# Potential Use of Pr **edictive To**  **xicology To**  **ols in Characterizing Effects of Chemical Str**  **essors to Aquatic**  **Animals Potential Use of Pr**  **edictive To**  **xicology To**  **ols in Characterizing Effects of Chemical Str**  **essors 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 4**  **0 CFR 158 FW**  Family Salmonidae Cold water fish (typically rainbow trout) One other family in the class O steichthyes Wa rm 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 f amily 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)

**4 -1**  **0 -8**  **-6 -4**  **-2 0** 

**-2 0**  **2 4**  **6 Log P** **F H** **M 9** **6 h** **r l** **o g** **M o** **l a** **r L** **C 5** **0**
Nar

cosis QSAR model with empirical data Wa ter 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. (V4.0)

– Developed by US EPA/ORD T.E.S.T. Developed 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 (R 2 

# ) 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 **freshw ater**  )

• mysid shrimp (96 hr)

• algae (72 and 96 hr)

• **daphnids** (48 hr) **8**

# T.E.S.T.: **EPA ORD models**

**Descriptions of modeling**  **methods ava**  **ilable within T**  **.E.S. T.**  **modeling tool**  **(tak en from**  **USEP A 2011)**  **Method Description**  **Species**

Hierarchical Clustering The toxicity for a given chemical is estimated using the weighted

average of the predictions from several diff erent models. The diff erent 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

FD A 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**

- 4.00 - 3.00 - 2.00 - 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h**  

### Comparison **of EcoSA R**  **V1.1 and Estimated Va**  **lues to Empirically** 

**D eriv**  **ed Te**  **st Data**

**12** - 9.00 - 8.00 - 7.00 - 6.00 - 5.00

- 9.00
- 8.00
- 7.00
- 6.00
- 5.00
- 4.00
- 3.00
- 2.00
- 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h** 

**Empirically Derived Log Molar**  **LC50 for Fish** 

## EcoSAR V1.1

# EcoSAR **models -**  **Fish**

- 4.00 - 3.00 - 2.00 - 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h**  

### Comparison **of EcoSA R**  **V1.1 and 1.0 Estimated V alues to**  **Empirically**

**D eriv**  **ed Te**  **st Data**

**13** - 9.00 - 8.00 - 7.00 - 6.00 - 5.00

- 9.00
- 8.00
- 7.00
- 6.00
- 5.00
- 4.00
- 3.00
- 2.00
- 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h** 

**Empirically Derived Log Molar**  **LC50 for Fish** 

## EcoSAR V1.1 EcoSAR V1.0

# EcoSAR **models -**  **Fish**

- 4.00 - 3.00 - 2.00 - 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h**  

### Comparison **of EcoSA R**  **V1.1 and 1.0 Estimated V alues to**  **Empirically**

**D eriv**  **ed Te**  **st Data**

**EcoSA R**  **V1.0 R**  **2 =0.093** 



### 14 - 9.00 - 8.00 - 7.00 - 6.00 - 5.00

- 9.00
- 8.00
- 7.00
- 6.00
- 5.00
- 4.00
- 3.00
- 2.00
- 1.00**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F i** **s h** 

**Empirically Derived Log Molar**  **LC50 for Fish** 

## EcoSAR V1.1 EcoSAR V1.0 **EcoSA R**  **V1.1 R**  **2 =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**

- 5 - 4 - 3**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D a** **p h** **n i** **d s**  

## Comparison of EcoSAR **Estimated V alues to Empirical Data**  **Ov erall**  **correlation of empirically**  **d eriv**  **ed data**  **to EcoSA R**  **daphnid model estimates**  **had an R 2**  

### =0.46

**16** - 10 - 9 - 8 - 7 - 6

- 10
- 9
- 8
- 7
- 6
- 5
- 4
- 3**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D a** **p h** **n i** **d s** 

**Empirically Derived Log Molar**  **LC50 for Daphnids**

# EcoSAR **models -**  **Daphnid**

- 5 - 4 - 3**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D a** **p h** **n i** **d s**  

## Comparison of EcoSAR **Estimated V alues to Empirical Data**  **Ov erall**  **correlation of empirically**  **d eriv**  **ed data**  **to EcoSA R**  **daphnid model estimates**  **had an R 2**  

### =0.46

**17** - 10 - 9 - 8 - 7 - 6

- 10
- 9
- 8
- 7
- 6
- 5
- 4
- 3**E c** **o S** **A R** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D a** **p h** **n i** **d s** 

**Empirically Derived Log Molar**  **LC50 for Daphnids R**  **2 =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** 

- 5 - 4.5 - 4 - 3.5**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F H** **M** 

## Comparison of EcoSA **R Estimated V**  **alues to Empirical Data R**  **2 =0.20**  **- 0.56** 



### 19 - 7.5 - 7 - 6.5 - 6 - 5.5-7.5-7.0-6.5-6.0-5.5-5.0-4.5-4.0-3.5**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F H** **M**

**Empirically Derived Log Molar**  **LC50 for Fathead Minnow s**  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** 

- 5 - 4.5 - 4 - 3.5**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F H** **M** 

## Comparison of EcoSA **R Estimated V**  **alues to Empirical Data R**  **2 =0.20**  **- 0.56** 



### 20 - 7.5 - 7 - 6.5 - 6 - 5.5-7.5-7.0-6.5-6.0-5.5-5.0-4.5-4.0-3.5**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **F H** **M**

**Empirically Derived Log Molar**  **LC50 for Fathead Minnow s**  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 ma gna 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** 

- 4 - 3 - 2**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D .** **m a** **g n** **a** 

### Comparison **of Empirically D**  **eriv ed**  **Data v s.**  **Estimated Va**  **lues Correlation**  **of empirically derived data to T** 

**.E.S.T . model estimates**  **ranged from R**  **2 =0.28**  **- 0.46** 

**Pirmicarb**



### 22 - 10 - 9 - 8 - 7 - 6 - 5

- 10
- 9
- 8
- 7
- 6
- 5
- 4
- 3
- 2**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** 

**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** 

- 4 - 3 - 2**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** **D .** **m a** **g n** **a** 

### Comparison **of Empirically D**  **eriv ed**  **Data v s.**  **Estimated Va**  **lues Correlation**  **of empirically derived data to T** 

**.E.S.T . model estimates**  **ranged from R**  **2 =0.28**  **- 0.46** 



### 23 - 10 - 9 - 8 - 7 - 6 - 5

- 10
- 9
- 8
- 7
- 6
- 5
- 4
- 3
- 2**T .** **E .** **S .** **T .** **E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0 f** **o r** 

**Empirically derived Log Molar LC50 for**  **D. magna** 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

- 6 - 5 - 4 - 3**E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0**



### 25 - 9 - 8 - 7

- 9
- 8
- 7
- 6
- 5
- 4
- 3**E s** **t i** **m a** **t e** **d L** **o g** **M o** **l a** **r L** **C 5** **0**

**Log M olar**  **LC50**
**– Empirical**  **fish data set**



### OECD Salmonid Model EcoSAR V1.1

# Conclusions: QSAR

• **How d**  **o QSAR estimates compare**  **to empirical test**  **data?** Acceptable models
- the estimated values are within one order of magnitude of empirical values



## • ECOSAR V

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



## • **What are the strengths,**  **we aknesses**  **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**  **Freshw ater MDRs Met by**  **M OA** 
**. Guidelines**  **MD Rs on y**  **- axis. Green**  **Box designates FIFRA**  **MD Rs**  **(** 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 T rout (log 96-h LC50)** **C a** **p e** **F e** **a r** **s h** **i n** **e r** **( l** **o g** **9 6** **- h** **L C** **5 0** **)**

**X = -0.025 + 1.098X**  **r = 0.991 2 1**  ICE model for chemicals tested in tr

out 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, w ith high R 2 

## value w ith tight 95 th 

## %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 w ithin the same family should be used. ICE models w ith species pairs in different phyla should be avoided

• Using multiple surrogate species toxicity values w ill provide more available models from w hich 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

• R 2 

## , p - value, cross - validation success
**30**

# Web **- ICE** 

We b

- 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 w ith 63% of values w ithin 5 

# Review of Web **- ICE** 

**Chemical N**  **< 10X**  **Δ <5X**  **Δ <=2X**  **Δ** 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 w ith 63% of values w ithin 5 fold of experimentally determined values

• Chlorpyrifos had the largest differences betw een estimated and experimentally determined acute values

• Overall, taxonomic distance betw een species pairs appeared to have the biggest influence on precision of estimates

• Other important correlation statistics that w ere consistent w ith < 5 X Δ betw een experimentally determined and estimated values w ere cross validation percentage (91% for <= 2X Δ ) and MSE (0.2

- 0.4)

• No trends w ere apparent for the correlations statistics of R 2 

## 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 FA V Input Va lue (μg/L ) Input Va lue

(μg/L ) Input Va lue

(μg/L ) (μg/L ) (μg/L ) X

- Fold Dif ference 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 v alues 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)

• How ever, using acute surrogate values from rainbow t rout and bluegill, the HC5 estimates w ere 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 w ere 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 a cetylcholinesterase inhibitors, w ould 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

m ean square error (MSE) (<0.22)

• Close taxonomic distance (≤ 3)

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

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

• High R 2 

## value (> 0.6) 

# Review of Web **- ICE** 

• High R value (> 0.6)

• Low p - values (<0.01)

• Narrow

c onfidence bands on the graph

• This Independent analysis confirms these recommendations

• How ever, rigid implementation of these rules w ill 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 wh en there are only acute surrogate data available for rainbow

t rout, bluegill and D. magna. This may change w ith 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 . w ithin the same family.)

• Using the “Rules of Thumb” provided in the Web
- ICE user’s manual is a good place to start w hen evaluating models. How ever, 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**