Charge Questions

1
1. QSARs for predicting 

acute toxicity for aquatic animals

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. 2
1. QSARs for predicting 

acute toxicity for aquatic animals

1. QSARs for predicting 

acute toxicity for aquatic animals

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), 

3
1. QSARs for predicting 

acute toxicity for aquatic animals

1. QSARs for predicting 

acute toxicity for aquatic animals

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. 

4
1. QSARs for predicting 

acute toxicity for aquatic animals

1. QSARs for predicting 

acute toxicity for aquatic animals

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.

5
1. QSARs for predicting 

acute toxicity for aquatic animals

1. QSARs for predicting 

acute toxicity for aquatic animals

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. 

6
1. QSARs for predicting 

acute toxicity for aquatic animals

1. QSARs for predicting 

acute toxicity for aquatic animals

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. 

7
2. Web-ICE for predicting 

acute toxicity for aquatic animals

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). 

8
2. Web-ICE for predicting 

acute toxicity for aquatic animals

2. Web-ICE for predicting 

acute toxicity for aquatic animals

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. 

9
2. Web-ICE for predicting 

acute toxicity for aquatic animals

2. Web-ICE for predicting 

acute toxicity for aquatic animals

a) 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.

10
2. Web-ICE for predicting 

acute toxicity for aquatic animals

2. Web-ICE for predicting 

acute toxicity for aquatic animals

b) 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. 

11
3. Use of SSDs to estimate HC5 

using varying amounts of data

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. 

12
3. Use of SSDs to estimate HC5 

using varying amounts of data

3. Use of SSDs to estimate HC5 

using varying amounts of data

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.

13
3. Use of SSDs to estimate HC5 

using varying amounts of data

3. Use of SSDs to estimate HC5 

using varying amounts of data

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.

14
3. Use of SSDs to estimate HC5 

using varying amounts of data

3. Use of SSDs to estimate HC5 

using varying amounts of data

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.

15
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). 

16
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

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. 

17
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

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. 

18
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

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. 

19
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

a) Please comment on the extent to which consideration of 

MOA decreases uncertainty associated with HC5 values 

that are estimated using EFs.

20
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

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. 

21
4. Extrapolation Factors for estimating acute 

HC5s with a specified level of confidence

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. 

22
5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

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). 

23
5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

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. 24
5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

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. 

25
5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

a) Please comment on the strengths and weaknesses of 

using chemical-specific ACRs to estimate chronic effect 

thresholds for other species and taxa.

26
5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

5. Use of ACRs to estimate 

chronic toxicity endpoints or HC5 values 

for acutely sensitive species

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 MOAspecific (e.g., specific (e.g., for AChE inhibitors) inhibitors) or more or more generalized generalized 

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

27
5. Use of ACRs to estimate 

chronic toxicity endpoints or 

HC5 values for acutely sensitive species

5. Use of ACRs to estimate 

chronic toxicity endpoints or 

HC5 values for acutely sensitive species

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 Douboudin et al. 2004; described et al. 2004; described in 

USEPA 2010b).

28
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. 

29
6. Estimating the HC5 for aquatic plants

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 estimating the HC5 using the FIFRA-5 will 

result in a reasonable approximation of the HC5 of an 

aquatic plant community. 

30
6. Estimating the HC5 for aquatic plants

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.

31
6. Estimating the HC5 for aquatic plants

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.

32
6. Estimating 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).

33
6. Estimating the HC5 for aquatic plants

d) Please comment on the extent to which consideration of 

MOA may decrease uncertainty associated with HC5 

values for aquatic plants. 

34