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Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic MOA to Predict Tumor Incidence

Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic MOA to Predict Tumor Incidence. Reeder Sams, Hisham El- Masri , Office of Research & Development USEPA.

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Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic MOA to Predict Tumor Incidence

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  1. Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic MOA to Predict Tumor Incidence Reeder Sams, Hisham El-Masri, Office of Research & Development USEPA Disclaimer: This presentation has been reviewed by the EPA. The views expressed in this presentation are those of the authors and do not necessarily reflect EPA policy

  2. Presentation Outline • Purpose • Biologically Based Dose Response Models • Human Health Risk Assessment Needs • Need for BBDR Models • Possible Avenues to Test Hypothesized MOAs • Development of a BBDR Model for a Cytotoxic MOA • Methods and Modeling (H El-Masri) • Results (H El-Masri) • Conclusions and Steps Forward (El-Masri &Sams)

  3. Purpose for DevelopingBBDR Models? • Help organize data and development of hypotheses • Allow more robust testing of hypotheses and assumptions based on quantitative information (test MOA hypothesis) • Inform shape of the dose response • Extrapolate results and make predictions outside experimental conditions • Assess relevance of animal model for human risk assessment

  4. MOA Established? No Quantitative Dose-response Assessment Yes • 1. Fit data in observable range • 2. Linear extrapolation from POD BBDR model? Yes Use model No Yes, nonlinear MOA informs low-dose extrapolation? No RfD/RfC or MOE Yes, linear (including mutagenic MOA)

  5. MOA data: Challenges for BBDR Models • Having sufficient information on MOA • Interpretation of data when multiple modes of action are operative • Application of well-established MOA from one chemical to another for which data are limited • Determination of MOA data that are not relevant to humans • Lack of dose-response information • Separating chemical-induced events from natural progression of cancer

  6. Mechanistically Based Models • Bottom Line • A BBDR is only as accurate as the sum of its individual components. Uncertainty in key assumptions (e.g. MOA), parameters, variables, etc…. must be characterized in multiple predictive outputs.

  7. Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic Mode of Action to Predict Tumor Incidence Nicholas S. Luke*, Reeder Sams II†, Michael J. DeVito‡, Rory B. Conolly§ and Hisham A. El-Masri§,1 1To whom correspondence should be addressed at U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Mail Drop B143-01, Research Triangle Park, NC 27711. Fax: (919) 541-4284. E-mail: el-masri.hisham@epa.gov.

  8. Hypothesized MOA: Cytotoxicity→ Cellular Regeneration • Multiple compounds proposed to induce tumors via cytotoxicity leading to cellular regeneration: • Chloroform • Carbon tetrachloride • 1,4-Dioxane • Dimethylarsinic acid • Furan • N,N-Dimethylformamide • Multiple MOAs may be operative

  9. Parent or Metabolite Hypothesized MOA: Cytotoxicity→ Cellular Regeneration Cytotoxicity Identifiable (Measurable) Key Events Sustained Regenerative Proliferation Hyperplasia Clonal expansion Tumors (e.g. liver, kidney)

  10. Choice of Pollutants for a Test Case BBDR Test case MOA based upon carcinogenicity endpoint • Considerations for toxicodyamic data • Tumor data is available for numerous pollutants hypothesized to result from a cytotoxic MOA • Is there data for the hypothesized key events? • Limit other variables that may decrease accuracy of a BBDR model • Route of exposure • Site concordance • Rodent species, strain, sex • Laboratory • Considerations for toxicokinetic data

  11. Key Events (hypothesized) Availability of Chemical-Specific Data

  12. Model Structure

  13. Data Needs to Populate Model • Pharmacokinetic Data • PBPK models for chloroform and carbon tetrachloride available in literature • Use of available data to develop a PBPK model for DMF • Labeling Index Data • Multiple times • Dose Response • Measure of Cytotoxicity • SDH, ALT, etc. • Tumor Incidence Data • 2 yr bioassay • time to tumor (or intermediate time points)

  14. Cytotoxicity Model

  15. Cytotoxicity Model Parameters

  16. SDH and kdam From Lundberg et al., 1986 Table: Comparison of SDH and the parameter

  17. Cytotoxicity Results

  18. Cytotoxicity Results

  19. Clonal Growth Model • Three cell populations: Normal, Initiated, Malignant • Cells move to next population via mutation • Normal and Initiated populations undergo division and death/differentiation • Malignant cells will lead to tumor after a delay.

  20. Initiated Cell Growth Initiated Cells Death disadvantage OR Initiated Cells Growth advantage

  21. Tumor Incidences Simulations

  22. Clonal Growth

  23. Cellular Proliferation and Tumor Incidences

  24. Conclusions • Quantitative modeling was useful to assess a generalized MOA across chemicals: • CHCL3,CCL4 and DMF cytotoxic MOA • SDH or other measures of cytotoxicity • Labeling Indices • Quantitative modeling indicates • Possibility of other key events • Possibility of multiple MOAs • Identified data needs • Time to tumor data • “Initiated” cells: death and proliferation rates

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