Compartment-Based Priority Setting

1. Compartment-Based Priority Setting Presented by Jim Darr EPA/OPPT

2. Information Categories in Compartment-Based Approach • Exposure Data • Effects Data • Combined Exposure and Effects Data • Specially Targeted Priorities • Mixtures • Nones

3. Features of Compartment-Based Priority Setting • Chemicals selected independently from each compartment • “Weight” means fraction of chemicals selected from a given compartment • Ranking occurs only within each compartment • Overall priority list is not rank-ordered

4. COMPARTMENT: A set of chemicals defined by common features that allow ranking Example: “Indoor Air” compartment consists of the set of chemicals with measured air concentrations from indoor environments; chemicals are ranked by median concentration

5. EDPSD is transparent but not simple

6. Endocrine Disrupter Priority Setting Database (EDPSD)Current StatusandFuture Plans Presented by Patrick Kennedy EPA/OPPT

7. Current Status of the EDPSD • Following the recommendation of the EDSTAC, EPA has developed a draft database. • The EDPSD uses a compartment-based priority setting approach • There are four categories of compartments • specially targeted priorities • exposure compartments • effects compartments • combined exposure/effects compartments

8. Current Status of the EDPSD (continued) • Focus of the database is on HPV/Inerts for selecting chemicals for Phase I of the program • Pesticide Active Ingredients for Phase I will be prioritized outside of the database • Exposure compartments contain data on HPV/Inerts and all the other chemicals in the underlying data sources • Effects compartments only contain data on HPV/Inerts

9. Current Status of the EDPSD (continued) • EPA has developed a draft approach for selecting Phase I priority chemicals from among the HPV/Inerts (i.e. the default scenario). We are looking for comment on our approach. • We expect the process will include the following steps: • Select draft priority list of chemicals • Through an internal EPA review, sort chemicals • Ask for public comment on resulting list of chemicals selected for Tier I testing

10. Future Plans for the EDPSD • Revise database based upon comments from this workshop • Add an additional ecological effects data source to the database • Add QSARs to the database • Conduct an external peer review of the EDPSD prior to using it to select chemicals for Phase I

11. Questions • Are the exposure and effects data sources adequate? Are important data sources missing? • For the effects compartments, can adequate data relevance judgments be made on the basis of the summary information shown in the database: in other words, does a relatively cursory review of a limited set of hazard data provide a sufficient basis for testing? • Are the compartment definitions clear? Should any compartments be added? Should any existing compartments be split or combined?

12. Questions (continued) • Does the ranking algorithm for each compartment make sense, e.g. rank based on average concentration in monitoring compartments, rank based on LOAEL in effects compartments? • Certain compartments have a lot of ties in their rankings. How should you break ties in rankings in the chemical selection process, e.g. if you want to pick the top 10 chemicals from a given compartment and there are 15 chemicals tied at rank #6, what do you do?

13. Questions (continued) • With respect to the EPA default scenario: Do the overall category weightings make sense, i.e. cumulative weights for human health vs. ecological concerns? Do the individual compartment weights make sense? Suggested alternatives? • Is the EDPSD sufficiently transparent in terms of its operation and documentation, i.e. is the basis of the ranking readily understandable to the user? • Should complex mixtures such as petroleum distillates and vegetable derivatives be handled differently than discrete chemicals? Should chemicals like these be moved to the mixtures compartment? • How should out-of-production chemicals be handled, e.g. ozone depleting chemicals that are being phased out?

14. Questions (continued) • The Endocrine Disruptor Screening Program includes an initial sorting step, in which chemicals are to be classified according to the availability of information on each chemical’s endocrine disrupting potential. Because such a determination involves a relatively detailed assessment, however, it appears that the only practical approach is to do the sorting after selecting an initial priority list. This means that some well-studied chemicals are likely to appear on the Tier 1 priority list before being moved to a Tier 2 or Hazard Assessment list. Is this a reasonable way to proceed? Are there low resource approaches that would allow more up-front sorting as envisioned in the EDSP plan?

15. Exposure Compartments Completeness and Data Quality Presented by Patrick Kennedy EPA/OPPT

16. Exposure Compartments-Completeness and Data Quality • Are the exposure compartment data sources adequate? • Are they complete? If not, what is missing? • Are the data of sufficient quality for priority setting? • Is the documentation describing the data sources adequate? • Is it clear what they are? • Is it clear how they are used within the compartments?

17. Exposure Compartment Ranking Algorithms Presented by Conrad Flessner EPA/OPPT

18. Exposure Compartment Ranking Algorithms • Algorithm Development Criteria • Are data suitable for compartment? • Example: Sediment/Soil Monitoring Compartment • Considered EMAP, NSI, and NHEXAS data sources • NHEXAS data too limited, not used • What data elements should be used per data source? • Example: NSI from Sediment/Soil Monitoring Compartment • Data elements included: Max and median concentrations, and measures of chemical prevalence, distribution, and frequency; the latter four were combined for ranking • Can data be configured for automated computation?

19. Exposure Compartment Ranking Algorithms • Algorithm Types • Weighted - Each data source ranked and weighted • Example: Surface Water Monitoring Compartment • Ranked chemicals from NASQAN, NAWQA, and NCOD • Each source weighted equally; if three chemicals are chosen from this compartment, one chemical would be selected from each source • Advanced - boolean logic permits flexible ranking • Example: Chemicals in Consumer/Cosmetics Products Compartment • Ranked chemicals from SRD and VCRP • Chemical in both sources, ranks are averaged • Chemical in one source, that rank is used

20. Exposure Compartment Ranking Algorithms • Do these algorithms make sense? • If not, how can they be improved, or replaced?

21. Human Health Effects CompartmentsCompleteness and Data Quality Present by Jim Kwiat EPA/OPPT

22. Human Health Effects Compartments • Epidemiological and Clinical Data on Endocrine-Related Effects • Reproductive/Developmental Toxicity in Laboratory Animals

23. Human Health Effects Compartments (cont.) • Chronic/Subchronic Toxicity in Laboratory Animals • Carcinogenicity in Endocrine Target Tissues in Laboratory Animals • Effects Multi-Hit Compartment

24. Effects Compartments-Completeness and Data Quality • Are the effects compartment data sources adequate? • Are they complete? If not, what is missing? • Are the data of sufficient quality for priority setting? • Is the documentation describing the data sources adequate? • Is it clear what they are? • Is it clear how they are used within the compartments?

25. Effects Compartments-Completeness and Data Quality (cont.) • Can adequate data relevance judgments be made using the summary information available in the database; i.e., does a cursory review of a limited set of hazard data provide a sufficient basis for priority setting? • Are the compartment definitions clear? Should any compartments be added? Should any existing compartments be split or combined?

26. EDSP Human Health Effects Compartments Ranking Algorithms Presented by Jim Kwiat EPA/OPPT

27. EDPSD Ranking Algorithms • Weighted: user selects order in which data are drawn from the different data sources (e.g., ATSDR, HSDB) by assigning a weight (i.e., preference value) to each source, selecting a numeric data element to key on, and determining a selection sequence

28. EDPSD Ranking Algorithms (cont.) • Advanced: user ranks data using boolean command statements (“IF” and “SET”) to identify the elements in data sources to be evaluated and how and in what order to evaluate them

29. EDPSD Ranking Algorithms (cont.) • Combined: user can average highest ranks of exposure and effects rankings (either using all or selected compartment rankings)

30. Compartment Ranking • Epidemiological and Clinical Data--Advanced • Reproductive/Developmental Toxicity--Advanced

31. Compartment Ranking (cont.) • Carcinogenicity--Advanced • Chronic/Subchronic Toxicity--Advanced • Effects Multi-Hit Compartment– descending order count by compartments per chemical

32. Questions • Does the specific ranking algorithm for each compartment make sense, e.g., rank based on LOAEL? • Suggested ways to improve or replace the algorithms?

33. Ecological Effects Compartments in EDPSD 2 as of June 2000 John D. Walker, Ph.D., M.P.H. Director TSCA Interagency Testing Committee

34. Contributions to Developing EDPSD 2 • ITC Contributions • Developed and published several screening, ranking and testing schemes for ITC and EPA • Evaluating Environmental Fate and Ecological Effects Data ( Non- ITC: that may be Used to Assign Chemicals to Tier 1 Screening) • Developing, Evaluating and Validating SARs (Non- ITC: to Assess Activity of Chemicals on Endocrine Function) • Non-ITC Contributions • Member of EDSTAC’s PSWG • Developed EDPSD 1 with Chris Waller and Stacy Kane • Editing 5 QSAR Handbooks – one on ED

35. Acknowledgmentsfor downloading AQUIRE from http://www.epa.gov/ecotox/ and for developing data files Grace Kitzmiller Molly Merrifield

36. Sources of Ecological Effects data for EDPSD 2 • June 2000 - AQUIRE • Future – TERRETOX, Canada’s Herring Gull database, Others?

37. Proposed and Actual Transparent Processes for Ranking AQUIRE data • Species examined and rationale for selection • Effects preferences and endpoints selected for ranking data • Concentration types and units

38. Aquatic vertebrates with sufficient AQUIRE data for EDPSD 2 • Fathead minnow - warm fresh water • Rainbow trout - cold fresh water • Sheepshead minnow - salt water

39. Aquatic invertebrates with sufficient AQUIRE data for EDPSD 2 • Daphnia sp. - fresh water • Mysidopsis bahia - salt water

40. Effects preferences considered for ranking data • Reproduction > Physiological > Behavioral > Growth

41. Effects preferences selected for ranking data • Reproduction = Physiological = Behavioral = Growth

42. Effects endpoints considered for ranking data MATC>LOEC>EC50>IC50>ET50>LC50>LT50>EC25>IC25>LT25>EC10 >NOEC

43. Effects endpoints selected for ranking data MATC>LOEC>EC50=IC50=ET50=LC50=LT50>EC25=IC25=LT25>EC10>NOEC

44. Concentration types considered for ranking data Type 1 > Type 2 Type 1 = mean or minimum Type 2 = maximum

45. Concentration types selected for ranking data Type 1 = Type 2 Example - Toxaphene only has concentration type 2 Reproductive MATC of 0.14 ug/L. Making Type 1 = Type 2 allows it rank above potassium chromate with concentration Type 1 Reproductive MATC of 2,500 ug/L

46. AQUIRE records with ug/L units • Total/ug/L 6701/6156 • Fathead Minnow 1687/1637 • Rainbow Trout 1981/1510 • Sheepshead Minnow 430/428 • Daphnia sp. 2259/2241 • Mysidopsis bahia 344/340

47. AQUIRE chemicals in EDPSD 2 • Total 308 • Integrated HPV/Other 37 • Fathead Minnow 10 (5 MATC, 3 NOEC) • Rainbow Trout 7 • Sheepshead Minnow 2 • Daphnia sp. 27 (7 MATC, 13 NOEC) • Mysidopsis bahia 0

48. Suggestions for using EDPSD 2 to rank chemicals for Tier 1 Screening Separate Organics and Inorganics Organize chemicals by Modes of Action Use QSARs to predict effects Collapsing fish and/or invertebrate data to avoid artificially assigning a higher weight to a that may have, e.g., the same EC50 in all 3 fish

49. Combined Exposure and Human Health Effects Compartments Definition and Ranking

50. Overview Diagram of EDPSD