Guidelines for
Carcinogen
Risk
Assessment
Risk
Assessment
Forum
U.S. Environmental
Protection
Agency
Washington,
DC
EPAJ63
O/P-03/OO
iF
March
2005
DISCLAIMER
This
document
has been reviewed
in
accordance
with
U.S.
Environmental
Protection
Agency
policy and
approved
for publication.
Mention
of trade
names
or
commercial
products
does
not constitute
endorsement
or
recommendation
for use.
CONTENTS
1.
iNTRODUCTION
.
1-1
1.1.
PURPOSE
AND
SCOPE
OF
THE
GUIDELiNES
1-1
1.2.
ORGANIZATION
AND
APPLICATION
OF
THE
GUIDELiNES
1-3
1.2.1.
Organization
1-3
1.2.2.
Application
1-5
1.3.
KEY
FEATURES
OF
THE
CANCER
GUIDELINES
1-7
1.3.1.
Critical
Analysis
of
Available
Information
as
the
Starting
Point
for
Evaluation
1-7
1.3.2.
Mode
of
Action
1-10
1.3.3.
Weight
of
Evidence
Narrative
1-11
1.3.4.
Dose-response
Assessment
1-12
1.3.5.
Susceptible
Populations
and
Lifestages
1-13
1.3.6.
Evaluating
Risks
from
Childhood
Exposures
1-15
1.3.7.
Emphasis
on
Characterization
1-21
2.
HAZARD
ASSESSMENT
2-1
2.1.
OVERVIEW
OF
HAZARD
ASSESSMENT
AND
CHARACTERIZATION
...
2-1
2.1.1.
Analyses
of
Data
2-1
2.1.2.
Presentation
of
Results
2-1
2.2.
ANALYSIS
OF
TUMOR
DATA
2-2
2.2.1.
Human
Data
2-3
2.2.1.1.
Assessment
of
evidence
of
carcinogenicity
from
human
data
2-4
2.2.1.2.
Types
of
studies
2-5
2.2.1.3.
Exposure
issues
2-6
2.2.1.4.
Biological
markers
2-7
2.2.1.5.
Confounding
actors
2-8
2.2.1.6.
Statistical
considerations
2-9
2.2.1.6.1.
Likelihood
of
observing
an
effect
2-9
2.2.1.6.2.
Sampling
and
other
bias
issues
2-10
2.2.1.6.3.
Combining
statistical
evidence
across
studies
.
...
2-11
2.2.1.7.
Evidence
for
Causality
2-11
2.2.2.
Animal
Data
2-15
2.2.2.1.
Long-term
Carcinogenicity
Studies
2-15
2.2.2.1.1.
Dosing
issues
2-16
2.2.2.1.2.
Statistical
considerations
2-19
2.2.2.1.3.
Concurrent
and
historical
controls
2-20
2.2.2.1.4.
Assessment
of
evidence
of
carcinogenicity
from
long
term
animal
studies
2-21
2.2.2.1.5.
Site
concordance
2-22
2.2.2.2.
Perinatal
Carcinogenicity
Studies
2-22
2.2.2.3.
Other
Studies
2-24
2.2.3.
Structural
Analogue
Data
2-25
2.3.
ANALYSIS
OF
OTHER
KEY
DATA
2-25
2.3.1.
Physicochemical Properties
.
2-25
2.3.2.
Structure-Activity
Relationships
2-26
2.3.3.
Comparative
Metabolism
and
Toxicokinetics
2-27
2.3.4.
Toxicological
and
Clinical
Findings
2-29
2.3.5.
Events
Relevant
to Mode
of Carcinogenic
Action
2-30
2.3.5.1.
Direct
DNA-Reactive
Effects
2-31
2.3.5.2.
Indirect
DNA
Effects
or
Other
Effects
on Genes/Gene
Expression
2-32
2.3.5.3.
Precursor
Events
and Biomarker
Information
2-34
2.3.5.4.
Judging
Data
2-36
2.4.
MODE
OF
ACTION—GENERAL
CONSIDERATIONS
AND
FRAMEWORK
FOR
ANALYSIS
2-36
2.4.1.
General
Considerations
2-36
2.4.2.
Evaluating a Hypothesized
Mode
of Action
2-40
2.4.2.1.
Peer
Review
2-40
2.4.2.2.
Use of
the
Framework
2-40
2.4.3. Framework
for
Evaluating
Each
Hypothesized
Carcinogenic
Mode
of
Action
2-41
2.4.3.1.
Description
of the
Hypothesized
Mode
of
Action
2-43
2.4.3.2.
Discussion
of the
Experimental
Support
for the
Hypothesized
Mode
of
Action
2-44
2.4.3.3.
Consideration
of
the
Possibility
of
Other
Modes
of Action
. 2-46
2.4.3.4.
Conclusions
About
the Hypothesized
Mode
of
Action
2-47
2.4.4. Evolution
with
Experience
2-49
2.5.
WEIGHT
OF EVIDENCE NARRATIVE
2-49
2.6.
HAZARD
CHARACTERIZATION
2-59
3.
DOSE-RESPONSE
ASSESSMENT
3-1
3.1.
ANALYSIS
OF
DOSE
3-3
3.1.1.
Standardizing Different
Experimental
Dosing
Regimens
3-4
3.1.2.
Toxicokinetic
Data
and
Modeling
3-5
3.1.3.
Cross-species Scaling
Procedures
3-6
3.1.3.1.
Oral
Exposures
3-6
3.1.3.2.
Inhalation
Exposures
3-8
3.1.4.
Route
Extrapolation
3-9
3.2.
ANALYSIS
IN
THE RANGE
OF OBSERVATION
3-11
3.2.1.
Epidemiologic
Studies
3-11
3.2.2.
Toxicodynamic
(“Biologically
Based”)
Modeling
3-13
3.2.3.
Empirical
Modeling
(“Curve
Fitting”)
3-14
3.2.4.
Point
of Departure
(POD)
3-16
3.2.5.
Characterizing
the
POD:
The
POD
Narrative
3-18
3.2.6.
Relative
Potency
Factors
3-20
3.3.
EXTRAPOLATION TO
LOWER
DOSES
3-20
3.3.1.
Choosing
an
Extrapolation
Approach
3-21
3.3.2.
Extrapolation
Using
a
Toxicodynamic
Model
3-21
3.3.3.
Extrapolation
Using
a
Low-dose
Linear
Model
3-23
3.3.4.
Nonlinear
Extrapolation
to
Lower
Doses
3-23
3.3.5.
Comparing
and
Combining Multiple
Extrapolations
3-24
3.4.
EXTRAPOLATION
TO
DIFFERENT
HUMAN
EXPOSURE
SCENARIOS
3-26
3.5.
EXTRAPOLATION
TO
SUSCEPTIBLE
POPULATIONS
AND
LIFESTAGES
3-29
3.6.
UNCERTAINTY
3-29
3.7.
DOSE-RESPONSE
CHARACTERIZATION
3-32
4.
EXPOSURE
ASSESSMENT
4-i
4.1.
DEFINING
THE
ASSESSMENT
QUESTIONS
4-1
4.2.
SELECTING
OR
DEVELOPING
THE
CONCEPTUAL
AND
MATHEMATICAL
MODELS
4-3
4.3.
COLLECTING
DATA
OR SELECTING
AND
EVALUATING
AVAILABLE
DATA
4-3
4.3.1.
Adjusting
Unit
Risks
for
Highly
Exposed
Populations
and
Lifestages
. 4-4
4.4. EXPOSURE
CHARACTERIZATION
4-5
5. RISK
CHARACTERIZATION
5-1
5.1.
PURPOSE
5-1
5.2.
APPLICATION
5-4
5.3.
PRESENTATION
OF
THE
RISK
CHARACTERIZATION
SUMMARY
5-5
5.4. CONTENT
OF THE
RISK
CHARACTERIZATION
SUMMARY
5-6
APPENDIX:
MAJOR
DEFAULT
OPTIONS
A-i
APPENDIX B:
EPA’s
GUIDANCE
FOR
DATA
QUALITY
ASSESSMENT
B-i
REFERENCES
R-l
LIST
OF
FIGURES
Figure
1-1.
Flow
chart
for
early-life
risk
assessment
using
mode
of
action
framework
....
1-23
Figure
3-1.
Compatibility
of
Alternative
Points
of
Departure
with
Observed
and
Modeled
Tumor
Incidences
3.35
Figure
3-2.
Crossing
between
10%
and
1% Dose-Response
Curves
for Bladder
Carcinomas
and
Liver
Carcinomas
Induced
by
2-AAF
3-35
1.
INTRODUCTION
1.1.
PURPOSE
AND
SCOPE
OF
THE
GUIDELINES
These
guidelines
revise
and
replace
the
U.S.
Environmental
Protection
Agency’s
(EPA’s,
or
the
Agency’s)
Guidelines
for
Carcinogen
Risk Assessmen4
published
in
51
FR
33992,
September
24,
1986
(U.S.
EPA,
1986a)
and
the 1999
interim
final
guidelines
(U.S. EPA,
1999a;
see
U.S.
EPA
2001b).
They
provide
EPA
staff with
guidance
for
developing
and using
risk
assessments.
They
also
provide
basic
information
to the
public
about
the Agency’s
risk
assessment
methods.
These
cancer
guidelines
are used
with
other
risk assessment
guidelines,
such
as
the
Guidelines
for
Mutagenicity
Risk
Assessment
(U.S.
EPA,
1986b)
and the
Guidelines
for
Exposure
Assessment
(U.S.
EPA,
1 992a).
Consideration
of other
Agency
guidance
documents
is
also
important
in
assessing
cancer
risks
where procedures
for
evaluating
specific
target
organ
effects
have
been developed
(e.g.,
assessment
of
thyroid
follicular
cell tumors,
U.S. EPA,
1998a).
All of EPA’s
guidelines
should
be
consulted
when
conducting
a risk
assessment
in
order
to
ensure
that information
from
studies
on
carcinogenesis
and
other
health
effects
are
considered
together
in the
overall
characterization
of
risk.
This
is particularly
true
in
the
case in
which
a
precursor
effect
for a tumor
is
also
a precursor
or
endpoint
of
other health
effects
or when
there
is
a concern
for a
particular
susceptible
life-stage
for
which
the
Agency
has
developed
guidance,
for
example,
Guidelines
for Developmental
Toxicity
RiskAssessment
(U.S.
EPA,
1991a).
The
developmental
guidelines
discuss
hazards
to children
that
may
result
from exposures
during
preconception and prenatal
or
postnatal
development
to sexual
maturity.
Similar
guidelines
exist
for
reproductive
toxicant
risk
assessments
(U.S.
EPA,
1 996a)
and
for neurotoxicity
risk
assessment
(U.S.
EPA,
1
998b).
The
overall
characterization
of
risk is
conducted
within
the
context
of
broader
policies
and guidance
such
as
Executive
Order
13045,
“Protection
of
Children
From
Environmental
Health
Risks
and Safety
Risks”(
Executive
Order
13045,
1997)
which
is
the
primary
directive
to federal
agencies
and
departments
to identifi
and
assess environmental
health
risks and
safety
risks
that may
disproportionately
affect
children.
1—1
The
cancer
guidelines
encourage
both
consistency
in the
procedures
that
support
scientific
components
of
Agency
decision
making
and
flexibility
to
allow
incorporation
of
innovations and
contemporaneous
scientific
concepts.
In
balancing
these
goals,
the
Agency
relies
on
established
scientific
peer
review
processes
(U.S.
EPA,
2000a;
0MB
2004).
The
cancer
guidelines
incorporate
basic
principles
and
science
policies
based
on
evaluation
of the
currently
available
information.
The Agency
intends
to revise
these
cancer
guidelines
when
substantial
changes
are necessary.
As
more
information
about
carcinogenesis
develops,
the
need
may
arise
to
make
appropriate
changes
in
risk
assessment
guidance.
In
the
interim,
the
Agency
intends
to
issue
special
reports,
after
appropriate
peer
review,
to
supplement
and
update
guidance
on
single
topics
(e.g.,
U.S.
EPA,
1991b).
One
such
guidance document,
Supplemental
Guidance
for
Assessing
Susceptibilityfrom
Early-Lfe
Exposure
to
Carcinogens
(“Supplemental
Guidance”),
was
developed
in
conjunction
with
these
cancer
guidelines
(U.S.
EPA.,
2005).
Because
both
the
methodology
and
the data
in
the
Supplemental
Guidance
(see Section
1.3.6)
are
expected
to
evolve
more
rapidly
than
the
issues
addressed
in
these
cancer
guidelines,
the
two
were
developed
as separate
documents.
The Supplemental
Guidance,
however,
as
well
as
any
other
relevant
(including
subsequent) guidance
documents,
should
be
considered
along
with
these
cancer
guidelines
as risk
assessments
for
carcinogens
are
generated.
The
use
of supplemental
guidance,
such
as
the
Supplemental
Guidance
for
Assessing
Cancer
Susceptibility
from
Early-life
Exposure
to
Carcinogens,
has
the
advantage
of
allowing
the Supplemental
Guidance
to be
modified
as
more
data become available.
Thus,
the
consideration
of
new,
peer-reviewed
scientific
understanding and
data
in
an
assessment
can
always
be
consistent
with
the purposes
of
these
cancer
guidelines.
These
cancer
guidelines
are
intended
as
guidance
only.
They
do not
establish
any
substantive “rules”
under
the
Administrative
Procedure
Act
or any
other
law
and have
no binding
effect
on
EPA
or any
regulated
entity,
but instead
represent
a non-binding
statement
of policy.
EPA
believes
that
the cancer
guidelines
represent
a
sound
and
up-to-date
approach
to
cancer
risk
assessment,
and
the
cancer
guidelines
enhance
the
application
of
the
best available
science
in
EPA’s
risk
assessments. However,
EPA
cancer
risk
assessments
may
be
conducted
differently
than
envisioned in the
cancer
guidelines
for
many
reasons,
including
(but
not limited
to)
new
1-2
information,
new scientific
understanding,
or new
science
policy
judgment.
The
science
of
risk
assessment
continues
to
develop
rapidly,
and
specific
components
of
the
cancer
guidelines
may
become
outdated
or may
otherwise
require
modification
in individual
settings.
Use
of
the cancer
guidelines
in
future
risk
assessments will
be based
on
decisions
by
EPA
that
the
approaches
are
suitable
and
appropriate
in the
context
of those
particular
risk
assessments.
These
judgments
will
be
tested
through
peer
review,
and
risk
assessments
will
be modified
to
use different
approaches
if appropriate.
1.2.
ORGANIZATION
AND
APPLICATION
OF
THE
CANCER
GUIDELINES
1.2.1.
Organization
Publications
by
the Office
of
Science
and
Technology
(OSTP,
1985)
and
the National
Research
Council
(NRC)
(NRC,
1983,
1994)
provide
information
and
general
principles
about
risk
assessment.
Risk
assessment
uses
available
scientific
information
on
the
properties
of an
agent’
and
its
effects
in
biological
systems to
provide
an
evaluation
of the
potential
for
harm
as
a
consequence
of
environmental
exposure. The
1983
and
1994
NRC
documents
organize
risk
assessment
information
into
four
areas:
hazard
identification,
dose-response
assessment,
exposure assessment,
and risk
characterization.
This
structure
appears
in these
cancer
guidelines,
with
additional
emphasis
placed
on
characterization
of
evidence
and
conclusions
in
each
area
of
the assessment.
In
particular,
the
cancer
guidelines
adopt
the
approach
of the
NRC’s
1994
report
in
adding
a
dimension
of
characterization
to the
hazard
identification
step:
an evaluation
of the
conditions under
which
its
expression
is
anticipated.
Risk
assessment
questions
addressed
in
these
cancer
guidelines
are
as follows.
•
For
hazard—Can
the
identified
agent
present
a carcinogenic
hazard
to
humans
and,
if
so, under
what
circumstances?
•
For
dose
response—At
what
levels
of
exposure
might
effects
occur?
The
term “agent”
refers
generally
to any
chemical
substance,
mixture,
or
physical
or biological
entity
being
assessed,
unless
otherwise
noted
(See
Section
1.2.2
for
a note
on
radiation.).
1-3
•
For
exposure—What
are
the
conditions
of
human
exposure?
•
For
risk—What
is the
character
of the
risk?
How
well
do
data
support
conclusions
about
the
nature
and extent
of the
risk
from
various
exposures?
The
risk
characterization
process
first
summarizes
findings
on
hazard,
dose
response,
and
exposure
characterizations
and
then
develops
an integrative
analysis
of the
whole
risk
case.
It
ends
in the
writing
of
a technical
risk characterization.
Other
documents,
such
as
summaries
for
the
risk
managers
and
the
public,
reflecting
the
key points
of the
risk
characterization
are
usually
written.
A
summary
for
managers
is a
presentation
for those
who
may
or
may
not be
familiar
with
the scientific details
of cancer
assessment.
It
also
provides
information
for other
interested
readers.
The
initial
steps
in
the risk
characterization
process
are
to make
building
blocks
in
the
form
of
characterizations
of the
assessments
of hazard,
dose
response,
and
exposure.
The
individual
assessments
and
characterizations
are then
integrated
to
arrive
at
risk
estimates
for
exposure
scenarios
of
interest.
As part
of the
characterization
process,
explicit
evaluations
are
made
of the
hazard
and
risk potential
for
susceptible
lifestages,
including
children
(U.S.
EPA,
1995,
2000b).
The
1994
NRC
document
also
explicitly
called
attention
to the
role
of
the risk
assessment
process
in
identifying
scientific
uncertainties
that,
if addressed,
could
serve
to
reduce
their
uncertainty
in
future
iterations
of
the
risk assessment.
NRC
recommended
that
when
the
Agency
“reports
estimates
of risk
to decisions-makers
and the
public,
it
should
present
not
only
point
estimates
of
risk,
but
also the
sources
and
magnitudes
of
uncertainty
associated
with these
estimates”
(p.
15). Thus,
the identified
uncertainties
serve
as a feedback
loop
to
the
research
community
and
decisionmakers,
specifying
areas
and
types
of
information
that
would
be
particularly
useful.
There
are
several
reasons
for individually
characterizing
the
hazard,
dose
response,
and
exposure
assessments.
One
is that
they
are
often
done
by
different
people
than
those
who
do
the
integrative
analyses.
The
second
is that
there
is
very
often
a lapse
of
time
between
the
conduct
of
hazard
and
dose-response analyses
and
the
conduct
of
exposure
assessment
and
integrative
1-4
analysis.
Thus,
it
is important
to capture
characterizations
of assessments
as
the
assessments
are
done to avoid
the
need
to
go
back and
reconstruct
them.
Finally, frequently
a single
hazard
assessment
is
used
by several
programs
for several
different
exposure
scenarios.
There
may
be
one or several
documents
involved.
“Integrative
analysis”
is
a generic
term; and many
documents
that
have other titles
may
contain
integrative analyses.
In
the
following
sections,
the
elements
of these
characterizations
are discussed.
1.2.2.
Application
The cancer
guidelines
apply within
the
framework
of policies
provided
by
applicable
EPA statutes
and
do
not alter
such
policies.
•
The cancer
guidelines
cover the assessment
of
available
data. They
do not
imply
that
one
kind of data
or
another
is prerequisite
for regulatory
action concerning
any
agent.
It is
important
that, when
evaluating
and
considering
the use
of any data,
EPA analysts
incorporate
the basic standards
of
quality,
as defined
by the
EPA
Information
Quality
Guidelines
(U.S.
EPA,
2002a
see Appendix
B) and
other
Agency guidance
on
data
quality
such as
the EPA Quality
Manual
for
Environmental
Programs
(U.S.
EPA,
2000e),
as well
as 0MB
Guidelines
for
Ensuring
and
Maximizing
the
Quality,
Utility, and
Integrity
of
Information
Disseminated
by
Federal
Agencies
(0MB,
2002).
It is very
important
that
all analyses
consider
the
basic
standards
of
quality,
including
objectivity,
utility,
and
integrity. A
summary
of the factors
and
considerations
generally
used
by
the
Agency
when
evaluating
and
considering
the use
of scientific
and
technical
infonnation
is
contained
in
EPA’s
A Summary
of General
Assessment
Factorsfor
Evaluating
the
Quality
ofScientific
and Technical
Information
(U.S.
EPA, 2003).
•
Risk
management
applies
directives
in
statutes,
which
may
require
consideration
of
potential
risk
or
solely
hazard
or exposure
potential,
along with
social,
economic,
technical,
and other
factors
in decision
making.
Risk assessments
may
be used
to
support
1-5
decisions,
but
in order
to
maintain
their
integrity
as
decision-making
tools,
they
are
not
influenced
by
consideration
of
the
social
or
economic
consequences
of
regulatory
action.
The
assessment
of
risk
from
radiation
sources
is
informed
by
the continuing
examination
of human
data
by the
National
Academy
of
Sciences/NRC
in its
series
of numbered
reports:
“Biological
Effects
of
Ionizing
Radiation.”
Although
some
of the
general
principles
of
these
cancer
guidelines
may
also
apply
to radiation
risk
assessments,
some
of
the details
of
their
risk
assessment
procedures
may
not,
as they
are
most
focused
on
other
kinds
of agents.
Therefore,
these
cancer
guidelines
are
not
intended
to
provide
the
primary
source
of, or
guidance
for,
the
Agency’s
evaluation
of
the
carcinogenic
risks
of radiation.
Not
every
EPA
assessment
has
the
same
scope
or
depth,
a
factor
recognized
by
the
National
Academy
of
Sciences
(NRC,
1996).
For
example,
EPA’s
Information
Quality
Guidelines
(U.S.
EPA,
2002a,
see Appendix
B)
discuss
influential
information
that
“will
have
or
does
have
a clear
and
substantial
impact
... on
important
public
policies
or
private
sector
decisions ... that
should
adhere
to a rigorous
standard
of quality.”
It is
often
difficult
to
know
a
priori
how
the results
of a
risk
assessment
are
likely
to
be
used
by
the
Agency.
Some
risk
assessments
may
be
used
by
Agency
economists
and
policy
analysts,
and
the
necessary
information
for such
analyses,
as discussed
in
detail
later in
this
document,
should
be
included
when
practicable
(U.S.
EPA,
2002a).
On the
other
hand,
Agency
staff
often
conduct
screening-
level
assessments
for
priority
setting
or separate
assessments
of
hazard
or
exposure
for
ranking
purposes
or
to
decide
whether
to invest
resources
in collecting
data
for a
full
assessment.
Moreover,
a
given
assessment
of
hazard
and
dose
response
may
be
used
with
more
than
one
exposure
assessment
that
may
be
conducted
separately
and
at different
times
as
the need
arises
in
studying
environmental
problems
related
to various
exposure
media.
The
cancer
guidelines
apply
to
these
various
situations
in
appropriate
detail,
given
the scope
and
depth
of
the
particular
assessment.
For
example,
a screening
assessment
may
be
based
almost
entirely
on
structure
activity
relationships
(SARs)
and
default
options,
when
other
data
are
not
readily
available.
When
more
data and
resources
are readily
available,
assessments
can
use
a critical
analysis
of all
of
the
available
data
as the
starting
point
of
the risk
assessment.
Under
these
conditions,
default
1-6
options
would
only
be
used
to address
uncertainties
or the
absence
of
critical
data.
Default
options
are inferences
based
on
general
scientific
knowledge
of the
phenomena
in
question
and
are also
matters
of
policy
concerning
the
appropriate
way
to bridge
uncertainties
that
concern
potential
risk
to human
health.
These
cancer
guidelines
do
not
suggest
that
all
of the
kinds
of
data
covered
here
will
need
to be
available
or used
for
either
assessment
or
decision
making.
The
level
of
detail
of an
assessment
is
a matter
of
Agency
management discretion
regarding
applicable
decision-making
needs.
The Agency
generally
presumes that
key
cancer
information
(e.g.,
assessments
contained
in
the
Agency’s
Integrated
risk
Information
System)
is
“influential
information”
as defined
by
the
EPA
Information
Quality
Guidelines
and
“highly
influential”
as
defined
by
OMB’s
Information
Quality
Bulletin
for Peer
Review
(0MB
2004).
1.3.
KEY
FEATURES
OF
THE
CANCER
GUIDELINES
1.3.1.
Critical
Analysis
of
Available
Information
as
the
Starting
Point
for
Evaluation
As
an increasing understanding
of
carcinogenesis
is becoming
available,
these
cancer
guidelines
adopt
a view
of
default
options
that
is
consistent
with
EPA’s
mission
to
protect
human
health
while
adhering
to the
tenets
of
sound
science. Rather
than
viewing
default
options
as the
starting
point
from
which
departures
may
be
justified
by
new
scientific
information,
these
cancer
guidelines
view
a
critical
analysis
of
all
of
the available
information
that
is relevant
to
assessing
the
carcinogenic
risk
as
the
starting
point
from which
a
default
option
may
be
invoked
if
needed
to
address
uncertainty
or
the
absence
of
critical
information.
Preference
is
given
to using
information that
has
been
peer reviewed,
e.g.,
reported
in
peer-reviewed
scientific
journals.
The
primary
goal
of EPA
actions
is
protection
of human
health;
accordingly,
as an
Agency
policy,
risk
assessment
procedures,
including
default
options
that
are
used
in
the
absence
of
scientific
data
to
the
contrary,
should
be
health
protective
(U.S.
EPA,
1 999b).
Use
of health
protective
risk
assessment
procedures
as described
in
these
cancer
guidelines
means
that
estimates,
while
uncertain,
are
more
likely
to
overstate
than
understate
hazard
and/or
risk.
NRC
(1994)
reaffirmed
the
use
of
default
options
as
“a
reasonable
way
to
cope
with
uncertainty
about
the choice
of
appropriate
models
or
theory”
(p.
104).
NRC
saw
the
1-7
need
to
treat
uncertainty
in
a
predictable
way
that
is
“scientifically
defensible,
consistent
with
the
agency’s
statutory
mission,
and
responsive
to
the
needs
of
decision-makers”
(p.
86).
The
extent
of
health
protection
provided
to
the
public
ultimately
depends
upon
what
risk
managers
decide
is
the
appropriate
course
of
regulatory
action.
When
risk
assessments
are
performed
using
only
one
set
of
procedures,
it
may
be
difficult
for
risk
managers
to
determine
how
much
health
protectiveness
is
built
into
a
particular
hazard
determination
or
risk
characterization.
When
there
are
alternative
procedures
having
significant
biological
support,
the
Agency
encourages
assessments
to
be
performed
using
these
alternative
procedures,
if
feasible,
in
order
to
shed
light
on
the
uncertainties
in
the
assessment,
recognizing
that
the
Agency
may
decide
to
give
greater
weight
to
one
set
of
procedures
than
another
in
a
specific
assessment
or
management
decision.
Encouraging
risk
assessors
to
be
receptive
to
new
scientific
information,
NRC
discussed
the
need
for
departures
from
default
options
when
a
“sufficient
showing”
is
made.
It
called
on
EPA
to
articulate
clearly
its
criteria
for
a
departure
so
that
decisions
to
depart
from
default
options
would
be
“scientifically
credible
and
receive
public
acceptance”
(p.
91).
It
was
concerned
that
ad
hoc
departures
would
undercut
the
scientific
credibility
of
a
risk
assessment.
NRC
envisioned
that
principles
for
choosing
and
departing
from
default
options
would
balance
several
objectives,
including
“protecting
the
public
health,
ensuring
scientific
validity,
minimizing
serious
errors
in
estimating
risks,
maximizing
incentives
for
research,
creating
an
orderly
and
predictable
process,
and
fostering
openness
and
trustworthiness”
(p.
81).
Appendices
N-i
and
N-2
of
NRC
(1994)
discussed
two
competing
standards
for
choosing
default
options
articulated
by
members
of
the
committee.
One
suggested
approach
would
evaluate
a
departure
in
terms
of
whether
“it
is
scientifically
plausible”
and
whether
it
“tends
to
protect
public
health
in
the
face
of
scientific
uncertainty”
(p.
601).
An
alternative
approach
“emphasizes
scientific
plausibility
with
regard
to
the
use
of
alternative
models”
(p.
631).
Reaching
no
consensus
on
a
single
approach,
NRC
recognized
that
developing
criteria
for
departures
is
an
EPA
policy
matter.
The
basis
for
invoking
a
default
option
depends
on
the
circumstances.
Generally,
if
a
gap
in
basic
understanding
exists
or
if
agent-specific
information
is
missing,
a
default
option
may
be
used.
If
agent-specific
information
is
present
but
critical
analysis
reveals
inadequacies,
a
default
1-8
option
may
also
be
used. If
critical analysis
of
agent-specific
information
is
consistent
with
one
or more biologically
based models
as well
as
with the
default
option,
the
alternative
models
and
the default
option
are both carried
through
the
assessment
and
characterized
for
the risk
manager.
In this case,
the default
model not
only
fits
the
data,
but also serves
as
a
benchmark
for
comparison
with
other
analyses.
This
case
also highlights
the importance
of extensive
experimentation
to
support
a conclusion
about mode
of action,
including
addressing
the
issue
of
whether alternative
modes
of action
are
also
plausible.
Section
2.4
provides
a
framework
for
critical analysis
of
mode
of action
information
to address
the extent
to
which
the available
information
supports
the hypothesized
mode
of
action,
whether
alternative
modes
of action
are
also
plausible,
and
whether
there
is confidence
that
the
same
inferences
can be
extended
to
populations
and
lifestages
that are not
represented
among
the experimental
data.
Generally,
cancer
risk decisions
strive
to be
“scientifically
defensible,
consistent
with
the
agency’s
statutory
mission,
and
responsive
to
the needs
of decision-makers”
(NRC,
1994).
Scientific
defensibility
would
be evaluated
through
use of EPA’s
Science
Advisory
Board,
EPA’s
Office
of Pesticide
Programs’
Scientific
Advisory
Panel,
or
other
independent
expert
peer
review
panels
to determine
whether
a consensus
among scientific
experts
exists.
Consistency
with
the
Agency’s
statutory
mission
would
consider
whether
the
risk assessment
overall
supports
EPA’s
mission
to protect
human health
and safeguard
the natural
environment.
Responsiveness
to
the
needs of
decisionmakers
would
take
into
account pragmatic
considerations
such
as the nature
of
the decision;
the
required
depth
of
analysis;
the
utility,
time, and
cost of
generating
new
scientific
data; and
the time,
personnel,
and resources
allotted to
the risk
assessment.
With
a
multitude
of
types of data,
analyses,
and risk
assessments,
as well
as
the
diversity
of needs of
decisionmakers,
it is neither
possible
nor desirable
to specif’
step-by-step
criteria
for
decisions
to
invoke a default
option.
A
discussion
of major
default
options
appears
in the
Appendix.
Screening-level
assessments
may
more readily
use
default parameters,
even
worst
case assumptions,
that would
not be appropriate
in a full-scale
assessment.
On
the
other
hand,
significant
risk management
decisions
will
often
benefit
from
a more comprehensive
assessment,
including
alternative
risk models
having
significant
biological
support.
To
the extent
practicable,
such
assessments
should
provide
central
estimates
of
potential
risks
in conjunction
with lower
1-9
and
upper
bounds
(e.g., confidence
limits)
and
a
clear
statement
of the
uncertainty
associated
with
these
estimates.
In
the
absence
of
sufficient
data
or
understanding
to develop
of
a robust,
biologically
based model,
an
appropriate
policy
choice
is to have
a single
preferred
curve-fitting
model
for
each
type
of data
set. Many
different
curve-fitting
models
have
been
developed,
and those
that
fit
the observed
data reasonably
well may
lead
to several-fold
differences
in estimated
risk
at the
lower
end of
the
observed
range.
In
addition,
goodness-of-fit
to the
experimental
observations
is
not
by itself
an
effective
means
of discriminating
among
models
that
adequately
fit
the
data
(OSTP,
1985). To
provide
some
measure
of consistency
across
different
carcinogen
assessments,
EPA
uses
a standard
curve-fitting
procedure
for tumor
incidence
data.
Assessments
that include
a
different
approach
should
provide
an
adequate
justification
and compare
their
results
with
those
from
the
standard
procedure.
Application
of
models
to data
should
be
conducted
in an
open
and
transparent
manner.
1.3.2.
Mode
of
Action
The
use
of mode
of action
2
in the
assessment
of potential
carcinogens
is a main
focus
of
these
cancer
guidelines.
This
area
of emphasis
arose
because
of the
significant
scientific
advances
that
have
developed
concerning
the
causes
of cancer
induction.
Elucidation
of a
mode
of
action
for a
particular
cancer
response
in
animals
or
humans
is a data-rich
determination.
Significant
information
should
be
developed
to
ensure
that a
scientifically
justifiable
mode
of
action
underlies
the process
leading
to cancer
at
a
given
site.
In
the
absence
of
sufficiently,
scientifically
justifiable
mode
of
action
information,
EPA
generally
takes
public
health-
protective,
default
positions
regarding
the
interpretation
of toxicologic
and
epidemiologic
data:
2
The
term
“mode
ofaction”
is defined
as a
sequence
of key
events
and processes,
starting with
interaction
of an agent
with a
cell,
proceeding
through
operational
and anatomical
changes,
and
resulting
in
cancer
formation.
A
“key
event”
is
an
empirically
observable
precursor
step that
is itself a
necessary
element
of the
mode
of action
or
is
a
biologically
based
marker
for
such an element.
Mode
of action
is
contrasted
with
“mechanism
of
action,”
which
implies
a more
detailed
understanding
and description
of
events,
often
at the molecular
level,
than is
meant
by
mode
of
action.
The
toxicokinetic
processes
that
lead
to formation
or distribution
of the
active
agent
to the target
tissue
are
considered
in
estimating
dose
but
are
not
part
of
the mode
of
action
as the term
is
used
here.
There
are many
examples
of possible
modes
of
carcinogenic
action, such
as mutagenicity,
mitogenesis,
inhibition
of cell
death,
cytotoxicity
with
reparative
cell
proliferation,
and
immune
suppression.
1-10
animal
tumor
findings
are judged
to
be relevant
to
humans,
and
cancer
risks
are
assumed
to
conform
with
low dose
linearity.
Understanding
of
mode
of
action
can
be a key
to identifying
processes
that
may
cause
chemical
exposures
to
differentially
affect
a
particular
population
segment
or
lifestage.
Some
modes
of action
are
anticipated
to be
mutagenic
and
are assessed
with
a linear
approach.
This
is
the mode
of
action
of
radiation
and
several
other
agents
that
are
known
carcinogens.
Other
modes
of
action
may
be modeled
with
either
linear
or
nonlinear
3
approaches
after
a
rigorous
analysis
of
available
data
under
the guidance
provided
in the
framework
for mode
of
action
analysis
(see
Section
2.4.3).
1.3.3.
Weight
of
Evidence
Narrative
The
cancer
guidelines
emphasize
the
importance
of weighing
all of
the
evidence
in
reaching
conclusions about
the
human
carcinogenic
potential
of agents.
This
is
accomplished
in
a single
integrative
step
after
assessing
all
of
the
individual
lines
of
evidence,
which
is
in
contrast
to
the step-wise
approach
in
the
1986 cancer
guidelines.
Evidence
considered
includes
tumor
findings,
or
lack thereof
in
humans
and
laboratory
animals;
an
agent’s
chemical
and
physical
properties;
its
structure-activity
relationships
(SARs)
as compared
with
other
carcinogenic
agents;
and
studies
addressing
potential
carcinogenic
processes
and
mode(s)
of action,
either
in
vivo
or
in
vitro.
Data
from
epidemiologic
studies
are
generally
preferred
for
characterizing
human
cancer
hazard
and
risk.
However,
all
of
the
information
discussed
above
could
provide
valuable
insights
into
the
possible
mode(s)
of
action
and
likelihood
of human
cancer
hazard
and
risk.
The
cancer
guidelines
recognize
the
growing
sophistication
of
research
methods,
3
The
term
“nonlinear”
is used
here
in a narrower
sense
than its
usual
meaning
in the
field
of
mathematical
modeling.
In
these
cancer
guidelines,
the
term
“nonlinear”
refers
to
threshold
models
(which
show
no
response
over
a range
of
low
doses
that include
zero)
and
some
nonthreshold
models
(e.g.,
a quadractic
model,
which
shows
some
response
at
all
doses
above
zero).
In these
cancer
guidelines,
a nonlinear
model
is one whose
slope
is
zero
at
(and
perhaps
above)
a dose
of zero.
A
low-dose-linear
model
is
one
whose
slope
is
greater
than
zero
at a dose
of zero.
A
low-dose-linear model
approximates
a straight
line only
at
very
low
doses;
at
higher
doses
near
the
observed
data,
a
low-dose-linear model
can
display
curvature.
The
term
“low-dose-linear”
is
often abbreviated
“linear,”
although
a
low-dose-linear model
is
not linear
at all
doses.
Use
of nonlinear
approaches
does
not imply
a
biological
threshold
dose
below
which
the
response
is zero.
Estimating
thresholds
can
be problematic;
for
example,
a response
that
is not
statistically
significant
can be
consistent
with a
small
risk that
falls
below
an
experiment’s
power
of
detection.
1—11
particularly
in their
ability
to
reveal
the
modes
of action
of carcinogenic
agents
at cellular
and
subcellular
levels
as
well
as toxicokinetic
processes.
Weighing
of the
evidence
includes
addressing not
only
the
likelihood
of
human
carcinogenic
effects
of
the
agent
but
also the
conditions
under
which
such
effects
may
be
expressed,
to
the
extent
that
these
are revealed
in the
toxicological
and
other
biologically
important
features
of
the agent.
The
weight
of
evidence
narrative
to characterize
hazard
summarizes
the results
of
the
hazard
assessment
and
provides
a
conclusion
with
regard
to human
carcinogenic
potential.
The
narrative
explains
the
kinds
of evidence
available
and how
they
fit
together
in
drawing
conclusions,
and
it points
out significant
issues/strengths/limitations
of
the
data and
conclusions.
Because
the narrative
also
summarizes
the
mode
of action
information,
it sets
the
stage
for
the
discussion
of
the
rationale
underlying
a
recommended
approach
to
dose-response
assessment.
In
order
to
provide
some
measure
of clarity
and consistency
in
an
otherwise
free-form,
narrative
characterization,
standard
descriptors
are used
as
part
of
the hazard
narrative
to
express
the
conclusion
regarding
the
weight
of
evidence
for
carcinogenic
hazard
potential.
There
are
five
recommended
standard
hazard
descriptors:
“Carcinogenic
to Humans,”
“Likely
to
Be
Carcinogenic to
Humans,”
“Suggestive
Evidence
of Carcinogenic Potential,”
“Inadequate
Information
to
Assess
Carcinogenic
Potential,”
and “Not
Likely
to
Be Carcinogenic
to
Humans.”
Each
standard
descriptor
may
be applicable
to
a wide
variety
of data
sets
and weights
of
evidence
and
is presented only in
the
context
of
a
weight
of
evidence
narrative.
Furthermore,
as
described
in
Section
2.5
of these
cancer
guidelines,
more
than
one
conclusion
may
be
reached
for
an
agent.
1.3.4.
Dose-response
Assessment
Dose-response
assessment evaluates
potential
risks
to humans
at
particular
exposure
levels.
The
approach
to
dose-response
assessment
for
a particular
agent
is
based
on the
conclusion reached
as
to its
potential
mode(s)
of
action
for each
tumor
type.
Because
an
agent
may
induce
multiple
tumor
types,
the dose-response
assessment
includes
an analysis
of all
tumor
types,
followed
by an
overall
synthesis
that includes
a
characterization
of the
risk estimates
across
tumor
types,
the
strength
of the
mode
of
action
information
of
each
tumor
type,
and
the
1-12
anticipated
relevance
of each
tumor type
to
humans,
including
susceptible
populations
and
lifestages
(e.g.,
childhood).
Dose-response
assessment
for
each
tumor
type
is
performed
in two
steps:
assessment
of
observed
data
to derive
a
point
of
departure
(POD),
4
followed
by extrapolation
to
lower
exposures
to
the
extent
that is
necessary.
Data
from
epidemiologic
studies,
of sufficient
quality,
are generally
preferred
for estimating
risks.
When
animal studies
are
the
basis
of the
analysis,
the estimation
of a
human-equivalent
dose
should
utilize
toxic
okinetic
data
to
inform
cross
species
dose
scaling
if appropriate
and if
adequate
data
are
available.
Otherwise,
default
procedures
should
be applied.
For
oral
dose,
based
on
current
science,
an
appropriate
default
option
is to
scale
daily
applied
doses experienced
for
a lifetime
in
proportion
to
body
weight
raised
to the
3/4
power
(U.S.
EPA, 1
992b).
For inhalation
dose,
based
on
current
science,
an
appropriate
default
methodology
estimates
respiratory
deposition
of
particles
and
gases
and
estimates
internal
doses
of gases
with different
absorption
characteristics.
When
toxicokinetic
modeling
(see
Section
3.1.2)
is used
without
toxicodynamic
modeling
(see
Section
3.2.2),
the
dose-response
assessment
develops
and
supports
an
approach
for
addressing
toxicodynamic
equivalence,
perhaps
by
retaining
some
of the
cross-species
scaling
factor
(see Section
3.1.3).
Guidance
is
also provided
for
adjustment
of dose
from
adults
to
children
(see
Section
4.3.1).
Response
data on
effects
of the
agent on
carcinogenic
processes
are analyzed
(nontumor
data)
in
addition
to
data on
tumor
incidence.
If
appropriate,
the analyses
of data
on tumor
incidence
and
on
precursor
effects
may
be
used in
combination.
To
the
extent the
relationship
between
precursor
effects
and
tumor
incidence
are
known,
precursor
data
may
be used
to
estimate
a
dose-response
function
below
the observable
tumor
data.
Study
of the
dose-response
function
for
effects
believed
to
be
part
of the
carcinogenic
process
influenced
by
the agent
may
also
assist
in
evaluating
the
relationship
of exposure
and response
in
the range
of observation
and
at
exposure
levels
below
the
range
of
observation.
‘
A
“point
of
departure”
(POD)
marks
the
beginning
of extrapolation
to
lower
doses.
The
POD
is an
estimated
dose
(usually
expressed
in
human-equivalent
terms)
near
the
lower end
of
the
observed
range,
without
significant
extrapolation
to lower
doses.
1-13
The
first step
of
dose-response
assessment
is
evaluation
within
the
range
of
observation.
Approaches
to
analysis
of
the
range
of
observation
of
epidemiologic
studies
are
determined
by
the
type
of
study
and
how
dose
and
response
are
measured
in the
study.
In
the
absence
of
adequate
human
data
for dose-response analysis,
animal
data
are
generally
used.
If there
are
sufficient
quantitative
data
and
adequate
understanding
of
the carcinogenic
process,
a
biologically
based
model
may
be
developed
to
relate
dose
and
response
data
on an
agent-specific
basis.
Otherwise,
as a
default
procedure,
a standard
model
can be
used
to curve-fit
the
data.
The
POD
for
extrapolating
the
relationship
to
environmental
exposure
levels
of
interest,
when
the latter
are
outside
the
range
of
observed
data,
is
generally
the
lower
95% confidence
limit
on
the
lowest
dose
level
that
can
be
supported
for
modeling
by
the data.
SAB
(1997)
suggested
that,
“it may
be
appropriate
to
emphasize
lower
statistical
bounds
in
screening
analyses
and
in
activities
designed
to develop
an
appropriate
human
exposure
value,
since
such
activities
require
accounting
for
various
types
of uncertainties
and a
lower
bound
on
the
central
estimate
is
a scientifically-based
approach
accounting
for
the
uncertainty
in
the
true
value
of
the
ED
10
[or
central
estimate].”
However,
the
consensus
of
the
SAB
(1997)
was
that,
“both
point
estimates
and
statistical
bounds
can
be
useful
in
different
circumstances,
and
recommended
that the
Agency
routinely
calculate
and
present
the
point
estimate
of
the
ED
10
[or
central
estimate]
and
the
corresponding
upper
and
lower
95%
statistical
bounds.”
For example,
it may
be
appropriate
to emphasize the central
estimate
in
activities
that
involve
formal
uncertainty
analysis
that
are
required
by
0MB
Circular
A-4
(0MB,
2003)
as
well
as ranking
agents
as
to their
carcinogenic
hazard.
Thus,
risk assessors should
calculate,
to
the
extent
practicable,
and
present
the
central
estimate
and
the corresponding
upper
and
lower
statistical
bounds
(such
as
confidence
limits)
to
inform
decisiomiiakers.
The
second
step
of
dose-response
assessment
is
extrapolation
to
lower
dose
levels,
if
needed.
This
extrapolation
is based
on
extension
of a
biologically
based
model
if supported
by
substantial data
(see Section
3.3.2).
Otherwise,
default
approaches
can be
applied
that are
consistent
with
current
understanding
of
mode(s)
of
action
of the
agent,
including
approaches
that
assume
linearity
or
nonlinearity
of the
dose-response
relationship,
or
both.
A
default
approach
for
linearity
extends
a
straight
line
from
the
POD
to
zero
dose/zero
response
(see
1-14
Section
3.3.3).
The
linear
approach
is used
when:
(1) there
is
an absence
of
sufficient
information
on
modes
of
action
or (2)
the mode
of
action
information
indicates
that
the
dose-
response
curve
at
low dose
is
or is
expected
to
be linear.
Where
alternative
approaches
have
significant
biological
support,
and
no
scientific
consensus
favors
a single
approach,
an
assessment
may
present
results
using
alternative
approaches.
A
nonlinear
approach
can
be
used
to develop
a
reference
dose
or
a reference
concentration
(see
Section
3.3.4).
1.3.5.
Susceptible Populations
and
Lifestages
An important
use
of mode
of
action
information
is
to identify
susceptible
populations
and
lifestages.
It
is rare
to
have
epidemiologic
studies
or
animal
bioassays
conducted
in
susceptible
individuals.
This
information
need
can
be
filled
by
identifying
the
key
events
of
the
mode
of
action
and
then
identifying
risk
factors,
such
as
differences
due
to
genetic
polymorphisms,
disease,
altered
organ
function,
lifestyle,
and
lifestage,
that
can augment
these
key
events.
To
do
this,
the information
about
the
key
precursor
events
is
reviewed
to
identify
particular
populations
or
lifestages
that
can
be particularly
susceptible
to
their
occurrence
(see
Section
2.4.3.4).
Any
information
suggesting
quantitative
differences
between
populations
or lifestages is
flagged
for
consideration in
the dose-response
assessment
(see
Section
3.5
and
U.S.
EPA
2002b).
1.3.6.
Evaluating
Risks
from
Childhood
Exposures
NRC
(1994)
recommended
that
“EPA
should
assess
risks
to
infants
and
children
whenever
it
appears
that
their
risks
might
be
greater
than
those
of
adults.”
Executive
Order
13045
(1997)
requires
that
“each
Federal
Agency
shall
make
it
a high
priority
to
identify
and
assess
environmental
health
and
safety
risks
that
may
disproportionately
affect
children,
and
shall
ensure
that
their
policies,
programs,
and
standards
address
disproportionate
risks
that
result
from
environmental health
risks
or safety
risks.”
In assessing
risks
to children,
EPA
considers
both
effects
manifest
during
childhood
and
early-life
exposures
that
can
contribute
to
effects
at any
time
later
in
life.
These
cancer
guidelines
view
childhood
as a
sequence
of
lifestages
rather
than
viewing
children
as
a
subpopulation, the distinction
being
that
a subpopulation
refers
to a portion
of
the
1-15
population,
whereas
a lifestage
is inclusive
of the
entire
population.
Exposures
that are
of
concern
extend
from
conception
through
adolescence
and
also
include
pre-conception
exposures
of
both
parents.
These
cancer
guidelines
use
the
term
“childhood”
in
this
more
inclusive
sense.
Rarely
are
there
studies
that
directly
evaluate
risks
following
early-life
exposure.
Epidemiologic
studies
of
early-life
exposure
to
environmental
agents
are
seldom
available.
Standard
animal
bioassays
generally
begin
dosing
after
the
animals
are several
weeks
old,
when
many
organ
systems
are
mature.
This
could
lead
to an
understatement
of
risk,
because
an
accepted
concept
in
the
science
of
carcinogenesis
is that
young
animals
are
usually
more
susceptible
to the
carcinogenic
activity
of
a chemical
than
are mature
animals
(McConnell,
1992).
At
this time,
there
is
some
evidence
of higher
cancer
risks
following
early-life
exposure.
For
radiation
carcinogenesis,
data
indicate
that
risks
for
several
forms
of cancer
are
highest
following
childhood exposure
(NRC,
1990;
Miller,
1995;
U.S.
EPA,
1999c).
These
human
results
are
supported
by
the
few
animal
bioassays
that
include
perinatal
(prenatal
or
early
posthatal)
exposure.
Perinatal
exposure
to some
agents
can
induce
higher
incidences
of
the
tumors
seen
in
standard
bioassays;
some
examples
include
vinyl
chloride
(Maltoni
et al.,
1981),
diethylnitrosamine
(Peto
et
al.,
1984),
benzidine,
DDT,
dieldrin,
and
safrole
(Vesselinovitch
et
al.,
1979).
Moreover,
perinatal
exposure
to
some
agents,
including
vinyl
chloride
(Maltoni
et
al.,
1981)
and
saccharin
(Cohen,
1995;
Whysner
and
Williams,
1996),
can
induce
different
tumors
that are
not
seen
in
standard
bioassays.
Surveys
comparing
perinatal
carcinogenesis
bioassays
with
standard
bioassays
for
a limited
number
of chemicals
(McConnell,
1992;
U.S.
EPA,
1
996b)
have
concluded
that
•
the
same
tumor
sites
are
usually
observed
following
either
perinatal
or
adult
exposure,
and
•
perinatal exposure
in
conjunction
with
adult
exposure
usually
increases
the
incidence
of
tumors
or
reduces
the
latent
period
before
tumors
are
observed.
1-16
The
risk attributable
to early-life
exposure
often
appears
modest
compared
with
the
risk
from
lifetime
exposure,
but
it can be
about
10-fold
higher than
the
risk
from
an
exposure
of
similar
duration
occurring
later in
life (Ginsberg,
2003).
Further
research
is
warranted
to
investigate
the
extent
to which
these
findings
apply to
specific
agents,
chemical
classes,
and
modes
of
action
or in
general.
These
empirical
results
are
consistent
with
current
understanding
of the
biological
processes
involved
in
carcinogenesis,
which
leads to
a reasonable
expectation
that
children
can
be
more
susceptible
to many
carcinogenic
agents
(Anderson
et al.,
2000; Birnbaum
and
Fenton,
2003;
Ginsberg,
2003;
Miller
et
al., 2002;
Scheuplein
et al.,
2002).
Some
aspects
potentially
leading
to
childhood
susceptibility
are listed
below.
•
Differences
in the
capacity
to metabolize
and clear
chemicals
can
result
in larger
or
smaller
internal
doses of
the active
agent(s).
•
More
frequent
cell
division
during
development
can
result
in
enhanced
expression
of
mutations
due to
the
reduced
time
available
for repair
of
DNA lesions
(Slikker
et
al.,
2004).
•
Some
embryonic
cells,
such
as brain
cells,
lack key
DNA
repair
enzymes.
•
More
frequent
cell
division
during
development
can
result
in
clonal
expansion
of
cells
with mutations
from
prior
unrepaired
DNA damage
(Slikker
et
al.,
2004).
•
Some
components
of
the
immune
system
are not
fully functional
during
development
(Holladay
and Smialowicz,
2000;
Holsapple
et al.,
2003).
•
Hormonal
systems
operate
at
different
levels
during
different
lifestages.
1-17
Induction
of
developmental
abnormalities
can
result
in
a predisposition
to
carcinogenic effects
later
in
life
(Anderson
et
al., 2000;
Bimbaum
and
Fenton,
2003;
Fenton
and
Davis,
2002).
To
evaluate
risks
from
early-life exposure,
these
cancer
guidelines
emphasize
the
role
of
toxicokinetic
information
to estimate
levels
of the
active
agent
in
children
and toxicodynamic
information
to identify
whether
any
key
events
of the
mode
of
action
are
of increased
concern
early
in life.
Developmental
toxicity
studies
can
provide
information
on
critical
periods
of
exposure
for
particular targets
of
toxicity.
An
approach
to
assessing
risks
from
early-life
exposure
is
presented
in
Figure
1-1.
In the
hazard
assessment,
when
there
are
mode
of
action
data,
the
assessment
considers
whether
these
data
have
special
relevance
during
childhood,
considering
the
various
aspects
of
development
listed
above.
Examples
of
such
data include
toxicokinetics
that
predict
a
sufficiently
large
internal
dose
in children
or a
mode
of
action
where
a
key
precursor
event
is
more
likely
to
occur
during
childhood.
There
is no
recommended
default
to settle
the question
of
whether
tumors
arising
through
a
mode
of
action
are
relevant
during
childhood;
and
adequate
understanding
the
mode
of
action
implies
that
there
are sufficient
data
(on
either
the specific
agent
or
the
general
mode
of action)
to
form
a
confident
conclusion
about
relevance
during
childhood
(see
Section
2.4.3.4).
In the
dose-response
assessment,
the
potential
for
susceptibility
during
childhood
warrants
explicit
consideration
in each
assessment.
These
cancer
guidelines
encourage
developing
separate
risk
estimates
for children
according
to a
tiered
approach
that
considers
what
pertinent
data
are
available
(see Section
3.5).
Childhood
may
be a
susceptible
period;
moreover,
exposures
during
childhood
generally
are not
equivalent
to
exposures
at other
times
and
may
be
treated
differently
from
exposures
occurring
later
in
life
(see
Section
3.5).
In
addition,
adjustment
of
unit risk
estimates
may
be
warranted
when
used
to
estimate
risks
from
childhood
exposure
(see
Section
4.4).
At this
time,
several
limitations
preclude
a full
assessment
of
children’s
risk.
There
are
no
generally
used
testing
protocols
to
identify
potential
environmental
causes
of
cancers
that
are
1-18
unique
to
children,
including
several
forms
of childhood
cancer
and
cancers
that
develop
from
parental
exposures,
and cases
where
developmental
exposure
may alter
susceptibility
to
carcinogen
exposure
in
the adult
(Bimbaum
and
Fenton,
2003).
Dose-response
assessment
is
limited
by
an
inability
to observe
how
developmental
exposure
can
modif’
incidence
and
latency
and
an inability
to estimate
the
ultimate
tumor
response
resulting
from
induced
susceptibility
to
later
carcinogen
exposures.
To partially
address
the
limitations
identified
above,
EPA
developed
in conjunction
with
these
cancer
guidelines,
Supplemental
Guidance
for
Assessing
Susceptibility
from
Early-Lfe
Exposure
to Carcinogens (“Supplemental
Guidance”).
The Supplemental
Guidance
addresses
a
number
of
issues
pertaining
to
cancer
risks
associated
with
early-life
exposures
generally,
but
provides
specific
guidance
on
procedures
for
adjusting
cancer
potency
estimates
only
for
carcinogens
acting
through
a
mutagenic
mode
of
action.
This Supplemental
Guidance
recommends,
for such
chemicals
when
no
chemical-specific
data
exist,
a default
approach
using
estimates
from
chronic
studies
(i.e., cancer
slope
factors)
with
appropriate
modifications
to
address
the
potential
for
differential
risk
of early-lifestage
exposure.
The
Agency
considered
both the
advantages
and
disadvantages
to extending
the
recormnended,
age
dependent
adjustment
factors
for
carcinogenic
potency
to carcinogenic
agents
for
which
the mode
of action
remains
unknown.
EPA decided
to recommend
these
factors
only
for carcinogens
acting
through
a
mutagenic
mode
of
action
based
on a combination
of
analysis
of
available
data
and
long-standing
science
policy
positions
which
govern
the
Agency’s
overall
approach
to
carcinogen
risk
assessment.
In
general,
the Agency
prefers
to rely
on
analyses
of
data, rather
than
general
defaults.
When
data
are
available
for
a sensitive
lifestage,
they
would
be
used directly
to
evaluate
risks
for
that
chemical
and
that
lifestage
on a case-by-case
basis.
In
the
case
of
nonmutagenic
carcinogens,
when
the
mode
of action
is unknown,
the
data were
judged
by
EPA
to be
too
limited
and
the
modes
of
action too
diverse
to use
this
as
a
category
for which
a
general
default
adjustment
factor
approach
can
be
applied.
In this
situation,
a
linear
low-dose
extrapolation
methodology
(without
further
adjustment)
is recommended.
It is
the
Agency’s
long-standing
science
policy
position
that
use
of the
linear
low-dose
extrapolation
approach
1-19
provides
adequate
public
health
conservatism
in the
absence
of
chemical-specific
data
indicating
differential
early-life
sensitivity
or when
the
mode
of
action
is not
mutagenic.
The
Agency
expects
to
produce
additional
supplemental
guidance
for other
modes
of
action,
as
data
from
new
research
and
toxicity
testing
indicate
it is
warranted.
EPA
intends
to
focus
its research,
and
work
collaboratively
with
its
federal
partners,
to improve
understanding
of
the implications
of
early
life
exposure
to
carcinogens.
Development
of
guidance
for
estrogenic
agents
and chemicals
acting
through
other
processes
resulting
in
endocrine
disruption
and
subsequent
carcinogenesis,
for
example,
might
be
a
reasonable
priority
in light
of
the human
experience
with
diethylstilbesterol
and
the
existing
early
life animal
studies.
It
is
worth
noting
that
each
mode
of
action
for
endocrine
disruption
will probably
require
separate
analysis.
As
the
Agency
examines
additional
carcinogenic
agents,
the
age
groupings
may
differ
from
those
recommended for assessing
cancer
risks
from
early-life
exposure
to
chemicals
with
a
mutagenic
mode
of action.
Puberty
and
its associated
biological
changes,
for
example,
involve
many
biological
processes
that
could
lead
to
changes
in
sensitivity
to the
effects
of
some
carcinogens,
depending
on
their
mode
of
action.
The
Agency
is interested
in identifying
lifestages
that
may
be
particularly
sensitive
or refractory
for carcinogenesis,
and believes
that
the
mode
of
action
framework
described
in
these
cancer
guidelines
is an appropriate
mechanism
for
elucidating
these
lifestages. For each
additional
mode
of
action
evaluated,
the
various
age
groupings
determined
to
be at
differential
risk may
differ
from
those
proposed
in
the
Supplemental Guidance.
For
example,
the
age
groupings
selected
for
the
age-dependent
adjustments
for
carcinogens
acting
through
a
mutagenic
mode
of
action
were
initially
selected
based
on the
available
data,
i.e.,
for
the laboratory
animal
age
range
representative
of
birth
to
<
2
years
in
humans.
More
limited
data
and
information
on human
biology
were
used
to
determine
a
science-informed
policy
regarding
2
to
<
16 years.
Data
were
not
available
to
refine
the
latter
age group.
If
more
data
become
available
regarding
carcinogens
with
a
mutagenic
mode
of
action,
consideration
may
be
given
to
further
refinement
of
these
age
groups.
1-20
1.3.7.
Emphasis
on
Characterization
The cancer
guidelines
emphasize
the
importance
of
a
clear
and
useful
characterization
narrative
that
summarizes
the
analyses
of
hazard,
dose-response,
and
exposure
assessment.
These
characterizations
summarize
the
assessments
to explain
the
extent
and weight
of
evidence,
major
points
of
interpretation
and
rationale
for
their
selection,
strengths
and
wealcnesses
of
the
evidence
and
the
analysis,
and
discuss
alternative
conclusions
and uncertainties
that
deserve
serious
consideration
(U.S.
EPA,
2000b).
They
serve
as starting
materials
for the
overall
risk
characterization
process
that
completes
the
risk assessment.
1-21
Figure 1-1.
Flow
chart for
early-life
risk assessment
using
mode
of
action
framework.
Use
linear
extmpolatinn
as
a default.
No
further
analys
of
tumo is.
Model
using
MOA or
use
REYRfC
method
as
default.
Adjuments
for
suseeptible
lifeages
or populatons
are pert
ofthe
process.
Use
the
same
linear
extrapolation
for
all
lifestages,
unless
have
c hemical
specific
information
on lifeages
or
populations
Use
framework
in Cancer
Guidelines
to
establish
MOA(s)
MOA
sufficiently
supported
in animals?
MOA
can
not
be
determined
Yes
MOA
relevant
to
humans?
No
Yes
Flag
lifestage(s)
orpopulation(s)
that
could
be susceptibè
(based
on
information
about
the specific
MOA)
for dose-response
analysis.
Determine
extrapolation
based
on infomiat
inn
about
specific
MOA.
Nonlinear
Line
ar, but
nonmutagenic
Linearitydueto
mutagenicMOA
V
Suppèmental
G.iidancefor
Early-Life
Expures
Were
chemical-specific
data available
Yes
[vebp
chemical-specific
in MOA
analysis
to
evaluate
differences
risk estimates
incorporiting
between
adults
and juveniles
(more,
lifestage
susceptibility.
,orthe
sume
susceptibility)?
No
V
Early- life
suscept
thility
assumed.
Apply
age-
dependent
adist ment
factors
(ADAFs)
as
appropnateto
develop
risk
estimates.
1-22
2.
HAZARD
ASSESSMENT
2.1. OVERVIEW
OF HAZARD
ASSESSMENT
AND
CHARACTERIZATION
2.1.1.
Analyses
of Data
The purpose
of
hazard
assessment
is to
review
and
evaluate
data
pertinent
to two
questions:
(1)
whether
an
agent
may
pose
a
carcinogenic
hazard
to human
beings,
and
(2)
under
what
circumstances
an identified
hazard
may be
expressed
(NRC,
1994). Hazard
assessment
involves
analyses
of a variety
of
data
that may
range
from
observations
of
tumor
responses
to
analysis
of
structure-activity
relationships
(SARs).
The
purpose
of
the
assessment
is
not simply
to
assemble
these
separate
evaluations;
its
purpose
is to construct
a
total
analysis
examining
what
the biological
data reveal
as a
whole
about carcinogenic
effects
and mode
of
action
of the
agent,
and
their
implications
for
human
hazard
and dose-response
evaluation.
Conclusions
are
drawn
from weight-of-evidence
evaluations
based on
the combined
strength
and
coherence
of
inferences
appropriately
drawn
from
all
of
the
available
information.
To
the
extent
that data
permit,
hazard
assessment
addresses
the
question
of
mode
of
action
of an agent
as both
an
initial
step
in
identifying
human
hazard
potential
and as a
component
in
considering
appropriate
approaches
to
dose-response
assessment.
The
topics
in this
chapter
include
analysis
of
tumor
data, both
human
and
animal,
and
analysis
of other
key
information
about
properties
and effects
that
relate
to carcinogenic
potential.
The
chapter
addresses
how
information
can be
used
to evaluate
potential
modes
of
action.
It also
provides
guidance
on performing
a
weight
of
evidence
evaluation.
2.1.2.
Presentation
of
Results
Presentation
of the
results
of
hazard
assessment
should
be informed
by Agency
guidance
as discussed
in
Section
2.6. The
results
are presented
in a technical
hazard
characterization
that
serves
as a
support
to later
risk
characterization.
It
includes:
•
a summary
of
the
evaluations
of hazard
data,
•
the rationales
for
its
conclusions,
and
2-1
an explanation
of the
significant
strengths
or
limitations
of the
conclusions.
Another
presentation
feature
is
the
use
of
a
weight
of evidence
narrative
that
includes
both
a conclusion
about
the
weight
of
evidence
of
carcinogenic
potential
and
a summary
of
the
data
on which
the conclusion
rests.
This
narrative
is a brief
summary
that
in
toto replaces
the
alphanumerical
classification
system
used
in
EPA’s
1986
cancer
guidelines
(U.S.
EPA,
1986a).
2.2.
ANALYSIS
OF
TUMOR
DATA
Evidence
of
carcinogenicity
comes
from
finding
tumor
increases
in
humans
or
laboratory
animals
exposed
to
a given
agent
or from
finding
tumors
following
exposure
to structural
analogues
to
the
compound
under
review.
The
significance
of observed
or
anticipated
tumor
effects
is evaluated
in reference
to
all the
other
key data
on
the agent.
This
section
contains
guidance
for
analyzing
human
and
animal
studies
to
decide
whether
there
is an
association
between
exposure
to
an
agent
or a structural
analogue
and
occurrence
of
tumors.
Note
that
the
use
of the
term
“tumor”
in
these
cancer
guidelines
is defined
as malignant
neoplasms
or
a
combination of
malignant
and corresponding
benign
neoplasms.
Observation of
only
benign
neoplasia
may
or
may
not have
significance
for
evaluation
under
these
cancer
guidelines.
Benign
tumors
that
are
not
observed
to
progress
to malignancy
are assessed on a case-by-case
basis.
There
is
a range
of
possibilities for
their
overall
significance.
They
may
deserve
attention
because
they
are
serious
health
problems
even
though
they are
not
malignant;
for
instance,
benign
tumors
may
be
a health
risk
because
of their
effect
on
the
function of a target
tissue
such
as
the
brain.
They
may
be
significant
indicators
of
the
need
for
further
testing
of an
agent
if they
are
observed
in
a
short-
term
test protocol,
or
such
an
observation
may
add
to the
overall
weight
of evidence
if the
same
agent
causes
malignancies
in a
long-term
study.
Knowledge of
the mode
of action
associated
with
a
benign
tumor
response
may
aid
in the
interpretation
of
other
tumor
responses
associated
with
the
same
agent.
In other
cases,
observation of
a
benign
tumor
response
alone
may have
no
significant
health
hazard
implications
when
other
sources
of
evidence
show
no
suggestion
of
carcinogenicity.
2-2
2.2.1.
Human
Data
Human
data
may
come
from
epidemiologic
studies
or
case
reports.
(Clinical
human
studies,
which
involve
intentional
exposures
to
substances,
may
provide
toxicokinetic
data,
but
generally
not
data
on
carcinogenicity.)
The
most
common
sources
of
human
data
for
cancer
risk
assessment
are
epidemiologic
investigations.
Epidemiology
is
the
study
of the
distribution
of
disease
in
human
populations
and
the
factors
that
may
influence
that
distribution.
The
goals
of
cancer
epidemiology
are
to
identify
distribution
of
cancer
risk
and determine
the
extent
to
which
the
risk
can
be attributed
causally
to
specific
exposures
to
exogenous
or
endogenous
factors
(see
Centers
for
Disease
Control
and Prevention
[CDC,
2004]).
Epidemiologic
data
are
extremely
valuable
in risk
assessment
because
they
provide
direct
evidence
on whether
a substance
is
likely
to
produce
cancer
in
humans,
thereby
avoiding
issues
such
as:
species-to-species
inference,
extrapolation
to
exposures
relevant
to
people,
effects
of concomitant
exposures
due to
lifestyles.
Thus,
epidemiologic
studies
typically
evaluate
agents
under
more
relevant
conditions.
When
human
data
of
high
quality
and
adequate
statistical
power
are
available,
they
are
generally
preferable
over animal
data
and
should
be given
greater
weight
in hazard
characterization
and
dose-response
assessment,
although
both
can
be used.
Null
results
from
epidemiologic
studies
alone
generally
do not
prove
the absence
of
carcinogenic effects
because
such
results
can
arise
either
from
an
agent
being
truly
not
carcinogenic or from
other
factors
such
as:
inadequate
statistical
power,
inadequate
study
design,
imprecise
estimates,
or
confounding
factors.
Moreover,
null
results
from
a
well-designed
and
well-conducted
epidemiologic
study
that
contains
usable
exposure
data
can
help
to
define
upper
limits
for
the
estimated
dose
of concern
for human
exposure
in
cases
where
the
overall
weight
of
the
evidence
indicates
that
the agent
is
potentially
carcinogenic
in
humans.
Furthermore,
data
from
a
well
designed
and
well
conducted
epidemiologic
study
that
does
not
show
positive
results,
in
conjunction
with
compelling
mechanistic
information,
can
lend
support
to a
conclusion
that
animal
responses
may
not
be predictive
of a
human
cancer
hazard.
Epidemiology
can
also
complement
experimental
evidence
in
corroborating
or
clarifying
the
carcinogenic
potential
of
the agent
in
question.
For
example,
epidemiologic
studies
that
show
elevated
cancer
risk
for
tumor
sites
corresponding
to
those
at
which
laboratory
animals
2-3
experience
increased
tumor
incidence
can
strengthen
the
weight
of
evidence
of
human
carcinogenicity.
Furthermore,
biochemical
or molecular
epidemiology
may
help
improve
understanding
of the
mechanisms
of
human
carcinogenesis.
2.2.1.1.
Assessment
of
Evidence
of
carcinogenicity
front
Human
Data
All
studies
that
are considered
to
be of
acceptable
quality,
whether
yielding
positive
or
null
results,
or
even
suggesting
protective
carcinogenic
effects,
should
be
considered
in
assessing
the
totality
of
the
human
evidence.
Conclusions
about
the
overall
evidence
for
carcinogenicity
from
available
studies
in
humans
should
be summarized
along
with
a
discussion
of
uncertainties
and
gaps
in
knowledge.
Conclusions
regarding
the
strength
of the
evidence
for positive
or
negative
associations
observed,
as well
as evidence
supporting
judgments
of
causality,
should
be
clearly
described. In assessing
the human
data
within
the
overall
weight
of
evidence,
determination about
the
strength
of the
epidemiologic
evidence
should
clearly
identify
the
degree
to
which
the
observed
associations
may
be
explained
by other
factors,
including
bias
or
confounding.
Characteristics that
are generally
desirable
in
epidemiologic
studies
include
(1)
clear
articulation
of
study
objectives
or hypothesis;
(2) proper
selection
and
characterization
of
comparison
groups
(exposed
and
unexposed
groups
or
case and
control
groups);
(3)
adequate
characterization of
exposure;
(4)
sufficient
length
of
follow-up
for disease
occunence;
(5)
valid
ascertainment
of
the
causes
of
cancer
morbidity
and
mortality;
(6)
proper
consideration
of bias
and
confounding
factors;
(7)
adequate
sample
size
to
detect
an
effect;
(8) clear,
well-documented,
and appropriate methodology
for
data
collection
and
analysis;
(9)
adequate
response
rate
and
methodology for handling
missing
data;
and
(10) complete
and
clear
documentation
of
results.
No
single
criterion
determines
the
overall
adequacy
of
a study.
Practical
and
resource
constraints
may limit
the
ability
to
address
all of
these
characteristics
in
a study.
The
risk assessor
is
encouraged
to
consider how
the limitations
of
the
available
studies
might
influence
the
conclusions.
While
positive
biases
may be
due,
for example,
to a
healthy
worker
effect,
it is
also
important
to
consider
negative
biases,
for
example,
workers
who
may
leave
the workforce
due
to
illness
caused
either
by
high
exposures
to
the
agent
or
to effects
of confounders
such
as
smoking.
2-4
The
following
discussions
highlight
the major
factors
included
in
an
analysis
of
epidemiologic
studies.
2.2.1.2.
Types
ofStudies
The
major
types
of cancer
epidemiologic
study
designs
used
for examining
environmental
causes
of
cancer
are analytical
studies
and descriptive
studies.
Each
study
type has
well-known
strengths
and
weaknesses
that
affect
interpretation
of results,
as
summarized
below
(Lilienfeld
and
Lilienfeld,
1979;
Mausner
and
Kramer,
1985;
Kelsey
et al.,
1996;
Rothman
and
Greenland,
1998).
Analytical
epidemiologic
studies,
which
include
case-control
and cohort
designs,
are
generally
relied
on
for identifring
a causal
association
between
human
exposure
and
adverse
health
effects.
In
case-control
studies,
groups
of
individuals
with
(cases)
and
without
(controls)
a
particular
disease
are identified
and
compared
to
determine
differences
in exposure.
In cohort
studies,
a group
of
“exposed”
and
“nonexposed”
individuals
are
identified
and studied
over
time
to
determine
differences
in
disease
occurrence.
Cohort
studies
can
be
performed
either
prospectively
or
retrospectively
from historical
records.
The
type of
study
chosen
may
depend
on
the
hypothesis
to be
evaluated.
For
example,
case-control
studies
may
be
more
appropriate
for
rare cancers
while
cohort
studies
may
be
more
appropriate
for
more
commonly
occurring
cancers.
On
the
other
hand, descriptive
epidemiologic
studies
examine
symptom
or disease
rates
among
populations
in relation
to
personal
characteristics
such
as age,
gender,
race,
and temporal
or environmental
conditions.
Descriptive
studies
are most
frequently
used
to
generate
hypotheses
about
exposure
factors,
but
subsequent
analytical
designs
are
necessary
to
infer
causality.
For
example,
cross-sectional
designs
might
be
used to
compare
the
prevalence
of
cancer
between
areas
near
and far
from
a Superfund
site.
However,
in
studies
where
exposure
and
disease
information
applies
only to
the
current
conditions,
it is
not possible
to infer
that
the
exposure
actually
caused
the
disease.
Therefore,
these
studies
are used
to identify
patterns
or
trends
in
disease
occurrence
over
time
or in different
geographical
locations,
but
typical
2-5
limitations
in
the
characterization
of
populations
in
these
studies
make
it difficult
to
infer
the
causal
agent
or
degree
of
exposure.
Case
reports
describe
a particular
effect
in
an individual
or
group
of individuals
who
were
exposed
to
a
substance.
These
reports
are
often
anecdotal
or highly
selective
in
nature
and
generally
are
of
limited
use
for hazard
assessment.
Specifically,
cancer
causality
can
rarely
be
infened
from
case
reports
alone.
Investigative
follow-up
may
or
may
not accompany
such
reports.
For
cancer,
the
most
common
types
of
case
series
are associated
with
occupational
and
childhood
exposures.
Case
reports
can
be
particularly
valuable
for
identifying
unique
features,
such
as
an
association
with
an uncommon
tumor
(e.g.,
inhalation
of
vinyl
chloride
and
hepatic
angiosarcoma
in workers
or ingestion
of
diethylstilbestrol
by
mothers
and clear-cell
carcinoma
of
the vagina
in
offspring).
2.2.1.3. Exposure
Issues.
For
epidemiologic data
to be useful
in
determining
whether
there
is
an association
between
health
effects
and
exposure
to
an
agent,
there
should
be
adequate
characterization
of
exposure information.
In general,
greater
weight
should
be
given
to studies
with more
precise
and
specific
exposure
estimates.
Questions
to address
about
exposure
are:
What
can
one
reliably
conclude
about
the
exposure
parameters including
(but
not limited
to) the
level,
duration,
route,
and
frequency
of
exposure
of
individuals
in one
population
as compared
with
another?
How
sensitive
are
study
results
to
uncertainties
in
these
parameters?
Actual
exposure
measurements
are not
available
for
many
retrospective
studies.
Therefore,
sunogates
are
often
used
to reconstruct
exposure
parameters.
These
may
involve
attributing
exposures
to job
classifications
in
a
workplace
or
to broader
occupational
or
geographic
groupings.
Use
of
surrogates
carries
a potential
for misclassification,
i.e.,
individuals
may
be
placed
in an
incorrect
exposure
group.
Misclassification
generally
leads
to
reduced
ability
of a
study
to
detect
differences
between
study
and
referent
populations.
When
either
current
or
historical
monitoring
data
are available,
the
exposure
evaluation
includes
consideration
of
the
error
bounds
of the
monitoring
and
analytic
methods
and
whether
2-6
the data
are
from
routine
or
accidental
exposures.
The potential
for misclassification
and
for
measurement
errors
is amenable
to both
qualitative
and
quantitative
analysis.
These
are
essential
analyses
for
judging
a
study’s
results,
because
exposure
estimation
is the
most
critical
part of
a
retrospective
study.
2.2.1.4.
Biological
Markers.
Biological
markers
potentially
offer
excellent
measures
of
exposure
(Hulka
and
Margolin,
1992;
Peto
and
Darby,
1994).
In
some
cases,
molecular
or cellular
effects
(e.g.,
DNA
or protein
adducts,
mutation,
chromosomal
aberrations,
levels
of
thyroid-stimulating
hormone)
can be
measured
in blood,
body
fluids, cells,
and
tissues
to
serve
as
biomarkers
of exposure
in
humans
and animals
(Callemen
et al.,
1978;
Birner
et
al., 1990).
As
such, they
can
act
as an
internal
surrogate
measure
of
chemical
dose,
representing,
as appropriate,
either
recent
exposure
(e.g.,
serum
concentration)
or
accumulated
exposure
over
some
period
(e.g.,
hemoglobin
adducts).
Validated
markers
of
exposure
such
as
alkylated
hemoglobin
from
exposure
to ethylene
oxide
(Van
Sittert
et
al., 1985)
or
urinary
arsenic
(Enterline
et al.,
1987)
can improve
estimates
of dose
over the
relevant
time
periods
for
the markers.
Markers
closely
identified
with
effects
promise
to
greatly
increase
the ability
of
studies
to distinguish
real effects
from
bias
at
low
levels
of relative
risk
between
populations
(Taylor
et
al.,
1994; Biggs
et al.,
1993)
and to
resolve
problems
of
confounding
risk
factors.
However,
when
using
molecular
or cellular
effects
as biomarkers
of
exposure,
since
many
of these
changes
are often
not
specific
to just
one type
of
exposure,
it is
important
to be
aware
that
changes
may be
due
to
exposures
unrelated
to the exposure
of interest
and attention
must
be
paid
to
controlling
for potential
confounders.
Biochemical
or
molecular
epidemiologic
studies
may
use biological
markers
of
effect
as
indicators
of
disease
or its precursors.
The
application
of
techniques
for measuring
cellular
and
molecular
alterations
due
to exposure
to specific
environmental
agents
may
allow
conclusions
to
be
drawn about
the mechanisms of
carcinogenesis
(see
section
2.4 for
more
information
on this
topic).
2-7
2.2.1.5.
Coifounding Factors.
Control
for potential
confounding
factors
is
an
important
consideration
in
the
evaluation
of
the
design
and
in the
analysis
of
observational
epidemiologic
studies.
A
confounder
is
a
variable
that
is related
to
both
the
health
outcome
of
concern
(cancer)
and exposure.
Common
examples
include
age, socioeconomic
status,
smoking
habits,
and
diet.
For
instance,
if older
people
are
more
likely
to
be
exposed
to
a given
contaminant
as well
as
more
likely
to
have
cancer
because
of
their
age,
age
is considered
a confounder.
Adjustment
for
potentially
confounding
factors
(from
a statistical
as contrasted with
an
epidemiologic
point
of
view)
can
occur
either
in
the design
of
the
study
(e.g.,
individual
or group
matching
on
critical
factors)
or in
the
statistical
analysis
of
the
results
(stratification
or
direct
or indirect
adjustment).
Direct
adjustment
in
the
statistical
analysis
may
not
be possible
owing
to
the presentation
of
the
data
or because
needed
information
was
not
collected
during
the
study.
In
this case,
indirect
comparisons
may
be
possible.
For
example,
in
the
absence
of
data
on smoking
status
among
individuals
in
the
study
population, an
examination of the
possible
contribution
of
cigarette
smoking
to increased
lung
cancer
risk
may
be based
on information
from
other
sources,
such
as
the American
Cancer
Society’s
longitudinal studies
(Hammand,
1966;
Garfinkel
and Silverberg,
1991).
The
effectiveness
of
adjustments
contributes
to the
ability
to
draw
inferences
from
a
study.
Different
studies
involving exposure
to an
agent
may
have
different
confounding
factors.
If consistent
increases
in
cancer
risk
are observed
across
a collection
of studies
with
different
confounding
factors,
the
inference
that
the
agent
under
investigation
was
the etiologic
factor
is
strengthened.
There
may
also
be
instances
where
the
agent
of
interest
is a risk
factor
in
conjunction
with
another
agent.
For
instance,
interaction
as
well
as effect-measure
modification
are
sometimes
construed
to
be
confounding,
but
they
are different
than
confounding.
Interaction
is
described
as
a situation
in
which
two
or
more
risk
factors
modify
the
effect
of
each
other
with
regard
to
the
occurrence
of
a
given
effect.
This
phenomenon
is
sometimes
described
as effect-measure
modification or
heterogeneity
of
effect
(Szklo
and
Nieto,
2000).
Effect-measure
modification
refers
to
variation
in
the
magnitude
of measure
exposure
effect
across
levels
of
another
variable
(Rothman
and
Greenland,
1998).
The
variable
across
which
the
effect
measure
varies
and
is
2-8
called
an
effect
mod/Ier
(e.g.,
hepatitis
virus
B and
afiatoxin
in hepatic
cancer).
Interaction,
on
the
other
hand,
means
effect
of
the exposure
on
the
outcome
differs,
depending
on
the
presence
of another
variable
(the
effect
modifier).
When
the
effect
of the
exposure
of interest
is
accentuated
by
another
variable,
it
is
said
to
be
synergistic
interaction.
Synergistic
interaction
can
be
additive
(e.g.,
hepatitis
virus
B and
aflatoxin
in
hepatic
cancer)
or multiplicative
(e.g.,
asbestos
and
smoking
in lung
cancer).
If the
effect
of
exposure
is diminished
or
eliminated
by
another
variable,
it
said
to
be antagonistic
interaction
(e.g.,
intake
of vitamin
E and
lower
occurrence
of
lung
cancer).
2.2.1.6.
Statistical
Considerations.
The
analysis
should
apply
appropriate
statistical
methods
to ascertain
whether
the
observed
association
between
exposure
and effects
would
be expected
by
chance.
A
description
of
the
method
or
methods
used
should
include
the
reasons
for
their
selection.
Statistical
analyses
of
the
bias,
confounding, and interaction
are
part
of
addressing
the
significance
of
an
association
and
the
power
of
a study
to
detect
an
effect.
The
analysis
augments
examination
of the
results
for
the
whole
population
with
exploration
of the
results
for
groups
with
comparatively
greater
exposure
or time
since
first
exposure. This
may
support
identifying
an association
or
establishing
a
dose-response
trend.
When
studies
show
no
association,
such
exploration
may
apply
to
determining
an
upper
limit
on
potential
human
risk
for
consideration
alongside
results
of
animal
tumor
effects
studies.
2.2.1.6.1.
Likelihood
of
observing
an
effect.
The
power
of
a
study
— the
likelihood
of
observing
an effect
if one
exists
— increases
with
sample
size,
i.e.,
the number
of subjects
studied
from
a
population. (For
example,
a quadrupling
of
a background
rate
in the
1 per
10,000
range
would
require
more
subjects
who
have
experienced
greater
or
longer
exposure
or
lengthier
follow-up,
than a
doubling
of
a
background
rate in
the 1
per 100
range.)
If the
size
of the
effect
is
expected
to
be
very
small
at
low
doses,
higher
doses
or longer
durations
of
exposure
may be
needed
to
have
an
appreciable likelihood
of
observing
an
effect
with
a
given
sample
size.
Because
of the
often
long
latency
period
in cancer
development,
the
likelihood
of
observing
an effect
also
2-9
depends
on whether
adequate
time
has
elapsed
since
exposure
began
for
effects
to
occur.
Since
the
design of
the study
and
the
choice
of analysis,
as well
as the design
level
of certainty
in the
results
and
the
magnitude
of
response
in
an unexposed
population
also
affect
the
likelihood
of
observing
an
effect,
it is important
to
carefully
interpret
the
absence
of
an observed
effect.
A
unique
feature
that
can be
ascribed
to the
effects
of a particular
agent
(such
as a tumor
type that
is
seen only
rarely
in the
absence
of
the
agent) can
increase
sensitivity
by
permitting
separation
of
bias and
confounding
factors
from
real
effects.
Similarly,
a biomarker
particular
to
the agent
can
permit
these
distinctions.
Statistical
re-analyses
of
data,
particularly
an
examination
of
different
exposure
indices,
can
give
insight
into potential
exposure-response
relationships.
These
are
all
factors
to explore
in
statistical
analysis
of the
data.
2.2.1.6.2.
Sampling
and
other
bias issues.
When
comparing
cases
and controls
or
exposed
and
non-exposed
populations,
it
would
be
preferable
for
the
two populations
to differ
only
in
exposure
to the
agent
in
question.
Because
this
is
seldom
the
case,
it
is important
to identif’
sources
of sampling
and other
potential
biases
inherent
in
a study
design
or data
collection
methods.
Bias
is a
systematic
error.
In
epidemiologic
studies,
bias
can
occur
in
the
selection
of
cases
and
controls
or exposed
and
non-exposed
populations,
as
well
as the
follow up
of
the
groups,
or the
classification
of
disease
or exposure.
The
size
of the
risks
observed
can
be
affected
by
noncomparability between
populations
of
factors
such as
general
health,
diet,
lifestyle,
or
geographic
location;
differences
in
the
way
case
and control
individuals
recall
past
events;
differences
in data
collection
that
result
in unequal
ascertainment
of health
effects
in
the
populations;
and unequal
follow-up
of
individuals
(Rothman
and
Greenland,
1998).
Other
factors
worth
consideration
can
be
inherent
in the
available
cohorts,
e.g., use
of
occupational
studies
(the
healthy
worker
effect),
absence
of
one
sex,
or
limitations
in sample
size for
one
or
more
ethnicities.
The
mere
presence
of
biases
does
not invalidate
a
study,
but
should
be reflected
in the
judgment
of its
strengths
or
weaknesses.
Acceptance
of
studies
for assessment
depends
on
identifjing
their
sources
of
bias
and the
possible
effects
on
study results.
2-10
2.2.1.6.3.
Combining
statistical
evidence
across
studies.
Meta-analysis
is
a means
of
integrating
the
results
of multiple
studies
of similar
health
effects and
risk
factors.
This
technique
is particularly
useful
when
various
studies
yield
varying
degrees
of risk
or even
conflicting
associations
(negative
and
positive).
It is intended
to introduce
consistency
and
comprehensiveness
into
what
otherwise
might
be
a more
subjective
review
of the
literature.
The
value
of such
an
analysis
is dependent
upon
a
systematic
review
of
the literature
that
uses
transparent
criteria
of inclusion
and
exclusion.
In
interpreting
such
analyses,
it is important
to
consider
the
effects
of differences
in study
quality,
as
well
as the
effect
of
publication
bias.
Meta-analysis
may
not
be
advantageous
in
some
circumstances.
These
include
when
the
relationship
between
exposure
and
disease
is obvious
from
the individual
studies;
when there
are
only
a few studies
of the
key
health
outcomes;
when
there
is insufficient
information
from
available
studies
related
to
disease,
risk estimate,
or exposure
classification
to insure
comparability;
or
when
there are
substantial
confounding
or
other
biases
that
cannot
be adjusted
for in
the
analysis
(Blair
et
al., 1995;
Greenland,
1987;
Peto,
1992).
2.2.1.7.
Evidence
for
Causality
Determining
whether
an observed
association
(risk)
is
causal rather
than
spurious
involves
consideration
of
a
number
of factors.
Sir
Bradford
Hill
(Hill,
1965)
developed
a set
of
guidelines
for
evaluating
epidemiologic
associations
that can
be
used
in
conjunction
with
the
discussion
of
causality
such
as the
2004
Surgeon
General’s
report
on
smoking
(CDC,
2004)
and
in
other
documents
(e.g.,
Rothman
and Greenland
1998;
IPCS,
1999)
. The
critical
assessment
of epidemiologic evidence
is conceptually
based
upon
consideration
of salient
aspects
of
the
evidence
of
associations
so
as to
reach
fundamental
judgments
as
to the likely
causal significance
of the observed
associations.
In
so
doing,
it is appropriate
to draw
from
those
aspects
initially
presented
in
Hill’s
classic
monograph
(Hill,
1965)
and
widely
used
by the scientific
community
in
conducting
such
evidence-based
reviews.
A
number
of
these aspects
are
judged
to be
particularly
salient
in evaluating
the
body
of evidence
available
in this
review,
including
the
aspects
described
by
Hill
as strength,
experiment,
consistency,
plausibility,
and
coherence.
Other
aspects
identified
by Hill,
including
temporality
and
biological
gradient,
are
also relevant
and
2-11
considered here
(e.g.,
in
characterizing
lag
structures
and
concentration-response
relationships),
but
are
more
directly
addressed
in
the
design
and
analyses
of the
individual
epidemiologic
studies
included
in
this
assessment.
As discussed
below,
these
salient
aspects
are
interrelated
and
considered
throughout
the
evaluation
of
the
epidemiologic
evidence
generally
reflected
in
the
integrative
synthesis
of
the mode
of
action
framework.
The
general
evaluation
of
the
strength
of
the epidemiological
evidence
reflects
consideration
not only
of
the magnitude
of
reported
effects
estimates
and
their statistical
significance,
but also
of
the precision
of
the effects
estimates
and
the
robustness
of the
effects
associations.
Consideration
of the
robustness
of
the associations
takes
into
account
a
number
of
factors,
including
in
particular
the
impact
of alternative
models
and
model
specifications
and
potential
confounding factors,
as
well
issues
related
to the
consequences
of
measurement
error.
Consideration
of
the consistency
of
the
effects
associations
involves
looking
across
the
results
of
studies
conducted
by
different
investigators
in different
places
and
times.
Particular
weight
may
be
given,
consistent
with
Hill’s
views,
to
the
presence
of
“similar
results
reached
in
quite
different
ways,
e.g.,
prospectively
and
retrospectively”
(Hill,
1965).
Looking
beyond
the
epidemiological
evidence,
evaluation
of the
biological
plausibility
of the
associations
observed
in
epidemiologic
studies
reflects
consideration
of both
exposure-related
factors
and
toxicological
evidence
relevant
to
identification
of
potential
modes
of
action
(MOAs).
Similarly,
consideration
of the
coherence
of health
effects
associations
reported
in
the epidemiologic
literature
reflects
broad
consideration
of
information pertaining
to the
nature
of the
biological
markers
evaluated
in
toxicologic
and
epidemiologic
studies.
In
identifying
these
aspects
as being
particularly
salient
in
this
assessment,
it
is
also
important
to
recognize
that
no
one
aspect
is
either
necessary
or
sufficient
for
drawing
inferences
of
causality.
As
Hill
(1965)
emphasized:
“None
of my
nine
viewpoints
can
bring
indisputable
evidence
for
or
against
the
cause-and-effect
hypothesis
and none
can
be
required
as a sine
qua non.
What
they
can
do,
with
greater
or less
strength,
is to
help us
to
make
up our
minds
on
the
fundamental
question
—
is
there
any
other
way
of explaining
the set
of
facts
2-12
before
us, is there
any
other answer
equally,
or more,
likely
than
cause
and
effect?”
While
these
aspects
frame
considerations
weighed
in
assessing
the epidemiologic
evidence,
they
do not
lend
themselves
to being
considered
in
terms
of simple
formulas
or
hard-and-fast
rules
of
evidence
leading
to answers
about
causality
(Hill, 1965).
One, for
example,
cannot
simply
count
up
the numbers
of studies
reporting
statistically
significant
results
or statistically
non-significant
results
for
carcinogenesis
and related
MOAs
and
reach
credible
conclusions
about
the
relative
strength
of the
evidence
and the
likelihood
of causality.
Rather,
these
important
considerations
are taken
into
account
throughout
the
assessment
with
a goal
of producing
an objective
appraisal
of
the evidence
(informed
by
peer and
public
comment
and
advice),
which
includes
the
weighing
of
alternative
views
on
controversial
issues.
Thus,
although
these
guidelines
have
become
known
as “causal
criteria,”
it is
important
to
note
that they
cannot
be
used as
a strictly
quantitative
checklist.
Rather,
these
“criteria”
should
be used
to determine
the strength
of the
evidence
for
concluding
causality.
In
particular,
the absence
of
one
or
more
of the
“criteria”
does
not
automatically
exclude
a study
from
consideration
(e.g., see
discussion
in
CDC,
2004).
The
list below
has
been
adapted
from
Hill’s
guidelines
as an
aid in judging
causality.
(a) Consistency
of
the observed
association.
An
inference
of
causality
is strengthened
when
a pattern
of elevated
risks is
observed
across
several
independent
studies.
The
reproducibility
of
findings
constitutes
one
of the
strongest
arguments
for
causality.
If
there
are
discordant
results
among
investigations,
possible
reasons
such
as differences
in
exposure,
confounding
factors,
and
the
power
of
the study
are
considered.
(b)
Strength
of
the observed
association.
The
finding
of
large,
precise
risks
increases
confidence
that
the
association
is
not
likely
due
to
chance,
bias,
or other
factors.
A modest
risk,
however,
does
not preclude
a
causal
association
and
may
reflect
a lower
level
of exposure,
an
agent
of lower
potency,
or
a
common
disease
with
a
high background
level.
(c) Specificity
of
the observed
association.
As
originally
intended,
this refers
to
increased
inference
of causality
if
one
cause is
associated
with
a single
effect
or disease
(Hill,
1965).
Based on
our current
understanding
that many
agents
cause
cancer
at
multiple
sites,
and
2-13
many
cancers
have
multiple
causes,
this
is
now
considered
one
of
the
weaker
guidelines
for
causality.
Thus,
although
the
presence of
specificity
may
support
causality,
its absence
does
not
exclude
it.
(d)
Temporal
relationship
ofthe
observed
association.
A
causal
interpretation
is
strengthened
when
exposure
is
known
to
precede
development
of the
disease.
Because
a latent
period
of up
to
20
years
or
longer
is
often
associated
with
cancer
development
in adults,
the
study
should
consider
whether
exposures
occurred
sufficiently
long
ago
to produce
an
effect
at
the
time
the
cancer
is
assessed.
This
is
among
the strongest
criteria
for an
inference
of
causality.
(e)
Biological
gradient
(exposure-response
relationship).
A
clear
exposure-response
relationship
(e.g.,
increasing
effects
associated
with
greater
exposure)
strongly
suggests
cause
and
effect,
especially
when
such relationships
are
also
observed
for
duration
of exposure
(e.g.,
increasing
effects
observed
following
longer
exposure
times).
There
are many
possible
reasons
that
an
epidemiologic
study
may
fail
to
detect
an exposure-response
relationship.
For
example,
an
analysis
that
included
decreasing
exposures
due
to
improved
technology
that
is combined
with
higher
prior
exposure
in an
initial
analysis
can
require
a
segmented
analysis
to
apportion
exposure.
Other
reasons
for
failure
to
detect
a relationship
may
include
a small
range
of
exposures.
Thus,
the
absence
of an
exposure-response
relationship
does
not exclude
a
causal
relationship.
U)
Biologicalplausibility.
An
inference
of causality
tends
to
be strengthened
by
consistency
with data
from
experimental
studies
or
other
sources
demonstrating
plausible
biological
mechanisms.
A
lack
of
mechanistic
data,
however,
is not
a reason
to reject
causality.
(g)
Coherence.
An
inference
of causality
may
be strengthened
by
other
lines
of
evidence
that
support
a
cause-and-effect
interpretation
of
the association.
Information
is
considered
from
animal
bioassays,
toxicokinetic
studies,
and
short-term
studies.
The
absence
of other
lines
of
evidence,
however,
is
not
a
reason
to
reject
causality.
(h)
Experimental
evidence
(from
human
populations).
Experimental
evidence
is
seldom
available
from
human
populations
and
exists
only
when
conditions
of human
exposure
have
occurred
to
create
a
“natural
experiment”
at different
levels
of
exposure.
Strong
evidence
2-14
for
causality
can be
provided
when
a change
in
exposure
brings
about
a
change
in
disease
frequency,
for
example,
the
decrease
in the
risk
of
lung
cancer that
follows
cessation
of
smoking.
(i)
Analogy.
SARs
and
information
on
the
agent’s
structural
analogues
can
provide
insight
into
whether
an
association
is
causal.
Similarly,
information
on mode
of action
for a
chemical,
as one of
many
structural
analogues,
can
inform
decisions
regarding
likely
causality.
2.2.2.
Animal
Data
Various
whole-animal
test systems
are
currently
used
or
are under
development
for
evaluating
potential
carcinogenicity.
Cancer
studies
involving
chronic
exposure
for
most
of
the
lifespan
of an
animal
are
generally
accepted
for
evaluation
of
tumor
effects
(Tomatis
et al.,
1989;
Rail,
1991;
Allen
et al.,
1988; but
see Ames
and
Gold,
1990).
Other
studies
of
special
design
are
useful
for observing
formation
of
preneoplastic
lesions
or
tumors
or investigating
specific
modes
of
action.
Their
applicability
is
determined
on a
case-by-case
basis.
2.2.2.1.
Long-term
Carcinogenicity
Studies
The
objective
of long-term
carcinogenesis
bioassays
is to
determine
the
potential
carcinogenic
hazard and
dose-response
relationships
of the
test agent.
Carcinogenicity
rodent
studies
are
designed
to examine
the
production
of
tumors
as well
as preneoplastic
lesions
and
other indications
of chronic
toxicity
that
may
provide
evidence
of treatment-related
effects
and
insights
into the
way
the test
agent
produces
tumors.
Current
standardized
carcinogenicity
studies
in
rodents
test
at least
50
animals
per
sex per
dose
group in
each of
three
treatment
groups
and in a
concurrent
control
group,
usually
for
18 to
24 months,
depending
on
the
rodent
species
tested
(OECD,
1981;
U.S.
EPA,
1 998c).
The
high
dose
in long-term
studies
is generally
selected
to
provide
the
maximum
ability
to
detect
treatment-related
carcinogenic
effects
while
not
compromising the
outcome
of the
study
through
excessive
toxicity
or inducing
inappropriate
toxicokinetics
(e.g.,
overwhelming
absorption
or
detoxification
mechanisms).
The
purpose
of
two
or more
lower doses
is
to provide
some
information
on
the
shape
of the
dose-response
curve.
Similar
protocols
have
been
and continue
to be
used
by many
laboratories
worldwide.
2-15
All available
studies
of
tumor effects
in whole
animals
should
be
considered,
at least
preliminarily.
The
analysis
should
discard studies
judged
to
be
wholly inadequate
in
protocol,
conduct,
or results.
Criteria
for
the technical
adequacy
of animal
carcinogenicity
studies
have
been
published
and should
be used as
guidance
to judge
the acceptability
of individual
studies
(e.g.,
NTP,
1984;
OSTP,
1985;
Chhabra et
al.,
1990).
As these
criteria,
in whole or
in part,
may
be
updated
by
the
National
Toxicology
Program (NTP)
and
others, the
analyst should
consult
the
appropriate
sources
to determine
both the
current
standards
as
well as those
that were
contemporaneous
with
the study. Care
should
be taken
to include
studies
that
provide
some
evidence
bearing
on
carcinogenicity
or
that help
interpret
effects
noted
in other studies,
even
if
these
studies have
some limitations
of
protocol or
conduct.
Such limited,
but
not
wholly
inadequate,
studies
can contribute
as their
deficiencies
permit.
The
findings
of long-term
rodent
bioassays
should be
interpreted
in conjunction
with results
of
prechronic
studies
along
with
toxicokinetic
studies
and other
pertinent information,
if
available.
Evaluation
of tumor
effects
takes into consideration
both
biological
and statistical
significance
of the findings
(Haseman,
1984,
1985, 1990,
1995).
The following
sections highlight
the major
issues in the
evaluation
of
long-term
carcinogenicity
studies.
2.2.2.1.1.
Dosing
issues.
Among
the many criteria
for
technical
adequacy
of animal
carcinogenicity
studies is the
appropriateness
of
dose selection.
The selection
of
doses
for
chronic bioassays
is
based
on scientific
judgments
and
sound
toxicologic
principles.
Dose
selection
should
be
made on the
basis of relevant
toxicologic
information
from
prechronic,
mechanistic,
and
toxicokinetic
and mechanistic
studies.
A
scientific
rationale
for dose
selection
should be
clearly
articulated
(e.g.,
NTP, 1984;
ILSI,
1997).
How
well the
dose selection
is
made
is evaluated
after the
completion
of the bioassay.
Interpretation
of carcinogenicity
study
results
is profoundly
affected
by study
exposure
conditions,
especially
by inappropriate
dose
selection.
This
is
particularly
important
in
studies
that
do not
show positive
results
for carcinogenicity,
because
failure
to use a sufficiently
high
dose reduces
the
sensitivity
of
the studies.
A
lack
of
tumorigenic
responses
at exposure
levels
that
cause
significant
impairment
of animal
survival
may
also not
be acceptable.
In
addition,
2-16
overt
toxicity
or qualitatively
altered
toxicokinetics
due
to
excessively
high
doses
may result
in
tumor
effects
that
are
secondary
to the
toxicity
rather
than
directly
attributable
to the
agent.
With
regard
to the
appropriateness
of the
high
dose,
an adequate
high
dose
would
generally
be
one
that produces
some toxic
effects
without
unduly
affecting
mortality
from
effects
other
than
cancer
or producing
significant
adverse
effects
on
the
nutrition
and
health
of the
test
animals
(OECD,
1981;
NRC, 1993a).
If
the
test
agent
does
not appear
to cause
any
specific
target
organ
toxicity
or
perturbation
of
physiological
function,
an
adequate
high
dose
can
be
specified
in
terms
of
a percentage
reduction
of body
weight
gain
over the
lifespan
of
the
animals.
The
high
dose
would
generally
be
considered
inadequate
if neither
toxicity
nor change
in
weight
gain is
observed.
On the
other
hand,
significant
increases
in
mortality
from
effects
other
than
cancer
generally
indicate
that
an
adequate
high
dose
has
been
exceeded.
Other
signs
of
treatment-related
toxicity
associated
with
an excessive
high dose
may
include
(a)
significant
reduction
of
body
weight
gain
(e.g.,
greater
than
10%),
(b) significant
increases
in
abnormal
behavioral
and clinical
signs,
(c)
significant
changes
in
hematology
or
clinical
chemistry,
(d)
saturation
of
absorption
and
detoxification
mechanisms,
or
(e) marked
changes
in
organ weight,
morphology,
and
histopathology.
It
should
be noted
that
practical
upper
limits have
been
established
to
avoid
the use
of excessively
high
doses in
long-term
carcinogenicity studies
of environmental
chemicals
(e.g.,
5%
of
the test
substance
in the feed
for
dietary
studies
or 1 g/kg
body
weight
for oral
gavage
studies
[OECD,
1981]).
For
dietary
studies,
weight
gain
reductions
should
be
evaluated
as
to whether
there
is
a
palatability problem
or an issue
with
food
efficiency;
certainly,
the latter
is a toxic
manifestation.
In the case
of
inhalation
studies
with
respirable
particles,
evidence
of
impairment
of normal
clearance
of
particles
from
the
lung
should
be
considered
along
with
other
signs
of toxicity
to
the
respiratory
airways
to determine
whether
the
high
exposure
concentration
has
been
appropriately
selected
(U.S.
EPA,
2001
a).
For
dermal
studies,
evidence
of skin
irritation
may
indicate
that
an
adequate
high
dose
has
been
reached
(U.S. EPA,
1989).
In
order
to obtain
the
most
relevant
information
from
a
long-term
carcinogenicity
study,
it
is
important
to
maximize
exposure
conditions
to the
test
material.
At
the
same time,
caution
is
appropriate
in
using excessive
high-dose
levels
that
would
confound
the
interpretation
of
study
2-17
results
to
humans.
The
middle
and
lowest
doses
should
be selected
to characterize
the shape
of
the
dose-response
curve
as
much
as possible.
It is
important
that the
doses
be adequately
spaced
so that
the
study
can provide
relevant
dose-response
data
for assessing
human
hazard
and risk.
If
the
testing
of
potential
carcinogenicity
is
being
combined
with
an evaluation
of noncancer
chronic
toxicity,
the
study should
be
designed
to
include
one
dose
in addition
to the
control(s)
that
is not expected
to elicit
adverse
effects.
There
are
several
possible
outcomes
regarding
the study
interpretation
of
the
significance
and
relevance
of
tumorigenic
effects
associated
with
exposure
or
dose
levels
below,
at,
or
above
an adequate
high
dose.
The
general
guidance
is given
here;
for
each case,
the
information
at
hand
should
be evaluated
and a rationale
should
be
given
for
the position
taken.
•
Adequately
high
dose. If
an
adequately high
dose has
been
used,
tumor
effects
are
judged
positive
or
negative
depending
on the
presence
or
absence
of significant
tumor
incidence
increases,
respectively.
•
Excessively
high
dose.
If toxicity
or mortality
is
excessive
at the
high
dose,
interpretation
depends
on
whether
or
not
tumors
are
found.
—
Studies
that show
tumor
effects
only
at excessive
doses
may be
compromised
and
may or
may not
carry
weight,
depending
on the
interpretation
in the
context
of other
study results
and
other
lines
of
evidence.
Results
of
such
studies,
however,
are
generally
not
considered
suitable
for dose-response
extrapolation
if
it
is
determined
that
the
mode(s)
of
action
underlying
the
tumorigenic
responses
at
high doses
is
not
operative
at
lower doses.
—
Studies
that
show
tumors
at lower
doses,
even
though
the
high
dose is
excessive
and
may
be
discounted,
should
be
evaluated
on their
own
merits.
2-18
—
If a study
does
not show
an
increase
in tumor
incidence
at a toxic
high
dose
and
appropriately
spaced
lower
doses
are
used without
such toxicity
or tumors,
the
study
is
generally
judged
as
negative
for carcinogenicity.
Inadequately
high
dose. Studies
of
inadequate
sensitivity
where
an
adequately
high
dose
has
not
been
reached
may
be
used
to
bound
the
dose range
where
carcinogenic
effects
might
be expected.
2.2.2.1.2.
Statistical
considerations.
The
main aim
of statistical
evaluation
is
to determine
whether
exposure
to the test
agent
is associated
with
an
increase
of
tumor
development.
Statistical
analysis
of a
long-term
study
should
be
performed
for
each
tumor
type separately.
The
incidence
of
benign
and
malignant
lesions
of the
same
cell
type,
usually
within
a
single tissue
or
organ,
are
considered
separately
but
may
be
combined
when
scientifically
defensible
(McConnell
etal.,
1986).
Trend
tests
and
pairwise
comparison
tests
are
the
recommended
tests
for determining
whether
chance,
rather
than
a treatment-related
effect,
is
a plausible
explanation
for
an
apparent
increase
in tumor
incidence.
A
trend
test such
as the
Cochran-Armitage
test
(Snedecor
and
Cochran,
1967)
asks
whether
the results
in
all dose
groups
together
increase
as
dose increases.
A
pairwise
comparison
test
such
as
the Fisher
exact
test
(Fisher,
1950)
asks whether
an
incidence
in
one
dose
group
is
increased
over
that
of
the
control
group.
By convention,
for both
tests
a
statistically significant
comparison
is one for
which
p
is less
than
0.05
that
the increased
incidence
is
due to
chance.
Significance
in either
kind
of
test is
sufficient
to reject
the
hypothesis
that chance
accounts
for
the result.
A statistically
significant
response
may
or
may
not
be
biologically
significant
and vice
versa. The
selection
of a
significance
level
is a
policy
choice
based
on a
trade-off
between
the
risks of
false
positives
and
false
negatives.
A
result
with
a significance
level
of greater
or
less
than
5%
(the
most common
significance
level)
is
examined
to see
if the result
confirms
other
scientific
information.
When the
assessment
departs
from
a simple
5%
level,
this
should
be
2-19
highlighted
in the
risk
characterization.
A
two-tailed
test
or
a
one-tailed
test
can
be
used.
In
either
case
a
rationale
is
provided.
Statistical
power
can affect
the
likelihood
that
a
statistically
significant
result
could
reasonably
be expected. This
is
especially
important
in
studies
or
dose groups
with
small
sample
sizes or
low
dose
rates.
Reporting
the
statistical
power
can
be
useful
for
comparing
and
reconciling
positive
and
negative
results
from
different
studies.
Considerations
of
multiple
comparisons
should
also be
taken
into
account.
Haseman
(1983)
analyzed
typical
animal
bioassays
that
tested
both
sexes
of
two
species
and
concluded
that,
because
of
multiple
comparisons,
a
single
tumor
increase
for
a
species-sex-site
combination
that
is statistically significant
at
the 1%
level
for
common
tumors
or
5%
for rare
tumors
corresponds
to
a 7—8%
significance
level
for
the
study
as
a whole.
Therefore,
animal
bioassays
presenting
only
one
significant
result
that
falls
short
of
the
1% level
for
a common
tumor
should
be treated
with
caution.
2.2.2.1.3.
Concurrent and
historical
controls.
The
standard
for
determining
statistical
significance
of tumor
incidence
comes
from
a comparison
of
tumors
in
dosed
animals
with
those
in
concurrent
control
animals.
Additional
insights
about
both
statistical
and
biological
significance can come
from
an
examination
of
historical
control
data
(Tarone,
1982;
Haseman,
1995).
Historical
control
data
can
add
to
the
analysis,
particularly
by enabling
identification
of
uncommon
tumor
types
or
high spontaneous
incidence
of
a tumor
in
a
given
animal
strain.
Identification of common
or uncommon
situations
prompts
further
thought
about
the
meaning
of
the
response
in the
current
study
in
context
with
other
observations
in
animal
studies
and
with
other
evidence
about
the carcinogenic
potential
of
the
agent.
These
other
sources
of
information
may
reinforce
or
weaken
the
significance
given
to the
response
in the
hazard
assessment.
Caution
should
be
exercised
in simply
looking
at the
ranges
of
historical
responses,
because
the
range
ignores
differences
in survival
of
animals
among
studies
and
is related
to
the number
of
studies
in
the
database.
In
analyzing
results
for uncommon
tumors
in
a
treated
group
that
are
not statistically
significant
in
comparison
with
concurrent
controls,
the analyst
may
be
informed
by
the
2-20
experience
of
historical
controls
to
conclude
that
the result
is
in fact
unlikely
to
be due
to chance.
However,
caution
should
be
used
in
interpreting
results.
In
analyzing
results
for common
tumors,
a
different
set of
considerations
comes
into
play.
Generally
speaking,
statistically
significant
increases
in
tumors
should
not
be
discounted
simply
because
incidence
rates
in
the
treated
groups
are within
the
range
of historical
controls
or because
incidence
rates
in the
concurrent
controls
are somewhat
lower
than
average.
Random
assignment
of animals
to
groups
and
proper
statistical
procedures
provide
assurance
that statistically
significant
results
are
unlikely
to be
due
to chance
alone.
However,
caution
should
be
used
in
interpreting
results
that
are
barely
statistically
significant
or in which
incidence
rates in
concurrent
controls
are unusually
low
in comparison
with
historical
controls.
In cases
where
there
may
be
reason
to
discount
the biological
relevance
to
humans
of
increases
in common
animal
tumors,
such considerations
should
be weighed
on
their
own
merits
and
clearly
distinguished
from
statistical
concerns.
When
historical
control
data
are
used,
the
discussion
should address
several
issues
that
affect
comparability
of historical
and
concurrent
control
data, such
as genetic
drift
in
the
laboratory
strains,
differences
in
pathology
examination
at different
times
and
in different
laboratories
(e.g.,
in
criteria
for
evaluating
lesions;
variations
in the
techniques
for the
preparation
or
reading
of
tissue
samples
among
laboratories),
and
comparability
of animals
from
different
suppliers.
The
most
relevant
historical
data come
from
the
same laboratory
and
the
same
supplier
and
are gathered
within
2 or
3
years
one
way
or
the
other of
the
study under
review;
other
data should
be used
only
with
extreme
caution.
2.2.2.1.4.
Assessment
of
evidence
of
carcinogenicity
from
long-term
animal
studies.
In
general,
observation
of
tumors
under
different
circumstances
lends
support
to
the
significance
of
the findings
for
animal
carcinogenicity.
Significance
is
generally
increased
by the
observation
of
more
of the
factors listed
below.
For
a factor
such
as malignancy,
the
severity
of
the
observed
pathology
can
also
affect
the
significance.
The
following
observations
add
significance
to
the
tumor
findings:
2-21
•
uncommon
tumor
types;
•
tumors
at
multiple
sites;
•
tumors
by
more
than
one
route
of
administration;
•
tumors
in
multiple
species,
strains,
or
both
sexes;
•
progression
of
lesions
from
preneoplastic
to
benign
to
malignant;
•
reduced
latency
of
neoplastic
lesions;
•
metastases;
•
unusual
magnitude
of
tumor
response;
•
proportion
of
malignant
tumors;
and
•
dose-related
increases.
In
these
cancer
guidelines,
tumors
observed
in
animals
are
generally
assumed
to
indicate
that
an
agent
may
produce
tumors
in
humans.
Mode
of
action
may
help
inform
this
assumption
on
a
chemical-specific
basis.
Moreover,
the
absence
of
tumors
in
well-conducted,
long-term
animal
studies
in
at
least
two
species
provides
reasonable
assurance
that
an
agent
may
not
be
a
carcinogenic
concern
for
humans.
2.2.2.1.5.
Site
concordance.
Site
concordance
of
tumor
effects
between
animals
and
humans
should
be
considered
in
each
case.
Thus
far,
there
is
evidence
that
growth
control
mechanisms
at
the
level
of
the
cell
are
homologous
among
mammals,
but
there
is
no
evidence
that
these
mechanisms
are
site
concordant.
Moreover,
agents
observed
to
produce
tumors
in
both
humans
and
animals
have
produced
tumors
either
at
the
same
site
(e.g.,
vinyl
chloride)
or
different
sites
(e.g.,
benzene)
(NRC,
1994).
Hence,
site
concordance
is
not
always
assumed
between
animals
and
humans.
On
the
other
hand,
certain
modes
of
action
with
consequences
for
particular
tissue
sites
(e.g.,
disruption
of
thyroid
function)
may
lead
to
an
anticipation
of
site
concordance.
2.2.2.2.
Perinatal
Carcinogenicity
Studies
The
objective
of
perinatal
carcinogenesis
studies
is
to
determine
the
carcinogenic
potential
and
dose-response
relationships
of
the
test
agent
in
the
developing
organism.
Some
2-22
investigators
have
hypothesized
that
the age
of
initial
exposure
to
a chemical
carcinogen
may
influence
the carcinogenic response
(Vesselinovitch
et
al., 1979;
Rice,
1979;
McConnell,
1992).
Cunent
standardized
long-term
carcinogenesis
bioassays
generally
begin
dosing
animals
at
6—8
weeks
of age
and
continue
dosing
for the
lifespan
of
the
animal
(18—24
months). This
protocol
has
been
modified
in
some
cases
to investigate
the
potential
of the
test agent
to
induce
transplacental
carcinogenesis
or
to investigate
the
potential
differences
following
perinatal
and
adult
exposures,
but
cunently
there
is not
a
standardized
protocol
for testing
agents
for
carcinogenic
effects
following
prenatal
or
early
postnatal
exposure.
Several
cancer
bioassay
studies
have
compared
adult
and
perinatal
exposures
(see
McConnell,
1992;
U.S.
EPA,
1996b).
A
review
of these
studies
reveals
that
perinatal
exposure
rarely
identifies
carcinogens
that
are
not
found
in
standard
animal
bioassays.
Exposure
that
is
perinatal
can
increase
the
incidence
of a
given
type
of
tumor.
The
increase
may
reflect
an
increased
length
of
exposure
and
a
higher
dose
for
the
developing
organism
relative
to
the
adult
or
an
increase
in
susceptibility
in
some
cases.
Additionally,
exposure
that
is perinatal
through
adulthood
sometimes
reduces
the
latency
period
for
tumors
to
develop
in the
growing
organism
(U. S.
EPA,
1
996b).
EPA
evaluates
the
usefulness
of
perinatal
studies
on
an agent-by-agent
basis
(e.g.,
U.S.
EPA,
1997a,
b).
Perinatal
study
data
analysis
generally
follows
the
principles
discussed
above
for
evaluating
other
long-term
carcinogenicity
studies.
When
differences
in
responses
between
perinatal
animals
and adult
animals
suggest
an
increased
susceptibility
of perinatal or
postnatal
animals,
such
as
the
ones
below,
a
separate
evaluation
of
the response
should
be
prepared:
•
a difference
in
dose-response
relationship,
•
the
presence
of
different
tumor
types,
•
an
earlier
onset
of tumors,
or
•
an increase
in
the incidence
of
tumors.
2-23
2.2.2.3.
Other Studies
Intermediate-term
and
acute dosing
studies
often
use
protocols
that
screen
for
carcinogenic
or preneoplastic
effects,
sometimes
in a single
tissue.
Some protocols
involve
the
development
of
various
proliferative
lesions,
such as
foci of
alteration
in the
liver
(Gold
sworthy
et al.,
1986).
Others
use
tumor
endpoints,
such
as
the
induction
of lung
adenomas
in
the
sensitive
strain
A
mouse
(Maronpot
et al.,
1986)
or
tumor
induction
in
initiation-promotion
studies
using
various
organs
such
as
the bladder,
intestine,
liver,
lung,
mammary
gland,
and
thyroid
(Ito
et al., 1992).
In
these
tests,
the
selected
tissue
rather
than
the
whole
animal
is,
in
a
sense,
the
test system.
Important
information
concerning
the
steps in
the
carcinogenic
process
and
mode
of
action
can
be obtained
from
“startlstop”
experiments.
In these
protocols,
an agent
is
given for
a
period
of time
to induce
particular
lesions
or
effects
and
then
stopped
in order
to
evaluate
the
progression
or
reversibility
of
processes
(Todd,
1986;
Marsman
and Popp,
1994).
Assays
in
genetically
engineered
rodents
may provide
insight into
the
chemical
and
gene
interactions
involved
in
carcinogenesis
(Tennant
et
al.,
1995).
These
mechanistically
based
approaches
involve
activated
oncogenes
that
are
introduced
(transgenic)
or tumor
suppressor
genes
that
are
deleted
(knocked
out). If
appropriate
genes are
selected,
not
only
may
these
systems
provide
information
on
mechanisms,
but
the rodents
typically
show
tumor
development
earlier than
in
the
standard
bioassay.
Transgenic
mutagenesis
assays
also
represent
a
mechanistic
approach
for
assessing
the
mutagenic
properties
of agents
as well
as
developing
quantitative
linkages
between
exposure,
internal
dose,
and
mutation
related
to
tumor
induction
(Morrison
and
Ashby,
1994;
Sisk
et al.,
1994;
Hayward
et al.,
1995).
The
support
that these
studies
give
to a determination
of
carcinogenicity
rests
on
their
contribution
to
the
consistency
of
other
evidence
about
an agent.
For
instance,
benzoyl
peroxide
has promoter
activity
on
the skin,
but
the overall
evidence
may
be
less
supportive
(Kraus
et
al.,
1995).
These
studies
also may
contribute
information
about
mode
of
action.
It is important
to
recognize
the
limitations
of these
experimental
protocols,
such
as short
duration,
limited
histology,
lack
of complete
development
of
tumors,
or experimental
manipulation
of
the
carcinogenic
process,
that
may
limit
their
contribution
to the
overall
assessment.
Generally,
their
results
are
appropriate
as
aids
in
the interpretation
of
other
toxicological
evidence
(e.g., rodent
2-24
chronic
bioassays),
especially
regarding
potential
modes
of action.
On
the
basis
of
cunently
available
information,
it is
unlikely
that
any of
these
assays,
which
are conducted
for
6 months
with
15
animals
per group,
will
replace
all chronic
bioassays
for
hazard
identification
(Spalding
et al.,
2000;
Gulezian
et al.,
2000;
ILSI, 2001).
2.2.3.
Structural
Analogue
Data
For
some chemical
classes,
there
is significant
available
information,
largely
from
rodent
bioassays,
on
the
carcinogenicity
of analogues.
Analogue
effects
are
instructive
in
investigating
carcinogenic
potential
of
an agent
as well
as
in
identifying
potential
target
organs,
exposures
associated
with
effects,
and
potential
functional
class
effects
or
modes
of
action.
All
appropriate
studies
should
be included
and
analyzed,
whether
indicative
of a
positive
effect
or not.
Evaluation
includes
tests
in
various
animal
species,
strains,
and
sexes;
with
different
routes
of
administration;
and
at various
doses, as
data are
available.
Confidence
in
conclusions
is
a
function
of
how
similar
the analogues
are to
the
agent
under
review
in structure,
metabolism,
and
biological
activity.
It is
important
to consider
this
confidence
to
ensure
a balanced
position.
2.3. ANALYSIS
OF OTHER
KEY
DATA
The
physical,
chemical,
and structural
properties
of
an agent,
as well
as data
on
endpoints
that
are thought
to be
critical
elements
of the
carcinogenic
process,
provide
valuable
insights
into
the
likelihood
of human
cancer
risk.
The
following
sections
provide
guidance
for analyses
of
these
data.
2.3.1.
Physicochemical
Properties
Physicochemical
properties
affect
an agent’s
absorption,
tissue
distribution
(bioavailability),
biotransformation,
and
degradation
in
the
body
and
are important
determinants
of
hazard
potential
(and
dose-response
analysis).
Properties
that
should
be analyzed
include,
but
are not
limited
to,
molecular
weight,
size,
and
shape;
valence
state;
physical
state
(gas,
liquid,
solid);
water
or
lipid
solubility,
which
can
influence
retention
and
tissue distribution;
and
potential
for
chemical
degradation
or stabilization
in
the
body.
2-25
An
agent’s
potential
for
chemical
reaction
with
cellular
components,
particularly
with
DNA
and proteins,
is also important.
The
agent’s
molecular
size
and
shape,
electrophilicity,
and
charge
distribution
are
considered
in
order
to
decide
whether
they
would
facilitate
such
reactions.
2.3.2.
Structure-Activity
Relationships
(SARs)
SAR
analyses
and models
can
be used
to predict
molecular
properties,
surrogate
biological
endpoints,
and
carcinogenicity
(see,
e.g.,
Richard,
1998a,
b;
Richard
and
Williams,
2002;
Contrera
et
al., 2003).
Overall,
these
analyses
provide
valuable
initial
information
on
agents,
they
may
strengthen
or
weaken
concern,
and
they
are part
of
the weight
of evidence.
Currently,
SAR
analysis
is most
useful
for
chemicals
and
metabolites
that are
believed
to
initiate
carcinogenesis
through
covalent
interaction
with
DNA
(i.e., DNA-reactive,
mutagenic,
electrophilic,
or
proelectrophilic
chemicals)
(Ashby
and
Tennant,
1991).
For organic
chemicals,
the
predictive
capability
of SAR
analysis
combined
with other
toxicity
information
has been
demonstrated (Ashby
and
Tennant,
1994).
The
following
parameters
are
useful
in
comparing
an
agent
to its
structural
analogues
and congeners
that
produce
tumors
and affect
related
biological
processes
such as
receptor
binding
and
activation,
mutagenicity,
and
general
toxicity
(Woo
and
Arcos,
1989):
•
nature
and
reactivity
of the
electrophilic
moiety
or moieties
present;
•
potential
to form
electrophilic
reactive
intermediate(s)
through
chemical,
photochemical,
or
metabolic
activation;
•
contribution
of
the
carrier
molecule
to
which
the electrophilic
moiety(ies)
is
attached;
•
physicochemical
properties
(e.g., physical
state,
solubility,
octanollwater
partition
coefficient,
half-life
in aqueous
solution);
2-26
•
structural
and
substructural
features
(e.g.,
electronic,
stearic,
molecular
geometric);
•
metabolic
pattern
(e.g.,
metabolic
pathways
and
activation
and
detoxification
ratio);
and
•
possible
exposure
route(s)
of the
agent.
Suitable
SAR
analysis
of non-DNA-reactive
chemicals
and of
DNA-reactive
chemicals
that
do not
appear
to
bind
covalently
to
DNA
should
be
based
on knowledge
or
postulation
of the
probable
mode(s)
of
action
of closely
related
carcinogenic
structural
analogues
(e.g.,
receptor
mediated,
cytotoxicity related).
Examination
of
the
physicochemical
and
biochemical
properties
of
the
agent
may then
provide
the rest
of
the
infonnation
needed
in order
to
make
an
assessment
of the
likelihood
of the
agent’s
activity
by
that
mode
of action.
2.3.3.
Comparative Metabolism
and
Toxicokinetics
Studies
of the
absorption,
distribution,
biotransformation,
and
excretion
of agents
permit
comparisons among
species
to
assist
in determining
the
implications
of
animal
responses
for
human
hazard
assessment,
supporting
identification
of
active
metabolites,
identifying
changes
in
distribution and
metabolic
pathway
or
pathways
over a
dose
range,
and
making
comparisons
among
different
routes
of
exposure.
If
extensive
data
are available
(e.g.,
blood/tissue
partition
coefficients
and
pertinent
physiological parameters
of the
species
of interest),
physiologically
based
toxicokinetic
models
can be
constructed
to
assist
in a determination
of tissue
dosimetry,
species-to-species
extrapolation
of dose,
and
route-to-route extrapolation
(Conolly
and Andersen,
1991;
see
Section
3.1.2).
If
sufficient
data
are
not available, it
may
be
assumed
as
a default
that
toxicokinetic
and
metabolic
processes
are
qualitatively
comparable
among
species.
Discussion
of appropriate
procedures for quantitative, interspecies
comparisons
appears
in
Chapter
3.
The
qualitative
question
of
whether
an
agent
is
absorbed
by
a particular
route
of
exposure
is important for weight
of
evidence
classification,
discussed
in Section
2.5.
Decisions
about
2-27
whether
route
of exposure
is a limiting
factor
on
expression
of
any hazard,
e.g., absorption
does
not occur
by
a
specified
route,
are generally
based
on
studies
in which
effects
of the
agent
or its
structural
analogues
have
been
observed
by different
routes,
on physical-chemical
properties,
or
on
toxicokinetics
studies.
Adequate
metabolism
and
toxicokinetic
data can
be
applied
toward
the following,
as
data
permit.
Confidence
in conclusions
is enhanced
when
in vivo
data
are
available.
•
Identing
inetabolites
and reactive
intermediates
of
metabolism
and
determining
whether
one
or more
ofthese
intermediates
is likely
to
be responsible
for the
observed
effects.
Information
on the
reactive
intermediates
focuses
on
SAR
analysis,
analysis
of
potential
modes
of
action,
and estimation
of
internal
dose
in
dose-response
assessment
(D’Souza
et
al.,
1987; Krewski
et al.,
1987).
•
Identifying
and
comparing
the
relative
activities
of
metabolic
pathways
in animals
and in
humans,
and
at
dfferent
ages.
This
analysis
can
provide
insights
for
extrapolating
results
of animal
studies
to humans.
•
Describing
anticipated
distribution
within
the
body
andpossibly
idenüfying
target
organs.
Use
of
water solubility,
molecular
weight,
and
structure
analysis
can
support
qualitative
inferences
about
anticipated
distribution
and
excretion.
In
addition,
describing
whether
the agent
or
metabolite
of
concern
will
be excreted
rapidly
or slowly
or
whether
it will
be
stored
in
a
particular
tissue
or
tissues
to
be
mobilized
later
can
identify
issues
in comparing
species
and
formulating
dose
response
assessment
approaches.
•
Ident/j’ing
changes
in toxicokinetics
and
metabolic
pathways
with
increases
in
dose. These
changes
may result
in important
differences
between
high
and low
dose
levels
in
disposition
of
the agent
or
generation
of
its
active
forms.
These
2-28
studies
play an
important
role
in providing
a rationale
for
dose
selection
in
carcinogenicity
studies.
Ident5’ing
and comparing
metabolic
process
dfferences
by
age, sex,
or
other
characteristic
so that
susceptible
subpopulations
can
be recognized.
For
example,
metabolic
capacity
with
respect
to
P450
enzymes
in
newborn
children
is
extremely
limited
compared
to
that
in
adults,
so
that
a carcinogenic
metabolite formed
through
P450
activity
will
have
limited
effect
in
the
young,
whereas
a
carcinogenic
agent
deactivated through
P450
activity
will
result
in
increased
susceptibility
of
this
lifestage
(Cresteil, 1998).
A
variety
of changes
in
toxicokinetics
and
physiology
occur
from
the
fetal
stage
to
post-weaning
to young
child.
Any
of
these
changes
may
make
a
difference
for
risk
(Renwick,
1998).
Determining
bioavailabiliiy
via
different
routes
of
exposure
by
analyzing
uptake
processes
under
various
exposure
conditions.
This
analysis
supports
identification
of
hazards
for
untested
routes.
In
addition,
use
of
physicochemical
data
(e.g.,
octanol-water
partition
coefficient
information)
can
support
an
inference
about
the
likelihood
of
dermal
absorption
(Flynn,
1990).
Attempts
should
be made
in
all
of
these
areas
to clarify
and
describe
as much
as
possible
the
variability
to be
expected
because
of
differences
in
species,
sex,
age, and
route
of
exposure.
The analysis takes
into
account
the
presence
of
subpopulations
of
individuals
who
are
particularly
vulnerable
to
the effects
of
an agent
because
of toxicokinetic
or
metabolic
differences
(genetically or environmentally
determined)
(Bois
et
al.,
1995)
and is
a special
emphasis
for
assessment
of
risks
to
children.
2.3.4.
Toxicological and
Clinical
Findings
Toxicological
findings
in
experimental
animals
and
clinical
observations
in
humans
are
important resources
for
the
cancer
hazard
assessment.
Such
findings
provide
information
on
2-29
physiological
effects
and
effects
on
enzymes,
hormones,
and
other
important
macromolecules
as
well
as on
target
organs
for toxicity.
For
example,
given
that
the cancer
process
represents
defects
in
processes
such as
terminal
differentiation,
growth
control,
and
cell
death,
developmental
studies
of
agents
may
provide
an understanding
of
the
activity
of an
agent
that
canies
over
to cancer
assessment.
Toxicity
studies
in
animals
by
different
routes
of
administration
support
comparison
of
absorption
and
metabolism
by
those
routes.
Data
on
human
variability
in standard
clinical
tests
may
also
provide
insight
into
the range
of
human
susceptibility
and
the
common
mechanisms
of
agents
that
affect
the
tested
parameters.
2.3.5.
Events
Relevant
to Mode
of
Carcinogenic
Action
Knowledge
of the
biochemical
and
biological
changes
that
precede
tumor
development
(which
include,
but are
not limited
to,
mutagenesis,
increased
cell proliferation,
inhibition
of
programmed
cell
death,
and
receptor
activation)
may provide
important
insight
for
determining
whether
a cancer
hazard
exists
and
may
help
inform
appropriate
consideration
of
the
dose-
response
relationship
below
the range
of observable tumor
response.
Because
cancer
can
result
from
a series
of genetic
alterations
in the
genes
that
control
cell
growth,
division,
and
differentiation
(Vogeistein
et
al.,
1988;
Hanahan
and Weinberg,
2000;
Kinzler
and
Vogeistein,
2002),
the
ability
of
an agent
to
affect
genotype
(and
hence
gene
products)
or gene
expression
is
of
obvious
importance
in
evaluating
its
influence
on
the
carcinogenic
process.
Initial
and
key
questions
to
examine
are:
Does
the agent
(or
its
metabolite)
interact
directly
with
DNA,
leading
to mutations
that
bring
about
changes
in
gene
products
or
gene
expression?
Does
the
agent
bring
about
effects
on
gene
expression
via other
nondirect
DNA
interaction
processes?
Furthermore, carcinogenesis
involves
a complex
series
and
interplay
of events
that
alter
the
signals
a
cell
receives
from
its extracellular
environment,
thereby
promoting
uncontrolled
growth.
Many,
but
not
all,
mutagens
are
carcinogens,
and
some,
but
not all,
agents
that
induce
cell
proliferation lead
to tumor
development.
Thus,
understanding
the
range
of
key steps
in
the
carcinogenic process
upon
which
an agent
might
act is
essential
for
evaluating
its
mode
of
action.
Determination of
carcinogens
that
are
operating
by
a
mutagenic
mode
of
action,
for example,
entails
evaluation of
in
vivo
or
in vitro
short-term
testing
results
for
genetic
endpoints,
metabolic
2-30
profiles,
physicochemical
properties,
and structure-activity
relationship
(SAR)
analyses
in a
weight-of-evidence
approach
(Dearfield
et al., 1991;
U.S. EPA,
1986b;
Waters
et al., 1999).
Key
data
for
a mutagenic
mode
of action
may be
evidence
that
the carcinogen
or
a metabolite
is
DNA-reactive
and/or
has
the ability
to bind
to
DNA.
Also, mutagenic
carcinogens
usually
produce
positive effects
in multiple
test
systems
for
different
genetic
endpoints,
particularly
gene
mutations
and structural
chromosome
aberrations,
and in
tests
performed
in
vivo which
generally
are
supported
by positive
tests in
vitro. Additionally,
carcinogens
may
be
identified
as
operating
via a mutagenic
mode
of
action
if they have
similar properties
and SAR
to
mutagenic
carcinogens.
Endpoints
that
provide insight
into an
agent’s
ability to
alter
gene
products
and
gene
expression,
together
with
other
features of
an agent’s
potential
mode
of
carcinogenic
action,
are discussed
below.
2.3.5.1.
Direct
DNA-Reactive
Effects
It
is
well
known that
many
carcinogens
are electrophiles
that interact
with DNA,
resulting
in DNA adducts
and
breakage (referred
to in
these cancer
guidelines
as
direct
DNA
effects).
Usually
during
the
process of
DNA
replication,
these
DNA
lesions
can be converted
into
and
fixed as mutations
and
chromosomal
alterations
that
then may initiate
and
otherwise
contribute
to
the carcinogenic
process
(Shelby
and
Zeiger,
1990;
Tinwell and
Ashby,
1991; IARC,
1999).
Thus,
studies
of
mutations
and other
genetic
lesions
continue
to inform
the
assessment
of
potential
human
cancer
hazard and
in
the
understanding
of an agent’s
mode
of
carcinogenic
action.
EPA has
published
testing
guidelines
for
detecting the
ability
of an agent
to
damage
DNA
and
produce
mutations
and
chromosomal
alterations
(as discussed
in Dearfield
et al.,
1991).
Briefly,
standard
tests
for gene mutations
in
bacteria and
mammalian
cells
in vitro
and in
vivo
and
for
structural chromosomal
aberrations
in
vitro and
in
vivo
are important
examples
of
relevant
methods.
New
molecular
approaches,
such as mouse
mutations
and
cancer
transgenic
models,
are providing
a means
to examine
mutation
at tissue sites
where
the tumor
response
is
observed
(Heddle
and
Swiger,
1996;
Tennant et
al., 1999).
Additionally,
continued
improvements
in
fluorescent-based
chromosome
staining
methods
(fluorescent
in
situ
2-31
hybridization
[FISH)
)
will
allow
the detection
of
specific
chromosomal
abnormalities
in relevant
target
tissues
(Tucker
and
Preston,
1998).
Endpoints
indicative
of DNA
damage
but
not
measures
of mutation
per
se,
such
as
DNA
adducts
or strand
breakage, may
be detected
in relevant
target
tissues
and
thus
contribute
to
evaluating
an agent’s
mutagenic
potential.
Evidence of
chemical-specific
DNA
adducts
(e.g.,
reactions
at oxygen
sites
in
DNA
bases
or
with
ring
nitrogens
of
guanine
and
adenine)
provides
information
on
a mutagen’s
ability
to
directly
interact
with
DNA
(La
and Swenberg,
1996).
Some
planar
molecules
(e.g.,
9-aminoacridine)
intercalate
between
base
pairs
of DNA,
which
results
in
a
physical
distortion
in
DNA
that
may
lead
to
mutations
when
DNA
replicates.
As
discussed
below,
some
carcinogens
do
not
interact
directly
with
DNA,
but
they can
produce
increases
in
endogenous
levels
of DNA
adducts
(e.g.,
8-hydroxyguanine)
by indirect
mechanisms.
2.3.5.2.
Indirect
DNA
Effects
or
Other
Effects
on
Genes/Gene
Expression
Although
some
carcinogens
may
result
in
an
elevation
of mutations
or
cytogenetic
anomalies,
as
detected
in
standard
assays,
they
may
do
so
by
indirect
mechanisms.
These
effects
may
be
brought
about
by
chemical-cell
interactions
rather
than
by
the
chemical
(or
its
metabolite)
directly
interacting
with
DNA.
An
increase
in mutations
might
be
due
to cytotoxic
exposures
causing
regenerative
proliferation
or
to mitogenic
influences
(Cohen
and
Ellwein,
1990).
Increased
cell
division
may
elevate
mutation
by
clonal
expansion
of
initiated
cells
or
by
increasing
the
number
of
genetic
errors
by
rapid
cell
division
and
reduced
time for
DNA
repair.
Some
agents
might
result
in an
elevation
of mutations
by interfering
with
the enzymes
involved
in DNA
repair
and
recombination
(Barrett
and
Lee,
1992).
Damage
to certain
critical
DNA
repair
genes
or
other
genes
(e.g.,
the
p53
gene)
may
result
in
genomic
instability,
which
predisposes
cells to
further
genetic
alterations
and
increases
the
probability
of neoplastic
progression (Harris
and
Hollstein,
1993;
Levine
et
al.,
1994;
Rouse
and
Jackson,
2002).
Likewise,
DNA
repair
processes
may
be
saturated
at
certain
doses
of
a chemical,
leading
to an
elevation
of
genetic
alterations.
2-32
The
initiation
of
programmed
cell
death
(apoptosis)
can
potentially
be
blocked
by
an
agent,
thereby
permitting
replication
of
cells
carrying
genetic
errors
that
would
normally
be
removed
from
the
proliferative
pooi.
At certain
doses
an
agent
may
also
generate
reactive
oxygen
species
that
produce
oxidative
damage
to
DNA
and
other
macromolecules
(Chang
et al.
1988;
Kehrer,
1993;
Clayson
et al.,
1994).
The
role of
cellular
alterations
that
are attributable
to
oxidative
damage
in tumorigenesis
(e.g.,
8-hydroxyguanine)
is currently
unclear.
Several
carcinogens have
been
shown
to
induce
aneuploidy
(the
loss
or
gain
of
chromosomes)
(Barrett,
1992;
Gibson
et
al.,
1995).
Aneuploidy
can result
in
the
loss
of
heterozygosity
or
genomic
instability
(Cavenee
et al.,
1986;
Fearon
and
Vogeistein,
1990).
Agents
that
cause
aneuploidy
typically
interfere
with
the
normal
process
of chromosome
segregation
by
interacting
with
non-DNA
targets
such
as
the
proteins
needed
for chromosome
segregation
and
chromosome
movement.
Whether
this
chromosome
imbalance
is
the
cause
or
the
effect
of
tumorigenesis
is not
clear.
Thus,
it
is important
to understand
if
the
agent
induces
aneuploidy
as a
key early
event
in
the
carcinogenic
process.
It is
possible
for
an
agent
to alter
gene
expression
by
transcriptional,
translational,
or
post-
translational
modifications. For
example,
perturbation
of DNA
methylation
patterns
may
cause
effects
that
contribute
to carcinogenesis
(Jones,
1986;
Holliday,
1987;
Goodman
and
Counts,
1993;
Chuang
et al.,
1996;
Baylin
and
Bestor,
2002).
Overexpression
of genes
by
DNA
amplification has
been
observed
in
certain
tumors
(Vainio
et al.,
1992).
Mechanisms
of
altering
gene
expression
may
involve
cellular
reprogramming
through
hormonal
or
receptor-mediated
mechanisms
(Barrett,
1992;
Ashby
et
al.,
1994).
Both
cell
proliferation
and programmed
cell
death
can
be
part
of the
maintenance
of
homeostasis in
many
normal
tissues,
and
alterations
in the
level
or
rate
of
either
can
be
important
elements
of
the
carcinogenic
process.
The
balance
between
the two
can
directly
affect
the
survival
and
growth
of initiated
cells
as well
as
preneoplastic
and
tumor
cell
populations
(i.e.,
increase
in
cell
proliferation
or decrease in cell
death)
(Moolgavkar,
1986;
Cohen
and
Ellwein,
1990,
1991;
Cohen
et al.,
1991;
Bellamy
et
al., 1995).
Thus,
measurements
of these
events
can
contribute
to
the
weight
of the
evidence
for
cancer
hazard
prediction
and to
mode
of action
2-33
understanding. In
studies
of
proliferative
effects,
distinctions
should
be made
between
mitogenesis
and
regenerative
proliferation
(Cohen
and Ellwein,
1990, 1991;
Cohen
et
al., 1991).
In
applying
information
from
studies
on
cell
proliferation
and
apoptosis
to
risk
assessment,
it
is important
to
identifi
the tissues
and
target
cells involved,
to
measure
effects
in
both
normal
and neoplastic
tissue,
to distinguish
between
apoptosis
and necrosis,
and to
determine
the dose
that
affects these
processes.
Gap-junctional
intercellular
communication
is
believed
to
play
a role
in
tissue
and organ
development
and
in
the
maintenance
of a normal
cellular
phenotype
within
tissues.
A
growing
body
of evidence
suggests
that
chemical
interference with
gap-junctional
intercellular
communication
is
a
contributing
factor
in tumor
development
(Swierenga
and Yamasaki,
1992;
Yamasaki,
1995).
2.3.5.3.
Precursor
Events
and
Biomarker
Information
Most
testing
schemes
for
mutagenicity
and
other short-term
assays
were
designed
for
hazard
identification
purposes;
thus,
these
assays
are
generally
conducted
using acute
exposures.
For
data on
“precursor
steps”
to
be
useful
in
informing
the
dose-response
curve
for tumor
induction
below
the level
of
observation,
it is often
useful
for
data
to come
from in
vivo
studies
and
from
studies
where
exposure
is repeated
or
given
over
an extended
period
of time.
Although
consistency
of
results
across
different
assays
and
animal
models
provides
a stronger
basis
for
drawing
conclusions,
it is
desirable
to have
data
on
the
precursor
event
in
the same
target
organ,
sex,
animal
strain, and
species
as
the
tumor
data. In
evaluating
an agent’s
mode
of
action,
it is
usually
not sufficient
to determine
that some
event
commences
upon
dosing.
It is
important
to
understand
whether
it is
a necessary
event
that
plays a
key
role
in
the
process
that
leads
to
tumor
development
versus
an effect
of
the cancer
process
itself
or
simply
an
associated
event.
Various
endpoints
can serve
as biological
markers
of
effects
in
biological
systems
or
samples.
These
may
help
identify
doses
at
which
elements
of the
carcinogenic
process
are
operating;
aid in
interspecies
extrapolations
when
data are
available
from
both
experimental
animal
and
human
cells;
and
under
certain
circumstances,
provide
insights
into the
possible
shape of
the
dose-response
curve
below
levels
where
tumor
incidences
are observed
(e.g.,
Choy,
1993).
2-34
Genetic
and other
findings
(such
as
changes
in proto-oncogenes
and
tumor
suppressor
genes
in preneoplastic
and
neoplastic
tissue
or,
possibly,
measures
of
endocrine
disruption)
can
indicate
the
potential
for
disease
and, as
such,
serve
as biomarkers
of
effect.
They,
too,
can
be
used
in different
ways.
•
The
spectrum
of
genetic
changes
in proliferative
lesions
and
tumors
following
chemical
administration
to experimental
animals
can
be
determined
and compared
with that
in spontaneous
tumors
in control
animals,
in
animals
exposed
to
other
agents
of varying
structural
and functional
activities,
and
in
persons
exposed
to
the
agent
under
study.
•
Biomarkers
of
effect
and/or
precursors
may
help
to
identify
subpopulations
of
individuals
who may
be at an
elevated
risk for
a certain
cancer
or
exposure
to a
certain
agent,
e.g.,
cytochrome
P450
2D6/debrisoquine
sensitivity
for
lung
cancer
(Caporaso
et
al., 1989)
or inherited
colon cancer
syndromes
(Kinzler
et
al.,
1991;
Peltomäki
et al., 1993).
•
As with
biomarkers
of exposure,
it
may be
justified
in some
cases
to use
biomarkers
of
effect
and/or
precursors
for
dose-response
assessment
or to
provide
insight
into
the potential
shape
of the
dose-response
curve
at doses
below
those
at which
tumors
are induced
experimentally.
In
applying
biomarker
data to
cancer assessment
an assessment
should
consider:
•
analytical
methodology,
•
routes
of exposure,
•
exposure
to mixtures,
•
time
after exposure,
•
sensitivity
and
specificity
of
biomarkers,
and
•
dose-response
relationships.
2-35
2.3.5.4.
Judging
Data
Criteria
that
are generally
applicable
for
judging
the adequacy
of mechanistically
based
data
include:
•
mechanistic
relevance
of the
data
to carcinogenicity,
•
number
of
studies
of each
endpoint,
•
consistency
of
results
in
different
test systems
and
different
species,
•
similar
dose-response
relationships
for
tumor
and
mode
of
action-related
effects,
•
conduct
of
the
tests in
accordance
with
generally
accepted
protocols,
and
•
degree
of
consensus
and
general
acceptance
among
scientists
regarding
interpretation
of
the
significance
and
specificity
of
the tests.
Although
important
information
can be
gained
from
in vitro
test
systems,
a higher
level
of
confidence
is generally
given
to
data
that are
derived
from
in
vivo
systems,
particularly
those
results
that
show
a site
concordance
with
the
tumor
data.
It is
important
to
remember
that
when
judging
and
considering
the use
of
any
data,
the
basic
standard
of
quality,
as defined
by
the
EPA
Information
Quality
Guidelines,
should
be
satisfied.
2.4.
MODE
OF
ACTION—GENERAL
CONSIDERATIONS
ANT)
FRAMEWORK
FOR
ANALYSIS
2.4.1.
General
Considerations
The
interaction
between
the
biology
of the
organism
and
the
chemical
properties
of
the
agent
determine
whether
there
is
an
adverse
effect.
Thus,
mode
of
action
analysis
is
based
on
physical,
chemical,
and
biological
information
that
helps
to explain
key
events
in
an
agent’s
influence
on
development
of tumors.
The
entire
range
of
information
developed
in
the
assessment
is
reviewed
to
arrive
at
a reasoned
judgment.
An
agent
may
work
by
more
than
one
mode
of
action,
both
at
different
sites
and
at the
same
tumor
site. Thus
the
mode
of
action
and
human
relevance
cannot
necessarily
be
generalized
to
other
toxic
endpoints
or tissues
or
cell
types
without
additional
analyses
(IPCS,
1999;
Meek
et
al., 2003).
At least
some
information
2-36
bearing on
mode of action
(e.g., SAR, screening
tests for mutagenicity)
is
present
for most
agents
undergoing
assessment
of carcinogenicity,
even though
certainty
about
exact
molecular
mechanisms
may
be rare.
Information
for
mode
of action
analysis generally
includes tumor
data in humans
and
animals
and among
structural analogues,
as well
as the other
key data. The more
complete
the
data
package and
the
generic
knowledge
about
a given mode
of action, the more
confidence
one
has and the
more one can
rely
on assessment
of available
data rather than
reverting to
default
options
to address
the
absence of information
on mode of
action.
Reasoned
judgments
are
generally
based
on a data-rich
source of chemical, chemical
class,
and
tumor type-specific
information.
Many
times there
will be conflicting
data
and
gaps
in the
information
base;
it is
important
to carefully evaluate
these
uncertainties
before reaching
any conclusion.
In
making decisions
about
potential
modes of action
and the relevance
of animal
tumor
findings
to humans
(Ashby et al.,
1990; Ashby and Tennant,
1991; Teimant,
1993; IPCS
1999;
Sonich-Mullin
et al., 2001; Meek
et al., 2003),
very often the results
of chronic
animal studies
may
give important clues.
Some of the
important
factors
to review include:
•
tumor types,
for example, those
responsive
to endocrine
influence
or those
produced
by
DNA-reactive carcinogens;
•
number of
studies and of tumor
sites, sexes,
and species affected
or unaffected
in
those
studies and if the data
present a coherent
story;
•
similarity of metabolic
activation
and detoxification
for a specific chemical
between
humans and
tested
species;
•
influence of route
of exposure
on the spectrum
of tumors and whether
they
occur
at
point
of exposure or systemic
sites;
2-37
•
effect
of high
dose
exposures
on
the
target
organ
or
systemic
toxicity
that
may
not
reflect
typical
physiological
conditions,
for example,
urinary
chemical
changes
associated
with
stone
formation,
effects
on
immune
surveillance;
•
presence
of
proliferative
lesions,
for
example,
hepatic
foci,
or hyperplasia;
•
effect
of dose
and
time
on
the progression
of
lesions
from
preneoplastic
to
benign
tumors,
then
to
malignant;
•
ratio
of
malignant
to
benign
tumors
as
a
function
of dose
and
time;
•
time
of
appearance
of tumors
after
commencing
exposure;
o
development
of tumors
that
invade
locally
or systemically,
or lead
to
death;
•
tumors
at
organ
sites
with
high
or
low
background
historical
incidence
in
laboratory
animals;
•
biomarkers
in
tumor
cells,
both
induced
and
spontaneous,
for
example,
DNA
or
protein
adducts,
mutation
spectra,
chromosome
changes,
oncogene
activation;
and/or
•
shape
of the
dose-response curve
in
the
range
of tumor
observation,
for
example,
linear
versus
nonlinear.
Some
of
the
myriad
ways
in
which
information
from
chronic
animal
studies
influences
mode
of
action
judgments
include,
but
are not
limited
to,
the following:
•
multisite
and
multispecies
tumor
effects
that
are
often
associated
with
mutagenic
agents;
2-38
•
tumors
restricted
to one
sex
or
species
suggesting
an influence
restricted
to gender,
strain,
or species;
•
late
onset
of tumors
that
are
primarily
benign,
are
at
sites
with
a high
historical
background
incidence,
or
show
reversal
of
lesions
on
cessation
of
exposure
suggesting
a
growth-promoting
mode
of action;
•
the possibility
that an
agent acting
differently
in different
tissues;
or
•
the
possibility
that
has more
than
one mode
of action
in
a single
tissue.
Simple
knowledge
of sites
of tumor
increase
in
rodent
studies
can
give
preliminary
clues
as
to mode
of
action.
Experience
at
the
National
Toxicology
Program
(NTP)
indicates
that
substances
that
are
DNA
reactive
and that
produce
gene
mutations
may be
unique
in producing
tumors
in
certain
anatomical
sites,
whereas
tumors
at
other
sites
may
arise
from
both
mutagenic
or
norunutagenic
influences
(Ashby
and
Tennant,
1991;
Huff
et aL, 1991).
The types
of data
and
their
influence
on
judgments
regarding
mode
of action
are
expected
to
evolve,
both
as science
advances
and as
the risk
assessment
community
gains
more
experience
with these
analyses.
This
section
contains
a framework
for
evaluating
hypothesized
mode(s)
of
action.
This
framework
has
similarities
to
and
differences
with
the
concepts
presented
in other
MOA
frameworks
(e.g.,
IPCS,
1999;
Sonich-Mullin
et al.,
2001;
Meek
et
aL, 2003).
Differences
are often
due to
the
context
of the
use
for
the framework.
For example,
the
Meek
et
al. (2003)
presents
a
stand-alone
document
for addressing
mode of
action
issues;
thus,
it
recommends
that
conclusions
concerning
MOA
be
rendered
separately.
In these
cancer
guidelines,
however,
they
are
incorporated
into
the context
of
all of
the data
regarding
weight
of
the
evidence
for
carcinogenicity.
2-39
2.4.2.
Evaluating
an
Hypothesized
Mode
of Action
2.4.2.1.
Peer Review
In
reaching
conclusions,
the
question
of “general
acceptance”
of
a mode
of action
should
be tested
as
part
of the
independent
peer
review
that EPA
obtains
for
its assessment
and
conclusions.
In
some cases
the
mode of
action
may
already
have
been
established
by
development
of
a large
body
of
research
information
and
characterization
of the
phenomenon
over
time.
In some
cases
there will
have
been development
of an Agency
policy
(e.g., mode
of
action
involving
alpha-2u-globulin
in the
male
rat
[U.S.
EPA,
1991
b])
or
a
series
of previous
assessments
in
which both
the
mode of
action
and
its
applicability
to
particular
cases
has
been
explored.
If so,
the assessment
and
its peer
review
can
focus
on
the
evidence
that a
particular
agent
acts
in
this mode.
The
peer
review
should
also evaluate
the strengths
and weaknesses
of
competing
modes
of action.
In other
cases,
the mode
of action
may
not
have
previously
been
the subject
of an Agency
document.
If
so, the
data
to
support
both
the mode
of
action
and
the
associated
activity
of the
agent
should
undergo
EPA
assessment
and subsequent
peer
review.
2.4.2.2.
Use
of
the Framework
The
framework
supports
a full
analysis
of mode
of
action
information,
but
it can also
be
used as
a screen
to
decide
whether
sufficient
information
is available
to evaluate
or whether
the
data
gaps are
too substantial
to
justify
further
analysis.
Mode
of
action
conclusions
are used
to
address
the
question
of human
relevance
of
animal
tumor
responses,
to
address
differences
in
anticipated
response
among
humans,
such
as between
children
and
adults
or
men
and
women;
and
as
the basis
of
decisions
about
the
anticipated
shape
of
the
dose-response
relationship.
Guidance
on
the latter
appears
in Section
3.
This
framework
is
intended
to provide
an
analytical
approach
for
evaluating
the
mode
of
action.
It is
neither
a
checklist
nor a
list of required
criteria.
As
the
type
and
amount
of
information
will
depend
on the mode
of
action postulated,
scientific
judgment
is
important
to
determine
if
the
weight
of
evidence
is
sufficient.
2-40
2.4.3.
Framework
for
Evaluating
Each Hypothesized
Carcinogenic
Mode
of Action
This
framework
is
intended
to be
an analytic
tool for
judging
whether
available
data
support
a mode
of carcinogenic
action
hypothesized
for
an
agent. It
is based upon
considerations
for causality
in
epidemiologic
investigations
originally
articulated
by Hill
(1965)
but
later
modified
by others
and extended
to
experimental
studies. The
original
Hill criteria
were
applied
to epidemiologic
data, whereas
this
framework
is
applied
to a much wider
assortment
of
experimental
data, so
it
retains the
basic principles
of Hill
but
is much
modified
in content.
The
modified Hill
criteria
can be
useful
for
organizing
thinking
about
aspects
of
causation,
and
they
are
consistent
with
the scientific
method
of developing
hypotheses
and
testing
those
hypotheses
experimentally.
During
analysis
by EPA,
and
as
guidance
for
peer
review, a key
question
is whether
the data to support
a mode
of action
meet the standards
generally
applied
in
experimental
biology regarding
inference
of causation.
All pertinent
studies are
reviewed
in analyzing
a mode
of action,
and
an
overall
weighing
of evidence
is
performed,
laying
out
the
strengths,
weaknesses,
and uncertainties
of
the
case
as
well
as potential
alternative
positions
and rationales.
Identifying
data gaps
and research
needs
is
also
part of the
assessment.
To evaluate
whether
an hypothesized
mode
of
action is operative,
an
analysis starts
with
an outline
of the
scientific
findings
regarding
the hypothesized
key events
leading
to cancer,
and
then weighing
information
to determine
whether
there is
a causal relationship
between
these
events
and cancer
formation,
i.e., that
the
effects
are critical
for
induction
of
tumors. It
is
not
generally
expected
that
the complete
sequence
will
be known
at the molecular
level.
Instead,
empirical
observations
made
at different
levels of
biological
organization—biochemical,
cellular,
physiological,
tissue,
organ,
and system—are
analyzed.
Several
important
points
should
be
considered
when working
with
the
framework:
The topics
listed for analysis
should
not be regarded
as a
checklist
of necessary
“proofs.”
The
judgment of
whether
an
hypothesized
mode
of
action
is supported
by
available
data
takes account
of the analysis
as
a whole.
2-41
•
The
framework
provides
a
structure
for
organizing
the
facts
upon
which
conclusions
as to
mode
of action
rest.
The
purpose
of
using
the
framework
is to
make
analysis
transparent
and
to
allow
the
reader
to
understand
the facts
and
reasoning
behind
a
conclusion.
•
The
framework
does
not
dictate
an answer.
The
weight
of
evidence
that is
sufficient
to support
a
decision
about
a
mode
of
action
may
be
less
or
more,
depending
on
the
purpose
of
the
analysis,
for
example,
screening,
research
needs
identification,
or full
risk
assessment.
To
make
the
reasoning
transparent,
the
purpose
of the
analysis
should
be made
apparent
to
the
reader.
•
Toxicokinetic
studies
may
contribute
to mode
of
action
analysis
by contributing
to
identifring
the
active
form(s)
of
an
agent
that
is
central
to
the
mode
of
action.
Apart
from contributing
in this
way,
toxicokinetics
studies
may
reveal
effects
of
saturation
of
metabolic
processes.
These
may not
be
considered
key
events
in
a
mode
of
action,
but
they
are
given
separate
consideration
in
assessing
dose
metrics
and
potential
nonlinearity
of
the
dose-response
relationship.
•
Generally, “sufficient”
support
is
a
matter
of scientific
judgment
in
the
context
of
the
requirements
of the
decisionmaker
or
in
the
context
of
science
policy
guidance
regarding
a
certain
mode
of action.
•
Even
when
an
hypothesized
mode
of
action
is supported
for
a described
response
in
a
specific
tissue,
it
may
not
explain
other
tumor
responses
observed,
which
should
get
separate
consideration
in
hazard
and
dose-response
assessment.
For
each tumor
site
being
evaluated,
the
mode
of
action
analysis
should
begin
with
a
description
of
the
relevant
data
and
key
events
that
may
be
associated
with
an
hypothesized
mode
of
action
and
its sequence
of key
events
(see
Section
2.4.3.1).
This
can be
followed
by a
2-42
discussion
of
various
aspects
of
the
experimental
support
for hypothesized
mode(s)
of
action
in
animals
and
humans
(see
Section
2.4.3.2).
The possibility
of other
modes
of
action
also
should
be considered
and
discussed
(see
Section
2.4.3.3);
if there
is evidence
for
more
than
one mode
of
action,
each
should
receive
a
separate
analysis.
Conclusions
about each
hypothesized
mode
of
action
should
address
whether
the mode
of
action
is
supported
in
animals
and
is
relevant
to
humans
and
which
populations
or
lifestages
can
be
particularly
susceptible
(see
Section
2.4.3.4).
In
a
risk
assessment
document,
the
analysis
of
an hypothesized
mode
of action
can
be
presented
before
or with
the
characterization
of an
agent’s
potential
hazard
to
humans.
2.4.3.1.
Description
ofthe
Hypothesized
Mode
of
Action
Summary
descrztion
of
the
hypothesized
mode
ofaction.
For
each tumor
site, the
mode
of action
analysis
begins
with
a
description
of
the hypothesized
mode
of action
and
its
sequence
of key
events.
If
there
is
evidence
for
more
than
one
mode
of action,
each
receives
a separate
analysis.
Identification
of
key events.
In
order
to
judge
how well
data
support
involvement
of a
key
event
in
carcinogenic
processes,
the
experimental
definition
of the
event
or events
should
be
clear
and
reproducible.
To support
an association,
experiments
should
define
and
measure
an
event
consistently.
•
Can
a list of
events
be identified
that
are key
to the
carcinogenic
process?
•
Are
the
events
well
defined?
Pertinent
observations
may include,
but
are not
limited
to, receptor-ligand
changes,
cytotoxicity,
cell
cycle
effects,
increased
cell
growth,
organ
weight
differences,
histological
changes,
hormone
or
other
protein
perturbations,
or
DNA
and
chromosome
effects.
2-43
2.4.3.2.
Discussion
of
the
Experimental
Support
for
the
Hypothesized
Mode
of
Action
The
experimental
support
for
the
hypothesized
mode
of
action
should
be
discussed
from
several
viewpoints
patterned
after
the
Hill
criteria
(see
Section
2.2.1.7).
For
illustration,
the
explanation
of
each
topic
includes
typical
questions
to
be
addressed
to
the
available
empirical
data
and
experimental
observations
anticipated
to
be
pertinent.
The
latter
will
vary
from
case
to
case.
For
a
particular
mode
of
action,
certain
observations
may
be
established
as
essential
in
practice
or
policy,
for
example,
measures
of
thyroid
hormone
levels
in
supporting
thyroid
hormone
elevation
as
a
key
event
in
carcinogenesis.
Strength,
consistency,
specflcity
of
association.
A
statistically
significant
association
between
events
and
a
tumor
response
observed
in
well-conducted
studies
is
generally
supportive
of
causation.
Consistent
observations
in
a
number
of
such
studies
with
differing
experimental
designs
increase
that
support,
because
different
designs
may
reduce
unknown
biases.
Studies
showing
“recovery,”
i.e,
absence
or
reduction
of
carcinogenicity
when
the
event
is
blocked
or
diminished,
are
particularly
useful
tests
of
the
association.
Specificity
of
the
association,without
evidence
of
other
modes
of
action,
strengthens
a
causal
conclusion.
A
lack
of
strength,
consistency,
and
specificity
of
association
weakens
the
causal
conclusions
for
a
particular
mode
of
action.
•
‘V1hat
is
the
level
of
statistical
and
biological
significance
for
each
event
and
for
cancer?
•
Do
independent
studies
and
different
experimental
hypothesis-testing
approaches
produce
the
same
associations?
•
Does
the
agent
produce
effects
other
than
those
hypothesized?
•
Is
the
key
event
associated
with
precursor
lesions?
2-44
Pertinent
observations include
tumor
response
associated
with
events
(site
of
action
logically
relates
to
event[s]),
precursor
lesions
associated
with
events,
initiation-promotion
studies,
and
stop/recovery
studies.
Dose-response
concordance.
If
a key
event
and
tumor
endpoints
increase
with
dose
such
that
the
key
events
forecast
the
appearance
of tumors
at a later
time
or
higher
dose,
a
causal
association
can be
strengthened.
Dose-response
associations
of the
key
event
with
other
precursor
events
can
add
further
strength.
Difficulty
arises
when
an event
is not
causal
but
accompanies the
process
generally.
For
example,
if
tumors
and the
hypothesized
precursor
both
increase
with
dose,
the
two
responses
will
be correlated
regardless
of
whether
a
causal
relationship
exists.
This
is
similar
to the
issue
of
confounding
in epidemiologic
studies.
Dose-
response
studies
coupled
with
mechanistic
studies
can
assist
in
clarifying
these
relationships.
What
are the
correlations
among
doses
producing
events
and cancer?
Pertinent observations
include,
but are
not
limited
to, 2-year
bioassay
observation
of
lesions
correlated
with
observations
of
hormone
changes
and
the same
lesions
in
shorter
term
studies
or
in
interim
sacrifice.
Temporal
relationship.
If an event
is shown
to
be causally
linked
to
tumorigenesis,
it
will
precede
tumor
appearance. An
event
may
also
be
observed
contemporaneously
or
after
tumor
appearance; these
observations may
add
to the
strength
of association
but
not
to
the temporal
association.
•
What
is
the
ordering
of
events
that
underlie
the
carcinogenic
process?
•
Is this
ordering
consistent
among
independent
studies?
Pertinent
observations include
studies
of varying
duration
observing
the
temporal
sequence
of
events
and
development
of
tumors.
2-45
Biological plausibility
and
coherence.
It
is important
that
the
hypothesized
mode
of
action
and
the
events
that
are part
of it
be based
on
contemporaneous
understanding
of
the
biology
of
cancer
to be
accepted.
If the
body
of infonnation
under
scrutiny
is
consistent
with
other
examples
(including
structurally
related
agents)
for which
the
hypothesized
mode
of
action
is accepted, the case
is
strengthened.
Because
some
modes
of
action
can
be
anticipated
to evoke
effects
other
than
cancer,
the
available
toxicity
database
on
noncancer
effects,
for
example,
reproductive
effects
of
certain
hormonal
disturbances,
can
contribute
to
this
evaluation.
•
Is the
mode
of
action
consistent
with
what is
known
about
carcinogenesis
in
general
and
for
the
case
specifically?
•
Are
carcinogenic
effects
and
events
consistent
across
structural
analogues?
•
Is the
database
on
the
agent
internally
consistent
in
supporting
the purported
mode
of action,
including
relevant
noncancer
toxicities?
Pertinent
observations
include
the scientific
basis
for considering
an
hypothesized
mode
of
action
generally, given
the
contemporaneous state
of knowledge
of
carcinogenic
processes;
previous
examples
of
data
sets
showing
the
mode
of
action;
data sets
on
analogues;
and
coherence
of
data
in
this case
from
cancer
and noncancer
toxicity
studies.
2.4.3.3.
Consideration
of
the
Possibility
of
Other
Modes
of
Action
The
possible
involvement
of
more
than
one
mode
of action
at the
tumor
site
should
be
considered. Pertinent
observations
that
are
not consistent
with
the
hypothesized
mode
of action
can
suggest
the
possibility
of other
modes
of action.
Some
pertinent
observations
can
be
consistent with
more
than
one
mode
of
action.
Furthermore,
different
modes
of
action
can
operate
in
different
dose
ranges;
for
example,
an
agent
can
act
predominantly
through
cytotoxicity at
high doses
and
through
mutagenicity
at
lower
doses
where
cytotoxicity
may
not
occur.
2-46
If there
is evidence
for
more
than
one
mode
of action,
each
should
receive
a separate
analysis.
There may
be an
uneven
level
of experimental
support
for
the different
modes
of
action.
Sometimes
this
can reflect
disproportionate
resources
spent
on
investigating
one
particular
mode
of
action and
not the
validity
or
relative
importance
of
the other
possible
modes
of action.
Ultimately,
however,
the information
on
all
of
the modes
of
action
should
be
integrated
to
better understand
how
and when
each
mode acts,
and
which
mode(s)
may
be
of
interest
for
exposure
levels
relevant
to human
exposures
of interest.
2.4.3.4.
Conclusions
About
the
Hypothesized
Mode
ofAction
Conclusions
about
the
hypothesized
mode
of action
should
address
the issues
listed
below.
For
those
agents
for
which the
mode
of
action
is
considered
useful
for the
risk
assessment,
the
weight
of the
evidence
concerning
mode
of action
in animals
as well
as
its
relevance
for
humans
would
be
incorporated
into
the
weight
of evidence
narrative
(Section
2.5).
(a)
Is the
hypothesized
mode
of
action
sufficiently
supported
in the test
animals?
Associations
observed
between
key events
and
tumors
may or
may
not
support
an
inference
of
causation.
The conclusion
that
the agent
causes
one or more
key
events
that results
in tumors
is
strengthened
as
more
aspects
of
causation
are satisfied
and weakened
as
fewer
are
satisfied.
Consistent
results
in different
experiments
that test
the
hypothesized
mode
of action
build
support
for
that
mode
of action.
Replicating
results
in
a
similar
experiment
does
not
generally
meaningfully
strengthen
the original
evidence,
and discordant
results generally
weaken
that
support.
Experimental
challenge
to the
hypothesized
mode
of action,
where
interrupting
the
sequence
of
key
events
suppresses
the
tumor
response
or enhancement
of
key events
increases
the
tumor
response,
creates
very
strong
support
for
the
mode of
action.
(b)
Is
the
hypothesized
mode
ofaction
relevant
to humans?
If
an hypothesized
mode
of
action
is sufficiently
supported
in
the
test
animals,
the sequence
of key
precursor
events
should
be
reviewed
to
identify
critical
similarities
and
differences
between
the test
animals
and
humans.
The
question
of
concordance
can
be
complicated
by
cross-species
differences
in
toxicokinetics
or
2-47
toxicodynamics.
For
example,
the
active
agent
can
be
formed
through
different
metabolic
pathways
in
animals
and
humans.
Any
information
suggesting
quantitative
differences
between
animals
and
humans
is
flagged
for
consideration
in
the
dose-response
assessment.
This
includes
the
potential
for
different
internal
doses
of
the
active
agent
or
for
differential
occurrence
of
a
key
precursor
event.
“Relevance”
of
a
potential
mode
of
action
is
considered
in
the
context
of
characterization
of
hazard,
not
level
of
risk.
Anticipated
levels
of
human
exposure
are
not
used
to
determine
whether
the
hypothesized
mode
of
action
is
relevant
to
humans.
Exposure
information
is
integrated
into
the
overall
risk
characterization.
The
question
of
relevance
considers
all
populations
and
lifestages.
It
is
possible
that
the
conditions
under
which
a
mode
of
action
operates
exist
primarily
in
a
particular
population
or
lifestage,
for
example,
in
those
with
a
pre-existing
hormonal
imbalance.
Other
populations
or
lifestages
may
not
be
analogous
to
the
test
animals,
in
which
case
the
question
of
relevance
would
be
decided
by
inference.
Special
attention
should
be
paid
to
whether
tumors
can
arise
from
childhood
exposure,
considering
various
aspects
of
development
during
these
lifestages.
Because
the
studies
that
support
a
mode
of
action
are
typically
conducted
in
mature
animals,
conclusions
about
relevance
during
childhood
generally
rely
on
inference.
There
is
currently
no
standard
Agency
position
regarding
the
issue
of
whether
tumors
arising
through
the
hypothesized
mode
of
action
are
relevant
during
childhood;
understanding
the
mode
of
action
implies
that
there
are
sufficient
data
(on
either
the
specific
agent
or
the
general
mode
of
action)
to
form
a
confident
conclusion
about
relevance
during
childhood.
(c)
Which
populations
or
lifestages
can
be
particularly
susceptible
to
the
hypothesized
mode
of
action?
If
an
hypothesized
mode
of
action
is
judged
relevant
to
humans,
information
about
the
key
precursor
event(s)
is
reviewed
to
identify
populations
or
lifestages
that
might
reasonably
expected
to
be
particularly
susceptible
to
their
occurrence.
Although
agent-specific
data
would
provide
the
strongest
indication
of
susceptibility,
this
review
may
also
rely
on
general
knowledge
about
the
precursor
events
and
characteristics
of
individuals
susceptible
to
these
2-48
events.
Any information
suggesting
quantitative
differences
between
populations
or
lifestages
should
be
flagged for
consideration
in the dose-response
assessment
(see
Section
3.5). This
includes
the potential
for a higher
internal
dose of
the active
agent
or for
an
increased
occurrence
of a key precursor
event.
Quantitative
differences
may
result in
separate
risk
estimates
for
susceptible
populations
or lifestages.
The possibility
that
childhood
is a susceptible
period
for exposure
should
be
explicitly
addressed.
Generic
understanding
of the mode
of action
can
be
used to
gauge
childhood
susceptibility,
and
this
determination
can be
refined through
analysis
of
agent-specific
data.
2.4.4
Evolution
with Experience
Several
groups
have
proposed
or
incorporated
mode of
action into
their
risk
assessments
(see,
e.g.,
U.S.
EPA,
1991b;
Sonich-Mullin
et
a!., 2001;
Meek
et al.,
2003). As
the
frameworks
and
mandates
under which
these evaluations
were
produced
differ,
the specific
procedures
described
in and
conclusions
drawn
may also
differ.
Nevertheless,
the
number
of case
studies
from
all venues
remains limited.
More
experience
with differing
modes
of action
are
expected
to
highlight
and
illustrate the
strengths
and
limitations
of
the
general
framework
proposed
in
these
cancer
guidelines.
Moreover,
additional toxicological
techniques
may expand
or change
scientific
judgments
regarding
which information
is
useful
for mode
of
action
determinations.
As warranted,
additional
guidance
may be proposed
as
experience
is gained
andlor as
toxicological
knowledge
advances.
2.5.
WEIGHT
OF
EVIDENCE
NARRATIVE
The weight
ofevidence
narrative
is
a
short
summary
(one
to two
pages)
that
explains
an
agent’s
human carcinogenic
potential and
the
conditions
that characterize
its expression.
It
should be
sufficiently
complete
to
be
able
to
stand
alone,
highlighting
the
key
issues
and
decisions
that were
the
basis
for
the evaluation
of the
agent’s potential
hazard.
It
should
be
sufficiently
clear
and transparent
to
be useful to
risk
managers
and non-expert
readers.
It
may
be
useful to
summarize
all
of the significant
components
and
conclusions
in the
first paragraph
of
the
narrative and
to
explain complex
issues in
more depth
in
the
rest
of the
narrative.
2-49
The
weight
of
the
evidence
should
be
presented
as
a
narrative
laying
out
the
complexity
of
information
that
is
essential
to
understanding
the
hazard
and
its
dependence
on
the
quality,
quantity,
and
type(s)
of
data
available,
as
well
as
the
circumstances
of
exposure
or
the
traits
of
an
exposed
population
that
may
be
required
for
expression
of
cancer.
For
example,
the
narrative
can
clearly
state
to
what
extent
the
determination
was
based
on
data
from
human
exposure,
from
animal
experiments,
from
some
combination
of
the
two,
or
from
other
data.
Similarly,
information
on
mode
of
action
can
specify
to
what
extent
the
data
are
from
in
vivo
or
in
vitro
exposures
or
based
on
similarities
to
other
chemicals.
The
extent
to
which
an
agent’s
mode
of
action
occurs
only
on
reaching
a
minimum
dose
or
a
minimum
duration
should
also
be
presented.
A
hazard
might
also
be
expressed
disproportionately
in
individuals
possessing
a
specific
gene;
such
characterizations
may
follow
from
a
better
understanding
of
the
human
genome.
Furthermore,
route
of
exposure
should
be
used
to
qualify
a
hazard
if,
for
example,
an
agent
is
not
absorbed
by
some
routes.
Similarly,
a
hazard
can
be
attributable
to
exposures
during
a
susceptible
lifestage
on
the
basis
of
our
understanding
of
human
development.
The
weight
of
evidence-of-evidence
narrative
should
highlight:
•
the
quality
and
quantity
of
the
data;
•
all
key
decisions
and
the
basis
for
these
major
decisions;
and
•
any
data,
analyses,or
assumptions
that
are
unusual
for
or
new
to
EPA.
To
capture
this
complexity,
a
weight
of
evidence
narrative
generally
includes
•
conclusions
about
human
carcinogenic
potential
(choice
of
descriptor(s),
described
below),
2-50
•
a summary of the
key
evidence
supporting
these
conclusions
(for each
descriptor
used),
including
information
on
the
type(s)
of
data
(human
and/or
animal,
in
vivo
and/or
in vitro)
used
to
support
the
conclusion(s),
•
available
information
on
the
epidemiologic
or
experimental
conditions
that
characterize
expression
of carcinogenicity
(e.g.,
if
carcinogenicity
is
possible
only
by
one
exposure
route
or only
above
a
certain
human
exposure
level),
•
a summary
of
potential
modes
of action
and
how they
reinforce
the
conclusions,
•
indications
of any
susceptible
populations
or
lifestages,
when
available,
and
•
a summary
of
the key
default
options
invoked
when
the
available
information
is
inconclusive.
To
provide
some
measure
of
clarity
and
consistency
in an
otherwise
free-form
narrative,
the
weight
of
evidence
descriptors
are
included
in
the
first
sentence
of the
narrative.
Choosing
a
descriptor
is
a
matter
ofjudgment
and
cannot
be
reduced
to
a formula.
Each descriptor
may
be
applicable
to
a wide
variety
of potential
data
sets and
weights
of
evidence.
These
descriptors
and
narratives
are
intended
to
permit
sufficient
flexibility
to accommodate
new
scientific
understanding
and
new
testing
methods
as
they are
developed
and
accepted
by the
scientific
community
and the
public.
Descriptors
represent
points
along
a
continuum
of
evidence;
consequently, there
are
gradations
and borderline
cases
that
are
clarified
by
the full
narrative.
Descriptors, as
well
as an
introductory
paragraph,
are a
short
summary
of
the
complete
narrative
that
preserves
the
complexity
that
is an
essential
part
of the
hazard
characterization.
Users
of
these
cancer
guidelines
and
of the
risk
assessments
that
result
from
the
use
of these
cancer
guidelines
should
consider
the
entire
range
of information
included
in
the
narrative
rather
than
focusing
simply
on
the
descriptor.
2-51
In
borderline
cases, the
narrative
explains
the
case
for choosing
one descriptor
and
discusses
the
arguments
for considering
but not
choosing
another.
For
example,
between
“suggestive”
and
“likely”
or
between
“suggestive”
and
“inadequate,”
the
explanation
clearly
communicates
the
information
needed
to
consider
appropriately
the
agents
carcinogenic
potential
in
subsequent
decisions.
Multiple
descriptors
can be
used for
a single
agent,
for
example,
when
carcinogenesis
is
dose-
or route-dependent.
For
example,
if an
agent
causes
point-of-contact
tumors
by
one
exposure
route
but
adequate
testing
is negative
by
another
route,
then
the agent
could
be
described
as
likely
to be carcinogenic
by the
first
route but
not
likely to
be carcinogenic
by
the
second.
Another
example
is when
the mode
of action
is
sufficiently
understood
to conclude
that
a
key event
in
tumor
development
would
not occur
below
a certain
dose
range.
In
this
case,
the
agent could
be
described
as likely
to be
carcinogenic
above
a certain
dose
range
but not
likely
to
be carcinogenic
below
that range.
Descriptors
can be
selected
for an
agent
that
has
not
been tested
in
a cancer
bioassay
if
sufficient
other
information,
e.g., toxicokinetic
and
mode
of
action
information,
is available
to
make
a strong,
convincing,
and logical
case
through
scientific
inference.
For
example,
if
an
agent
is one of
a well-defined
class of
agents
that are
understood
to operate
through
a
common
mode
of
action
and
if that agent
has
the
same
mode
of
action, then
in
the
narrative
the
untested
agent would
have
the same
descriptor
as the class.
Another
example
is when
an untested
agent’s
effects
are
understood
to be caused
by
a human
metabolite,
in which
case
in
the
narrative
the
untested
agent
could
have
the
same
descriptor
as
the metabolite.
As
new
testing
methods
are
developed
and
used,
assessments
may
increasingly
be
based
on inferences
from
toxicokinetic
and
mode
of
action
information
in
the
absence
of tumor
studies
in animals
or
humans.
When
a
well-studied
agent produces
tumors
only
at a
point
of
initial
contact,
the
descriptor
generally
applies
only
to
the
exposure
route
producing
tumors
unless
the mode
of
action
is
relevant
to other
routes.
The
rationale
for
this conclusion
would
be
explained
in
the
narrative.
W]aen
tumors
occur
at a site
other than
the
point
of initial
contact,
the
descriptor
generally
applies
to
all
exposure
routes that
have
not been
adequately
tested
at
sufficient
doses.
An
2-52
exception
occurs
when
there
is
convincing
information,
e.g.,
toxicokinetic
data
that absorption
does
not
occur
by
another
route.
When
the
response
differs
qualitatively
as well
as quantitatively
with dose,
this
information
should
be part
of the
characterization
of the
hazard.
In
some
cases
reaching
a
certain
dose range
can
be a precondition
for effects
to
occur,
as when
cancer
is secondary
to
another
toxic
effect that
appears
only
above
a certain
dose.
In other
cases
exposure
duration
can be
a
precondition
for hazard
if effects
occur
only after
exposure
is
sustained
for a
certain
duration.
These
considerations
differ
from
the issues
of relative
absorption
or
potency
at different
dose
levels
because
they
may
represent
a discontinuity
in
a
dose-response
function.
When
multiple
bioas
says
are
inconclusive,
mode of
action
data are
likely
to hold
the
key
to resolution
of
the more
appropriate
descriptor.
When
bioas
says
are few,
further
bioassays
to
replicate
a study’s
results
or to
investigate
the
potential
for effects
in
another
sex, strain,
or
species
may
be
useful.
When
there are
few pertinent
data, the
descriptor
makes
a statement
about
the
database,
for
example,
“Inadequate
Information
to
Assess
Carcinogenic
Potential,”
or
a database
that
provides
“Suggestive
Evidence
of
Carcinogenic
Potential.”
With
more information,
the
descriptor
expresses
a conclusion
about
the
agent’s
carcinogenic
potential
to
humans.
If the
conclusion
is
positive,
the
agent
could
be
described
as
“Likely
to Be
Carcinogenic
to
Humans”
or,
with strong
evidence,
“Carcinogenic
to
Humans.”
If the
conclusion
is
negative,
the
agent
could
be
described
as “Not
Likely
to Be
Carcinogenic
to
Humans.”
Although
the term
“likely”
can
have a probabilistic
connotation
in
other contexts,
its
use
as
a
weight
of
evidence
descriptor
does
not
correspond
to
a quantifiable
probability
of
whether
the
chemical
is
carcinogenic.
This is
because
the
data
that
support
cancer
assessments
generally
are not
suitable
for
numerical
calculations
of the
probability
that
an agent
is a
carcinogen.
Other
health
agencies
have expressed
a comparable
weight of
evidence
using
terms
such
as
“Reasonably Anticipated
to Be a
Human
Carcinogen”
(NTP)
or
“Probably
Carcinogenic
to
Humans”
(International
Agency
for Research
on Cancer).
The
following
descriptors
can
be used
as an
introduction
to
the weight
of evidence
narrative.
The
examples
presented
in the
discussion
of the
descriptors
are illustrative.
The
2-53
examples
are
neither
a
checklist
nor
a limitation
for
the descriptor.
The
complete
weight
of
evidence
narrative,
rather
than
the
descriptor
alone,
provides
the
conclusions
and
the
basis
for
them.
“Carcinogenic
to
Humans”
This
descriptor
indicates
strong
evidence
of
human
carcinogenicity.
It
covers
different
combinations
of evidence.
•
This
descriptor
is
appropriate
when
there
is
convincing
epidemiologic
evidence
of
a
causal
association
between
human
exposure
and cancer.
•
Exceptionally,
this
descriptor
may
be
equally
appropriate
with
a lesser
weight
of
epidemiologic
evidence
that
is strengthened
by other
lines
of
evidence.
It can
be
used
when
of
the
following
conditions
are
met:
(a)
there
is
strong
evidence
of
an
association
between
human
exposure
and
either
cancer
or
the
key
precursor
events
of the
agent’s
mode
of
action
but
not enough
for a
causal
association,
(b) there
is extensive
evidence
of
carcinogenicity
in animals,
(c)
the
mode(s)
of
carcinogenic
action
and
associated
key
precursor
events
have
been
identified
in
animals,
and
(d) there
is
strong
evidence
that
the
key
precursor
events
that
precede
the
cancer
response
in animals
are
anticipated
to occur
in humans
and
progress
to
tumors,
based
on
available
biological
information.
In
this
case,
the
narrative
includes
a summary
of both
the
experimental
and
epidemiologic
information
on
mode
of action
and
also
an
indication
of
the relative
weight
that
each
source
of
information
carries,
e.g.,
based
on
human
information,
based
on
limited
human
and
extensive
animal
experiments.
“Likely
to
Be
Carcinogenic
to Humans”
This
descriptor
is appropriate
when
the weight
of the
evidence
is
adequate
to
demonstrate
carcinogenic
potential
to
humans
but
does
not reach
the
weight
of evidence
for
the descriptor
2-54
“Carcinogenic
to Humans.”
Adequate
evidence
consistent
with
this descriptor
covers
a broad
spectrum.
As
stated
previously,
the
use
of the
term
“likely”
as
a
weight
of
evidence
descriptor
does
not correspond
to a quantifiable
probability.
The
examples
below
are meant
to represent
the
broad
range
of data
combinations
that
are
covered
by
this
descriptor;
they
are
illustrative
and
provide
neither
a checklist
nor a
limitation
for the
data
that
might
support
use
of
this
descriptor.
Moreover,
additional
information,
e.g.,
on
mode
of
action,
might
change
the choice
of descriptor
for
the illustrated
examples.
Supporting
data for
this descriptor
may
include:
•
an agent
demonstrating
a plausible
(but
not definitively
causal)
association
between
human
exposure
and
cancer,
in most
cases with
some
supporting
biological,
experimental
evidence,
though
not
necessarily
carcinogenicity
data
from
animal
experiments;
•
an agent
that
has
tested
positive
in
animal
experiments
in more
than
one
species,
sex,
strain,
site,
or
exposure
route,
with or
without
evidence
of
carcinogenic
ity
in
humans;
•
a
positive
tumor
study
that
raises additional
biological
concerns
beyond
that
of a
statistically
significant
result,
for example,
a
high
degree
of
malignancy,
or
an
early
age at
onset;
•
a
rare animal
tumor
response
in a
single
experiment
that
is assumed
to be
relevant
to
humans;
or
•
a
positive
tumor
study
that
is strengthened
by other
lines
of evidence,
for example,
either
plausible
(but
not definitively
causal)
association
between
human
exposure
and
cancer
evidence
that
the
agent or
an
important
metabolite
causes
events
generally
known
to be
associated
with
tumor
formation
(such
as DNA
reactivity
or
effects
on
cell growth
control)
likely
to be
related
to
the
tumor response
in this
case.
2-55
“Suggestive Evidence
of
Carcinogenic
Potential”
This
descriptor
of the
database
is
appropriate
when
the
weight
of
evidence
is
suggestive
of
carcinogenicity;
a
concern
for
potential
carcinogenic
effects
in
humans
is raised,
but
the
data
are
judged
not
sufficient
for a
stronger
conclusion.
This
descriptor
covers
a spectntm
of
evidence
associated
with
varying
levels
of
concern
for
carcinogenicity,
ranging
from
a positive
cancer
result
in
the
only
study
on
an agent
to
a
single
positive
cancer
result
in an
extensive
database
that
includes
negative
studies
in other
species.
Depending
on
the
extent
of the
database,
additional
studies
may
or may
not
provide
further
insights.
Some
examples
include:
•
a small,
and possibly
not
statistically
significant,
increase
in tumor
incidence
observed
in
a
single
animal
or
human
study
that
does
not
reach
the
weight
of
evidence
for the
descriptor
“Likely
to Be
Carcinogenic
to Humans.’
The
study
generally
would
not
be
contradicted
by
other
studies
of equal
quality
in the
same
population
group
or
experimental
system
(see
discussions
of
conflicting
evidence
and
differing
results,
below);
•
a small
increase
in a
tumor
with
a high
background
rate
in
that
sex and
strain,
when
there
is some
but
insufficient
evidence
that the
observed
tumors
may
be
due
to
intrinsic
factors
that
cause
background
tumors
and
not
due
to the
agent
being
assessed.
(When
there
is a
high
background
rate
of
a specific
tumor
in
animals
of
a
particular
sex
and
strain,
then
there
may
be biological
factors
operating
independently
of
the
agent
being
assessed
that
could
be
responsible
for
the
development
of
the
observed
tumors.)
In
this
case, the
reasons
for determining
that
the tumors
are not
due
to the
agent
are explained;
•
evidence
of
a positive
response
in a
study
whose
power,
design,
or
conduct
limits
the
ability
to
draw
a
confident
conclusion
(but
does
not
make
the
study
fatally
2-56
flawed),
but
where
the carcinogenic
potential
is
strengthened
by other
lines
of
evidence
(such
as structure-activity
relationships);
or
•
a
statistically
significant
increase
at
one
dose
only,
but
no
significant
response
at
the
other
doses
and
no
overall
trend.
“Inadequate
Information
to
Assess
Carcinogenic
Potential”
This
descriptor
of
the
database
is appropriate
when
available
data
are
judged
inadequate
for
applying
one
of
the other
descriptors.
Additional
studies
generally
would
be
expected
to
provide
further
insights.
Some
examples
include:
•
little
or no
pertinent
information;
•
conflicting
evidence,
that
is,
some
studies
provide
evidence
of carcinogenicity
but
other
studies
of
equal
quality
in
the
same
sex and
strain
are
negative.
Differing
results,
that
is, positive
results
in some
studies
and
negative
results
in one
or
more
different
experimental
systems,
do not
constitute
conflicting
evidence,
as
the
term
is
used
here.
Depending
on
the
overall
weight
of
evidence,
differing
results
can
be
considered
either
suggestive
evidence
or
likely
evidence;
or
•
negative
results
that
are
not sufficiently
robust
for
the
descriptor,
“Not
Likely
to
Be
Carcinogenic
to Humans.”
“Not
Likely
to
Be
Carcinogenic
to
Humans”
This
descriptor
is
appropriate
when
the
available
data
are considered
robust
for
deciding
that
there
is no
basis
for
human
hazard
concern.
In
some
instances,
there
can
be
positive
results
in
experimental animals
when
there
is
strong,
consistent
evidence
that
each
mode
of action
in
experimental animals
does
not operate
in
humans.
In
other
cases,
there
can be
convincing
2-57
evidence
in both
humans
and
animals
that
the agent
is
not
carcinogenic.
The
judgment
may
be
based
on
data
such
as:
•
animal
evidence
that
demonstrates
lack
of carcinogenic
effect
in
both
sexes
in
well-
designed
and
well-conducted
studies
in
at least
two
appropriate
animal
species
(in
the
absence
of other
animal
or human
data
suggesting
a
potential
for cancer
effects),
•
convincing
and
extensive
experimental
evidence
showing
that
the only
carcinogenic
effects
observed
in animals
are
not
relevant
to
humans,
•
convincing
evidence
that
carcinogenic
effects
are
not
likely
by
a
particular
exposure
route
(see
Section
2.3), or
•
convincing
evidence
that
carcinogenic
effects
are
not
likely
below
a defined
dose
range.
A
descriptor
of
“not
likely”
applies
only
to the
circumstances
supported
by
the
data.
For
example,
an agent
may
be “Not
Likely
to
Be Carcinogenic”
by
one
route
but not
necessarily
by
another.
In
those
cases
that
have
positive
animal
experiment(s)
but
the
results
are
judged
to
be
not relevant
to
humans,
the
narrative
discusses
why
the
results
are not
relevant.
Multzple
Descriptors
More
than
one
descriptor
can be
used
when
an
agent’s
effects
differ
by
dose
or
exposure
route.
For
example,
an agent
may
be
“Carcinogenic
to
Humans”
by
one
exposure
route
but
“Not
Likely
to
Be
Carcinogenic”
by
a route
by
which
it is
not absorbed.
Also,
an agent
could
be
“Likely
to Be
Carcinogenic”
above
a specified
dose
but
“Not
Likely
to
Be
Carcinogenic”
below
that
dose because a key
event
in tumor
fonnation
does
not
occur
below
that
dose.
2-58
2.6. HAZARD CHARACTERIZATION
The
hazard
characterization
contains
the
hazard
information
needed
for
a full
risk
characterization
(U.S.
EPA,
2000b).
It
presents
the
results
of
the hazard
assessment
and
explains
how
the
weight
of
evidence
conclusion was
reached.
The
hazard
characterization
summarizes,
in
plain
language, conclusions
about
the agent’s
potential
effects,
whether
they
can be
expected
to
depend
qualitatively
on
the
circumstances
of
exposure,
and if
anyone
can
be
expected
to
be
especially
susceptible.
It discusses
the
extent
to
which
these
conclusions
are
supported
by
data
or
are the
result
of
default
options
invoked
because
the
data
are
inconclusive.
It
explains
how
complex
cases
with
differing
results
in
different
studies
were
resolved.
The
hazard
characterization
highlights
the major
issues
addressed
in
the
hazard
assessment
and
discusses
alternative
interpretations of
the
data
and
the
degree
to
which
they
are supportable
scientifically
and
are
consistent
with
EPA
guidelines.
When
the
conclusion
is
supported
by
mode
of
action
information,
the hazard
characterization
also
provides
a
clear summary
of
the mode
of
action
conclusions
(see Section
2.4.3.4),
including
the completeness
of the
data,
the
strengths
and
limitations
of
the
inferences
made,
the
potential
for
other
modes
of
action,
and
the
implications
of
the mode
of
action
for
selecting
viable
approaches
to
the dose-response
assessment.
The
hazard
characterization
also
discusses
the extent
to
which
mode
of
action
information
is
available
to address
the
potential
for
disproportionate
risks
in specific
populations
or lifestages
or
the potential
for
enhanced
risks
on
the
basis
of
interactions
with
other
agents
or stressors,
if
anticipated.
Topics
that
can
be
addressed
in
a
hazard
characterization
include:
•
summary
of
the
results
of
the hazard
assessment;
•
identification
of
any
likely
susceptible
populations
and
lifestages,
especially
attending
to
children,
infants,
and
fetuses;
•
conclusions
about
the agent’s
mode
of
action,
and
implications
for
selecting
approaches
to
the
dose-response
assessment;
2-59
•
identification
of
the
available
lines
of
evidence
(e.g.,
animal
bioassays,
epidemiologic
studies,
toxicokinetic
information,
mode
of
action
studies,
and
information
about
structural
analogues
or
metabolites),
highlighting
data
quality
and
coherence
of
results
from
different
lines
of
evidence;
and
•
strengths
and
limitations
of
the
hazard
assessment,
highlighting
significant
issues
in
interpreting
the
data,
alternative
interpretations
that
are
considered
equally
plausible,
critical
data
gaps,
and
default
options
invoked
when
the
available
information
is
inconclusive.
2-60
3.
DOSE-RESPONSE
ASSESSMENT
Dose-response assessment
estimates
potential
risks
to
humans
at exposure
levels
of
interest.
Dose-response
assessments
are
useful
in
many
applications:
estimating
risk
at different
exposure
levels,
estimating
the
risk
reduction
for
different
decision
options,
estimating
the
risk
remaining
after
an
action
is taken,
providing
the
risk
information
needed
for
benefit-cost
analyses
of different
decision
options,
comparing
risks
across
different
agents
or
health effects,
and
setting
research
priorities.
The
purpose
of the
assessment
should
consider
the
quality
of the data
available,
which
will
vary
from
case
to
case.
A
dose-response
analysis
is
generally
developed
from
each
study
that
reports
quantitative
data on
dose
and response.
Alternative
measures
of
dose
are
available
for analyzing
human
and
animal
studies
(see Section
3.1).
A
two-step
approach
distinguishes
analysis
of
the
dose-
response
data
from
inferences
made about
lower doses.
The
first
step
is an
analysis
of dose
and
response
in the
range
of
observation
of the
experimental
or epidemiologic
studies
(see Section
3.2).
Modeling
is encouraged
to incorporate
a
wide
range
of experimental
data
into
the dose-
response
assessment
(see
Sections
3.1.2,
3.2.1,
3.2.2, 3.2.3).
The
modeling
yields
a point of
departure
(POD)
near the
lower
end of
the
observed
range,
without
significant
extrapolation
to
lower
doses
(see Sections
3.2.4,
3.2.5).
The
second
step
is extrapolation
to lower
doses
(see
Section
3.3).
The
extrapolation
approach
considers
what
is
known
about the
agent’s
mode
of
action
(see Section
3.3.1).
Both
linear
and nonlinear
approaches
are
available
(see Sections
3.3.3,
3.3.4).
When
multiple
estimates
can
be developed,
the strengths
and
weaknesses
of each
are presented.
In
some cases,
they
may
be combined
in
a way
that
best
represents
human
cancer
risk
(see Section
3.3.5).
Special
consideration
is given
to describing
dose-response
differences
attributable
to
different
human
exposure
scenarios
(see
Section
3.4)
and
to
susceptible
populations
and
lifestages
(see
Section
3.5).
It
is
important
to
discuss
significant
uncertainties
encountered
in
the
analysis
(see
Section
3.6) and
to
characterize
other
important
aspects
of the
dose-response
assessment
(see Section
3.7).
The
scope,
depth,
and use
of a dose-response
assessment
vary
in different
circumstances.
Although
the
quality
of
dose-response
data
is not necessarily
related
to
the
weight
of
evidence
3-1
descriptor,
dose-response
assessments
are
generally
completed
for
agents
considered
“Carcinogenic
to
Humans”
and
“Likely
to Be
Carcinogenic
to Humans.”
When
there
is
suggestive
evidence,
the Agency
generally
would
not
attempt
a dose-response
assessment,
as
the
nature
of the
data
generally
would
not
support
one;
however,
when
the evidence
includes
a
well-
conducted
study,
quantitative
analyses
may
be useful
for
some
purposes,
for
example,
providing
a
sense of
the
magnitude
and uncertainty
of potential
risks,
ranking
potential
hazards,
or
setting
research
priorities.
In
each
case,
the rationale
for
the quantitative
analysis
is
explained,
considering
the
uncertainty
in the
data
and
the suggestive
nature
of
the
weight
of evidence.
These
analyses
generally
would
not
be
considered
Agency
consensus
estimates.
Dose-response
assessments
are
generally
not
done when
there
is inadequate
evidence,
although
calculating
a
bounding
estimate
from
an
epidemiologic
or
experimental
study
that does
not show
positive
results
can
indicate
the study’s
level
of sensitivity
and capacity
to detect
risk
levels
of concern.
Cancer
is a
collection
of
several
diseases
that
develop
through
cell and
tissue
changes
over time.
Dose-response
assessment
procedures
based
on tumor
incidence
have
seldom
taken
into
account
the
effects
of
key precursor
events
within
the whole
biological
process
due
to lack
of
empirical
data
and
understanding
about
these
events.
In
this
discussion,
response
data
include
measures
of key
precursor
events
considered
integral
to
the carcinogenic
process
in addition
to
tumor
incidence.
These
responses
may
include
changes
in DNA,
chromosomes,
or other
key
macromolecules;
effects
on
growth
signal transduction,
including
induction
of
hormonal
changes;
or
physiological
or toxic
effects
that
include
proliferative
events
diagnosed
as
precancerous but
not
pathology
that is
judged
to
be
cancer.
Analysis
of
such responses
may
be
done
along
with
that
of tumor
incidence
to
enhance
the
tumor dose-response
analysis.
If
dose-
response
analysis
of
nontumor
key
events
is
more
informative
about
the carcinogenic
process
for
an
agent,
it can
be used
in
lieu
of,
or in
conjunction
with, tumor
incidence
analysis
for
the overall
dose-response assessment.
As
understanding
of mode
of action
improves
and new
types
of data
become
available,
dose-response assessment
will
continue
to
evolve.
These
cancer
guidelines
encourage
the
development
and
application
of
new methods
that
improve
dose-response
assessment
by
reflecting
new
scientific
understanding
and
new sources
of information.
3-2
3.1.
ANALYSIS
OF
DOSE
For
each
effect
observed,
dose-response
assessment
should
begin
by
determining
an
appropriate
dose
metric.
Several
dose
metrics
have
been
used,
e.g.,
delivered
dose,
body
burden,
and
area
under
the curve,
and others
may be
appropriate
depending
on
the data
and
mode
of
action.
Selection
of an
appropriate
dose
metric
considers
what
data
are available
and
what
is
known
about
the
agent’s
mode
of
action
at
the target
site,
and
uncertainties
involved
in
estimation
and
application
of
alternative
metrics.
The
dose
metric
specifies:
•
the
agent
measured,
preferably
the
active
agent
(administered
agent
or
a
metabolite);
•
proximity
to
the
target
site
(exposure
concentration,
potential
dose,
internal
dose,
or
delivered
dose,
5
reflecting
increasing
proximity);
and
•
the
time
component
of the
effective
dose
(cumulative
dose,
average
dose,
peak
dose,
or body
burden).
Analyses
can
be
based
on
estimates
of
animal
dose
metrics
or
human
dose
metrics.
The
assessment
should
describe
the
approach
used
to
select
a
dose
metric
and the
reasons
for
this
approach.
The
final
analysis,
however,
should
determine
a human
equivalent
dose metric.
This
facilitates
comparing results
from
different
datasets
and
effects
by
using
human
equivalent
dose/concentrations
as
common
metrics.
When
appropriate,
it may
be
necessary
to convert
dose
metrics
across
exposure
routes.
When
route-to-route
extrapolations
are made,
the
underlying
data,
algorithms,
and
assumptions
are
clearly
described.
Exposure
is contact
of
an
agent
with
the
outer
boundary
of
an organism.
Exposure
concentration
is
the
concentration
of
a
chemical
in its
transport
or carrier
medium
at
the
point of
contact.
Dose
is
the amount
of
a
substance
available
for
interaction
with
metabolic
processes
or
biologically
significant
receptors
after crossing
the
outer
boundary
of
an organism.
Potential
dose
is
the
amount
ingested,
inhaled,
or applied
to the
skin.
Applied
dose
is the
amount
of
a substance
presented
to an
absorption
barrier
and
available
for
absorption
(although
not
necessarily
having
yet
crossed
the outer
boundary
of the
organism).
Absorbed
dose
is
the
amount
crossing
a specific
absorption
barrier
(e.g.,
the
exchange
boundaries
of
skin, lung,
and digestive
tract)
through
uptake
processes.
Internal
dose
is a
more
general
term,
used
without
respect
to
specific
absorption
barriers
or
exchange
boundaries.
Delivered
dose
is
the
amount
of
the
chemical
available
for interaction
by
any
particular
organ
or cell
(U.S. EPA,
1992a).
3-3
Timing
of exposure
can
also
be
important.
When
there
is
a susceptible
lifestage,
doses
during
the
susceptible period
are
not
equivalent
to doses
at
other
times,
and
they
would
be
analyzed
separately.
3.1.1.
Standardizing
Different
Experimental
Exposure
Regimens
Complex
exposure
or dosing
regimens
are
often
present
in
experimental
and
epidemiologic
studies.
The
resulting
internal
dose
depends
on
many
variables,
including
concentration,
duration, frequency
of
administration,
and
duration
of recovery
periods
between
administrations.
Internal
dose also
depends
on
variables
that
are intrinsic
to
the exposed
individual,
such
as
lifestage
and
rates
of
metabolism
and clearance.
To
facilitate
comparing
results
from
different
study
designs
and
to
make
inferences
about
human
exposures,
a
summary
estimate
of
the dose
metric,
whether
the
administered
dose
or inhalation
exposure
concentration
or
an internal
metric,
may be
derived
for
a complex
exposure
regimen.
Toxicokinetic
modeling
is the
preferred
approach
for estimating
dose
metrics
from
exposure.
Toxicokinetic
models
generally
describe
the
relationship
between
exposure
and
measures
of
internal
dose
over
time.
More
complex
models
can
reflect
sources
of intrinsic
variation, such
as polymorphisms
in metabolism
and
clearance
rates.
When
a robust
model
is not
available,
or
when
the
purpose
of the
assessment
does
not
warrant
developing
a
model,
simpler
approaches may
be used.
For
chronic
exposure
studies,
the
cumulative
exposure
or
dose
administered
often
is
expressed
as
an average
over
the duration
of the
study,
as
one consistent
dose metric.
This
approach
implies
that
a
higher
dose
administered
over
a short
duration
is
equivalent
to
a
commensurately
lower
dose
administered
over
a longer
duration.
Uncertainty
usually
increases
as the
duration
becomes
shorter
relative
to
the
averaging
duration
or
the
intennittent
doses
become
more
intense
than
the
averaged
dose.
Moreover,
doses
during
any
specific
susceptible
or
refractory
period
would
not
be
equivalent
to doses
at
other times.
For these
reasons,
cumulative
exposure
or
potential
dose may
be
replaced
by
a more
appropriate
dose
metric
when
indicated
by
the
data.
3-4
For
mode
of
action
studies,
the
dose
metric
should
be
calculated
over a
duration
that
reflects
the
time to
occurrence
of the
key
precursor
effects.
Mode
of
action
studies
are
often
of
limited
duration,
as
the
precursors
can
be
observed
after
less-than-chronic
exposures.
When
the
experimental
exposure
regimen
is specified
on
a weekly
basis
(for
example,
4
hours
a
day,
5 days
a week),
the
daily
exposure
may
be
averaged
over
the week,
where
appropriate.
Doses
in studies
at
the cellular
or
molecular
level
can
be difficult
to relate
to organ-
or
organism-level
dose
metrics.
Toxicokinetic
modeling
can
sometimes
be
used
to
relate
doses
at
the
cellular
or molecular
level
to doses
or
exposures
at
higher
levels
of
organization.
3.1.2.
Toxicokinetic
Data
and
Modeling
In the
absence
of
chemical-specific
data,
physiologically
based
toxicokinetic
modeling
is
potentially
the most
comprehensive
way to
account
for
biological
processes
that determine
internal
dose.
Physiologically
based
models
commonly
describe
blood
flow
between
physiological
compartments
and
simulate
the
relationship
between
applied
dose
and internal
dose.
Toxicokinetic models
generally
need
data
on
absorption,
distribution,
metabolism,
and
elimination of the
administered
agent
and
its metabolites.
Additionally,
in
the
case
of
inhalation
exposures,
models
can
explicitly
characterize
the
geometry
of
the
respiratory
tract and
the
airflow
through
it,
as
well
as
the
interaction
of
this
airflow
with
the
entrained
particles
or
fibers
and gases
(Kimbell
et
al., 2001;
Subramaniam
et
al.,
2003).
Because
of
large
interspecies
differences
in airway
morphometry
such
models
can
be
particularly useful
in interspecies extrapolations.
When
employed,
however,
the
potential
for
large
inter-individual
differences
in
airway
morphometry,
are
considered
to ensure
that
the
models
provide
information
representative
of
human
populations.
Toxicokinetic models
can
improve
dose-response
assessment
by
revealing
and
describing
nonlinear relationships between
applied
and
internal
dose.
Nonlinearity
observed
in a dose
response
curve
often
can
be
attributed
to toxicokinetics
(Hoel
et al.,
1983;
Gaylor
et
aL, 1994),
involving, for
example,
saturation
or
induction
of
enzymatic
processes
at high
doses.
In
some
cases,
toxicokinetic processes
tend
to
become
linear
at sufficiently
low
doses
(Hattis,
1990).
3-5
A
discussion
of
confidence
should
accompany
the
presentation
of
model
results
and
include
consideration
of model
validation
and sensitivity
analysis,
stressing
the predictive
performance
of
the model
and
whether
the
model
is
sufficient
to support
decision-making.
Quantitative uncertainty
analysis
is important
for
evaluating
the
performance
of
a
model,
whether
the
model
is
based
primarily
on default
assumptions
or
chemical-specific
data.
The
uncertainty
analysis
covers
questions
of
model
uncertainty
(e.g.,
Is the
model
based
on the
appropriate
biology
and
how
does
that
affect
estimates
of dose
metrics?)
and
parameter
uncertainty
(e.g., Do
the
data
support
unbiased and
stable
estimates
of the
model
parameters?).
When
a
delivered
dose
measure
is
used
in animal-to-human
extrapolation,
the assessment
discusses
the
confidence
of
the
target
tissue
and
its
toxicodynamics
being
the
same
in
both
species
(see
Section
3.6).
Toxicokinetic
modeling
results
may be
presented
alone
as the
preferred
method
of
estimating
human
equivalent
exposures
or
doses,
or
these
results
may
be presented
in
parallel
with
default
procedures
(see
Section
3.1.3),
depending on the
confidence
in
the
modeling.
3.1.3.
Cross-species
Scaling
Procedures
Standard
cross-species
scaling
procedures
are
available
when
the
data
are
not
sufficient
to
support
a
toxicokinetic model
or
when
the
purpose
of
the
assessment
does
not
warrant
developing one.
The
aim
is to define
exposure
levels
for
humans
and animals
that
are
expected
to produce the same
degree
of
effect
(U.S.
EPA,
1992b),
taking
into
account
differences
in
scale
between
test
animals
and
humans,
such
as
size
and
lifespan.
3.1.3.1.
Oral
Exposures
For oral
exposures,
administered
doses
should
be
scaled
from
animals
to humans
on
the
basis
of
equivalence
of
mg/kg
314
-d
(milligrams
of the
agent
normalized
by
the
3/4 power
of
body
weight
per
day)
(U.S.
EPA,
1 992b).
The
3/4
power
is consistent
with current
science,
including
empirical
data
that
allow
comparison
of
potencies
in
humans
and
animals,
and
it is also
supported
by
analysis
of
the
allometric
variation
of key
physiological
parameters
across
mammalian
species.
It is
generally
more
appropriate
at low
doses,
where
sources
of
nonlinearity
such
as
saturation
of enzyme activity
are
less
likely
to occur.
This scaling
is
intended
as
an
3-6
unbiased
estimate
rather
than
a conservative
one.
Equating
exposure
concentrations
in
food
or
water
is an
alternative
version
of
the
same
approach,
because
daily
intakes
of
food
or
water
are
approximately
proportional
to
the 3/4
power
of body
weight.
The
aim
of these
cross-species
scaling
procedures
is
to
estimate
administered
doses
in
animals
and
humans
that
result
in equal
lifetime
risks.
It
is useful
to
recognize
two
components
of this
equivalence:
toxicokinetic
equivalence,
which
determines
administered
doses
in
animals
and
humans
that
yield
equal
tissue
doses,
and
toxicodynamic
equivalence,
which
determines
tissue
doses
in
animals
and
humans
that
yield
equal
lifetime
risks
(U.S.
EPA,
1 992b).
Toxicokinetic
modeling
(see Section
3.1.2)
addresses
factors
associated
with
toxicokinetic
equivalence,
and
toxicodynamic
modeling
(see
Section
3.2.2)
addresses
factors
associated
with
toxicodynamic
equivalence.
When
toxicokinetic
modeling
is used
without
toxicodynamic
modeling,
the
dose-response
assessment
develops
and
supports
an
approach
for
addressing
toxicodynamic
equivalence,
perhaps
by
retaining
some
of the
cross-species
scaling
factor
(e.g.,
using
the
square
root of
the
cross-species
scaling
factor
or
using
a factor
of
3 to
cover
toxicodynamic
differences
between
animals
and humans,
as
is
currently
done
in
deriving
inhalation
reference
concentrations
[U.S.
EPA,
1994]).
When
assessing
risks
from
childhood
exposure,
the
mg/kg
3
”
4
-d
scaling
factor
does
not
use
the
child
T
s
body
weight
(U.S.
EPA,
1 992b).
This
reflects
several
uncertainties
in extrapolating
risks
to
children:
•
The data
supporting
the
mg/kg
314
-d
scaling
factor
were
derived
for
differences
across
species
and
may
not apply
as
well
to differently
sized
individuals
of
the same
species
or
to
different
lifestages.
•
In addition
to
metabolic
differences,
there
are also
important
toxicodynamic
differences; for
example,
children
have
faster
rates
of
cell
division
than
do
adults,
so
scaling
across
different
lifestages
and
species
simultaneously
may
be
particularly
uncertain.
3-7
3.1.3.2.
Inhalation
Exposures
For inhalation
exposures
experimental
exposure
concentrations
are
replaced
with
human
equivalent
concentrations
calculated
using
EPA’s
methods
for
deriving
inhalation
reference
concentrations
(U.S.
EPA,
1994),
which
give
preference
to
the
use
of
toxicokinetic
modeling.
When
toxicokinetic
models
are unavailable,
default
dosimetry
models
are
employed
to
extrapolate
from
experimental
exposure
concentrations
to human
equivalent
concentrations.
When
toxicokinetic
modeling
or
dosimetry
modeling
is used
without
toxicodynamic
modeling,
the dose-response
assessment
develops
and supports
an approach
for
addressing
toxicodynamic
equivalence.
The
default
dosimetry
models
typically
involve
the use
of species-specific
physiologic
and
anatomic
factors
relevant
to the
form
of the
agent
(e.g.,
particle
or
gas)
and
categorized
with
regard
to
whether
the response
occurs
either
locally
(i.e., within
the
respiratory
tract)
or
remotely.
For
example,
cunent
default
models
(U.S.
EPA,
1994)
use parameters
such
as:
•
inhalation
rate
and
surface
area
of the
affected
part
of
the respiratory
tract
for
gases
eliciting
the
response
locally,
•
blood:gas
partition
coefficients
for remote
acting
gases,
•
fractional deposition
with
inhalation
rate
and surface
area
of
the
affected
part
of
the
respiratory
tract for
particles
eliciting
the
response
locally,
and
•
fractional
deposition
with
inhalation
rate
and
body
weight
for particles
eliciting
the
response
remotely.
The
current
default
values
for
some
parameters
used
in
the default
models
(e.g.,
breathing
rate
and
respiratory
tract
surface
area)
are
based
on
data
from
adults
(U.S.
EPA,
1994).
The
human
respiratory system
passes
through
several
distinct
stages
of
maturation
and growth
during
the first
several
years
of life
and
into adolescence
(Pinkerton
and Joad,
2000),
during
which
3-8
characteristics
important
to disposition
of
inhaled
toxicants
may
vary.
Children
and
adults
breathing
the
same
concentration
of an
agent
may
receive
different
doses
to
the body
or
lungs
(U.S.
EPA,
2002b).
Consequently,
it may
be appropriate
to evaluate
the default
models
by
considering
physiologic
and
anatomic
factors
representative
of
early
lifestages,
for
example
through
the
substitution
of
child-specific
parameters
(U.S.
EPA,
2002b).
Such
evaluation
uses
the
default
model
and
dosimetric
adjustment
in
use
at
the
time
of the
assessment
coupled
with
the
best
understanding
of child-specific
parameters
at that
time
(e.g.,
drawn
from the
scientific
literature).
This
analysis
is
undertaken
with
caution:
(I)
because
of
the
correlations
between
activity
level,
breathing
rate,
respiratory tract
dimensions,
and
body
weight
and
(2) to
avoid
the
possibility
of
mismatching
the
type
of agent
(gas
or
particle)
and
its
site
of
response
(within
the
respiratory
tract
or remote
from
the
respiratory
tract)
with
the relevant
dosimetry
factors
in
use
at
the time
of
the
assessment.
Analyses
of
children’s
inhalation
dosimetry
are
also considered
when
using
model
structures
beyond
the
default
models
(e.g.,
physiologically
based
toxicokinetic
models).
When
using
dosimetry
modeling,
the
comparison
of human-equivalent
concentrations
for
different lifestages
(e.g.,
for
an
adult
and
a
child)
can
indicate
whether
it is
important
to
carry
both
concentrations
forward
in
the
dose-response
assessment
or
whether
a verbal
characterization
of
any findings will
suffice.
3.1.4.
Route
Extrapolation
In
certain
situations,
an
assessment
based
on
studies
of one
exposure
route
may
be
applied
to
another
exposure
route.
Route-to-route
extrapolation
has both
qualitative
and
quantitative
aspects.
For
the
qualitative
aspect,
the
assessor
should
weigh
the degree
to
which
positive
results
by one
exposure
route
support
a judgment
that
similar
results
would
be
expected
by
another
route.
In
general,
confidence
in
making
such
a judgment
is strengthened
when
tumors
are
observed
at a
site
distant
from
the
portal
of
entry
and
when
absorption
is similar
through
both
routes.
In the
absence
of
contrary
data,
a qualitative default
option
can
be used:
if
the
agent
is
absorbed
through
an exposure
route
to
give
an
internal
dose,
it may
be
carcinogenic
by
that
route.
3-9
When
a qualitative
extrapolation
can
be supported,
quantitative
extrapolation
may
still
be
problematic
due
to the
absence
of adequate
data.
The
differences
in
biological
processes
among
routes
of
exposure
(oral,
inhalation,
dermal)
can
be
great
because
of
for example,
first-pass
effects
and
different
results
from
different
exposure
patterns.
There
is
no
generally
applicable
method
for
accounting
for
these differences
in uptake
processes
in
a
quantitative
route-to-route
extrapolation
of dose-response
data
in the
absence
of
good data
on
the
agent
of interest.
Therefore,
route-to-route
extrapolation
of dose
data
relies
on
a case-by-case
analysis
of
available
data.
When
good
data
on the
agent itself
are
limited,
an extrapolation
analysis
can
be based
on
expectations
from
physical
and
chemical
properties
of the
agent,
properties
and
route-specific
data
on structurally
analogous
compounds,
or in
vitro
or in
vivo
uptake
data
on
the agent.
Route-to-route
uptake
models
may be
applied
if model
parameters
are
suitable
for the
compound
of
interest.
Such
models
are currently
considered
interim
methods;
further
model
development and validation
is
awaiting
the
development
of more
extensive
data.
For screening
or hazard
ranking,
route-to-route
extrapolation
may
be
based
on assumed
quantitative
comparability
as a
default,
as long
as it is
reasonable
to assume
absorption
by compared
routes.
When
route-to-route
extrapolation
is used,
the
assessor’s
degree
of
confidence
in
both
the
qualitative
and
quantitative
extrapolation
is
discussed
in
the
assessment
and
highlighted
in
the
dose-response
characterization.
Toxicokinetic
modeling
can
be used
to
compare
results
of
studies
by
different
exposure
routes.
Results
can
also
be
compared
on
the
basis
of internal
dose
for
effects
distant
from
the
point
of
contact.
Route
extrapolation
can
be
used
to
understand
how
internal
dose
and
subsequent
effects
depend
on
exposure
route.
If
testing
by
different
exposure
routes
is
available,
the
observation
of
similar
or
dissimilar
internal
doses
can
be
important
in
determining
whether
and
what
conclusions
can
be made
concerning
the dose-response
function(s)
for
different
routes
of
exposure.
3-10
3.2.
ANALYSIS
IN
THE RANGE
OF OBSERVATION
The
principle
underlying
these
cancer
guidelines
is
to
use
approaches
that
include
as
much
information
as possible.
Quantitative
information
about
key precursor
events
can be
used
to
develop
a
toxicodynamic
model.
Alternatively,
such
information
can
be fitted
by empirical
models
to
extend
the
dose-response
analysis
of
tumor
incidence
to
lower
doses
and
response
levels.
The
analysis
in the
range
of observation
is
used
to establish
a
POD near
the
lower
end
of
the observed
range
(see
Section
3.3).
3.2.1.
Epidemiologic
Studies
Ideally,
epidemiologic
data
would
be
used
to select
the
dose-response
function
for
human
exposures.
Because
epidemiologic
data
are
usually
limited
and
many
models
may
fit the
data
(Samet
et al.,1998),
other
factors
may
influence
model
choice.
For
epidemiologic
studies,
including
those
with
grouped
data,
analysis
by
linear
models
in
the
range
of
observation
is
generally
appropriate
unless
the
fit
is poor.
The
relatively
small
exposure
range
observed
in
many
epidemiologic
studies,
for example,
makes
it
difficult
to
discern
the
shape
of the
exposure-
or
dose-response
curve.
Exposure
misclassification
and errors
in exposure
estimation
also
obscure
the
shape
of the
dose-response
curve.
‘When
these errors
are
unsystematic
or random,
the
result is
frequently
to bias
the
risk
estimates
toward
zero.
When
a linear
model
fits
poorly,
more
flexible
models
that
allow
for
low-dose
linearity,
for example,
a
linear-quadratic
model
or
a
Hill
model
(Murrell
et
al., 1998),
are
often
considered
next.
Analysis
of epidemiologic
studies
depends
on
the
type
of
study and
quality
of
the
data,
particularly
the availability
of
quantitative
measures
of
exposure.
The
objective
is
to develop
a
dose-response
curve
that estimates
the incidence
of
cancer
attributable
to the
dose
(as estimated
from
the
exposure)
to
the agent.
In
some
cases,
e.g.,
tobacco
smoke
or
occupational
exposures,
the
data are
in
the range
of the
exposures
of
interest.
In other
cases,
as
with
data
from
animal
experiments,
information
from
the observable
range
is
extrapolated
to
exposures
of interest.
Analysis
of effects
raises
additional
issues:
3-li
•
Many
studies
collect
information
from
death
certificates,
which
leads
to
estimates
of
mortality
rather
than
incidence.
Because
survival
rates
vary
for
different
cancers,
the
analysis
may
be
improved
by
adjusting
mortality
figures
to reflect
the relationship
between
incidence
and mortality.
•
Epidemiologic
studies,
by
their
nature,
are
limited
in
the
extent
to which
they
can
control
for effects
due to
exposures
from other
agents.
In
some
cases,
the
agent
can
have
discernible
interactive
effects
with
another
agent,
making
it
possible
to
estimate
the
contribution
of
each
agent
as
a
risk
factor
for
the
effects
of
the
other.
For example,
competing
risks
in
a study
population
can
limit
the
observed
occurrence
of
cancer,
while
additive
effects
may
lead
to an
increase
occurrence
of
cancer.
In
the
case
of rates
not
already
so
adjusted,
the
analysis
can
be
improved
by correcting
for
competing
or
additive
risks
that
are
not
similar
in
exposed
and
comparison
groups.
•
Comparison groups
that
are
not
free from
exposure
to
the
agent
can bias
the
risk
estimates
toward
zero.
The
analysis
can
be
improved
by
considering
background
exposures
in the
exposed
and
comparison
groups.
•
The
latent
period
for most
cancers
implies
that
exposures
immediately
preceding
the
detection
of a
tumor
would
be less
likely
to
have
contributed
to its
development
and,
therefore,
may
count
less
in
the
analysis.
Study
subjects
who
were
first exposed
near
the
end
of
the
study
may
not
have
had
adequate
time
since
exposure
for
cancer
to
develop;
therefore, analysis
of their
data
may
be
similar
to
analysis
of
data
for
those
who were
not
exposed. However,
for
carcinogens
that act
on
multiple
stages
of the
carcinogenic
process,
especially
the
later
stages,
all periods
of exposure.
including
recent
exposures,
may
be important.
Some
study
designs
can
yield
only
a
partial
characterization
of the
overall
hazard
and
therefore
risk
as,
for
example,
in
studies
that:
(1)
investigate
only
one
effect
(typical
of many
3-12
case-control
studies),
(2)
include
only
one
population segment
(e.g., male
workers
or
workers
of
one socioeconomic
class),
or
(3)
include
only
one
lifestage
(e.g.,
childhood
leukemia
following
maternal
exposure
to
contaminated
drinking
water).
To obtain
a
more
complete
characterization
that includes
risks
of other
cancers,
estimates
from these
studies
can
be
supplemented
with
estimates
from
other
studies
that
investigated
other
cancers,
population
segments,
or
lifestages
(see
Section
3.5).
When
several
studies
are available
for
dose-response
analysis,
ineta-analysis
can
provide
a systematic
approach
to weighing
positive
studies
and those
studies
that
do not
show
positive
results,
and
calculating
an
overall
risk
estimate
with
greater
precision.
Issues
considered
include
the
comparability
of
studies,
heterogeneity
across
studies,
and the
potential
for a
single
large
study
to
dominate
the analysis.
Confidence
in a
meta-analysis
is
increased
when
it
considers
study
quality,
including
definition
of the
study
population
and comparison
group,
measurement
of
exposure,
potential
for exposure
misclassification,
adequacy
of
follow-up
period,
and
analysis
of
confounders
(see
Section
2.2.1.3).
3.2.2.
Toxicodynamic
(“Biologically
Based”)
Modeling
Toxicodynamic modeling
can be
used
when
there
are sufficient
data
to ascertain
the
mode
of
action
(see
Section
2.4)
and quantitatively
support
model
parameters
that
represent
rates
and
other
quantities
associated with
the
key
precursor
events
of
the
mode
of action.
Toxicodynamic
modeling
is
potentially
the
most
comprehensive
way
to account
for
the
biological
processes
involved
in a
response.
Such
models
seek
to
reflect
the
sequence
of
key precursor
events
that
lead
to
cancer.
Toxicodynamic
models
can
contribute
to dose-response
assessment
by
revealing
and describing nonlinear
relationships
between
internal
dose
and
cancer
response.
Such
models
may
provide
a useful
approach
for
analysis
in
the
range
of
observation,
provided
the
purpose
of
the
assessment
justifies
the
effort
involved.
If
a
new
model
is
developed
for
a
specific
agent,
extensive
data
on
the
agent
are
important
for
identifying
the
form
of the
model,
estimating
its
parameters,
and
building
confidence
in its
results.
Conformance to
the
observed
tumor
incidence
data alone
does
not
establish
a
model’s
validity,
as a
model
can
be
designed
with
a sufficiently
large
number
of parameters
so as to
fit
3-13
any given
dataset.
Peer
review,
including
both
an
examination
of
the
scientific
basis
supporting
the
model
and
an
independent
evaluation
of
the
model’s
performance,
is
an
essential
part
of
evaluating
the
new
model.
If
a
standard
model
already
exists
for
the
agent’c
mode
of
action,
the
model
can
be
adapted
for
the agent
by
using
agent-specific
data
to
estimate
the
model’s
parameters.
An
example
is
the two-stage
clonal
expansion
model
developed
by
Moolgavkar
and
Knudson
(1981)
and
Chen
and
Farland
(1991).
These
models
continue
to be
improved
as
more
information
becomes available.
It
is
possible
for
different
models
to provide
equivalent
fits
to
the
observed
data
but to
diverge
substantially
in their
projections
at lower
doses.
When
model
parameters
are
estimated
from
tumor
incidence
data,
it
is
often
the case
that
different
combinations
of
parameter
estimates
can yield
similar
results
in the
observed
range.
For this
reason,
critical
parameters
(e.g.,
mutation
rates
and
cell
birth
and
death
rates)
are estimated
from
laboratory
studies
and
not
by
curve-fitting
to
tumor
incidence
data
(Portier,
1987).
This
approach
reduces
model
uncertainty
(see
Section
3.6)
and
ensures
that the
model
does
not
give
answers
that
are
biologically
unrealistic.
This
approach
also
provides
a
robustness
of
results,
where
the
results
are
not
likely
to
change
substantially if fitted
to
slightly
different
data.
Toxicodynamic
modeling
can
provide
insight
into
the
relationship
between
tumors
and
key precursor events.
For
example,
a
model
that
includes
cell
proliferation
can
be
used
to
explore
the
extent
to which
small
increases
in
the
cell proliferation
rate can
lead
to
large
lifetime
tumor
incidences
(Gaylor
and
Zheng,
1996).
In this
way,
toxicodynamic
modeling
can
be
used
to
select
and
characterize
an
appropriate
precursor
response
level
(see Section
3.2.2,
3.2.5).
3.2.3.
Empirical
Modeling
(“Curve Fitting”)
When
a toxicodynamic
model
is not
available
or when
the
purpose
of the
assessment
does
not
warrant
developing such
a
model,
empirical
modeling
(sometimes called
“curve
fitting”)
should
be
used
in
the
range
of observation.
A
model
can be
fitted
to
data
on either
tumor
incidence
or a
key
precursor
event.
Goodness-of-fit
to
the
experimental
observations
is
not
by
itself
an
effective
means
of discriminating
among
models
that adequately
fit
the
data
(OSTP,
3-14
1985).
Many
different
curve-fitting
models
have
been
developed,
and those
that
fit
the
observed
data
reasonably
well
may lead
to
several-fold
differences
in estimated
risk
at the lower
end of
the
observed
range.
Another
problem
occurs
when
a multitude
of
alternatives
are
presented
without
sufficient
context
to
make
a
reasoned
judgment
about
the
alternatives.
This
form
of model
uncertainty
reflects
primarily
the
availability
of
different
computer
models
and
not biological
information
about
the
agent being
assessed
or
about
carcinogenesis
in general.
In cases
where
curve-fitting
models
are used
because
the data
are
not adequate
to
support
a
toxicodynamic
model,
there
generally
would
be
no
biological
basis
to
choose
among
alternative
curve-fitting
models.
However,
in situations
where
there
are
alternative
models
with
significant
biological
support,
the
decisionmaker
can
be informed
by the presentation
of
these
alternatives
along with
their strengths
and uncertainties.
Quantitative
data
on
precursors
can
be used
in
conjunction
with,
or
in
lieu
of,
data
on
tumor
incidence
to
extend
the
dose-response
curve
to lower
doses.
Caution
is used
with
rates
of
molecular
events
such
as mutation
or
cell proliferation
or
signal
transduction.
Such rates
can
be
difficult
to
relate to
cell or
tissue
changes
overall.
The timing
of
observations
of these
phenomena,
as
well
as the
cell
type
involved,
is
linked
to
other
precursor
events
to ensure
that
the
measurement
is truly
a key
event (Section
2.4).
For
incidence
data
on either
tumors
or
a
precursor,
an
established
empirical
procedure
is
used to
provide
objectivity
and
consistency
among assessments.
The
procedure
models
incidence,
corrected
for
background,
as
an
increasing
function
of dose.
The
models
are
sufficiently
flexible
in the
observed
range
to
fit
linear
and
nonlinear
datasets.
Additional
judgments
and
perhaps
alternative
analyses
are
used
when
the
procedure
fails
to
yield reliable
results.
For
example,
when
a model’s
fit is
poor, the
highest
dose
is often
omitted
in cases
where
it is
judged
that
the highest
dose
reflects
competing
toxicity
that
is more
relevant
at high
doses
than at
lower
doses.
Another
example
is
when
there are
large differences
in
survival
across
dose
groups;
here,
models
that
includes
time-to-tumor
or
time-to-event
information
may
be
useful.
For
continuous
data
on key
precursor
effects,
empirical
models
can
be
chosen
on
the
basis of
the
structure
of the
data.
The rationale
for the
choice
of model,
the
alternatives
3-15
considered
and
rejected,
and a
discussion
of model
uncertainty
are
included
in
the
dose-response
characterization.
3.2.4.
Point
of
Departure
(POD)
For
each
tumor
response,
a
POD
from the
observed
data
should
be
estimated
to
mark
the
beginning
of
extrapolation
to
lower
doses.
The
POD
is
an
estimated
dose
(expressed
in
human-
equivalent
tenns)
near
the lower
end
of
the
observed
range
without
significant
extrapolation
to
lower
doses.
The POD
is used as
the starting
point
for subsequent
extrapolations
and
analyses.
For
linear
extrapolation,
the
POD is
used to
calculate
a
slopefactor
(see Section
3.3.3),
and for
nonlinear
extrapolation
the
POD
is used
in
the calculation
of a
reference
dose
or reference
concentration
(see
Section
3.3.4).
In a risk
characterization,
the
POD
is part
of the
determination
of
a margin
of
exposure
(see Section
5.4).
With
appropriate
adjustments,
it can
also
be used
as
the basis
for
hazard
rankings
that compare
different
agents
or
health effects.
The
lowest
POD is
used that
is adequately
supported
by the
data.
If the POD
is above
some
data points,
it can
fail to
reflect
the shape
of
the dose-response
curve
at
the lowest
doses
and
can introduce
bias into
subsequent
extrapolations
(see Figure
3-1).
On the
other hand,
if
the
POD
is far
below
all observed
data points,
it
can introduce
model
uncertainty
and
parameter
uncertainty
(see Section
3.6)
that
increase
with
the
distance
between
the
data
and
the
POD.
Use
of a POD
at the
lowest
level
supported
by
the
data
seeks
to
balance
these
considerations.
It
uses
information
from
the
model(s)
a
small
distance
below
the
observed
range
rather
than
discarding
this
information
and using
extrapolation
procedures
in a
range
where
the model(s)
can provide
some
useful
information.
Statistical
tests
involving
the
ratio
of
the
central
estimate
and
its
lower
bound
(i.e.,
ED/LED)
can
be
useful
for
evaluating
how
well
the data
support
a model’s
estimates
at
a
particular
response
level.
(Note
that
the ability
to model
at a
particular
response
level is
not the
same
as
the study’s
ability
to
identify
an increase
at that response
level
as
statistically
significant.)
The POD
for
extrapolating
the
relationship
to
environmental
exposure
levels
of interest,
when the
latter
are
outside
the range
of observed
data, is
generally
the
lower
95%
confidence
3-16
limit
on the
lowest
dose level
that
can be
supported
for
modeling
by the
data.
SAB (1997)
suggested
that,
“it
may
be
appropriate
to emphasize
lower
statistical
bounds
in screening
analyses
and
in
activities
designed
to
develop
an appropriate
human
exposure
value,
since
such
activities
require
accounting
for various
types
of
uncertainties
and
a
lower
bound
on
the
central
estimate
is
a scientifically-based
approach
accounting
for the
uncertainty
in
the
true
value
of the
ED
10
[or
central
estimate].”
However,
the
consensus
of the
SAB
(1997)
was
that,
“both
point
estimates
and
statistical
bounds
can
be
useful
in different
circumstances,
and
recommended
that the
Agency
routinely
calculate
and
present
the point
estimate
of
the
ED
10
[or central
estimate]
and
the corresponding
upper
and
lower
95%
statistical
bounds.”
For
example,
it
may
be appropriate
to emphasize
the central
estimate
in
activities
that
involve
fonnal
uncertainty
analysis
that are
required
by
0MB
Circular
A-4
(0MB,
2003)
as
well as
ranking
agents
as to their
carcinogenic
hazard.
Thus,
risk
assessors
should
calculate,
to
the extent
practicable,
and
present
the central
estimate
and
the
corresponding
upper
and lower
statistical
bounds
(such
as
confidence
limits)
to
inform
decisionmakers.
When
tumor
data
are
used,
a POD
is obtained
from
the
modeled
tumor
incidences.
Conventional
cancer
bioassays,
with approximately
50 animals
per
group,
generally
can
support
modeling
down to
an increased
incidence
of 1—10%;
epidemiologic
studies,
with larger
sample
sizes,
below 1%.
Various
models
commonly
used
for
carcinogens
yield similar
estimates
of the
POD
at
response
levels
as low
as
1%
(Krewski
and
Van
Ryzin,
1981;
Gaylor
et al., 1994).
Consequently,
response
levels
at
or
below
10%
can
often
be
used as
the POD.
As
a
modeling
convention,
the
lower
bound
on
the
doses
associated
with standard
response
levels
of 1,
5,
and
10%
can
be
analyzed,
presented,
and
considered.
For
making
comparisons
at
doses within
the
observed
range,
the
ED
10
and
LED
10
are
also
reported
and
can
be used,
with
appropriate
adjustments,
in
hazard
rankings
that compare
different
agents
or health
effects
(U.S.
EPA,
2002c).
A
no-observed-adverse-effect
level (NOAEL)
generally
is
not used
for assessing
the
potential
for
carcinogenic
response
when
one or
more
models
can be
fitted to
the data.
When
good quality
precursor
data
are
available
and
are
clearly
tied
to the
mode
of
action
of
the
compound
ofinterest,
models
that include
both
tumors
and
their precursors
may
be
advantageous
for
deriving
a POD.
Such
models
can provide
insight
into quantitative
3-17
relationships
between
tumors
and
precursors
(see
Section
3.2.2),
possibly
suggesting
the
precursor
response
level
that
is
associated
with
a
particular
tumor
response
level.
The
goal
is
to
use
precursor
data
to
extend
the
observed
range
below
what
can
be
observed
in
tumor
studies.
EPA
is
continuing
to
examine
this
issue
and
anticipates
that
findings
and
conclusions
may
result
in
supplemental
guidance
to
these
cancer
guidelines.
If
the
precursor
data
are
drawn
from
small
samples
or
if
the
quantitative
relationship
between
tumors
and
precursors
is
not
well
defined,
then
the
tumor
data
will
provide
a
more
reliable
POD.
Precursor
effects
may
or
may
not
be
biologically
adverse
in
themselves;
the
intent
is
to
consider
not
only
tumors
but
also
damage
that
can
lead
to
subsequent
tumor
development
by
the
agent.
Analysis
of
continuous
data
may
differ
from
discrete
data;
Murrell
et
al.
(1998)
discuss
alternative
approaches
to
deriving
a
POD
from
continuous
data.
3.2.5.
Characterizing
the
POD:
The
POD
Narrative
As
a
single-point
summary
of
a
single
dose-response
curve,
the
POD
alone
does
not
convey
all
the
critical
information
present
in
the
data
from
which
it
is
derived.
To
convey
a
measure
of
uncertainty,
the
POD
should
be
presented
as
a
central
estimate
with
upper
and
lower
bounds.
A
POD
narrative
summarizes
other
important
features
of
the
database
and
the
POD
that
are
important
to
account
for
in
low-dose
extrapolations
or
other
analyses.
(a)
Nature
of
the
response.
Is
the
POD
based
on
tumors
or
a
precursor?
If
on
tumors,
does
the
POD
measure
incidence
or
mortality?
Is
it
a
lifetime
measure
or
was
the
study
terminated
early?
The
relationships
between
precursors
and
tumors,
incidence
and
mortality,
and
lifetime
and
early-termination
results
vary
from
case
to
case.
Modeling
can
provide
quantitative
insight
into
these
relationships,
for
example,
linking
a
change
in
a
precursor
response
to
a
tumor
incidence
(see
Section
3.2.2).
This
can
aid
in
evaluating
the
significance
of
the
response
at
the
POD
and
adjusting
different
PODs
to
make
them
comparable.
(b)
Level
of
the
response.
What
level
of
response
is
associated
with
the
POD,
for
example,
1%
cancer
risk,
10%
cancer
risk,
or
10%
change
in
a
precursor
measure?
3-18
(c) Nature
of
the
study
population.
Is
the
POD
based
on
humans
or animals?
How
large
is the
effective
sample
size?
Is the
study
group representative
of
the
general
population,
of
healthy
adult
workers,
or
of a susceptible
group?
Are
both
sexes
represented?
Did
exposure
occur
during
a
susceptible
lifestage?
(d)
Slope
ofthe
dose-response
curve
at the
POD.
How
does
response
change
as
dose
is
reduced
below
the POD?
A
steep
slope indicates
that risk
decreases
rapidly
as
dose decreases.
On the
other
hand,
a
steep
slope
also
indicates
that
errors
in an exposure
assessment
can
lead
to
large
errors
in
estimating
risk.
Both
aspects
of
the slope
are important.
The slope
also
indicates
whether
dose-response
curves
for different
effects
are
likely
to
cross
below the
POD.
For
example,
in the
ED
01
study
where
2-acetylaminofluorene
caused
bladder
carcinomas
and
liver
carcinomas
in
mice
(Littlefield
et
al.,
1980),
the dose-response
curves
for
these
tumors
cross
between
10%
and
1% response
(see
Figure
3-2).
This crossing,
which
can
be inferred
from
the
slopes
of the
curves
at a
10% response,
shows
how
considering
the
slope can
lead
to better
inferences
about
the
predominant
effects
expected
at lower
doses.
Mode
of action
data
can
also
be useful;
quantitative
information
about
key precursor
events
can be
used to
describe
how
risk
decreases
as
dose
decreases
below
the
POD.
(e)
Relationship
ofthe
POD with
other
cancers.
How
does
the
POD
for
this cancer
relate
to PODs
for other
cancers
observed
in
the
database?
For example,
a
POD
based
on male
workers
would
not reflect
the
implications
of
mammary
tumors
in female
rats
or mice.
(1)
Extent
ofthe
overall
cancer
database.
Have
potential
cancer
responses
been
adequately
studied
(e.g.,
were
all
tissues
examined),
or is the
database
limited
to particular
effects,
population
segments,
or life
stages?
Do the
mode
of
action
data
suggest
a potential
for
cancers
not
observed
in the
database
(e.g.,
disruption
of
particular
endocrine
pathways
leading
to
related
cancers)?
3-19
3.2.6.
Relative
Potency
Factors
Relative
potency
factors
(of
which
toxicity
equivalence
factors
are
a special
case)
can
be
used
for
a well-defined
class
of
agents
that
operate
through
a common
mode
of
action
for
the
same
toxic
endpoint.
A
complete
dose-response
assessment
is
conducted
for
one well-studied
member
of the
class
that
serves
as
the
index
chemical
for
the
class.
The
other
members
of
the
class are
tied
to the
index
chemical
by
relative
potency
factors
that
are
based
on
characteristics
such
as
relative
toxicological
outcomes,
relative
metabolic
rates,
relative
absorption
rates,
quantitative
SARs,
or
receptor
binding
characteristics
(U.S.
EPA,
2000c).
Examples
of
this
approach
are
the
toxicity
equivalence
factors
for
dioxin-like
compounds
and
the
relative
potency
factors
for
some
carcinogenic
polycyclic
aromatic
hydrocarbons.
Whenever
practicable,
toxicity
equivalence
factors
should
be
validated
and
accompanied
by quantitative
uncertainty
analysis.
3.3.
EXTRAPOLATION
TO
LOWER DOSES
The
purpose
of
low-dose
extrapolation
is
to
provide
as
much
information
as
possible
about
risk in
the
range
of
doses
below
the observed
data.
The
most
versatile
forms
of
low-dose
extrapolation
are
dose-response
models
that
characterize
risk
as
a probability
over
a
range
of
environmental
exposure
levels.
These
risk
probabilities
allow
estimates
of the
risk
reduction
under
different
decision
options
and
estimates
of the
risk
remaining
after
an
action
is
taken
and
provide
the
risk
information
needed
for
benefit-cost
analyses
of different
decision
options.
When
a dose-response
model
is not
developed
for
lower
doses,
another
form
of
low-dose
extrapolation is a
safety
assessment
that
characterizes
the
safety
of
one
lower
dose,
with
no
explicit
characterization
of
risks
above
or
below
that
dose.
Although
this
type
of
extrapolation
may be
adequate
for
evaluation
of some
decision
options,
it
may
not be
adequate
for
other
purposes
(e.g.,
benefit-cost
analyses)
that
require
a quantitative
characterization
of risks
across
a
range
of
doses.
At
this
time,
safety
assessment
is the
default
approach
for
tumors
that
arise
through
a
nonlinear
mode
of action;
however,
EPA
continues
to explore
methods
for
quantifying
dose-response
relationships
over
a range
of environmental
exposure
levels
for
tumors
that
arise
through
a
nonlinear
mode
of action
(U.S.
EPA,
2002c).
EPA
program
offices
that
need
this
more
3-20
explicit
dose-response
information
may
develop
and
apply
methods
that
are
informed
by the
methods
described
in
these
cancer
guidelines.
3.3.1.
Choosing
an
Extrapolation
Approach
The
approach
for
extrapolation
below
the
observed
data
considers
the
understanding
of
the
agent’s
mode
of
action
at
each
tumor
site
(see
Section
2.4).
Mode
of
action
information
can
suggest
the
likely
shape
of
the
dose-response
curve
at
lower
doses.
The
extent
of
inter-individual
variation
is
also
considered,
with
greater
variation
spreading
the
response
over
a
wider
range
of
doses.
Linear
extrapolation
should
be
used
when
there
are
MOA
data
to indicate
that the
dose-
response
curve
is expected
to
have
a linear
component
below
the POD.
Agents
that
are
generally
considered
to
be linear
in
this
region
include:
•
agents
that
are
DNA-reactive
and
have
direct
mutagenic
activity,
or
•
agents
for
which
human
exposures
or
body
burdens
are
high
and near
doses
associated
with
key
precursor
events
in the
carcinogenic
process,
so
that
background exposures
to this
and
other
agents
operating
through
a
common
mode
of
action
are in
the increasing,
approximately
linear,
portion
of
the
dose-response
curve.
When
the
weight
of
evidence
evaluation
of
all
available
data
are insufficient
to
establish
the
mode
of
action
for
a
tumor
site
and when
scientifically
plausible
based
on
the
available
data,
linear
extrapolation
is used
as a
default
approach,
because
linear
extrapolation
generally
is
considered
to
be a
health-protective
approach.
Nonlinear
approaches
generally
should
not
be
used
in
cases
where
the mode
of
action
has not
been
ascertained.
Where
alternative
approaches
with
significant
biological support
are
available
for
the
same
tumor
response
and
no
scientific
consensus favors
a single
approach,
an
assessment
may
present
results
based
on more
than
one
approach.
3-21
A
nonlinear
approach
should
be selected
when
there
are
sufficient
data
to
ascertain
the
mode
of
action
and
conclude
that it
is not
linear
at low
doses
j
the
agent
does
not
demonstrate
mutagenic
or
other
activity
consistent
with
linearity
at low
doses.
Special
attention
is
important
when
the
data
support
a
nonlinear
mode
of
action
but
there
is
also a
suggestion
of
mutagenicity.
Depending
on the
strength
of the
suggestion
of mutagenicity,
the
assessment
may
justify
a
conclusion
that
mutagenicity
is
not operative
at
low doses
and
focus
on a
nonlinear
approach,
or
alternatively,
the
assessment
may
use
both
linear
and
nonlinear
approaches.
Both
linear
and
nonlinear
approaches
may
be
used
when
there
are
multiple
modes
of
action.
If
there
are
multiple
tumor
sites,
one
with
a linear
and
another
with
a
nonlinear
mode
of
action,
then
the
corresponding
approach
is
used
at each
site.
If
there
are
multiple modes
of action
at a
single
tumor
site, one
linear
and
another
nonlinear,
then
both
approaches
are
used
to
decouple
and
consider
the
respective
contributions
of
each
mode
of
action
in
different
dose
ranges.
For example,
an
agent
can
act
predominantly
through
cytotoxicity
at
high
doses
and
through
mutagenicity
at
lower
doses
where
cytotoxicity
does
not
occur.
Modeling
to
a low
response level
can be
useful
for
estimating
the response
at doses
where
the
high-dose
mode
of
action
would
be
less
important.
3.3.2.
Extrapolation
Using
a Toxicodynamic
Model
The
preferred
approach
is
to develop
a
toxicodynamic
model
of
the agent’s
mode
of
action
and
use
that
model
for
extrapolation
to
lower
doses
(see
Section
3.2.2).
The
extent
of
extrapolation is governed
by
an analysis
of
model
uncertainty,
where
alternative
models
that
fit
similarly
in
the
observed
range
can
diverge
below
that
range
(see
Section
3.6).
Substantial
divergence
is
likely
when
model
parameters
are estimated
from
tumor
incidence
data,
so that
different
combinations
of
parameter
estimates
yield
similar
fits
in the
observed
range
but
have
different
implications
at
lower
doses.
An
analysis
of
model
uncertainty
can be
used
to
determine
the range
where
extrapolation
using
the
toxicodynamic
model
is
supported
and where
further
extrapolation would
be
based
on
either
a
linear
or
a nonlinear
default,
as
appropriate
(see
Sections
3.3.3,
3.3.4).
3-22
3.3.3.
Extrapolation
Using
a Low-dose,
Linear
Model
Linear
extrapolation
should
be
used
in two
distinct
circumstances:
(1)
when
there
are
data
to indicate
that
the
dose-response
curve
has
a linear
component
below
the POD,
or
(2)
as a
default
for
a
tumor
site
where
the
mode
of action
is
not established
(see
Section
3.3.1).
For
linear
extrapolation,
a line
should
be
drawn
from
the
POD
to the
origin,
corrected
for
background.
This
implies
a proportional
(linear)
relationship
between
risk
and dose
at
low
doses.
(Note
that
the
dose-response
curve
generally
is not
linear
at
higher
doses.)
The slope
of
this
line,
known
as
the slope
factor,
is an
upper-bound
estimate
of risk
per
increment
of
dose
that can
be used
to
estimate
risk
probabilities
for
different
exposure
levels.
The slope
factor
is equal
to
0.0
1/LED
01
if
the
LED
01
is
used
as
the
POD.
Unit
risk estimates
express
the slope
in
terms
of
g/L
drinking
water
or
g/m
3
or
ppm
air.
In
general,
the
drinking
water
unit
risk is
derived
by
converting
a
slope
factor
from
units
of
mg/kg-d
to units
of
ig/L,
whereas
an inhalation
unit
risk
is developed
directly
from
a dose-
response
analysis
using
equivalent
human
concentrations
already
expressed
in
units
of
ig/m
3
.
Unit
risk
estimates
often
assume
a
standard
intake
rate
(L/day
drinking
water
or
m
3
/day
air)
and
body
weight
(kg), which
may need
to
be
reconciled
with
the
exposure
factors
for
the
population
of
interest
in
an
exposure
assessment
(see
Section
4.4).
Alternatively,
when
the
slope
factor
for
inhalation
is
in
units
of ppm,
it may
sometimes
be
termed
the
inhalation
unit risk.
Although
unit
risks
have
not
been
calculated
in the
past
for
dermal
exposures,
both
exposures
that
are
absorbed
into
the
systemic
circulation
and
those
that
remain
in
contact
with
the
skin
are
also important.
Risk-specific
doses
are derived
from
the slope
factor
or unit
risk
to estimate
the dose
associated with a
specific
risk
level,
for example,
a one-in-a-million
increased
lifetime
risk.
3.3.4.
Nonlinear
Extrapolation
to Lower
Doses
A
nonlinear
extrapolation
method
can
be used
for
cases
with
sufficient
data
to
ascertain
the
mode
of action
and
to
conclude
that it
is not
linear
at low
doses
but
with
not enough
data
to
support
a
toxicodynamic model
that may
be
either
nonlinear
or linear
at
low doses.
Nonlinear
extrapolation
having
a
significant
biological
support
may
be
presented
in addition
to a linear
approach
when
the available
data
and
a
weight
of
evidence
evaluation
support
a
nonlinear
3-23
approach, but the data are not strong
enough to ascertain the mode of action
applying the
Agency’s mode of action
framework. If the mode of action and other information
can support
chemical-specific
modeling at low doses, it is preferable to default
procedures.
For cases where the tumors arise
through
a
nonlinear
mode
of action,
an oral reference
dose or an inhalation reference
concentration, or both, should be developed in
accordance
with
EPA’s
established practice for developing
such values, taking into consideration
the factors
summarized in the
characterization of the POD (see Section 3.2.5). This approach expands
the
past focus of such
reference values (previously reserved for effects other than cancer)
to include
carcinogenic effects
determined to have a nonlinear mode of action. As with other
health
effects
of concern, it is important to put
cancer in perspective with the overall health
impact of an
exposure by
comparing reference
value calculations for cancer with those for
other health
effects.
For effects other than cancer, reference values have been
described
as being based
on the
assumption of biological thresholds. The Agency’s more current
guidelines
for these effects
(U.S.
EPA, 1996a, l998b),
however,
do
not use this assumption, citing the
difficulty of
empirically
distinguishing a true threshold from a dose-response curve that is nonlinear
at low
doses.
Economic and policy
analysts need
to
know how
the
probability
of cancer varies
at
exposures
above
the reference value and whether, and to what extent, there are health
benefits
from
reducing exposures below the reference value.
The
risk assessment community is
working
to develop
better methods to provide more useful information to economic and policy
analysts.
3.3.5.
Comparing and
Combining Multiple
Extrapolations
When multiple estimates can be
developed, all
datasets should be considered and
a
judgment made
about how best to represent the human cancer risk. Some options
for presenting
results
include:
adding risk estimates derived from different tumor sites (NRC, 1994),
3-24
combining
data
from
different
datasets
in
a
joint
analysis
(Putzrath
and
Ginevan,
1991;
Stiteler
et
al.,
1993;
Vater
et
al.,
1993),
•
combining
responses
that
operate
through
a
common
mode
of
action,
•
representing
the
overall
response
in
each
experiment
by
counting
animals
with
any
tumor
showing
a
statistically
significant
increase,
•
presenting
a
range
of
results
from
multiple
datasets
(in
this
case,
the
dose-response
assessment
includes
guidance
on
how
to
choose
an
appropriate
value
from
the
range),
•
choosing
a
single
dataset
if
it
can
be
justified
as
most
representative
of
the
overall
response
in
humans,
or
•
a
combination
of
these
options.
Cross-comparison
of
estimates
from
human
and
animal
studies
can
provide
a
valuable
risk
perspective.
•
Calculating
an
animal-derived
slope
factor
and
using
it
to
estimate
the
risk
expected
in
a
human
study
can
provide
information
with
which
to
evaluate
the
human
study
design,
for
example,
adequacy
of
exposure
level
and
sample
size.
•
Calculating
an
upper-bound
slope
factor
from
a
human
study
that
does
not
show
positive
results
but
that
has
good
exposure
information,
and
comparing
it
to
an
animal-derived
slope
factor
can
indicate
whether
the
animal
and
humans
studies
are
consistent.
3-25
3.4.
EXTRAPOLATION
TO DIFFERENT
HUMAN
EXPOSURE
SCENARIOS
As
described
in
the
previous
cancer
guidelines,
special
problems
arise
when
the
human
exposure
situation
of
concern
suggests
exposure
regimens,
e.g.,
route
and
dosing
schedule,
that
are
substantially
different
from
those
used
in
the
relevant
animal
studies.
Unless
there
is
evidence
to
the
contrary
in a particular
case,
the
cumulative
dose
received
over
a
lifetime,
expressed
as average
daily
exposure
prorated
over
a
lifetime,
is recommended
as
an
appropriate
measure
of
exposure
to a
carcinogen.
That
is, the
assumption
is made
that
a high
dose
of
a
carcinogen
received
over
a short
period
of
time
is equivalent
to a
corresponding
low dose
spread
over
a
lifetime.
This
approach
becomes
more
problematical
as
the
exposures
in
question
become
more
intense
but
less
frequent,
especially
when
there
is evidence
that
the
agent
has
shown
dose-
rate
effects
(U.S.
EPA
l986a).
Accordingly,
for
lfetime
human
exposure
scenarios
that
involve
intermittent
or varying
levels
of
exposure,
the
prevailing
practice
has
been
to
assess
exposure
by
calculating
a lifetime
average
daily
exposure
or
dose
(U.S.
EPA,
1992a).
For
less-than-lifetime
human
exposure
scenarios,
too,
the
lifetime
average
daily
exposure
or
dose
has
often
been
used.
The
use
of
these
lifetime
average
exposure
metrics
was
adopted
with
low-dose
linear
cancer
assessments
in
mind.
The
lifetime
averaging
implies
that
less-than-
lifetime
exposure
is associated
with
a linearly
proportional
reduction
of the
lifetime
risk,
regardless
of
when
exposures
occur.
Such
averaging
may
be
problematic
in
some
situations.
This
can
be
illustrated
using
both
the
multistage
model
and
the
two-stage
clonal
expansion
model
that
predict
that
short-duration
risks
are
not necessarily
proportional
to
exposure
duration
and
can
depend
on
the
nature
of
the
carcinogen
and
the
timing
of exposure
(Goddard
et
al.,
1995;
Murdoch
et
al., 1992).
These
examples
indicate
some
circumstances
in which
use of
a
lifetime
average
daily
dose
(LADD)
would
underestimate
cancer
risk
by two-
to
fivefold,
and
others
in
which
it
might
overestimate
risk
(Murdoch
et al.,
1992).
Thus,
averaging
over
the
duration
of
a
lifestage
or a
critical
window
of exposure
may
be
appropriate.
As
methodological
research
focuses
on
new
approaches
for
estimating
risks
from
less-than-lifetime
exposures,
methods
and
defaults
can
be
expected
to
change.
3-26
This
highlights
the
importance
for
each
dose-response
assessment
to
critically
evaluate
all
information
pertaining
to
less-than-lifetime
exposure.
For
example,
detailed
stop-exposure
studies
can
provide
information
about
the
relationship
between
exposure
duration,
precursor
effects,
potential
for
reversibility,
and
tumor
development.
Toxicokinetic
modeling
can
investigate
differences
in internal
dose
between
short-term
and
long-term
exposure
or
between
intermittent
and
constant
exposure.
Persistence
in
the body
can
be
useful
in
explaining
long-term
effects
resulting
from
shorter-term
exposures.
For
nonlinear
cancer
analyses,
it may
be
appropriate
to
assess
exposure
by
calculating
a
daily
dose
that
is
averaged
over
the
exposure
duration
for the
study
(see
Section
3.1.1).
For
example,
when
the
analysis
is
based
on
precursor
effects
that
result
from
less than
a
lifetime
exposure,
that
exposure
period
may
be used.
This
reflects
an expectation
that
the
precursor
effects
on which
the
analysis
is based
can result
from
less-than-lifetime
exposure,
bringing
consistency
to the
methods
used
for dose-response
assessment
and
exposure
assessment
in
such
cases.
The
dose-response
assessment
can
provide
a
recommendation
to exposure
assessors
about
the
averaging time
that is
appropriate
to
the
mode
of
action
and to
the exposure
duration
of
the
scenario.
3.5.
EXTRAPOLATION
TO
SUSCEPTIBLE
POPULATIONS
AND
LIFESTAGES
The
dose-response assessment
strives
to derive
separate
estimates
for susceptible
populations and
lifestages
so
that these
risks can
be
explicitly
characterized.
For
a
susceptible
population,
higher
risks
can
be
expected
from
exposures
anytime
during
life,
but
this
applies
to
only
a
portion
of
the
general
population
(e.g.,
those
bearing
a particular
genetic
susceptibility).
In contrast, for a
susceptible
lifestage,
higher
risks can
be
expected
from
exposures
during
only
a
portion
of a
lifetime,
but
everyone
in the
population
may
pass
through
those
lifestages.
Effects
of
exposures
during
a
susceptible
period
are
not
equivalent
to effects
of
exposures
at
other
times;
consequently,
it is useful
to
estimate
the
risk attributable
to exposures
during
each
period.
Depending
on the
data
available,
a
tiered
approach
should
be
used
to
address
susceptible
populations and
lifestages.
3-27
•
When
there
is an
epidemiologic
study
or
an animal
bioassay
that
reports
quantitative
results
for
susceptible
individuals,
the
data
should
be
analyzed
to
provide
a
separate
risk
estimate
for those
who
are
susceptible.
If susceptibility
pertains
to a
lifestage,
it
is
useful
to
characterize
the
portion
of
the lifetime
risk
that
can
be
attributed
to
the
susceptible
lifestage.
•
When
there are
data
on
some
risk-related
parameters
that allow
comparison
of
the
general
population
and
susceptible
individuals,
the
data should
be
analyzed
with
an
eye toward
adjusting
the general
population
estimate
for
susceptible
individuals.
This
analysis
can
range
from
toxicokinetic
modeling
that
uses
parameter
values
representative
of susceptible
individuals
to
more
simply
adjusting
a
general
population
estimate
to
reflect
differences
in important
rate-governing
parameters.
Care
is taken
to not
make
parameter
adjustments
in isolation,
as the
appropriate
adjustment
can
depend
on
the
interactions
of
several
parameters;
for
example,
the
ratio
of
metabolic
activation
and
clearance
rates
can be
more
appropriate
than
the
activation
rate
alone
(U.S.
EPA,
1
992b).
•
In the
absence
of
such
agent-specific
data,
there
is
some
general
information
to
indicate
that
childhood
can
be
a
susceptible
lifestage
for
exposure
to
some
carcinogens
(U.S.
EPA,
2005);
this
warrants
explicit
consideration
in
each
assessment. The
potential
for
susceptibility
from
early-life
exposure
is
expected
to
vary
among
specific
agents
and
chemical
classes.
In
addition,
the
concern
that
the
dose-averaging
generally used
for
assessing
less-than-lifetime
exposure
is
more
likely
to
understate
than
overstate
risk
(see
Section
3.4)
contributes
to the
suggestion that
alternative
approaches
be
considered
for
assessing
risks
from
less
than-lifetime exposure
that
occurs
during
childhood.
Accompanying
these
cancer
guidelines
is
the
Supplemental
Guidance
that
the
Agency
will
use
to assess
risks
from
early-life
exposure
to potential
carcinogens
(U.S.
EPA,
2005).
The
Supplemental Guidance
may
be updated
to
reflect
new
data
and
new
understanding
that
may
become
available
in the
future.
3-28
3.6. UNCERTAINTY
The
NRC
(1983,
1994,
1996,
2002)
has
repeatedly
advised
that
proper
characterization
of
uncertainty
is
essential
in risk
assessment.
An
assessment
that
omits
or
underestimates
uncertainty
can
leave
decisionmakers
with
a false
sense
of confidence
in
estimates
of
risk.
On
the
other
hand,
a
high level
of uncertainty
does
not
imply
that
a risk
assessment
or a risk
management
action
should
be
delayed
(NRC,
2002).
Uncertainty
in dose-response
assessment
can
be
classified
as either
model
uncertainty
orparanzeter
uncertainty.
A related
concept,
human
variation,
is
discussed
below.
Assessments
should
discuss
the
significant
uncertainties
encountered
in
the
analysis,
distinguishing,
if
possible,
between
model
uncertainty,
parameter
uncertainty,
and
human
variation.
Origins
of
these
uncertainties
can
span
a
range,
from
a
single
causal
thread
supported
by
sparse
data,
to
abundant
information
that
presents
multiple
possible
conclusions
or
that
does not
coalesce.
As
described
in Section
2.6
and
in
Section
5.1,
all
contributing features
should
be
noted.
Model
uncertainty
refers
to a lack
of
knowledge
needed
to
determine
which
is
the
correct
scientific
theory
on
which
to base
a model.
In
risk
assessment,
model
uncertainty
is
reflected
in
alternative
choices
for model
structure,
dose
metrics,
and
extrapolation
approaches.
Other
sources
of
model
uncertainty
concern
whether
surrogate
data are
appropriate,
for example,
using
data on
adults
to make
inferences
about children.
The
full extent
of model
uncertainty
usually
cannot
be quantified;
a partial
characterization
can be
obtained
by
comparing
the
results
of
alternative
models.
Model
uncertainty
is expressed
through
comparison
of
separate
analyses
from each
model,
coupled
with
a
subjective
probability
statement,
where
feasible
and
appropriate,
of
the
likelihood
that
each
model
might
be correct
(NRC,
1994).
Some
aspects
of model
uncertainty
that
should
be addressed
in an
assessment
include
the
use
of
animal
models
as a
surrogate
for humans,
the influence
of cross-species
differences
in
metabolism
and
physiology,
the
use of effects
observed
at high
doses
as
an
indicator
of the
potential
for
effects
at
lower doses,
the
effect
of using
linear
or nonlinear
extrapolation
to
estimate
risks,
the
use of using
small
samples
and subgroups
to make
inferences
about
entire
human
populations
or
subpopulations
with differential
susceptibilities,
and
the
use
of
3-29
experimental
exposure
regimens
to
make
inferences
about
different
human
exposure
scenarios
(NRC,
2002).
Toxicokinetic
and
toxicodynamic
models
are
generally
premised
on site
concordance
across
species,
modeling,
for example,
the
relationship
between
administered
dose
and
liver
tissue
concentrations
to
predict
increased incidences
of
liver
cancer.
This
relationship,
which
can
be observed in
animals,
is
typically
only
inferred
for humans.
There
are,
however,
numerous
examples
of
an
agent
causing
different
cancers
in
different
species.
The
assessment
should
discuss
the
relevant
data
that bear
on
this
form
of
model
uncertainty.
Parameter
uncertainty
refers
to
a
lack
of
knowledge
about
the
values
of a
model’s
parameters.
This
leads
to
a distribution of
values
for each
parameter.
Common
sources
of
parameter uncertainty
include
random
measurement
errors,
systematic
measurement
errors,
use
of
surrogate
data instead
of direct
measurements,
misclassification
of
exposure
status,
random
sampling
errors,
and use
of an
unrepresentative
sample.
Most
types
of parameter
uncertainty
can
be
quantified
by statistical analysis.
Human
variation
refers
to
person-to-person
differences
in biological
susceptibility
or
in
exposure.
Although
both
human
variation
and uncertainty
can
be
characterized
as
ranges
or
distributions,
they
are
fundamentally
different
concepts.
Uncertainty
can
be reduced
by
further
research
that
supports
a model
or
improves
a
parameter
estimate,
but
human
variation
is
a reality
that
can be
better
characterized,
but
not reduced,
by
further
research.
Fields
other
than
risk
assessment use “variation”
or
“variability”
to
mean
dispersion
about
a
central
value,
including
measurement errors
and
other
random
errors
that
risk
assessors
address
as uncertainty.
Probabilistic
risk
assessment,
informed
by
expert
judgment,
has
been
used
in
exposure
assessment
to
estimate
human
variation
and uncertainty
in
lifetime
average
daily
exposure
concentration or
dose.
Probabilistic
methods
can
be used
in
this exposure
assessment
application
because
the pertinent variables
(for
example,
concentration,
intake
rate,
exposure
duration,
and
body
weight)
have
been
identified,
their
distributions
can
be
observed,
and
the
formula
for
combining
the
variables
to
estimate
the
lifetime
average
daily
dose
is
well
defined
(see
U.S.
EPA,
1 992a).
Similarly,
probabilistic methods
can
be
applied
in
dose-response
assessment
when
there
is an
understanding
of
the
important
parameters
and their
relationships,
such
as
3-30
identification
of
the
key
determinants
of human
variation
(for example,
metabolic
polymorphisms,
hormone
levels,
and cell
replication
rates),
observation
of the
distributions
of
these
variables,
and
valid models
for
combining
these
variables.
With
appropriate
data
and
expert
judgment,
formal
approaches
to
probabilistic
risk assessment
can
be
applied
to
provide
insight
into
the
overall
extent
and
dominant
sources
of
human
variation
and
uncertainty.
In
doing
this,
it is
important
to
note that
analyses
that
omit or
underestimate
some
principal
sources
of
variation
or
uncertainty
could
provide
a
misleadingly
narrow
description
of
the true
extent
of
variation
and
uncertainty
and give
decisionmakers
a
false
sense
of
confidence
in estimates
of
risk.
Specification
ofjoint
probability
distributions
is
appropriate
when
variables
are not
independent
of
each other.
In
each case,
the
assessment
should
carefully
consider
the questions
of
uncertainty
and human
variation
and
discuss
the
extent to
which
there are
data
to address
them.
Probabilistic
risk
assessment
has
also
been
used
in
dose-response
assessment
to
determine
and
distinguish
the
degree
of uncertainty
and variability
in
toxicokinetic
and
toxicodynamic
modeling.
Although
this
field
is
less
advanced
that probabilistic
exposure
assessment,
progress
is
being
made and
these
cancer
guidelines
are flexible
enough
to
accommodate
continuing
advances
in these
approaches.
Advances
in uncertainty
analysis
are expected
as
the field
develops.
The
cancer
guidelines
are
intended
to be
flexible
enough
to
incorporate
additional
approaches
for
characterizing
uncertainty
that have
less
commonly
been
used by
regulatory
agencies.
In
all
scientific
and
engineering
fields,
data and
research
limitations
often
limit
the application
of
established
methods.
A
dearth
of
data
is
a
particular
problem
when quantifying
the
probability
distribution
of
model
outputs.
In
many
of these
scientific
and
engineering
disciplines,
researchers
have used
rigorous
expert
elicitation
methods
to
overcome
the
lack of
peer-reviewed
methods
and
data.
Although
expert
elicitation
has
not been
widely
used
in
environmental
risk
assessment,
several
studies
have applied
this
methodology
as
a
tool
for understanding
quantitative
risk.
For
example,
expert
elicitation
has been
used
in chemical
risk
assessment
and its associated
uncertainty
(e.g.,
Richmond,
1981;
Renn,
1999;
Florig
et al.,
2001;
Morgan
et al.,
2001; Willis
et
al., 2004),
components
of
risk
assessment
such
as
hazard
assessment
and
dose-response
3-31
evaluation
(e.g.,
Hawkins
and Graham
1988; Jelovsek
et
al.,
1990;
Evans
et al.,
1994;
lEe, 2004;
U.S.
EPA
2004)
and
exposure
assessment
(e.g., Whiffield
and
Wallsten,
1989;
Hawkins
and
Evans,
1989;
Winkler
et
al., 1995;
Stiber
et al.,
1999;
Walker
et al.,
2001, 2003;
Van Der
Fels
Klerx
et al.,
2002),
and
for
evaluating
other
types of
risks
(e.g.,
North
and
Merkhofer,
1976;
Fos
and
McLin,
1990).
These
cancer
guidelines
are
flexible
enough
to
accommodate
the use
of
expert
elicitation
to
characterize
cancer
risks,
as a complement
to
the methods
presented
in the
cancer
guidelines.
According
to
NRC
(NRC,
2002),
the
rigorous
use
of expert
elicitation
for
the
analyses
of
risks is
considered
to
be
quality
science.
3.7. DOSE-RESPONSE
CHARACTERIZATION
A dose-response characterization
extracts
the dose-response
information
needed
in
a full
risk
characterization
(US.
EPA, 2000b),
including:
•
presentation
of
the recommended
estimates
(slope
factors,
reference
doses,
reference
concentrations)
and alternatives
with
significant
biological
support,
•
a
summary
of the data
supporting
these
estimates,
•
a
summary
and
explanation
of the modeling
approaches
used,
•
a
description
of
any special
features
such
as the
development
and
consolidation
of
multiple
estimates
as
detailed
in
Section
3.3.5,
•
the
POD narrative
(see
Section
3.2.5),
•
a
summary
of the
key defaults
invoked,
•
identification
of susceptible
populations
or
lifestages
and
quantification
of their
differential
susceptibility,
and
3-32
a
discussion
of
the
strengths
and
limitations
of the
dose-response
assessment,
highlighting
significant
issues
in
developing
risk
estimates,
alternative
approaches
considered equally
plausible,
and
how
these
issues
were resolved.
All
estimates
should
be
accompanied
by the
weight
of
evidence
descriptor and
its
narrative
(see Section
2.5)
to
convey
a
sense
of
the qualitative uncertainty
about
whether
the
agent
may
or may
not
be
carcinogenic.
Slope
factors
generally
represent
an
upper
bound
on the
average
risk
in
a
population
or
the
risk for
a
randomly
selected
individual
but
not
the
risk
for
a
highly
susceptible
individual
or
group.
Some
individuals face
a
higher
risk
and
some
face
a lower
risk.
The use
of
upper
bounds
generally
is
considered
to
be
a health-protective
approach
for
covering
the risk
to
susceptible
individuals,
although
the
calculation
of upper
bounds
is
not
based
on
susceptibility
data.
Similarly,
exposure
during
some
lifestages
can
contribute
more
or
less
to
the total
lifetime
risk
than
do
similar
exposures
at
other
times.
The
dose-response
assessment
characterizes,
to the
extent
possible,
the
extent
of
these
variations.
Depending
on
the
supporting
data
and
modeling
approach,
a
slope
factor
can
have
a
mix
of
traits
that tend
to
either
estimate,
overestimate,
or underestimate
risk.
Some
examples
of
traits
that
tend
to overestimate
risk
include
the
following.
•
The slope
factor
is
derived
from
data
on
a highly
susceptible
animal
strain.
•
Linear
extrapolation
is used
as
a
default
and
extends
over
several
orders
of
magnitude.
•
The
largest
of
several
slope
factors
is chosen.
Some
examples
of
traits
that
tend
to underestimate
risk
include
the
following.
3-33
•
Several
tumor
types
were
observed,
but
the
slope
factor
is
based
on
a
subset
of
them.
•
The
study
design
does
not
include
exposure
during
a
susceptible
lifestage,
for
example,
perinatal
exposure.
•
The
study
population
is
of
less-than-average
susceptibility,
for
example,
healthy
adult
workers.
•
There
is
random
exposure
misclassification
or
random
exposure
measurement
error
in
the
study
from
which
the
slope
factor
is
derived.
Some
examples
of
traits
that
inherently
neither
overestimate
nor
underestimate
risk
include
the
following.
•
The
slope
factor
is
derived
from
data
in
humans
or
in
an
animal
strain
that
responds
like
humans.
•
Linear
extrapolation
is
appropriate
for
the
agent’s
mode
of
action.
•
Environmental
exposures
are
close
to
the
observed
data.
•
Several
slope
factors
for
the
same
tumor
are
averaged
or
a
slope
factor
is
derived
from
pooled
data
from
several
studies.
•
The
slope
factor
is
derived
from
the
only
suitable
study.
3-34
Figure
3-1.
Compatibility
of alternative
points
of
departure
with
observed
and modeled
tumor
incidences
15%-
X
Observed
tumor
incidence
Modeled
tumor
incidence
Extrapolations
from
LED
10 and
LEDO1
10%-
5%-
-
0
0%-
“..
0
5
10
15
Figure 3-2.
Crossing--between 10%
and 1%--of
dose-response
curves
for
bladder
carcinomas
and
liver
carcinomas
induced
by
2-AAF
X
Observed
bladder
tumors
10%
—
bladder
Extrapolations
Modeled
tumorsbladder
from
tumors
LED1O
and
LEDO1
for
/
/
Observed
liver
tumors
Modeled
liver
tumors
Extrapolation
from LED1O
and LEDO)
ftr
liver
tumors
5%
——
—
_
0%
.
.
.
-..
0
50
100
150
3-35
4.
EXPOSURE
ASSESSfVEENT
Exposure
assessment
is the
determination
(qualitative
and quantitative)
of
the magnitude,
frequency,
and
duration
of exposure
and
internal
dose
(U.S.
EPA,
1
992a).
This
section
provides
a brief
overview
of exposure
assessment
principles,
with
an emphasis
on
issues
related
to
carcinogenic
risk assessment.
The
information
presented
here
should
be
used
in conjunction
with other
guidance
documents,
including
Guidelines
for Exposure
Assessment
(U.S.
EPA,
1992a),
Science
Policy
Council
Handbook:
Risk Characterization
(U.S.
EPA,
2000b),
Exposure
Factors
Handbook
(U.S.
EPA,
1997c),
the
1997 Policy
for
Use
ofProbabilistic
Analysis
in
Risk
Assessments
(U.S.
EPA, 1997d),
and the
1997 Guiding
Principles
for
Monte
Carlo
Analysis
(U.S.
EPA,
1997e).
In addition,
program-specific
guidelines
for exposure
assessment
should
be
consulted.
Exposure
assessment
generally
consists
of
four major
steps: defining
the assessment
questions,
selecting
or
developing
the conceptual
and
mathematical
models,
collecting
data
or
selecting
and
evaluating
available
data,
and
exposure
characterization.
Each
of these
steps
is
briefly
described
below.
4.1. DEFINING
THE
ASSESSMENT
QUESTIONS
In providing
a clear
and
unambiguous
statement
of
the
purpose
and
scope
of
the
exposure
assessment
(U.S.
EPA,
1997e),
consider
the
following.
•
The
management
objectives
of
the assessment
will
determine
whether
deterministic
screening
level
analyses
are
adequate
or whether
full
probabilistic
exposure
characterization
is
needed.
•
Identify
and
include
all important
sources
(e.g.,
pesticide
applications),
pathways
(e.g.,
food
or water),
and
routes
(e.g.,
ingestion,
inhalation,
and
derrnal)
of
exposure
in
the
assessment.
If
a
particular
source,
pathway,
or
route
is
omitted,
a clear
and
transparent
explanation
should
be
provided.
4-1
Separate
analyses
should
be
conducted
for
each definable
subgroup
within
the
population
of
interest.
In
particular,
subpopulations
or
life
stages
that
are
believed
to
be
highly
exposed
or
susceptible
to a
particular
health
effect
should
be studied.
These
include
people
with
certain
diseases
or
genetic
susceptibilities
and
others
whose
behavior
or
physiology
may
lead
to higher
exposure
or
susceptibility.
Consider
the
following
examples:
—
Physiological
differences
between
men
and
women
(e.g.,
body
weight
and
inhalation
rate)
may
lead
to
important
differences
in
exposures.
See,
for
example,
the
discussion
in
Exposure
Factors
Handbook
(U.S.
EPA,
1997c,
Appendix
1A).
Pregnant
and
lactating
women
may
have
exposures
that
differ
from
the
general
population
(e.g.,
slightly
higher
water
consumption)
(U.S.
EPA,
1 997c).
Further, exposure
to
pregnant
women
may
result
in
exposure
to
the
developing
fetus
(NRC,
1993b).
—
Children
consume
more
food
per
body
weight
than
do adults
while
consuming
fewer
types
of foods,
i.e.,
have
a more
limited
diet (ILSI,
1992;
NRC,
l993b;
U.S.
EPA,
1997c).
In
addition,
children
engage
in
crawling
and
mouthing
(i.e.,
putting
hands
and
objects
in the
mouth)
behaviors,
which
can
increase
their exposures.
—
The
elderly
and
disabled
may
have
important
differences
in their
exposures
due
to a
more
sedentary
lifestyle
(U.S.
EPA,
1997c).
In
addition,
the
health
status
of
this
group
may
affect
their susceptibility
to
the
detrimental
effects
of exposure.
For
further
guidance,
see
Guidelines
for
Exposure
Assessment
(U.S.
EPA,
1992a,
§
3).
4-2
4.2.
SELECTING
OR
DEVELOPING
THE
CONCEPTUAL
AND
MATHEMATICAL
MODELS
Carcinogen
risk
assessment
models
have
generally
been
based
on
the
premise
that
risk
is
proportional
to
cumulative
lifetime
dose.
For
lifetime
human
exposure
scenarios,
therefore,
the
exposure
metric
used
for
carcinogenic
risk
assessment
has
been
the
lifetime
average
daily
dose
(LADD)
or,
in
the
case
of
inhalation
exposure,
the
lifetime
average
exposure
concentration.
These
metrics
are
typically
used
in
conjunction
with
the
corresponding
slope
factor
to
calculate
individual
excess
cancer
risk.
The
LADD
is
typically
an
estimate
of
the
daily
intake
of
a
carcinogenic
agent
throughout
the
entire
life
of
an
individual,
while
the
lifetime
average
exposure
concentration
is
the
corresponding
estimate
of
average
exposure
concentration
for
the
carcinogenic
agent
over
the
entire
life
of
an
individual.
Depending
on
the
objectives
of
the
assessment,
the
LADD
or
lifetime
average
exposure
concentration
may
be
calculated
deterministically
(using
point
estimates
for
each
factor
to
derive
a
point
estimate
of
the
exposure)
or
stochastically
(using
probability
distributions
to
represent
each
factor
and
such
techniques
as
Monte
Carlo
analysis
to
derive
a
distribution
of
the
LADD)
(U.S.
EPA,
1997e).
Stochastic
analyses
may
help
to
identify
certain
population
segments
or
lifestages
that
are
highly
exposed
and
may
need
to
be
assessed
as a
special
subgroup.
For
further
guidance,
see
Guidelines
for
Exposure
Assessment
(U.S.
EPA,
l992a,
§
5.3.5.2).
As
methodological
research
focuses
on
new
approaches
for
estimating
risks
from
less-than-lifetime
exposures,
methods
and
defaults
can
be
expected
to
change.
There
may
be
cases
where
the
mode
of
action
indicates
that
dose
rates
are
important
in
the
carcinogenic
process.
In
these
cases,
short-term,
less-than-lifetime
exposure
estimates
may
be
more
appropriate
than
the
LADD
for
risk
assessment.
This
may
be
the
case
when
a
nonlinear
dose-response
approach
is
used
(see
Section
3.3.4).
4.3.
COLLECTING
DATA
OR
SELECTING
AND
EVALUATING
AVAILABLE
DATA
After
the
assessment
questions
have
been
defined
and
the
conceptual
and
mathematical
models
have
been
developed,
it
is
important
to
compile
and
evaluate
existing
data
or,
if
necessary,
to
collect
new
data.
Depending
on
the
exposure
scenario
under
consideration,
data
on
4-3
a
wide
variety
of
exposure
factors
may
be
needed.
EPA’s
Exposure
Factors
Handbook
(U.S.
EPA,
1 997c)
contains
a
large
compilation
of exposure data,
with
some
analysis
and
recommendations.
Some
of these
data
are
organized
by
age groups
to assist
with
assessing
such
subgroups
as
children.
See,
for
example,
Exposure
Factors
Handbook
(U.S.
EPA,
1997c,
Volume
1,
Chapter
3).
When
using
these
existing
data,
it
is important
to evaluate
the
quality
of
the data
and
the
extent
to
which
the
data
are representative
of
the
population
under
consideration.
EPA’s
(U.S.
EPA,
2000d)
and OMB’s
(0MB
2002)
guidance
on
information
quality,
as
well
as
program-specific
guidances
can
provide
further
assistance
for
evaluating
existing
data.
When
existing
data
fail
to provide
an adequate
surrogate
for
the
needs
of
a particular
assessment,
it
is important
to
collect
new
data.
Such
data
collection
efforts
should
be
guided
by
the
references
listed
above
(e.g.,
Guidance
for
Data
Quality
Assessment
and program-specific
guidance).
Once
again,
subpopulations
or lifestages
of
concern
are
an
important
consideration
in
any
data
collection
effort.
4.3.1.
Adjusting
Unit
Risks
for
Highly
Exposed
Populations
and
Lifestages
Unit
risk estimates that
have
been
developed
in the
dose-response
assessment
often
assumed standard
adult
intake
rates.
When
an
exposure
assessment
focuses
on
a population
or
lifestage
with
differential
exposure,
good
exposure
assessment
practice
would
replace
the
standard
intake
rates
with
values
representative
of
the
exposed
population.
Small
changes
in
exposure assessments
can
be
approximated
by using
linearly
proportional
adjustments
of
exposure
parameters,
but
a more
accurate
integrative
analysis
may
require
an
analysis
stratified
by
exposure
duration
(see
Section
5.1).
For
example, to adjust
the
drinking
water
unit
risk for
an
active
population
that
drinks
4
L/day
(instead
of
2 Llday),
multiply
the
unit
riskby2.
4-4
Because
children
drink more
water
relative
to
their body
weight
than
do
adults
(U.S.
EPA,
2002d),
adjustments
to
unit
risk
estimates
are warranted
whenever
they
are applied
in
an
assessment
of
childhood
exposure.
For
example,
to
adjust
the drinking
water
unit risk
for
a
9-kg infant
who
drinks
1
L/day
(instead
of
a 70-kg
adult who
drinks
2 L/day),
multiply
the unit
risk
by [(1 L/day)
/ (9
kg)]
/
[(2
L/day)
/ (70 kg)]
=
3.9.
Inhalation
dosimetry
is
employed
to
derive
the
human
equivalent
exposure
concentrations
on which
inhalation
unit
risks,
and
reference
concentrations,
are
based
(U.S.
EPA, 1994).
As
described
previously
(see
Sections
3.1.2,
3.1.3),
different
dosimetry
methods
may
be employed
depending
on
the
availability
of
relevant
data
and
chemical-specific
characteristics
of
the
pollutant.
Consideration
of
lifestage-particular
physiological
characteristics
in the
dosimetry
analysis
may
result
in a
refinement
to the
human
equivalent
concentration
(HEC)
to insure
relevance
in
risk
assessment
across
lifestages,
or might
conceivably
conclude
with
multiple
HECs,
and
corresponding
inhalation
unit
risk
values (e.g.,
separate
for childhood
and
adulthood).
The
dose-response
assessment
discusses
the
key sources
of uncertainty
in
estimating
dosimetry,
including
any
related
to
lifestage.
Review
of
this discussion
and
of the
dosimetric
analysis
performed
in
deriving
the
HEC
and resultant
unit risk
will
assist
in the appropriate
application
of
inhalation
unit
risk
values
to
exposure
across
lifestages.
4.4.
EXPOSURE
CHARACTERIZATION
The
exposure
characterization
is a technical
characterization
that
presents
the
assessment
results
and
supports
the risk
characterization.
It
provides
a statement
of
the
purpose,
scope,
and
approach
used
in
the assessment,
identifying
the exposure
scenarios
and
population
subgroups
covered.
It
provides
estimates
of
the magnitude,
frequency,
duration,
and distribution
of
exposures
among
members
of
the
exposed
population
as the
data
permit.
It identifies
and
compares
the
contribution
of
different
sources,
pathways,
and routes
of
exposure.
In particular,
a
4-5
qualitative
discussion
of
the strengths
and
limitations
(uncertainties)
of the
data and
models
are
presented.
The discussion
of uncertainties
is a
critical
component
of
the exposure
characterization.
Uncertainties
can arise
out of problems
with
the conceptual
and
mathematical
models.
Uncertainties
can
also
arise
from poor
data quality
and
data
that are
not quite
representative
of
the
population
or scenario
of interest.
Consider
the
following
examples
of uncertainties.
•
National
data (i.e.,
data
collected
to represent
the
entire
U.S.
population)
may not
be
representative
of
exposures
occurring
within
a regional
or
local
population.
•
Use
of short-term
data
to infer
chronic,
lifetime
exposures
should
be done
with
caution.
Use
of
short-term
data to
estimate
long-term
exposures
has
the
tendency
to
underestimate
the
number
of
people
exposed while
overestimating
the
exposure
levels
experienced
by
those in
the
upper
end (i.e., above
the
9O
percentile)
of
the
exposure
distribution.
For
further
guidance,
refer to Guidelines
for
Exposure
Assessment
(U.S.
EPA,
1992a,
§
5.3.1).
•
Children’s
behavior,
including
their
more
limited
diet, may
lead to
relatively
high
but
intermittent
exposures.
This
pattern of
exposure,
“one that
gradually
declines
over
the
developmental
period
and which
remains
relatively
constant
thereafter”
is
not
accounted
for in the LADD
model
(ILSI,
1992). Further,
the
physiological
characteristics
of
children
may
lead
to
important
differences
in exposure.
Some
of
these
differences
can
be
accounted
for in
the LADD
model.
For
further
guidance,
see
Guidelines
for Exposure
Assessment
(U.S.
EPA,
l992a,
§
5.3.5.2).
Overall, the
exposure
characterization
should
provide a
full
description
of the
sources,
pathways,
and routes
of exposure.
The
characterization
also
should include
a full
description
of
the populations
assessed.
In
particular,
highly
exposed
or susceptible
subpopulation
or
lifestage
should
be
discussed.
For further
guidance
on
the exposure
characterization,
consult
Guidelines
4-6
for
Exposure
Assessment
(U.S.
EPA,
1
992a),
the
Policy
and
Guidance
for
Risk
Characterization
(U.S.
EPA,
2000b,1995)
and
EPA’s
Rule
Writer’s’
Guide
to
Executive
Order
13045
(especially
Attachment
C:
Technical
Support
for
Risk
Assessors—Suggestions
for
Characterizing
Risks
to
Children
[U.S.
EPA,
1998d]).
4-7
5. RISK
CHARACTERIZATION
5.1. PURPOSE
EPA has
developed
general
guidance
on risk
characterization
for
use in its
risk
assessment
activities.
The
core of EPA’s
risk
characterization
policy
(U.S.
EPA,
2000b,
1995)
includes
the
following.
Each
risk
assessment
prepared
in support
of
decision making
at EPA
should
include
a
risk
characterization
that
follows
the principles
and
reflects
the
values
outlined
in this
policy.
A
risk
characterization
should
be
prepared
in
a
manner that
is clear,
transparent,
reasonable,
and
consistent
with other
risk
characterizations
of similar scope
prepared
across
programs
in
the Agency.
Further,
discussion
of risk
in all EPA
reports,
presentations,
decision
packages,
and
other
documents
should
be
substantively
consistent
with the
risk
characterization.
The
nature
of the
risk characterization
will
depend
upon
the information
available,
the
regulatory
application
of the
risk
information,
and the
resources
(including
time) available.
In
all
cases,
however,
the
assessment
should identif,
and discuss
all
the major
issues
associated
with
determining
the
nature
and
extent of
the risk
and
provide
commentary
on any
constraints
limiting
fuller
exposition.
Risk
characterization
should
be carried
out in accordance
with
the
EPA
(U.S.
EPA,
2002a) and
0MB (2002)
information
quality
guidelines.
EPA’s
risk characterization
handbook
(U.S.
EPA,
2000b)
provides detailed
guidance
to Agency
staff.
The discussion
below
does
not
attempt
to duplicate
this material,
but it
summarizes
its applicability
to
carcinogen
risk
assessment.
The risk
characterization
includes
a summary
for
the risk manager
in a
nonteclmical
discussion
that
minimizes
the use of
technical
terms. It
is an appraisal
of
the
science
that
informs
the
risk
manager
in public health
decisions,
as do other
decision-making
analyses
of
economic,
5-1
social,
or
technology
issues.
It also
serves
the
needs
of other
interested
readers.
The
summary
is
an information
resource
for
preparing
risk
communication
information,
but
being
somewhat
more
technical
than
desired
for
communication
with
the
general
public,
is
not
itself
the
usual
vehicle
for
communication
with
every
audience.
The
risk
characterization
also
brings
together
the
assessments
of hazard,
dose
response,
and
exposure
to
make
risk
estimates
for
the
exposure
scenarios
of
interest.
This
analysis
that
follows
the
summary
is generally
much
more
extensive.
It
typically
will
identify
exposure
scenarios
of
interest
in decision
making
and
present
risk
analyses
associated
with
them.
Some
of
the
analyses
may
concern
scenarios
in
several
media;
others
may
examine,
for
example,
only
drinking
water
risks.
As these
cancer
guidelines
allow
different
hazard
characterizations
and
different
potencies
for
specified
conditions,
e.g.,
exposure
level,
route
of exposure,
or
lifestage,
some
of the
integrative
analyses
may
need
to be
stratified
to accommodate
the
appropriate
combinations
of
parameters
across
relevant
exposure
durations.
In
constructing
high
end
estimates
of risk,
the
assessor
should
bear
in
mind
that
the
high-
end
risk is
a
plausible
estimate
of
the risk
for
those
persons
at
the upper
end
of the
risk
distribution
(U.S.
EPA,
1992a).
The intent
of this
approach
is
to
convey
an
estimate
of
risk
in
the
upper
range
of the
distribution,
but
to avoid
estimates
that
are beyond
the
true
distribution.
Overly
conservative
assumptions,
when
combined,
can
lead
to unrealistic
estimates
of
risk.
This
means
that
when
constructing
estimates
from
a
series
of factors
(e.g.,
emissions,
exposure,
and
unit
risk
estimates)
not
all factors
should
be set
to values
that maximize
exposure,
dose,
or effect,
since
this
will
almost
always
lead
to
an
estimate
that is
above
the
99th-percentile
confidence
level
and
may
be of
limited
use
to decisionmakers.
This
is
particularly
problematic
when
using
unbounded lognormal
factor
distributions.
While
it is an
appropriate
aim
to
assure
protection
of
health
and
the environment
in
the
face
of
scientific
uncertainty,
common
sense,
reasonable
applications
of
assumptions
and
policy,
and
transparency
are
essential
to
avoid
unrealistically
high
estimates.
It
is
also
important
to
inform
risk
managers
of
the
final
distribution
of risk
estimates
(U.S.
EPA,
2000b;
1995).
Otherwise, risk
management
decisions
may
be
made
on
varying
levels
of conservatism,
leading
5-2
to
misplaced
risk
priorities and
potentially
higher
overall
risks.
(Nichols
and
Zeckhauser,
1986;
Zeckhauser
and
Viscusi,1990).
The
risk characterization
presents
an
integrated
and balanced
picture
of the
analysis
of
the
hazard,
dose-response,
and
exposure.
The
risk
analyst
should
provide
summaries
of
the
evidence
and results
and describe
the
quality
of
available
data
and
the
degree
of confidence
to
be
placed
in
the
risk estimates.
Important
features
include
the
constraints
of available
data
and
the
state
of
knowledge, significant
scientific
issues,
and significant
science
and
science
policy
choices
that
were
made
when
alternative
interpretations
of
data
exist
(U.S.
EPA,
1995,
2000b).
Choices
made
about
using
data
or
default
options
in
the
assessment
are
explicitly
discussed
in
the
course
of
analysis,
and
if a
choice
is a
significant
issue,
it
is
highlighted
in
the
summary.
In
situations
where
there
are
alternative approaches
for
a risk
assessment
that
have
significant
biological
support,
the
decisionmaker
can
be
informed
by
the presentation
of
these
alternatives
along
with
their
strengths
and
uncertainties.
5.2.
APPLICATION
Risk
characterization
is
a
necessary
part
of
generating
any
Agency
report
on
risk,
whether
the report
is
preliminary
— to
support
allocation
of
resources
toward
further
study
or
comprehensive
— to
support
regulatory
decisions.
In
the
former
case,
the
detail
and
sophistication
of the
characterization
are
appropriately
small
in scale;
in
the
latter
case,
appropriately extensive.
Even
if a document
covers
only
parts
of a
risk
assessment
(hazard
and
dose-response analyses,
for
instance),
the
results
of
these
are
characterized.
Risk
assessment
is
an
iterative
process
that
grows
in
depth
and scope
in
stages
from
screening
for
priority
making
to
preliminary
estimation
to fuller
examination
in
support
of
complex
regulatory
decision
making.
Default
options
may
be used
at
any
stage,
but they
are
predominant at screening
stages
and
are
used
less as
more
data
are gathered
and
incorporated
at
later
stages.
Various
provisions
in EPA-administered
statutes
require
decisions
based
on
differing
findings
for
which
differing
degrees
of
analysis
are
appropriate.
There
are
close
to
30
provisions
within
the
major
statutes
that
require
decisions
based
on
risk,
hazard,
or
exposure
assessment.
For
example,
Agency
review
of
pre-manufacture
notices
under
Section
5 of
the Toxic
Substances
5-3
Control
Act
relies
on
screening
analyses,
whereas
requirements
for
industry
testing
under
Section
4 of
that
Act
rely
on
preliminary
analyses
of
risk
or
simply
of exposure.
In
comparison,
air
quality
criteria
under
the
Clean
Air
Act
rest
on
a rich
data
collection
and are
required
by
statute
to undergo
periodic
reassessment.
There
are
provisions
that
require
ranking
of
hazards
of
numerous
pollutants
—
which
may
be addressed
through
a screening level
of analysis
—
and
other
provisions
for which
a
full assessment
of risk
is
more
appropriate.
Given
this
range
in the
scope
and
depth
of analyses,
not all
risk
characterizations
can
or
should
be equal
in
coverage
or
depth.
The
risk
assessor
should
carefully
decide
which
issues
in a
particular
assessment
are important
to present,
choosing
those
that
are
noteworthy
in
their
impact
on results.
For
example,
health
effect
assessments
typically
rely
on animal
data
because
human
data
are
rarely
available.
The objective
of
characterization
of the
use
of animal
data
is
not
to
recount
generic
issues
about
interpreting
and
using
animal
data;
Agency
guidance
documents
cover
these
issues.
Rather,
the
objective is to
highlight
any significant
issues
that
arose
within
the
particular
assessment
being
characterized
and
inform
the
reader
about
significant
uncertainties
that
affect
conclusions.
5.3.
PRESENTATION
OF
THE
RISK
CHARACTERIZATION
SUMMARY
The
presentation
is
a
nontechnical
discussion
of
important
conclusions,
issues,
and
uncertainties that
uses
the
hazard,
dose
response,
exposure,
and
integrative
analyses
for
technical
support.
The
primary
technical
supports within
the
risk
assessment
are the
hazard
characterization,
dose-response
characterization,
and
exposure
characterization
described
in
these
cancer
guidelines.
The
risk
characterization
is
derived
from
these.
The
presentation
should
fulfill
the
aims
outlined
in
the
purpose
section
above.
5.4.
CONTENT OF THE
RISK
CHARACTERIZATION
SUMMARY
Specific
guidance
on
hazard,
dose-response,
and
exposure
characterization
appears
in
previous
sections.
Overall,
the
risk
characterization
routinely
includes
the
following,
capturing
the
important
items
covered
in hazard,
dose
response,
and
exposure
characterization:
5-4
•
primary
conclusions
about
hazard,
dose
response,
and exposure,
including
alternatives
with significant
biological
support;
•
nature
of
key
supporting
information
and analytic
methods;
•
risk estimates
and their
attendant
uncertainties,
including
key
uses
of default
options
when
data are
missing
or uncertain.
—
With
linear
extrapolations,
risk
below
the POD
is
typically
approximated
by
multiplying
the
slope
factor
by
an
estimate
of
exposure,
i.e.,
Risk
=
Slope
Factor
x Exposure.
For
exposure
levels
above
the
POD,
the dose-
response
model
is
used
instead
of
this approximation.
—
With
nonlinear
extrapolations,
the method
of
risk assessment
depends
on
the
procedure
used.
If
a
nonlinear
dose-response
function
has
been
determined,
it
can be
used with
the expected
exposure
to
estimate
a risk.
If
an RID
or
RfC
was calculated,
the hazard
can
be expressed
as a hazard
quotient
(HQ),
defined
as the
ratio
of an
exposure
estimate
over the
reference
dose
(RID)
or
reference
concentration
(RIO),
i.e.,
HQ =
Exposure
/
(RID
or RfC).
From
the
hazard
quotient,
it
can generally
be
inferred
whether
the nonlinear
mode of
action
is
relevant
at
the
environmental
exposure
level
in question;
•
statement
of the
extent
of extrapolation
of risk
estimates
from observed
data
to
exposure
levels
of
interest
and
its
implications
for certainty
or
uncertainty
in
quantifying
risk.
The
extent of
extrapolation
can
be
expressed
as
a margin
of
exposure
(MOE),
defined
as the
ratio of
the POD
over
an
exposure
estimate
(MOE
=
POD
/ Exposure);
5-5
•
significant
strengths
and
limitations
of
the
data
and
analyses,
including
any
major
peer
review
issues;
•
appropriate
comparison
with
similar
EPA
risk
analyses
or
common
risks
with
which
people
may
be
familiar; and
•
comparison
with
all
appropriate
assessments
of
the
same
problem
by
others.
It is
often
difficult
to know
a
priori
when
or
how
different
results
of a
cancer
risk
assessment
are
likely
to be
used
by Agency
economists,
policy
analysts,
and
decisionmakers,
so
it
is important
that
the
resulting
characterizations
include
the necessary
infonnation
for these
analyses
to
the
extent
practicable.
0MB
and
EPA
guidelines
for
benefit-cost
analysis
require
expected
or
central
estimates
of risk
and
information
on
the
uncertainty
of
the
estimate
when
it is
possible
or
practicable.
The
extent
of
the uncertainty
information
needed
for
analysis
depends,
in
part,
on the
scale
of the
policy
being
considered,
with
formal
quantitative
analysis
of
uncertainty
being
required
in
some
cases.
6
0MB
Circular
A-4
(0MB,
2003)
emphasizes
that
agencies
“should
try
to
provide
some
estimate
of the
probability
distribution
of regulatory
benefits
and
costs.”
These
0MB
guidelines
note,
“Whenever
it is possible
to characterize
quantitatively
the
probability
distribution,
some
estimates
of
expected
value
... must
be
provided
in
addition
to
ranges,
variances,
specified
low-end
and
high-end
percentile
estimates,
and
other
characteristics
of
the
distribution.”
The risk
characterization
should
therefore
include,
where
practicable,
expected
or
central
estimates
of
risk,
as
well
as
upper
and
lower
bounds,
e.g.,
confidence
limits,
based
on the
POD,
if not
a
full
characterization
of uncertainty
of the
risk.
As discussed
in
EPA’s
Guidelines
for
Ensuring
and
Maximizing the
Quality,
Objectivity,
Utility,
and
Integrity
of
Information
Disseminated
by the
Environmental
Protection
Agency
(Appendix
B),
statutory
mandates, such
as the
Safe
Drinking
Water
Act,
the
Food
Quality
Protection
Act, and
the
Clean
6
Specifically,
0MB
guidelines
state:
“For rules
that
exceed
the
$1
billion
annual
[economic
effects]
threshold,
a
formal
quantitative
analysis
of
uncertainty
is required.
For rules
with
annual
benefits
and/or
costs
in
the
range from
100
million
to
$1
billion,
you should
seek
to use
more
rigorous
approaches
with
higher
consequence
rules”
(0MB,
2003,
page 158)
5-6
Air
Act, call
for
the
Agency
to generate
specific
kinds
of
risk information,
and thus
these
updated
cancer
assessment
guidelines
should
be read
in
conjunction
with
the
Agency’s statutory
mandates
regarding
risk
assessment.
5-7
APPENDIX
A:
MAJOR
DEFAULT
OPTIONS
This
discussion
covers
the
major
default
options
commonly
employed
when
data
are
missing
or
sufficiently
uncertain
in
a
cancer
risk
assessment,
as
adopted
in
these
cancer
guidelines.
These
options
are
predominantly
inferences
that
help
use
the
data
observed
under
empirical
conditions
in
order
to
estimate
events
and
outcomes
under
environmental
conditions.
Several
inferential
issues
arise
when
effects
seen
in
a
subpopulation
of
humans
or
animals
are
used
to
infer
potential
effects
in
the
population
of
environmentally
exposed
humans.Several
more
inferential
issues
arise
in
extrapolating
the
exposure-effect
relationship
observed
empirically
to
lower-exposure
environmental
conditions.
The
following
issues
cover
the
major
default
areas.
Is
the
presence
or
absence
of
effects
observed
in
a
human
population
predictive
of
effects
in
another
exposed
human
population?
Is
the
presence
or
absence
of
effects
observed
in
an
animal
population
predictive
of
effects
in
exposed
humans?
How
do
metabolic
pathways
relate
across
species
and
among
different
age
groups
and
between
sexes
in
humans?
How
do
toxicokinetic
processes
relate
across
species
and
among
different
age
groups
and
between
sexes
in
humans?
V/hat
is
the
relationship
between
the
observed
dose-response
relationship
to
the
relationship
at
lower
doses?
A-i
Is the
Presence
or Absence
of
Effects
Observed
in a Human
Population
Predictive
of
Effects
in Another
Exposed
Human
Population?
When
cancer
effects
in exposed
humans
are
attributed
to
exposure
to
an agent,
the
default
option
is
that
the resulting
data
are predictive
of
cancer
in
any
other
exposed
human
population.
Most
studies
investigating
cancer
outcomes
in
humans
from
exposure
to
agents
are
often
studies
of occupationally
exposed
humans.
By
sex,
age,
and
general
health,
workers
may
not
be
representative
of the
general
population
exposed
environmentally
to the
same
agents.
In
such
studies
there
is no
opportunity
to
observe
subpopulations
who
are
likely
to be
under
represented,
such as
fetuses,
infants
and children,
women,
or people
in
poor
health,
who
may
respond
differently
from
healthy
workers.
Therefore,
it is
understood
that
this
option
could
still
underestimate
the
response of certain
human
subpopulations
(NRC,
1
993b,
1994).
When
cancer
effects
are not
found
in
an exposed
human
population,
this information
by
itself
is
not generally
sufficient
to
conclude
that
the
agent
poses
no carcinogenic
hazard
to
this
or other
populations
ofpotentially
exposed
humans,
including
susceptible
subpopulations
or
lifestages. This
is because epidemiologic
studies
often
have
low
power
to detect
and
attribute
responses
and
typically
evaluate
cancer
potential
in
a restricted
population
(e.g.,
by
age,
healthy
workers).
The
topic
of
susceptibility
and
variation
is addressed
further
in
the
discussion
below
of
quantitative
default
options
about
dose-response
relationships.
Well-conducted
studies
that
fail
to detect
a
statistically
significant
positive
association,
however,
may
have
value
and
should
be
judged
on
their
merits,
including
population
size,
duration
of
the study,
the
quality
of
the
exposure
characterization and
measures
of
outcome,
and
the
magnitude
and duration
of
the
exposure.
There
is not
yet
enough
knowledge
to
form
a
basis
for
any
generally
applicable
qualitative
or
quantitative
inference
to
compensate
for the
gap
in
knowledge
concerning
other
populations.
In these
cancer
guidelines,
this
problem
is
left
to analysis
in
individual
cases,
to
be attended
to
with
further
general
guidance
as future
research
and
information
allow.
When
information
on
a
susceptible
subpopulation
or
lifestage
exists,
it will
be
used.
For
example,
an
agent
such
as
diethylstilbestrol
(DES)
causes
a
rare
form
of
vaginal
cancer
(clear-cell
adenocarcinoma)
(Herbst
A-2
et
al., 1971)
in about
1 per
1000
of
adult
women
whose
mothers
were
exposed
during
pregnancy
(Hatchet
al.,
1998).
Is the
Presence
or
Absence
ofEffects
Observed
in
an
Animal
Population
Predictive
of
Effects
in Exposed
Humans?
The default
option
is that
positive
effects
in animal
cancer
studies
indicate
that
the
agent
under
study
can
have
carcinogenic
potential
in humans.
Thus,
if
no
adequate
human
or
mode
of action
data
are
present,
positive
effects
in
animal
cancer
studies
are
a
basis
for
assessing
the
carcinogenic
hazard
to
humans.
This
option
is
a public
health-protective
policy,
and
it is both
appropriate
and
necessary,
given
that
we
do not
test
for carcinogenicity
in
humans.
The
option
is
supported
by
the
fact
that
nearly
all of
the agents
known
to
cause
cancer
in
humans
are
carcinogenic
in animals
in
tests
that
have
adequate
protocols
(IARC,
1994;
Tomatis
et
al., 1989;
Huff,
1994).
Moreover,
almost
one-third
of
human
carcinogens
were
identified subsequent
to animal
testing
(Huff,
1993).
Further
support
is provided
by
research
on
the
molecular
biology
of
cancer
processes,
which
has shown
that the
mechanisms
of
control
of
cell
growth
and differentiation
are
remarkably
homologous
among
species
and highly
conserved
in
evolution.
Nevertheless, the same
research
tools
that
have
enabled
recognition
of the
nature
and
commonality
of cancer
processes
at
the molecular
level
also
have
the
power
to reveal
differences and
instances
in which
animal
responses
are not
relevant
to
humans
(Lijinsky,
1993;
U.S.
EPA,
1991 b).
Under
these
cancer
guidelines,
available
mode
of action
information
is
studied
for
its
implications
in both
hazard
and
dose-response
assessment
and
its
ability
to
obviate
default
options.
There
may
be
instances
in
which
the
use
of
an
animal
model
would
identify
a
hazard
in
animals
that
is not
truly
a
hazard
in
humans
(e.g.,
the
alpha-2u-globulin
association
with
renal
neoplasia
in
male
rats
[U.S.
EPA,
1991
bJ).
The extent
to which
animal
studies
may
yield
false
positive
indications
for
humans
is
a
matter
of scientific
debate.
To demonstrate
that
a
response
in
animals
is
not
relevant
to
any human
situation,
adequate
data
to assess
the
relevancy
issue
are
important.
In
general,
while
effects
seen
at
the highest
dose
tested
are
assumed
to
be
appropriate
for
assessment, it
is
necessary
that the
experimental
conditions
be
scrutinized.
Animal
studies
A-3
are
conducted
at high
doses in
order
to
provide
statistical
power,
the
highest
dose
being
one
that
is minimally
toxic (maximum
tolerated
dose
or MTD).
Consequently,
the
question
often arises
of whether
a
carcinogenic
effect
at
the
highest
dose may
be a consequence
of cell
killing with
compensatory
cell replication
or
of general
physiological
disruption
rather
than
inherent
carcinogenicity
of
the tested
agent.
There
is little
doubt
that
this may
happen
in
some cases,
but
skepticism
exists
among
some scientists
that
it
is
a
pervasive
problem
(Ames
and Gold,
1990;
Melnick
et
al.,
1993; Barrett,
1993).
If
adequate
data
demonstrate
that the
effects
are
solely
the
result
of excessive
toxicity
rather
than
carcinogenicity
of
the tested
agent
per
Se, then
the effects
may
be regarded
as
not
appropriate
to include
in
assessment
of
the potential
for human
carcinogenicity
of the
agent.
This
is a matter
of expert
judgment,
with
consideration
given
to
all
of
the
data
available
about
the agent,
including
effects
in
other
toxicity
studies,
structure-activity
relationships,
and
effects
on
growth
control
and
differentiation.
When
cancer
effects
are
notfound
in well-conducted
animal
cancer
studies
in
two or
more
appropriate
species
and
other
information
does
not
support
the
carcinogenic
potential
of
the agent,
these
data
provide
a basis
for concluding
that the
agent
is
not
likely
to possess
human
carcinogenic
potential
in
the absence
of
human
data to
the
contrary.
This default
option
about
lack
of cancer
effects
has limitations.
it is recognized
that animal
studies
(and epidemiologic
studies
as
well)
have
very low
power
to detect
cancer
effects.
Detection
of a
10%
tumor
incidence
is generally
the
limit
of power
with
standard
protocols
for
animal
studies
(with
the
exception
of
rare
tumors
that
are
virtually
markers
for
a particular
agent,
e.g.,
angiosarcoma
caused
by
vinyl
chloride).
In some
situations,
the
tested animal
species
may
not
be
predictive
of
effects
in humans;
for example,
arsenic
shows
only
minimal
or
no effect
in animals,
whereas
it is
clearly
positive
in
humans.
Therefore,
it
is important
to
consider
other
information
as well;
absence
of
mutagenic
activity
or
absence
of
carcinogenic
activity
among
structural
analogues
can
increase
the
confidence
that
negative
results
in animal
studies
indicate
a
lack
of human
hazard.
Another
limitation
is
that
standard
animal
study
protocols
are
not yet
available
for
effectively
studying
perinatal
effects.
The
potential
for effects
on the very
young
generally
should
be considered
separately.
Under
existing
Agency
policy
(U.S.
EPA,
1
997a,
b),
perinatal
studies
A-4
accomplished
by
modification
of
existing
adult
bioassay
protocols
are
important
in
special
circumstances.
Target
organ
concordance
is
not
a
prerequisite
for
evaluating
the
implications
ofanimal
study
results
for
humans.
Target
organs
of
carcinogenesis
for agents
that
cause
cancer
in
both
animals
and
humans
are
most
often
concordant
at
one
or
more
sites
(Tomatis
et al.,
1989;
Huff,
1994).
However,
concordance
by
site
is not
uniform.
The
mechanisms
of control
of cell
growth
and
differentiation
are concordant
among
species,
but
there
are
marked
differences
among
species
in
the
way
control
is
managed
in
various
tissues.
For example,
in
humans,
mutations
of
the
tumor
suppressor
genes
p53
and retinoblastoma
are
frequently
observed
genetic
changes
in
tumors. These
tumor-suppressor
genes
are
also observed
to be
operating
in
some
rodent
tissues,
but other
growth
control
mechanisms
predominate
in
other
rodent
tissues.
Thus,
an
animal
response
may
be
due
to
changes
in
a
control
that are
relevant
to humans
but
appear
in
animals
in
a different
way.
However,
it
is
appropriate
under
these
cancer
guidelines
to
consider
the
influences
of
route
of
exposure,
metabolism,
and,
particularly,
some
modes
of
action
that
may
either
support
or
not
support
target
organ
concordance
between
animals
and
humans.
When
data
allow,
these
influences
are
considered
in
deciding
whether
agent-,
species-,
or
organ-specific
situations
are
appropriate
to
use in
preference
to this
default
assumption
(NRC,
1994).
In
contrast,
use
of
toxicokinetic modeling inherently
assumes
site
concordance,
as these
models
are
used
to
estimate
delivered dose
to a
particular
tissue
or
organ
in
humans
on the
basis
of the
same
tissue
or
organ
from
animal
data.
The
default
is
to
include
benign
tumors
observed
in animal
studies
in the
assessment
of
animal
tumor
incidence,
fsuch
tumors
have
the
capacity
to
progress
to
the malignancies
with
which
they
are
associated.
This
default
is
consistent
with
the
approach
of
the National
Toxicology
Program
and
the International
Agency
for
Research
on
Cancer
and
is more
protective
of public
health
than
not including
benign
tumors
in the
assessment;
benign
and malignant
tumors
are
treated
as representative
of
related
responses
to
the
test
agent
(McConnell
et
al.,
1986),
which
is
scientifically
appropriate.
Nonetheless,
in assessing
findings
from
animal
studies,
a
greater
proportion
of
malignancy
is weighed
more
heavily
than
is a response
with
a
A-S
greater
proportion
of benign
tumors.
Greater
frequency
of
malignancy
of
a particular
tumor
type
in
comparison
with
other tumor
responses
observed
in an animal
study
is
also a factor
to be
considered
in selecting
the response
to be used in
dose-response
assessment.
Benign
tumors
that are not
observed
to
progress
to malignancy
are
assessed
on a case-
by-case
basis.
There
is a range
of possibilities
for
the overall
significance
of benign
tumors.
They
may
deserve
attention
because
they
are
serious
health
problems
even though
they
are not
malignant;
for instance,
benign tumors
may
be
a health
risk because
of
their
effect
on the
function
of a target
tissue,
such
as the
brain.
They may
be significant
indicators
of the
need
for
further
testing
of
an
agent if they
are
observed
in a short-term
test
protocol,
or such
an
observation
may
add to the
overall
weight of
evidence
if
the same
agent
causes
malignancies
in
a
long-term
study.
Knowledge
of the mode
of
action
associated
with
a benign tumor
response
may
aid in the interpretation
of other tumor
responses
associated
with
the
same
agent.
How
Do Metabolic
Pathways
Relate
Across Species
andAinong
Different
Age
Groups
and
Between
Sexes
in
Humans?
The default
option
is that
there is
a similarity
ofthe
basic pathways
ofmetabolism
and
the occurrence
of
metabolites
in
tissues
in
regard
to the species-to-species
extrapolation
of
cancer
hazard
and
risk.
If comparative
metabolism
studies were
to
show
no
similarity
between
the
tested species
and
humans
and
a metabolite(s)
was the active
form,
there would
be
less
support
for
an
inference
that the animal
response(s)
relates
to humans.
In other
cases,
parameters
of
metabolism
may
vary
quantitatively
between
species;
this
becomes
a factor
in deciding
on
an
appropriate
human-equivalent
dose
based
on
animal
studies,
optimally
in
the context
of a
toxicokinetic
model.
Although
the
basic
pathways
are assumed
to
be the
same
among
humans,
the presence
of
polymorphisms
in
the general
population
and factors
such
as
the
maturation
of
the
pathways
in infants
should
be
considered.
The active
form
of an agent
may be present
to
differing
degrees,
or it may
be completely
absent, which
may
result
in
greater or
lesser
risk
for
subpopulations.
A-6
How
Do
Toxicokinetic
Processes Relate
Across
Species
and Among
Dfferent
Age
Groups
and
Between
Sexes
in Humans?
A
major
issue
is how
to
estimate
human-equivalent
doses
in
extrapolating
from
animal
studies.
As
a
default
for
oral
exposure
a
human
equivalent
dose
for
adults
is estimated
from
data
on
another
species
by
an
adjustment
of
animal
applied
oral
dose
by
a scaling
factor
based
on
body
weight
to
the 3/4
power.
The
same
factor
is
used
for
children
because
it
is
slightly
more
protective
than using
children’s
body
weight
(see
Section
3.1.3).
This
adjustment
factor
is
used
because
it
represents
scaling
of metabolic
rate
across
animals
of different
size.
Because
the
factor
adjusts
for a parameter
that
can
be
improved
on
and
brought
into
more
sophisticated
toxicokinetic modeling
when
such
data
become
available,
they
are
usually
preferable
to
the
default
option.
For
inhalation exposure,
a
human
equivalent
dose
for
adults
is estimated
by
default
methodologies
that
provide
estimates
of
lung
deposition
and
internal
dose
(U.S.
EPA,
1994).
The
methodologies
can
be
refined
to
more
sophisticated
forms
with
data
on
toxicokinetic
and
metabolic parameters
of
the
specific
agent.
This
default
option,
like
the
one for
oral
exposure,
is
selected
in
part because
it
lays
a foundation
for
incorporating
better
data.
The
use
of
information
to
improve
dose
estimation from
applied
to
internal
to delivered
dose
is encouraged,
including
use
of toxicokinetic modeling
instead
of
any default,
where
data
are
available.
There
are
important
differences
between
infants,
adults,
and
older
adults
in
the
processes
of absorption, distribution,
and
elimination;
for
example,
infants
tend
to
absorb
metals
through
the gut
more
rapidly
and
more
efficiently
than
do older
children
or
adults
(Calabrese,
1986).
Renal
elimination
is
also
not
as
efficient
in infants.
Although
these
processes
reach
adult
competency
at
about
the
time
of
weaning,
they may
have
important
implications,
particularly
when
the
dose-response
relationship
for
an
agent
is considered
to
be
nonlinear
and
there
is
an
exposure scenario
disproportionately
affecting
infants,
because
in
these
cases
the magnitude
of
dose
is
more
pertinent
than
the
usual
approach
in
linear
extrapolation
of
averaging
dose
across
a
lifetime.
Efficiency
of
intestinal
absorption
in
older
adults
tends
to
be
generally
less
overall
for
most
chemicals.
Another
notable
difference
is
that, post-weaning
(about
1
year),
children
have
a
A-7
higher
metabolic
rate
than
do
adults
(Renwick,
1998),
and
they
may toxify
or detoxify
agents
at
a
correspondingly
higher
rate.
For a route-to-route exposure
extrapolation,
the
default
option
is
that an
agent that
causes
internal
tumors
by
one route
of
exposure
will
be
carcinogenic
by another
route
fit
is
absorbed
by
the second
route
to give
an
internal
dose. This
is a
qualitative
option
and
is
considered
to
be
public-health
protective.
The
rationale
is that
for internal
tumors
an internal
dose
is significant
no matter
what
the
route
of
exposure.
Additionally,
the
metabolism
of
the
agent
will
be
qualitatively
the same
for
an
internal
dose.
The
issue
of
quantitative
extrapolation
of the dose-
response
relationship
from
one
route
to another
is
addressed
case
by
case.
Quantitative
extrapolation
is complicated
by
considerations
such
as
first-pass
metabolism.
What
Is the
Correlation
of
the Observed
Dose-Response
Relationship
to the
Relationship
at
Lower
Doses?
If
sufficient
data
are
available,
a
biologically
based
model for
both
the
observed
range
and
extrapolation
below
that range
may
be used.
Although
no
standard
biologically
based
models
are
in
existence,
an
agent-specific
model
may
be developed
if
extensive
data exist
in
a particular
case
and
the
purpose
of the
assessment
justifies
the
investment
of the
resources
needed.
The default
procedure
for the
observed
range
ofdata
when
a biologically
based
model
is
not used
is
to
use
a
curve-fitting
model
for
incidence
data.
In
the
absence
of data
supporting
a biologically
based
model
for
extrapolation
outside
of
the
observed
range,
the
choice
of
approach
is
based
on the
view
of mode
of action
of the agent
arrived
at in the
hazard
assessment.
If more
than
one approach
(e.g.,
both a nonlinear
and
linear
approach)
are
supported
by
the
data,
they should
be
used and
presented
to the
decisionmaker.
A
linear
extrapolation approach
is
used
when
the mode
of
action
information
is
supportive
of
linearity
or mode
of
action
is not understood.
The
linear
approach
is
used when
a
view
of
the mode
of action
indicates
a linear
response,
for
example,
when a
conclusion
is made
that
an agent
directly
causes
alterations
in DNA,
a
kind of
interaction
that
not
only
theoretically
requires
one
reaction
but
also is
likely
to be additive
to
ongoing,
spontaneous
gene
mutation.
Other
kinds
of
activity
may
have
linear
implications,
for
example,
linear
rate-limiting
steps
A-8
would
also
support
a
linear
procedure.
The
linear
approach
is
to
draw
a
straight
line
between
a
point
of
departure
from
observed
data,
generally
as a
default,
an
LED
chosen
to
be
representative
of
the
lower
end
of
the
observed
range,
and
the
origin
(zero
incremental
dose,
zero
incremental
response).
This
approach
is
generally
considered
to
be
public-health
protective.
The
linear
default
is
thought
to
generally
provide
an
upper-bound
calculation
of
potential
risk
at
low
doses,
for
example,
a
1/100,000
to
1/1,000,000
risk.
This
upper
bound
is
thought
to
be
public-health
protective
at
low
doses
for
the
range
of
human
variation,
considering
the
typical
Agency
target
range
for
risk
management
of
1/1,000,000
to
1/10,000,
although
it
may
not
completely
be
so
(Bois
et
al.,
1995)
if
pre-existing
disease
or
genetic
constitution
place
a
percentage
of
the
population
at
greater
risk
from
exposure
to
carcinogens.
The
question
of
what
may
be
the
actual
variation
in
human
susceptibility
is
one
that
was
discussed
in
general
in
the
NRC
(1994)
report,
as
well
as
the
NRC
report
on
pesticides
in
children
and
infants
(NRC,
1
993b).
NRC
has
recommended
research
on
the
question,
and
EPA
and
other
agencies
are
conducting
such
research.
Given
the
current
state
of
knowledge,
EPA
will
assume
that
the
linear
default
procedure
adequately
accounts
for
human
variation
unless
there
is
case-specific
information
for
a
given
agent
or
mode
of
action
that
indicates
a
particularly
susceptible
subpopulation
or
lifestage,
in
which
case
the
special
information
will
be
used.
When
adequate
data
on
mode
of
action
provide
sufficient
evidence
to
support
a
nonlinear
mode
of
action
for
the
general
population
and/or
any
subpopulations
of
concern,
a
different
approach
—
a
reference
dose/reference
concentration
that
assumes
that
nonlinearity
—
is used.
The
POD
is
again
generally
an
BMDL
when
incidence
data
are
modeled.
A
sufficient
basis
to
support
this
nonlinear
procedure
is
likely
to
include
data
on
responses
that
are
key
events
integral
to
the
carcinogenic
process.
This
means
that
the
POD
may
be
based
on
these
precursor
response
data,
for
example,
hormone
levels
or
mitogenic
effects
rather
than
tumor
incidence
data.
When
the
mode
of
action
iiformation
indicates
that
the
dose-response
function
may
be
adequately
described
by
both
a
linear
and
a
nonlinear
approach,
then
the
results
of
both
the
linear
and
the
nonlinear
analyses
are
presented.
An
assessment
may
use
both
linear
and
nonlinear
approaches
if
different
responses
are
thought
to
result
from
different
modes
of
action
or
a
response
appears
to
be
very
different
at
high
and
low
doses
due
to
influence
of
separate
A-9
modes
of
action.
The results
may
be
needed
for
assessment
of
combined
risk
from
agents
that
have
common
modes
of
action.
Absent
data
to
the
contrary,
the
default
assumption
is
that
the
cumulative
dose
received
over
a
lifetime,
expressed
as
a lifetime
average
daily
dose
or
lifetime
average
daily
exposure,
is
an appropriate measure
of
dose
or exposure.
This assumes
that
a
high
dose
of
such
an
agent
received
over
a shorter
period
of time
is equivalent
to a low
dose
spread
over
a
lifetime.
This
is
thought
to be
a
relatively
public-health-protective
option
and
has some
empirical
support
(Monro,
1992).
A counter
example,
i.e.,
effects
of short-term,
high
exposure
levels
that
result
in
subsequent
cancer
development,
is treatment
of
cancer
patients
with
certain
chemotherapeutic
agents.
When
sufficient
information
is available
to
support
a different
approach,
it can
be
used.
For
example,
short-term
exposure
estimates
(several
days
to several
months)
may
be
more
appropriate than
the lifetime
average
daily
dose.
In these
cases,
both
agent
concentration
and
duration
are
likely
to
be
important, because
such
effects
may
be reversible
at
cessation
of
very
short-term exposures.
A-
10
APPENDIX
B: EPA’s
GUIDANCE
FOR
DATA
QUALITY
ASSESSMENT
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2000d)
Guidance
for
data
quality
assessment:
practical
methods
for
data
analysis.
Office
of Environmental
Information,
Washington,
DC.
EPAI600/R-96/084.
Available
from:
ht://www.epa.gov/gualitv/gs-docsk9-finaI.Ddf
B-i
REFERENCES
Allen,
BC;
Crump,
KS; Shipp,
AM.
(1988)
Correlation
between
carcinogenic
potency
of
chemicals
in
animals
and
humans.
Risk
Anal 8:531—544.
Ames,
BN;
Gold,
LS.
(1990)
Too
many rodent
carcinogens:
mitogenesis
increases
mutagenesis.
Science
249:970—971.
Anderson,
LM;
Diwan,
BA;
Fear,
NT;
et al.
(2000)
Critical
windows
of exposure
for
children’s
health:
cancer
in human
epidemiological
studies
and
neoplasms
in
experimental
animal
models.
Environ
Health
Perspect
108(Suppl
3):573-594
Ashby,
J;
Tennant,
RW.
(1991)
Definitive
relationships
among
chemical
structure,
carcinogenicity
and
mutagenicity
for 301
chemicals
tested
by
the
U.S.
NTP.
Mutat
Res
257:229—306.
Ashby,
J;
Tennant,
RW.
(1994)
Prediction
of
rodent
carcinogenicity
for 44
chemicals:
results.
Mutagenesis
9:7—15.
Ashby,
J;
Doerrer,
NG;
Flamm,
FG;
et
al. (1990)
A scheme
for
classifying
carcinogens.
Regul
Toxicol
Pharmacol
12:270—295.
Ashby,
J;
Brady,
A;
Elcombe,
CR;
et al.
(1994)
Mechanistically
based
human
hazard
assessment
of
peroxisome
proliferator-induced
hepatocarcinogenesis.
Hum
Exper
Toxicol
13:1—117.
Barrett,
JC.
(1992)
Mechanisms
of
action
of known
human
carcinogens.
In: Mechanisms
of
carcinogenesis
in risk
identification.
IARC
Sci
Pubs No.
116,
115—134.
International
Agency
for
Research
on Cancer,
Lyon,
France.
Barrett,
JC.
(1993) Mechanisms
of
multistep
carcinogenesis
and
carcinogen
risk
assessment.
Environ
Health
Perspect
100:9-20.
Barrett,
JC;
Lee,
TC. (1992)
Mechanisms
of
arsenic-induced
gene amplification.
In: Kellems,
RE,
ed.
Gene
amplification
in
mammalian
cells:
a comprehensive
guide.
New York:
Marcel
Dekker.
Baylin,
5;
Bestor,
TH. (2002)
Altered
methylation
patterns
in
cancer
cell
genomes:
causes
or
consequence?
Cancer
Cell
1:299—305.
Bellamy,
CO;
Malcomson,
RD; Harrison,
DJ; et
al. (1995)
Cell
death
in
health
and
disease:
the
biology
and
regulation
of
apoptosis.
Seminars
in Cancer
Biology,
Apoptosis
in
Oncogenesis
and
Chemotherapy
6:3—16.
R-1
Biggs,
PJ; Wanen,
W;
Venitt,
S;
et
al. (1993)
Does
a
genotoxic
carcinogen
contribute
to human
breast
cancer?
The
value
of
mutational
spectra
in unraveling
the etiology
of
cancer.
Mutagenesis
8:275—283.
Bimbaum,
LS;
Fenton,
SE. (2003)
Cancer
and
developmental
exposure
to
endocrine
disruptors.
Environ
Health
Perspect
111:389-394.
Bimer,
G;
Albrecht,
W;
Neumann,
HG.
(1990)
Biomonitoring
of aromatic
amines.
III:
hemoglobin
binding
and
benzidine
and
some
benzidine
congeners.
Arch
Toxicol
64(2):97—102.
Blair,
A; Burg,
J;
Foran,
J;
et
al. (1995)
Guidelines
for
application
of
meta-analysis
in
environmental epidemiology.
Regul
Toxicol
Pharmacol
22:189—197.
Bois,
FY;
Krowech,
G;
Zeise,
L.
(1995)
Modeling
human
interindividual
variability
in
metabolism
and
risk:
the
example
of
4-aminobiphenyl.
Risk
Anal
15:205—213.
Calabrese,
EJ.
(1986) Age
and
susceptibility
to
toxic
substances.
New York:
Winter-Interscience
Publication,
John Wiley
and
Sons,
Inc.
Callemen,
CJ;
Ehrenberg,
L; Jansson,
B;
et al.
(1978) Monitoring
and
risk
assessment
by means
of alkyl
groups
in hemoglobin
in
persons
occupationally
exposed
to
ethylene
oxide.
J
Environ
Pathol
Toxicol
2:427—442.
Caporaso,
N;
Hayes,
RB;
Dosemeci,
M;
et
al. (1989)
Lung
cancer
risk,
occupational
exposure,
and
the
debrisoquine
metabolic
phenotype.
Cancer
Res
49:3675—3679.
Cavenee,
WK;
Koufos,
A; Hansen,
MF.
(1986)
Recessive
mutant
genes
predisposing
to
human
cancer.
Mutat
Res
168:3—14.
CDC (Centers
for
Disease
Control
and
Prevention).
(2004)
The
health
consequences
of
smoking:
a
report
of the
surgeon
general.
Dept.
of
Health
and Human
Services,
Washington,
D.C.
Available
from: http://www.cdc,gov/tobacco/sgr/sgr
2004/index.htm.
Chang,
CC;
Jone,
C;
Trosko,
JE;
et
al. (1988)
Effect
of
cholesterol
epoxides
on the
inhibition
of
intercellular
communication
and
on
mutation
induction
in
Chinese
hamster
V79
cells.
Mutat
Res
206:471—478.
Chuang,
LS; Ng,
HH;
Chia,
IN;
Li,
BF.
(1996)
Characterisation
of independent
DNA
and
multiple
Zn-binding
domains
at
the
N
terminus
of
human
DNA-(cytosine-5)
methyltransferase:
modulating
the
property
of
a DNA-binding
domain
by contiguous
Zn-binding
motifs.
J
Mol
Biol
257(5):935-
48.
Chen, C;
Farland,
W.
(1991)
Incorporating
cell proliferation
in quantitative
cancer
risk
assessment:
approaches,
issues,
and
uncertainties.
In: Butterworth,
B.,
Slaga,
T., Farland,
W.,
et
R-2
al.,
eds. Chemical
induced
cell proliferation:
implications
for
risk assessment.
New
York:
Wiley
Liss,
pp.
481—499.
Chhabra,
RE;
Huff,
JE; Schwetz,
BS;
Selkirk,
J.
(1990)
An overview
of prechronic
and chronic
toxicity/carcinogenicity
experimental
study
designs
and
criteria
used by
the National
Toxicology
Program.
Environ.
Health
Perspect.
86:313-321.
Choy,
WN.
(1993)
A
review
of the
dose-response
induction
of DNA
adducts
by
aflatoxin
B
2
and
its
implications
to
quantitative
cancer-risk
assessment.
Mutat
Res
296:181—198.
Clayson,
DB; Mehta,
R;
Iverson,
F. (1994)
Oxidative
DNA
damage—the
effects
of certain
genotoxic
and
operationally
non-genotoxic
carcinogens.
Mutat
Res
317:25-42.
Cohen,
SM.
(1995)
Role of
urinary
physiology
and
chemistry
in bladder
carcinogenesis.
Fd
Chem
Toxicol
33:715—30.
Cohen,
SW;
Ellwein,
LB.
(1990)
Cell
proliferation
in carcinogenesis.
Science
249:1007—1011.
Cohen,
SM; Ellwein,
LB. (1991)
Genetic
enors,
cell
proliferation
and carcinogenesis.
Cancer
Res
51:6493—6505.
Cohen,
SM;
Purtilo,
DT;
Ellwein,
LB.
(1991)
Pivotal
role
of increased
cell
proliferation
in
human
carcinogenesis.
Mod
Pathol
4:371—375.
Conolly,
RB;
Andersen,
ME. (1991)
Biologically
based
pharmacodynamic
models:
tools
for
toxicological
research
and risk
assessment.
Ann
Rev
Pharmacol
Toxicol
3 1:503—523.
Contrera,
IF;
Matthews,
EJ;
Benz,
RD. (2003)
Predicting
the
carcinogenic
potential
of
pharmaceuticals
in
rodents
using
molecular
structural
similarity
and
E-state
indices.
Regul.
Toxicol.
Pharmacol.
38:243—259.
Cresteil,
T.
(1998)
Onset of
xenobiotic
metabolism
in
children:
toxicological
implications.
Food
Addit
Contam
15, Supplement
45—51.
Dearfield,
K.
L.;
Auletta,
A. E.;
Cimino,
M.
C.,
et al. (1991)
Considerations
in the
U.S.
Environmental
Protection
Agency
T
s
testing
approach
for
mutagenicity.
Mutat.
Res.
25
8:259-283.
D’Souza,
RW;
Francis,
WR;
Bruce,
RD;
et
al. (1987)
Physiologically
based
pharmacokinetic
model
for
ethylene
chloride
and
its application
in risk
assessment.
In: Pharmacokinetics
in
risk
assessment:
drinking
water
and
health.
Vol.
8.
Washington,
DC: National
Academy
Press.
Enterline,
PE;
Henderson,
VL; Marsh,
GM.
(1987)
Exposure
to
arsenic.
Amer
J
Epidemiol
125:929—93
8.
R-3
Evans,
JS;
Gray,
GM;
Sielken,
RL
Jr;
Smith,
AE;
Valdez-Flores,
C;
Graham,
JD.
(1994
)
Use
of
probabilistic
expert
judgment
in
uncertainty
analysis
of
carcinogenic
potency.
Regul
Toxicol
Pharmacol.
2:
15-36.
Executive
Order
13045
(1997)
Protection
of children
from
environmental
health
risks
and
safety
risks,
issued
April
21, 1997.
Fearon,
E; Vogelstein,
B.
(1990)
A
genetic
model
for
colorectal
tumorigenesis.
Cell
61:959—967.
Fenton,
SE;
Davis,
CC.
(2002)
Atrazine
exposure
in
utero
increases
dimethylbenz
a
anthracene
induced
mammary
tumor
incidence
in long
evans
offspring.
Toxicol
Sci
66(1-2):
185.
“The
Toxicologist, Abstracts
of
the
41st
Annual
Meeting
of
the
Society
of
Toxicology.”
(Abstract
903)
Fisher,
RA.
(1950)
Statistical
methods
for
research
workers.
Edinburgh,
Scotland:
Oliver
and
Boyd.
Florig,
HK;
Morgan,
MG;
Morgan,
KM;
Jenni,
KE;
Fischhoff,
B; Fischbeck,
PS;
DeKay,
ML.
(2001)
A
deliberative
method
for
ranking
risks
(I):
Overview
and test
bed
development.
Risk
Anal.
21:913-21.
Flynn,
GL.
(1990)
Physicochemical
determinants
of
skin
absorption.
In:
Gerrity,
TR,
Henry,
Ci,
eds. Principles
of
route
to route
extrapolation
for
risk
assessment.
New
York:
Elsevier
Science;
pp.
93—127.
Fos,
PJ;
McLin,
CL.
(1990)
The
risk of
falling
in the
elderly:
a
subjective
approach.
Med
Decis
Making
10:195-200.
Garfinkel,
L; Silverberg,
E.
(1991)
Lung
cancer
and
smoking
trends
in the
United
States
over
the
past
25 years.
Cancer
41:137—145.
Gaylor,
DW;
Zheng,
Q.
(1996)
Risk
assessment
of nongenotoxic
carcinogens
based
on
cell
proliferationldeath
rates
in rodents.
Risk
Anal
16(2)
:221—225.
Gaylor,
DW;
Kodell,
RL; Chen,
JJ; et
al.
(1994)
Point
estimates
of
cancer
risk
at
low
doses.
Risk
Anal
14(5):843—850.
Gibson,
DP;
Aardema,
MJ;
Kerckaert,
GA;
et
al. (1995)
Detection
of
aneuploidy-inducing
carcinogens
in the
Syrian
hamster
embryo
(SHE)
cell transformation
assay.
Mutat
Res
343:7—24.
Ginsberg,
GL.
(2003)
Assessing
cancer
risks
from
short-term
exposures
in
children.
Risk
Anal
23(1):19-34.
Goddard,
Mi;
Murdoch,
DJ;
Krewski,
D.
(1995).
Temporal
aspects
of
risk
characterization.
Inhal
Toxicol
7:1005—1018.
R-4
Goldsworthy,
TL;
Hanigan,
MH;
Pitot,
HC. (1986)
Models
of
hepatocarcinogenesis
in the
rat—contrasts
and
comparisons.
CRC Crit
Rev Toxicol
17:61—89.
Goodman,
JI; Counts,
JL. (1993) Hypomethylation
of
DNA:
A possible
nongenotoxic
mechanism
underlying
the role
of cell proliferation
in carcinogenesis.
Environ
Health
Perspect
101
Supp. 5:169—1
72.
Greenland,
S.
(1987)
Quantitative
methods
in
the
review
of
epidemiologic
literature.
Epidemiol
Rev 9:1—29.
Gulezian,
D;
Jacobson-Kram,
D;
McCullough,
CB; et al.
(2000)
Use of transgenic
animals
for
carcinogenicity
testing: considerations
and implications
for risk
assessment.
Toxicol
Pathol
28:482—499.
Hanimand,
BC.
(1966)
Smoking
in relation
to the death
rates
of one million
men and
women.
In:
Haenxzel,
W,
ed.
Epidemiological
approaches
to the
study of
cancer
and
other
chronic
diseases.
National
Cancer Institute
Monograph
No. 19. Washington,
DC.
Hanahan,
D; Weinberg,
RA.
(2000) The
hallmarks
of cancer.
Cell
100:57—70.
Harris,
CC;
Holistein,
M. (1993)
Clinical implications
of the
p53 tumor
suppressor
gene.
N Engi
J Med
329:1318—1327.
Haseman,
JK. (1983)
Issues: a
reexamination
of false-positive
rates
for
carcinogenesis
studies.
Fundam
Appl
Toxicol
3:334—339.
Haseman,
JK. (1984)
Statistical
issues
in the
design, analysis
and
interpretation
of animal
carcinogenicity
studies.
Environ
Health
Perspect
58:385—392.
Haseman, JK.
(1985)
Issues
in carcinogenicity
testing:
dose
selection.
Fundam
Appl
Toxicol
5:66—78.
Haseman,
JK. (1990)
Use
of statistical
decision
rules
for evaluating
laboratory
animal
carcinogenicity
studies.
Fundam
Appl
Toxicol
14:637—648.
Haseman,
JK. (1995)
Data
analysis:
Statistical
analysis
and use of historical
control
data.
Regul
Toxicol
Pharmacol
2 1:52—59.
Hatch,
BE; Palmer,
JR; Titus-Ernstoff
L;
Noller, KL
et al.
(1998)
Cancer
risk
in women
exposed
to
diethyistilbestrol
in utero.
JAMA 280:630-634.
Hattis,
D. (1990)
Pharmacokinetic
principles
for
dose-rate extrapolation
of carcinogenic
risk
from genetically
active agents.
Risk Anal
10:303—316.
R-5
Hawkins,
NC;
Evans,
JS.
(1989)
Subjective
estimation
of
toluene
exposures:
a
calibration
study
of industrial
hygienists,
Applied
Industrial
Hygiene,
4:61
-68.
Hawkins,
NC;
Graham,
JD.
(1988)
Expert
scientific
judgment
and
cancer
risk
assessment:
a
pilot
study
of
pharmacokinetic
data,
Risk Anal.
8:615-25.
Hayward,
JJ;
Shane,
BS;
Tindall,
KR;
et
al. (1995)
Differential
in
vivo
mutagenicity
of
the
carcinogen-noncarcinogen
pair
2,4-
and
2,6-diaminotoluene.
Carcinogenesis
10:2429—2433.
Heddle,
JA;
Swiger,
RR.
(1996)
Risk
estimation
from
somatic
mutation
assays.
Mutat
Res
365(1-3):107-17.
Herbst,
AL,
Ulfelder,
H,
Poskanzer,
DC.
(1971)
Adenocarcinoma
of
the
vagina:
association
of
maternal
stilbestrol
therapy
with
tumor
appearance
in young
women.
N
Engl
J
Med
284:878-881.
Hill,
AB.
(1965)
The
environment
and
disease:
association
or
causation?
Proc
R
Soc
Med
58:295—300.
Hoel,
DG;
Kaplan,
NL;
Anderson,
MW.
(1983)
Implication
of
nonlinear
kinetics
on
risk
estimation
in
carcinogenesis.
Science
219:1032—1037.
Holliday,
R. (1987)
DNA
methylation
and
epigenetic
defects
in
carcinogenesis.
Mutat
Res
181:215—217.
Holladay
SD,
Smialowicz RJ.
2000.
Development
of
the
murine
and
human
immune
system:
differential
effects
of
immunotoxicants
depend
on
time
of
exposure.
Environ
Health
Perspect
108
Suppl
3:463-473.
Holsapple
MP,
West
U,
Landreth
KS.
2003.
Species
comparison
of anatomical
and
functional
immune
system
development.
Birth
Defects
Res
B Dev
Reprod
Toxicol
68(4):321-334.
Huff,
JE. (1993)
Chemicals
and
cancer
in
humans:
first
evidence
in
experimental
animals.
Environ
Health
Perspect
100:201-210.
Huff,
JE.
(1994)
Chemicals causally
associated
with
cancers
in humans
and
laboratory
animals.
A perfect
concordance.
In:
Carcinogenesis.
Waalkes,
MP, Ward,
JM,
eds.,
New
York:
Raven
Press;
pp.
25-3
7.
Huff
J,
Cirvello
J,
Haseman
J,
Bucher
J
(1991)
Chemicals
associated
with
site-specific
neoplasia
in
1394
long-term carcinogenesis
experiments
in laboratory
rodents.
Environ
Health
Perspect
93
:247-70.
Erratum
in:
Environ
Health
Perspect
1991
Nov;95:213.
Hulka,
BS;
Margolin,
BH.
(1992)
Methodological
issues
in epidemiologic
studies
using
biological
markers.
Am
J
Epidemiol
135:122—129.
R-6
IARC
(International
Agency
for Research
on
Cancer).
(1994)
IARC
monographs
on
the
evaluation
of
carcinogenic
risks to
humans.
Vol.
60.
Some
industrial
chemicals.
Lyon,
France:
IARC;
pp.
13-33.
IARC.
(International
Agency
for
Research
on Cancer)
(1999)
The use
of short-
and
medium-term
tests
for
carcinogens
and
data
on
genetic
effects
in carcinogenic
hazard
evaluation.
Lyon,
France.
lEc (Industrial
Economics,
Incorporated).
2004.
“An
Expert
Judgment
Study
of
the
Concentration-Response
Relationship
Between
PM2.5
Exposure
and Mortality,”
Available
at:
www.epa.ov/ttn/ecas/benefits.htm1.
ILSI (International
Life
Sciences
Institute).
(1992)
Similarities
and
differences
between
children
and adults;
implications
for risk
assessment.
Washington,
DC:
ILSI
Press.
ILSI
(International
Life
Sciences
Institute).
(1997)
Principles
for
the selection
of doses
in
chronic
rodent
bioassays.
Foran,
JA,
ed. Washington,
DC:
ILSI Press.
ILSI
(International
Life
Sciences
Institute).
(2001)
Proceedings
of workshop
on
the
evaluation
of
alternative
methods
for
carcinogenesis
testing.
Toxicol
Pathol
29:1—351.
IPCS
(International
Programme
on Chemical
Safety).
(1999)
IPCS
workshop
on
developing
a
conceptual
framework
for cancer
risk assessment,
February
16-18,
1999,
Lyon,
France.
IPCS/99.6.
World
Health
Organization,
Geneva.
Ito,
N;
Shirai,
T;
Hasegawa,
R.
(1992)
Medium-term bioassays
for carcinogens.
In: Vainio,
H,
Magee,
PN,
McGregor,
DB,
et
al.,
eds.
Mechanisms
of
carcinogenesis
in
risk
identifications.
International
Agency
for Research
on
Cancer,
Lyon,
France;
pp.
353—388.
Jelovsek
,FR;
Mattison,
DR;
Young,
IF. (1990)
Eliciting
principles
of
hazard
identification
from
experts.
Teratology
42:521-533.
Jones,
PA.
(1986)
DNA
methylation
and
cancer.
Cancer
Res
46:461—466.
Kehrer,
JP.
(1993)
Free
radicals
as
mediators
of tissue
injury
and
disease.
Crit
Rev Toxicol
23:21—48.
Kelsey,
JL; Whittemore,
AS;
Evans,
AS;
Thompson,
WD.
(1996)
Methods
in observational
epidemiology.
New York:
Oxford
University
Press.
Kimbell,
JS;
Subramaniam,
RP;
Gross,
EA; Schlosser,
PM; Morgan,
KT. (2001)
Dosimetry
modeling
of
inhaled
formaldehyde:
comparisons
of
local flux
predictions
in the
rat, monkey
and
human
nasal
passages.
Toxicol
Sci
64(1):100-110.
R-7
Kinzler,
KW;
Vogeistein, B.
(2002)
Colorectal
tumors.
In:
Vogeistein,
B; Kinzler,
KW,
eds.
The
genetic
basis
of
human
cancer.
New
York:
McGraw-Hill.
Kinzler,
KW;
Nilbert,
MC;
Su,
L-K;
et
al.
(1991)
Identification
of
FAP locus
genes
from
chromosome
5q21.
Science
253:661—665.
Kraus,
AL;
Munro,
IC;
Orr,
JC;
et
al. (1995)
Benzoyl
peroxide:
an
integrated
human
safety
assessment
for
carcinogenicity.
Regul
Toxicol
Pharmacol
21:87—1
07.
Krewski,
D;
Van
Ryzin,
J.
(1981)
Dose
response
models
for quantal
response
toxicity
data.
In:
Csorgo;
Dawson;
Rao;
et al.,
eds.
Statistics
and
related
topics.
Amsterdam:
North-Holland,
pp.
201—231.
Krewski,
D;
Murdoch,
DJ;
Withey,
JR.
(1987)
The
application
of
phannacokinetic
data
in
carcinogenic risk
assessment.
In:
Pharmacokinetics
in
risk
assessment:
drinking
water
and
health.
Vol.
8.
Washington,
DC:
National
Academy
Press;
pp.
441—468.
La, DK;
Swenberg,
JA.
(1996)
DNA
adducts:
biological
markers
of
exposure
and potential
applications
to
risk
assessment.
Mutat
Res 365(1-3):129-
46.
Levine,
AJ;
Perry,
ME;
Chang,
A;
et al.
(1994)
The
1993
Walter
Hubert
lecture:
the
role
of the
p53
tumor-suppressor
gene
in
tumorigenesis.
Br
J
Cancer
69:409—416.
Lijinsky,
W.
(1993)
Species
differences
in
carcinogenesis.
In Vivo
7:65-72.
Lilienfeld, AIVI;
Lilienfeld,
D. (1979)
Foundations
of epidemiology,
2nd
ed.
New
York:
Oxford
University
Press.
Littlefield, NA;
Farmer,
JH;
Gaylor,
DW.
(1980)
EDO1
study.
J
Environ
Pathol
Toxicol
3:17.
Maltoni,
C;
Lefemine,
G;
Ciliberti,
A;
et
al.
(1981)
Carcinogenicity
bioassay
of
vinyl
chloride
monomer:
a
model
of risk
assessment
on
an
experimental
basis.
Environ
Health
Perspect
41:3—29.
Maronpot,
RR;
Shimkin,
MB;
Witschi, HP;
et al.
(1986)
Strain
A
mouse
pulmonary
tumor
test
results
for
chemicals previously
tested
in National
Cancer
Institute
carcinogenicity
test.
J Nati
Cancer
Inst
76:1101—1112.
Marsman,
DS;
Popp,
JA.
(1994)
Biological
potential
of basophilic
hepatocellular
foci
and
hepatic
adenoma
induced
by
the
peroxisome
proliferator,
Wy-
14,643.
Carcinogenesis
15:111—117.
Mausner,
JS;
Kramer,
S.
(1985)
Epidemiology,
2nd
ed.
Philadelphia:
W.B.
Saunders.
R-8
McConnell,
EE. (1992)
Comparative
response
in
carcinogenesis
bioassay
as
a
function
of age
at
first
exposure.
In: Guzelian,
P;
Henry,
CJ;
Olin,
SS,
eds.
Similarities
and
difference
between
children
and
adults:
implications
for
risk
assessment.
Washington,
DC:
ILSI
Press;
pp.
66-78.
McConnell,
EE;
Solleveld, HA;
Swenberg,
JA; et
al. (1986)
Guidelines
for
combining
neoplasms
for
evaluation
of rodent
carcinogenesis
studies.
J Nati
Cancer
Inst
76:283—289.
Meek,
ME;
Bucher,
JR;
Chohen,
SM;
Dellarco,
V;
Hill,
RN;
Lehman-McKeeman,
LD;
Longfellow,
DG;
Pastoor,
T; Seed,
J.;
and Patton,
DE. (2003)
A
framework
for humand
relevance
analysis
of
information
on carcinogenic
modes
of
action.
Crit
Rev
Toxicol
33:591
-
653.
Melnick,
RL,
Huff,
JE,
Barrett,
JC,
Maronpot,
RR,
Lucier,
G,
Portier,
CJ.
(1993)
Cell
proliferation and
chemical
carcinogenesis:
A symposium
overview.
Mol
Carcinog
7:135-138.
Miller,
RW.
(1995)
Special
susceptibility
of the
child
to
certain
radiation-induced
cancers.
Environ
Health
Perspect
l03(suppl
6):41—44.
Miller,
MD;
Marty,
MA;
Arcus,
A;
et al.
(2002)
Differences
between
children
and
adults:
implications
for risk
assessment
at
California
EPA.
Tnt
J
Toxicol
21:403-418.
Monro,
A.
(1992)
What
is an appropriate measure
of
exposure
when
testing
drugs
for
carcinogenicity
in
rodents?
Toxicol
Appl
Pharmacol
112:171-181.
Moolgavkar,
SH.
(1986)
Carcinogenesis
Modelin:
From
Molecular
Biology
to Epidemiology.
Am
Rev
Public
Health
7:15
1-169.
Moolgavkar,
SH;
Knudson,
AG.
(1981)
Mutation
and
cancer:
a model
for
human
carcinogenesis.
J
Nati Cancer
Inst
66:1037—1052.
Morgan,
KM;
DeKay,
ML;
Fischbeck,
PS;
Morgan,
MG;
Fischhoff
B;
Florig,
HK.
(2001)
A
deliberative
method
for
ranking
risks
(II):
Evaluation
of validity
and
agreement
among
risk
managers. Risk Anal.
21:923-37.
Morrison,
V; Ashby,
J.
(1994)
A
preliminary
evaluation
of the
performance
of the
muta
mouse
(lacZ)
and Big
BlueTM
(lad)
transgenic
mouse
mutation
assays.
Mutagenesis
9:367—375.
Murdoch,
DJ;
Krewski,
D;
Wargo,
J.
(1992)
Cancer
risk assessment
with
intermittent
exposure.
Risk
Anal
12(4):569—577.
Murrell,
JA;
Portier,
CJ;
Morris,
RW.
(1998)
Characterizing
dose-response
I:
critical
assessment
of
the
benchmark
dose
concept.
Risk
Anal
18(1):13—25.
R-9
NRC (National
Research
Council).
(1983)
Risk assessment
in
the
federal
government:
managing
the
process.
Committee
on
the
Institutional
Means
for
Assessment
of
Risks
to Public
Health,
Commission
on
Life
Sciences,
NRC.
Washington,
DC:
National
Academy
Press.
NRC
(National
Research
Council).
(1990)
Health
effects
of exposure
to low
levels
of
ionizing
radiation
(BEIR
V). Washington,
DC:
National
Academy
Press.
NRC
(National
Research
Council).
(1993a)
Issues
in risk
assessment.
Committee
on Risk
Assessment
Methodology.
Washington,
DC: National
Academy
Press.
NRC
(National
Research
Council).
(l993b)
Pesticides
in
the diets
of
infants
and
children.
Washington,
DC: National
Academy
Press.
NRC
(National
Research
Council).
(1994)
Science
and
judgment
in
risk assessment.
Washington,
DC:
National
Academy
Press.
NRC (National
Research
Council).
(1996)
Understanding
risk: informing
decisions
in a
democratic
society.
Washington,
DC:
National
Academy
Press.
NRC
(National
Research
Council).
(2002)
Estimating
the
public
health
benefits
of
proposed
air
pollution
regulations.
Washington,
DC:
National
Academy
Press.
NTP (National
Toxicology
Program).
(1984)
Report
of the
ad hoc
panel on
chemical
carcinogenesis
testing
and evaluation
of
the
National
Toxicology
Program,
Board
of
Scientific
Counselors.
Washington,
DC:
U.S.
Government
Printing
Office.
1984-421-132:4726.
Nichols,
AL;
Zeckhauser,RJ.
(1986).
The dangers
of
caution:
Conservatism
in
the
assessment
and the
mismanagement
of risk.
In:
Smith,
VK,
ed., Advances
in
Applied
Micro-Economics:
Risk,
Uncertainty,
and
the
Valuation
of Benefits
and
Costs, Vol.
4, Greenwich,
Conn.:
JAI
Press,
pp.
55-82.
North,
DW;
Merkhofer,
MW.
(1976).
A
methodology
for
analyzing
emission
control
strategies.
Comput
Oper
Res
3:187-207.
OECD
(Organization for Economic
Cooperation
and
Development).
(1981)
Guidelines
for
testing
of
chemicals.
Carcinogenicity
studies.
No. 451.
Paris,
France.
0MB
(Office
of Management
and
Budget).
(2002)
Guidelines
for ensuring
and
maximizing
the
quality,
objectivity,
utility,
and
integrity
of information
disseminated
by
federal
agencies.
Federal
Register
67(36):
8451-8460.
Available
from:
http://www.epa.gov/oei/gualitygu
idelines/fr22fe02-
II 7.htm.
0MB
(Office
of Management
and Budget).
(2003)
Circular
A-4:
Regulatory
Analysis.
September
17.
Available
from:
http://www.whitehouse.gov/omb/circulars!a004/a-4.pdf
R-l0
0MB
(Office
of
Management
and
Budget).
(2004)
Revised
information
quality
bulletin
for
peer
review.
April
15. Available
from:
ht://www.whitehouse.ov/ornb/inforeu/peer
review04
1404.pdf.
OSTP
(Office
of Science
and Technology
Policy).
(1985)
Chemical
carcinogens:
review
of the
science and
its associated
principles.
Federal
Register
50:10372-10442.
Peltomäki,
P; Aaltonen,
LA;
Sisonen,
P; et
al.
(1993)
Genetic
mapping
of
a locus
predisposing
human
colorectal
cancer.
Science
260:810—812.
Peto,
J. (1992)
Meta-analysis
of
epidemiological
studies
of carcinogenesis.
In:
Mechanisms
of
carcinogenesis
in
risk
assessment.
IARC
Sci.
Pubs.
No. 116, Lyon,
France;
pp.
57
1—577.
Peto,
J; Darby,
S.
(1994)
Radon
risk
reassessed.
Nature
368:97—98.
Peto,
R; Gray,
R;
Brantom,
P;
et al.
(1984)
Nitrosamine
carcinogenesis
in
5120
rodents:
chronic
administration
of
sixteen
different
concentrations
of NDEA,
NDMA,
NPYR
and NPIP
in
the
water of 4440
inbred rats,
with parallel
studies
on NDEA
alone of
the effect
of age
of starting
(3,6,
or 20
weeks)
and
of
species
(rats, mice
or hamsters).
IARC
Sci PubI
57:627—665.
Pinkerton,
KE;
Joad, J.
(2000)
The
mammalian
respiratory
system
and critical
windows
of
exposure
for
children’s
health. Environ
Health
Perspect
1 08(suppl):457—462.
Portier,
C.
(1987)
Statistical
properties
of a
two-stage
model
of carcinogenesis.
Environ
Health
Perspect
76:125—131.
Putzrath,
RM; Ginevan,
ME (1991)
Meta-analysis:
Methods
for combining
data
to improve
quantitative
risk
assessment.
Regul Toxicol
Pharmacol
14:178-188
Rall, DP.
(1991) Carcinogens
and
human health:
part 2.
Science
25 1:10—11.
Renn,
0.
(1999)
Model
for
an analytic-deliberative
process
in
risk management.
Environ
Sci Technol
33:3049-3055.
Renwick,
AG.
(1998) Toxicokinetics
in infants
and
children
in relation
to
the
ADI and
TDI.
Food
Addit
Contam
15,
Suppl 17—35.
Rice,
JM.
(1979) Problems
and
perspective
in perinatal
carcinogenesis:
a summary
of the
conference.
NCI
Monogr
51:271-278.
Richard,
AM. (1
998a) Structure-based
methods
for
predicting
mutagenicity
and
carcinogenicity:
are
we there
yet?
Mutat
Res 400:493-507.
R-11
Richard,
AM, (1
998b)
Commercial
toxicology
prediction
systems:
A regulatory
perspective.
Toxicol.
Left.
102-103:611-616.
Richard,
AM;
Williams,
CR.
(2002)
Distributed
structure-searchable
toxicity
(DSSTox)
public
database
network:
a
proposal:
Mutat.
Res.
499:27-52.
Richmond,
HM.
(1981).
A
framework
for assessment
of
health
risks
associated
with
national
ambient
air
quality
standards.
Environ
Prof
3:225-234.
Rothman,
KJ;
Greenland,
S.
(1998)
Modem
Epidemiology.
Philadelphia:
Lippincott
Williams
and
Wilkins
Publishers.
Rouse,
J;
Jackson,
SP.
(2002)
Interfaces
between
the detection,
signaling,
and
repair
of
DNA
damage.
Science
297:547—551.
Samet,
JM;
Schnatter,
R;
Gibb,
H.
(1998)
Invited
Commentary:
Epidemiology
and risk
assessment. Am
J
Epidemiol
148:929-93
6.
Scheuplein,
R;
Chamley,
G;
Dourson,
M.
(2002)
Differential
sensitivity
of children
and
adults
to
chemical
toxicity.
I:
biological
basis.
Regul
Toxicol
Pharmacol
35:429-447.
SAB
(Science
Advisory
Board).
(1997)
An
SAB
report:
guidelines
for cancer
risk
assessment.
Washington
DC:
U.S.
Environmental
Protection
Agency,
September.
EPA-SAB-EHC-97-010.
Available
from:
http://www.epa.gov/sab/pdf!ehc97
1 0.pdf
Shelby,
MD;
Zeiger,
E. (1990)
Activity
of
human
carcinogens
in
the
Salmonella
and rodent
bone-marrow
cytogenetics
tests.
Mutat
Res 234:257—261.
Silberstein,
GB.
(2001)
Tumour-stromal
interactions:
role
of
the stroma
in mammary
development.
Breast
Cancer
Res
3:218-223.
Slikker
W,
3rd,
Mei
N,
Chen
T. 2004.
N-ethyl-N-nitrosourea
(ENU)
increased
brain
mutations
in
prenatal
and
neonatal
mice
but not
in the
adults.
Toxicol
Sci 81(1):112-120.
Sisk,
SC;
Pluta,
U;
Bond,
JA;
et
al.
(1994)
Molecular
analysis
of
lad
mutants
from
bone
marrow
of
B6C3F1
transgenic
mice
following
inhalation
exposure
to
1 ,3-butadiene.
Carcinogenesis
1 5(3):47
1—477.
Snedecor,
GW;
Cochran,
WG.
(1967)
Statistical
methods,
6th
ed. Ames,
Iowa:
Iowa
State
University Press.
Sonich-Mullin,
C;
Fielder,
R; Wiltse,
J;
Baetcke,
K;
Dempsey.
K;
Fenner-Crisp,
P;
Grant,
D;
Hartley,
M;
Knaap,
A;
Kroese,
D;
Mangelsdorf,
I; Meek,
E;
Rice,
TM;
and
Yones,
M.
(2001)
IPCS
conceptual
framework
for
evaluating a
mode
of
action
for
chemical
carcinogenesis.
Regul
Toxicol
Phamacol
34:146-152.
R-12
Spalding,
JW;
French,
JE; Stasiewicz,
S;
Furedi-Machacek,
M; Conner,
F;
Tice,
RR;
Tennant,
RW.
(2000)
Responses
of
transgenic
mouse
lines
p53(+I-)
and Tg.AC
to agents
tested
in
conventional
carcinogenicity
bioassays.
Toxicol
Sci
53(2)213-223.
Stiber,
NA;
Pantazidou,
M;
Small,
MJ.
(1999)
Expert
system
methodology
for
evaluating
reductive
dechlorination
at
TCE
sites.
Environ
Sci Technol
33:3012-3020.
Stiteler,
WH;
Knauf
LA;
Hertzberg,
RC;
et
al.
(1993)
A
statistical
test
of compatibility
of data
sets
to a
common
dose-response
model.
Regul
Toxicol
Pharmacol
18:392—402.
Subramaniam,
RP;
Asgharian,
B;
Freijer,
ii;
Miller,
FJ; Anjilvel,
S.
(2003)
Analysis
of
differences
in particle
deposition
in
the
human
lung.
Inhal
Toxicol
15:1-21.
Swierenga,
SHH;
Yamasaki,
H. (1992)
Performance
of
tests for
cell
transformation
and
gap
junction
intercellular
communication
for
detecting
nongenotoxic
carcinogenic
activity.
In:
Mechanisms
of
carcinogenesis
in
risk
identification.
IARC
Sci. Pubs.
No.
116,
Lyon,
France;
pp.
165—193.
Szklo,
M; Nieto,
FJ.
(2000):
Epidemiology
Beyond
the
Basics.
Gaithersburg,
MD:
Aspen
Publishers,
Inc.
Tarone,
RE.
(1982)
The
use
of
historical
control
information
in
testing
for
a trend
in
proportions.
Biometrics
38:215—220.
Taylor,
JH;
Watson,
MA;
Devereux,
TR;
et
al. (1994)
p
53
mutation
hotspot
in
radon-associated
lung
cancer.
Lancet
343:86—87.
Tennant,
RW.
(1993)
Stratification
of
rodent
carcinogenicity
bioassay
results
to
reflect
relative
human
hazard.
Mutat
Res
286:111—118.
Tennant,
RW;
French,
JE;
Spalding,
JW.
(1995)
Identifying
chemical
carcinogens
and
assessing
potential
risk
in
short-term
bioassays
using
transgenic
mouse
models.
Environ
Health
Perspect
103:942—950.
Tennant,
RW;
Stasiewicz,
5; Mennear,
J;
et
al. (1999)
Genetically
altered
mouse
models
for
identifying
carcinogens.
In: McGregor,
DB;
Rice,
IM;
Venitt,
5,
eds.
The
use
of
short-
and
medium-term
tests
for
carcinogens
and
data
on
genetic
effects
in
carcinogenic
hazard
evaluation.
Lyon,
France:
International
Agency
for
Research
on
Cancer.
Tinwell,
H;
Ashby,
J.
(1991)
Activity
of
the human
carcinogen
MeCCNTJ
in
the
mouse
bone
marrow
mironucleus
test.
Environ
Molec
Mutagen
17:152—154.
Todd,
GC.
(1986)
Induction
of
reversibility
of thyroid
proliferative
changes
in rats
given
an
antithyroid compound.
Vet
Pathol
23:110—117.
R-13
Tomatis,
L;
Aitio,
A; Wilboum,
J; et al.
(1989)
Human
carcinogens
so far identified.
Jpn J
Cancer
Res
80:795—807.
Tucker,
JD;
Preston,
RJ. (1996)
Chromosome
aberrations,
micronuclei,
aneuploidy,
sister
chromatid
exchanges,
and
cancer
risk
assessment.
Mutat
Res 365(1-3):147-59.
Review.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
986a)
Guidelines
for
carcinogen
risk
assessment.
Federal
Register
51(1
85):33
992—34003.
Available
from:
http://www.epa.gov/nceatrafY.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
986b) Guidelines
for
mutagenicity
risk
assessment.
Federal
Register
51(1
85):34006-34012.
Available
from:
http://cfpub.epa.ov/ncea/ra17recordisp1ay.c:Em
?deid23
160.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1989)
Summary
of
the second
workshop
carcinogenesis
bioassay
with
the
dermal
route.
May
18-19,
1988,
Research
Triangle
Park,
NC.
EPA/560/6-89/003.
Available
from
NTIS,
Springfield,
VA
22161.
U.S. EPA
(U.
S.
Environmental
Protection
Agency).
(1991
a)
Guidelines
for
developmental
toxicity
risk
assessment.
Federal
Register
56(234):63798-63826.
Available
from:
http://cfpub.epa.ov/ncea/raf/recordisp1ay.cfin?deid=23
162.
U.S.
EPA
(U.S.
Environmental Protection
Agency).
(1
991b)
Alpha-2u-globulin:
association
with
chemically
induced
renal
toxicity
and
neoplasia
in the male
rat.
Risk
Assessment
Forum,
Washington,
DC.
EPA/625/3-9
1/01
9F.
U.S.
EPA
(U.S.
Environmental Protection
Agency).
(1 992a)
Guidelines
for
exposure
assessment.
Federal
Register
57(1
04):22888-2293
8.
Available
from:
http://cfpub.epa.gov/ncea/raflrecord
isplay.cfm?deid=
15263.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
992b)
Draft
report:
a
cross-species
scaling
factor
for
carcinogen
risk assessment
based
on equivalence
of
mg/kg
314
/day.
Federal
Register
57(109):24152-24173.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1994)
Methods
for
derivation
of inhalation
reference
concentrations
and
application
of
inhalation
dosimetry.
Office
of
Health
and
Environmental Assessment,
Environmental
Criteria
and
Assessment
Office,
Research
Triangle
Park,
NC.
EPAJ600/8-90/066F.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1995)
Policy
for
risk
characterization.
Memorandum
of
Carol
M.
Browner,
Administrator,
March
21, 1995,
Washington,
DC.
Available
from:
http:!/www.epa.gov/osp/spc/2riskchr.htrn.
R-l4
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
996a)
Guidelines
for
reproductive
toxicity
risk
assessment.
Federal
Register
61(212):56274-56322.
Available
from:
http://cfpub.epa.gov/ncea/raflrecordisplay.cfin?deid=283
8.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
996b)
Comparison
of
the
effects
of
chemicals
with
combined
perinatal
and
adult
exposure
vs.
Adult
only
exposure
in
carcinogenesis
studies.
Office
of
Pesticide
Programs.
Washington,
DC.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1997a)
A
proposed
OPP
policy
on
determining
the
need
for
perinatal
carcinogenicity
testing
on
a
pesticide.
Office
of
Pesticide
Programs.
Washington,
DC.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1997b)
A
set
of
scientific
issues
being
considered
by
the
Agency
in
connection
with
the
criteria
for
requiring
in-utero
cancer
studies.
Office
of
Pesticide
Programs.
FIFRA
Scientific
Advisory
Panel.
September
1997
meeting
report.
Available
from:
http:/!www.epa.gov/pesticides/SAP/archive/september/finalsep.htm.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
997c)
Exposure
factors
handbook.
National
Center
for
Environmental
Assessment,
Washington,
DC.
EPAI600/P-95/002F.
Available
from:
http://cfpub.epa.gov/nceaicfm/recordisplay.cfin?deid=1
2464.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
997d)
Policy
for
use
of
probabilistic
analysis
in
risk
assessment.
Memorandum
of
Fred
Hansen,
Deputy
Administrator,
May
15,
1997.
Available
from:
http://www.epa.gov/osp/spc!probpol.htm.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
997e)
Guiding
principles
for
Monte
Carlo
analysis.
Risk
Assessment
Forum,
Washington,
DC.
EPAJ63O/R-97/00
1.
Available
from:
http:!/cfpuh.epa.gov/ncea!raf’record
isplay.cfln?deid29596.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
998a)
Assessment
of
thyroid
follicular
cell
tumors.
Risk
Assessment
Forum,
Washington,
DC.
EPA!630/R-97/002.
Available
from:
http:/icfpub.epa.govincea/rafirecordisplav.cfm’?deid=i
3102.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
998b)
Guidelines
for
neurotoxicity
risk
assessment.
Federal
Register
63(93):26926-26954.
Available
from:
http://cfpuh.epa.ov/nceaJrafJrecord
isplav.cfrn?deid=
12479.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
998c)
Health
effects
test
guidelines:
OPPTS
870.4300
combined
chronic
toxicity/carcinogenicity.
Office
of
Prevention,
Pesticides
and
Toxic
Substances,
Washington,
DC.
EPAI7
1
2/C-98/2
12.
Available
from:
http://www.cpa.gov/opDtsfrs/OPPTS
Flarmonized/870_Health
Effects
Test
Guidelines/Series/
R-15
U.S. EPA
(U.S.
Environmental
Protection
Agency).
(1
998d)
EPA’s
rule writer’s
guide
to
Executive
Order
13045. Available
from:
http://vosemite.epa.ov/ochp/ochpweb.nsf/content/whaewe
rezulate. htm
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1
999a) Guidelines
for
carcinogen
risk
assessment
(review
draft).
Risk
Assessment
Forum,
Washington,
DC.
NCEA-F-0644.
Available
from: httr://www.epa.gov/ncea/rat7cancer.htm.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(1 999b) Review
of
revised
sections
of the
proposed
guidelines
for
carcinogen
risk
assessment.
Science
Advisory
Board,
Washington,
DC.
EPA/SAB/EC-99/0
15.
Available
from:
http://www.epa.gov/ncea/raficancer.htrn.
U.S. EPA
(U.S.
Environmental
Protection
Agency).
(1
999c)
Cancer
risk coefficients
for
environmental
exposure
to
radionuclides:
federal
guidance
report no.
13.
Office
of Air
and
Radiation.
EPA/402/R-99/00
1. Available
from:
http://www.epa.gov!radiation/federal.
U.S. EPA
(U.S.
Environmental
Protection
Agency).
(2000a) Science
Policy
Council
handbook:
peer review.
Office
of Research
and Development,
Office
of Science
Policy,
Washington,
DC.
EPA/l
00/B-98/00
1.
Available
from:
http://www.epa.gov/osp/spc/prhandbk.pdf.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2000b)
U.S.
EPA.
Science
Policy
Council
handbook:
risk
characterization.
EPA
Science Policy
Council,
Washington,
DC.
EPA/i
00/B-
00/002.
Available
from: http://www.epa.ov/osp/spc/rchandbk.pdf
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2000c)
Supplementary
guidance
for
conducting
health
risk assessments
of chemical
mixtures.
Risk Assessment
Forum,
Washington,
DC. EPA/630/R-00/002.
Available
from:
http://cfoub.epa.gov/ncea!ratYrecordisplay.cfin?deid=20533.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2000d)
Guidance
for
data
quality
assessment:
practical
methods
for
data analysis.
Office
of Environmental
Information,
Washington,
DC.
EPA/600/R-96/084.
Available
from:
http://www.epa.gov/gualitv/gs-docs/g9-final.pdf.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2000e)
EPA
quality
manual
for
environmental
programs
5360 Al.
Available
from: http://www.epa.gov/qualitv/qs
docs/5360.pdf.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(200la)
Health
effects
test
guidelines.
Combined
chronic
toxicity/carcinogenicty
testing of
respirable
fibrous
particles.
OPPTS
870.8355.
Available
from: http://www.epa.cov/opptsfrs/horne/guidelin.htm.
R-16
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2001b)
Notice
of
opportunity
to provide
additional
information
and
comment.
Fed
Reg
66:59593-59594.
Available
from:
http://cfpub.eia.gov/nceai’raf7recordisp1ay.cfm?deid=55868.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2002a)
Guidelines
for
ensuring
and
maximizing
the
quality,
objectivity,
utility
and
integrity
for
information
disseminated
by the
Environmental
Protection
Agency.
Office
of
Environmental
Information,
Washington,
DC.
EPA!260/R-02/008.
Available
from:
http://www.epa.gov/oei/qualitvguidelines/index.html.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2002b)
A
review
of
the
reference
dose
and
reference
concentration
process
. Risk
Assessment
Forum,
Washington,
DC.
EPAJ63O/P-
02/002F.
Available
from:
http://cfpub.epa.gov/ncea/raf/recordisplav.cfm?deid=553
65.
U.S.
EPA
(U.S.
Environmental
Protection
Agency). (2002c)
Workshop
on
the
benefits
of
reductions
in
exposure
to hazardous
air
pollutants:
developing
best
estimates
of
dose-response
functions.
Science
Advisory
Board,
Washington,
DC.
EPAJSAB-EC/WKSHP/02/00
1.
Available
from:
http:!/www.epa.gov/science
i/fiscai02.htm.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2002d)
Child-specific
exposure
factors
handbook (interim
report).
EPAJ600/P-00/002B.
Office
of
Research
and Development,
National
Center
for
Environmental
Assessment,
Washington,
DC,
448
pp.
Available
from:
.http://cfpub.epa.ovtncea/cfm!recordisplay.cfin?deid=55
145.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2003)
A
summary
of
general
assessment
factors
for
evaluating
the
quality
of
scientific
and
technical
information.
Science
Policy
Council,
Washington, DC.
EPA
l00/B-03/00l.
Available from:
http://www.epa.gov/osa/spc/htmiassess2.pdf.
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2004).
Final
Regulatory
Analysis:
Control
of Emissions from
Nonroad
Diesel
Engines.
Prepared
by
U.S.
EPA,
Office
of Transportation
and
Air
Quality,
Washington,
DC,
May;
EPA report
no. EPA42O-R-04-007.
See
chapter
9 and
Appendix
B.
Available
from:
http://www.epa.gov/nonroad-dieselI2004fr.htm#ria
U.S.
EPA
(U.S.
Environmental
Protection
Agency).
(2005)
Supplemental
guidance
for
assessing
cancer
susceptibility
from
early-life
exposure
to
carcinogens.
Risk
Assessment
Forum,
Washington, DC.
Available
from:
http:I/www.epa.gov/ncea/raf.
Vainio,
H;
Magee,
P;
McGregor,
D; et
al. (1992)
Mechanisms
of
carcinogenesis
in
risk
identification.
IARC
Sci.
Pubs.
No.
116.
Lyon,
France:
IARC.
Van Der
Fels-Klerx,
IHJ;
Goossens,
LHJ;
Saatkamp,
HW;
Horst,
SHS.
(2002)
Elicitation
of
quantitative
data
from
a
heterogeneous
expert
panel:
formal
process
and
application
in animal
health.
Risk
Anal.22:67-81.
R-17
Van
Sittert,
NJ;
De
Jong,
G;
Clare,
MG;
et al.
(1985)
Cytogenetic,
immunological,
and
hematological
effects
in workers
in an
ethylene
oxide
manufacturing
plant.
Br
J
Indust
Med
42:19—26.
Vater,
ST;
McGinnis,
PM;
Schoeny,
RS; et
al. (1993)
Biological
considerations
for
combining
carcinogenicity
data
for
quantitative
risk
assessment.
Regul.
Toxicol
Pharrnacol
18:403—418.
Vesselinovitch,
SD; Rao,
KVN;
Mihailovich,
N. (1979)
Neoplastic response
of
mouse
tissues
during
perinatal
age
periods
and
its significance
in
chemical
carcinogenesis.
NCI
Monogr
51:239.
Vogelstein,
B;
Fearon,
ER;
Hamilton,
SR;
et
al. (1988)
Genetic
alterations
during
colorectal
tumor
development.
N
Eng
J
Med
319:525—532.
Walker,
KD;
MacIntosh,
D; Evans,
JS.
(2001)
Use
of
expert
judgment
in exposure
assessment.
Part
I.
Characterization
of
personal
exposure
to benzene.
J Exposure
Environ
Epidemiol
11:308-
322.
Walker,
KD;
Catalano,
P; Hammitt,
JK;
Evans,
JS.
(2003)
Use
of
expert
judgment
in
exposure
assessment: part
2.
Calibration
of
expert
judgments
about
personal
exposures
to
benzene.
J Expo
Anal
Environ
Epidemiol.
13:1-16.
Waters,
MD;
Stack,
H; F.Jackson,
MA.
(1999)
Short-term
tests
for defining
mutagenic
carcinogens.
In: McGregor,
DB;
Rice,
JM;
Venitt,
5,
eds.
The
use
of
short
term
tests
for
carcinogens
and
data
on
genetic
effects
in
carcinogenic
hazard
evaluation.
Lyon,
France:
International
Agency
for
Research
on
Cancer.
IARC
Sci. Pubi.
No.
146,
pp.
499
-
536
.
Whitfield, RG;Wallsten,
TS.
(1989).
A
risk
assessment
for
selected
lead-induced
health
effects:
an
example
of
a general
methodology.
Risk
Anal.
9:197-208.
Whysner,
J;
Williams,
GM.
(1996)
Saccharin
mechanistic
data and
risk
assessment:
urine
composition, enhanced
cell proliferation,
and tumor
promotion.
Pharmacol
Ther
71:225:252.
Willis,
HH;
DeKay,
ML;
Morgan,
MG;
Florig,
HK;
Fischbeck,
PS.
(2004)
Ecological
risk
ranking:
development
and
evaluation
of
a method
for
improving
public
participation
in
environmental
decision
making,
Risk
Anal.
24:363-78.
Winkler,
RL;
Wallsten,
TS;
Whitfield,
RG;
Richmond,
HM;
Rosenbaum,
AS.
(1995).
An
assessment
of
the
risk of
chronic
lung
injury
attributable
to
long-term
ozone
exposure.
Operations
Research
43:19-28.
Woo,
YT;
Arcos,
JC.
(1989)
Role
of
structure-activity
relationship
analysis
in evaluation
of
pesticides
for
potential
carcinogenicity.
In:
Ragsdale,
NN;
Menzer,
RE,
eds. Carcinogenicity
and
R-18
pesticides:
principles,
issues,
and
relationship.
ACS
Symposium
Series
No. 414.
San Diego:
Academic
Press;
pp.
175—200.
Yamasaki,
H.
(1995)
Non-genotoxic
mechanisms
of carcinogenesis:
Studies of cell
transformation
and
gap junctional
intercellular
communication.
Toxicol Lett
77:55—61.
Zeckhauser,
RJ; Viscusi,
WK.
(1990). Risk
Within Reason,
Science 248:559-564.
R-19