State of the Science Workshop:
Low Dose-Response Extrapolation
for Environmental Health Risk
Assessment
Mary Fox, PhD, MPH
Ronald White, MST
Risk Sciences and Public Policy Institute
Johns Hopkins Bloomberg School of Public Health
Society of Toxicology
Risk Assessment Specialty Section
January 14, 2009
Contemporary Principles of Risk Assessment
• Risk assessment involves a critical analysis of available
scientific information
– Chemical/pollutant toxicity (hazard identification)
– Exposure assessment
– Dose - response assessment
– Risk Characterization (cancer/non-cancer risk metrics – “safe”
levels)
• Quantitative risk metrics are ideally
– Biologically-based
– Data-driven
• When there is insufficient data, default methods are used that
– Ensure scientific validity (i.e., scientifically plausible and
extensively peer-reviewed)
– Create an orderly and predictable process
– Protect public health (account for uncertainties)
EPA 2004; Preuss 2006
Dose - Response Assessment
R
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Observable Range
Range of Inference
Dose
State-of-the-Science Project on Low DoseResponse Extrapolation in EH Risk Assessment
•
Increasing biological knowledge and analytical
capabilities are challenging existing dose-response
assessment procedures
•
Project provides opportunity to:
– Summarize the current body of science
– Integrate multi-disciplinary expertise
– Advance the field
•
Funded by U.S. Environmental Protection Agency
Background Information Document
• Prepared by Johns Hopkins Bloomberg School of Public
Health
• Literature review – 889 papers (1953 - 2006) compiled,
covering broad scope of topics related to workshop issue.
• Selected key studies annotated
• Historical review of low dose extrapolation in EH risk
assessment
• Discussion of key concepts and approaches
• Preliminary findings and conceptual approach
• Case studies: ambient & diesel particulate matter, radon,
naphthalene
State-of-the-Science Workshop: Issues and Approaches in Low Dose–Response
Extrapolation for Environmental Health Risk Assessment
April 23 - 24, 2007
Baltimore, Maryland, U.S.A.
Co-Chairs
Ila Cote, U.S. Environmental Protection Agency
Jonathan M. Samet, Johns Hopkins Bloomberg School of Public Health
Participants
Linda S. Birnbaum, U.S. Environmental Protection Agency
Thomas A. Burke, Johns Hopkins Bloomberg School of Public Health
Kenny S. Crump, Environ Corp
Francesca Dominici, Johns Hopkins Bloomberg School of Public Health
Elaine M. Faustman, University of Washington School of Public Health
& Community Medicine
Mary Fox, Johns Hopkins Bloomberg School of Public Health
Seymour Garte, University of Pittsburgh Cancer Institute
Dale B. Hattis, Clark University
Ralph L. Kodell, University of Arkansas for Medical Sciences
Frederick J. Miller, consultant
Peter Preuss, U.S. Environmental Protection Agency
Louise M. Ryan, Harvard School of Public Health
Paul D. White, U.S. Environmental Protection Agency
Ronald H. White, Johns Hopkins Bloomberg School of Public Health
Lauren Zeise, California Environmental Protection Agency
Provided by D. Hattis
Outline
• Where we’ve been
– Current approaches and their origins
• What we’re doing and learning
– Mode of action
– Refinements
– Interindividual variability
– Additivity to dose and on-going disease
process
• Workshop discussions
Origins of ‘Linear No Threshold’ (1)
• One ‘hit’ to DNA in one cell…
• Studies of Japanese atomic bomb
survivors in 1950’s
• Marked differences in individual response
to carcinogens (individual thresholds) are
not a basis for predicting a no-effect level
in other individuals (IRLG 1979)
Origins of ‘Linear No Threshold’ (2)
Additivity to Background
• Under the assumption of
additivity to background,
even for dose-response
models that do not exhibit
linearity, the low-doseresponse will approximately
linear at low-doses (Crump et
al 1976,Hoel 1980,Krewski and
Van Ryzin 1981).
• That is true for any doseresponse model that is
increasing in d, because its first
derivative at the background
dose will be positive and
therefore the curve will be
“locally” linear.
Origins of the “Threshold”
(Lehman and Fitzhugh 1954)
• Biological resilience, adaptability, repair
• When using animal data to set a human “safety”
standard
– Animals are more resistant to toxics
– Considerable variations within a species;
animals tested are homogeneous, healthy
– Reviewed data supporting 10-fold difference
for resistance and variability (a.k.a inter- and
intra-species “uncertainty factors”)
Definition: Mode of Action
“A
mode of action is composed of key events and
processes starting from the interaction of an
agent with a cell, and through operational and
anatomical changes, resulting in cancer
formation. “Mode” is contrasted to “mechanism”
of action, which implies a more detailed
understanding and description of events than is
meant by mode of action.”
EPA Guidelines for Carcinogen Risk Assessment, 2005
Advances in Understanding Mode of Action (MOA)
Continuum of Practice: Mode of Action
• Pharmacokinetic models to extrapolate
dose across species
• Pharmacodynamic understanding
qualitatively evaluate effects across
species
• Well-developed biologically based doseresponse models (few)
Refinements to Inputs: “Uncertainty Factors”
Inter-individual Variability
Hattis et al. 1999
Refinements to Procedures: Benchmark Dose
A devil
in the
details
Procedure (Crump 1984); Figure (Rodricks 2007)
State of Practice: UF and Procedures
• Pharmacokinetic or dynamic information
used to determine UF when available
• Benchmark dose approach applied moreor-less routinely but results not always
used in risk assessment
State of the Science: Statistical Research
• Sources of uncertainty in extrapolation to low
doses
– Statistical (overwhelming majority of statistical
literature)
– Choice of model (most important uncertainty
amenable to statistical investigation)
– Uncertainty in mode of action
– Uncertainty in additivity to background expo
and biological processes
– Uncertainty in animal to human extrapolation
State of the Science: Quantitative Methods
• Increasing interest in and application of
– Probabilistic methods and simulations
– Bayesian/Hierarchical models
MOA, Additivity, Repair and Inter-individual Variability
Conolly et al. 2005
Conclusions: State of the Science
• Research and risk decision making should be based on a mode-ofaction hypothesis when possible
• There is a growing body of evidence critical of the “threshold” doseresponse model as a population-protective default
• Multiple dose-response shapes are to be expected in the population
• There is recognition that better modeling requires more data and that
biologically-based dose-response models for the majority of
chemicals of regulatory concern will not be available for some time
(ever?)
• There are competing ideas on how to address dose-response
modeling and extrapolation to low doses for risk assessment
• Extrapolation (risk estimation) for all toxicities
• Benchmark dose approach for all toxicities given the
considerable uncertainties
Workshop Discussions: Major Topics
• How/if to do biologically based DR modeling
• New/Alternative approaches to DR
• Mode of action
– Promise/Pitfalls
– Evaluating evidence of MOA
• Statistical methods
• Research needs
Debate: BIOLOGICAL DATA IN MODELS
• TWO DIVERGENT VIEWS
– INCORPORATION OF BIOLOGICAL DATA INTO RISK
ASSESSMENT MODELS IS USEFUL AND/OR
NECESSARY
– SUCH USE OF BIOLOGICAL DATA IS HOPELESS FOR
QUANTITATIVE LOW DOSE RISK ESTIMATION
Approaches Discussed (1)
• CATEGORICAL APPROACH TO A PRIORI
- MODEL SELECTION BASED ON
MECHANISM
• HATTIS CATEGORIES
1. LOW DOSE REVERSIBLE MECHANISM
2. SMALL NUMBER OF IRREVERSIBLE
EVENTS (MUTATIONS)
3. CHRONIC CUMMULATIVE LARGE
NUMBER OF IRREVERSIBLE EVENTS
Approaches Discussed (2)
• USE OF LINEAR LOW DOSE NO THRESHOLD
EXTRAPOLATION AS A DEFAULT WITH
ADJUSTMENT OF SLOPE WITH
MECHANISTIC DATA (CRUMP 1)
• POINT OF DEPARTURE (eg. ANIMAL DATA)
WITH UNCERTAINTY FACTORS BASED ON
MECHANISM, SEVERITY ETC. TO ARRIVE AT
AN ADVISORY LEVEL WITHOUT A RISK
ESTIMATE (CRUMP 2)
Approaches Discussed (3) :New Low Dose
Linear Formulation (Zeise)
RiskH = SlopeBMD × MS × FH-A × D
where
- RiskH is the low dose human risk.
- SlopeBMD is the slope of the dose response curve at
what would be chosen as the benchmark dose under the
current practice
- MS adjusts for the differences in slope at the high dose
compared to low dose
(0  MS  1)
- D is the dose
- FH-A adjusts for interspecies differences.
Zeise 2007
Zeise Alternative: Base Model Incorporating
Uncertainty and Variability
RiskH = SlopeBMD × MS × FH-A × D × U
Log Risk = log SlopeBMD + log MS + log FH-A + log D + e
• where the random variable e is distributed normally with mean
zero and variance 2. Thus, for this simplistic case,
• 2 = 2 logSlope + 2 logM + 2 logF + 2logD
RiskH yth = SlopeBMD × MS × FH-A × D × VH yth × U
where VH yth is the yth quantile of the distribution that describes the
ratio of the yth percentile individual to the median individual.
Zeise 2007
The ultimate goal is to enable expressions such as
“the risk of effect does not exceed x level for the yth
percentile individual, stated with confidence z%”
Additivity to Background Risk
• IF THERE IS EVIDENCE FOR ADDITIVITY TO
BACKGROUND, LINEAR NO THRESHOLD IS
APPROPRIATE
• THERE ARE IMPORTANT SITUATIONS IS
WHICH ADDITIVITY DOES NOT OCCUR.
Focus on Mode of Action
• 1980s: Rationale for risk assessment based on additivity
to ongoing processes (vs independence), population
variability and statistical considerations lead to low dose
linear modeling for carcinogens
• Early 2000s: Emphasis on defining mode of action (MOA)
with expectation that such information would answer
dose-response questions
• Challenge: How can considerations of MOA be combined
with basic disease causation issues (population variability
and background processes)?
MOA: Promise and Pitfalls
Determine human relevance
(yes/no)
Allows deviation from
defaults
Build unified models (all
relevant processes)
Diversity in human
population complicates
answer
Lack of data (MOA
research not addressing
key info, e.g., additivity to
ongoing processes)
Not there yet
Current Criteria for MOA Evidence
Assessment
• “Hill criteria”: temporality, strength of
association, specificity, consistency,
coherence—developed for consideration of
observational evidence.
• Hill criteria of uncertain relevance to
evaluating evidence on mode or
mechanism of action
• Need for new criteria?
Assessing Weight of Evidence for MOA
• Formal and transparent
process for gathering
relevant evidence
• Systematic approach for
assessing the quality of the
evidence
• Approaches for combining
evidence, e.g., metaanalysis
• Criteria for evaluating the
informativeness of the
evidence
• Standard terminology for
classifying the strength of
evidence.
An Example Classification Scheme
• Evidence on MOA is sufficient to inform development of a
biologically-based model;
• Evidence on MOA is sufficient to support a particular
dose-response model
• Evidence on MOA is suggestive in supporting a particular
dose-response model
• Evidence on MOA is insufficient to inform the selection of
the dose-response model/shape
Discussion: Statistical Modeling
• Model choice is challenging, even if MOA is known
• Model averaging may avoid need to chose a single model
• Models that combine data from multiple endpoints and/or
multiple studies
– Gain power/precision
– Inform biology
– Help quantify uncertainty
• Research needed on how to incorporate biomarkers, especially
high dimensional genomics data
• Greater effort to understand mechanism may be needed if
effects seen in animal studies are close to exposures seen in
humans
Discussion: Bayesian Model Averaging
• Specify a set of suitable models, usually
weighted equally a priori
• Compute “posterior” model probabilities,
reflecting the likelihood that a model holds, given
the observed data
• Average the results with respect to these
posterior probabilities – “bad” models get downweighted, several “good” models can contribute.
(Example presented used method of Carlin and
Chib 1995.)
Discussion: Epi vs Animal Studies
• Epi studies avoid inter-species problem, but many
other problems (Measurement error, confounding)
• Default approaches developed for risk assessment
based on animals may not work for epi data
– Appropriate point of departure generally lower (10%
typical for animal studies maybe at upper end of
dose range for human studies)
– Application of standard uncertainty factors may lead
to infeasibly low exposure levels
On Bioassays
• Current bioassays
– Expensive
– Fewer being conducted
– Generate limited data to
support BBDR models and
quantitative risk
assessments
– Provide limited insights on
MOA
Better Bioassays: Design Considerations
• Serially kill animals
• Include endpoints critical for BBDR models (cell
proliferation, lesion location, toxicogenomics)
• Incorporate endpoints likely to help identify, refine MOA
• Preserve tissue for future research
– So new methodologies can be applied or other
endpoints can be assessed
– Link to comparable human responses, e.g.,
proteomics
Discussion Highlights
•
From MOA to risk (dose-response) model—how?
– Families of MOA based models (Hattis)
– New/refined default models, e.g., Crump and Zeise alternatives
– Operational definition of MOA
•
Approach for evaluating and classifying evidence related to MOA
– Need systematic approach
– Relevance of Hill criteria for this purpose?
•
Statistical Methods
– BMA is a tool for quantifying uncertainty in the shape of the dose-response for
extrapolation of risk at low doses (that is, at dose levels below the observable
ranges)
– Averaged model may reflect true population heterogeneity
– Methods for data integration, combined models
•
Considerations when extrapolating to low doses
•
Consider additivity (vs not) to background (exposure & bio process)
•
Consider population variability
•
Does this suggest a LNT default for non-cancer effects?
A Controversial Topic
• Linear No-Threshold Default Assumption for All Toxicities
• Controversial but not new
– See Biological Effects of Low Level Exposure (BELLE) newsletter
vol. 6, no. 1, March 1997
• http://www.belleonline.com/newsletters/volume6/vol6-1.html
– Useful for first pass screening (R. Wilson), reasonable for public
health purposes where there is no evidence to the contrary (D.
Hoel)
– Does not mean that low-dose slope should be same as high-dose
slope
• Since then …
– Results from population studies – Informative for this
controversy?
– Residential radon exposure, Criteria pollutants, ETS, Pb
Residential Radon Exposure and Lung Cancer
Krewski et al. 2006
Ozone and Mortality
Bell et al. 2006
PM10 and Mortality
8
8
10 U.S. Cities
(Schwartz and Zanobetti 2000)
8 Spanish Cities
(Schwartz et al. 2001)
b
6
% increase in deaths
% increase in deaths
a
4
2
0
6
4
2
0
0
20
40
60
80
100
0
20
40
3
80
100
BS (g/m )
8
8
20 U.S. Cities
(Daniels et al. 2000;
Dominici et al. 2003)
c
6
88 U.S. Cities
(Dominici et al. 2002;
Dominici et al. 2003)
d
% increase in deaths
% increase in deaths
60
3
PM10 (g/m )
Selected PM10 concentrationresponse relationships from
various multi-city daily timeseries studies. (Adapted from
original publications and
rescaled for comparison.)
4
2
0
6
• Schwartz and Zanobetti 2000
• Schwartz et al. 2001
4
• Daniels et al. 2000
2
0
0
20
40
60
80
100
0
20
40
3
60
80
100
3
PM10 (g/m )
PM10 (g/m )
• Dominici et al. 2002
• Dominici et al. 2003
8
22 European Cities
(Samoli et al. 2005)
% increase in deaths
e
• Samoli et al. 2005
6
4
2
0
0
20
40
60
80
100
120
140
160
180
200
220
1.4
1050
a
1000
b
U.S. cross-sectional mortality
Adjusted mortality relative risk
1980 adjusted mortality(deaths/yr/100,000)
PM2.5 and Mortality, Children’s Lung Growth
950
900
850
800
750
700
650
1.3
Harvard Six-cities
(Laden et al. 2006)
Period 1 (upper case)
1.2
H
L
1.1
h
T
1.0
p P
10
15
20
25
W
s
t
Period 2 (lower case)
l
0.9
w
0.8
5
S
30
5
10
3
PM2.5 (g/m )
15
20
25
30
25
30
3
PM2.5 (g/m )
1.4
1.3
ACS cohort
(Pope et al. 2002)
Cardiopulmonary
1.2
1.1
All cause
1.0
All other
0.9
Children's Lung Growth
(Gauderman et al. 2004)
d
12
Lung Cancer
FEV1 <80% of predicted (%)
Adjusted mortality relative risk
c
10
8
6
4
2
0
0.8
5
10
15
20
3
PM2.5 (g/m )
25
30
5
10
15
20
3
PM2.5 (g/m )
Figure 2. Selected PM2.5 concentration-response relationships from studies of
long-term exposure. (Adapted from original publications and rescaled for
comparison.) (Pope et al. 2002; Gauderman et al. 2004; Laden et al. 2006)
Tobacco Smoke, Lead Exposure and Multiple
Outcomes
• Report of the Surgeon General 2006
– “The scientific evidence indicates
that there is no risk free level of
exposure to second-hand smoke.”
• CDC 2005
– “…data demonstrating that no
“safe” threshold for blood lead
levels in young children has been
identified…”
The Controversial Topic
• An argument for a ‘no-threshold’ default for
noncancer outcomes
• Significant Implications
– A change in risk assessment practice to reflect
growing knowledge
– A significant change in regulatory approach to
identify protective exposure levels
Workshop Recommendations:
What We Can Do Now
• Develop operational definition of MOA
• Develop approaches for evaluating evidence on
MOA
• Incorporate variability, background incidence
and background exposures to current doseresponse models
• Adjust for severity with uncertainty factors
• Use model averaging
Workshop Recommendations: Research Needs
• As information on MOA advances – translate into
dose-response models
• Attention to background risk in MOA-based
research
• Explore statistical approaches to model selection
(e.g. model averaging)
• What are the implications for risk assessment
practice?
• Develop hybrid modeling approaches to integrate
data across multiple species and health
endpoints/outcomes
Workshop Report
State-of-the-Science Workshop Report: Issues and
Approaches in Low Dose–Response Extrapolation for
Environmental Health Risk Assessment
Ronald H. White (JHU), Ila Cote (EPA), Lauren Zeise (CA
EPA), Mary Fox (JHU), Francesca Dominici (JHU),
Thomas A. Burke (JHU), Paul D. White (EPA), Dale B.
Hattis (Clark U), Jonathan M. Samet (JHU)
Environmental Health Perspectives (On-line),
19 September, 2008.
A Recent Development
Science and Decisions: Advancing Risk Assessment
(NAS 2008)
– Unification of cancer and noncancer dose-response
assessment approaches
• Noncancer effects do not necessarily have a threshold, or lowdose nonlinearity, and the mode of action of carcinogens
varies.
• Background exposures and underlying disease processes
contribute to population background risk and can lead to
linearity at the population doses of concern.
• Recommends a consistent, unified approach for doseresponse modeling that includes formal, systematic
assessment of background disease processes and exposures,
possible vulnerable populations, and modes of action that may
affect a chemical’s dose-response relationship in humans.
• RfD or RfC redefined as “risk-specific dose”