluate whether the model outputs are consistent with what has been 

measured. The ability of the (Q)SARs to reliably predict the toxicity of other chemicals depends 

on the extent to which structurally similar chemicals with similar MOAs are represented in the 

training set of data used to populate the model. As 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 MOA 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 consistent with their understanding of the 

chemical. 

With any model, as with measured toxicity data, there are uncertainties regarding the variability 

and relevancy of predicted values. (Q)SAR estimates 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 available. 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 of evidence and must be viewed in the 

context of the model’s domain of applicability; these 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 model outputs carefully to determine whether 

predictions are consistent with what may be known about the chemical however limited. 

 
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7.2 Selection of Descriptors in (Q)SAR models 

There are a large number of descriptor variables that can be used in the development of a QSAR 

to predict the toxicity of a substance. Many of these descriptors are calculated using first order 

principles, i.e., calculated based on theory as it relates to chemical structure and not 

experimentation. In theory, these have less error because they would not have error related to 

experimentation; however, selection of these parameters 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 since it is by definition a measure of 

lipophilicity and membranes are by nature lipophilic. But it is less clear what parameters should 

be used in defining inhibition of an acetylcholinesterase inhibitor. Due to limited data, many 

QSAR models combine data from various mechanisms and use a global QSAR approach 

(Netzeva et al., 2007) which incorporates parameters associated with hydrophobicity, electronic, 

and possibly steric information regarding each compound 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 the experimental error associated with the 

biological test data, which is not recommended (Wold 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-dimensional nature of the chemical which may 

be critical in its interactions with biological matrices. Log P has been used historically to 

describe the partitioning of chemicals into biological 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 phosphorylation, 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 polar 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 describing all aspects of the toxicity. This makes 

sense when you consider that log P captures only the partitioning aspect and not the hydrophilic 

or polar nature of the chemical in question. 

Table 8. Examples of baseline narcosis and polar narcosis models using only log P as a means of predicting 

toxicity. 
Page 53 of 126 

Organism Endpoint Slope Intercept R

2 N Source 

Guppy 

Baseline 

narcosis LC50 0.871 1.13 0.976 50 Könemann 1981 

Guppy Polar narcosis LC50 0.46 3.04 0.824 11 Könemann and Musch 1981 

Fathead minnow 

Baseline 

narcosis LC50 0.94 1.25 0.94 60 Russom et al., 1997 

Fathead minnow Polar narcosis LC50 0.65 2.29 0.90 39 Veith and Broderius, 1987 

Ciliate 

Baseline 

narcosis IGC50 0.929 2.639 0.986 20 Schultz 1996 

Ciliate Polar narcosis IGC50 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, predictivity, and appropriateness of the model is related to 

the knowledgebase behind the predictive method. For instance, Benfenati and coworkers, 

predicted the log P for a series of pesticides using 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 octanol/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 predictions using QSARs. 
Page 54 of 126 

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

Another reason that log P calculation methods may be a poor representation of the actual 

physical/chemistry properties of a substance is that the calculation is based on fragments of the 

chemical, typically added up with correction factors 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. measurements of octanol/water partitioning. 

Liposomes, which are an artificial phospholipid bilayer, are better mimics of a biological 

membrane, having regions with hydrophobic, hydrophilic, charged or neutral properties (Smejtek 

and Wang, 1993; Escher and Schwarzenbach, 1996). 

Although the partitioning coefficients developed with liposomes are better at describing activity 

in biological membranes, there is not a large database of measured values for use in model 

development, such as the database that exists for octanol/water partition coefficient 

measurements. Additionally, models are not as readily available for predicting the membrane / 

water partition coefficient; however, this is changing with the advancement in QSPR 

(quantitative structure-property relationship) prediction methods using linear solvation free 

energy relationships (LSERs). LSER approaches to predicting 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 / polarizability
Page 55 of 126 

of the chemical, electrostatic interactions, hydrogen bonding interactions, and steric measures 

such as molar volume to predict lipophilicity. Previously, these models were not readily 

available due to computational requirements, but recent advances have allowed for availability of 

these calculations via personal computers. Once again, 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 prediction 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 not as well on structures not previously made 

available (i.e., not part of the models domain of applicability). 

7.3 Empirical Test Data 

In keeping with the first and fourth OECD validation principles, some QSAR models use 

training sets of test data generated by single research 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 literature. 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 test methodologies. 

Pesticides are complicated by the fact that they exist as formulated products which may include 

‘inert’ ingredients that may not be completely identified, 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 based on the active ingredient. 

It is presumed that for the OW/OPP common assessment efforts, most data gathering will be on 

the parent compound, but some active ingredients become more toxic upon degradation. Having 

a clear understanding of degradate exposure and toxicity 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 should be kept in mind though that a degradate 

may not have the same MOA or even fall within the same chemical category as the parent 

compound. As such, toxicity predictions for the degradate may be based on a different working 

QSAR model than the parent compound. It is also recommended 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 structures and AOPs; however, conventional pesticides 

have been engineered to be toxic, oftentimes to specific 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 entire chemical structure may impart a 

specific AOP and toxicity. For example, the insecticide carbaryl, is classified in EcoSAR as a 

neutral organic and it predicts the toxicity of carbaryl based on an ester moiety contained within 

the compound. However, the overall MOA of this N-methyl carbamate is to inhibit 

acetylcholine esterase. The extent to which the component toxicities contribute to the overall 

activity of the compound is an important consideration and can be a challenge to decipher. As 

compounds are transformed/degraded to smaller components, the importance/relevance of 

substructure-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 

structural similarities to the parent and/or chemicals 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 determining 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 the methods for determining MOA are based 

on acute toxicity data, and may not be reflective of chronic exposure. 

7.5 ICE Models 

While the taxonomic relatedness of surrogate and predicted species within ICE models is an 

important consideration for model selection, evaluation of ICE models should be a holistic 

evaluation of all available models and their attributes. Models are available to predict to a 

species, genus, and family, and models predicting to 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 used 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 predicted toxicity confidence intervals may 

indicate otherwise. Additionally, MOA-specific models may provide more accurate predictions 

for two species with greater taxonomic distances. Additionally, it is important that the input 

value of the surrogate species is within or close to the range of toxicity values used to develop 

the model. 
Page 57 of 126 

8 Example Applications and Proposed Analyses of Predictive 

Methods 

In order to begin assessing the feasibility of using 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 of Chemical 

Stressors to Aquatic Animals”), and to perform a preliminary comparison of acute and chronic 

ALSVs with actual FAVs from published criteria documents. 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 ALWQC 

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 identified that met these criteria: Acrolein, 

Diazinon, Chlorpyrifos and Tributyltin (TBT). The acute MOAs for these chemicals were listed 

in Web-ICE as reactive (Acrolein), anticholinesterase –OP (Diazinon and Chlorpyrifos) and 

uncoupler/inhibitor of oxidative phosphorylation (TBT). Important values (Table 8) for these 

chemicals for the example applications were obtained 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 regarding imposex in female snails. 

3 - FAV recalculated to reflect the addition of sensitive 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 values (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) are 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 cladoceron species (eg. 21 day life-cycle test, 

D. magna). Therefore, criteria documents were used as a source 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 chlorpyrifos 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-Merhibi et al. 2003) and two acceptable chronic 

tests (Rose et al. 2001 and Rose et al. 2002). Mean acute and chronic values (the geometric 

mean (geomean) of the NOEC and LOECs ) were calculated from the studies for each species. 

C.dubia was the most sensitive species tested for chlorpyrifos 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 data available to develop 

ALSVs using the various predictive methods, assuming 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, Chlorpyrifos and TBT 

12-14). 

Table 9. Species mean acute and chronic values found 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 2,3

 
Page 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 corresponding C.V., therefore C. dubia was used. 

5 - Geomean of two ELS tests (Norberg King 1989, Jarvinen 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 would be acceptable for 

 inclusion in criteria development consistent with the 1985 Guidelines. AVs from Harmon et al. 2003 & 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 additional toxicity data 

are available and that additional data can be predicted using tools described in this paper, the 

decision from Box 3 would be, “Yes”. Moving to Box 4 , the next step is to use the appropriate 

tools to then predict toxicity data. The rest of Section 8.2 is divided into providing examples 

where 1) empirical data are supplemented with predicted values from Web-ICE and the OECD 

Toolbox to populate a species sensitivity distribution 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 currently accepted paradigm of the 1985 

Guidelines and an ALSV was calculated in an identical manner as an FAV. Second, the ALSV 

was calculated as the HC5 concentration using examples of SSD approaches independent of the 

1985 Guidelines. 

8.2.1 Supplementing Empirical Data with Predicted Values for Missing 

MDRs (Box 4 of Figure 11).

The FIFRA registration process for pesticides results in a minimum of toxicity data for three of 

the eight acute FW MDRs required by the 1985 Guidelines. The following sections provide 

examples of two methods used to populate the remaining acute freshwater MDRs with predicted 

acute values and the resultant ALSVs are then compared 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 specified 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 fish 
Page 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 Chordata 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 acute 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 beyond rainbow trout (MDR #1), bluegill (MDR #2) 

and cladoceron species (MDR #4), there are no predicted species available that meet the 7th

MDR and correlate with any of those 3 surrogate species. In choosing species that met MDRs, 

care was taken to choose the most appropriate surrogate 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 reduce 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 exposure becomes 

limited and, therefore, predictions may fall outside 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 that met minimum data requirements and had 

the highest number of toxicity test results correlating 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 R2

 ranged from 0.311 

(Rainbow trout → the midge) to 0.825 (Rainbow trout → fathead minnow). After predicted 

values were obtained, acute ALSVs were calculated for each chemical using formulae from the 

1985 Guidelines (n=8, the four most sensitive species are bolded in Table 10). 
Page 61 of 126 

Table 10. Meeting minimum data requirements of the 1985 Guidelines using Web-ICE (acute values, 95% confidence limits are 

Chemical Species Mean Acute Values (ug/L) Predicted Acute Values (LC/EC50 ug/L) 

Oncorhynchus mykiss1 Lepomis macrochiris 2 Ceriodaphnia sp.4 Pimephales promelas3 Gammarus fasciatus5 Chironomus plumosus6 Crassostrea virginica 

RBT 9 RBT 9 RBT 9 Bluegill 10 

Acrolein (2009) 16 27.19 <39.76 54.9 16.7 14.9 36.8 

(33.7 - 89.3) (3.6 - 76.7) (2.6 - 84.5) (20 - 67.6) 

Diazinon (2005) 425.8 459.6 0.3773 1011.6 153.1 185.3 299.9 

(736.7 - 

1389.2) (53.3 - 439.6) (61.2 - 561) (203.5 - 442.1) 

Chlorpyrifos (1986) 8.485 10 0.053 31.2 10.9 9.2 17.5 

(18.3 - 53.2) (2.0 - 58.7) (1.4 - 62.2) (8.5 - 36.0) 

TBT (2003) 4.571 8.3 4.3 18 7.6 5.7 15.3 

(10.1 - 32.2) (1.1 - 45.7) (0.7 - 46.6) (7.3 - 32.0) 

Note: Bolded values were used to calculate ALSV with an n=8 1 - The family Salmonidae in the class Osteichthyes; 2 - One other family (preferably a commercially or recreationally important, warmwater species) in the class Osteichthyes (e.g., blueg

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 species 10 - Bluegill (L. macrochiris) was used as a surrogate for these species 11-No predicted freshwater species were available that met MDR #7 using SMAVs from available freshwater surrogates (i.e.. RBT, b
Page 62 of 126 

Table 11. Summary of model statistics used to predict acute values. 

Surrogate species Predicted species N DF R2

 

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 in that the 

Web-ICE derived ALSV was 1.4 fold less than the criteria FAV, thus achieving the goal of 

having the ALSV being a somewhat conservative estimate of the FAV. For acrolein, diazinon 

and chlorpyrifos, the Web-ICE ALSVs were 6.6, 9.2 and 4.3 fold respectively lower than the 

criteria FAV (Table 12). The Web-ICE ALSV for diazinon is particularly troublesome in that it 

is 9 fold lower the most sensitive species in the restricted 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 species predictions to experimentally 

determined values; 2) comparing the shapes of predicted species sensitivity distributions to 

experimentally determined distributions; 3) explore species sensitivity distributions developed 

using different predicted taxa meeting the 1985 Guidelines MDRs; 4) consideration of biases in 

surrogate species that may, in-turn, bias the predicted values; 5) compare the accuracy of ICE 

predictions among types of MOA; 6) determine if there are characteristics of surrogate data sets 

that limit, or enhance, the predictability of regression models used in ICE. 

8.2.1.2 Read-Across 
Page 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) was used to fill data gaps using read-across 

techniques for single chemicals. The toolbox software, 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_34379_42923638_1_1_1_1,00.html#Downl

oad_qsar_application_toolbox. 

Step by step instruction is available in the guidance 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 Track” was used 

to identify the target chemical, gather analogue data, and fill data gaps. 

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

After identifying the target chemical, users are prompted 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 for aquatic species, the toolbox guidance 

recommends using the OASIS Acute toxicity MOA, Verhaar 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 pesticide. OASIS classifies it simply as a 

“Reactive Unspecified”, while the Verhaar scheme classifies 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 interest. 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 identifying 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 toolbox. 

After gathering the toxicity data for the target chemical, 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 other 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 boxes 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 definition screen for Diazinon in the OECD toolbox. 

The final step is to move onto the “Filling data gap” 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 according to a “descriptor”. The default 

descriptor is Log_Kow_EPISUITE. In the diazinon example, there were 18 analogues with 48 hr 

LC50 data for fathead minnow (Figure 16). The toolbox then identified 5 (may be more or less) 

of the closest analogues (as defined by the descriptor) and calculated an average LC50 value for 

those 5 points. In Figure 16, the 5 analogues used to calculate the average LC50 are identified 

as the maroon colored points, the target value is in red and all others are in blue. At this point, 

users can further refine the points by subcategorization. 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 pesticides 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 calculated ASLVs are illustrated in Table 13. 
Page 67 of 126 

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

Figure 17. Analogue structure similarity indexs in the OECD toolbox. 
Page 68 of 126 

Table 13. Meeting minimum data requirements of the 1985 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) 

Oncorhynchus mykiss1 Lepomis macrochiris 2 Cladoceran sp.4 Pimephales promelas3 Gammarus fasciatus5 Pteronarcys californicus 6 Crassostrea virginica 7, 9 

Diazinon (2005) 425.8 459.6 0.3773 1350 3.18 6.81 437 

(31 - 58.2) (0.014 - 700) (0.67-690) (96.5 - 1970) 

Chlorpyrifos (1986) 8.485 10 0.053 1310 41.7 9.99 140 

(56.4 - 30300) (0.29 -5820) (0.35 -286) (14.3 - 1360) 

Note: Bolded values were used to calculate ALSV with n = 8 1 - The family Salmonidae in the class Osteichthyes; 2 - One other family (preferably a commercially or recreationally important, warmwater species) in the class Osteichthyes (e.g., blueg3 - 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-ICE 
Like WEB-ICE, the OECD toolbox using the read-across 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 chlorpyrifos (-3.5 fold). And, like Web-ICE, 

diazinon had the largest discrepancy between the ALSV and the criteria-derived FAV. 

Proposed analyses will further explore the use of the OECD toolbox with its read-across, trend 

analysis and (Q)SAR model applications. In this example, some observations and points of 

investigation are apparent. Obviously, the more data that are available from similarly structured 

chemicals, the more useful this tool will be. In the case of acrolein and TBT, sufficient data are 

not available to begin using any of these predictive methods as employed currently. In the case 

of the two organophosphates, sufficient data were available for a number of different analogues 

with similar structures to consider their use. Additionally, more work needs to be completed to 

identify profiles more closely linked to adverse outcome pathways. For example, using the 

default recommended aquatic acute toxicity classifications (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 inference 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 Kow. The toolbox has dozens of other physical and 

chemical descriptors that would likely be more appropriate for anticholinesterase pesticides such 

as diazinon or chlorpyrifos. Proposed analyses will attempt to identify the most appropriate 

descriptors for various pesticide MOAs. Also in our examples, the Dice similarity index for 

chemical structure was used with a 70% similarity cut 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 similarity indexes with MOA or structure type. 

Finally, this was a very quantitative example of read-across. Proposed analysis will also describe 

how the technique can be used in a qualitative manner to rank relative potencies of chemical 

structures among taxa types. 

8.2.2 Only Use Results of Toxicity Tests for Chemical and Taxa of 

Interest 

The previous two predictive methods were examples of how one may be able to supplement data 

sets to meet MDRs and subsequently calculate an ALSV using an SSD approach. The following 

section provides examples of how one may generate an 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, or 2) using an SSD methodology. The 

companion paper (“Exploration of Methods for Characterizing Effects of Chemical Stressors to 

Aquatic Animals”) illustrates an example of using GLI extrapolation factors and therefore will 

not be duplicated here. Therefore, this section focuses on two examples of SSD approaches. 

8.2.2.1 Calculating an ALSV using a modified SSD Methodology (Box 7, 

Figure 11) 
70

Two different methods are readily available for estimating 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 species. In this example, the 3 acute values 

(Figure 18) from Table 9. Species mean acute and chronic values found in criteria documents. 

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

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

Acrolein. 

The HC5 estimates using the Web-ICE log-logistic assumed 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) were 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 to 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 trout, bluegill and cladoceran acute 

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

9 were used with an assumed Log-logistic SSD according to methods proposed by de Zwart 

(2002). The log-logistic equation from the Species Sensitivities Distribution section (Appendix 
71

B) of the companion white paper “Exploration of Methods for Characterizing Effects of 

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 log10 of the 3 acute values for each chemical, while β was taken from 

de Zwart (2002) specific to the chemicals MOA as listed 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 conservative 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 

βdeZwart1HC5 

 Α β Μg/L μg/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 dependent of MOA designation 

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

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

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

One obvious and perhaps important difference in these two SSD examples is de Zwart’s 

incorporation of MOA in the HC5 estimate as parameters 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 environmental fate and toxicity values when 

chemical-specific data are not available for EFED risk assessment is provided in Appendix A. 

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

8.3.2 Comparison of endosulfan and its degradate, endosulfan sulfate,

toxicities using Web-ICE
72

Section 8.2.1.1 discussed the application of Web-ICE models for predicting acute toxicity values 

to meet MDRs. This section demonstrates another potential application of Web-ICE models that 

compares toxicities of a chemical and it’s degradate, which may have limited test data. The 

organochlorine insecticide, endosulfan, and it’s degradate 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 endosulfan 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 magnitude of each other (Error! 

Reference source not found.). Since direct comparisons 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/fchain/webice/) application was used to predict 

toxicity to endosulfan sulfate for additional species. 

Endosulfan sulfate toxicity was predicted for species for which a measured endosulfan toxicity 

value existed, but the degradate toxicity was lacking. Predicted toxicity values could only be 

obtained where models exist for both the taxa of interest (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 the measured data and where models were 

available. 

 

For aquatic species, surrogate selection and assessment of model predictions for aquatic species 

followed Web-ICE user guidance (Raimondo et al. 2010). 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 for each potential surrogate (bluegill, carp, 

sheepshead minnow, Daphnia magna, and the mysid shrimp) and predicted species. Where 

multiple models existed for a predicted species (multiple surrogates, multiple taxonomic levels) 

models were sorted by MSE in ascending order. For all predicted species except the scud, 

Gammarus lacustris, low MSE correlated with high R2

, close taxonomic distance, and high 

cross-validation success rate. For predicted species 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 limits were calculated using the top 2 or 3 

models based on the criteria above (low MSE). Confidence intervals were used to confirm that 

the models with the lowest MSE, highest R2

, highest cross-validation success rate, and closes

taxonomic distances yielded the most robust predictions. 

 All models for the scud contained relatively equal and large MSE (>1.48), low R2 (<0.53) 

and low cross-validation success rates (< 51%), which indicate high model uncertainty. Since 

these model criteria were generally the same for all scud models and confidence intervals of all 

model predictions were generally equal, taxonomic relatedness was used to select the best 

surrogate (Mysid shrimp). Results of the extrapolations support the measured data in 

demonstrating similar toxicity of endosulfan and endosulfan sulfate, with the degradate being 

slightly less toxic than the parent compound. 
73

Table 14. Comparison of the acute toxicity predictions for endosulfan and endosulfan sulfate using Web-ICE 

and measured data for aquatic species. The upper table includes species with toxicity values for both

endosulfan and endosulfan sulfate. The lower table contains species for which endosulfan sulfate toxicity 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 45421402a

Daphnia magna 

166 5008271 300 45421403a

Sheepshead minnow 

1.3b

Shimmel 1981; 

(Ecotox 3740) 3.1 46382603 

Mysid shrimp 

0.83b

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 supplemental 
74

b

 geometric means 

c

 confidence interval 

 
75

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Appendix A: EFED Guidance on Use of Structure-Activity Relationships

Executive Summary 

This document is intended as an example of how quantitative structure-activity relationships 

([Q]SARs can be used for estimating chemical/physical 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, the 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 capacity of the models among other lines of 

evidence is a critical factor for determining whether the information can be used qualitatively 

versus quantitatively. This weight-of-evidence approach 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 Ecological 

Structure Activity Relationships (ECOSAR)1 and the Assessment Tools for the Evaluation of 

Risk2 (ASTER). Both tools rely on the chemical name, CAS number, and the chemical’s 

structure as represented by a Simplified Molecular Input Line Entry System (SMILES) string. 

Both have undergone Agency peer review and are posted to the Agency’s intranet. Information 

on chemical-specific SMILES strings can be found using some freely accessible tools on the 

internet. ChemiSpider3 maintained by the Royal Society of Chemistry and the Toxicology Data 

Network (ToxNet)4 maintained by the National Library of Medicine both provide SMILES string 

information along with chemical/physical properties of a chemical. 

The user manual for ECOSAR5 can be found at 

http://www.epa.gov/oppt/newchems/tools/ecosarusersguide.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 estimates 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 2009a. Ecological Structure Activity Relationships (ECOSAR). 

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

2

 USEPA 2009b. Assessment Tools for the Evaluation of Risk (ASTER). 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. http://www.chemspider.com/ 

4

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

5

 USEPA. 2009d. User’s Guide for the ECOSAR Class Program, MS-Windows Version 0.99d November 1998. 

Prepared by W. Meylan and P. H. Howard, Syracuse Research Corporation Environmental Sciences Center, 6225 

Running ridge Road, North Syracuse, NY 13210. http://www.epa.gov/oppt/newchems/tools/ecosarusersguide.pdf 
91

chemical CAS Number, chemical name and SMILES string as inputs. The model allows the use 

to modify the mode of action leading to the most toxic response to ensure protection. The user 

should review the model’s output to determine whether its estimates of chemical/physical 

properties are consistent with available measured values 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 and 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 filter. For carbaryl, the mean 96-hr LC50 value 

for fish is 3.253 mg/L. 

SAR programs typically used by ecological risk assessors in OPP estimate toxicity to aquatic 

organisms and do not currently estimate the toxicity 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 physiochemical properties (Kow) and its aquatic 

toxicity within specific chemical modes of action. QSARs within ASTER use the Kow as the 

chemical attribute (descriptor) for estimating toxicity and ASTER will not provide estimates for 

chemicals with Kow values outside the range of its training set. 

Other useful tools include the Distributed Structure-Searchable Toxicity (DSSTox)6 public 

database network. DSSTox is a project of EPA's Office of Research and Development National 

Center for Computational Toxicology that is helping to build a public data foundation for 

improved structure-activity and predictive toxicology capabilities. The DSSTox website 

(http://www.epa.gov/ncct/dsstox/) provides a public forum for publishing downloadable, 

structure-searchable, standardized chemical structure 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 match for carbaryl. Figure A8 depicts where 

there are exact matches on data for carbaryl; in the 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 acute toxicity data and mode of action of 

carbaryl (Figure A9). The output indicates that carbaryl has a mean LC50 of 8.75 mg/L 

representing the geometric mean of 4 experiments and 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 degradates of concern is MetaPath7. MetaPath is a 

 

6

 USEPA 2009f. Distributed Structure-Searchable Toxicity (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 Metabolite 

Database and Simulator of Xenobiotic Metabolism. Presented at BOSC Computational Toxicology Research 

Program Review, Research Triangle Park, NC, September 29 - 30, 2009.
92

computational tool developed for the storage and analysis 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, species, and/or experimental conditions. The 

database has been constructed primarily from in vivo rat metabolism studies of pesticides. This 

system also serves as a foundation of an expert system to predict metabolite formation. Figure 

A10 depicts the MetaPath window for selecting chemicals of interest and the associate 

degradation pathway reported in rat studies. 

For ECOSAR and ASTER, the user should determine whether model outputs for both fate and 

effects parameters are consistent with what is known for the chemical under evaluation. In some 

cases, some toxicity data on either fish and/or invertebrates may exist and the reviewer should 

evaluate whether the model outputs are consistent with 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 relationships. 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 provided by the model. The reviewer should be 

aware of this output and make an effort to determine whether these moieties and/or mode of 

action are consistent with their understanding of the chemical. 

The use of SARs is dependent on multiple lines of evidence 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 chemical structures are not in their domain. 

Users are cautioned to examine model outputs carefully to determine whether estimates are 

consistent with what may be known about the chemical however limited. 

With any model as with measured toxicity data, there are uncertainties regarding the variability 

and relevancy of estimates. The decision to use such 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 the values must be discussed in the assessment. 

Ultimately, the use of SARs is dependent on the best professional judgment of the user and are 

part of a weight-of-evidence approach toward characterizing 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 structures and modes of action; however, conventional 

pesticides have been engineered to be toxic, oftentimes to specific taxa, through relatively 

specific modes of action. Thus, a chemical may have a number of substructures (moieties) that 

have associated toxicities based on what may be dissimilar modes of action while the entire 

chemical structure may impart a specific mode of action 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 contained within the compound. However, the 

overall mode of action of this N-methyl carbamate is to inhibit acetylcholine esterase. The 

 
93

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 substructure 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 structural 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; however, this relatively broad mode of action 

may include chemicals with widely divergent modes of 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 the mode of action across those species is the 

same. 

Criteria for Use of [Q]SARs 

Using OECD validation principles as template/guidance for the use of QSAR models,8

 the 

following attributes should be examined for completeness. The model must estimate a defined 

endpoint, e.g., a 96-hr lethal concentration to 50% of the test organisms (LC50), a no-observed 

adverse effect concentration (NOAEC), or a maximum acceptable toxic concentration (MATC). 

As such, the model must be transparent as to the types of studies included in model database. 

The user must in turn determine whether these defined 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 clearly identified. If the model is regressionbased and uses Kow, then the exact mathematic expression (function) should be identified and its 

derivation transparent. Known limitations of the model 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 of the output table indicates the method used 

to calculate the vapor pressure and the percent error (47%) associated with the estimate. 

As with any mathematical relationship it is constructed 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 estimation capabilities of the model could lead to 

erroneous values. As such, the user should be cognizant of the domain of applicability for the 

model and should not extend analyses beyond that range. For example, in Figure A2 depicting 

output from ECOSAR, the user is warned that chemical solubility may limit the model's ability 

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

 

8

 OECD. 1007. Report on the Regulatory Uses and Applications in OECD Member Countries of (Quantitative 

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/NT00003B0A/$FILE/JT03221927.PDF 
94

toxicity for compounds with log octanol-water partition coefficient (log Kow) values of 5 and that 

for green algae, the cutoff is a log Kow 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 mathematical 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 goodness of fit, robustness and predictivity. 

Typically, the mean square error term and/or regression coefficient (r2

) of the model serve as 

measures of the ability of the model to represent the data in its training set. Robustness of the 

model can be evaluated by examining how toxicity estimates 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 log 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 environmental fate and ecological risk assessment 

written in support of the reregistration eligibility decision (RED) on carbaryl are near identical 

for log P; however, the ASTER estimate for vapor pressure differs from the measured value by 3 

orders of magnitude and the ASTER-estimated solubility is roughly 5X greater than what is 

reported in the RED. In cases where the model allows the user to modify environmental fate 

characteristics to reflect measured values, the user should consider doing so to determine the 

extent to which the toxicity values are affected. 

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

consistent with what has been demonstrated for similarly structured chemicals, e.g., analogs. 

Additional guidance on the use of QSAR models may also 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 example, in Figure A2 depicting output from the 

ECOSAR model for carbaryl, the toxicity estimates are 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. However, as stated previously, it is also known that 

carbaryl's primary mode of action is to inhibit acetyl cholinesterase. As such, the toxicity 

estimates based on the ester moiety by itself may or may not be reflective of the toxicity 

associated with the entire chemical. 

Finally, the strengths and weakness of the data estimation method used should be clearly 

described by the user when such estimates are included in assessments. When any SAR model 

has been used to estimate toxicity values, the output 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, consider 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 the left of the output provides the SMILES 

string for the compound. Figure A11 depicts chemical information on 1-naphthol entered into 
95

ECOSAR and Figure A12 depicts the ECOSAR output for the chemical with a fish 96-hr LC50

of 7.959 mg/L. The ASTER outputs for 1-naphthol are depicted in Figures A13 and A14 and 

indicate the mean acute LC50 value for freshwater fish is 1.78 mg/L. When naphthol is entered 

into DSSTox (Figure A15), the tool indicates that there are no exact matches 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 LC50 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 values for 

carbaryl and its primary degradate 1-naphthol; the most sensitive measured values used in the 

carbaryl risk assessment are also provided for comparison. Although QSAR model estimates for 

carbaryl appear to be considerably higher than the most sensitive toxicity value (Atlantic salmon 

96-hr LC50=0.22 mg/L) used in the ecological risk assessment of carbaryl, the mean measured 

96-hr LC50 value across all freshwater fish species is roughly 2.6 mg/L and is relatively 

consistent with QSAR estimates. Similarly for 1-napthol, 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 degradate are not substantially different than 

measured toxicity values. . 

Table 1. Summary of acute toxicity estimates for carbaryl and its primary degradate 1 naphthol from three 

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 toxicity data passing the ASTER filter. 

** Most sensitive freshwater fish 96-hr LC50

 
96

Figure A1. ECOSAR chemical input screen. Information depicted is for the carbamate insecticide carbaryl. 
97

Figure A2. ECOSAR output window screen shot. Screen is depicting estimates for the carbamate insecticide 

carbaryl. 
98

Figure A3. ASTER chemical data entry screen.
99

Figure A4. ASTER chemical property screen. Screen is depicting estimates for the carbamate insecticide 

carbaryl (1-naphthalenol methylcarbamate; CAS 63252). 
100

Figure A5. ASTER ecptoxicological hazard assessment. Screen is depicting estimates for the carbamate

insecticide carbaryl (1-naphthalenol methylcarbamate; 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 carbaryl 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 DSSTox 

search window indicates that for carbaryl, there is one exact match and no partial matches. The lower box 
102

indicates there are records (data) for carbaryl in 7 of the databases in which it searched. Double clicking on 

the box labeled "Exact matches" will provide more detail on those matches.

 
103

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

entitled "Exact matches" has been double clicked and the lowermost box now displays the similarity score, 

structure, CAS number, and hot links to each of the databases containing chemical-specific information on 

carbaryl. Double clicking on the hotlinks will display the chemical-specific data.
104

Figure A9. DSSTox substance results for EPA fathead minnow (EPAFHM) acute toxicity data using carbaryl 

as an example. In this example, the EPAFHM chemical/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 (map) in lower right is the open literature citation from 

which the metabolism data were derived. In this example, 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 been 

entered as the Value. The metabolic pathway for carbaryl is displayed in the window to the right showing 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. Information depicted is for the carbaryl degradate naphthol. 

In this example, the SMILES string, Name and CAS Number have been entered. Alternatively, the tab "CAS 

Input" could be selected and by entering the CAS Number (1321671), ECOSAR will automatically populate 

the window. Afterward, the user would double-click the tab "Calculate". 
107

Figure A12. ECOSAR output window screen shot. Screen is depicting estimates for the carbaryl 

degradatenaphthol. Toxicity estimates are calculated for both neutral organics and phenols. Both acute 

(LC50 and EC50) and chronic (ChV) toxicity values for 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 screen for carbaryl degradate 1-naphthol. The 

uppermost window contains acute and chronic toxicity estimates based on behavior (BEH), growth (GRO), 

mortality (MOR) and physiology (PHY) across both aquatic and terrestrial animals; these estimates 

represent the geometric mean (plus minimum and maximum values) of all the data passing the ASTER filter 

by species group. The lowermost window depicts median acute toxicity values for specific species. 
110
111

Figure A15. DSSTox search details for naphthol.

 
112

Figure A16. DSSTox substance results for 1-naphthol 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 Reference 

Acetylcholinesterase Inhibition - Carbamate Carbamates 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 Fish 

Russom et al, 

1997 

B-Halogenated Alcohol Based Reactivity Beta-halogenated 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 carrier 

protein reductase Chlorinated phenoxyphenol Irgasan (Triclosan) Fungi, Bacteria Russell 2004 

Blocks potassium ion channels in nerve fibers Aminopyridine 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 Insect IRAC 2010 

Ecdysone agonists / moulting disruptors Diacylhydrazines Chromafenozide Insect IRAC 2010 

Ester narcosis Esters Methyl acetate Fish 

Russom et al, 

1997 

GABA-gated chloride channel antagonists - Cyclodiene 

organochlorines Cyclodiene organochlorines Endosulfan Insect IRAC 2010 

GABA-gated chloride channel antagonists - 

Phenylpyrazoles Phenylpyrazoles (Fiproles) Fipronil Insect IRAC 2010 

Host defense inducer: salicylic pathway Benzothiadiazole Probenazole Fungi FRAC 2010 

Hydrazine Based Reactivity Hydrazines 1,2-Diethylhydrazine Fish 

Russom et al, 

1997 

Hypercalcemia resulting in calcification of soft tissue Vitamin D analogs Cholecalciferol Rodent Marshall 1984 

Inhibition of AA and Protein Synthesis: methionin 

biosynthesis Anilinopyrimidines Cyprodinil Fungi FRAC 2010 

Inhibition of AA and Protein Synthesis: Protein synthesis 

Hexopyranosyl antibiotic, 

Glucopyranosyl antibiotic, 

Tetracycline antibiotic Blasticidin-S Fungi FRAC 2010 

Inhibition of acetyl CoA carboxylase (ACCase) 

Aryloxyphenoxyproprionates; 

Cyclohexanediones Diclofop-methyl Plant HRAC 2010 

Inhibition of ALS/AHAS (acetolactate/actohydroxy acid 

synthase) 

Sulfonylureas; Imidazolinones; 

Triazolopyrimidines; 

Pyrimidinyl(thio)benzoates; 

Sulfonylaminocarbonyltriazolinones Imazapyr Plant HRAC 2010 

Inhibition of ATP production Thiophenecarboximides Silthiofam Fungi FRAC 2010 
114

Inhibition of auxin transport Phthalamates; Semicarbazones Naptalam Plant HRAC 2010 

Inhibition of cell division; Inhibition of very long chain 

fatty acids (VLCFA) 

Chloroacetamides; Acetamides; 

Oxyacetamides; Tetrazolinones, 

Unclassified Metolachlor Plant HRAC 2010 

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

Inhibition of cell wall (cellulose) synthesis 

Nitriles; Benzamides; 

Triazolocarboxamides Dichlobenil Plant HRAC 2010 

Inhibition of Complex I - NADH oxidoreductase Pyrimidinamines 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 (ubiquinone 

reductase) at Q1 site Cyanoimidazole, Sulfamoyltriazole Amisulbrom Fungi FRAC 2010 

Inhibition of DHP (dihydropteroate) synthase Carbamates Asulam Plant HRAC 2010 

Inhibition of EPSP (5-enolpyruvylshikimate-3-phosphate ) 

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 inhibition 

Thiocarbamates; Phosphorodithioates; 

Benzofuranes; Chloro-carbonic acids EPTC Plant HRAC 2010 

Inhibition of Lycopene cyclase Triazole Amitrol Plant 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 2010 

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

Inhibition of Mitosis/Cell Division: delocalisation of 

spectrin-like proteins Pyridinylmethylbenzamides Fluopicolide Fungi FRAC 2010 

Inhibition of nucleic acid synthesis: DNA topoisomerase 

Type II Carboxylic acids Oxolinic acid Fungi FRAC 2010 

Inhibition of nucleic acid synthesis: DNA/RNA synthesis Isoxazoles, Isothiazolones Octhilinone Fungi FRAC 2010 

Inhibition of nucleic acid synthesis: Purine metabolism 

(adenosin-demainase) Hydroxy-(2-amino)pyrimidines Ethirimol Fungi FRAC 2010 

Inhibition of nucleic acid synthesis: RNA polymerase I 

Acylalanines, Oxazolidinones, 

Butyrolactones Metalaxyl Fungi FRAC 2010 

Inhibition of phospholid biosynthesis: methyltransferase Phosphorothiolates; Dithiolanes Iprobenfos Fungi FRAC 2010 

Inhibition of phospholipid biosynthesis and cell wall 

deposition 

Cinnamic acid amides, Valinamide 

carbamates, Mandelic acid amides Dimethomorph Fungi FRAC 2010 

Inhibition of photosynthesis at photosystem II, Site A 

Triazine, Triazinones, Triazolinone, 

Uracils, Pyridazinones, Phenylcarbamates Atrazine Plant HRAC 2010 

Inhibition of photosynthesis at photosystem II, Site A but 

different binding behavior Ureas; Amides Linuron Plant HRAC 2010 

Inhibition of photosynthesis at photosystem II, Site 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 2010 
115

Unclassified 

Inhibition of signal transduction: G-proteins Quinolines, Quinazolinones Quinoxyfen Fungi FRAC 2010 

Inhibition of signal transduction: Osmotic (MAP/histidine 

kinase) Phenylpyrroles; Dicarboximides Iprodione Fungi FRAC 2010 

Inhibition of sterol biosynthesis class I (demethylation): 

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 2010 

Inhibition of sterol biosynthesis class IV: Squalene 

epoxidase Thiocarbamates, Allyamines Terbinafine Fungi FRAC 2010 

Inhibitors of acetyl CoA carboxylase - Lipid synthesis, 

growth regulation Tetronic acid Spiromesifen Insect IRAC 2010 

Inhibitors of cell wall synthesis: chitin synthase Peptidyl pyrimidine nucleoside Polyoxin B Fungi FRAC 2010 

Inhibitors of cell wall synthesis: trehalase and inositol 

biosynthesis Glucopyranosyl antibiotic Validamycin Fungi FRAC 2010 

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

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

Inhibitors of melanin synthesis in cell wall: dehydratase 

Cyclopropanecarboxamide, 

Carboxamide, Propionamide Fenoxanil Fungi FRAC 2010

Inhibitors of melanin synthesis in cell wall: reductase 

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 synthase Triphenyl tin compounds Fentin acetate Fungi FRAC 2010 

Isocyanate based reactivity Isocyanates, Isothiocyanates 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 2010 

Microbial disruptors of pathogen cell membranes 

Bacillus subtilius and fungicidal 

lipopeptides they produce Bacillus sp. Fungi FRAC 2010 

Microtubule assembly inhibition 

Dinitroanilines, Phosphoroamidates, 

Pyridines, Benzamides, 

Benzenedicarboxylic acids Trifluralin Plant HRAC 2010 

Miscellaneous nonspecific (multi-site) inhibitors 

Alkyl halides, Unclassified, Inorganic 

compounds Chloropicrin Insect IRAC 2010 

Mitochondrial complex I electron transport inhibitors 

Unclassified, Phenoxypyrazole, 

Pyridazinones, Pyrazole, 

Biopesticides Rotenone Insect IRAC 2010 

Mitochondrial complex II electron transport inhibitors Pyrazole Cyenopyrafen Insect IRAC 2010 

Mitochondrial complex III electron transport inhibitors 

(Coupling site II) 

Unclassified, Naphthoquinone 

derivative, Strobilurin 

Hydramethylnon, 

Antimycin Insect, Fish IRAC 2010 

Mitochondrial complex IV electron transport inhibitors 

(cytochrome oxidase) Inorganic compounds, Unclassified 

Zinc phosphide, Sodium 

azide 

Insect, Rodent, 

Bacteria IRAC 2010 

Mitochondrial complex V electron transport inhibitor - 

ATP synthase Sulfite ester Propargite Insect IRAC 2010 

Moulting disruptor, Dipteran Triazine Cyromazine Insect IRAC 2010 

Multi-site contact inhibitor activity 

Inorganic compounds, 

Dithiocarbamates and relatives, 

Phthalimides, Chloronitriles, 

Sulfamides, Guanidines, Triazines, 

Quinones Captafol Fungi FRAC 2010 
116

N-Halogenated Acetophenone Based Reactivity Beta-halogenated acetophenones 

alpha-Bromo-pnitroacetophenone Fish Russom et al, 

1997 

Nicotinic Acetylcholine receptor agonists Neonicotinoids, Botanical Dinotefuran Insect IRAC 2010 

Nicotinic Acetylcholine receptor allosteric activators Spinosyns Spinosad Insect IRAC 2010 

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

Nitroso Based Reactivity Nitroso compounds N-Nitrosomethylamine Fish 

Russom et al, 

1997 

Octopaminergic receptor agonists Amidine Amitraz Insect IRAC 2010 

Oxime Based Reactivity Oximes Nifuroxime Fish 

Russom et al, 

1997 

Pheromone: Attract thru odor Pheromone analogs Calcium lactate Insect IRAC 2010 

Photosystem I electron diversion Bipyridilium Paraquat 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 Chlorantraniliprole Insect IRAC 2010 

Selective homopteran feeding blockers 

Pyridine azomethines, Pyridine 

carboxamides Flonicamid Insect IRAC 2010 

Sodium channel modulators - organochlorines Organochlorine Organochlorines Insect IRAC 2010 

Sodium channel modulators - pyrethoids Pyrethroid Pyrethroid 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 Oxadiazine, Semicarbazone Metaflumizone Insect IRAC 2010 
117

Appendix C. Computational tools available via government, open access, or 

commercial sources that are available to risk assessors 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 Aventail AAA access. System is 

windows-based. ASTER is designed to provide high quality 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 models are MOA-based. System can be run 

in either a batch mode or interactive / single chemical mode. Outputs to Excel format or HTML. 

Coverage: Version 2.00 links to the most recent version of ECOTOX and EcoChem databases 

for empirical data. Includes 9 acute QSARs models for 7 MOAs (nonpolar narcosis, polar 

narcosis, ester narcosis, uncouplers of oxidative phosphorylation, reactive diesters, reactive 

carbonyls (3 equations), and reactive acrylates), and 3 chronic QSAR models for three MOA 

(nonpolar narcosis, polar narcosis, uncouplers of oxidative phosphorylation) for fathead 

minnow. Predicts toxicity for rainbow trout, bluegill, daphnid, and catfish for 2 MOA (nonpolar 

narcosis and polar narcosis) based on species extrapolation from fathead minnow LC50. When 

QSAR models do not exist, the system provides an estimated 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 number 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. Within the domain of applicability, MOA 

assignments default to nonpolar narcosis, if substructure fragments associated with other MOA 

are not identified. Models not available for metals or organometallics. QSAR models do not 

function outside of the domain of applicability. MOA assessments will function for organic 

structures within the domain of applicability. Models were built for use under TSCA and 

therefore do not include QSAR models for MOA related 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 Evaluation of 

Toxicity of pesticide Residues in Agriculture) 

URL: http://www.demetra-tox.net/index.php?option=com_frontpage&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-linear regression (MLR), and classification 

algorithms (e.g., adaptive fuzzy partition). The main 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 specifically developed using a training set 

including 20 pesticide classes including: organotins, organochlorines, organophosphates, 

carbamates, formamidines, terpenes, pyrethroids, phenols, 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 independent 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 available 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 on data submitted by 

manufacturers following OPPT guidelines, much of which is confidential business information 

(CBI), and data collections developed for use in QSAR 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/EC50 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 freshwater), mysid shrimp, daphnid, and algae. 

Most acute models are based on a QSAR model, but some 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 number 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. Models 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-congeneric databases. MCASE (and MC4PC) 

evaluate the structural features of a set of non-congeneric 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 models are developed. The descriptors of 
119

these models are called modulators and consist of fragments found within the individual sets as 

well as calculated transport and partition properties 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” responsible 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 every 

three bonded non-hydrogen atom groups has been seen and therefore evaluated by the model 

builder or not seen and therefore of questionable effect 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 predicting 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 number and chemical information will be 

populated. 

Known limitations: The data used in development of the aquatic toxicity models is unknown at 

this time, therefore it is unknown how the models would 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_34379_42923638_1_1_1_1,00.html 

Owner: OECD 

Overview:. Freely –available; PC-Based application. 

Coverage: Includes ECOTOX database, and other large collections for empirical data; includes 

EcoCHEM, EcoSAR, and many other chemical information 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 available, provide predictive methods to build 

chemical categories and read-across tables, includes 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 number and chemical information will be 

populated. 

Known limitations: System is currently in beta-testing, and limitations 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 number 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/predictive-toxicology.html 

Owner: Accelrys, Inc. 

Overview: Commercially available; TOPKAT runs on PCs under Windows 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 descriptors to build statistically robust 

Quantitative Structure Toxicity Relationship (QSTR) models for over 18 endpoints. TOPKAT 

will validate its assessments via a univariate analysis of the descriptors, a multivariate analysis of

the fit of the query structure in Optimum Prediction Space (OPS), and by similarity searching in 

descriptor space. QSAR models are preselected by the software based on the chemical class. The 

program can be executed in batch mode and the result is available in a format that can be 

imported into Microsoft Excel for Windows. TOPKAT makes 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 number and chemical information will be 

populated. 

Known limitations: TOPKAT produces information for the (Q)SAR applicability 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 are 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 fathead models is unknown at this time; 

therefore it is unknown how the models would perform with pesticides. 

Similarity Tools 

AIM: (Analog Identification Methodology) 

URL: https://aim.epa.gov 

Owner: USEPA OPPT 

Overview: AIM was designed to identify structurally analogous compounds based on a three 

pass atom-fragment matching algorithm. The substructure library contains 645 atom-fragment 

definitions along with super fragments for identifying important ring systems. The library 

focuses mostly on molecular features of neutral organic chemicals, but the predictive method 

will also provide results for salts and organometallic compounds. However, AIM does not 
121

consider oxidation state which is often an important consideration when performing a toxicity 

assessment on metal containing compound and compounds with complex organic cations and 

anions may give unexpected result. 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 ecotoxicology data, although ECOTOX data set is to be included by 

2011. The current AIM methodology requires exact matching 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. dichlorodiphenylsilane will not be identified as an 

analog for trichlorophenylsilane). Methodology to remove 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; therefore, some additional good analogs may not 

appear in the results.. 

Coverage: AIM accesses 10 data sources: TSCATS (USEPA TSCA test submission), HSDB 

(National Library of Medicine’s Toxicology Data Network, Hazardous Substances Data Bank), 

IRIS (USEPA Integrated Risk Information System), National Institute of Health’s NTP (National 

Toxicology Program), Centers for Disease Control and Prevention’s ATSDR (Agency for Toxic 

Substances and Disease Registry), US EPA’s HPV (High Production Volume) Challenge 

Program, US EPA’s DSSTox website (see below), NIOSH’s RTECS (Registry of Toxic Effects 

of Chemical Substances), OECD’s IUCLID (International Uniform Chemical Information 

Database) of HPV chemical data reported by European industry, and the National Advisory 

Committee for the Development of Acute Exposure Guideline Levels (AEGL) for Hazardous 

Substances, AEGL rank. 

Known limitations: AIM was designed to identify analogs only for neutral organic compounds. 

Other chemical classes should not be run through AIM. 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 ecotoxicology data, although ECOTOX data 

set is to be included by 2011. The current AIM methodology requires exact matching 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. dichlorodiphenylsilane will not be 

identified as an analog for trichlorophenylsilane). Methodology to remove 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; 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 (NCCT) 

Overview: The USEPA DSSTox Structure-Browser, developed from available structure-viewing 

freeware and open-source programming methods, delivers 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, SMILES string, CAS Registry number, InChITM 

(International Chemical Identifier) code, drawing the chemical, or structural formula (e.g., 
122

C4H5Cl). Users can also select how they want to search for similarity; i.e., exact match, 

substructure search, or similarity with defined threshold percentage. 

Coverage: System searches all the DSSTox databases including the European Bioinformatics 

Institute (EBI) ArrayExpress Repository for Gene Expression 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 Carcinogenic Potency data set, USEPA fathead 

minnow acute toxicity data, USEPA water disinfection 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 limit searches to specific data sets. 

Known limitations: Current data sets cover 7,410 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 ecotoxicology data. 

OECD (Q)SAR Application Toolbox

URL: http://www.oecd.org/document/54/0,3343,en_2649_34379_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 similarity based on Tanimoto coefficient, Dice, 

Ochial, and Kulcynski-2 methods. Users can set similarity threshold limits (% similarity) and 

molecular features (e.g., atom pairs, functional groups). 

Input requirements: Chemical structure; via SMILES, drawing, etc., but if structure is in the 

supporting database, you only need enter the CAS number and chemical information will be 

populated. 

Known limitations: System is currently in beta-testing, and limitations 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 predicting toxicity to aquatic species, 

genera, and families and 852 models predicting to wildlife 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 species and 951 chemicals. Models described in 

Section 4.3 are available on Web-ICE . All models within Web-ICE are statistically significant 
123

and those with at least four datapoints have been cross-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 endangered 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 guidance provides assistance with model 

selection and results interpretation. 

Input requirements: Acute EC/LC50 value in ug/L for aquatic species, acute 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 model 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 datasets: 1) acute aquatic 

organism (4890 EC/LC50 tests), 2) aquatic plant (1439 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 provide 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 model uncertainty. Chronic tests were not 

standardized for test duration or most sensitive endpoint 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 include the number of 

individuals alive at each time step and each concentration. 
124

Known limitations: Model output is for chronic lethality. Model uncertainty 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 models: 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 potential differences in algorithms that result 

in predictions to MATC and NOECs, respectively, are unclear. 

Input requirements: time-course data for acute toxicity studies to include the number of 

individuals alive at each time step and each concentration. 

Known limitations: Model output is for chronic lethality. Model uncertainty analyses have not 

been thoroughly conducted. 

 
125

Appendix D: Predictive methods for use in determining mode of action and physical chemical properties

TITLE Brief description Web address Owner 

Tools and Websites for use in Determining MOA of chemical of interest 

ASTER 

Predicts acute mode of action based on fish model http://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 formula, 

etc. for more than 1100 substances. Site is indexed by 

chemical name, CAS number and pesticide classification 

http://www.alanwood.net/pesticides

/index.html Alan Wood 

Fungicide 

Resistance Action 

Committee 

Includes link to fungide mode of action maps as well 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 HRAC 

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 specific 

information on mode of action http://irac-online.org/ 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 be 

used by governments, chemical industry and other 

stakeholders in filling gaps in (eco)toxicity data needed 

for assessing the hazards of chemicals. The Toolbox 

incorporates information and predictive methods from 

various sources into a logical workflow. Crucial to this 

workflow is grouping chemicals into chemical 

categories. Has several mechanistically-based categories 

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, and 

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 

Damage 
126

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 environmental 

assessment http://cfint.rtpnc.epa.gov/aster/ USEPA 

EPISuite 

Predict p-chem and fate properties to assist in environmental 

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 parameters 

from pollutant molecular structure and basic information 

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 be used by 

governments, chemical industry and other stakeholders in 

filling gaps in (eco)toxicity data needed for assessing the 

hazards of chemicals. The Toolbox incorporates information 

and predictive methods from various sources into a logical 

workflow. Crucial to this workflow is grouping chemicals 

into chemical categories. 

 

The seminal features of the Toolbox are: 

1. Identification of relevant structural characteristics and 

potential mechanism or mode of action of a target chemical. 

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 data 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 better 

evaluate chemical risks 

http://nomiracle.jrc.ec.europa.

eu/Lists/Toolbox/Exposure.as

px EU