08 rita schoeny


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  • Policy for any endpoint.
  • Examples of setting POD based on precursors. Discussion about DNA adducts.
  • Can also do a MOE.
  • Can also do a MOE.
  • USEPA - max factor 10,000 (RfD), 3000 RfC Health Canada - max factor 10,000 Uses Renwick scheme for apportioning
  • Gaylor and Kodell (2000) combined unc and var into single dist. – target range 955 – 99% certainly. When only one UF used , default of 10 underprotective by about 3. When 3 or 4, UF of 1000-3000 overprotective by about 3. General UF D is not used for chemicals with clear portal of entry effects – e.g ammonia, HCl, acrolein. Baird and Hattis independently decided that RfDs are about risk = 1/100,000.
  • Risk assessment should be viewed as a method for evaluating the relative merits of various options for managing risk rather than as an end in itself “ Science and Decisions: Advancing Risk Assessment”
  • Lots of data for phosgene production, cell death and proliferation In rodent bioassay studies, Chlorofom has been shown to result in liver and kidney tumors. The postulated MOA to explain these tumor responses involves bioactivation through CYP2E1 oxidative metabolism as the rate limiting step in chloroform’s mode of action. Metabolism by this pathway produces cytotoxic metabolite within the target organ, in particular phosgene that injures and kills cells, cytotoxicity is followed by regenerative cell proliferation, and it cytotoxicity/regenerative proliferation is sustained, eventually tumor development. So,these are the key sequence of events that will be considered with respect to chloroform;s included tumorigenesis in the rodent kidney and liver.
  • Metabolism to phosphoramide mustard (PAM) DNA damage (e.g.,DNA adduct formation) Induction of multiple adverse genetic events (mutation and/or chromosomal aberrations) and/or cytotoxicity Regenerative proliferation (  cell proliferation, organ weight, hyperplasia) Bladder tumors
  • 08 rita schoeny

    1. 1. Brazilian Benzene Seminar Brasilia, Brazil December 6, 2012Low Dose Extrapolation forCarcinogens – U.S. EPA PerspectiveRita Schoeny, Ph.D.Senior Science Advisor,Office of Science Policy, Office of Research and DevelopmentU.S. EPA 1
    2. 2. Disclaimer The views expressed in this presentation are those do the author and do not represent the policy of the U.S. EPA. I am still a Federal employee 2
    3. 3. Cancer Guidelines: What’s Differentfrom 1986? Analyze data before invoking default options. Mode of action is key in decisions Weight-of-evidence narrative replaces the previous “A-B-C-D-E” classification scheme. Two step dose response assessment  Model in observed range  Extrapolate from point of departure Consider linear and non-linear extrapolation Address differential risks to children12/10/12 3
    4. 4. High dose data – what do theytell us? Response Dose 4
    5. 5. PossibilitiesResponse Dose Interspecies 5
    6. 6. Two Step Approach  Model data in the observed range – to a point of Response (Tumor or Nontumor Data) e) os nD i to e Lim x Environmental nc Empirical Exposure Levels of Interest Co nf ide ma te) Range of Observation departure sti % 95 E st al e tr ow en (L Extrapolate (C x x  below the POD 10% ault Range of ar Def Line Extrapolation x x 0% LED10 ED10 UF x NOAEL Nonlinear Default Dose x LOAEL 6
    7. 7. Extend the Observed Range UsingPrecursor Data Objective of choosing POD is to set it as close to environmental levels as Supported by data Appropriate to model Cancer Guidelines say precursor data are useful for this Must have MOA Section 3.2.2 7
    8. 8. Mode of Action: Bladder Tumors, Key Events Cytotoxicity and Regenerative Hyperplasia DMAIII Measurable Key Metabolite Events in Target SEM Tissue Urothelial Toxicity BrdUSustained BrdU Labeling Labeling Regenerative Proliferation Hyperplasia Tumor
    9. 9. Cacodylic Acid: BMDs and BMDLs Feeding Drinking water 10% 1% 10% 1%Endpoint Duration Duration BMD BMDL BMD BMDL BMD BMDL BMD BMDL (mg/kg/d) (mg/kg/d) (mg/kg/d) (mg/kg/d) (mg/kg/d) (mg/kg/d) (mg/kg/d) (mg/kg/d) 104 104Tumor weeks 7.74 5.96 6.80 2.22 weeks 1.92 1.21 0.88 0.14 10 weeks 1.36 1.04 0.42 0.32 104Hyperplasia weeks 1.63 1.04 0.74 0.14 104 weeks 1.97 1.61 0.93 0.66BrdU 10 weeks 0.65 0.29 0.54 0.07 Not determined. Available data not suitable for modeling.labeling 3 weeks 0.68 0.18 0.31 0.02Cytotoxicity No reliable dose-response data available 10 weeks 0.02 0.008 0.002 0.0007 9
    10. 10. What Else Could Be Used? Pre-neoplastic lesions (e.g. altered enzyme foci) Mutations? Chromosomal changes? DNA damage?
    11. 11. So Many Models! Response (Tumor or Nontumor Data) ) se Do o n it m Li en ce x Environmental Empirical e) id nf at Exposure Levels Co Range of m sti of Interest % Observation 95 lE st ra e nt ow e (L (C x x 10% ult efa Range of earD Lin Extrapolation x x 0% LED10 ED10 UF x NOAEL Nonlinear Default Dose x LOAEL
    12. 12. Linear or Non-linear? Two Step Dose Response Process Response (Tumor or Nontumor Data) e) os D on it Another Question First m Li ce en x e) Environmental fid Empirical at on im Exposure Levels C Range of st % 95 lE of Interest Observation st ra e nt o w e (L (C x x 10% lt fa u De Range of e ar Lin Extrapolation x x 0% LED10 ED10 UF x NOAEL Nonlinear Default Dose x LOAEL 12
    13. 13. Is There Something Better? Analyze the available data Is there too much uncertainty or is Invoke a critical information lacking? Y default option* N Conduct risk assessment 13
    14. 14. Source Exposure PBPK Tissue dose BBDR Mode of actionBiologically BasedDose ResponseModel Response 14
    15. 15. BBDR – Based on Knowledge of KeyEvents dosimetry Key event B1 Key event B2 Key event A1 Mode of Action Assessment endpoint Key event A2 Key event A3 15
    16. 16. Applied M ulDose of Phenobarbital tipl ed and ose (PBPK) tim -resp e-c ons our ses es 16
    17. 17. Reality check (I) There are always data gaps Arsenic Formaldehyde TCDD phenobarbital A BBDR model is a description of biological structure with embedded empirical linkages that cover the parts of the overall exposure- dose-response linkage for which data are missing. 17
    18. 18. Reality check (II) As research improves our understanding of the overall exposure-dose-response linkage, the sophistication of the description of the mode of action increases. Corresponding iteration of the BBDR model leads to more accurate predictions of dose- response and time-course behaviors. Will always be some degree of residual uncertainty. But is the default more uncertain? 18
    19. 19. And if no BBDR? Two Step Dose Response Process Response (Tumor or Nontumor Data) e) os D on it m Li Linear or Non-linear ce en x e) Environmental fid Empirical at on im Exposure Levels C Range of st % 95 lE of Interest Observation st ra e nt o w e (L (C x x 10% lt fa u De Range of e ar Lin Extrapolation x x 0% LED10 ED10 UF x NOAEL Nonlinear Default Dose x LOAEL 19
    20. 20. Mutagenesis Paradigm Mutagens/Spontaneous DNA Damaged DNA Damage Sensing Cellular Response DNA Repair Incorrect No repair Repair/ReplicationRepaired DNA Mutant DNA Dead Cell Demarini 70 20
    21. 21. Threshold? Demonstrated  Based on MOA By inspection of the Mutagenic MOA dose response has been linear curve But should consider Fitting models and biology of mutation checking goodness of fit Statistical tests for one model or another Does mutagenic MOA mean low dose linear? BBDR should be first choice 21
    22. 22. In vitro Mutation Dose-Response: MMS & MNU Doak et al., 2007 HPRT MF MMS MMS NOEL = 1 µg/ml MNU MNU No NOEL 2011 EMS Annual Meeting Pottenger 22
    23. 23. In vitro Mutation Dose-Response: ENU Johnson et al., 2009 HPRT MFENU threshold dose-response (Lutz & Lutz model) Slide from Pottenger 23
    24. 24. RfV = POD / UF UFs Health IPCS RIVM ATSDR EPA CanadaInterhuman 10 10 10 10 1-10 (3.16 X 3.16Animal to 10 10 10 10 1-10human (2.5 X 4)Subchronic 10 NA 1-10to chronic 1-100 1- 100LOAEL to 10 10 1-10NOAELIncomplete NA NA 1-10database 24
    25. 25.  Example: Inhalation, RfCs – use RfC methodology guidance (U.S. EPA 1994) in determining interspecies UF. (Generally use UF = 3 when dosimetric adjustment of animal data). Example: Methylmercury PK UF of 3 (based on analyses of interindividual variability) and default PD UF of 3 U.S. EPA Risk Assessment Forum working on Guidance for Data-derived Extrapolation Factors. Divides UFA into toxicokinetic and toxicodynamic components Same for UFH 25
    26. 26. Take Home Message MOA informs dose response assessment DNA damage is not mutation Mutation is not cancer Some genotoxicity endpoints may be reasonable biomarkers May be useful for extending the lower end of dose response curve Useful in MOA 26
    27. 27. 27
    28. 28. NRC 2009 Silver Book 1 Framing questions and design step. Risk Assessment is not an end in itself. Characterize uncertainty and variability Default before data? These are strictly my own opinions 28
    29. 29. NRC 2009 Silver Book 2 Dose response Additivity to background is a major theme ○ How differentiate between exogenous and endogenous damage? ○ DNA adducts biomarkers, could have major role ○ Does this mean linear all the time? EPA has expressed preference for BBDR ○ Low dose data for adduct formation ○ Low dose data for mutation ○ Low dose data for other markers Again my own opinions 29
    30. 30. Breaking Down the Dichotomy Cancer Non-Cancer  Threshold  Non-Threshold  Reversible  Irreversible  Safety Value  Risk value  RfD/RfC  Slope Factor  ADI/TDI  Unit Risk  MRL  Risk-Specific Dose 30
    31. 31. Postulated Mode Of Action ve CYP2E1 etabolism Chloroform MOx idati Ph o s g e ne Sustained Toxicity Regenerative Cell Proliferation Key Events Tumor Development 31
    32. 32. Postulated Mode Of Action Metabolism CP Cyt p 450s Phosp h Acrole oramide m in ustard , PAM DNA damageTumorDevelopment Mutations 32