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Exploring Carcinogen Risk Analysis Through Benzene

Exploring Carcinogen Risk Analysis Through Benzene. Image from Matthew J. Dowd Department of Medicinal Chemistry Virginia Commonwealth University. Objective. Use benzene as a case for exploring Toxicology Epidemiology Uncertainty Regulatory Science. Toolbox Building.

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Exploring Carcinogen Risk Analysis Through Benzene

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  1. Exploring Carcinogen Risk Analysis Through Benzene Image from Matthew J. Dowd Department of Medicinal Chemistry Virginia Commonwealth University  2002 David M. Hassenzahl

  2. Objective • Use benzene as a case for exploring • Toxicology • Epidemiology • Uncertainty • Regulatory Science  2002 David M. Hassenzahl

  3. Toolbox Building • Likelihood Maximization • Curve fitting • Bootstrapping • Z-Scores • Relative Risk • Dose-Response extrapolation  2002 David M. Hassenzahl

  4. Overview of benzene • Fairly common hydrocarbon • Manufacturing • Petroleum products • Strongly suspected human carcinogen • Animal assays • Many epidemiological studies • Leukemia as important endpoint  2002 David M. Hassenzahl

  5. Benzene structure Image from Matthew J. Dowd Department of Medicinal Chemistry Virginia Commonwealth University  2002 David M. Hassenzahl

  6. Benzene Data in Should We Risk It? • Toxicological Data, p. 175 et seq. • Epidemiological Data p 211 – 216 • But many other data sets • Other toxicological data (rare) • Chinese workers • Turkish workers  2002 David M. Hassenzahl

  7. Toxicology Data Set  2002 David M. Hassenzahl Crump and Allen 1984

  8. What are risks from benzene? • Risk as potency times exposure • How do we determine potency? • Extrapolate from animal data? • Extrapolate from epidemiological data? • How wrong will we be? • What are “real” exposures? • What are effects at these levels?  2002 David M. Hassenzahl

  9. Toxicology • Paracelsus “the dose makes the poison” • Regulatory assumptions! • This is not Dr. Gerstenberger’s Toxicology!  2002 David M. Hassenzahl

  10. Reading • SWRI Chapter 5 • US EPA Proposed guidelines (US EPA 1996) • Cox 1996  2002 David M. Hassenzahl

  11. General idea • Applied doses • Greater specificity about exposure than epidemiology • Observed effects • Artificial control of exposure  2002 David M. Hassenzahl

  12. Physiologically Based Pharmacokinetics • PBPK • Investigate flows of materials through bodies • System dynamics models • More on these in exposure lecture  2002 David M. Hassenzahl

  13. Studies • Animals • Rarely humans • Parts • Cell • tissue  2002 David M. Hassenzahl

  14. Effects • Chronic • cancer fatality • increasing interest in other issues • lead and intelligence in children. • Acute • Reversible • Irreversible  2002 David M. Hassenzahl

  15. Crump and Allen Benzene data set • Animals at various concentrations • Four data points • “Designer” mice  2002 David M. Hassenzahl

  16. Relevance to Humans • How to get from • high level, lifetime studies of animals to • anticipated low dose effects in humans?  2002 David M. Hassenzahl

  17. Questions about benzene • Is benzene a mouse carcinogen? • Is benzene a human carcinogen? • If so, how potent?  2002 David M. Hassenzahl

  18. Benzene data set I Crump and Allen data set (Crump and Allen 1984) Note: the actual doses are not stated correctly here. See “notes for more information  2002 David M. Hassenzahl

  19. Benzene data set II 1.0 0.8 0.6 P(cancer) 0.4 0.2 0 0 25 50 75 100 Dose (mg/kg/day) Crump and Allen data set.  2002 David M. Hassenzahl

  20. Uncertainty Pervades • Often understated • Creates (or at least prolongs) conflict • Think as we go! (Part of Homework PS 2)  2002 David M. Hassenzahl

  21. Animal Test Issues • Interspecific comparison • Statistical uncertainty • Heterogeneity • Extrapolation • Dose Metric  2002 David M. Hassenzahl

  22. Interspecific comparison • Mouse-human • Metabolism as a function of body weight • Dosehuman = sf  Dosemouse • sf = (BWhuman/BWmouse)1-b • b is empirically derived as 0.75a a. See SWRI page 177.  2002 David M. Hassenzahl

  23. Interspecific comparison • Lifetime of human = lifetime mouse? • Mice age 30 days per human day • Total mouse lifetime is much shorter • Analogous organs or processes? • Do mice have cancer points we do not? • Do we have cancer points mice do not? a. See SWRI page 177.  2002 David M. Hassenzahl

  24. Interspecific comparison 1. Hallenbeck, 1993 2. Finley et al., 1994  2002 David M. Hassenzahl

  25. Interspecific comparison sf = (BWhuman/BWmouse)1-b sf = (70/0.03)0.25 = 7.0 Dosehuman = 7.0  Dosemouse  2002 David M. Hassenzahl

  26. Interspecific comparison Crump and Allen data set, converted to humans  2002 David M. Hassenzahl

  27. Animal Test Issues • Interspecies comparison • Statistical uncertainty • Heterogeneity • Extrapolation • Dose Metric  2002 David M. Hassenzahl

  28. Binomial Distribution • 50 genetically “identical” mice…binomial distribution? • Can use this to generate “likelihood function” to compare the likelihood that any given probability is  2002 David M. Hassenzahl

  29. Likelihood Maximization • More appropriate than Least Squares when you know something about likelihoods • “Bootstrapping” method needed • We will work through likelihood maximization  2002 David M. Hassenzahl

  30. Statistical Uncertainty Can calculate standard deviation using the binomial Recall that two standard deviations to either side represents a 95% confidence interval, and...  2002 David M. Hassenzahl

  31. Statistical Uncertainty 1.0 0.8 0.6 P(cancer) 0.4 0.2 0 0 175 350 525 700 Human Dose (mg/kg/day) Crump and Allen data set, applied to humans  2002 David M. Hassenzahl

  32. Animal Test Issues • Interspecies comparison • Statistical uncertainty • Heterogeneity • Extrapolation • Dose Metric  2002 David M. Hassenzahl

  33. Heterogeneity • Epidemiology and toxicology • Genetically identical mice compared to diverse humans • Predictable versus unpredictable susceptibility • Male and female differences (observed cancer rates are different)  2002 David M. Hassenzahl

  34. Heterogeneity • Genetic diversity among humans • Early insights into cancer mechanism: subpopulation born with one of two “steps” competed • Variability as a function of age  2002 David M. Hassenzahl

  35. Animal Test Issues • Interspecies comparison • Statistical uncertainty • Heterogeneity • Extrapolation • Dose Metric  2002 David M. Hassenzahl

  36. Extrapolation • Theoretical or “Mechanistic” models: • one-hit • two-hit • two-stage • Empirical • Cox “data-driven, model free curve fitting” • EPA Proposed Guidelines  2002 David M. Hassenzahl

  37. Overestimation Tautological effects Thresholds Hormesis, or “Vitamin” effect Underestimation Saturation Synergistic effects Susceptibility Omission Extrapolation Concerns  2002 David M. Hassenzahl

  38.  2002 David M. Hassenzahl

  39. After EPA (1996)  2002 David M. Hassenzahl

  40. Statistical Uncertainty 1.0 0.8 0.6 P(cancer) 0.4 0.2 0 0 175 350 525 700 Human Dose (mg/kg/day) Crump and Allen data set, applied to humans  2002 David M. Hassenzahl

  41. 1.0 LED(10) = 100 mgb/kg/day 0.8 0.6 P(cancer) 0.4 0.2 0 0 175 350 525 700 Human Dose (mg/kg/day)  2002 David M. Hassenzahl

  42. Extrapolation If LED(10) = 100 mg/kg/day, then LED(10-6) = 100  10-6 / 0.1 = 1  10-4 mg/kg/day  2002 David M. Hassenzahl

  43. Animal Test Issues • Interspecies comparison • Statistical uncertainty • Heterogeneity • Extrapolation • Dose Metric  2002 David M. Hassenzahl

  44. Dose Metric • Assumption: exposure is irrelevant to effect • Area under the curve/expected value. • Lifetime dose leads to average daily dose. • Particularly problematic if there are threshold effects or extreme effects  2002 David M. Hassenzahl

  45. Risk to Humans? • Lifetime cancer risk • 40 hours per week • 50 weeks per year • 30 years • Average 10 ppm(v) exposure?  2002 David M. Hassenzahl

  46. Calculate Risk • 10ml benzene/liter air • 0.313 ml/mg • 20m3 air / day • 1000 liters/ m3 • 70kg person  2002 David M. Hassenzahl

  47. Cancer Risk • Lifetime Cancer Probability is a function of Dose and Potency • Assume cumulative dose • Use Daily Dose per kg body weight, averaged over lifetime • Potency usually given as q* • Additional risk per unit dose  2002 David M. Hassenzahl

  48. Cancer Risk: Exposure Term  2002 David M. Hassenzahl

  49. Computed Exposure Terms  2002 David M. Hassenzahl

  50. Computed Exposure Terms  2002 David M. Hassenzahl

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