Extrapolation Factors for Estimation of HC5

s

for Acute Toxicity to Aquatic Animals, 

with Emphasis on AChE Inhibitors

Russell Erickson

1

Office of Research and Development

Mid-Continent Ecology Division, Duluth, MN

U.S.EPA Scientific Advisory Panel Review 

January 31, 2012 

Arlington, VA
Organization of Talk

Problem Formulation

Methods

Extrapolation Factor (EF) Concept 

EF Development Approaches

2

Results

Effects of Dataset Size and Taxonomy

Resampling vs. Distributional Approach

Effects of Mode of Action

Summary and Conclusions
Problem Definition

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100

“Species sensitivity distributions” (SSDs) often used 

in risk assessments to characterize toxicity across taxa

GMAV (g Cu/L) 3

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Percentiles within the SSD are used to designate

“hazard concentrations” (HCs) for use in assessments

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100 HC5

=14.8 g/L = Final Acute Value (FAV) for 

Aquatic Life Criteria (ALC)

GMAV (g Cu/L) 4

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Aquatic Life Criteria FAVs (HC5s) can be based on as few

as 8 genera meeting minimum data requirements (MDRs)

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GMAV (g Cu/L) 5

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

FAV (HC5) will lie below the lowest GMAV and be

subject to more uncertainty than for larger dataset

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100 HC5

(FAV)=8.7 g/L

GMAV (g Cu/L) 6

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Are useful estimates of HC5 possible with fewer data?

Example one, N=6, without less commonly tested phyla

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100 Reference HC5

=14.8 g/L

GMAV (g Cu/L) 7

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Are useful estimates of HC5 possible with fewer data?

Example two, N=6, without most sensitive genera

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100 Reference HC5

=14.8 g/L

GMAV (g Cu/L) 8

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Are useful estimates of HC5 possible with fewer data?

Example three, N=3, OPP minimum requirements

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100 Reference HC5

=14.8 g/L

GMAV (g Cu/L) 9

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla
Problem Definition

Are useful estimates of HC5 possible with fewer data?

Example three, N=3, OPP minimum requirements

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100 Reference HC5

=14.8 g/L

GMAV (g Cu/L) 10

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla

Range of 

Individual 

Tests
Methods: Extrapolation Factor Concept

• As noted in the presentation on SSD analysis 

methods, direct application of these methods to

such smaller sets results in large uncertainties.

• An alternative approach is to use datasets with 

known HC5s to derive extrapolation factors (EFs) to 

estimate HC5s from the data in these smaller sets.

11

estimate HC5s from the data in these smaller sets.

• When the HC5is known:

• When the HC5is unknown:
Extrapolation factors will depend on the size and

taxonomy of the toxicity dataset of interest.

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100 Reference HC5

=14.8 g/L

Methods: Extrapolation Factor Concept

12

GMAV (g Cu/L)

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla EF=2.6
Extrapolation factors will depend on the size and

taxonomy of the toxicity dataset of interest.

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100 Reference HC5

=14.8 g/L

Methods: Extrapolation Factor Concept

13

GMAV (g Cu/L)

10 100 1000 10000

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Arthropods

Molluscs 

Vertebrates

Other Phyla

EF=4.0

EF=1.3
• Extrapolation factors for this type of use were 

specified in EPA’s Great Lakes water quality 

guidance for deriving Tier II aquatic life values. 

These extrapolation factors were based on 

analyses by Host, Regal, and Stephan (1995), 

using data from existing aquatic life criteria. 

Methods: EF Development Approaches

14

• Current work is expanding on this as follows: 

(1) Use of additional data, and data for chemicals 

without published aquatic life criteria.

(3) Consideration of more taxonomic dependencies, 

with specific attention to OPP acute MDRs.

(2) Separate analyses for different toxic modes of 

action (MOAs).
“Resampling Approach” – Host et al. (1995) 

Aquatic Life Criteria Database

of Acute Values (LC50s)

Randomly Sample

One LC50 per MDR

Methods: EF Development Approaches

15

“N=8 Dataset”

EF Distribution 

Across Multiple 

Samples, Chemicals

MinVal for Subset of 

Size, Taxa of Interest

Subsample of Size,

Taxa of Interest

Final Acute Value

(Reference HC5)

Compute FAV per

ALC Guidelines

EF for Size, Taxa

of Interest 
“Resampling Approach” – Current Efforts

Database of LC50s for data-rich 

AChE-inhibiting chemical

Randomly Sample

One LC50 per MDR

Methods: EF Development Approaches

16

“N=8 Dataset”

EF Distribution 

Across Multiple 

Samples, Chemicals

MinVal for Subset of 

Size, Taxa of Interest

Subsample of Size,

Taxa of Interest

Final Acute Value

(Reference HC5)

Compute FAV per

ALC Guidelines

EF for Size, Taxa

of Interest 
• “Data rich” AChE-inhibiting chemicals are required 

to have at least 15 genera and 20 toxicity tests with 

arthropods.

“Resampling Approach” – Current Efforts

• Subset sizes and taxonomic compositions that were 

assessed included:

Methods: EF Development Approaches

17

Taxonomic Composition N

any fish; planktonic crustacean (other MDRs random) 2,3,4,5,6

salmonid&nonsalmonid fish; planktonic crustacean; 2

nd arthropod 4,5,6

any fish; planktonic crustacean; 2

nd arthropod 3

salmonid&nonsalmonid fish; planktonic crustacean 3

Daphnia magna; rainbow trout; bluegill – OPP FW animal MDRs 3

Subset sizes and taxonomic compositions that were 

assessed included:
“Distributional Approach” – Host et al. (1995) 

Log-Triangular Distribution with 

Specified Variance

Randomly Sample

Eight LC50s

Methods: EF Development Approaches

18

“N=8 Dataset”

EF Distribution 

Across Multiple 

Samples

MinVal for Subset of 

Size of Interest

Subsample of Size

of Interest

Final Acute Value

(Reference HC5)

Compute FAV per

ALC Guidelines

EF for Size, Variance

of Interest 
“Distributional Approach” – Current Efforts

Separate Distributions for Each 

MDR Based on Toxicity Data

Randomly Sample

One LC50 per MDR

Methods: EF Development Approaches

19

“N=8 Dataset”

EF Distribution 

Across Multiple 

Samples

MinVal for Subset of 

Size, Taxa of Interest

Subsample of Size,

Taxa of Interest

Reference HC5

5

th percentile of 

composite dist.

EF for Size, Taxa

of Interest 
• Pooled analysis of all chemicals within an MOA 

to determine variance of logLC50 with each MDR 

and mean difference of logLC50 between MDRs.

ACHE Inhibition Narcosis

Salmonid Fish

“Distributional Approach” – Current Efforts

Methods: EF Development Approaches

20

LC50 for Designated MDR/Median LC50 for Planktonic Crustaceans

0.01 0.1 1 10 100

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0.2

0.4

0.6

0.8

1.0

1.2

1.4 Narcosis

1 10 100 1000 10000

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0.2

0.4

0.6

0.8

1.0

1.2

1.4 ACHE Inhibition Salmonid Fish

Nonsalmonid Fish

Third Chordate Family

Planktonic Crustacean

Benthic Crustacean

Insect

Third Phylum

Fourth Phylum or

2nd Insect Order
Results: Effects of Dataset Size & Taxa

EFs for AChE inhibitors, by resampling approach.

(any fish; daphnid)

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Carbaryl 

Chlorpyrifos 

Diazinon 

Fenitrothion 

Malathion 

Methylparathion 

Geometric Mean EFs

21 Number of MDRs Satisfied

2 3 4 5 6 7 8

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Composite

Geometric Mean
EFs for AChE inhibitors, by resampling approach.

(any fish; daphnid)

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Carbaryl 

Chlorpyrifos 

Diazinon 

Fenitrothion 

Malathion 

Methylparathion 

Results: Effects of Dataset Size & Taxa

22 Number of MDRs Satisfied

2 3 4 5 6 7 8

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Composite 

90th Percentile

Composite

Geometric Mean
EFs for AChE inhibitors, by resampling approach.

(salmonid&nonsalmonid fish; daphnid; 2nd arthropod)

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Carbaryl 

Chlorpyrifos 

Diazinon 

Fenitrothion 

Malathion 

Methylparathion 

Results: Effects of Dataset Size & Taxa

23 Number of MDRs Satisfied

2 3 4 5 6 7 8

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Composite 

90th Percentile

Composite

Geometric Mean
EFs for AChE inhibitors, by resampling approach.

(N=3, different taxonomic composition)

Geometric means of EFs for samples of size 3 with different taxonomic compositions

Chemical

Any Fish

Daphnid

Any other MDR

Any Fish

Daphnid

2

nd Arthropod

Salmonid Fish

Nonsalmonid Fish

Daphnid

Rainbow Trout

Bluegill Sunfish

Daphnia Magna

Carbaryl 1.8 1.5 2.2 3.8

Results: Effects of Dataset Size & Taxa

24

Carbaryl 1.8 1.5 2.2 3.8

Chlorpyrifos 1.7 1.4 1.9 6.1

Diazinon 1.1 1.1 1.2 2.3

Fenitrothion 2.9 1.4 5.6 2.7

Malathion 1.2 1.1 1.3 1.1

Methylparathion 2.9 2.7 3.1 3.8

Composite 1.8 1.4 2.2 2.9

Composite 90th Pct 11.0 4.8 12.1 16.0
Distributional vs. Resampling Approach; AChE Inhibition

(taxonomic requirements: any fish; daphnid)

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Red: Distributional Approach

Blue: Resampling Approach

Results: Resampling vs. Distributional

Number of MDRs Satisfied 25

2 3 4 5 6 7 8

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90th Percentile

Geometric Mean
AChE-inhibition vs. Narcosis MOA; distributional approach

(taxonomic requirements: any fish; daphnid)

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Red: AChE-Inhibition MOA

Green: Narcosis MOA

Results: Effects of Mode of Action

Number of MDRs Satisfied 26

2 3 4 5 6 7 8

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90th Percentile

Geometric Mean
• Comparison of EFs for AChE Inhibitors by resampling

approach in current effort (for requirement of one fish

and one daphnid test to EFs from Host et al. (for 

requirement of one salmonid and one daphnid test). 

Number of 

MDRs 

Satisfied

Median EF 90th Overall Percentile EF

Current Effort

AChE Inhibitors

Host et al.

All Chemicals

Current Effort

AChE Inhibitors

Host et al.

All Chemicals

Results: Current Efforts vs. Host et al. 

27

Satisfied AChE Inhibitors All Chemicals AChE Inhibitors All Chemicals

2 2.19 2.93 12.1 25.5

4 1.55 2.28 7.6 18.3

8 1.13 1.86 3.5 4.9

• However, this comparison is confounded by different 

approaches for the reference HC5, underscoring a need 

to resolve this issue. Other issues for generalized EFs 

that warrant attention are chemical selection and 

uncertainty definition.
Summary and Conclusions

• As reported by Host et al. (1995), existing toxicity data 

can be used to develop extrapolation factors to derive 

useful estimates of HC5s based on data sets with 3 to 8 

toxicity values, and to provide information regarding the 

uncertainty of such estimates.

• EFs can be developed by resampling discrete toxicity 

test results from individual "data rich" chemicals or by 

28

test results from individual "data rich" chemicals or by 

sampling from a composite distribution of the relative 

sensitivity of different taxa, estimated by analysis of 

variance of toxicity tests for all chemicals. 

• EFs will depend to some degree on MOA, which affects 

the variability of toxicity within and differences between 

taxonomic groups. For the two MOAs examined here, EF 

differences were not great, but sufficient to warrant 

further development of MOA-specific EFs.
Summary and Conclusions

• A critical aspect of this process is the definition of the 

desired reference HC5, which involves definition of the 

• EFs will be a function of the size and taxonomic 

composition of the datasets to which they applied and 

should be further developed regarding this, including 

consideration of multiple tests with a species and multiple 

species within a taxonomic group. 

29

desired reference HC5, which involves definition of the 

composite distribution in the simulation approach and 

dataset size/composition issues in the resampling

approach. Efforts to resolve this issue are needed. 

• Even with the distributional approach, some MOAs will 

have insufficient data to support EF development, 

necessitating use of generalized EFs such as from Host et 

al. (1995). Further consideration is warranted of the data 

used for such EFs and the expression of uncertainty.