A Biologically-Based Model for Low-Dose Extrapolation of Cancer Risk from Ionizing Radiation

1. A Biologically-Based Model for Low-Dose Extrapolation of Cancer Risk from Ionizing Radiation Doug Crawford-Brown School of Public Health Director, Carolina Environmental Program

2. What’s our task? Extrapolate downwards in dose and dose-rate

3. Having trouble finding the right functional form? No problem. We have in vitro studies to show us that.

4. Cells also die from radiation, so we need to account for that

5. Just use these to create a phenomenological model PTSC(D) = αD + βD2 S(D) = e-kD PT(D) = (αD + βD2) x e-kD

6. So what’s the big deal? Just fit it! in vitro Kd “Fitted” Kd

7. Why does it not work?? • Model mis-formulation even at lower level of biological organization • New processes appear at the new level of biological organization (emergent properties) • Processes disappear at the new level of biological organization • Incorrect equations governing processes • Parameter values differ at the new level of biological organization

8. Why does it not work (continued)?? • Dose distributions different at the new level of biological organization • Computational problems somewhere • Anatomy, physiology and/or morphometry differ at the new level of biological organization • Errors in the data provided (exposures, transformation frequency, probability of cancer, etc)

9. Then let’s get a generic modeling framework Exposure conditions Environmental conditions Deposition and clearance Probability of effect Dose- response Dose distribution

10. The environmental, exposure and dosimetry conditions • In vitro doses are uniform as given by the authors, and at the dose-rates provided • Rat exposures are from Battelle and Monchaux et al studies, under the conditions indicated by the authors • Human exposures are from the uranium miner studies in Canada • Rat and human dosimetry models using Weibel bifurcating morphology • Uses mean bronchial dose in TB region, or dose distributions throughout the TB region and depth in the epithelium

11. The multi-stage nature of cancer Initiation Promotion Progression Cell Death

12. The state vector model

13. k_d k_mi 3 k_23 k_34 The Mathematical Development of the SVM • Let Ni(t) be the number of cell in State i at any time t: • Vector represents the state of the • cellular community where • The total cells in all states is denoted: • Transformation frequency is calculated by: • Six Differential equations describe the movement of cells through states • Example:

14. And now for some parameter values: chromosomal aberrations

15. Rate constants for repair rates and transformation rate constants.

16. Inactivation rate constants

17. D D D I D D D Then for promotion: removal of contact inhibition Showing: Complete removal of cell-cell contact inhibition

18. So, does this work for x-rays? The in-vitro data on transformation Pooled data from many experiments for the transformation rate for single () and split (O) doses of X-rays (Miller et al. 1979)

19. Model fit to in vitro data

20. Sensitivity to Pci value

21. Low dose behavior (no adaptive response)

22. Low dose behavior (with adaptive response)

23. But does it work for in vivo exposures to high LET radiation with very inhomogeneous patterns of irradiation? Helpful scientific picture from EPA web site

24. The rat data (Battelle in circles and Monchaux et al in triangles)

25. So, does this work for rats?? Well, not so much……..

26. With dose variability PC(D) = ∫ PDF(D) * (αD + βD2) * e-kD dD

27. Incorporating dose variability GSD = 1, 5, 10 Empirically: lognormal with GSD = 8

28. Deterministic or stochastic?

29. Deterministic or stochastic?

30. Back to the issue of differentiation, Rd/s in the kinetics model

31. Changes in Rd/s 1, 2, 4

32. Fits to mining data With depth-dose information Without depth-dose information

33. Inverting the dose-rate effect

34. Conclusions (continued) • Good fit to the in vitro data, even at low doses if adaptive response is included (IF you believe the low-dose data!) • Reasonable fit to rat and human data at low to moderate doses, but only with dose variability folded in • Best fit with Rd/s included to account for differentiation pattern in vivo

35. Conclusions • Under-predicts human epidemiological data at higher levels of exposure • Under-predicts rat data at higher levels of exposure, especially for Battelle data (not as bad for the Monchaux et al data)

36. Why did it not work?? • Model mis-formulation even at lower level of biological organization: compensating errors that only became evident at higher levels of biological organization • New processes appear at the new level of biological organization: clusters of transformed cells needed to escape removal by the immune system • Processes disappear at the new level of biological organization: cell lines too close to immortalization to be valid at higher levels • Incorrect equations governing processes: dose-response model assumes independence of steps • Parameter values differ at the new level of biological organization: not true for cell-killing, but may be true for repair processes

37. Why does it not work (continued)?? • Dose distributions different at the new level of biological organization: we account for the distributions, but we don’t know the locations of stem cells • Computational problems somewhere: what exactly are you suggesting here (but perhaps a problem of numerical solutions under stiff conditions)??? • Anatomy, physiology and/or morphometry differ at the new level of biological organization: we think we are accounting for this • Errors in the data provided: well, not all mistakes are introduced by theoreticians