# Extr apola tion Fa ct or s f or Es tima tion of HC 5 

# s fo r Acut e T

ox icity t o Aqua tic Animals, with Emphasis on A ChE Inhibit or s

Russell Erickson

**1** 

## Office of R esear ch and Development Mid

- Con tinen t Eco logy Division, Duluth, MN

U. S.EP A Scien tific Advisory P anel R eview January 31, 2012 Arling ton, V

A

# Or ga niz at ion of T alk

Pr oblem Fo rmula tion Me thods Extr apolation F act or (EF) Concept EF Dev elopmen t Appr oaches

**2** 

# Re sults Ef fe cts of Dat aset Siz e and T ax onom y Re sampling vs. Dis tributional Appr oach Ef fe cts of Mode of Action 

# Summary and Conclusions

# Pr oblem De finitionPercentile 80

100 

## “Species sensitivity dis tributions” (SSDs) oft en used in risk assessmen ts to char act eriz e to xicity acr oss t ax a

**3**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

Pe rc en tiles within the SSD ar e used to designate “haz ar d concen tr ations” (HCs) f or use in assessmen tsPercentile 80 100 **HC 5**  **= 14.8**   **g/L = Final Acut**  **e V alue (F**  **AV )**  **fo r**  **Aquatic Lif e Crit**  **eria (AL C)** 

**4** 

### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition
Aquatic Lif e Criteria F AVs (HC 5 

## s) c an be based on as f ew as 8 gener a meeting minimum dat a r equir emen ts (MDRs)Percentile 80 100

**5**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

FA V (HC 5 

## ) will lie below the lowes t GMA V and be subject to mor e uncert ain ty than f or lar ger dat asetPercentile 80 100 **HC 5**  **(FA**

**V) =**  **8.7**  **g/L**

**6** 

### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

Ar e useful es timates of HC 5 

## possible with f ewer dat a? Ex ample one, N=6, without less c ommonly t es ted ph ylaPercentile 80 100 **Reference HC 5**  **= 14.8**   **g/L**

**7**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

Ar e useful es timates of HC 5 

## possible with f ewer dat a? Ex ample two

, N=6, without mos t sensitive generaPercentile 80 100 **Reference HC 5**  **= 14.8**   **g/L**

**8**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

Ar e useful es timates of HC 5 

## possible with f ewer dat a? Ex ample three, N=3, OPP minimum requirementsPercentile 80 100 **Reference HC 5**  **= 14.8**   **g/L**

**9**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la

# Pr oblem De finition

Ar e useful es timates of HC 5 

## possible with f ewer dat a? Ex ample three, N=3, OPP minimum requirementsPercentile 80 100 **Reference HC 5**  **= 14.8**   **g/L**

**10**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la Range of Individual Te sts

# Methods: Extr apolation F act or Concept

• As not ed in the pr esen tat ion on SSD analy sis methods, dir ect application of these methods to such smaller sets r esults in lar ge uncert ain ties.

• An alt ernative appr oach is t o use dat asets with known HC 5 

## s to deriv e e xtr apolation f act or s (EF s) t o es timate HC 5 

## s fr om the dat a in these smaller sets.

**11** 

## es timate HC 5 

## s fr om the dat a in these smaller sets.

• When the HC 5 

## is known:

• When the HC 5 

## is unknown:

## Extr apolation f act or s will depend on the siz e and

tax onom y of the t ox icity dat aset of in te re st

.Percentile 60 80 100 **Reference HC 5**  **= 14.8**   **g/L** 

# Methods: Extr apolation F act or Concept

**12**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la **EF=2.6**

## Extr apolation f act or s will depend on the siz e and

tax onom y of the t ox icity dat aset of in te re st

.Percentile 60 80 100 **Reference HC 5**  **= 14.8**   **g/L** 

# Methods: Extr apolation F act or Concept

**13**



### GMAV (  g Cu/L) 10 100 1000 10000Percentile

0 20 40 60

Ar thropods Molluscs Ve rtebrates Other Phy la **EF=4.0 EF=1.3** 

## • Extr apolation f act or s f or this type of use wer e specified in EP

A’ s Gr eat Lak es w ater quality guidance f or deriving Tier II aquatic lif e v alues. These e xtr apolation fa ct or s wer e based on analy ses by Hos t, Re ga l, and Stephan (1995), using dat a fr om e xis ting aquatic lif e crit eria. 

# Methods: EF Dev elopmen t Appr oaches

**14** 

## • Curr en t work is e xpanding on this as f ollows: (1) Use of additional dat a, and dat a f or chemicals without published aquatic lif e criteria.

(3) Consider at ion of mor e t ax onomic dependencies, with specific atte nt ion to OPP acute MDRs. (2) Separ ate analyses f or dif fe re nt t ox ic modes of action (MOAs).

# “ Re sampling Approach”

– Hos t et al. (1995)

Aqua tic Lif e Criteria Da tabase of Acute V alues (L C 50 s)

Randomly Sample One LC 50 per MDR 

# Methods: EF Dev elopmen t Appr oaches

**15**

“N=8 Da taset ”

EF Dis tribution Acr oss Multiple Samples, Chemicals MinV al fo r Subset of Siz e, T axa of Interest Subsample of Siz e, Ta xa of Inter est

Final Acute V alue (R ef er ence HC 5 ) Compute F AV per ALC Guidelines

EF fo r Siz e, T

axa of Interest

# “ Re sampling Approach”

– Curren t E ff orts

Da tabase of LC 50 s f or data

- rich AChE

- inhibiting chemical

Randomly Sample One LC 50 per MDR 

# Methods: EF Dev elopmen t Appr oaches

**16**

“N=8 Da taset ”

EF Dis tribution Acr oss Multiple Samples, Chemicals MinV al fo r Subset of Siz e, T axa of Interest Subsample of Siz e, Ta xa of Inter est

Final Acute V alue (R ef er ence HC 5 ) Compute F AV per ALC Guidelines

EF fo r Siz e, T

axa of Interest

## • “Dat a rich” A ChE

- inhibiting chemicals ar e r equir ed to ha ve at leas t 15 gener a and 20 t ox icity t es ts with arthr opods. 

# “ Re sampling Approach”

– Curren t E ff orts

• Subset siz es and t ax onomic compositions that wer e assessed included: 

# Methods: EF Dev elopmen t Appr oaches

**17 Ta**  **xo nomic**  **Composition N** 

**an y**  **fish; planktonic crustacean**  **(other MDRs r**  **andom) 2,3,4,5,6** 
**salmonid &**  **nonsalmonid fish; planktonic crustacean;**  **2 nd**  **arthr opod**  **4,5,6**

**an y fish**  **; planktonic**  **crustacean ;**  **2 nd**  **arthr opod**  **3**
**salmonid &**  **n onsalmonid**  **fish; planktonic crustacean**  **3 Daphnia**  **magna ;**  **ra inbow**  **tr out; bluegill** 

**– OPP FW animal**  **MDRs 3**  

## Subset siz es and t ax onomic compositions that wer e assessed included:

# “Dis tributional Approach”

– Hos t et al. (1995)

Log

- Tr iangular Dis tribution with Specified V ariance

Randomly Sample Eight LC 50 s 

# Methods: EF Dev elopmen t Appr oaches

**18**

“N=8 Da taset ”

EF Dis tribution Acr oss Multiple Samples MinV al fo r Subset of Siz e of Interest Subsample of Siz e of Inter est

Final Acute V alue (R ef er ence HC 5 ) Compute F AV per ALC Guidelines

EF fo r Siz e, V ariance of Interest

# “Dis tributional Approach”

– Curren t E ff orts

Separ ate Distributions f or E ach MDR Based on T

ox icity Data

Randomly Sample One LC 50 per MDR 

# Methods: EF Dev elopmen t Appr oaches

**19**

“N=8 Da taset ”

EF Dis tribution Acr oss Multiple Samples MinV al fo r Subset of Siz e, T axa of Interest Subsample of Siz e, Ta xa of Inter est

Ref er ence HC 5 5 th per centile of composite dist.

EF fo r Siz e, T

axa of Interest

### • 

## Po oled analy sis of all chemicals within an MO A to determine v ariance of logL C 50 

## with each MDR and mean dif fe re nce of logL C 50 

## between MDRs.

Narcosis ACHE Inhibition Salmonid Fish 

# “Dis tributional Approach”

– Curren t E ff orts 

# Methods: EF Dev elopmen t Appr oaches

**20** LC 50 for Designated MDR/Median LC 50 for Planktonic Crustaceans 0.01 0.1 1 10 100Frequency

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Narcosis

1 10 100 1000 10000Frequency

0.0 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

# Re sults: Ef

fe cts of Dat aset Siz e & T

ax a
EF s f or AChE inhibitor s, by re sampling appr oach.

(an y fish; daphnid

)ExtrapolationFactor

5 20

10 Carbaryl Chlorpyrifos Diazinon Fenitrothion Ma lathion Me thylparathion Geometric Mean EFs

**21** Number of MDRs Satisfied 2 3 4 5 6 7 8ExtrapolationFactor

2 5

1 Composite Geom etric Mean

## EF s f or AChE inhibitor s, by re sampling appr oach.

(an y fish; daphnid

)ExtrapolationFactor

5 20

10 Carbaryl Chlorpyrifos Diazinon Fenitrothion Malathion Methylparathion 

# Re sults: Ef

fe cts of Dat aset Siz e & T

ax a

**22** Number of MDRs Satisfied 2 3 4 5 6 7 8ExtrapolationFactor

2 5

1 Com posite 90th Percentile

Composite Geometric Mean

## EF s f or AChE inhibitor s, by re sampling appr oach. ( salmonid&nonsalmonid fish; daphnid ; 2 nd 

## arthropod)ExtrapolationFactor

5 20

10 Carbaryl Chlorpyrifos Diazinon Fenitrothion Ma lathion Me thylparathion 

# Re sults: Ef

fe cts of Dat aset Siz e & T

ax a

**23** Number of MDRs Satisfied 2 3 4 5 6 7 8ExtrapolationFactor

2 5

1 Composite 90th Percentile

Composite Geom etric Mean

## EF s f or AChE inhibitor s, by re sampling appr oach.

(N=3, diff erent t ax onomic c omposition)

Geometric means of EF s f or samples of siz e 3 with diff er ent tax onomic compositions

Chemical An y Fish

Daphnid An y other MDR An y Fish

Daphnid 2 nd Arthropod Salmonid Fish

Nonsalmonid Fish Daphnid Rainbow Tr out

Bluegill Sunfish Daphnia Magna Carbaryl **1.8 1.5**  **2.2 3.8**  

# Re sults: Ef

fe cts of Dat aset Siz e & T

ax a

**24** Carbaryl **1.8 1.5**  **2.2 3.8** 
Chlorpyrif os **1.7 1.4**  **1.9 6.1** 

Diazinon **1.1 1.1**  **1.2 2.3** 

Fenitr othion **2.9 1.4**  **5.6 2.7** 

Malathion **1.2 1.1**  **1.3 1.1** 
Methylpar athion **2.9 2.7**  **3.1 3.8** 
Composite **1.8 1.4**  **2.2 2.9** 
Composite 90 th Pc t **11.0 4.8**  **12.1 16.0** 

## Dis tributional vs. Re sampling Appr oach; AChE Inhibition (t ax onomic requirements: an y fish; daphnid )ExtrapolationFactor

5 20

10 Red: Distributional Approach Blue: Resampling Approach 

# Re sults: Re sampling vs. Dis tributional

**25**

Number of MDRs Satisfied 2 3 4 5 6 7 8ExtrapolationFactor

2 5

1 90th Percentile

Geometric Mean

## AChE

- inhibition vs. Nar cosis MO A; dis tributional appr oach (t ax onomic requirements: an y fish; daphnid )ExtrapolationFactor

5 20

10 Red: AChE-Inhibition M OA Green: Narcosis M OA 

# Re sults: Ef

fe cts of Mode of Action

**26** Number of MDRs Satisfied 2 3 4 5 6 7 8ExtrapolationFactor

2 5

1 90th Percentile

Geometric Mean

## • Comparison of EF s f or A ChE Inhibitor s by re sampling appr oach in curr en t eff ort (f or r equir emen t of one fish and one daphnid te st t o EF s fr om Hos t et al. (f or re quir emen t of one salmonid and one daphnid te st ).

**Number of**  **MDRs Satisfied**  **Median EF**  **90 th**  **Over all Percentile**  **EF**

**Curr ent**  **Ef fo**  **rt AChE**  **Inhibit ors**  **Hos t et al** 

**.**

**All Chemicals**  **Curr ent**  **Ef fo**  **rt**

**AChE Inhibit**  **ors Hos**  **t et al** 

**.**

**All Chemicals**  

# Re sults: Curr en t E ff orts vs. Hos t et al.

**27 Satisfied**  **AChE Inhibit**  **ors All**  **Chemicals AChE**  **Inhibit ors**  **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 conf ounded by diff er en t appr oaches f or the r ef er ence HC 5 

## , under scoring a need to r esolv e this issue. Other issues fo r gener aliz ed EF s that w arr an t at te nt ion ar e chemical selection and uncert ain ty definition.

# Summary and Conclusions

• As r eport ed by Hos t et al. (1995), e xis ting to xicity data can be used to develop e xtr apolation f act or s to derive useful es timates of HC 5 

### s based on data sets with 3 to 8 to xicity va lues, and to pr ovide inf ormation r eg ar ding the uncert ain ty of such es timates.



## • EF s can be developed by re sampling discr ete to xicity te st re sults fr om individual "data rich" chemicals or by

**28** 

### te st re sults fr om individual "data rich" chemicals or by sampling fr om a composite distribution of the r ela tiv e sensitivity of dif

fe re nt t axa , es timated by analysis of
va riance of t ox icity te st s f or all chemicals.



## • EF s will depend to some degr ee on MO A, which aff ects the v ariability of t ox icity within and diff er ences between tax onomic gr oups. F or the two MO As e xa mined her e, EF dif fe re nces w er e not gr ea t, but sufficient to warr an t further dev elopmen t of MO A

- specific EF s.

# Summary and Conclusions

• A critical aspect of this pr ocess is the definition of the desir ed r ef er ence HC 5 

### , which in volves definition of the • EF s will be a function of the siz e and tax onomic composition of the datasets to which they applied and should be further dev eloped r eg ar ding this, including consider ation of multiple tests with a species and multiple species within a tax onomic gr oup.

**29** 

### desir ed r ef er ence HC 5 

### , which in volves definition of the composite distribution in the simulation appr oach and dataset siz e/ composition issues in the re sampling appr oach. E ffo rts to r esolv e this issue ar e needed.

• Ev en with the dis tributional appr oach, some MO As will ha ve insufficient data to support EF development, necessit ating use of g ener aliz ed EF s such as fr om Hos t et al. (1995). Further consider ation is warr an te d of the data used f or such EF s and the e xpr ession of uncertainty

.