Potential Use of Predictive Toxicology 

Tools in Characterizing Effects of 

Chemical Stressors to Aquatic Animals 

Potential Use of Predictive Toxicology 

Tools in Characterizing Effects of 

Chemical Stressors to Aquatic Animals 

Chris Russom and Dale Hoff

Office of Research and Development

1

Presentation to the FIFRA Scientific Advisory Panel 

January 31, 2012
Purpose

Evaluate predictive toxicology tools for use in 

filling data gaps in SSDs (e.g., ALC MDRs) 

for the purpose of estimating an HC5.

1985 Guidelines (one species from:) FIFRA 40 CFR 158

FW Family Salmonidae Cold water fish (typically rainbow trout)

One other family in the class Osteichthyes Warm water fish (typically bluegill sunfish)

A third family in the phylum Chordata -

2

A third family in the phylum Chordata -

Planktonic crustacean Aquatic invertebrate (typically D. magna)

Benthic crustacean -

An insect -

Family in phylum that does not include arthropods or chordata -

A family in any order of insect or phylum not represented -

SW

Family in phylum Chordata Estuarine/marine fish (typically sheepshead 

minnow)

Second family in phylum Chordata -

Family in not in Chordata or Arthropoda One estuarine/marine mollusk (typically Eastern 

oyster)

Either Mysidae or Penaeidae family One estuarine/marine invertebrate (typically mysid 

shrimp)

Three other families not in the phylum Chordata -

Any other family -
Use of PredictiveTools

• What is the potential utility for each model/tool 

in developing quantitative toxicity values?

• For each model, what are the strengths, 

weaknesses and uncertainties as they relate to 

use in predicting toxicity of a chemical to 

specific taxa and species distribution?

• How do model predictions compare to an 

independently compiled experimentally derived 

data collection?

• What future research could improve the utility 

of the predictive tools considered in this 

review? 3
QSAR Models

2

• Chemical extrapolation technique

• Relates structure to activity (e.g., log P is used to estimate the 

LC50)

• QSARs can be based on chemical categories, mode of action 

or be global (range of chemical classes / MOA)

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Log P

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Narcosis QSAR model 

with empirical data

Water solubility
QSAR: Approach

• Gather chemical information for 127 

acetylcholinesterase inhibitors

• Structure (SMILES)

• CAS Registry Number

• Chemical name

• Acute mode of action

• Molecular descriptors 

5

Acetylcholinesterase inhibition
QSAR: Approach

• Compiled an empirical data set

• Data sets should meet a level of rigor for use 

in QSAR validation or model development

• Selected empirical data set used in building 

web-ICE models

• Filled data gaps with ECOTOX data (released after 

Feb 2009), but used the same filters as employed 

under ICE

• Within the OECD toolbox, used ICE model data 

sets and used resident OECD data sets, but 

applied similar data filters as used under ICE

6
QSAR: Acute toxicity

• Examine 3 publicly available QSAR Tools

• EcoSAR – Developed by US EPA/OPPT 

• V1.1 (chemical domain expanded to include some pesticide AIs) 

with comparisons to V1.0 (minimal pesticide data)

• T.E.S.T. T.E.S.T. (V4.0) – Developed Developed by US EPA/ORD by US EPA/ORD 

• QSAR Toolbox (V2.2.1) – Developed by the OECD

• Evaluated QSAR model performance by 

comparing model estimates to empirical test 

data 

• Ratio of the estimated value to empirical value

• Correlation (R2) of estimated value to empirical value 7
EcoSAR models

• EcoSAR (US EPA OPPT)

• Models based on chemical classes

• Includes 111 chemical classes and 709 QSAR 

models

• Log P is the molecular descriptor for models 

evaluated

• Models relevant to this effort - LC/EC50:

• fish (saltwater and freshwater)

• mysid shrimp (96 hr)

• algae (72 and 96 hr)

• daphnids (48 hr) 8
T.E.S.T.: EPA ORD models

Descriptions of modeling methods available within T.E.S.T. modeling tool (taken from USEPA 2011)

Method Description Species

Hierarchical Clustering

The toxicity for a given chemical is estimated using the weighted 

average of the predictions from several different models. The different 

models are obtained by using hierarchical clustering techniques. A 

genetic algorithm based method is used to generate models for each 

cluster. The models are generated prior to runtime.

Fathead minnow

Daphnia magna

FDA

The prediction for each test chemical is made using a new model that 

is fit to the chemicals that are most similar to the test compound. Each 

model is generated at runtime.

Fathead minnow

Daphnia magna

9

Single Model

Predictions are made using a multilinear regression model that is fit to 

the training set (using molecular descriptors as independent variables) 

using a genetic algorithm based approach. The regression model is 

generated prior to runtime.

Fathead minnow

Daphnia magna

Nearest Neighbor

The predicted toxicity is estimated by taking an average of the 3 

chemicals in the training set that are most similar to the test chemical.

Fathead minnow

Daphnia magna

Group Contribution

Predictions are made using a multilinear regression model that is fit to 

the training set (using molecular fragment counts as independent 

variables). The regression model is generated prior to runtime.

Fathead minnow

Consensus Model

The predicted toxicity is estimated by taking an average of the 

predicted toxicities from the above QSAR methods (provided the 

predictions are within the respective applicability domains).

Fathead minnow

Daphnia magna
OECD Toolbox

• Decision Support System

• Includes extensive data collections

• Repository of existing models

• Fill data gaps

10
EcoSAR models - Fish

• Of the 127 AChE inhibitors

• 64 chemicals had acute lethality data for 

freshwater fish in the empirical data set

• 17 carbamates and 47 organophosphates

• The averages of the test data for rainbow trout 

11

and/or bluegills were used to represent the 

empirical test data

• These species had data for all 64 substances 

• These were typically the most sensitive fish species

• Selected the EcoSAR model for the OP or 

carbamate chemical categories
EcoSAR models - Fish

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Comparison of EcoSAR V1.1 and Estimated Values to 

Empirically Derived Test Data

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Empirically Derived Log Molar LC50 for Fish 

EcoSAR V1.1
EcoSAR models - Fish

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Comparison of EcoSAR V1.1 and 1.0 Estimated Values to 

Empirically Derived Test Data

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Empirically Derived Log Molar LC50 for Fish 

EcoSAR V1.1 EcoSAR V1.0
EcoSAR models - Fish

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Comparison of EcoSAR V1.1 and 1.0 Estimated Values to 

Empirically Derived Test Data

EcoSAR V1.0 R2=0.093

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Empirically Derived Log Molar LC50 for Fish 

EcoSAR V1.1 EcoSAR V1.0

EcoSAR V1.1 R2=0.23
EcoSAR models - Daphnid

• Of the 127 AChE inhibitors

• 51chemicals had acute lethality data for 

daphnids in the empirical data set

• 14 carbamates and 37 organophosphates

• The averages of the test data for Daphnia 

15

magna and Daphnia pulex were used to 

represent the empirical test data

• These species had data for all 51substances 

• Selected the EcoSAR model for the OP or 

carbamate chemical categories
EcoSAR models - Daphnid

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Comparison of EcoSAR Estimated Values to Empirical Data

Overall correlation of empirically derived data to EcoSAR daphnid 

model estimates had an R2=0.46

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Empirically Derived Log Molar LC50 for Daphnids 
EcoSAR models - Daphnid

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Comparison of EcoSAR Estimated Values to Empirical Data

Overall correlation of empirically derived data to EcoSAR daphnid 

model estimates had an R2=0.46

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Empirically Derived Log Molar LC50 for Daphnids 

R2=0.46
T.E.S.T. models - Fish

• Of the 127 structures only 100 were 

accepted by T.E.S.T

• 30 chemicals had acute lethality data for 

fathead minnow in the empirical data set

• Not all models provided an estimated value; 

18

20-27 chemicals had both an estimated and 

empirical data point depending on the 

T.E.S.T. model

• The average of the test data for fathead 

minnow was used in the analysis
T.E.S.T. models – FHM

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Comparison of EcoSAR Estimated Values to Empirical Data

R2=0.20-0.56

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Empirically Derived Log Molar LC50 for Fathead Minnows

TEST Consensus Method TEST FDA Method TEST Group Method

TEST Heir.Clust Method TEST Nearest Neigh. Method TEST Single Model Method
T.E.S.T. models – FHM

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Comparison of EcoSAR Estimated Values to Empirical Data

R2=0.20-0.56

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Empirically Derived Log Molar LC50 for Fathead Minnows

TEST Consensus Method TEST FDA Method TEST Group Method

TEST Heir.Clust Method TEST Nearest Neigh. Method TEST Single Model Method
T.E.S.T. models – D. magna

• Of the 127 structures only 100 were 

accepted by T.E.S.T for modeling

• 45 chemicals had acute lethality data for Daphnia 

magna in the empirical data set

• Not all models provided an estimated value; 38-45 

21

• Not all models provided an estimated value; 38-45 

chemicals had both an estimated and empirical data 

point depending on the T.E.S.T. model

• The average of the test data for D. magna was used 

in the analysis

• Group Contribution model not available for D. magna
T.E.S.T. models – D. magna

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Comparison of Empirically Derived Data vs. Estimated Values 

Correlation of empirically derived data to T.E.S.T. model estimates ranged 

from R2=0.28-0.46

Pirmicarb

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Empirically derived Log Molar LC50 for D. magna 

Concensus Method FDA Method Heir.Clust Method Nearest Neigh. Method Single Model Method
T.E.S.T. models – D. magna

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Comparison of Empirically Derived Data vs. Estimated Values 

Correlation of empirically derived data to T.E.S.T. model estimates ranged 

from R2=0.28-0.46

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Concensus Method FDA Method Heir.Clust Method Nearest Neigh. Method Single Model Method
OECD Toolbox

• Models could be built, but were either not 

significant or failed upon final validation

• Difficulty finding appropriate molecular 

descriptors within the toolbox to describe 

toxicity

• Difficulty filtering resident empirical data 

sets to include studies meeting rigorous 

acceptability requirements

24
OECD Toolbox

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Log Molar LC50 – Empirical fish data set

OECD Salmonid Model EcoSAR V1.1
Conclusions: QSAR

• How do QSAR estimates compare to empirical test data? Acceptable 

models - the estimated values are within one order of magnitude of empirical values

• ECOSAR Version 1.1 QSAR models: 

• Fish : 73.4% (carbamates 100%; OPs 63.8%) met benchmark 

• Daphnid: 76% (carbamates 92.3%; OPs 70%) met benchmark 

• T.E.S.T. QSAR models :

• Fathead minnow: 68-88% of the model estimates met benchmark

• D. magna D. magna: 47-56% of the model estimates met benchmark of the model estimates met benchmark

• What are the strengths, weaknesses and uncertainties of models?

• EcoSAR / T.E.S.T.: Existing models do not include pesticides in chemical domain

• OECD Toolbox: Need toxicologically-relevant molecular descriptors for use in 

predicting pesticide MOAs

• All QSAR Tools: Availability of models is driven by availability of suitable empirical 

data sets

• Can these tools be used to fulfill data gaps in SSDs?

• Available models are primarily those for species groups not having MDR 

data gaps (i.e., data typically obtained under FIFRA) 26
Insect

Family in phylum that does not include 

Chordate or Arthropod

Family in any order of Insect or phylum 

not represented

Across MOA

Reactive 

Conclusions: QSAR

Percentage of Individual Freshwater MDRs Met by MOA. 

Guidelines MDRs on y-axis. Green Box designates FIFRA MDRs (i.e., cold water fish / salmonid

warm water fish / other family in Osteichthyes, and aquatic invertebrate / planktonic crustacean).

0.00 20.00 40.00 60.00 80.00 100.00

Salmonid

One other family in Osteichtyes

3rd Chordate family

Planktonic crustacean

Benthic crustacean

Reactive 

Cellular

Uncouplers

Narcosis

Organochlorines

Pyrethroids

AChE-OP

AChE-Carbamate

Percentage of chemicals meeting the data requirement
Interspecies Correlation Estimation 

(ICE)

• Estimates the toxicity of a chemical to a species that has no 

measured result from the know toxicity of another species.

• Correlates toxicity results for two species for as many chemicals 

as are available

• Once a correlation is developed, the regression equation is used 

to estimate the toxicity of additional chemicals regardless of 

MOA.

28 1.0 2.0 3.0 4.0

1.0

2.0

3.0

Rainbow Trout (log 96-h LC50)

C

a

p

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ar shiner (lo

g

9

6-h L

C

50)

X = -0.025 + 1.098X

r = 0.991

2 1

ICE model for chemicals 

tested in trout and shiner
Background of ICE

• First described by authors (e.g. Kenaga, Maki, Doherty) starting 

in late 1970s principally in response to TSCA when the USEPA 

was given the authority to require pre-manufacture testing of 

chemicals

• Development is ongoing, but some consensus opinions exist

• Model II regressions need to be used to account for random 

variation in both the X and Y axis. The regression needs to have a 

significant slope, preferably positive, with high R2 value with tight 

95th %tile confidence limits.

• The toxicity values being used in the regression are of a high data 

quality using chemicals of high purity

• Whenever possible, species pairs within the same family should be 

used. ICE models with species pairs in different phyla should be 

avoided

• Using multiple surrogate species toxicity values will provide more 

available models from which to predict toxicity and improve 

correlation statistics 29
Web-ICE

• Evaluation for this analyses used EPA’s Web-ICE 

Version 3.1 

• Includes ICE models for wildlife (birds and mammals), 

and aquatic (fish and invertebrates) species, genera, 

and families

• Three separate modules: 

• Single species estimates

• Endangered species and 

• Species sensitivity distributions. 

• Provides correlation statistics for species pair models

• R2, p-value, cross-validation success

30
Web-ICE

Web-ICE SSD: HC5 estimate from bluegill, rainbow trout and Daphnia magna toxicity values

31

HC5 = 0.486 CI= 0.0161-8.06 HC5 = 4.69 CI= 0.564-52.42
Review of Web-ICE

• Two potential uses of Web-ICE for “benchmark” development

• Using single species estimates to populate MDRs of the 1985 

guidelines, then calculate a benchmark value using the FAV (final 

acute value) method

• Using the SSD module to directly calculate an HC5 as the FAV

Note: Assumed only acute data available were for FIFRA MDRs of 96 hr 

LC50s from a cold water fish species (rainbow trout), warm water fish 

species (blue gill) and a 48 hr invertebrate LC50 (Daphnia magna).

• Compared estimated and experimentally determined single species 

acute toxicity values and HC5 estimates (Web-ICE HC5 vs. AWQC 

FAV).

• Used 4 model chemicals: diazinon, chlorpyrifos, acrolein, and 

cadmium.

• Chemicals were chosen because of high quality toxicity data available in AWQC 

documents

• 2 acetylcholinesterase inhibitors (diazinon and chlorpyrifos)

• 2 alternate MOAs: 1 reactive (acrolein) and 1 metal (cadmium)

32
• Single species Web-ICE estimates

• Use of Web-ICE to populate 1985 Guidelines MDRs 

is currently not possible when only acute values for 

rainbow trout, bluegill and D.magna are available.

• Empirical data are still necessary for Web-ICE 

Review of Web-ICE

• Empirical data are still necessary for Web-ICE 

estimates

• Best correlations are those that closely 

taxonomically related; at least within the same 

family

• May be useful for estimating toxicity for endangered 

species

33
• Species Sensitivity Distributions and HC5 Web-ICE estimates

• Diazinon had the most precise estimates with 63% of values within 5 

Review of Web-ICE

Chemical N < 10XΔ <5XΔ <=2XΔ

Diazinon 19 14 12 5

Chlorpyrifos 13 6 3 2

Cadmium 22 13 11 5

Acrolein 8 6 4 2

• Diazinon had the most precise estimates with 63% of values within 5 

fold of experimentally determined values

• Chlorpyrifos had the largest differences between estimated and 

experimentally determined acute values

• Overall, taxonomic distance between species pairs appeared to have 

the biggest influence on precision of estimates

• Other important correlation statistics that were consistent with < 5 X Δ

between experimentally determined and estimated values were crossvalidation percentage (91% for <= 2XΔ) and MSE (0.2 - 0.4)

• No trends were apparent for the correlations statistics of R2 or df

34
• The number of precise individual models within an SSD does not necessarily

Review of Web-ICE

Chemical D. magna Rainbow 

trout

Bluegill HC5 FAV

Input Value 

(μg/L)

Input Value 

(μg/L)

Input Value 

(μg/L)

(μg/L) (μg/L) X-Fold 

Difference

Diazinon 1.048 425.8 459.6 12.05 0.3397 + 35.5

Chlorpyrifos 1.7 8.485 10 0.906 0.1669 +5.4

Acrolein 57 16 27.19 1.75 5.92 -3.4

Cadmium 13.41 2.108 6028 0.709 2.763 -3.9

• The number of precise individual models within an SSD does not necessarily

influence the precision of HC5 estimate; need to pay attention to the species 

represented in the distribution

• Species pairs for highly toxic values of invertebrates ( ~ < 2 μg/L) not well 

represented and are missing from distributions; leads to elevated estimates of 

HC5s (diazinon)

• For insecticides (e.g.diazinon) with specific acting MOA influencing large 

differences in responses among taxa without taxonomic representation in the 

distribution, HC5 estimates will not be precise

• HC5 estimates are better when input toxicity values are moderate and similar 

(acrolein) or empirical data are available for species pairs in the extreme ends of 

the distribution (Cadmium).

35
• Importance of choice and number of surrogate 

species used in HC5 estimates

• Diazinon Example:

• The HC5 estimate for diazinon using only the D. magna acute value 

is 0.322 ug/L (0.111 – 1.05)

• However, using acute surrogate values from rainbow trout and 

bluegill, the HC5 estimates were 67.6 (16.8 – 148.32) and 28.41 

(5.01 – 82.31) respectively

Review of Web-ICE

(5.01 – 82.31) respectively

• If a user had only one of these 3 measured results, their HC5 

estimate could range from being virtually identical to the diazinon 

FAV (0.3397 ug/L), or be as much as 200 fold higher if only rainbow

trout data were available.

• Importance of variability in species responses. 

• Extreme example: Carbaryl LC50 values in Daphnia genus range 

from 5.6-5400 ug/L. Given this variability for carbaryl, and only one 

value available for new acetylcholinesterase inhibitors, would you 

have confidence enough in that one genus response to define the 

entire SSD?

36
• Species Sensitivity Distributions and HC5 Web-ICE estimates

• Influence on “Rules of Thumb”

• Relatively low mean square error (MSE) (<0.22)

• Close taxonomic distance (≤ 3)

• High cross-validation success rate (> 85%)

• High degrees of freedom (df > 8, N > 10)

• High R2 value (> 0.6)

Review of Web-ICE

• High R value (> 0.6)

• Low p-values (<0.01)

• Narrow confidence bands on the graph

• This Independent analysis confirms these recommendations

• However, rigid implementation of these rules will significantly decrease 

species availability for SSD analysis

• Diazinon: From 59 models to 6, representing only closely related 

fish species

• Cadmium: From 51 models to 6, representing only closely related 

fish species 37
• The current version of Web-ICE does not have sufficient data to fulfill all of 

the MDRs of the 1985 Guidelines when there are only acute surrogate data 

available for rainbow trout, bluegill and D. magna. This may change with 

future updates.

• The frequency of precise models for individual species in Web-ICE does not 

necessarily influence the accuracy of an HC5 estimate of an SSD.

Conclusions: 

Review of Web-ICE

necessarily influence the accuracy of an HC5 estimate of an SSD.

• When using Web-ICE to develop SSDs, it is ill-advised to use only one 

surrogate species.

• The precision of Web-ICE predictions is better for chemicals that do not 

target specific taxa in a distribution of species. 

• The precision of Web-ICE predictions are best for closely related species 

(e.g. within the same family.) 

• Using the “Rules of Thumb” provided in the Web-ICE user’s manual is a 

good place to start when evaluating models. However, applying them all 

rigidly can dramatically reduce the number of models available to develop an 

SSD. 38
Needs and Issues

• Related to using estimated values to fill MDRs:

• The improvement of QSAR models requires that the chemical 

domain within the models be expanded to include pesticide 

registration data

• Use in filling MDRs will require taxonomic groups that for the most 

part are underrepresented in the open literature, and if present may 

not be of sufficient quality to use in model development. 

• As QSAR models for pesticide active ingredients are developed, 

there is a need to identify molecular descriptors that are linked to key 

events in the mode of action

• Even if these models improve significantly, a consideration will be 

whether the levels of uncertainty related to estimated values will be 

appropriate for use in filling data gaps within an HC5. 

• These tools require a level of understanding of theory and the how 

model works in order to apply professional judgement in a 

scientifically-defensible way. 39