1. 1 Dose-Response Modeling: Past, Present, and Future Rory B. Conolly, Sc.D. Center for Computational Systems Biology & Human Health Assessment CIIT Centers for Health Research (919) 558-1330 - voice rconolly@ciit.org - e-mail SOT Risk Assessment Specialty Section, Wednesday, December 15, 2004

2. 2 Outline Why do we care about dose response? Historical perspective –Brief, incomplete! Formaldehyde Future directions

3. 3 Perspective This talk mostly deals with issues of cancer risk assessment, but I see no reason for any formal separation of the methodologies for cancer and non cancer dose-response assessments –PK –Modes of action –Tumors, reproductive failure, organ tox, etc.

4. 4 Typical high dose rodent data – what do they tell us? Response Dose

5. 5 Not much! Response Dose Interspecies

6. 6 Possibilities Response Dose Interspecies

7. 7 Possibilities Response Dose Interspecies

8. 8 Possibilities Response Dose Interspecies

9. 9 Possibilities Response Dose Interspecies

10. 10 Benzene Decision of 1980 U.S. Supreme Court says that exposure standards must be accompanied by a demonstration of “significant risk” –Impetus for modeling low-dose dose response

11. 11 1984 Styrene PBPK model (TAP, 73:159-175, 1984) A physiologically based description of the inhalation pharmacokinetics of styrene in rats and humans John C. Ramsey a and Melvin E. Andersen b a Toxicology Research Laboratory, Dow Chemical USA, Midland, Michigan 48640, USA b Biochemical Toxicology Branch, Air Force Aerospace Medical Research Laboratory (AFAMRL/THB), Wright-Patterson Air Force Base, Ohio 45433, USA

12. 12 Biologically motivated computational models (or) Biologically based computational models Biology determines –The shape of the dose-response curve –The qualitative and quantitative aspects of interspecies extrapolation Biological structure and associated behavior can be –described mathematically –encoded in computer programs –simulated

13. 13 Experiments to understand mechanisms of toxicity and extrapolation issues Computational models Risk assessment Biologically-based computational models: Natural bridges between research and risk assessment

14. 14 Garbage in – garbage out Computational modeling and laboratory experiments must go hand-in-hand

15. 15 Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action) Response Dose Interspecies

16. 16 Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action) Response Dose Interspecies

17. 17 Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action) Response Dose Interspecies

18. 18 Response Refining the description with research on pharmacokinetics and pharmacodynamics (mode of action) Dose Interspecies

19. 19 Formaldehyde nasal cancer in rats: A good example of extrapolations across doses and species

20. 20

21. 21 Swenberg JA, Kerns WD, Mitchell RI, Gralla EJ, Pavkov KL Cancer Research, 40:3398-3402 (1980) Induction of squamous cell carcinomas of the rat nasal cavity by inhalation exposure to formaldehyde vapor. 1980 - First report of formaldehyde-induced tumors

22. 22 Formaldehyde bioassay results 0 10 20 30 40 50 60 Tumor Response (%) 00.7261015 Exposure Concentration (ppm) Kerns et al., 1983 Monticello et al., 1990

23. 23 Mechanistic Studies and Risk Assessments

24. 24 What did we know in the early ’80’s? Formaldehyde is a carcinogen in rats and mice Human exposures roughly a factor of 10 of exposure levels that are carcinogenic to rodents.

25. 25 1982 – Consumer Product Safety Commission (CPSC) voted to ban urea- formaldehyde foam insulation.

26. 26 Casanova-Schmitz M, Heck HD Toxicol Appl Pharmacol 70:121-32 (1983) Effects of formaldehyde exposure on the extractability of DNA from proteins in the rat nasal mucosa. 1983 - Formaldehyde cross-links DNA with proteins - “DPX”

27. 27 DPX

28. 28 Starr TB, Buck RD Fundam Appl Toxicol 4:740-53 (1984) The importance of delivered dose in estimating low-dose cancer risk from inhalation exposure to formaldehyde. 1984 - Risk Assessment Implications

29. 29 1985 – No effect on blood levels Heck, Hd’A, Casanova-Schmitz, M, Dodd, PD, Schachter, EN, Witek, TJ, and Tosun, T Am. Ind. Hyg. Assoc. J. 46:1. (1985) Formaldehyde (C 2 HO) concentrations in the blood of humans and Fisher-344 rats exposed to C 2 HO under controlled conditions.

30. 30 1987 – U.S. EPA cancer risk assessment Linearized multistage (LMS) model –Low dose linear –Dose input was inhaled ppm –U.S. EPA declined to use DPX data

31. 31 Summary: 1980’s Research –DPX – delivered dose –Breathing rate protects the mouse (Barrow) –Blood levels unchanged Regulatory actions –CPSC ban –US EPA risk assessment

32. 32 Key events during the ’90s Greater regulatory acceptance of mechanistic data for risk assessment (U.S. EPA) Cell replication dose-response Better understanding of DPX (Casanova & Heck) Dose-response modeling of DPX (Conolly, Schlosser) Sophisticated nasal dosimetry modeling (Kimbell) Clonal growth models for cancer risk assessment (Moolgavkar)

33. 33 1991 – US EPA cancer risk assessment Linearized multistage (LMS) model –Low dose linear –DPX used as measure of dose

34. 34 Monticello TM, Miller FJ, Morgan KT Toxicol Appl Pharmacol 111:409-21 (1991) Regional increases in rat nasal epithelial cell proliferation following acute and subchronic inhalation of formaldehyde. 1991, 1996 - regenerative cellular proliferation

35. 35 Normal respiratory epithelium in the rat nose

36. 36 Formaldehyde-exposed respiratory epithelium in the rat nose (10+ ppm)

37. 37 Dose-response for cell division rate ppm formaldehyde (Raw data)

38. 38 DPX submodel – simulation of rhesus monkey data

39. 39 Summary: Dose-response inputs to the clonal growth model Cell replication –J-shaped DPX –Low dose linear

40. 40 CFD Simulation of Nasal Airflow (Kimbell et. al)

41. 41 2-Stage clonal growth model (MVK model) Normal cells (N) Division  N ) Death/ differentiation (  N ) Mutation (  N ) Initiated cells (I) (I)(I) (I)(I) Mutation (   ) Cancer cell Tumor (delay)

42. 42 Dose-response for cell division rate ppm formaldehyde (Raw data) ppm formaldehyde (Hockey stick transformation)

43. 43 Simulation of tumor response in rats

44. 44 CIIT clonal growth cancer risk assessment for formaldehyde (late ’90’s) Risk assessment goal –Combine effects of cytotoxicity and mutagenicity to predict the tumor response

45. 45 1987 U.S. EPA Tumor response Inhaled ppm Cancer model (LMS)

46. 46 1991 U.S. EPA Inhaled ppm Tissue dose (DPX) Cancer model (LMS) Tumor response

47. 47 1999 CIIT Cell killing Cell proliferation Mutagenicity (DPX) Cancer model (Clonal growth) Tumor response Inhaled ppm Tissue dose CFD modeling

48. 48 Formaldehyde: Computational fluid dynamics models of the nasal airways F344 Rat Rhesus Monkey Human

49. 49 Human assessment

50. 50 Baseline calibration against human lung cancer data

51. 51 DPX and direct mutation Direct mutation is assumed to be proportional to the amount of DPX: Is KMU big or small?

52. 52 Grid search

53. 53 Optimal value of KMU is zero

54. 54 Upper bound on KMU

55. 55 Calculation of the value of KMU Grid search Optimal value of KMU was zero –Modeling implies that direct mutation is not a significant action of formaldehyde 95% upper confidence limit on KMU was estimated

56. 56 Human risk modeling Normal cells (N) Division  N ) Death/ differentiation (  N ) Mutation (  N ) Initiated cells (I) (I)(I) (I)(I) Mutation (   ) Cancer cell Tumor (delay)

57. 57 Final model: Hockey stick and 95% upper confidence limit on value of KMU 95% UCL on KMU

58. 58 Predicted human cancer risks (hockey stick-shaped dose-response for cell replication; optimal value for KMU) Optimal value of KMU KMU = 0.

59. 59 “Negative risk” using raw dose-response for cell replication 95% UCL on KMU

60. 60 Make conservative choices when faced with uncertainty Use hockey stick-shaped cell replication Use a 95% upper bound on the dose-response for the directly mutagenic mode of action –Statistically optimal model has 0 (zero) slope Risk model predicts low-dose linear risk. Optimal, data based model predicts negative risk at low doses

61. 61 Summary: CIIT Clonal Growth Assessment Either no additional risk or a much smaller level of risk than previous assessments Consistent with mechanistic database –Direct mutagenicity –Cell replication

62. 62 Summary: CIIT Clonal Growth Assessment International acceptance –Health Canada –WHO –MAK Commission (Germany) –Australia –U.S. EPA (??) Peer-review

63. 63 IARC 2004 Classified 1A based on nasopharyngeal cancer Myeloid leukemia data suggestive but not sufficient –Concern about mechanism –British study negative Reclassification driven by epidemiology In my opinion inadequate consideration of regional dosimetry

64. 64 nasopharynx Anterior nose Whole nose

65. 65 IARC: hazard characterization vs. dose- response assessment

66. 66 Formaldehyde summary Nasal SCC in rats Mechanistic studies Risk Assessments Implications of the data IARC

67. 67 The future

68. 68 Outline Long-range goal Systems in biological organization Molecular pathways Data Example –Computational modeling –Modularity

69. 69 Long-range goal A molecular-level understanding of dose- and time- response behaviors in laboratory animals and people. –Environmental risk assessment –Drug development –Public health

70. 70 Levels of biological organization Populations Organisms Tissues Cells Organelles Molecules (systems) Descriptive Mechanistic

71. 71 Levels of biological organization Populations Organisms Tissues Cells Organelles Molecules (systems) Today

72. 72 Molecular pathways

73. 73 Segment polarity genes in Drosophila Albert & Othmer, J. Theor Biol. 223, 1 – 18, 2003

74. 74 ATM curated Pathway from Pathway Assist®

75. 75 Approach Initial pathway identification –Static map Existing data New data Computational modeling –Dynamic behavior –Iterate with data collection

76. 76 Initial pathway identification Use commercial software that can integrate data from a variety of sources (Pathway Assist) –Scan Pub Med abstracts to identify “facts” –Create pathway maps –Incorporate other, unpublished data Quality control –Curate pathways

77. 77 Computational modeling To study the dynamic behavior of the pathway Analyze data –Are model predictions consistent with existing data? Make predictions –Suggest new experiments –Ability to predict data before it is collected is a good test of the model

78. 78 DNA damage and cell cycle checkpoints

79. 79 p21 time-course data and simulation Experimental data

80. 80 IR Mutation Fraction Rate model calculated values (Redpath et al, 2001) Mutations dose-response and model prediction

81. 81 Data

82. 82 (Fat) Liver Rest of Body Air-blood interface Venous blood Tissue dosimetry is the “front end” to a molecular pathway model

83. 83 Gain-of-function and loss-of-function screens to study network structure Selectively alter behavior of the network –Loss-of-function SiRNA –Gain-of-function full-length genes Look for concordance between lab studies and the behavior of the computational model –Mimic gain-of-function and loss-of-function changes in the computer

84. 84 Example Skin irritation MAPK, IL-1 , and NF-  B computational “modules” High throughput overexpression data to characterize IL-1  – MAPK interaction with respect to NF-  B

85. 85 Skin Irritation Epidermis Dermis Dead cells Blood vessels (keratinocytes) Nerve Endings (fibroblasts) Chemical Tissue damage A cascade of inflammatory responses (cytokines) Study on the dose response of the skin cells to inflammatory cytokines contributes to quantitative assessment of skin irritation

86. 86 Modular Composition of IL-1 Signaling IL-1R IL-1 Secondary messenger NF-  B MAPKOthers Extracellular Intracellular IL-6, etc. Transcriptional factors IL-1 specific top module Constitutive downstream NF-  B module

87. 87 IL-1 IL-1R MyD88 P IRAK Degraded IRAK gene IRAK P TRAF6 TAB2 IRAK P TRAF6 TAK1 TAB1 P IKIK P IKIK Nucleus Cytoplasm Top IL-1 Signaling Module NF-kB module Self-limiting mechanism

88. 88 Top Module Simulation IL-1 receptor number and ligand binding parameters from human keratinocytes Other parameters constrained by reasonable ranges of similar reactions/molecules, and tuned to fit data Time (hrs) TAK1* IRAKp Increasing IRAKp degradation

89. 89 IKIK P NF BB IBIB P BB I  B Degraded I  B gene NF BB IBIB IL-6 gene NF BB BB IBIB P BB IBIB Nucleus Cytoplasm Constitutive NF-  B Signaling Module IKIK Negative feedback Input signal

90. 90 NF-  B Module Simulation Parameters from existing NF-  B model (Hoffmann et al., 2002) and refined to fit experimental data in literature Time (hrs) Concentration (  M) IBIB NF-  B + _ Time (hrs) Concentration (  M) IL-6 Add constant input signal Longer delay Smoothened oscillations

91. 91 The IB–NF-B Signaling Module: Temporal Control and Selective Gene Activation Alexander Hoffmann, Andre Levchenko, Martin L. Scott, David Baltimore Science 298:1241 – 1245, 2002 6 hr

92. 92 MAPK intracellular signaling cascades http://www.weizmann.ac.il/Biology/open_day/book/rony_seger.pdf

93. 93

94. 94 MAPK time-course and bifurcation after a short pulse of PDGF Input pulse

95. 95 IL-1 MAPK crosstalk and NFkB activation

96. 96 Gain-of-function screen

97. 97 Model prediction

98. 98 Future directions Computational modeling and data collection at higher levels of biological organization –Cells Intercellular communication –Tissues –Organisms NIH initiatives Environmental health risk, drugs ==> in vivo

99. 99 Summary Biological organization and systems Molecular pathways –identification –Computational modeling Data –Gain-of-function –Loss-of-function Skin irritation example –3 modules –Crosstalk –Targeted data collection

100. 100 Acknowledgements Colleagues who worked on the clonal growth risk assessment –Fred Miller, Julian Preston, Paul Schlosser, Julie Kimbell, Betsy Gross, Suresh Moolgavkar, Georg Luebeck, Derek Janszen, Mercedes Casanova, Henry Heck, John Overton, Steve Seilkop

101. 101 Acknowledgements CIIT Centers for Health Research –Rusty Thomas –Maggie Zhao –Qiang Zhang –Mel Andersen Purdue –Yanan Zheng Wright State University –Jim McDougal Funding –DOE –ACC

102. 102 End