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Charge Questions for SAP on Review of Methods for Characterizing Effects 

of Pesticides and Other Chemical Stressors to Aquatic Organisms

1. QSARs for predicting acute toxicity for aquatic animals

Effect characterizations in both United States Environmental Protection Agency’s (USEPA) 

Office of Water (OW) and Office of Pesticide Programs (OPP) rely on toxicity estimates for 

various taxa. The USEPA investigated the use of some readily available Quantitative Structure

Activity Relationship (QSAR) tools (described in Appendix A) to predict acute toxicity values

to represent sensitive taxa which could potentially fill gaps in available empirical toxicity data. 

Three publicly available QSAR models were examined, including: Ecological Structure Activity 

Relationships (ECOSAR), Toxicity Estimation Software Tool (TEST), and the Organization for 

Economic Cooperation and Development (OECD) QSAR Toolbox. These tools are increasingly 

used by USEPA risk assessors to determine the potential toxicity of various chemicals, including 

pesticide degradates. The white paper’s analyses of ECOSAR, TEST and the OECD Toolbox 

suggest that, at this time, 1) the chemical domains for most models within these QSAR tools do

not include sufficient data for pesticide active ingredients and therefore these models are not 

currently robust estimators of the toxicity for modes of action (MOAs) that are unique to 

pesticides (e.g., acetylcholinesterase (AChE) inhibitors), 2) current models are generally 

populated with data for species for which toxicity data are typically submitted under FIFRA, and 

generally, could not be used to fulfill other taxonomic data requirements defined in the 1985 

guidelines, and 3) more toxicologically relevant molecular descriptors and structural alerts need 

to be identified for use in building models for MOAs associated with pesticide active ingredients.

a) Appendix A of the white paper includes a review of the utility of ECOSAR, TEST and the 

OECD toolbox for quantifying sensitive acute toxicity values for aquatic animals exposed 

to pesticides. Please comment on the strengths and weaknesses of the methods, results 

and conclusions associated with this review.

b) The analysis discussed in Appendix A of the white paper used empirical toxicity data for 

AChE inhibitors to evaluate the predictions of specific QSAR tools. Please discuss the 

applicability of the conclusions based on AChE inhibitors to other pesticide MOAs. 

Although not part of the current analysis, please comment on the ability of SAR tools to 

quantitatively predict the toxicity of chemicals with non-specific MOAs for sensitive

animals (e.g., pesticide degradates and some herbicide with a narcosis MOA), that may 

be within the domain of applicability of these tools. 

c) Please comment on potential modifications to the existing versions of ECOSAR, TEST 

and the OECD toolbox that could improve their quantitative capability relative to 

estimating acute toxicity values for aquatic animals exposed to specific pesticides. For 

example, expansion of QSAR models within existing tools to better include pesticide 

active ingredients within their domain of applicability, expansion of the model training 

sets to cover taxa associated with data gaps, development of models based on MOA, 

inclusion of parameters other than the logarithm of the octanol-water partition 

coefficient (i.e., log P) to describe toxicity. 
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2. Web-ICE for predicting acute toxicity for aquatic animals

The white paper investigated the use of Web-based Interspecies Correlation Estimation models 

(Web-ICE) to predict acute toxicity values and potentially fill data gaps (described in Appendix 

B). The review of the current version of Web-ICE suggests that, when using the minimum acute 

toxicity data for aquatic animals that are typically submitted to fulfill requirements under FIFRA 

(i.e., data for Daphnia magna, rainbow trout and bluegill sunfish), Web-ICE appears to generate 

the most precise predictions of toxicity for closely related fish species (e.g., within the same 

family). If toxicity data are available for either rainbow trout or bluegill sunfish, Web-ICE may

be useful for predicting the toxicity of a chemical to some species of fish. The current review of 

this tool indicates that Web-ICE does not have sufficient data to fulfill the minimum data 

requirements across all taxa defined in the 1985 Guidelines when there are only acute surrogate 

data available for rainbow trout, bluegill and D. magna; however, this may change with future 

updates. 

d) Appendix B of the white paper includes a review of the utility of Web-ICE for quantifying 

sensitive acute toxicity values for aquatic animals exposed to pesticides. Please comment 

on the strengths and weaknesses of the methods, results and conclusions associated with 

this review.

a) Please comment on potential modifications to the existing version of Web ICE that could 

improve its ability to predict the acute toxicity values of pesticides to different species of 

aquatic animals. For example, expansion of the tool to consider MOA, addition of data 

for infrequently tested species that may fulfill the data requirements defined in the 1985 

guidelines. 

3. Use of SSDs to estimate HC5 using varying amounts of data

As described in Appendix C, USEPA investigated the use of species sensitivity distributions 

(SSDs) with varying amounts of empirical data to estimate the fifth percentiles of sensitivity 

distributions of toxicity data (termed the “HC5”). Analyses performed by USEPA for this effort 

suggest that SSDs have limited utility when test data points are limited, specifically where 

biological diversity is not sufficient to represent the distribution of taxa sensitivity. The analyses 

further suggest that SSDs applied to datasets that satisfy existing taxonomic requirements with 

extrapolation constants (as defined in Appendix C) would likely result in lower and yet 

reasonable approximations of the HC5. 

a) Appendix C of the white paper includes a description of the analyses of SSDs. Please 

comment on the strengths and weaknesses of the methods, results and conclusions 

associated with this analysis.

In Appendix C, several potential avenues for future work on SSDs were described, including i)

extension of methods for ordered data to all continuous distributions ii) development of stepwise

methods, including decision points, for data analysis to incorporate information on the 

relationship of MOA and taxa sensitivity, and iii) the use of random effects methods for handling 

cases in which more than one test result is available for a given taxon.
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b) Please comment upon additional analyses relative to SSDs that could be considered by 

USEPA that may result in improved confidence in estimates of HC5 values using various 

amounts of data that are typically available for registered pesticides.

4. Extrapolation Factors for Estimating Acute HC5s with a Specified Level of Confidence

The USEPA OW’s aquatic life criteria (ALC) involve calculation of the "Final Acute Value" 

(FAV) as the fifth percentile of acute toxicity values for genera of aquatic animals, and impose 

certain minimum data requirements (MDRs) for such calculations. To provide some assessment 

of effects when insufficient data are available for calculating an FAV, USEPA’s Great Lakes 

Water Quality Guidance (GLWQG) specified extrapolation factors (EFs) by which the acute 

toxicity value for the most sensitive tested aquatic animal genus could be divided to provide a 

conservative estimate for what the FAV should be if sufficient data was available (U.S.EPA 

1995). In this case, conservative estimates of the FAV are generated by accounting for 

uncertainty in possible EF values, which generally results in an underestimate of the FAV that 

may be generated using a data set that would be considered complete according to the 1985 

Guidelines. The EFs incorporated into the GLWQG were based on analyses by Host et al. (1995) 

of toxicity test datasets used in freshwater ALC that existed at that time; these analyses involved 

repeatedly subsampling (resampling) of these datasets to assess the relationship of the lowest 

toxicity values in datasets of a specified size and composition to FAVs based on larger datasets. 

The analyses described in Appendix D of this white paper builds upon the analysis conducted by 

Host et al. (1995) by (a) utilizing more recent aquatic toxicity data and data for chemicals that do 

not have an ALC, (b) developing EFs for different MOAs to reduce uncertainty, (c) paying 

specific attention to EFs for OPP minimum data requirements for acute toxicity testing of aquatic 

animals, and (d) generating EFs using statistical distributions descriptive of acute toxicity values 

for different taxonomic groups and based on multiple chemicals, rather than resampling toxicity 

data for individual chemicals. These analyses established the feasibility of establishing EFs for 

certain MOAs, demonstrated how a distributional rather than a resampling approach can more 

effectively exploit data to develop MOA-specific EFs, and documented the relationship between 

OPP and OW benchmarks. In addition, the current efforts considered how EF applicability and 

meaning might be affected, relative to the GLWQG, by different strategies for assigning 

reference HC5 and for summarizing EF distributions. 

a) Please comment on the extent to which consideration of MOA decreases uncertainty 

associated with HC5 values that are estimated using EFs.

b) Please comment on the strengths and limitations of both the resampling and 

distributional methodologies for developing MOA-specific EFs, and provide suggestions 

on their further development and application to other MOAs. Please provide suggestions

for deriving EFs for chemicals with MOAs that have limited amounts of empirical toxicity 

data. 

c) Please comment on the strengths and limitations of the different approaches in the Host 

et al. (1995) and the current work for assigning reference HC5s and summarizing EF 

distributions. 
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5. Use of ACRs to estimate chronic toxicity endpoints or HC5 values for acutely sensitive 

species 

Chronic toxicity data are generally available for fewer species than are tested to determine acute 

toxicity. Acute-chronic ratios (ACRs) are a common tool used to estimate chronic effect 

thresholds for taxa lacking chronic toxicity data. Both OPP and OW currently use ACRs 

developed for specific chemicals to estimate chronic effect levels. ACR distributions have been 

developed for broad groups of chemicals without consideration of MOA or taxa (e.g., mixed 

pesticides by TenBrook et al. 2010; all chemicals by Host et al. 1995). Appendix E of the white 

paper considers the application of ACRs developed for chemicals within the same MOA to 

estimate chronic effects for chemicals with the same MOA and lacking any chemical-specific 

ACRs. Analysis of ACR data specifically for AChE inhibitors identified patterns in the ACR 

distributions. Most notably, ACRs for invertebrates were smaller than those for fish. Although 

fish had larger ACRs, this did not translate to higher chronic sensitivity of fish to AChE 

inhibitors, because of their very low acute sensitivity. In fact, analyses showed that 

concentrations protective of acute toxicity to invertebrates would simultaneously protect fish 

from chronic effects. By extension, for AChE inhibitors, applying an invertebrate-based ACR to 

the acute HC5 (i.e., fifth percentile of a sensitivity distribution) should provide a chronic HC5 

protective of both fish and invertebrates. When comparing ACRs developed for broad groups of 

chemicals and taxa to ACRs for aquatic invertebrates exposed to AChE inhibitors, they were 

approximately 2 fold different. 

a) Please comment on the strengths and weaknesses of using chemical-specific ACRs to 

estimate chronic effect thresholds for other species and taxa.

b) Please comment on the strengths and weakness of applying “default” ACRs derived from 

other chemicals to extrapolate from an acute HC5 to a chronic HC5, including the 

relative merits of values derived for MOA-specific (e.g., for AChE inhibitors) or more 

generalized (e.g., Host et al. 1995) distributions.

c) Are there other methods for estimating a chronic HC5 that the panel believes would be 

technically superior to ACR-based approaches? For example, TCE models (described in 

USEPA 2010a), distributional approaches (e.g., de Zwart 2002, Douboudin et al. 2004; 

described in USEPA 2010b).

6. Estimating the HC5 for aquatic plants

As with animals, there has been uncertainty regarding the extent to which available data reflect 

the range of sensitivities that may be present for aquatic plants. This effort focuses on the 

potential uses of EFs and SSDs to estimate the HC5 relevant to aquatic plants. As described in 

Appendix F, the primary analyses investigated the use of the standard aquatic plant species 

submitted to fulfill FIFRA data requirements in estimating the HC5. These species, which are 

referred to in the white paper as “the FIFRA-5” include: Pseudokirchneriella subcapitata (a 

freshwater green alga), Anabaena flos-aquae (a freshwater cyanobacterium), Navicula 

pelliculosa (a freshwater diatom), Skeletonema costatum (a saltwater diatom) and Lemna gibba 

(the aquatic vascular plant, duckweed). The analysis described in Appendix F suggested that 
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estimating the HC5 using the FIFRA-5 will result in a reasonable approximation of the HC5 of 

an aquatic plant community.

a) Appendix F of the white paper includes an analysis of the use of EFs and SSDs combined 

with the FIFRA-5 to estimate the HC5 for aquatic plants. Please comment on the 

strengths and weaknesses of the methods, results and conclusions associated with this 

analysis.

b) Please comment on other aquatic plant species or test endpoints that may be considered 

in order to generate more certain estimates of the HC5 for aquatic plants.

c) Please comment on the strengths and limitations of normalizing plant toxicity endpoints 

to a standard metric (i.e., EC50 for growth rate).

d) Please comment on the extent to which consideration of MOA may decrease uncertainty 

associated with HC5 values for aquatic plants.