P. Schmieder EPA, ORD, NHEERL, MED-Duluth, MN 2 nd McKim Cancer Workshop

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 treatmentenv - 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 vitroAssay focus area Toxicity Pathway Adverse Outcome Pathway In vivo Inerts; Antimicrobial Chemicals Chemicals POPULATION CELLULAR Response TISSUE/ORGAN MOLECULAR Target INDIVIDUAL Skewed Sex Ratios; Yr Class Liver Altered proteins(Vtg)& hormones; Gonad Ova-testis; Complete ovary in male Sex reversal; Altered behavior; Repro. Liver Cell Protein Expression Vitellogenin (egg protein transported to ovary) Receptor Binding ER Binding Greater Risk Relevance Greater Toxicological Understanding

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

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

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

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

19. Log Kow = 3.65 Log Kow = 1.97 Log Kow = 4.06 Log Kow = 1.50 Log Kow = 3.20 Log Kow = 2.47 C6 C7 C8 C9 Log Kow = 4.15 Log Kow = 4.62 Log Kow = 5.68 Log Kow = 5.76 ER Binding Site A Homologous Series 4-n-Alkylphenols C1 C2 C3 C4 C5 C0 Note: pink shading indicates RBA > 0.00001%; grey shading indicates RBA < 0.00001%

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

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

22. Trout ER Knowledge base C0 Log Kow = 0.90 C3 C4 C5 C6 C8 C1 C2 Log Kow = 1.39 Log Kow = 1.96 Log Kow = 2.40 Log Kow = 3.05 Log Kow = 3.39 Log Kow = 4.06 Log Kow = 5.12 ER Binding Site B Homologous Series 4-n-Alkylanilines Note: pink shading indicates RBA > 0.00001%; grey shading indicates RBA < 0.00001%

23. 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. Site A & Site B Chemicals with RBA > 0.00001% 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 + 500 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 + 88non-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 vitroAssay focus area Toxicity Pathway Adverse Outcome Pathway Inerts; Antimicrobial Chemicals POPULATION CELLULAR Response TISSUE/ORGAN MOLECULAR Target INDIVIDUAL Skewed Sex Ratios; Yr Class Liver Altered proteins(Vtg) Sex reversal; Altered behavior; Repro. Liver Cell Protein Expression Vitellogenin (egg protein transported to ovary) Receptor Binding ER Binding Greater Risk Relevance Greater Toxicological Understanding

31. Test Chemicals: Positive response Test Chemicals: Negative response Positive Control: Estradiol Positive Control: Estradiol Data Example - primary In vitro assay used : Estrogen Receptor Binding Displacement Assay CytortER rtER 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. Positive Control: Estradiol Positive Control: Estradiol Test Chemicals: Positive response Test Chemical: Negative response Data example – Confirmatory in vitro Assay: Gene Activation

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

34. Meets specific distance criteria for: Sites “A-B”; Sites “A-C”; Sites “A-B-C” Contains some attenuating feature, steric or other Yes No High Affinity “A-B”;“A-C” or “A-B-C” RBA > 0.1% Strength of attenuation factors Weak Strong RBA < 0.00001% Low Affinity “A-B”,“A-C” or “A-B-C” 0.00001% < RBA < 0.1% No IV Start Here: Chemical List 393 Food Use Inerts II III I Contains a Cycle Contains a Charge Contains a phenolic OH, and additional OH and/or =0 Belongs to known Inactive Sub-class Yes Yes No No Yes Yes No “Special Rule” Applies V Yes Alkylaromatic Sulfonic Acids Log Kow (-0.6 - 5.7) Sulfonic Acid Dyes Log Kow (-0.4 - 6.0) 4-Alkylbenzthiols Log Kow ( 2.9 - 4.2) Miscellaneous Functional Groups w/ Charge Log Kow Range 4-n-Alkylfluorobenzenes ( 2.3 - 4.9) Alkylphenol (2,6-subst) ( 4.2) Benzamides( 1.7 - 2.1) Borate Esters (-0.5 - 1.0) Benzoates (non-ring subst) ( 3.6 - 4.2) Bis-anilines ( 1.6 - 6.2) Hydrofurans (alcohol & ketone) ( 0.0 - 1.2) Imidazolidines(-0.9 - 0.6) Isothiazolines( 0.6 - 2.5) Mono-cyclic Hydrocarbons ( 2.7 - 4.6) Oxazoles(<0.3) Phenones (n-alkyl) ( 1.6 - 4.1) Pyrrolidiones(-0.4 - 3.3) Sorbitans( 3.2 - 5.9) Triazines (-0.4 - 3.4) RBA < 0.00001% No DDT-Like Tamoxifen-Like Multicyclic hydrocarbons Alkylchlorobenzenes Thiophosphate Esters Possible Low Affinity Site A-Type, Site B-Type Yes RBA > 0.00001% Log KOW <1.3 Exact match to Group Training Set Structure and measured RBA Yes RBA < 0.00001% No RBA < 0.00001% Unknown Binding Potential No Site “A” Contains Phenol Fragment VI Belongs to known Active Sub-class • Log Kow Range • Alkylphenols (1.5 - 8.0) • Alkoxy phenols (2.3 - 4.3) • Parabens (1.9 - 5.4) • Salicylates (2.5 - 5.1) Yes Yes RBA > 0.00001% Yes RBA > 0.00001% No No Exact match to Mixed Phenols Training Set Structure and measured RBA RBA < 0.00001% Mixed Phenols No Unknown Binding Potential Log Kow Range 4-Alkylanilines ( 1.4 - 5.1) 4-Alkoxy Anilines ( 2.6 - 3.7) Phthalates ( 2.5 - 6.8) Phenones-(branched) ( 2.7 - 4.8) 4-Alkyl cyclohexanols ( 2.4 - 3.8) 4-Alkyl cyclohexanones ( 2.4 - 3.8) * 2-; 4-; or 2,4,6-Benzoates (substitution-dependent Log Kows) Belongs to known Active Sub-class Site “B” Contains “Specified” Fragment VII Yes Yes RBA > 0.00001% No No RBA > 0.00001% Yes Belong to untested class Exact match to Mixed Organics Training Set Structure and measured RBA RBA < 0.00001% 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. Meets specific distance criteria for: Sites “A-B”; Sites “A-C”; Sites “A-B-C” Contains some attenuating feature, steric or other Yes No High Affinity “A-B”;“A-C” or “A-B-C” RBA > 0.1% Strength of attenuation factors Weak Strong RBA < 0.00001% Low Affinity “A-B”,“A-C” or “A-B-C” 0.00001% < RBA < 0.1% No IV Start Here: Chemical List 393 Food Use Inerts II III I Contains a Cycle Contains a Charge Contains a phenolic OH, and additional OH and/or =0 Belongs to known Inactive Sub-class Yes Yes No No Yes Yes No “Special Rule” Applies V Yes Alkylaromatic Sulfonic Acids Log Kow (-0.6 - 5.7) Sulfonic Acid Dyes Log Kow (-0.4 - 6.0) 4-Alkylbenzthiols Log Kow ( 2.9 - 4.2) Miscellaneous Functional Groups w/ Charge Log Kow Range 4-n-Alkylfluorobenzenes ( 2.3 - 4.9) Alkylphenol (2,6-subst) ( 4.2) Benzamides( 1.7 - 2.1) Borate Esters (-0.5 - 1.0) Benzoates (non-ring subst) ( 3.6 - 4.2) Bis-anilines ( 1.6 - 6.2) Hydrofurans (alcohol & ketone) ( 0.0 - 1.2) Imidazolidines(-0.9 - 0.6) Isothiazolines( 0.6 - 2.5) Mono-cyclic Hydrocarbons ( 2.7 - 4.6) Oxazoles(<0.3) Phenones (n-alkyl) ( 1.6 - 4.1) Pyrrolidiones(-0.4 - 3.3) Sorbitans( 3.2 - 5.9) Triazines (-0.4 - 3.4) RBA < 0.00001% No DDT-Like Tamoxifen-Like Multicyclic hydrocarbons Alkylchlorobenzenes Thiophosphate Esters Possible Low Affinity Site A-Type, Site B-Type Yes RBA > 0.00001% Log KOW <1.3 Exact match to Group Training Set Structure and measured RBA Yes RBA < 0.00001% No RBA < 0.00001% Unknown Binding Potential No Site “A” Contains Phenol Fragment VI Belongs to known Active Sub-class • Log Kow Range • Alkylphenols (1.5 - 8.0) • Alkoxy phenols (2.3 - 4.3) • Parabens (1.9 - 5.4) • Salicylates (2.5 - 5.1) Yes Yes RBA > 0.00001% Yes RBA > 0.00001% No No Exact match to Mixed Phenols Training Set Structure and measured RBA RBA < 0.00001% Mixed Phenols No Unknown Binding Potential Log Kow Range 4-Alkylanilines ( 1.4 - 5.1) 4-Alkoxy Anilines ( 2.6 - 3.7) Phthalates ( 2.5 - 6.8) Phenones-(branched) ( 2.7 - 4.8) 4-Alkyl cyclohexanols ( 2.4 - 3.8) 4-Alkyl cyclohexanones ( 2.4 - 3.8) * 2-; 4-; or 2,4,6-Benzoates (substitution-dependent Log Kows) Belongs to known Active Sub-class Site “B” Contains “Specified” Fragment VII Yes Yes RBA > 0.00001% No No RBA > 0.00001% Yes Belong to untested class Exact match to Mixed Organics Training Set Structure and measured RBA RBA < 0.00001% 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 < 0.00001 378 (96%) 196 (93%) Predicted RBA > 0.00001 15 (4%) 15 (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 + 1465 non-discrete substances (49% discrete : 51% non-discrete) 1423discrete 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 Groups to build the mechanism model Food Use Inerts • 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 • 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 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 (rechERα). • Recombinant rainbow trout ERα (recrtERα) shown for species comparison with same chemicals in assays with chemical bioavailability (i.e., assay total protein) comparable to rechERα. • Cytosolic rainbow trout ER (cytortER) also shown. 4-alkylphenols RBA more comparable when assay chemical bioavailability is the same regardless of species (rechERα vs. recrtERα), than for same species if assay bioavailability is different (recrtERαvs. cytortER).

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

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)

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 • 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. 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 hepatocellularneoplasia 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. 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.