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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 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
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)
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
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)
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
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.
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.
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
Parent or Metabolite Hypothesized MOA: Cytotoxicity→ Cellular Regeneration Cytotoxicity Identifiable (Measurable) Key Events Sustained Regenerative Proliferation Hyperplasia Clonal expansion Tumors (e.g. liver, kidney)
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
Key Events (hypothesized) Availability of Chemical-Specific Data
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)
SDH and kdam From Lundberg et al., 1986 Table: Comparison of SDH and the parameter
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.
Initiated Cell Growth Initiated Cells Death disadvantage OR Initiated Cells Growth advantage
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