DoseResponse Modeling: Past, Present, and Future

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
• [phone] - voice
• rconolly_at_ciit.org - e-mail
• SOT Risk Assessment Specialty Section, Wednesday,
  December 15, 2004

2
Outline

• Why do we care about dose response?
• Historical perspective
• Brief, incomplete!
• Formaldehyde
• Future directions

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
Typical high dose rodent data what do they tell
us?
Response
5
Not much!
Response
?
6
Possibilities
Response
7
Possibilities
Response
8
Possibilities
Response
9
Possibilities
Response
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
1984 Styrene PBPK model(TAP, 73159-175, 1984)
A physiologically based description of the
inhalation pharmacokinetics of styrene in rats
and humans John C. Ramseya and Melvin E.
Andersenb a Toxicology Research Laboratory, Dow
Chemical USA, Midland, Michigan 48640, USAb
Biochemical Toxicology Branch, Air Force
Aerospace Medical Research Laboratory
(AFAMRL/THB), Wright-Patterson Air Force Base,
Ohio 45433, USA
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
Biologically-based computational models Natural
bridges between research and risk assessment
Computational models
14
Garbage in garbage out

• Computational modeling and laboratory experiments
  must go hand-in-hand

15
Refining the description with research on
pharmacokinetics and pharmacodynamics (mode of
action)
Response
16
Refining the description with research on
pharmacokinetics and pharmacodynamics (mode of
action)
Response
17
Refining the description with research on
pharmacokinetics and pharmacodynamics (mode of
action)
Response
18
Refining the description with research on
pharmacokinetics and pharmacodynamics (mode of
action)
Response
19
Formaldehyde nasal cancer in ratsA good
example of extrapolations across doses and species
20
(No Transcript)
21
Swenberg JA, Kerns WD, Mitchell RI, Gralla EJ,
Pavkov KL Cancer Research, 403398-3402
(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
Formaldehyde bioassay results
23
Mechanistic Studies and Risk Assessments
24
What did we know in the early 80s?

• Formaldehyde is a carcinogen in rats and mice
• Human exposures roughly a factor of 10 of
  exposure levels that are carcinogenic to rodents.

25
1982 Consumer Product Safety Commission (CPSC)
voted to ban urea-formaldehyde foam insulation.
26
Casanova-Schmitz M, Heck HD Toxicol Appl
Pharmacol 70121-32 (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
DPX
28
Starr TB, Buck RD Fundam Appl Toxicol 4740-53
(1984)The importance of delivered dose in
estimating low-dose cancer risk from inhalation
exposure to formaldehyde.
1984 - Risk Assessment Implications
29
1985 No effect on blood levels

• Heck, HdA, Casanova-Schmitz, M, Dodd, PD,
  Schachter, EN, Witek, TJ, and Tosun, T
• Am. Ind. Hyg. Assoc. J. 461. (1985)
• Formaldehyde (C2HO) concentrations in the blood
  of humans and Fisher-344 rats exposed to C2HO
  under controlled conditions.

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
Summary 1980s

• Research
• DPX delivered dose
• Breathing rate protects the mouse (Barrow)
• Blood levels unchanged
• Regulatory actions
• CPSC ban
• US EPA risk assessment

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
1991 US EPA cancer risk assessment

• Linearized multistage (LMS) model
• Low dose linear
• DPX used as measure of dose

34
Monticello TM, Miller FJ, Morgan KT Toxicol
Appl Pharmacol 111409-21 (1991)Regional
increases in rat nasal epithelial cell
proliferation following acute and subchronic
inhalation of formaldehyde.
1991, 1996 - regenerative cellular proliferation
35
Normal respiratory epithelium in the rat nose
36
Formaldehyde-exposed respiratory epitheliumin
the rat nose (10 ppm)
37
Dose-response for cell division rate
38
DPX submodel simulation of rhesus monkey data
39
Summary Dose-response inputs to the clonal
growth model

• Cell replication
• J-shaped
• DPX
• Low dose linear

40
CFD Simulation of Nasal Airflow(Kimbell et. al)
41
2-Stage clonal growth model(MVK model)
42
Dose-response for cell division rate
43
Simulation of tumor response in rats
44
CIIT clonal growth cancer risk assessment for
formaldehyde(late 90s)

• Risk assessment goal
• Combine effects of cytotoxicity and mutagenicity
  to predict the tumor response

45
1987 U.S. EPA
Inhaled ppm
46
1991 U.S. EPA
Inhaled ppm
47
1999 CIIT
Inhaled ppm
48
Formaldehyde Computational fluid dynamics
models of the nasal airways
F344 Rat
Rhesus Monkey
Human
49
Human assessment
50
Baseline calibration against human lung cancer
data
51
DPX and direct mutation

• Direct mutation is assumed to be proportional to
  the amount of DPX
• Is KMU big or small?

52
Grid search
53
Optimal value of KMU is zero
54
Upper bound on KMU
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
Human risk modeling
57
Final model Hockey stick and 95 upper
confidence limit on value of KMU
95 UCL on KMU
58
Predicted human cancer risks(hockey stick-shaped
dose-response for cell replication optimal value
for KMU)
Optimal value of KMU KMU 0.
59
Negative risk using raw dose-response for cell
replication
95 UCL on KMU
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
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
Summary CIIT Clonal Growth Assessment

• International acceptance
• Health Canada
• WHO
• MAK Commission (Germany)
• Australia
• U.S. EPA (??)
• Peer-review

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
(No Transcript)
65
IARC hazard characterization vs. dose-response
assessment
66
Formaldehyde summary

• Nasal SCC in rats
• Mechanistic studies
• Risk Assessments
• Implications of the data
• IARC

67
The future
68
Outline

• Long-range goal
• Systems in biological organization
• Molecular pathways
• Data
• Example
• Computational modeling
• Modularity

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
Levels of biological organization

• Populations
• Organisms
• Tissues
• Cells
• Organelles
• Molecules

Mechanistic
Descriptive
71
Levels of biological organization

• Populations
• Organisms
• Tissues
• Cells
• Organelles
• Molecules

(systems)
72
Molecular pathways
73
Segment polarity genes in Drosophila
Albert Othmer, J. Theor Biol. 223, 1 18, 2003
74
ATM curated Pathway from Pathway Assist
75
Approach

• Initial pathway identification
• Static map
• Existing data
• New data
• Computational modeling
• Dynamic behavior
• Iterate with data collection

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
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
DNA damage and cell cycle checkpoints
79
p21 time-course data and simulation
80
Mutations dose-response and model prediction
model calculated values
Mutation Fraction Rate
IR
81
Data
82
Tissue dosimetry is the front end to a
molecular pathway model
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
Example

• Skin irritation
• MAPK, IL-1a, and NF-kB computational modules
• High throughput overexpression data to
  characterize IL-1a MAPK interaction with
  respect to NF-kB

85
Skin Irritation
Chemical
Dead cells
Epidermis
Tissue damage
(keratinocytes)
Tissue damage
Dermis
Nerve Endings
A cascade of inflammatory responses (cytokines)
(fibroblasts)
Blood vessels

• Study on the dose response of the skin cells to
  inflammatory cytokines contributes to
  quantitative assessment of skin irritation

86
Modular Composition of IL-1 Signaling
IL-1
Extracellular
IL-1R
Intracellular
IL-1 specific top module
Secondary messenger
MAPK
Others
Constitutive downstream NF-kB module
NF-kB
IL-6, etc.
Transcriptional factors
87
Top IL-1 Signaling Module
IL-1
IL-1R
TAB2
TAK1
TAB1
MyD88
TRAF6
NF-kB module
Degraded
Cytoplasm
Nucleus
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

Increasing IRAKp degradation
IRAKp
TAK1
Time (hrs)
Time (hrs)
89
(No Transcript)
90
NF-kB Module Simulation

• Parameters from existing NF-kB model (Hoffmann et
  al., 2002) and refined to fit experimental data
  in literature

IkB
IL-6
_

NF-kB
Smoothened oscillations
Concentration (mM)
Concentration (mM)
Time (hrs)
Add constant input signal
Time (hrs)
Longer delay
91
The IBNF-B Signaling Module Temporal Control
and Selective Gene Activation Alexander Hoffmann,
Andre Levchenko, Martin L. Scott, David
Baltimore Science 2981241 1245, 2002
6 hr
92
MAPK intracellular signaling cascades
http//www.weizmann.ac.il/Biology/open_day/book/ro
ny_seger.pdf
93
(No Transcript)
94
MAPK time-course and bifurcation after a short
pulse of PDGF
95
IL-1 MAPK crosstalk and NFkB activation
96
Gain-of-function screen
97
Model prediction
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 gt in vivo

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
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
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
End