1. HESI ILSI Health and Environmental Sciences Institute Workshop Organizing Committee Melvin Andersen (CIIT) Matthew Bogdanffy (DuPont) James Bus (Dow) Rory Conolly (CIIT) Raymond David (Kodak) Christopher DeRosa (ATSDR) Nancy Doerrer (HESI) John Doull (University of Kansas) William Farland (EPA) Penelope Fenner-Crisp (ILSI RSI) David Gaylor (Gaylor and Associates) Dale Hattis (Clark University) Gary Kimmel (EPA) Christopher Portier (NIEHS) Bernard Schwetz (FDA) R. Woodrow Setzer, Jr. (EPA) William Slikker, Jr. (FDA) Bob Sonawane (EPA) James Swenberg (University of NC) Kendall Wallace (University of MN) Mildred Williams-Johnson (ATSDR)

2. HESI ILSI Health and Environmental Sciences Institute Publications Slikker, W., Jr., Andersen, M.E., Bogdanffy, M.S., Bus, J.S., Cohen, S.D., Conolly, R.B., David, R.M., Doerrer, N.G., Dorman, D.C., Gaylor, D.W., Hattis, D., Rogers, J.M., Setzer, R.W., Swenberg, J.A., Wallace, K., 2004a. Dose-dependent transitions in mechanisms of toxicity. Toxicol. Appl. Pharmacol. 201, 203-225. Slikker, W., Jr., Andersen, M.E., Bogdanffy, M.S., Bus, J.S., Cohen, S.D., Conolly, R.B., David, R.M., Doerrer, N.G., Dorman, D.C., Gaylor, D.W., Hattis, D., Rogers, J.M., Setzer, R.W., Swenberg, J.A., Wallace, K., 2004b. Dose-dependent transitions in mechanisms of toxicity: case studies. Toxicol. Appl. Pharmacol. 201, 226-294

3. HESI ILSI Health and Environmental Sciences Institute Examples of Dose-Dependent Transitions in Kinetic Disposition and Dynamic Expression Absorption Active or passive via GI or respiratory tract Distribution Protein binding, active transport Elimination Renal organic anion transport Chemical transformation –Activation Butadiene –Detoxification Vinyl chloride, Methylene chloride Enzyme saturationVinylidine chloride, Ethylene glycol Co-substrate depletionAcetaminophen Receptor interaction PPAR, progesterone/hydroxyflutamide Repair or reversal Vinyl chloride Altered homeostasis Propylene oxide, Formaldehyde –Induction Vinyl acetate, Manganese, Zinc –Metabolic switch –Cell proliferation

4. Mode of action CYP 2E1 catalyzed: CH 2 Cl 2  CHOHCl 2  HCOCl  CO + CO 2 formyl chloride COHb GST catalyzed: CH 2 Cl 2  GSCH 2 Cl GSCH 2 OH  HCHO chloromethylglutathione  GSCHO  HCOOH  CO 2

5. Using Bradford Hill criteria (Framework analysis) for MOA CriterionData to support Identify key eventsYes (most) – value of genomics Biological plausibilityYes based on dose-response and association with GST activity (reactive intermediate not isolated, but DNA-metabolite interaction demonstrated ) Strength, consistency, and specificity of association with tumor data Consistency demonstrated by dose-response (use of PK models); tissue localization/ of enzyme activity consistent with tumor response Dose-Response and Temporal Association Dose-response and temporal association consistent with genetic reactivity in bacteria with GST activity Alternate MOANo plausible alternative proposed ConfidenceHigh confidence that MOA reflects cellular events in animals

6. 25 30 35 40 123456789 10111213 A BC D E Clewell (1995) Jonsson and Johanson (2001) Expert Elicitation OSHA Prior OSHA Posterior Individual Values (Sweeney et al., 2004) Individual Values (Jonsson et al. (2001) Population Values Vmaxc/Km (/hr) Human data

7. Key components of the formaldehyde risk assessment (I) Regional dosimetry and effects data in the respiratory tract –DPX –Labeling index Time-course and dose-response data –Labeling index –DPX –tumors Sophisticated extrapolation tools –CFD modeling –Rat and rhesus

8. Key components of the formaldehyde risk assessment (II) Sophisticated extrapolation tools –CFD modeling –Effects data from rat and rhesus monkey –Human physiology

9. DPX submodel – simulation of rhesus monkey data

12. Uptake Patterns

13. Q1--Improvements to Exposure and Dose Monitoring--Beyond “Dose-Response ” Need to think in terms of dose-time-response relationships to inform collection or modeling of external exposure and dose information in relevant time periods. Both exposure duration and age-at-exposure and are relevant, especially for developmental effects. Sensitivity is not necessarily constant within a “window of vulnerability” (e.g. per modeling by Luecke)

14. Q3--Does modeling of adaptive responses require any changes in current regulatory testing strategies? In assessment approaches? In general it is not sufficient for a good assessment to establish the presence of “adaptive responses” at particular dose levels to assure safety. Such responses are not necessarily biologically costless or perfectly effective in preventing adverse effects in all people.

15. Q4--What type of dose-response models or approaches might be “better” used to integrate the diverse data? For characterizing variability and uncertainty? There is need to replace all the “uncertainty factor’s with distributions based on empirical data for analogous cases. See, as a preliminary effort, http://www2.clarku.edu/faculty/dhattis. This is, among other things, the only way to produce estimates of finite exposure control benefits to juxtapose with exposure control costs. http://www2.clarku.edu/faculty/dhattis In general, the more non-linear the model is at relevant exposure levels, the more important it is to make quantitative assessments of uncertainty and variability—both for judging risks to relatively sensitive segments of the population and for producing “expected value” estimates of risk and cost.

16. Relationships of Exposure and Dose to Risk Individual versus Population Risks Risk Descriptors ~Central Estimates ~High End ~Reasonable Worst Case ~Theoretical Upper Bound Estimate (TUBE)

17. Typical non-linear, “threshold”, dose-response relationship (R=Ad 3 )* * Adapted from Heitzmann and Wilson (1997) R(Response) d(Dose)

18. * Adapted from Heitzmann and Wilson (1997) R(Response) d(Dose) Additivity to Background *

19. * Adapted from Heitzmann and Wilson (1997) R(Response) d(Dose) Comparison of Slopes * RORO dOdO dhdh Linear response (high dose) ß inc (high dose) ß inc (low dose)

20. Tumor Incidence in Heterogeneous Population Monogenic Determination of Sensitivity Carcinogen Dose Spont. Max. Population A Population B Lutz, 1990

21. Tumor Incidence in Heterogeneous Population Polygenic Determination of Sensitivity Carcinogen Dose Spont. Max. Lutz, 1990

22. Tumor Incidence in Heterogeneous Population Sensitivity Governed by Multiple Genes + Modulation by Lifestyle Carcinogen Dose Spont. Max. Lutz, 1990

23. Tumor Incidence in Heterogeneous Population Lutz, 1990 Sensitivity Governed by Multiple Genes + Modulation by Lifestyle Polygenic Determination of Sensitivity Monogenic Determination of Sensitivity

24. Food and Drug Administration Dose-Dependent Transitions in Mechanisms of Toxicity: Impact of Testing Strategies and Risk Assessment Approaches Society of Toxicology, March 7, 2005 David Jacobson-Kram, Ph.D., DABT Center for Drug Evaluation and Research Office of New Drugs

25. Food and Drug Administration Center for Drug Evaluation and Research, FDA  CDER generally does not perform quantitative risk assessment except for drug impurities and degradation products  CDER generally has rigorous exposure and metabolism data in humans and animals, often at comparable doses  Safety studies for specific human populations can be modeled in parallel animal studies, eg. Juvenile animal tox studies, geriatric possible but not practical

26. Food and Drug Administration Challenges for CDER  Detection of rare adverse events (eg Vioxx)  Development of animal models capable of predicting rare AEs  Animals engineered with rare genetic polymorphisms  Animal models compromised because of other exposures, pharmaceutical, life style or environment