1. Potential Role for Endocrine Disruptor Expert Systems in Investigating ‘Endocrine System-Epigenome’ Interactions
P. Schmieder
EPA, ORD, NHEERL, MED-Duluth, MN
2nd McKim Cancer Workshop

2. Effects-based Expert System
Automated rule-based decision trees to predict which chemicals have the potential to disrupt endocrine systems.
This is done by:
testing key chemicals within a chemical class to set boundaries on biological/toxicological activity, to predict activity of other members of the “class”
Grouping chemicals by a common biological activity, then determining what is similar about the chemical structures and properties that explain their activity
writing rules that help categorize similar but untested chemicals.
The Program Offices use these tools to decide which, of the hundreds of chemicals on Agency chemical lists, should be evaluated first for likelihood of disrupting endocrine-mediated pathways the results in hormonal imbalances.

3. ER-mediated AOP
Significant evidence exists linking ER binding chemicals to adverse outcomes related to reproductive effects (ER-mediated reproductive impairment AOP): hh - drug design of anti-estrogens for breast cancer research and treatment env - multiple studies linking chemical ER binders to adverse effects -potent pharmaceuticals - e.g., EE2 -weak affinity environmental chemicals - e.g., APs
If there is evidence, or evidence found in future, for NR mediated AOPs linked to cancer, then the approach described here will be relevant for hypothesizing what chemical might do this.
This presentation will focus on: using an ER-mediated AOP to form chemical categories for addressing a specific risk assessment application

4. ER-mediated AOP
The risk context to which AOP is applied influences the approach:
This example: Risk context – USEPA needs to evaluate large lists of data-limited chemicals for ED potential; how can predictive tools be used to identify which of these chemicals have the greatest potential to cause an adverse effect because of their estrogenic potential; Goal - Given limited testing resources, prioritize chemicals on targeted inventories, so that those with the highest likelihood of producing an adverse outcome are tested first Targeted chemical inventories: -inert ingredients in pesticides used on food crops -antimicrobial active ingredients
-inert ingredients in pesticides not used on food crops

5. OECD Principles for QSAR Validation
Well-Defined Endpoint
Well-defined biological endpoint –
Informing important risk endpoint – Adverse Outcome Pathway (AOP) ending in impaired reproduction; plausible linkage of measure (initiating event) to higher level adversity
Well-defined chemistry
Does assay allow testing of the types of chemicals (range of properties) found on regulatory inventories?
Is the chemical form and concentration in the assay understood?
Mechanistic interpretation
Can estimates be explained mechanistically - chemistry & biology ?
ER-mediated Reproductive Impairment Adverse Outcome Pathway
Relationship of chemical parameters to activity

6. OECD Principles for QSAR Validation (cont.)
Defined Model Applicability Domain
Well-defined application
Is the regulatory question well-defined – priority setting is different than risk assessment?
Is the QSAR model domain coverage well-defined?
Does the QSAR chemical domain adequately cover the regulatory chemical domain i.e., the regulatory question?
Appropriate measures of goodness of fit, robustness, ability to predict
Measures appropriate for a regression model likely not appropriate to evaluate an expert system
Unambiguous algorithm
Expert Systems – logic tree, rules/queries, supporting information

7. Application of OECD Principles to Forming Chemical Categories:
Key Questions
Transparency
How reasonable is the estimate compared with data for similar chemicals ?
Can the QSAR estimate be explained mechanistically?
Usefulness
Are the predictions applicable to all the chemicals of regulatory concern?
Does the model/expert system answer the regulatory question?

8. Mechanistic Basis of an Expert System to Predict
Potential for Chemical Binding to the Estrogen Receptor
ES development based on a defined AOP
ER-mediated reproductive impairment adverse outcome pathway
ER-mediated liver cell proliferation in fish
ER Binding Domain
Knowledge/theories of chemical-receptor interactions
How chemicals interact with ER protein – LBD sub-pockets
The Regulatory Chemical Domain
Characterizing the FI and AM inventory chemicals
Building from existing information
The Receptor Binding Assay Domain
Optimizing assays considering properties of inventory chemicals

9. Adverse Outcome Pathway ER-Mediated Reproductive Impairment
Chemical effects across levels of biological organization
QSAR focus area
In vitro Assay
focus area
In vivo
Inerts; Antimicrobial
Chemicals
Chemicals
CELLULAR
Response
POPULATION
MOLECULAR
Target
TISSUE/ORGAN
INDIVIDUAL
Skewed
Sex Ratios;
Yr Class
Liver
Altered proteins(Vtg)& hormones;
Gonad
Ova-testis;
Complete ovary in male
Liver Cell
Protein Expression
Vitellogenin (egg protein transported to ovary)
Sex reversal;
Altered behavior;
Repro.
Receptor
Binding
ER Binding
Series of events that can be measured across levels of biological organization
Toxicity Pathway
Adverse Outcome Pathway
Greater Toxicological Understanding
Greater Risk Relevance

10. Adverse Outcome Pathway ER-mediated Reproductive Impairment
QSAR focus area
In vitro Assay
focus area
Inerts; Antimicrobial
Chemicals
CELLULAR
Response
POPULATION
MOLECULAR
Target
TISSUE/ORGAN
INDIVIDUAL
Skewed
Sex Ratios;
Yr Class
Liver
Altered proteins(Vtg)
Liver Cell
Protein Expression
Vitellogenin (egg protein transported to ovary)
Sex reversal;
Altered behavior;
Repro.
Receptor
Binding
ER Binding
Toxicity Pathway
Adverse Outcome Pathway
Greater Toxicological Understanding
Greater Risk Relevance

11. Mechanistic Basis of the Expert System
ER Binding Affinity: An Indicator of Potential Reproductive Effects
ER-mediated reproductive impairment adverse outcome pathway
ER Binding Domain
Knowledge/theories of chemical-receptor interactions
ER sub-pockets
The Regulatory Chemical Domain
Characterizing the FI and AM inventory chemicals
Building from existing information
The Receptor Binding Assay Domain
Optimizing assays considering properties of inventory chemicals

12. ER Binding Domain
- Bioassays for ER binding were available from drug-design, although extant
methods were focused on potent anti-estrogens
- Drug-design research provides mechanistic insights on multiple types of interaction
within the ER binding site

13. Distance = 10.8 for 17-Estradiol
A_B Interaction
T 347 C
QOxygen= -0.25
QOxygen= -0.32
E 353
H 524 H
CH3 H A B HO OH
R 394 H H
Distance = 10.8 for 17-Estradiol
J. Katzenellenbogen

14. Mechanistic Basis of the Expert System
ER Binding Affinity: An Indicator of Potential Reproductive Effects
ER-mediated reproductive impairment adverse outcome pathway
ER Binding Domain
Knowledge/theories of chemical-receptor interactions
ER sub-pockets
The Regulatory Chemical Domain
Characterizing the FI and AM inventory chemicals
Building from existing information
The Receptor Binding Assay Domain
Optimizing assays considering properties of inventory chemicals

15. Apply knowledge/theory of ER Binding Domain to
The Regulatory Chemical Domain
(continuing to seek MECHANISTIC understanding)
Hypothesize ER interactions of Inventory Chemicals
Inert ingredients and antimicrobial pesticides are non-steroidal and do not
contain multiple H-bonding groups at distance needed for
steroid-like interactions
Hypotheses:
- Any pesticide inert or antimicrobial that does bind ER will do so
through an interaction mechanism that results in
low affinity binding
- Only a small % of these chemicals are likely to bind ER
- A chemical group approach will facilitate regulatory application
- Chemicals can be grouped based on how they interact with the
ER (within specific ER sub-pockets)

16. C A B T 347 E 353 H 524 HO R 394 CH3 “A_site only” Interaction
QOxygen= -0.25
E 353
H 524 A B HO
R 394
CH3
J. Katzenellenbogen

17. C A B T 347 E 353 H 524 NH2 R 394 H3C “B_site only” Interaction
QOxygen= -0.32
E 353
H 524 A B
NH2
R 394
H3C
J. Katzenellenbogen

18. HO OH
CH3 H
QOxygen= -0.32 B A
QOxygen= -0.25
A_site
B_site

19. ER Binding Site A Homologous Series 4-n-AlkylphenolsC0
Log Kow = 1.50
Log Kow = 1.97
Log Kow = 2.47
Log Kow = 3.65
Log Kow = 3.20 C1 C2 C3 C4 C5
Log Kow = 4.06
Log Kow = 5.68
Log Kow = 5.76
Log Kow = 4.62
Log Kow = 4.15 C7 C6 C8 C9
Note: pink shading indicates RBA > %; grey shading indicates RBA < %

20. 4-t-Alkylphenols were also assayed
Log Kow = 1.50
Log Kow = 1.97
Log Kow = 2.47
Log Kow = 3.65
Log Kow = 3.20
Log Kow = 4.06 C0 C2 C1 C3 C4 C5
Log Kow = 2.90 C3
Log Kow = 3.83
Log Kow = 3.31
Log Kow = 3.32 C4 C5
Log Kow = 5.76
Log Kow = 4.62
Log Kow = 4.15 C7 C6
Log Kow = 5.68 C8 C9
Log Kow = 4.36 C6
Log Kow = 4.89 C7
Log Kow = 5.16 C8
C10
C10
Log Kow = 6.61
Log Kow = 7.91 +
C12

21. Relationship between Log Kow and RBA for 4-alkylphenols
Site A
Relationship between Log Kow and RBA for 4-alkylphenols

22. ER Binding Site B Homologous Series 4-n-Alkylanilines
Log Kow = 3.05
Log Kow = 5.12
Log Kow = 3.39
Log Kow = 4.06
Log Kow = 0.90
Log Kow = 1.96
Log Kow = 2.40
Log Kow = 1.39
Trout ER Knowledge base C0 C1 C2 C8 C3 C4 C5 C6
Note: pink shading indicates RBA > %; grey shading indicates RBA < %

23. Relationship between Log Kow and RBA for 4-alkylanilines
Site B
Relationship between Log Kow and RBA for 4-alkylanilines

24. RBA ranges of low ER affinity (Site A or Site B only) chemicals relative to high ER affinity Site A-B (estrogens) or Site A-C (anti-estrogens)
Site A-B or A-C
Estradiol
Ethinyl Estradiol
Site A
Site B
Note: high affinity estrogens indicated by black dots; high affinity anti-estrogens indicated by orange dots.

25. Chemicals with RBA > 0.00001%
Site A & Site B
Chemicals with RBA > %
Site A
Site B
Symbols - Site A chemical groups - diamonds
Symbols - Site B chemical groups - triangles

26. Mechanistic Basis of the Expert System to Predict Relative Estrogen Receptor Binding Affinity
ER Binding Affinity: An Indicator of Potential Reproductive Effects
ER-mediated reproductive impairment adverse outcome pathway
ER Binding Domain
Knowledge/theories of chemical-receptor interactions
ER sub-pockets
The Regulatory Chemical Domain
Characterizing the FI and AM inventory chemicals
Building from existing information
The Receptor Binding Assay Domain
Optimizing assays considering properties of inventory chemicals

27. Food use Inert Ingredients
Inert chemicals in pesticides used on food crops
The 2004 List included:
893 entries = 393 discrete chemicals non-discrete substances
(44% discrete : 56% non-discrete)
393 discrete chemicals include:
366 organic chemicals (93%)
24 inorganic chemicals (6%)
3 organometallic compounds (1%)
500 non-discrete substances include:
147 polymers of mixed chain length
170 mixtures
183 undefined substances

28. Antimicrobial Pesticides
Antimicrobials and sanitizers list included:
299 = 211 discrete chemicals + 88 non-discrete substances
(71% discrete : 29% non-discrete)
211 discrete chemicals include:
153 organic chemicals (72%)
52 inorganic chemicals (25%)
6 acyclic organometallic compounds (3%)
88 non-discrete substances include:
25 polymers of mixed chain length
35 mixtures
28 undefined substances

29. Mechanistic Basis of the Expert System to Predict Relative Estrogen Receptor Binding Affinity
ER Binding Affinity: An Indicator of Potential Reproductive Effects
ER-mediated reproductive impairment adverse outcome pathway
ER Binding Domain
Knowledge/theories of chemical-receptor interactions
ER sub-pockets
The Regulatory Chemical Domain
Characterizing the FI and AM inventory chemicals
Building on existing information
The Receptor Binding Assay Domain
Optimizing assays considering properties of inventory chemicals

30. Adverse Outcome Pathway ER-mediated Reproductive Impairment
QSAR focus area
In vitro Assay
focus area
Inerts; Antimicrobial
Chemicals
CELLULAR
Response
POPULATION
MOLECULAR
Target
TISSUE/ORGAN
INDIVIDUAL
Skewed
Sex Ratios;
Yr Class
Liver
Altered proteins(Vtg)
Liver Cell
Protein Expression
Vitellogenin (egg protein transported to ovary)
Sex reversal;
Altered behavior;
Repro.
Receptor
Binding
ER Binding
Toxicity Pathway
Adverse Outcome Pathway
Greater Toxicological Understanding
Greater Risk Relevance

31. Estrogen Receptor Binding Displacement Assay
Data Example - primary In vitro assay used :
Estrogen Receptor Binding Displacement Assay
Cyto rtER
rtER
Test Chemicals:
Positive response
Test Chemicals:
Negative response
Positive Control:
Estradiol
Positive Control:
Estradiol
RBA = relative binding affinity; (a ratio of measured chemical affinity for the ER relative to 17-beta-Estradiol = 100%)
Log Kow = Log of octanol/water partition coefficient ); indicator of lipophilicity

32. Data example – Confirmatory in vitro Assay: Gene Activation
Positive Control:
Estradiol
Positive Control:
Estradiol
Test Chemicals:
Positive response
Test Chemical:
Negative response

33. Chemicals with RBA > 0.00001%
Site A & Site B
Chemicals with RBA > %
Site A
Site B
Symbols - Site A chemical groups - diamonds
Symbols - Site B chemical groups - triangles

34. 393 Food Use Inerts I II III IV V VI VII Start Here: Chemical ListNo IV
Meets specific distance criteria for:
Sites “A-B”;
Sites “A-C”;
Sites “A-B-C”
Contains some
attenuating feature,
steric or other
High Affinity
“A-B”;“A-C” or “A-B-C”
RBA > 0.1%
RBA < %
Low Affinity
“A-B”,“A-C” or “A-B-C”
% < RBA < 0.1%
Strength of
attenuation factors
Yes No
Strong
Weak
Contains
a Cycle
Contains
a Charge
Belongs to
known Inactive
Sub-class
Contains a phenolic
OH, and additional
OH and/or =0
Yes
Yes No No No
Yes
Yes
“Special Rule”
Applies V
Yes
Alkylaromatic Sulfonic Acids
Log Kow ( )
Sulfonic Acid Dyes
Log Kow ( )
4-Alkylbenzthiols
Log Kow ( )
Miscellaneous Functional
Groups w/ Charge
Log Kow Range
4-n-Alkylfluorobenzenes ( )
Alkylphenol (2,6-subst) ( 4.2)
Benzamides ( )
Borate Esters ( )
Benzoates (non-ring subst) ( )
Bis-anilines ( )
Hydrofurans (alcohol & ketone) ( )
Imidazolidines ( )
Isothiazolines ( )
Mono-cyclic Hydrocarbons ( )
Oxazoles (<0.3)
Phenones (n-alkyl) ( )
Pyrrolidiones ( )
Sorbitans ( )
Triazines ( )
RBA < % No
DDT-Like
Tamoxifen-Like
Multicyclic hydrocarbons
Alkylchlorobenzenes
Thiophosphate Esters
Possible Low Affinity
Site A-Type, Site B-Type
Yes
RBA > %
Log KOW <1.3
Exact match to Group Training Set Structure and measured RBA
Yes
RBA < % No
RBA < %
Unknown
Binding Potential No
Site “A”
Contains Phenol
Fragment VI
Belongs to
known Active
Sub-class
Log Kow Range
Alkylphenols ( )
Alkoxy phenols ( )
Parabens ( )
Salicylates ( )
Yes
Yes
RBA > %
Yes
RBA > % No No
Exact match to Mixed Phenols Training Set Structure and measured RBA
RBA < %
Mixed Phenols No
Unknown
Binding Potential
Log Kow Range
4-Alkylanilines ( )
4-Alkoxy Anilines ( )
Phthalates ( )
Phenones-(branched) ( )
4-Alkyl cyclohexanols ( )
4-Alkyl cyclohexanones ( ) *
2-; 4-; or 2,4,6-Benzoates
(substitution-dependent Log Kows)
Site “B”
Contains “Specified”
Fragment
Belongs to
known Active
Sub-class
VII
Yes
Yes
RBA > % No No
RBA > %
Yes
Belong to untested class
Exact match to Mixed Organics Training Set Structure and measured RBA
RBA < %
Mixed Organics No
Unknown Binding Potential
Unknown
Binding Potential

35. RBA ranges of low ER affinity (Site A or Site B only) chemicals relative to high ER affinity Site A-B (estrogens) or Site A-C (anti-estrogens)
Site A-B or A-C
Estradiol
Ethinyl Estradiol
Site A
Site B
Note: high affinity estrogens indicated by black dots; high affinity anti-estrogens indicated by orange dots.

36. 393 Food Use Inerts I II III IV V VI VII Start Here: Chemical ListNo IV
Meets specific distance criteria for:
Sites “A-B”;
Sites “A-C”;
Sites “A-B-C”
Contains some
attenuating feature,
steric or other
High Affinity
“A-B”;“A-C” or “A-B-C”
RBA > 0.1%
RBA < %
Low Affinity
“A-B”,“A-C” or “A-B-C”
% < RBA < 0.1%
Strength of
attenuation factors
Yes No
Strong
Weak
Contains
a Cycle
Contains
a Charge
Belongs to
known Inactive
Sub-class
Contains a phenolic
OH, and additional
OH and/or =0
Yes
Yes No No No
Yes
Yes
“Special Rule”
Applies V
Yes
Alkylaromatic Sulfonic Acids
Log Kow ( )
Sulfonic Acid Dyes
Log Kow ( )
4-Alkylbenzthiols
Log Kow ( )
Miscellaneous Functional
Groups w/ Charge
Log Kow Range
4-n-Alkylfluorobenzenes ( )
Alkylphenol (2,6-subst) ( 4.2)
Benzamides ( )
Borate Esters ( )
Benzoates (non-ring subst) ( )
Bis-anilines ( )
Hydrofurans (alcohol & ketone) ( )
Imidazolidines ( )
Isothiazolines ( )
Mono-cyclic Hydrocarbons ( )
Oxazoles (<0.3)
Phenones (n-alkyl) ( )
Pyrrolidiones ( )
Sorbitans ( )
Triazines ( )
RBA < % No
DDT-Like
Tamoxifen-Like
Multicyclic hydrocarbons
Alkylchlorobenzenes
Thiophosphate Esters
Possible Low Affinity
Site A-Type, Site B-Type
Yes
RBA > %
Log KOW <1.3
Exact match to Group Training Set Structure and measured RBA
Yes
RBA < % No
RBA < %
Unknown
Binding Potential No
Site “A”
Contains Phenol
Fragment VI
Belongs to
known Active
Sub-class
Log Kow Range
Alkylphenols ( )
Alkoxy phenols ( )
Parabens ( )
Salicylates ( )
Yes
Yes
RBA > %
Yes
RBA > % No No
Exact match to Mixed Phenols Training Set Structure and measured RBA
RBA < %
Mixed Phenols No
Unknown
Binding Potential
Log Kow Range
4-Alkylanilines ( )
4-Alkoxy Anilines ( )
Phthalates ( )
Phenones-(branched) ( )
4-Alkyl cyclohexanols ( )
4-Alkyl cyclohexanones ( ) *
2-; 4-; or 2,4,6-Benzoates
(substitution-dependent Log Kows)
Site “B”
Contains “Specified”
Fragment
Belongs to
known Active
Sub-class
VII
Yes
Yes
RBA > % No No
RBA > %
Yes
Belong to untested class
Exact match to Mixed Organics Training Set Structure and measured RBA
RBA < %
Mixed Organics No
Unknown Binding Potential
Unknown
Binding Potential

37. Expert System Predictions for Food use Inerts and Antimicrobials
Food use Inerts Antimicrobials
Total Chemicals (%) Total Chemicals (%)
393 (100%) 211 (100%)
Predicted RBA < (96%) (93%)
Predicted RBA > (4%) (7%)

38. Expert System Profiler
Series of nodes with possible yes/no logic applied. Structure categorized as alkylphenol w/ 1.5<LogKow <8.0
Chemical categorized as high EDC Potential – prioritization model.

39. Alkylphenol node – training set

40. Expansion of an Expert System Non-Food Use Inert Ingredients
Non-Food Use Inerts list included:
2888 = 1423 discrete chemicals non-discrete substances
(49% discrete : 51% non-discrete)
1423 discrete chemicals include:
1192 organic chemicals (84%)
205 inorganic chemicals (14%)
26 organometallic compounds (2%)
1465 non-discrete substances include:
596 polymers of mixed chain length
174 mixtures
695 undefined substances

41. Expansion of an Expert System
Food Use Inerts
Groups to build the mechanism model
Alkylphenols
Alkoxyphenols
Parabens
Gallates
Salicylates
Phthalates
2,6-Substituted Alkylphenols
Thiophosphate Esters
Mixed Organics
Mixed Phenols
Acyclic
Alkylaromatic Sulfonic Acids
Cyclohexanols
Mono-Cyclic Hydrocarbons
Multi-Cyclic Hydrocarbons
Cyclohexanones
Hydrofurans
Chlorobenzenes
Phenones – n-chain
Pyrrolidiones
Sorbitans
Sulfonic Acid Dyes
Fluorobenzenes
Alkylbenzthiols
Alkylanilines
Alkoxyanilines
DDT-like
Tamoxifen-like
Benzamides
Phenones-branched-chain
Phenones – Cyclic
Ring-subst Benzoates
Non-ring subst Benzoates
Acetanilides
High Affinity Binders
Non-food Use Inerts
Antimicrobials
Aminoanthracenedione
Naphthalenol Azophenyl
Benzotriazole Phenol
2-Hydroxy Benzophenone
Dimethylamino Phenol
Phenolsulfonphthaleins
Fluorosceins
Iso-phthalates
Tribenzoates
Phosphorous Acid Esters
Phosphoric Acid Esters
Sugars
Oxazoles
Isothiazolines
Borate Esters
Imidazolidines
Triazines

42. Human ER Binding Affinity and Gene Activation

43. Relationship between Log Kow and RBA demonstrated for
Site A chemicals binding human ER.
Chemical ER binding assayed using recombinant human ERα (rec hERα).
Recombinant rainbow trout ERα (rec rtERα) shown for species comparison with same chemicals in assays with chemical bioavailability (i.e., assay total protein) comparable to rec hERα.
Cytosolic rainbow trout ER (cyto rtER) also shown.
4-alkylphenols
RBA more comparable when assay chemical bioavailability is the same regardless of species (rec hERα vs. rec rtERα), than for same species if assay bioavailability is different (rec rtERα vs. cyto rtER).

44. Relationship between Log Kow and RBA demonstrated for
Site B chemicals binding human ER.
Chemical ER binding assayed using recombinant human ERα (rec hERα).
Recombinant rainbow trout ERα (rec rtERα) shown for species comparison with same chemicals in assays with chemical bioavailability (i.e., assay total protein) comparable to rec hERα.
Cytosolic rainbow trout ER (cyto rtER) also shown.
4-alkylanilines
RBA more comparable when assay chemical bioavailability is the same regardless of species (rec hERα vs. rec rtERα), than for same species if assay bioavailability is different (rec rtERα vs. cyto rtER).

45. rat uterine cytosol ER and trout liver cytosol ER
for 55 diverse chemicals.
(similar chemical bioavailability in both cytosol receptor assays with similar total protein concentration)
Rat data from the literature

46. Working with EPA NCCT to determine how mammalian model-based ER HTS assays correlate with the in vitro assay used to build ES
focus was on selection of chemicals to cover chemical classes used in development of ER Expert System
The NCCT ER assays are:
Human ER, Bovine ER, Mouse ER-alpha competitive binding assays
Human ER-alpha reporter gene assays: agonist & antagonist mode
(2 vendors)
ERE interaction assay
Future: plan to extend to AR activation in the future with goal to build class-based expert system
Goal: use hER data (low-, medium-, or high-throughput) to build ES with expanded chemical classes.

47. Conclusions ICPS MOA An Expert System based upon the ER-mediated AOP
Knowledge of common initiating event across chemical classes facilitates development of QSARs and read-across methods to predict toxicity potential of untested chemicals
OECD QSAR Validation Principles
ICPS MOA

48. Relevance of ER model to predicting xenobiotic epigenetic chemicals?

49. endogenous estrogens stimulate substantial increases in
Promotion by 17b-estradiol and b-hexachlorocyclohexane of hepatocellular tumors in medaka, Oryzias latipes. J.B. Cooke, D.E. Hinton. Aquatic Toxicology 45 (1999) 127–145
Hypothesis: Many laboratory and field studies with various fish species show a higher prevalence
Of hepatocellular neoplasia in females than in males. During female sexual maturation,
endogenous estrogens stimulate substantial increases in
synthetic activity (e.g., vitellogenin, choriogenin productions) and hepatocytes proliferation.
-tested hypothesis that estrogens promote growth of hepatic preneoplastic lesions and tumors.
Medaka (Oryzias latipes) –
-low dose of diethylnitrosamine (DEN; 200 mg /1 ,24 h) at 3 weeks of age
then fed purified casein-based diet daily from 1 to 7 months of age
with or w/o 17b-estradiol (E2; 0.01–10.0 mg g1 dry diet)
With xenoestrogen, b-hexachlorocyclohexane (bHCH, 0.01–100.0 mg g1 dry diet).
Livers examined for foci of cellular alteration (FCA) & and hepatocellular tumors.
E2 increased prevalences of hepatocellular adenoma or carcinoma
(26% in DEN plus 10ppm E2 group versus 4.6% in DEN only group, PB0.01).
With incr E2, avg# basophilic FCA (BF) rose; #eosinophilic FCA (EF) sharply declined.
DEN plus bHCH treatment groups
> numbers of tumors in most, and greater numbers of BF in all

50. E2 increased prevalences of hepatocellular adenoma or carcinoma
Promotion by 17b-estradiol and b-hexachlorocyclohexane of hepatocellular tumors in medaka, Oryzias latipes. J.B. Cooke, D.E. Hinton. Aquatic Toxicology 45 (1999) 127–145
Results:
Livers examined for foci of cellular alteration (FCA) & and hepatocellular tumors.
E2 increased prevalences of hepatocellular adenoma or carcinoma
(26% in DEN plus 10ppm E2 group versus 4.6% in DEN only group, PB0.01).
With incr E2, avg# basophilic FCA (BF) rose; #eosinophilic FCA (EF) sharply declined.
DEN plus bHCH treatment groups
> numbers of tumors in most, and greater numbers of BF in all
DEN only:
For all DEN-treated groups, BF were more common in female medaka,
and EF more common in males.
No tumors were found in fish fed E2 or bHCH without DEN exposure
Liver wts
-control medaka, significantly larger in females
-E2 treatments (0.1, 1.0 or 10.0 ppm E2) elevated liver weights in males similar to that in females.
-bHCH had no effect on liver weights.

51. -E2 is a tumor promoter in medaka.
Promotion by 17b-estradiol and b-hexachlorocyclohexane of hepatocellular tumors in medaka, Oryzias latipes. J.B. Cooke, D.E. Hinton. Aquatic Toxicology 45 (1999) 127–145
Conclusions:
-E2 is a tumor promoter in medaka.
-Because tumor increases were not statistically significant, bHCH was considered a weakly
positive modulator.
-E2 particularly promoted tumor development in male medaka, indicating xenobiotics
with mechanism of action like that of E2 may escalate growth in wild fish of previously
initiated cells into tumors.

52. Epigenetic Adverse Outcome Pathway?
ER-Mediated Hormonal Imbalance in fish
DNA Binding
DEN initation
Chemical (parent or metabolite)
DEN
Chemical (parent or metabolite)
ER agonist
Individual
Cell
Organ
CELLULAR
Response
POPULATION
MOLECULAR
Target
MIE
TISSUE/ORG
INDIVIDUAL
Liver Cell
Replication
Vitellogenin
Hypertrophy
Liver
Tumors
Cancer
Receptor
Binding
ER Binding
Cooke & Hinton, Aquatic Toxicology, 1999

53. ER model Relevance? Wildlife species:
- reproductive impairment is more relevant than cancer from population perspective, so ER ES focused on repro impairment AOP
Rodents:
Rodent ER binding – good correlation with fishER - Estrogens and anabolic steroids can increase liver adenomas in rats/mice
Mammals in general:
Many of same chemicals bind ER and result in gene activation
-possible role in promotion, progression?
Liver is not considered one of the classic targets for hormonal carcinogenesis but liver cancer development is influenced by sex hormones (estrogen, testosterone).