Richard Hertzberg Biomathematics Consulting Atlanta, GA Beyond Science and Decisions

1. Categorical Regression as a Predictive Tool for Determining Risks atDoses above the Reference Dose (RfD) Richard Hertzberg Biomathematics Consulting Atlanta, GA Beyond Science and Decisions From Issue Identification to Dose-Response Assessment Austin, TX March 16, 2010

2. Overview • Goal: Estimate risk for dose > RfD • Why RfD and BMD will not work • How categorical regression works • Pros and Cons • Future

3. The RfD is Limited(pun intended) “If it's zero degrees outside today and it's supposed to be twice as cold tomorrow, how cold is it going to be?” (ba-da-bum) • For lower doses, the RfD does not inform us of risk: • Safe is still safe. What does the RfD or critical effect say about toxicity at higher doses?

4. Why Severity Categories? • Reference Dose Limitations • Bounding value: minimal to no risk for lower doses • Benchmark dose based on modeling the critical effect • Other effects not included in calculation of RfD • Doses higher than the RfD • Cannot estimate risk except of the critical effect • Need dose-response information on all “toxic effects” • Multiple responses and measures (need lots of data) • Such information is rarely in any single study Meta-analysis?

5. Desirable Effects Information • Critical Effect—effect observed at the lowest dose • Do NOT want: P(critical effect | dose>RfD) • Secondary Effects—observed at higher doses, also includes: • Effects mediated by chemical metabolites • Effects that are not adverse (e.g., enzyme induction) • Variations in Effects (e.g., from chemical mixture exposures) • Co-occurring effects might be worse than any by itself • Usually require toxicologist’s judgment on severity • Mixture Issues • Joint toxic action may occur: dose- or response-additivity; toxicological interactions (e.g., synergism, antagonism)

6. What Are We Modeling? RfD: An estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (includingsensitive subgroups) that islikely to bewithout an appreciable riskof deleterious effectsduring a lifetime. sensitive subgroups be conservative likely P( ... ) > 0.95 (?) deleterious effects “adverse effects” without an appreciable risk r < 0.01 (?) P( "risk of adverse effect" < 0.01 | dose<RfD) > 0.95

7. What Are We Modeling? P( “adverse effect” | dose=D) for this presentation, D is a dose > RfD

8. How Categorical Regression Works • Meta-analysis of exposure-response data • A method for combining health effects data across studies, endpoints, exposure durations and species • Basic approach to modeling (e.g., Dourson et al., 1997) • Using toxicological judgment, each dose group (or individual animal) of every study is assigned to a severity category • Using link function, e.g. logistic regression, severity categories are regressed on dose (and possibly duration) • Models predict the probability that an effect severity will be observed, given dose • Example also of five pesticides (Teuschler et al., 1999) • Toxicological effects data from multiple bioassays • Results compared with RfD

9. Categorical Regression Model • Logistic model for the i th severity category • The Probability that severity is less than or equal to level i, given dose is expressed as: where s = severity i = an effect level (1=NOEL/NOAEL, 2=AEL, 3=FEL) αi = an intercept term for level i β = a slope factor related to dose

10. Example 1. Frequency of Effect Categories for Aldicarb Exposure in Humans* *Source: Dourson et al. 1997. Categorical Regression of Toxicity Data, A Case Study Using Aldicarb. Reg. Toxicology and Pharmacology, 25:121-129 **Numbers reflect a judgment that whole blood (Haines, 1971) or red blood cell (Wyld et al., 1992) cholinesterase inhibition of 20% or greater is considered an adverse effect. This percentage can be debated and is a source of uncertainty.

11. Example Severity Assignments for Human Health Effects from Aldicarb Exposures (Adapted from Dourson et al. ,1997)

12. Aldicarb: Multiple Severities Information for the risk manager!

13. Example 2. Health Effects of Concern for 5 Pesticides* *Source: Teuschler et al. 1999. Health Risk Above the Reference Dose for Multiple Chemicals. Reg. Tox. And Pharm. 30:S19-S26.

14. Animal Study Data Records Modeled Using a 3 Category Model *NOAEL = No Observed Adverse Effect Level, AEL = Adverse Effect Level, FEL = Frank Effect Level

15. Pros and Cons • Advantages: • Provides a consistent basis for calculating risk above the RfD • Can use available data, even marginal studies and dose group level information • Accounts for severity of toxic effect by combining studies on multiple effects • Limitations: • Animal to human extrapolation is still needed • Data are transformed into categories, losing information • Cannot track toxic MOA progression with increasing dose • More tox judgment needed than merely NOAEL vs AEL

16. Closer to Goal of P(toxicity | dose>RfD) RfD & BMD Categorical Regression clear, off-target more on target, but fuzzy

17. DDT: oral, many species

18. More Pros and Cons • Is this human RISK estimation? • If includes human incidence data, then YES • If human data are on dose groups, then Not Exactly • If animal data, then No • Not Exactly? No? • Dose group data: risk=P(dose group shows toxicity) • Animal data: unknown relevance to human probability (tolerance) distribution (same with BMD) • But, in either case: “risk” can inform regulatory decisions

19. Categorical RegressionModeling Results P(Adverse or Frank Effect) is equal to 1 – P(s < 1), i.e., one minus the probability of observing a nonadverse or no effect. *Tests proportional odds assumption of common slope parameter across categories Source: Teuschler and Hertzberg, 2008, presented at SRA.

20. Reference Doses (RfDs) and All Effects Toxicity Doses (AETDs) Meta-analysis used multiple durations and species so did not need those UFs *RfDs from IRIS (accessed 2008), except for Diazinon which was derived in Teuschler et al., 1999 **Uncertainty Factors (UF) = 10 for Interspecies, 10 for Intraspecies, and 10 for subchronic to chronic (Lindane), 10 for LOAEL to NOAEL (Disulfoton) ***AETD = ED05 from categorical regression, UF of 10 for intraspecies

21. Future? • Multiple effect ED’s appear to provide a less conservative screen for risk than RfD-based ED’s • More data = better modeling • Duration influence • ‘omics data for precursors and adverse effects • Improved species conversions • Uncertainties need to be considered and discussed • Expert judgment used in identifying severity categories • Interpretation of using dose group data

22. Acknowledgements • Linda Teuschler, US EPA • Mike Dourson and Lynne Haber, Toxicology Excellence for Risk Assessment • Bill Stiteler, Syracuse Research Corporation