Dose-Response Analysis in Environmental Risk Assessment: Where are we, and where are we going

1. Dose-Response Analysis in Environmental Risk Assessment: Where are we, and where are we going? Weihsueh Chiu, Ph.D. National Center for Environmental Assessment U.S. Environmental Protection Agency Presentation for Society for Risk Analysis Dose-Response Specialty Group Teleseminar June 3, 2008 Disclaimer: The views expressed in this presentation are those of the author, and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

2. Acknowledgements Contributors: Jane Caldwell, U.S. EPA Chao Chen, U.S. EPA Kenny Crump, (formerly) Environ Anthony Fristachi, Battelle Kate Guyton, U.S. EPA Karen Hogan, U.S. EPA Jennifer Jinot, U.S. EPA Bob Lordo, Battelle John Lipscomb, U.S. EPA Leonid Kopylev, U.S. EPA Cheryl Siegel Scott, U.S. EPA Ravi Subramaniam, U.S. EPA Paul White, U.S. EPA U.S. EPA Management Support: David Bussard Peter Preuss

3. Outline Background and Key Questions Current and developing practices Model-independent approaches Integrating Pharmacokinetics Integrating Mode-of-Action (MOA) Future Directions: Beyond �Uncertainty� Factors

4. Environmental risk assessment (mostly) estimates risks at exposures (often well) below the range of data

5. Low-dose extrapolation has both biological and statistical components Population dose-response combines Individual dose-response [deterministic or stochastic] Inter-individual variability In a decision-making context, also should include uncertainty (not shown)

6. Key Question: To understand effects at �low dose� over multiple levels of organization

7. A Simple Conceptual Framework

8. Outline Background and Key Questions Current and developing practices Model-independent approaches Integrating Pharmacokinetics Integrating Mode-of-Action (MOA) Future Directions: Beyond �Uncertainty� Factors

9. Model-independent approaches (EPA standard practice) �Linear� Used for cancer endpoints if Insufficient knowledge of MOA MOA indicates expectation of linearity �Non-linear� Used for non-cancer endpoints Used for cancer endpoints if MOA indicates non-linearity

10. Linear extrapolation: Implicit Assumptions

11. Non-linear extrapolation: Implicit Assumptions

12. Outline Background and Key Questions Current and developing practices Model-independent approaches Integrating Pharmacokinetics Integrating Mode-of-Action (MOA) Future Directions: Beyond �Uncertainty� Factors

13. Addressing Pharmacokinetics in Dose-Response (step 1)

14. Addressing Pharmacokinetics in Dose-Response (step 2)

15. Addressing Pharmacokinetics in Dose-Response (step 3)

16. Carrying over to (rodent) dose-response the �usual� way Use a �central estimate� MCMC is �fancy� way of getting a point estimate Select mean, geometric mean, median, mode, etc. Proceed deterministically. �Tried and true� but does not account for uncertainty and variability. Examples: Rhomberg 2000 (TCE), David et al. (2005)

17. Using �Central parameters� �Central parameters� -- taking an estimate from the marginal distribution of each parameter, and then running through the model. External-to-internal dose relationship reflects a single set of parameters. Parameter correlation not accounted for due to use of marginal distributions.

18. Using �Central predictions� �Central predictions� -- -- generating model predictions from the full posterior distribution, and then taking the distribution. Reflects distribution of external-to-internal dose relationships (not any particular one). Parameter correlation accounted for by use of full multi-variate distribution. No systematic comparison of two approaches (compare to what?)

19. Proposed probabilistic method for carrying over to (rodent) dose-response Use full distribution of predictions Do not run BMDS 1000+ times. Dose metric is unobserved, so treat as a latent variable MCMC PBPK outputs naturally lend themselves to bootstrap methods for calculating likelihood and confidence intervals Result is a distribution of BMDs combining Binomial (statistical) variance [usual output of BMDS] PBPK uncertainty/variability. Software still being tested!!! Will be interesting to compare with �usual� method...

20. Issues with current approaches to PK

21. Outline Background and Key Questions Current and developing practices Model-independent approaches Integrating Pharmacokinetics Integrating Mode-of-Action (MOA) Future Directions: Beyond �Uncertainty� Factors

22. What is a Mode-of-Action? (U.S. EPA perspective) MOA is a sequence key events and processes: Starting from interaction of agent with a cell ? Operational and anatomical changes ? Cancer formation A �key event� is: Empirically observable A precursor step Necessary element of carcinogenic MOA Or is a biologically-based marker for such an element Contrasts with a �mechanism of action� which Implies a more detailed understanding and description of events Is often at the molecular level

23. How does MOA information currently inform Dose-Response? Human relevance (binary decision). Choice of linear vs. non-linear extrapolation (binary decision) For non-linear extrapolation, choice of precursor endpoint In the case of use of PBPK model, choice of dose metric (categorical decision). In rare cases, development of a biologically-based dose-response model.

24. Human relevance (qualitative) or human sensitivity (quantitative)? �Toxicokinetic and toxicodynamic factors� Often invoked to dismiss human relevance of a particular MOA. Lack of transparency in translating quantitative measures into a binary decision. Should be a �dose-response,� not a �hazard identification� issue �Any information suggesting quantitative differences between animals and humans is flagged for consideration in dose-response assessment.� � U.S. EPA Cancer Guidelines (2005) Key issues when considering MOA in dose-response Extent of inter- and intra-species variability Quantitative relationships between key events and the toxic effect

25. �Key events� are not necessarily all rate-limiting �Rate-limiting� really means a �sensitive� modulator of the result. Related to concept of biomarkers (e.g., BP, HDL, LDL, CRP,�). In context of a model, can be identified via sensitivity analysis. Identifying causal events is not enough � need additional data to identify events/processes that are �rate-limiting� to understand: Precursor effects most suitable for dose-response assessment. Shape of the dose-response-curve (what is �rate-limiting� may change across the dose-response curve, e.g., due to saturation at various points). Impact of different background rates (e.g., inter-individual, inter-strain, inter-species).

26. Role of biologically-based dose-response models Still require data and analysis to address issues of variability in the tail biological relationships at low dose overall uncertainty Two-stage clonal growth (MVK) models for carcinogenesis: Crump (1994) showed examples where low-dose extrapolation of such models could change by >105 with 1% change in initiated cell birth/death rates (a or b) Recently borne out in sensitivity/uncertainty analysis for formaldehyde (Crump et al. 2008, submitted):  Changes to I- and N-cell division rate That fit in vivo cell replication and tumor data equally well Lead to 106-fold range in low-dose risk in rats

27. Issues with current approaches to integrating MOA in Dose-Response assessment

28. Outline Background and Key Questions Current and developing practices Model-independent approaches Integrating Pharmacokinetics Integrating Mode-of-Action (MOA) Future Directions: Beyond �Uncertainty� Factors

29. Conceptual Framework: A Path to Go Beyond �Uncertainty� Factors

30. Mathematical Formulation

31. Disaggregating Population Dose-Response

32. Can do with already with PBPK models using Bayesian methods Given individual data can disaggregate Uncertainty in population parameters Variability in population, given those parameters.

33. Implementation Issues PBPK provides biological basis for inter- and intra-species model/parameter extrapolation, but limited by available data. Usually lack individual data in both rodents and humans. What individual data exists may not representative of the general population. Usually lack PK data on the particular subjects in the toxicity studies. Lack of an analogous mathematical framework for PD/MOA Begin with an empirical approach, per clinical biomarkers? Complicated by need to address multiple MOAs/pathways of toxicity. Low-dose extrapolation still involves assumptions, but perhaps can be specified with more biological and statistical basis.

34. Summary A unified conceptual framework can Integrate different contributions to dose-response � biology, stochasticity, heterogeneity, and uncertainty Make assumptions in current practices explicit Identify needs for progress Progress is being made integrating PK information through PBPK models and Bayesian methods. Areas for improvement: Data on inter-individual variability (humans and rodents) Disentangling sources of variance Carry-over of uncertainty and variability to dose-response assessment Integration of mechanistic data in dose-response still rather primitive, with a need to: Identify not only causal, but �rate-limiting� events that are quantitatively predictive of dose-response across strains/species/chemicals. Understand generic properties of biological systems at low dose (since BBDR may not be helpful for extrapolation). Routinely examine the range of possible alternatives.