Understanding Uncertainties and Confidence in Hazard Databases: An Example Using IRIS

1. Understanding Uncertainties and Confidence in Hazard Databases: An Example Using IRIS Nancy B. Beck, PhD, DABT May 21, 2014 Alliance for Risk Assessment Workshop VIII

2. Center for Advancing Risk Assessment Science and Policy (ARASP) Coalition of independent groups and associations that promotes the development and application of up-to-date, scientifically sound methods for conducting chemical assessments. Acrylonitrile Group ACC Chlorine Chemistry Division ACC Ethylene Oxide Panel ACC Formaldehyde Panel ACC Hexavalent Chromium Panel ACC High Phthalates Panel ACC Hydrocarbon Solvents Panel ACC Vinyl Chloride Health Committee ACC Propylene Oxide/Propylene Glycol Panel ACC Public Health and Science Policy Team ACC Olefins Panel ACC Oxo Process Panel ACC Silicones Environmental, Health and Safety Center American Cleaning Institute American Petroleum Institute CropLife America Halogenated Solvents Industry Alliance Nickel Producers Environmental Research Association Styrene Information and Research Center

3. 2013 ARASP WorkshopInforming Risk Assessment: Understanding and Communicating Uncertainty in Hazard Assessment Workshop goals including bringing together diverse stakeholders (government, industry, academia, NGO) to: • Learn about the ways that uncertainty information is currently analyzed and presented by different organizations. • e.g., the margin of exposure approach • Gain a better understanding of how the public generally processes and perceives risks and associated uncertainties. • Explore approaches to improving the way uncertainty is presented in federal assessment programs.

4. 2013 ARASP WorkshopInforming Risk Assessment: Understanding and Communicating Uncertainty in Hazard Assessment Focused on IRIS as an example. • Considered experiences in other programs with varied mandates Used specific case studies to explore diverse approaches (including some very basic ideas). Work is continuing to developimplementable approaches showing how information can be presented differently to improve the consideration and use of uncertainty information by risk managers. Four approaches: • Comparing Values to Other Peer Reviewed Numbers • Unpacking Toxicity Assessments to Understand and Improve Confidence • Presenting Toxicological Information Visually in the Context of Alternative Values, Exposure Levels, and Biomonitoring Equivalents • Improving Transparency in Dose-Response Decision Making

5. 1. Comparing IRIS Values to Other Peer Reviewed Numbers Carbon TetrachlorideProposed Risk Summary Comparison Table • [1] If a value was derived the status will contain the derived risk value; if no value was derived, but the data were evaluated the status is considered qualitative; and if there was no evaluation this is designated by a “-” sign. Data sources include EPA and ITER.

6. 1. Comparing IRIS Values to Other Peer Reviewed Numbers Carbon Tetrachloride Proposed Oral Non-Cancer Risk Value Summary Table Data sources include EPA and ITER.

7. 2. Unpacking Toxicity Assessments to Understand and Improve Confidence Look at 8 major ‘elements’ of a toxicity assessment and 3 ‘confirmatory elements’ • how confident are we that the value is likely to be correct. Uses clear criteria to judge confidence and implement scaling • 1 = low confidence; 5= high confidence For the purposes of our example, each element is treated as being equally important. Audience = risk assessor • help explain the approach and confidence to a risk manager/decision maker.

8. Table 1 Non-Cancer Toxicity Assessment Elements

9. Table 2 Cancer Toxicity Assessment Elements

10. Table 3 Elements for a Confirmation of Toxicity Assessment

11. Table A3 Hazard Identification: Quality of Key Study(ies)

12. Figure 1 Confidence Scoring for Inhalation RfC for Carbon Tetrachloride (CCl4)

13. Figure 2 Confidence Scoring for Inhalation ReVfor 4-vincylcyclohexene (4-VCH)

14. Figure 1 Confidence Scoring for Inhalation RfC for Carbon Tetrachloride (CCl4) Figure 2 Confidence Scoring for Inhalation Reference Value for 4-vinylcyclohexene (4-VCH)

15. 3. Presenting Toxicological Information Visually Using IRIS as the example, we provide a working model of a visualization approach for cancer/non-cancer risks to help put relationships in context. Presents a series of figures that capture different types of information. Allows for inclusion of exposure information, where appropriate. • E.g., intake levels, environmental levels Gives risk managers and others the opportunity to put toxicity values in the context of exposure, which may be valuable.

16. 3. Presenting Toxicological Information Visually Intended for use by a risk assessor. • The risk assessor can then use the figures as helpful tools to explain the data and its derivation and context to risk managers. In describing relationships, it may appear that each toxicity value is known with certainty and represents a bright line between safe and unsafe. • Although toxicity value point estimates are often used in this manner, it is important to note that the true definition, at least for an IRIS value, recognizes that the value could be a range that spans an order of magnitude of uncertainty. In going through figures, you must imagine an approach that uses scroll overs and pop-up boxes.

17. Oral Noncancer Basic Figure The dose-response point that marks the beginning of a low-dose extrapolation. This point can be the lower bound estimate on dose for an estimated incidence or a change in response level from a dose-response model (BMD), or a NOAEL or LOAEL for an observed incidence, or change in level of response. The range for the values are not on scale but are allowing the visualization of the uncertainty between the POD and the risk value. Point of Departure (POD) Uncertainty Factors can range from 0 to 3000 (maximum). The possible types of UFs are: interspecies uncertainty (UFA); intraspeciesvariability (UFH); subchronicto chronic extrapolation (UFS); use of a LOAEL in absence of a NOAEL (UFL); database incomplete (UFD) Uncertainty Factor (UF) Dose (mg/kg/day) Refernce Value An estimate of an exposurefor a given duration to the human population (including susceptible subgroupsthat is likely to be without an appreciable risk of adverse health effects over a lifetime. It is derived from a BMDL, NOAEL, LOAEL or suitable point of departure, with uncertainty/variability factors applied to reflect limitations of the data used. Durations include acute, short-term, subchronic, and chronic and are defined individually in this glossary. The shading in the figure represents a decrease in the value and the potential risk of effects; higher value (darker shade) to a lower value (lighter shade).

18. Carbon tetrachloride: Oral Noncancer (USEPA) • Chosen PODs • BMDL2x-ADJ = 3.9mg/kg-day for elevated serum SDH activity • Based on subchronic oral rat study • Alternative PODs • Alternative PODs • NOAEL = 1 mg/kg-day (EPA) • LOAEL = 10 mg/kg-day for liver lesions (EPA) 3.9 BMDL2x-ADJ(EPA) Uncertainty Factor is based on default 10-fold for intraspecies differences (UFH), 10-fold for interspecies extrapolation (UFA), 3 for subchronic to chronic extrapolation (UFS) UF = 10x10x3 = 1000 mg/kg-day 1000-fold UF 4x10-3 RfD (EPA) 3.9 mg/kg-day (BMDL2x-ADJ) 1000 (UF) RfD = RfD= 0.004 mg/kg-day

19. Carbon tetrachloride: Oral Noncancer (RIVM) • Alternative PODs • LOAEL 10 mg/kg-day for liver lesions (RIVM) • Chosen POD • NOAEL = 1 mg/kg-day for liver effects • Based on a rat subchronic oral study • Alternative PODs mg/kg-day 1 NOAEL (RIVM) The basis for the uncertainty factor was not provided. 250-fold UF 4x10-3 TDI (RIVM) 1 mg/kg-day (NOAEL) 250(UF) RfD = RfD= 0.004 mg/kg-day

20. Carbon tetrachloride: Inhalation Noncancer Comparison* mg/m3 14.3 BMCL10(HEC) (EPA) 6.4 NOAECADJ (RIVM) 5.8 100-fold UF NOAELHEC (ATSDR) 30-fold UF 0.19 cMRL (ATSDR) 100-fold UF 0.1 RfC (EPA) 0.06 TCA (RIVM) mg/m3 0.19 cMRL (ATSDR) 0.1 RfC (EPA) 0.06 TCA (RIVM) *represents available alternative reference values

21. Carbon Tetrachloride: Comparison to Background and BE 60 Background Range (ATSDR) mg/m3 10 Equivalent Concentration µg/L 0.1 RfC (EPA) 75th 50th 0.07 BE 25th

22. Oral Carcinogenic Basic Figure The dose-response point that marks the beginning of a low-dose extrapolation. This point can the lower limit on Effective dose10 (LED10) which is the 95% lower confidence limit of the dose of a chemical needed to produce an adverse effect in 10 percent of those exposed to the chemical, relative to control. It can also be the Effective dose10 (ED10), which is the dose corresponding to a 10% increase in an adverse effect, relative to the control response. The range for the values are not on scale but are allowing the visualization of the uncertainty between the POD and the risk value. Point of Departure (POD) An oral slope factor is an upper bound, approximating a 95% confidence limit, on the increased cancerrisk from a lifetime oral exposureto an agent. This estimate, usually expressed in units of proportion (of a population) affected per mg/kg-day, is generally reserved for use in the low-dose region of the dose-response relationship, that is, for exposure corresponding to risks less than 1 in 100. Dose (mg/kg/day) Risk Specific Dose = Target Risk/CSF Cancer Classifications: There are five recommended US EPA standard hazard descriptors: “Carcinogenic to Humans,” “Likely to Be Carcinogenic to Humans,” “Suggestive Evidence of Carcinogenic Potential,” “Inadequate Information to Assess Carcinogenic Potential,” and “Not Likely to Be Carcinogenic to Humans.” Other Agencies such as IARC and RIVM use other cancer classification descriptions. The shading in the figure represents a decrease in the value and the potential risk of effects; higher value (darker shade) to a lower value (lighter shade).

23. Carbon tetrachloride: Oral Cancer (USEPA) • Chosen POD • LED10, lower 95% bound on exposure at 10% extra risk: 1.54 mg/kg-day  • Alternative POD • Alternative POD • ED10, central estimate of exposure at 10% extra risk: 2.27 mg/kg-day 1.54 (EPA) mg/kg-day • Risk Specific Dose (RSD): 1.4x 10-4mg/kg-day • Oral slope factor: 7x10-2 mg/kg-day • Alternative Oral Slope Factor • Target Organ: liver • Species: Mouse (Nagano et al., 2007b; JRBC, 1998) • Extrapolation method: multistage model, linear extrapolation from LED10 • Classification: likely to be carcinogenic to humans • Other Agency Classifications • RSD Calculation • Divide 1E-5 (1 in 100,000 (E-5) risk level) by the slope factor of 7E-2 per (mg/kg)-day 1.4x10-4(EPA) • Alternative Oral Slope Factor • If ED10 was used the Oral slope factor: 4x10-2 mg/kg-day IARC Cancer classification: 2B (possible carcinogenic to humans)

24. 4. Improving Transparency in Dose-Response Decision Making Uses a template for consideration of uncertainty and variability in reference dose derivation as a basis to increase transparency. • RfD for Acrylamide is the example The information in the figure is presented in the order of conduct, and provides a summary that identifies: (1) what decisions are important; and (2) the degree of conservatism in the selected option. Table 1 provides: • Detailed information used to create the figure • Sources of uncertainty vs. variability • Relationships between decisions (e.g., MOA impacts multiple decisions) • Degree of confidence the assessors have in the decision made for each step For purposes of prioritization, the table provides a grid for all decisions, organized by their relative importance (high, medium, low) and their degree of confidence (high, medium, low).

25. Figure 1. Summary of Decisions Made for the Acrylamide Oral RfD Assessment Neurotoxicity is attributed to acrylamide Neurotoxicity is attributed to glycidamide *This decision has qualitative rather than quantitative options (see Table 1) The solid red lines indicates the “selected” option or value (column 4 in Table 1). The dashed line indicates the normalizing value (column 3 in Table 1). The shading gradient of the bars indicates the direction of higher or lower conservatism. Values in the dark blue region result in lower RfDs than the light blue region.

26. Table 1 Structure

27. R a n g e R e f l e c t s U n c e r t a i n t y o r C o n f i d e n c e i n D e c i s i o n ( S c i e n c e - o r a D e c i s i o n P o i n t V a r i a b i l i t y B a s i s f o r N o r m a l i z i n g V a l u e s D e c i d e d O p t i o n P o l i c y - b a s e d ) D a t a S e t / E n d p o i n t V a r i a t i o n i n t h e e f f e c t i v e c h r o n i c M e a n e f f e c t i v e c h r o n i c N O A E L N O A E L f o r p e r i p h e r a l n e r v e e f f e c t s M e d i u m / H i g h c o n f i d e n c e i n k e y b N O A E L v a l u e s ( m i n i m u m a n d v a l u e a c r o s s c a n d i d a t e s t u d i e s ( 6 . 1 ( 0 . 5 m g / k g - d a y ; J o h n s o n e t a l . , s t u d y . S e l e c t i o n o f a s e n s i t i v e S e l e c t i o n m a x i m u m v a l u e s c a l c u l a t e d f r o m m g / k g - d a y , b a s e d o n d a t a p r o v i d e d 1 9 8 6 ) e n d p o i n t a n d s t u d y r e f l e c t s a p o l i c y c i n E P A T a b l e 5 - 1 ) d e c i s i o n t o b e p r o t e c t i v e E P A T a b l e 5 - 1 ) C a u s a t i v e A g e n t 1 ) N e u r o t o x i c i t y i s a t t r i b u t e d t o N A N e u r o t o x i c i t y i s a t t r i b u t e d t o N o t e x p l i c i t l y s t a t e d b y E P A b a c r y l a m i d e a c r y l a m i d e D e t e r m i n a t i o n ( M O A ) 2 ) N e u r o t o x i c i t y i s a t t r i b u t e d t o g l y c i d a m i d e D o s e - R e s p o n s e M o d e l V a r i a t i o n i n P O D a c r o s s m o d e l s , M e a n P O D o f a c c e p t a b l e m o d e l s L o g - l o g i s t i c m o d e l ( 1 . 2 m g / k g - d a y ; H i g h c o n f i d e n c e ( E P A S e c t i o n b b a s e d o n m i n i m u m ( 1 . 2 m g / k g - d a y ) ( 1 . 4 m g / k g - d a y ; E P A T a b l e C - 2 ) E P A T a b l e C - 2 ) 5 . 3 . 1 . 3 ) . S e l e c t i n g t h e b e s t f i t t i n g S e l e c t i o n a n d m a x i m u m ( 1 . 8 m g / k g - d a y ) f o r m o d e l r e f l e c t s a s c i e n c e - b a s e d a l t e r n a t i v e B M D v a l u e s d e c i s i o n t o b e p r e d i c t i v e C o n f i d e n c e L i m i t S e l e c t i o n U n c e r t a i n t y i n m o d e l p a r a m e t e r s P O D = B M D ( 1 . 2 m g / k g - d a y ; c e n t r a l P O D = B M D L ( 0 . 6 m g / k g - d a y ; 9 5 % N o t e x p l i c i t l y s t a t e d b y E P A , f o r l o g - l o g i s t i c m o d e l , b a s e d o n t e n d e n c y ) l o w e r c o n f i d e n c e l i m i t ) h o w e v e r s e l e c t i n g l o w e r c o n f i d e n c e B M D L 1 0 ( 0 . 5 7 m g / k g - d a y ) a n d l i m i t r e f l e c t s a p o l i c y - b a s e d B M D 1 0 ( 1 . 2 m g / k g - d a y ) f r o m E P A d e c i s i o n t o b e p r o t e c t i v e T a b l e C - 2 B e n c h m a r k R e s p o n s e R a t e U n c e r t a i n t y i n P O D r e s p o n s e , b a s e d B M R = 1 0 % ( B M D L 1 0 = 0 . 5 7 m g / k g - B M R = 5 % ( B M D L 0 5 = 0 . 2 7 m g / k g - N o t e x p l i c i t l y s t a t e d b y E P A , S e l e c t i o n o n r a n g e d e f i n e d b y t h e B M D L 0 1 d a y ) f o r t h e d e f a u l t r e s p o n s e r a t e d a y ) h o w e v e r s e l e c t i n g a B M R v a l u e ( 0 . 0 5 m g / k g - d a y ) a n d B M D L 1 0 ( 0 . 5 7 f o r d i c h o t o m o u s d a t a ( 5 % ) t h a t i s b e l o w t h e d e f a u l t v a l u e m g / k g - d a y ) f r o m E P A T a b l e 5 - 3 ( 1 0 % ) a p p e a r s t o r e f l e c t a p o l i c y - b a s e d d e c i s i o n t o b e p r o t e c t i v e Table 1. Summary of Decisions Made for the Acrylamide Oral RfD Assessment

28. Table 1 Structure (cont’d)

29. Table 1(cont’d)

30. Table 1. Summary of Decisions Made for the Acrylamide Oral RfD Assessment

31. Table 2. Summary of Confidence and Importance of the Decisions Made in the RfD Assessment for Acrylamide

32. Summary Four distinct and independent approaches have been presented. Each one addresses and portrays confidence and uncertainty in slightly different ways. There is no ‘right’ way to communicate this type of information • Different approaches may appeal to different people Consideration should be given to testing (e.g. focus groups) before adopting or adapting approaches. The working groups welcome input and feedback on the approaches.

33. With Great Thanks To: ARASP Workshop Nov 6-7, 2013Participants (1) Angela Lynch, American Chemistry Council Anita Meyer* , US Army Corps Anna Lowit, US EPA Ann Mason, American Chemistry Council Beth Holman, US EPA Bette Meek, University of Ottawa Bill Farland, Colorado State University Bruce Fowler, ICF International Candace Prusiewicz*, NC DENR Chris Frey, North Carolina State University Chris Kirman, Summit Toxicology Christine Palermo* , Exxon Mobil Chuck Elkins, Chuck Elkins & Associates Dale Strother, ToxSolve LLC David Adenuga*, ExxonMobil Biomedical David Dunlap*, Koch Industries Drew Rak*, Noblis EevaLeinala, Health Canada Elena Craft*, Environmental Defense Fund Flora Ratpan, Nova Chemicals Corporation Fran Kruszewski, American Cleaning Institute George Cruzan, ToxWorks George Gray, George Washington University Greg Dolan, Methanol Institute Heather Burleigh-Flayer*, PPG James Kim, Office of Management and Budget Jeff Lewis, ExxonMobil Biomedical Jennifer Foreman, ExxonMobil Biomedical

34. With Great Thanks To: ARASP Workshop Nov 6-7, 2013 Participants (2) Jennifer Taylor, American Chemistry Council Jim Laity, Office of Management and Budget Joanna Klapacz*, Dow John Norman, ExxonMobil Biomedical Judith Zelikoff, New York University Langone Medical Center Judy LaKind, LaKind Associates Karluss Thomas, American Chemistry Council Kathy Stanton, ACI Kevin Bromberg, US Small Business Administration Linda Abbott, US Department of Agriculture Lynn Flowers, US EPA Lynn Pottenger, The Dow Chemical Company Michael Kosnett, University of Colorado Denver Mike Dourson, Toxicology Excellence in Risk Assessment Mike Taylor, Nickel Producers Environmental Research Association Min Chen*, Exxon Mobil Nadira De Abrew*, Proctor & Gamble Nathan Perchacek*, Ecolab Nancy Beck, American Chemistry Council Neeraja Erraguntla*, TCEQ Patricia Nance, Toxicology Excellence in Risk Assessment Patricia Underwood, US Department of Defense Patrick Beatty, American Petroleum Institute Raj Sharma, Georgia-Pacific LLC Rich Sedlak*, ACI

35. With Great Thanks To: ARASP Workshop Nov 6-7, 2013 Participants (3) Rick Becker, American Chemistry Council Robert Roy*, 3M Roberta Grant, TCEQ Stephanie Shirley*, TCEQ Stephen Broomell, Carnegie Mellon University Steve Risotto, American Chemistry Council Stewart Holm*, American Forest and Paper Association Susan Dudley, George Washington University Susan Santos, FOCUS GROUP Risk Communication and Environmental Management Consultants Ted Simon, Ted Simon LLC Tim Pastoor, Syngenta Vincent Cogliano, US EPA *Denotes participation via teleconference Bold denotes continued work on developing examples and approaches

36. Questions and Discussion

37. Table 5 Confidence Scoring for the RfC for CCl4