Slide
1
of
84
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005
Slide
2
of
84
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Background
and
Overview
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Background
and
Overview
Dr.
Anna
Lowit
Health
Effects
Division
Office
of
Pesticide
Programs
Dr.
Anna
Lowit
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
3
of
84
Introduction

Fourth
in
a
series
of
scientific
peer
reviews
regarding
cumulative
risk
assessment
of
the
N­
methyl
carbamates

Previous
reviews:

°
December
2003:


Physiologically­
Based
Pharmacokinetic/

Pharmacodynamic
Modeling:
Preliminary
Evaluation
and
Case
Study
for
the
N­
Methyl
Carbamate
Pesticides
°
December
2004:


The
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:
Strategies
and
Methodologies
for
Exposure
Assessment
°
February
2005:


N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Pilot
Cumulative
Analysis
Slide
4
of
84
Background
Presentation

A
"
roadmap"
for
the
documents
and
presentations
for
the
four
day
SAP
meeting

Scope
of
the
preliminary
CRA

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(

Multipathway
Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
5
of
84
Slide
6
of
84
Cumulative
Risk
Assessment
Background

Food
Quality
Protection
Act
(
1996)

°
Requires
EPA
to
take
into
account
when
setting
pesticide
tolerances:

"
available
evidence
concerning
the
cumulative
effects
on
infants
and
children
of
such
residues
and
other
substances
that
have
a
common
mechanism
of
toxicity."
Slide
7
of
84
Cumulative
Risk
Assessment
Background

Aggregate
risk
assessment:

°
Single
chemical,
multipathway
°
Includes
various
durations
of
exposure
and
variety
of
toxicity
endpoints

Cumulative
risk
assessment:

°
Multichemical,
multipathway
°
Limited
to
the
common
mechanism
endpoint
Slide
8
of
84
General
Steps
Outlined
in
the
OPP
Cumulative
Guidance

Identify
common
mechanism
effect
and
common
mechanism
group
(
CMG)


Identify
cumulative
assessment
group
(
CAG)


Determine
relevant
exposure
scenarios/
pathways

Consider
appropriate
method(
s)


Conduct
cumulative
risk
assessment
Nerve
Axon
U.
S.
EPA
2001
Policy
Paper
Determining
the
Common
Mechanism
Group
(
CMG)


Identified
based
on:

°
Structural
characteristics
°
AChE
inhibition
by
carbamylation
of
serine
hydroxyl
group
°
AChE
inhibition
in:


Brain,


Peripheral
nervous
system,


Surrogate
measures
in
blood
(
RBC
or
plasma)
Slide
9
of
84
Slide
10
of
84
Pesticides
Included:


Aldicarb/
Aldoxycarb

Carbaryl

Carbofuran

Formetanate
HCl

Methiocarb

Methomyl

Oxamyl

Pirimicarb

Propoxur

Thiodicarb
Slide
11
of
84
Develop
Exposure
Scenarios
for
CAG

Use
available
information
to
develop
exposure
scenarios
°
Food

USDA
Pesticide
Data
Program

FDA
Monitoring
Data
°
Drinking
Water

Use/
Usage
Information

Labels

PRZM­
EXAMS
and
ground
water
model
°
Residential

Use/
Usage
Information

Labels
Slide
12
of
84
General
Steps
Outlined
in
the
OPP
Cumulative
Guidance

Identify
common
mechanism
effect
and
common
mechanism
group
(
CMG)


Identify
cumulative
assessment
group
(
CAG)


Determine
relevant
exposure
scenarios/
pathways

Consider
appropriate
method(
s)


Conduct
cumulative
risk
assessment
Slide
13
of
84

February
2005
SAP:

°
Focused
on
methods
development
and
proposed
approaches

Current
SAP
Meeting:

°
Application
of
methods
and
approaches
°
Risk
characterization
Purpose
and
Scope
Slide
14
of
84

Key
Issues
NOT
yet
addressed:

°
Extrapolation
and
Uncertainty
Factors

Intraspecies,
Interspecies

FQPA
10X
Factor
°
Sensitivity
analyses
for
food,
water,

and
residential
still
on­
going
°
Residential
exposure
with
LifeLineTM
and
CARESTM
°
Percentile
of
exposure
for
regulation
Purpose
and
Scope
Slide
15
of
84
Session
1:

Hazard
Assessment

Collaborative
efforts
of
OPP
and
ORDNHEERL
ORD­
NCCT

Relies
on
rat
toxicity
studies
for
estimation
of
relative
potency

Issues:

°
Dose
and
time­
response
modeling

Empirical
modeling
&
Data
used
°
Index
chemical
°
Endpoint
selected
for
Relative
Potency
Factors
and
Points
of
Departure
Slide
16
of
84
Session
2:

Drinking
Water
Exposure

Surface
water
exposure
estimates
similar
to
OP
methods
°
Estimated
exposures
from
surface
water
do
not
contribute
substantially
to
cumulative
exposure

Ground
water
exposure
estimates
using
3
models
(
new
for
NMC)

°
Estimates
for
shallow
private
wells
in
highly
permeable
soils
°
Estimates
to
other
localized
areas
Slide
17
of
84
Session
2:

Drinking
Water
Exposure

Seeking
panel
input
on:

°
Revised
conceptual
model
for
ground
water
sources
of
drinking
water
°
Comparisons
of
the
three
ground
water
models
°
Extrapolation
of
ground
water
model
exposure
estimates
to
other
potential
high
exposure
areas
Slide
18
of
84
Session
3:

Food
&
Residential
Exposure

Methodology
similar
to
OP
assessment

Four
age
groups
°
Children
1­
2,
Children
3­
5,
Adults
20­
49,
Adults
50+


Three
models
°
DEEM/
Calendex
 
,
LifeLine
 
,

CARES
 
Slide
19
of
84
Session
3:

Food
&
Residential
Exposure

Seeking
panel
input
on:

°
Methodologies
and
assumptions
°
Statistical
and
related
issues
involving
the
selection
and
parameterization
of
distributions
°
Information
sources
used
in
the
preliminary
residential
assessment
Slide
20
of
84
Session
4:

Cumulative
Analysis
&
Risk
Characterization

Methodology
similar
to
OP
assessment

Three
pathways
°
Food,
drinking
water,
residential

Four
age
groups
°
Children
1­
2,
Children
3­
5,
Adults
20­
49,

Adults
50+


Multipathway
results
for
DEEM/
Calendex
 
°
Analyses
with
LifeLine
 
and
CARES
 
are
on­
going
Slide
21
of
84
Session
4:

Cumulative
Analysis
&
Risk
Characterization

Seeking
panel
input
on:

°
Issues
associated
with
characterization
and
timeframes
of
exposure
°
Additional
data
sources
which
may
enhance
the
assessment

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(

Multipathway
Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
22
of
84
Slide
23
of
84
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Public
Comments
Public
Comments

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(

Multipathway
Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
24
of
84
Slide
25
of
84
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Hazard
Assessment:

Introduction
Session
1
Preliminary
N­
Methyl
Carbamate
Cumulative
Hazard
Assessment:

Introduction
Dr.
Anna
Lowit
Health
Effects
Division
Office
of
Pesticide
Programs
Dr.
Anna
Lowit
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
26
of
84
Introduction

Preliminary
CRA
relies
on
the
Relative
Potency
Factor
(
RPF)
method

Ongoing
research
effort
to
develop
PBPK/
PD
model
for
carbaryl
°
Presentation
at
02/
05
SAP
meeting
°
Insufficient
data
to
develop
multichemical/
multipathway
PBPK
or
BBDR
model
°
Lessons
learned
will
aid
the
Agency
in
future
assessments

EPA
encourages
the
development
of
PK
and
PD
data
to
characterize
internal
dosimetry
and
timing
of
toxic
effects
Slide
27
of
84
Introduction

Relative
Potency
Factor
Method

Relative
toxic
potency
of
each
chemical
was
calculated
in
comparison
to
"
index
chemical"


Exposure
equivalents
of
index
chemical
are
combined
in
the
cumulative
risk
assessment
Slide
28
of
84
Session
1:
Topics

Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates

Laboratory
Studies
with
the
NMethyl
Carbamate
Insecticides

Relative
Potency
Factors
and
Points
of
Departure
Slide
29
of
84
Session
1
Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates
Session
1
Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates
Dr.
R.
Woodrow
Setzer
National
Center
for
Computational
Toxicology
Office
of
Research
and
Development
Dr.
R.
Woodrow
Setzer
National
Center
for
Computational
Toxicology
Office
of
Research
and
Development
Slide
30
of
84
Some
Responses
to
February,
2005
SAP
Comments

Dose
model
may
be
overparameterized
°
Preliminary
analysis
of
"
estimability"
triggers
default
values
of
some
parameters

Possible
need
for
dose­
time
interaction
term
(
product
term
suggested)

°
Interaction
term
included
when
designs
allowed

Greater
emphasis
on
statistical
hypothesis
testing
°
Models
simplified
from
"
full"
to
"
reduced"
using
likelihood
ratio
tests
and
Wald
tests
of
linear
combinations
of
fixed
parameters
as
appropriate

Possibly
use
a
simpler
model
for
time
component
°
Biphasic
model
replaced
with
a
model
giving
exponential
decay
of
inhibition
Slide
31
of
84
What
Do
We
Want
From
Modeling?

1.
Estimates
of
potency
and
relative
potency
for
AChE
inhibition
2.
Estimates
of
recovery
rate
or
half­
life
of
AChE
inhibition
3.
Tissues:
brain
and
RBC
Slide
32
of
84
What
Kind
of
Data
Do
We
Have?


Designs:

°
Dose­
response:


Measures
of
AChE
activity
at
the
time
of
peak
effect
for
a
range
of
doses
(
multiple
tissues,

one
or
both
sexes)

°
Time­
course:


Measures
of
AChE
activity
at
one
or
a
few
dose
levels
at
several
time
points
soon
after
a
single
gavage
exposure
°
Repeated
gavage
exposures
on
a
subchronic
time
scale

Possibly
with
time
course
information
for
some
of
them
Slide
33
of
84
What
Kind
of
Data
Do
We
Have?


Data
Characteristics
°
For
most
chemicals,
we
have
more
than
one
study,
usually
a
mix
of
design
types
°
Aggregated
(
means,
standard
deviations,

sample
sizes)
for
brain
AChE,
individual
data
for
RBC
AChE
°
Units
of
AChE
activity
vary
among
studies
°
Background
(
control)
AChE
activity
varies
among
studies

Even
for
the
same
species­
strain
°
In
time­
course
designs,
there
are
few
if
any
measurements
before
the
time
of
peak
effect
Slide
34
of
84
Strategy

Develop
a
single
model
that
describes
AChE
activity
as
a
function
of
dose
and
time
post
dosing

Parameters
include:

°
log(
BMD)
=
lBMD
for
10%
(
or
other
specified)

inhibition
°
Recovery
half­
life

Fit
the
model
to
all
relevant
data­
sets,
treating
some
of
the
variation
among
data­
sets
as:

°
Random

e.
g.
lBMD
°
Fixed
effects

With
specific
values
for
each
data­
set
and
sex

e.
g.
background
levels

This
gives
a
nonlinear
mixed­
effects
model
Slide
35
of
84
The
Model

Inhibition
is
the
product
of
a
function
that
depends
only
on
dose
and
estimated
parameters,
and
a
function
that
depends
only
on
time
and
estimated
parameters:


Activity
is
(
ignoring
the
parameters
for
the
moment):
(
)
(
)

1
A
g
d
h
t


×
 
×


(
)

;
,
,
,
(
;
)

Fractional
Inhibition
R
R
g
d
R
P
D
h
t
T
 
=
×
Slide
36
of
84
Time­
Course
Model

Time
course
for
fractional
inhibition
represents
exponential
decay
of
the
inhibition:

 
represents
the
time
of
the
first
sample
ln(
2)

(
;
)
R
t
T
R
h
t
T
e
 
 

 

=

e
R
lT
R
T
=
Slide
37
of
84
Alternative
Form
of
Time­

Course
Model

In
this
parameterization,

TR
is
a
"
recovery"
half­
life,

and
TA
is
an
"
absorption"
half­
life
(
)
(
)

(
)(
)

(
)
(
)
(
)

(
)

(
)(
)

(
)
(
)

*
*

ln
2
ln
2
0
*
*

0
ln
2
ln
2
(
)
ln
ln
ln
2
1
ln
2
ln
1
R
A
R
A
t
t
T
T
R
A
R
A
R
R
A
R
T
T
A
T
T
h
t
C
e
e
T
T
T
T
T
T
T
T
T
T
C
T
e
e
 
 
 
 
 

 
 





=
 





 

 
=

=
 

=

=
 
Slide
38
of
84
Comparison
of
Time
Course
Models
0
5
10
15
20
25
30
35
0.0
0.2
0.4
0.6
0.8
1.0
T*
=
1
Time
Fractional
AChE
Inhibition
 
=
10
 
=
50
Slide
39
of
84
Dose­
Response
Model

The
dose­
response
model
gives
the
fraction
of
inhibition
at
the
time
of
peak
effect.
In
this
model,
any
low­
dose
shoulder
is
modeled
with
the
shape
parameter
 :
(
)
1
log
1
(
;
,
,
,
)
1
1
1
:
:

1
e
e
e:

:
benchmark
response
1
­
max.
inhibition
:
benchmark
dose
shape
parameter
dose
R
R
P
d
P
D
R
tz
lD
l
R
g
d
R
P
D
P
e
R
R
P
D
d
 
 
 
 


 
 







 





 





=
 
 





 

=
+

=
=
Slide
40
of
84
Dose­
Response
Examples
0
10
20
30
40
0.0
0.2
0.4
0.6
0.8
1.0
DR
=
3
Dose
(
mg/
kg)

Max.

Fraction
Inhibition
P
=
0.1
P
=
0.4
 
=
1
 
=
2
Slide
41
of
84
Estimation
Issues

Want
to
fit
all
data
sets
for
a
chemical
simultaneously

Allow
for
variation
among
studies,
sexes,

time­
on­
study
(
in
sub­
chronic
studies)


Time
course
parameter
TR
may
(
probably
does)
depend
on
dose

Some
parameters
or
sets
of
parameters
may
not
be
uniquely
estimable
with
current
data

Error
variance
probably
depends
upon
activity
level,
and
may
differ
among
studies
Slide
42
of
84
Statistical
Model

The
full
statistical
model
used
for
estimating
parameters
is,
using
the
following
indexes:
d
l
k
Index
Dose
Subject
Time­
on­
study
Factor
j
Sex
i
Study
(
MRID)
Index
Factor
(
)(
)

(
)

(
)

(
)
*
*
2
2
2
E
|
,
,
,
,
,
,
,
,
,

Var
|
,
,
,
,
,
,
,
(
)

,
,
,
if
repeated
observations
on
same
subject
ijkl
ijkl
k
d
ijk
ijkl
ijkl
k
d
ijk
e
ijk
jk
lD
ijkl
ijk
lA
y
dose
time
lA
lT
la
tP
lD
lg
f
dose
time
y
dosetimelA
lT
la
tPlD
lg
f
lD
lA
A
 
 
µ
 
 
=
=
 



N
N
Slide
43
of
84
Estimation
Strategy

Evaluate
estimability
of
parameters
given
the
data
set(
s)


Compare
variance
models
°
Constant
variance
°
Power
model
for
variance

Fit
a
"
full"
model
to
the
data
set(
s)
with
separate
values
of
BMD
and
recovery
half­
life
among
sexes,
studies
(
and
time
points
in
subchronic
studies);
differences
among
half­
lives
among
doses
°
Lindstrom
and
Bates
approach
to
nonlinear
mixed
effects
models
(
nlme()
in
R)

°
Generalized
non­
linear
least
squares
(
gnls()
in
R)
in
the
absence
of
random
effects

Test
differences:

°
Likelihood
ratio
tests
for
variance
models
first
°
Wald
tests
(
conditional
on
estimates
of
variance
parameters)
for
differences
in
parameter
values

Pinheiro
and
Bates,
2000
recommend
against
likelihood
ratio
tests
for
fixed
effects

Fit
simplified
model
for
estimates;
confidence
limits
based
on
standard
errors
Slide
44
of
84
Problems
Estimating
Parameters
(
Estimability)


Single
parameter:

°
Design
does
not
allow
estimate

e.
g.
doses
too
low
to
estimate
P
in
dose­
response
model
°
Time
interval
too
wide
to
estimate
TR

Response
has
recovered
by
the
next
time
point

Multiple
parameters:

°
Redundancy
 
design
does
not
allow
unique
estimate
of
two
or
more
parameters

e.
g.
lg
and
lD

Often
a
range
of
values
for
these
two
parameters
result
in
very
similar
fitted
values
at
design
points.


Diagnosis
of
both
based
on
analyzing
the
(
unscaled)

parameter
covariance
matrix:

°
(
XWX')­
1,
where
each
row
of
X
is
the
matrix
of
the
derivatives
of
the
model
function
wrt
to
the
parameters
evaluated
at
one
of
the
design
points

i.
e.
sex,
dose,
time,
study,
etc.

°
W
is
the
(
diagonal,
in
this
case)
matrix
of
weights

Derived
from
the
error
variance
model
Slide
45
of
84
More
on
Diagnosing
Estimability
Problems

Single
parameter
estimability
determined
by
estimated
relative
standard
error
°
If
there
is
a
problem,
the
estimated
relative
standard
error
for
the
implicated
parameter
will
be
orders
of
magnitude
greater
than
for
the
other
parameters

Multi­
parameter
estimability
determined
using
eigen
analysis
of
(
XW
½
)
,
analogously
to
the
analysis
of
collinearity
in
linear
models
°
Belsley,
et
al,
1980;
Reich,
1981
Slide
46
of
84
Example:
Oxamyl
Dose­

Time
Response,
EPA
data
Slide
47
of
84
Example:
Oxamyl
Dose­
Time
Response,
MRID
44420301
Slide
48
of
84
Oxamyl
Dose­
Response,
All
Data:
Observed
versus
Fitted
Slide
49
of
84
Summary

BMDs
and
relative
potencies
are
estimated
along
with
recovery
half
lives
from
rat
dose­
time
response
data

The
dose­
response
portion
of
model
is
similar
to
that
used
for
AChE
inhibition
by
organophosphates

The
time
course
model
reflects
an
exponential
decay
of
inhibition

The
estimation
process
accounts
for
variability
among
studies
°
When
appropriate
data
are
available
Slide
50
of
84
Session
1:
Topics

Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates

Laboratory
Studies
with
the
NMethyl
Carbamate
Insecticides

Relative
Potency
Factors
and
Points
of
Departure
Slide
51
of
84
Session
1
Laboratory
Studies
with
the
N­
Methyl
Carbamate
Insecticides
Session
1
Laboratory
Studies
with
the
N­
Methyl
Carbamate
Insecticides
Dr.
Stephanie
Padilla
National
Health
and
Environmental
Effects
Research
Laboratory
Office
of
Research
and
Development
Dr.
Stephanie
Padilla
National
Health
and
Environmental
Effects
Research
Laboratory
Office
of
Research
and
Development
Slide
52
of
84
Outline
of
the
Presentation

Studies
to
address
questions
raised
at
the
previous
SAP
meeting

Mixture
Study
using
seven
Nmethyl
carbamates
Slide
53
of
84
Questions
Raised
in
the
February
2005
SAP
Meeting

Length
of
waiting
time
for
samples
between
homogenization
and
assay

Panel
suggested
the
Agency
investigate
the
potential
for
reactivation
before
the
assay
Study
of
Reactivation
Parameters
Brains
from
Oxamyl­
Treated
Animals
TIME
(
minutes)

2
4
6
8
10
12
14
Cholinesterase
Activity
(%

Control)
0
10
20
30
40
50
60
70
80
90
100
110
120
Control
0.0666
mg/
kg
0.1
mg/
kg
0.5
mg/
kg
1.0
mg/
kg
1.5
mg/
kg
Original
Rad
3/
27/
04
1:
3
immed
1:
3,
1.5
hrs
1:
9
immed
Slide
54
of
84
Slide
55
of
84
Conclusions
from
Studies

Storage
of
unhomogenized
tissue
at
­
80

C
for
one
year
did
not
affect
reactivation

Dilution
of
samples
promoted
reactivation
(
7­
10%)

°
This
level
of
reactivation
probably
happens
when
the
tissue
is
homogenized

Samples
waiting
90
minutes
on
ice
caused
some
reactivation
(
0­
5%)
Slide
56
of
84
Brain
Dose
Response
Cholinesterase
Activity
Dose
(
mg/
kg)

0.1
1
10
Cholinesterase
(%

control)
0
10
20
30
40
50
60
70
80
90
100
110
120
Propoxur
Carbaryl
Oxamyl
Methomyl
Methiocarb
Folrmetanate
Carbofuran
Slide
57
of
84
29.10%

Propoxur
1.49%

Oxamyl
5.05%

Methomyl
19.60%

Methiocarb
1.63%

Formetanate
1.46%

Carbofuran
41.60%

Carbaryl
Percentage
of
Mixture
Carbamate
Slide
58
of
84
Mixture
Study
Design

13
dose
groups:

°
Mixture
at
5
levels,
each
carbamate
alone,

and
control
°
10
animals
per
group

Adult
male,
Long­
Evans
rats

Dosed
orally
°
Two
vehicles
°
All
animals
received
both
vehicles

Tissues
taken
at
40
min.
after
dosing
Slide
59
of
84
Dose
of
Mixture
(
mg/
kg)

0
2
4
6
8
10
12
14
16
Cholinesterase
activity
(%

control)
20
30
40
50
60
70
80
90
100
110
120
Expected
Values
(+
95%
confidence
limits)

Actual
Data
(+
sem)

Carbamate
Mixture
Study:

Brain
Cholinesterase
Activity
Dose­
Additive
Design
Equipotent
Mixture
Control
Comparison
of
the
Brain
Dose
Response
and
Single
Dosing
in
the
Mixture
Study
Dose
(
mg/
kg)

0.1
1
10
100
Cholinesterase
(%

control)
0
10
20
30
40
50
60
70
80
90
100
110
120
Propoxur
Carbaryl
Oxamyl
Methomyl
Methiocarb
Formetanate
Carbofuran
Open
Symbols
are
Single
Dose
Repeats
in
Mixture
Study
Slide
60
of
84
Slide
61
of
84
Conclusions

Increasing
dosages
of
the
mixture
decreased
brain
cholinesterase
activity

The
changes
in
brain
cholinesterase
activity
in
the
animals
dosed
with
the
carbamate
mixture
appears
to
fit
the
dose­
additive
model
Slide
62
of
84
Session
1:
Topics

Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates

Laboratory
Studies
with
the
NMethyl
Carbamate
Insecticides

Relative
Potency
Factors
and
Points
of
Departure
Slide
63
of
84
Session
1:

Relative
Potency
Factors
and
Points
of
Departure
Session
1:

Relative
Potency
Factors
and
Points
of
Departure
Dr.
Elissa
Reaves
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
64
of
84
Relative
Potency
Factors

Steps:

°
Estimate
chemical
potency

Calculate
benchmark
dose
estimates
°
Selection
of
the
endpoint
for
Relative
Potency
Factors
and
Points
of
Departure
°
Select
index
chemical
Slide
65
of
84
ChE
Data

Toxicity
studies
in
the
rat
provide
the
most
extensive
and
robust
database
of
ChE
inhibition
data
and
were
focus
of
assessment

RBC
and
brain
(
whole
and
half)

ChE
extracted
°
Appendix
II.
B.
I
Slide
66
of
84
Oral
Studies

Studies
submitted
for
registration:

°
Gavage
administration
°
At
or
near
time
of
peak
effect
°
Single
or
repeated
dosing
with
or
without
recovery
data

Acute
neurotoxicity,
subchronic
toxicity,

developmental
neurotoxicity

EPA­
NHEERL
data
available
for
°
Aldicarb,
carbaryl,
formetanate,

methiocarb,
methomyl,
oxamyl,
propoxur

Number
of
available
studies
varies
among
chemicals
°
At
least
one
study
per
NMC
°
Most
chemicals
two
or
more
studies
Slide
67
of
84
ChE
SOPs

Registration
studies
used
modified
Ellman
method
°
Spectrophotometric
method

EPA
NHEERL
used
radiometric
method

Good
concordance
between
registration
studies
and
EPA's
radiometric
experiments

Preliminary
evaluation
of
submitted
registration
SOPs
provided
in
CRA
°
Additional
SOPs
submitted
in
recent
weeks
Slide
68
of
84
Dermal
and
Inhalation
Studies

Compiled
for
pesticides
with
residential
exposure
potential
and
index
chemical

Dermal
studies
include
both
rat
and
rabbit
studies

Inhalation
studies
available
only
for
propoxur
and
oxamyl
°
Oral
studies
used
for
carbaryl
and
methiocarb
Slide
69
of
84
Index
Chemical:
Oxamyl

Considered
all
NMCs
as
possible
candidates
for
index
chemical

Oxamyl:

°
Most
robust
database
for
oral,

dermal,
and
inhalation
studies
°
High
quality
dose­
response
data
for
RBC
and
brain
ChE
inhibition
Slide
70
of
84
Brain
ChE
for
RPFs
and
PoDs

Represents
a
direct
measure
of
the
common
mechanism
of
toxicity

BMD10
estimates
for
ChE
inhibition
generally
similar
for
brain
and
RBC

Tighter
confidence
intervals
compared
to
than
RBC
ChE
Slide
71
of
84
Comparison
of
RBC
and
Brain
ChE
BMD
10'
s
Chemical
Name
Aldicarb
Carbofuran
Formetanate
Oxamyl
Methomyl
Thiodicarb
Propoxur
Methiocarb
Carbaryl
Pirimicarb
BMD
10
(

95%

confidence
limits)
0.01
0.1
1
10
100
RBC
Brain
RBC,
index
chemical
Brain,
index
chemical
Slide
72
of
84
Rat
Brain
ChE
Inhibition
Chemical
Name
Aldicarb
Formetanate
Carbofuran
Oxamyl
Thiodicarb
Methomyl
Methiocarb
Carbaryl
Propoxur
Pirimicarb
BMD
10
(

95%

confidence
limits)
0.01
0.1
1
10
100
Slide
73
of
84
Half­
Life
Time
to
Recovery

Brain
and
RBC
estimates
generally
range
from
<
1
hour
to
6
hours

Recovery
is
dose­
dependent
for
some
chemicals

Overall,
half­
life
to
recovery
data
support
the
use
of
acute,
single
day
exposures
Slide
74
of
84
Points
of
Departure

Point
of
departure
is
a
point
estimate
on
the
index
chemical's
dose­
response
curve
used
to
extrapolate
risk
to
exposure
levels
anticipated
in
the
human
population
Slide
75
of
84
0.00024
mg/
L
0.050
mg/
kg
17.05
mg/
kg
0.14
mg/
kg
BMDL10
0.00040
mg/
L
0.083
mg/
kg
34.91
mg/
kg
0.18
mg/
kg
BMD10
Inhalation
Dermal
Oral
Endpoint
Brain
BMD10s
and
BMDL10s
for
Oxamyl
Slide
76
of
84
Conclusions

Oxamyl
contained
most
robust
database
for
all
3
routes
of
exposure

Brain
ChE
data
provide
a
supportable
common
endpoint
for
extrapolating
cumulative
risk
Slide
77
of
84
Session
1:
Topics

Empirical
Models
for
Acetylcholinesterase
Inhibition
by
N­
Methyl
Carbamates

Laboratory
Studies
with
the
NMethyl
Carbamate
Insecticides

Relative
Potency
Factors
and
Points
of
Departure
Slide
78
of
84
Session
1
Preliminary
Cumulative
Hazard
Assessment:

Questions
to
the
Panel
Session
1
Preliminary
Cumulative
Hazard
Assessment:

Questions
to
the
Panel
Dr.
Elissa
Reaves
Health
Effects
Division
Office
of
Pesticide
Programs
Dr.
Elissa
Reaves
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
79
of
84
Hazard
Question
#
1

Empirical
Dose­
Response
and
Time
Course
Modeling
°
At
the
February,
2005
meeting
of
the
FIFRA
SAP,

EPA
proposed
an
empirical
model
for
use
in
the
cumulative
risk
assessment
of
the
N­
methyl
carbamates.
This
model
contains
a
dose­
response
and
a
time
to
recovery
component.
Based
on
the
comments
from
the
Panel
and
following
experience
with
its
application,
EPA
made
some
modifications
to
this
proposed
model.
EPA
has
applied
this
revised
empirical
model
to
the
available
RBC
and
brain
cholinesterase
data
for
the
N­
methyl
carbamates.
BMD
and
BMDL
estimates
provided
in
the
preliminary
assessment
were
derived
from
cholinesterase
data
from
multiple
studies
and
in
some
cases,
using
different
cholinesterase
measurement
techniques.
Slide
80
of
84
Hazard
Question
#
1
H1a.
Please
comment
on
the
mathematical/
statistical
approach
to
modeling
cholinesterase
data
used
to
estimate
benchmark
dose
values
and
time
to
half­
life
recovery
in
the
preliminary
cumulative
risk
assessment.

Please
address
biological
and
mathematical/
statistical
considerations
in
your
response.

H1b.
Please
comment
on
the
adequacy,

clarity,
and
transparency
of
the
documentation
provided
for
the
empirical
dose­
response
and
time
course
modeling.
Slide
81
of
84
Hazard
Question
#
2

Selection
of
the
Index
Chemical
°
EPA's
cumulative
risk
assessment
guidance
indicates
that
the
index
chemical
should
be
selected
based
on
the
availability
of
high
quality
toxicity
database
for
the
common
mechanism
endpoint.

The
selection
of
the
index
chemical
is
an
important
step
in
the
cumulative
risk
assessment;
the
BMD
for
oxamyl
was
used
to
calculate
RPFs
and
the
BMDL
for
oxamyl
was
used
as
the
PoD
for
extrapolating
cumulative
risk.
Slide
82
of
84
Hazard
Question
#
2
H2.
Please
comment
on
the
rationale
provided
for
the
selection
of
the
index
chemical.
Should
any
additional
factors
be
included
in
the
rationale
for
the
selection
of
oxamyl
as
the
index
chemical?
Slide
83
of
84
Hazard
Question
#
3

Selection
of
Brain
ChE
data
for
developing
RPFs
and
PoDs
°
EPA
has
used
data
for
brain
ChE
as
the
basis
for
the
RPFs
and
PoDs.

The
rationale
for
this
selection
was
provided
in
I.
B.
Slide
84
of
84
Hazard
Question
#
3
H3.
Please
comment
on
the
rationale
provided
for
the
selection
of
the
brain
ChE
as
the
basis
for
RPFs
and
PoDs
in
the
preliminary
cumulative
risk
assessment.
Should
any
additional
factors
be
considered?
