II.
B.
6
­
Page
1
of
162
Appendix
II.
B.
6
Prepared
by:
U.
S.
Environmental
Protection
Agency
II.
B.
6
­
Page
2
of
162
Physiologically
Based
Pharmacokinetic
Modeling
for
the
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
Prepared
by
U.
S.
Environmental
Protection
Agency
Office
of
Research
and
Development
Human
Exposure
and
Atmospheric
Sciences
Division
Exposure
and
Dose
Research
Branch
P.
O.
Box
93478
Las
Vegas,
NV
89193­
3478
II.
B.
6
­
Page
3
of
162
A.
Introduction
and
Background
The
Food
Quality
Protection
Act
(
FQPA)
of
1996
requires
EPA
to
consider
potential
human
health
risks
from
all
pathways
of
dietary
and
non­
dietary
exposure
to
more
than
one
pesticide
acting
through
a
common
mechanism
of
toxicity.
In
2001,
EPA
established
the
N­
methyl
carbamate
pesticide
as
a
common
mechanism
group
based
on
their
structural
characteristics
and
shared
ability
to
inhibit
acetylcholinesterase
(
AChE)
by
carbamylation
of
the
serine
hydroxyl
group
located
in
the
active
site
of
the
enzyme.

Physiologically
based
pharmacokinetic
and
pharmacodynamic
(
PBPK/
PD)
models
are
powerful
tools
to
evaluate
risk.
The
mathematical
representation
accounts
for
relevant
anatomic
structures
and
specific
physiological
and
biochemical
processes
associated
with
chemical
absorption,
distribution,
metabolism,
excretion,
(
ADME)
and
effects.
All
variables
in
the
model
are
accessible
as
dose
metrics,
which
can
be
investigated
under
diverse
exposure
scenarios
and
implementation
of
hypotheses
about
the
biological
processes
that
may
affect
ADME
and
the
pharmacodynamics.
Due
to
the
absence
of
the
specific
biological
pathways,
simple
mathematical
models
are
limited
in
their
ability
to
generate
testable
hypotheses
and
systematically
address
important
issues,
such
as
lifestage
and
species
extrapolation,
risk
to
susceptible
subpopulations
and
mechanisms
of
chemical
interactions.

A
current
limitation
of
applying
PBPK/
PD
models
to
risk
assessment
is
in
the
model
development
process.
The
available
data
on
the
ADME
and
effects
of
the
N­
methyl
carbamates,
and
methods
to
evaluate
model
simulations
against
the
data,
are
limited
relative
to
the
model
complexity
required
to
address
the
questions
of
interest.
Active
research
to
address
these
areas
includes
computational
prediction
of
model
parameters,
including
QSAR
methods,
extrapolation
of
in
vitro
experiments
to
an
in
vivo
context,
and
development
of
preliminary
models
to
illustrate
their
utility
and
investigate
methods
for
model
evaluation.

This
appendix
contains
descriptions
of
two
research
activities
in
PBPK/
PD
modeling
relevant
to
the
N­
methyl
carbamate
cumulative
risk
assessment.
An
abstract
briefly
describes
a
cumulative
PBPK/
PD
model
that
simulates
the
ADME
and
effects
of
a
mixture
of
aldicarb,
carbaryl,
and
propoxur
(
Use
of
Exposure­
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessment
of
Aggregate
Exposure
of
Infants
and
Children
to
N­
Methyl
Carbamate
Insecticides").
The
considerations
and
details
associated
with
modeling
each
individual
chemical
is
conceptually
similar
to
that
described
in
the
comprehensive
report
for
a
PBPK/
PD
model
of
carbaryl
("
Assessment
of
Carbaryl
Exposure
Following
Turf
Application
Using
a
Physiologically
Based
Pharmacokinetic
Model").
The
carbaryl
PBPK/
PD
model
was
presented
to
the
FIFRA
SAP
in
February,
2005.
The
panel
was
complimentary
of
EPA's
effort
and
suggested
some
key
revisions
that
included
II.
B.
6
­
Page
4
of
162
the
application
of
quantitative
methods
to
evaluate
model
sensitivity
and
simulation
of
data,
reduction
in
the
number
of
chemicals
tracked,
and
enhancement
of
the
pharmacodynamic
modeling
of
cholinesterase
inhibition.
The
panel
also
emphasized
the
importance
of
communicating
and
documenting
the
data
considered
in
model
development
in
a
transparent
and
clear
way.
EPA
has
not
made
any
revisions
to
the
carbaryl
PBPK/
PD
model
or
the
report
since
February,
2005.
The
report
provided
here
for
the
carbaryl
PBPK/
PD
model
is
the
same
as
that
reviewed
by
the
SAP.
II.
B.
6
­
Page
5
of
162
B.
Use
of
Exposure­
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessment
of
Aggregate
Exposure
of
Infants
and
Children
to
N­
Methyl
Carbamate
Insecticides
(
Abstract
and
poster
presentation
from
SOT,
2005)

Abstract
Presented
at
the
2005
Society
of
Toxicology
Annual
Meeting
(
New
Orleans,
LA,
March
6­
10,
2005)

Power
F.
W.,
Okino
M.
S.,
Tornero­
Velez
R.,
Blancato
J.
N.
and
Dary
C.
C.

U.
S.
Environmental
Protection
Agency
Office
of
Research
and
Development
Human
Exposure
and
Atmospheric
Sciences
Division
Exposure
and
Dose
Research
Branch
P.
O.
Box
93478
Las
Vegas,
NV
89193­
3478
A
physiologically
based
pharmacokinetic
(
PBPK)
model
was
developed
within
the
Exposure
Relational
Dose
Estimating
Model
(
ERDEM)
framework
to
investigate
selected
exposure
inputs
related
to
recognized
exposure
scenarios
of
infants
and
children
to
N­
methyl
carbamate
pesticides
as
specified
under
the
Food
Quality
Protection
Act
(
FQPA).
Assumptions
underlying
residential
exposure
and
cumulative
risk
were
examined
as
inputs
for
particular
exposure
scenarios
using
residential
dermal
transfer
coefficients,
ambient
air
concentrations,
and
dietary
intake.
Physiological,
pharmacokinetic
and
pharmacodynamic
parameters
describing
the
fate
and
effects
of
carbaryl,
aldicarb,
and
propoxur
in
rats
were
scaled
to
establish
the
model
structure
for
exposure
to
humans.
Adjustments
were
made
for
differences
in
metabolism
and
physiology
between
children
and
adults.
Michaelis­
Menten
kinetics
were
used
to
describe
metabolism,
where
the
chemical
species
compete
for
the
catalytic
enzymes.
Bimolecular
rate
constants,
ki
(
pM­
1
hr­
1),
were
used
to
describe
inhibition
of
acetylcholinesterase
by
the
parent
compounds,
where
the
doses
to
the
target
tissue
are
assumed
to
be
additive.
The
simulation
results
over
a
day
reveal
the
effects
of
residential
exposure
on
cholinesterase
activity,
and
highlight
the
scenarios
and
biological
pathways
where
chemical
interactions
are
important.
The
interactions
are
a
cause
for
differences
in
risk
between
cumulative
and
single­
chemical
exposures.
II.
B.
6
­
Page
6
of
162
II.
B.
6
­
Page
7
of
162
C.
Assessment
of
Carbaryl
Exposure
Following
Turf
Application
Using
a
Physiologically
Based
Pharmacokinetic
Model
Prepared
by
Miles
S.
Okino
Frederick
W.
Power
Rogelio
Tornero­
Velez
Jerry
N.
Blancato
Curtis
C.
Dary
U.
S.
Environmental
Protection
Agency
Office
of
Research
and
Development
Human
Exposure
and
Atmospheric
Sciences
Division
Exposure
and
Dose
Research
Branch
P.
O.
Box
93478
Las
Vegas,
NV
89193­
3478
Disclaimer:
The
information
in
this
document
has
been
funded
in
part
by
the
United
States
Environmental
Protection
Agency
under
interagency
agreement
number
DW47939443
to
the
General
Services
Administration
for
a
work
assignment
to
Anteon
Corporation.
It
has
been
subjected
to
the
Agency's
peer
and
administrative
review
and
has
been
approved
for
publication
as
an
EPA
document.
II.
B.
6
­
Page
8
of
162
Preface
The
document
entitled
"
Assessment
of
carbaryl
exposure
following
turf
application
using
a
physiologically
based
pharmacokinetic
model"
was
developed
by
EPA's
National
Exposure
Research
Laboratory
in
collaboration
with
the
Office
of
Pesticide
Programs
(
OPP).
This
effort
represents
one
part
of
EPA's
on­
going
efforts
to
improve
risk
assessment
methodologies
and
approaches.
This
document
and
the
relevant
attachments
and
models
reflect
research
efforts
only.
All
results
contained
in
this
draft
are
considered
preliminary
in
nature
and
therefore,
OPP
does
not
anticipate
using
them
as
part
of
the
either
the
risk
assessment
for
carbaryl
or
the
cumulative
risk
assessment
for
N­
methyl
carbamates.
EPA
issued
the
Interim
Reregistration
Eligibility
Decision
for
carbaryl
on
June
30,
2003.
That
assessment
contains
EPA's
most
recent
aggregate
risk
assessment
for
carbaryl.
The
IRED
is
available
to
the
public
at
http://
www.
epa.
gov/
oppsrrd1/
REDs/
carbaryl_
ired.
pdf.
Moreover,
given
the
preliminary
nature
of
the
methodology,
it
is
important
to
note
that
they
do
not
reflect
current
OPP
risk
assessment
policies
regarding
evaluation
of
inter­
and
intra­
species
extrapolation.
Specific
considerations
mandated
by
the
Food
Quality
Protection
Act
(
FQPA)
of
1996
such
as
aggregation
of
multiple
pathways
of
exposure
and
specific
consideration
of
potential
sensitivity
of
infants
and
children
are
not
considered
here.
As
this
methodology
matures,
OPP
will
need
to
examine
how
it
can
be
incorporated
into
the
overall
approach
to
the
assessment
of
risks
to
human
health.
II.
B.
6
­
Page
9
of
162
Abstract
Carbaryl
(
1­
naphthol
N­
methylcarbamate,
CAS#
63252)
is
a
widely
used
neurotoxic
insecticide
in
agriculture,
residential
applications,
and
professional
turf
management.
A
member
of
the
N­
methylcarbamate
class
of
pesticides,
carbaryl
is
a
reversible
inhibitor
of
acetylcholinesterase
(
AChE).

A
physiologically
based
pharmacokinetic
(
PBPK)
model
was
used
to
evaluate
the
available
pharmacokinetic
data
and
perform
necessary
species­
to­
species
and
route­
to­
route
extrapolations
to
evaluate
the
dose
metrics
relevant
to
risk.
The
National
Exposure
Research
Laboratory
(
NERL)
has
developed
a
PBPK
modeling
system
that
includes
a
comprehensive
description
of
chemical
absorption,
distribution,
metabolism
and
excretion
(
ADME),
as
well
as
the
flexibility
to
evaluate
diverse
chemical
inputs
corresponding
to
different
exposure
scenarios.
This
model,
the
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
has
been
used
in
the
current
assessment
as
a
tool
for
aiding
in
risk
characterization
of
carbaryl
following
turf
exposure.
The
approach
illustrated
by
this
assessment
is
representative
of
the
investigations
possible
with
PBPK
models.

The
PBPK
model
was
first
developed
to
be
consistent
with
rat
data
from
the
literature
and
registrant,
Bayer
CropScience.
The
human
scenarios
were
then
implemented
by
substituting
human
physiologic
parameters
in
the
PBPK
model.
Oral
hand­
to­
mouth
and
dermal
exposure
scenarios
were
implemented
for
children,
and
the
degree
of
acetylcholinesterase
inhibition
and
tissue
concentrations
were
evaluated.
The
magnitude
of
exposure
was
constrained
by
a
biomonitoring
study
conducted
by
Bayer
CropScience
that
measured
carbaryl
metabolite
mass
in
urine.
The
urine
metabolite
levels
corresponded
to
absorbed
doses
of
approximately
0.08
mg/
kg,
relative
to
the
no
observed
adverse
effect
level
(
NOAEL)
of
1
mg/
kg.

Acetylcholinesterase
activity
based
on
the
assumptions
in
this
analysis
was
predicted
to
remain
>
99%
of
the
baseline
levels
for
the
children's
exposure
scenarios.
The
simulated
scenarios
were
intentionally
designed
to
be
conservative.
All
the
excreted
metabolite
was
associated
with
a
single
activity
period,
which
results
in
high
peak
tissue
concentrations.
The
expected
variability
in
model
parameters
associated
with
brain
concentration
levels
and
acetylcholinesterase
inhibition
were
investigated.
Minimal
changes
were
observed
for
the
predicted
acetylcholinesterase
inhibition,
but
peak
brain
concentrations
were
found
to
be
dependent
on
values
used
for
the
blood:
brain
partition
coefficient
and
the
metabolism
rates.
Additional
data
on
these
pathways
would
reduce
the
uncertainties
in
the
assessment
of
carbaryl.
(
Note:
The
results
contained
in
this
report
are
preliminary
in
nature
and
do
not
reflect
current
OPP
and
EPA
policies
regarding
evaluation
of
inter­
and
intra­
species
extrapolation,
aggregate
risk,
and
application
of
the
FQPA
safety
factor
for
potential
sensitivity
of
infants
and
children).
II.
B.
6
­
Page
10
of
162
Table
of
Contents
1.0
INTRODUCTION.....................................................................................
11
1.1
Overview....................................................................................................
11
1.2
Carbaryl
ADME
Characteristics...............................................................
11
1.2.1
Absorption..................................................................................................
14
1.2.2
Distribution.................................................................................................
15
1.2.3
Metabolism
and
Excretion........................................................................
16
1.2.4
Carbaryl
Pharmacodynamics...................................................................
18
2.0
METHODS.................................................................................................
18
2.1
PBPK
Models
............................................................................................
19
2.1.1
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
...........................
19
2.1.2
Carbaryl
Model
Structure
in
ERDEM
......................................................
21
2.2
PBPK/
PD
Model
Parameters
...................................................................
28
2.2.1
Physiological
Parameters.........................................................................
28
2.2.2
Physiochemical
Parameters
....................................................................
30
2.2.3
Biochemical
Parameters
..........................................................................
32
2.2.4
Pharmacodynamic
Parameters
...............................................................
34
2.3
Exposure
Pathways
Following
Turf
Application
.....................................
35
3.0
RESULTS................................................................................................
37
3.1
Overview....................................................................................................
37
3.2
Rat
Model...................................................................................................
37
3.3
Oral
Exposure
by
Hand­
to­
Mouth
Activities
...........................................
45
3.3.1
Estimates
of
absorbed
dose
from
oral
exposure
...................................
46
3.3.2
Estimates
of
blood
and
tissue
concentrations
following
oral
exposure47
3.4
Dermal
Exposure
......................................................................................
48
3.4.1
Estimates
of
absorbed
dose
from
dermal
exposure..............................
49
3.4.2
Estimates
of
blood
and
tissue
concentrations
following
dermal
exposure
....................................................................................................
50
4.0
DISCUSSION
..........................................................................................
52
5.0
REFERENCES........................................................................................
58
Appendix
A.
Model
...........................................................................................
63
Appendix
B.
QA................................................................................................
96
Appendix
C.
Biomonitoring
Study
Data
Analysis........................................
103
Appendix
D.
Cardiac
Output
and
Partition
Coefficients
.............................
110
Appendix
E.
Additional
Model
Simulation
Results......................................
134
II.
B.
6
­
Page
11
of
162
1.0
INTRODUCTION
1.1
Overview
Carbaryl
(
1­
naphthol
N­
methylcarbamate,
CAS#
63252)
is
a
widely
used
neurotoxic
insecticide
in
agriculture,
residential
applications,
and
professional
turf
management.
A
member
of
the
N­
methylcarbamate
class
of
pesticides,
carbaryl
is
a
reversible
inhibitor
of
acetylcholinesterase
(
AChE).
In
response
to
the
widespread
use
of
this
carbamate
pesticide,
the
U.
S.
EPA
evaluated
the
risks
of
carbaryl
and,
in
June
2003,
reached
an
Interim
Reregistration
Eligibility
Decision
(
IRED).
The
IRED
identifies
additional
pharmacokinetic
data
as
a
means
to
refine
post­
application
risks
from
broadcast
applications
to
turf
lawns
with
liquid
formulations.

At
the
present
time,
appropriate
pharmacokinetic
studies
with
blood
or
urine
levels
of
carbaryl
or
its
metabolites
in
humans
exposed
to
carbaryl
following
turf
application
are
not
available.
Biomonitoring
studies,
such
as
Bayer
2004b,
provide
information
on
the
amount
of
chemical
absorbed
over
the
observation
period,
but
it
is
not
feasible
to
record
or
control
all
exposure
activities.
The
relevant
health
effect,
acetylcholinesterase
inhibition,
occurs
over
a
much
shorter
time­
scale
(
hours)
than
can
be
estimated
from
daily
urine
collection.
However,
physiologically
based
pharmacokinetic
(
PBPK)
models
may
be
used
to
evaluate
the
available
pharmacokinetic
data
and
perform
necessary
species­
to­
species
and
route­
to­
route
extrapolations
to
evaluate
the
dose
metrics
relevant
to
risk.
PBPK
models
describe
the
time
course
disposition
of
chemicals
and
their
metabolites.
PBPK
models
are
well
suited
to
help
assess
risk
from
carbaryl
following
turf
application.
They
provide
a
representation
of
the
absorption,
distribution,
metabolism,
and
excretion
(
ADME)
of
xenobiotics
that
are
believed
to
contribute
to
the
potential
of
inducing
adverse
human
health
responses.

The
National
Exposure
Research
Laboratory
(
NERL)
has
developed
a
PBPK
modeling
system
that
includes
a
comprehensive
description
of
chemical
ADME,
as
well
as
the
flexibility
to
evaluate
diverse
chemical
inputs
corresponding
to
different
exposure
scenarios.
This
model,
ERDEM
(
Exposure
Related
Dose
Estimating
Model)
has
been
used
in
the
current
assessment
as
a
tool
for
aiding
in
risk
characterization
of
carbaryl
following
exposure
to
treated
turf.
The
approach
illustrated
by
this
assessment
is
representative
of
the
investigations
possible
for
the
cumulative
assessment
for
the
N­
methyl
carbamate
pesticides.

1.2
Carbaryl
ADME
Characteristics
Pharmacokinetic
models
provide
a
framework
in
which
to
organize
the
available
pharmacokinetic
and
pharmacodynamic
(
PD)
data.
The
laboratory
studies
in
rats
are
suitable
for
characterizing
various
ADME
and
PD
behaviors
of
carbaryl,
dependent
on
the
type
of
study
and
measurements
collected.
The
studies
used
II.
B.
6
­
Page
12
of
162
to
characterize
the
behavior
of
carbaryl,
and
ultimately
for
development
of
the
PBPK/
PD
model
are
listed
in
Table
1.
II.
B.
6
­
Page
13
of
162
Table
1.
Pharmacokinetic
studies
in
rats
for
estimation
of
kinetic
parameters
Source
Scenario
Measurements
Bayer
2004a
Oral
1
mg/
kg
Brain
14C,
blood
14C
Bayer
2004a
Oral
10
mg/
kg
Brain
14C,
blood
14C,
brain
carbaryl,
brain
naphthol,
fat
14C,
liver
14C,
plasma
naphthyl
sulfate,
plasma
naphthol,
plasma
carbaryl
(
n.
d.)
Bayer
2004a
IV
1
mg/
kg
Brain
14C,
blood
14C
Bayer
2004a
IV
10
mg/
kg
Brain
14C,
blood
14C,
brain
carbaryl,
brain
naphthol,
fat
14C,
liver
14C,
plasma
naphthyl
sulfate,
plasma
naphthol,
plasma
carbaryl,
fat
carbaryl,
fat
naphthol,
liver
carbaryl,
liver
naphthol
Bayer
2004a
Oral
0.075
mg/
kg
x2
with
dermal
0.75
mg/
kg
Brain
14C,
blood
14C,
brain
carbaryl,
brain
naphthol
Brooks
and
Broxup
1995a
Oral
10,
50,
125
mg/
kg
Blood,
brain
cholinesterase
inhibition
Brooks
and
Broxup
1995b
Oral
10,
30,
90
mg/
kg
Blood,
brain
cholinesterase
inhibition
Cheng
1994
Dermal
0.793
mg
Urine
14C,
total
carcass
14C,
blood
14C,
skin
carbaryl,
skin
surface
carbaryl
Knaak
et
al.
1984
Dermal
1.74
mg
Urine
14C,
fat
14C,
liver
14C,
rapidly
perfused
tissue
14C,
slowly
perfused
tissue
14C,
kidney
14C,
blood
14C,
skin
carbaryl,
skin
surface
carbaryl,
evaporated
carbaryl
Knaak
et
al.,
1965
Oral
and
IP
doses
of
20
mg/
kg
Major
urine
metabolites:
urine
Naphthyl
sulfate,
urine
naphthyl
glucuronide,
urine
4­
OH
carbaryl
sulfate,
urine
4­
OH
carbaryl
glucuronide,
urine
5,6
DIOH
carbaryl
glucuronide
Marshall
and
Dorough
1979
Oral
dose
0.01
mg/
kg
to
bile
cannulated
animals
Urine
14C,
bile
14C
II.
B.
6
­
Page
14
of
162
1.2.1
Absorption
Important
absorption
pathways
following
turf
application
are
dermal
for
direct
contact
with
treated
surfaces
and
oral
resulting
from
hand­
to­
mouth
activities.
Several
types
of
studies
are
considered
to
characterize
absorption.
The
comparison
of
intravenous
(
IV)
studies
to
oral
or
dermal
studies
illustrates
the
kinetics
of
absorption.
The
completeness
of
the
absorption
is
characterized
by
comparison
to
IV
studies,
and
by
total
mass
balance
studies
that
account
for
the
excretion
pathways.

Oral
absorption
of
carbaryl
into
the
blood
is
rapid,
where
peak
blood
concentrations
of
14C
(
ring
label)
occur
within
15
minutes
in
rats
after
dosing
with
1
mg/
kg
(
Figure
1).
The
fast
kinetics
from
Bayer
2004a
of
oral
absorption
are
consistent
with
other
oral
studies
(
Casper
et
al.
1973,
Ahdaya
and
Guthrie
1982).

0
0.2
0.4
0.6
0.8
1
­
5
0
5
10
15
20
25
Time
(
hours)
ppm
14C
residue
Figure
1.
Blood
14C
concentrations
(
ring
label)
after
1
mg/
kg
carbaryl
oral
dosing
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

Oral
absorption
is
essentially
complete,
where
the
peak
concentration
observed
after
an
oral
dose
of
1
mg/
kg
(
Figure
1)
is
similar
to
the
peak
observed
after
1
mg/
kg
IV
dose
(
Bayer
2004a).
This
observation
is
consistent
with
the
high
percent
recovery
of
radiolabeled
carbaryl
in
urine
(>
85%)
relative
to
feces
(<
10%,
Bayer
2004b,
Struble
1994).

As
expected,
dermal
absorption
is
slower,
where
peak
concentrations
in
blood
are
observed
10­
12
hours
after
initial
application
(
Figure
2).
The
slower
kinetics
of
dermal
absorption
are
consistent
with
other
dermal
absorption
studies
(
Cheng
1994,
Shah
et
al.
1981).
II.
B.
6
­
Page
15
of
162
0
0.00002
0.00004
0.00006
0.00008
0.0001
0.00012
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

Figure
2.
Blood
14C
concentrations
(
ring
label)
after
1.74
mg
(
20
cm2)
carbaryl
dermal
application
on
rats.
Data
from
Knaak
et
al.
1984,
average
of
3
animals
at
each
sample
time.

1.2.2
Distribution
Once
a
chemical
enters
the
blood
stream,
its
disposition
in
blood,
other
fluids
(
e.
g.
bile),
organs
and
tissues
determines
its
access
to
the
site
or
sites
of
action.
Chemical
disposition
involves
distribution
from
blood
and
fluids
to
tissues
and
organs,
metabolism
in
liver
and
other
organs
of
metabolism,
and
elimination
in
exhaled
breath,
fluids,
e.
g.
milk,
and
excreta.

Measurements
of
each
specific
chemical,
the
parent
compound
and
metabolites,
in
the
tissues
of
interest
are
ideal
to
assess
distribution.
Most
of
the
available
data
are
for
14C
(
Table
1),
which
are
not
sufficient
for
characterization
since
each
chemical
distributes
to
the
tissues
differently
based
on
its
physical
and
chemical
properties.

Due
to
its
high
octanol:
water
partition
coefficient,
carbaryl
is
expected
to
favor
sequestration
in
tissues,
while
its
more
water
soluble
metabolites
will
favor
the
blood.
The
distribution
of
carbaryl
to
tissues
is
limited
by
its
rapid
metabolism,
although
its
metabolites
are
available
for
distribution
in
the
body
for
longer
time
periods.
At
a
high
intravenous
(
IV)
dose
in
rats,
carbaryl
was
detected
in
blood,
brain,
liver
and
fat
(
Bayer
2004a),
and
the
sharp
peak
in
brain
(
Figure
3)
provides
II.
B.
6
­
Page
16
of
162
evidence
for
the
fast
metabolism.
Similar
profiles
were
observed
in
the
other
compartments.

0
2
4
6
8
10
12
14
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

Figure
3.
Brain
carbaryl
concentration
after
10
mg/
kg
carbaryl
IV
dose
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

Measurements
of
14C
for
a
variety
of
doses
and
exposures
illustrates
the
distribution
of
the
metabolites.
In
addition
to
transport
by
the
blood,
the
glucuronidated
species
are
recirculated
through
the
bile.
14C
residues
were
found
in
blood
(
Figures
1
and
2),
fat,
brain,
liver,
bile,
and
rapidly
and
slowly
perfused
tissues
(
Bayer
2004a,
Knaak
et
al.
1984,
Marshall
and
Dorough
1979).
Few
measurements
have
been
taken
for
specific
chemicals
in
the
tissues,
but
their
identity
can
be
inferred
from
metabolism
studies.

1.2.3
Metabolism
and
Excretion
The
metabolism
of
carbamate
pesticides
has
been
the
subject
of
several
reviews
(
Roberts
and
Hutson
1999,
Cool
and
Jankowski
1985,
Kuhr
and
Dorough
1976).
The
pathways
are
shown
in
Figure
4.
II.
B.
6
­
Page
17
of
162
Figure
4.
Metabolism
pathways
for
carbaryl.
Adapted
from
Ross
and
Driver
2002.

The
metabolism
of
carbaryl
in
rats
has
been
studied
by
Knaak
et
al.
(
1965),
Hassan
et
al.
(
1966),
Bend
et
al.
(
1971),
and
Sullivan
et
al.
(
1972).
A
comparison
of
the
metabolite
profiles
in
urine
showed
that
the
rat
metabolism
pathways
were
conserved
in
humans
(
Knaak
et
al.
1965;
Knaak
et
al.
1968).

The
primary
metabolites
of
carbaryl
are
4­
hydroxy
carbaryl,
3,4­
dihydroxy
carbaryl,
5,6­
dihydroxycarbaryl,
and
1­
naphthol.
Based
on
the
chemistry,
5­
hydroxycarbaryl
and
hydroxymethylcarbaryl
are
possible
products,
but
these
have
not
been
detected
in
in
vivo
studies
in
rats
or
humans.
The
carbaryl
products
can
be
further
metabolized
to
the
corresponding
naphthol
species,
and
II.
B.
6
­
Page
18
of
162
all
the
metabolites
are
subject
to
conjugation
with
sulfate
or
glucuronide.
Conjugation
increases
the
aqueous
solubility
to
facilitate
urinary
excretion.
Minimal
excretion
occurs
via
the
feces
(
Struble
1994,
Krishna
and
Casida
1966).
Hydroxylation
reactions
are
catalyzed
by
liver
microsomal
P450
enzymes
(
Tang
et
al.
2002),
while
hydrolases,
including
carboxylesterase
(
Sogorb
et
al.
2002),
catalyze
the
hydrolysis
reactions.

Following
an
intraperitoneal
dose
of
20
mg/
kg
14C
carbaryl
(
ring
label)
in
rats,
the
major
urinary
metabolites
identified
after
24
hours
were
naphthyl
sulfate,
naphthyl
glucuronide,
4­
hydroxycarbaryl
glucuronide,
4­
hydroxycarbaryl
sulfate,
and
5,6­
dihydroxycarbaryl
glucuronide
(
Knaak
et
al.
1965,
Sullivan
et
al.
1972).

Measurements
of
total
14C
in
rat
urine
indicate
that
excretion
of
carbaryl
and
its
metabolites
after
an
oral
dose
occurs
within
1­
3
days
(
Knaak
et
al.
1965,
Benson
and
Dorough
1984).
Excretion
occurs
over
a
longer
time­
scale
following
dermal
exposure
due
to
the
slower
absorption
rate
that
results
in
continuous
absorption
over
a
longer
period
and
susceptibility
of
the
carbaryl
on
the
skin
surface
to
other
removal
mechanisms,
i.
e.,
evaporation
(
Knaak
et
al.
1984,
Cheng
1994).

1.2.4
Carbaryl
Pharmacodynamics
Acetylcholinesterase
(
AChE)
inhibition
in
the
blood
and
brain
is
a
health
effect
of
interest
for
the
N­
methyl
carbamates,
including
carbaryl.
The
inhibition
occurs
through
carbamylation
of
the
serine
hydroxyl
group
located
in
the
active
site
of
the
enzyme,
and
results
in
accumulation
of
acetylcholine
at
a
nerve
synapse
or
neuromuscular
junction.
Continued
accumulation
of
the
neurotransmitter
acetylcholine
may
result
in
the
overstimulation
of
cholinergic
pathways
in
the
central
and
peripheral
nervous
systems
and
possibly
to
the
expression
of
cholinergic
signs
and
symptoms
such
as
nausea,
gastrointestinal
distress,
vomiting,
tremors,
paralysis
and
depression
of
respiratory
function.

Generally,
cholinesterase
(
ChE)­
inhibiting
chemicals
compete
with
the
acetylcholine
for
binding
to
the
enzyme
(
AChE).
As
more
ChE­
inhibiting
chemical
binds
with
the
enzyme,
the
acetylcholine
is
subject
to
slower
or
less
hydrolysis
and
its
activity
is
prolonged.
In
rat
studies,
carbaryl
was
given
orally
at
doses
from
10
to
125
mg/
kg,
and
the
blood
and
brain
ChE
activity
was
measured
over
time.
The
time­
scale
of
ChE
recovery
to
baseline
activity
levels
was
observed
to
be
less
than
a
day
(
Brooks
and
Broxup
1995a,
b).

2.0
METHODS
The
objective
of
this
study
is
to
develop
a
structure
to
organize
and
evaluate
the
existing
data
on
carbaryl.
Physiologically
based
pharmacokinetic
models
(
PBPK),
with
integrated
PK
components,
are
suitable
for
this
purpose
since
the
known
physiologic
and
biochemical
processes
that
affect
chemical
ADME
are
explicitly
represented.
The
deterministic
framework
of
the
model
enables
the
II.
B.
6
­
Page
19
of
162
extension
of
the
model
to
other
exposure
scenarios
(
route­
to­
route
extrapolation),
and
provides
the
basis
for
systematic
scaling
among
species
and
age­
groups.

An
established
PBPK
modeling
system
was
utilized,
the
Exposure
Related
Dose
Estimating
Model
(
ERDEM).
The
system
is
adapted
for
the
carbaryl
scenarios
by
implementing
parameters
specific
to
the
subjects
and
chemicals.
The
suitability
of
these
parameter
values
is
then
evaluated
by
comparing
the
model
results
to
the
available
data.

2.1
PBPK
Models
2.1.1
Exposure
Related
Dose
Estimating
Model
(
ERDEM)

ERDEM
is
an
exposure
and
dose­
modeling
system
developed
by
ORD
scientists.
The
heart
of
ERDEM
is
a
physiologically
based
pharmacokinetic
(
PBPK)
model
that
simulates
the
absorption,
distribution,
metabolism,
and
elimination
of
chemicals
in
mammals.
Simulated
chemicals
are
introduced
into
the
physiological
system
by
any
of
several
routes
including
injection,
ingestion,
inhalation,
and/
or
dermal
absorption.
The
ERDEM
system
is
complex
and
flexible,
with
over
30
physiological
compartments
such
as
arterial
and
venous
blood,
brain,
derma,
fat,
intestine,
kidney,
liver,
rapidly
and
slowly
perfused
tissue,
and
stomach
(
Figure
5).
Any
subset
of
compartments
can
be
included
for
the
PBPK
model
of
a
specific
chemical.
II.
B.
6
­
Page
20
of
162
LV
Liver
CR
Carcass
KD
Kidney
FT
Fat
SL
Slowly
Perfused
RP
Rapidly
Perfused
DR
Dermis
VB
Venous
AB
Arterial
Inputs
Bolus
Dose
Ingestions
Rate
Ingestions
Intraperitoneal
Injection
Kidney
Metabolites...

Kidney
Elimination
Intramuscular
injection
Skin
Surface
Bolus
Dose
Injections
Infusions
Open
Chamber
Inhalation
Open
Chamber
Exhalation
CC
Closed
Chamber
Inhalation
Liver
Metabolites
Carcass
Metabolites
QBCR
QBLV
QBKD
QBFT
QBSL
QBRP
QBDR
PU
Lung
(
Pulmonary)

Lung
Metabolites
QA
QB
GI
Tract
Lymph
Flow
Bile
Flow
Portal
Blood
Arterial
Blood
SP
Spleen
Fat
Metabolites
Slowly
Perfused
Metabolites
Rapidly
Perfused
Metabolites
QBSP
Spleen
Metabolites
BR
Brain
QBBR
Brain
Metabolites
There
are
N
chemicals
modeled.
Most
compartments
have
binding,
elimination,
and
metabolism.

Enzyme
inhibition
is
modeled
in
brain,
blood,
kidney,
and
liver.

There
are
up
to
K
metabolites
of
up
to
N
chemicals.

There
is
binding
modeled
in
the
blood.

Figure
5.
ERDEM
System
Flow
with
Static
Lung
and
Full
GI
Metabolism
can
occur
in
many
of
these
compartments
and
multiple
metabolites
are
tracked
as
they
and
the
parent
compound(
s)
circulate
through
the
system.
It
is
important
to
note
that
adjustments
are
made
for
differences
in
metabolism
and
physiology
between
children
and
adults.
ERDEM
is
programmed
in
the
Advanced
Continuous
Simulation
Language
(
ACSL).
To
execute
ERDEM,
the
II.
B.
6
­
Page
21
of
162
user
enters
physiological,
biological,
and
pharmacodynamic
modeling
data
specific
to
their
chemical
and/
or
scenario
of
interest
(
U.
S.
EPA,
2002).

Any
PBPK
model,
including
ERDEM,
is
made
up
of
a
series
of
the
differential
equations
which
describe
the
rates
of
inflow,
distribution,
metabolism,
or
outflow
of
a
chemical
and
various
metabolites
in
each
separate
biological
compartment.
Appendix
A
describes
in
detail
the
equations
for
each
of
the
biological
compartments
contained
in
ERDEM.

2.1.2
Carbaryl
Model
Structure
in
ERDEM
The
structure
of
the
carbaryl
model
is
defined
by
the
scenarios
and
metrics
of
interest,
and
available
data
for
evaluation.
Exposure
following
turf
application
is
expected
to
occur
through
the
dermal
and
oral
routes.
The
relevant
dose
and
effect
metrics
for
carbaryl
are
the
concentrations
in
blood
and
brain,
and
level
of
acetylcholinesterase
inhibition
in
those
compartments.

Based
on
the
model
requirements
and
available
measurements,
the
model
for
carbaryl
includes
the
following
compartments:
blood
(
venous,
arterial,
portal),
brain,
liver,
fat,
rapidly
and
slowly
perfused
tissues,
and
bile.
The
liver
is
required
as
the
primary
site
of
metabolism,
the
brain
as
the
target
tissue,
and
kidney
as
the
site
of
urinary
elimination.
Also,
the
skin,
stomach
and
other
GI
tract
components
are
modeled
to
account
for
the
absorption
routes
of
interest.

2.1.2.1
Absorption
Model
in
ERDEM
Mathematical
representations
of
oral
and
dermal
absorption
are
included
in
ERDEM
to
simulate
exposure
following
turf
application.
Dermal
absorption
occurs
due
to
direct
contact
with
treated
surfaces,
while
oral
absorption
would
occur
through
hand­
to­
mouth
activities
of
young
children.

Oral
absorption
is
modeled
as
a
first
order
kinetic
process
as
transfer
from
the
luminal
volume
to
the
walls
of
the
GI
tract.
Chemical
can
be
input
as
either
a
bolus
dose
or
as
a
rate
process
into
the
stomach
lumen.
The
stomach
and
other
GI
components
(
duodenum,
small
and
large
intestine,
colon)
are
modeled,
with
absorption
in
each
compartment,
and
transfer
from
the
stomach
to
the
remainder
of
the
GI
tract.
The
equation
for
flow
through
the
stomach
lumen
is
below:

i
i
i
i
i
i
STL
SW
NL
STL
STL
F
BIG
RIG
STL
A
K
C
Q
dt
dA
dt
dA
dt
dA
,
,
 
 
+
=

where
i
STL
A
=
amount
of
ith
chemical
in
stomach
lumen
i
STL
C
=
concentration
of
ith
chemical
in
stomach
lumen
II.
B.
6
­
Page
22
of
162
dt
dA
i
STL
=
the
rate
of
change
of
the
ith
chemical
in
the
stomach
lumen
STL
F
Q
,
=
the
volume
rate
of
food
flowing
through
the
stomach
lumen
to
the
duodenum
i
SW
NL
K
,
=
the
rate
constant
for
the
amount
of
the
ith
chemical
from
the
stomach
lumen
to
the
stomach
wall
dt
dA
i
RIG
=
the
rate
of
change
of
the
amount
of
the
ith
chemical
as
a
rate
ingestion
dt
dA
i
BIG
=
the
rate
of
change
of
the
amount
of
the
ith
chemical
as
a
bolus
ingestion
Complete
equations
for
GI
absorption
are
in
Appendix
A.

Two
compartments
are
used
for
GI
absorption
(
stomach;
and
lumped
duodenum,
small
and
large
intestines,
colon)
to
account
for
the
known
biological
processes
that
affect
the
ADME
of
carbaryl.
The
glucuronidated
metabolites
of
carbaryl
are
known
to
circulate
from
the
liver
to
the
duodenum
through
the
bile,
so
the
lumped
compartment
is
necessary.

In
the
dermal
model,
movement
of
carbaryl
from
the
vehicle
into
the
skin
is
determined
by
the
concentration
(
mg/
L)
of
carbaryl
on
the
skin
surface,
the
area
of
exposed
skin
(
cm2),
and
permeation
coefficient
(
cm/
hr)
as
follows:

dA
sks
dr
i
dt
K
sks
dr
prm
i
A
sk
C
sks
i
,

,
,
=

where
C
A
V
sks
sks
sk
i
i
=

and
A
sk
=
area
of
skin
exposed
V
sk
=
volume
of
skin
exposed
A
sks
i
=
amount
of
the
ith
chemical
on
the
skin
K
sks
dr
prm
i
,
,
=
the
permeation
coefficient
for
the
ith
chemical
from
the
skin
surface
to
dermis
II.
B.
6
­
Page
23
of
162
dA
sks
dr
i
dt
,
=
the
rate
that
the
ith
chemical
moves
from
the
skin
surface
into
the
dermis
The
mass
of
carbaryl
absorbed
into
the
skin
over
the
exposure
period
slowly
enters
the
vascular
compartment
(
Figure
2).
Mass
on
the
skin
surface
may
also
be
removed
by
evaporation
or
washing.
Only
the
mass
absorbed
into
the
skin
contributed
to
the
internal
dose.
The
complete
equations
for
dermal
exposure
are
in
Appendix
A.

2.1.2.2
Distribution
Model
in
ERDEM
Distribution
of
chemicals
from
the
blood
to
tissues
is
modeled
as
perfusion
limited,
where
it
is
dependent
on
the
blood
flow
rate
and
equilibrium
partition
coefficient
between
the
blood
and
tissue.
The
transport
of
the
ith
chemical
to
the
brain
(
BN)
through
the
circulation
is
described
below:

V
dC
dt
Q
C
Q
C
R
BN
BN
B
BN
AB
B
BN
BN
BN
VB
i
i
i
i
=
 
,
,
,

where
V
BN
=
volume
of
the
brain
Q
B
BN
,
=
blood
flow
to
the
brain
C
BNi
=
concentration
of
ith
chemical
in
the
brain
C
ABi
=
concentration
of
ith
chemical
in
the
arterial
blood
R
BN
VBi
,
=
partition
coefficient
for
the
ith
chemical
between
the
brain
and
blood
The
chemical
in
tissues
is
also
subject
to
changes
due
to
metabolism
and
excretion.
The
complete
equations
are
described
in
Appendix
A.

2.1.2.3
Metabolism
Model
in
ERDEM
For
this
assessment,
metabolism
is
modeled
to
occur
in
the
liver.
Based
on
the
metabolism
studies
(
Knaak
et
al.
1965),
the
pathways
to
the
following
metabolites
are
included
in
the
model:
naphthyl
sulfate,
naphthyl
glucuronide,
4­
hydroxycarbaryl
glucuronide,
4­
hydroxycarbaryl
sulfate,
and
5,6­
dihydroxycarbaryl
glucuronide.
Other
metabolites
consistent
with
the
carbaryl
scheme
account
for
the
unidentified
species
in
the
studies.
The
modeled
pathways
are
summarized
in
Table
2.
II.
B.
6
­
Page
24
of
162
Table
2.
Metabolism
Structure
for
Carbaryl
in
Rats
and
Humans
Metabolic
Reaction
Enzyme
Compartment
Carbaryl
to
4­
OH
carbaryl
Cytochrome
P450
isozymes
liver
Carbaryl
to
3,4­
diOH
carbaryl
Cytochrome
P450
isozymes
liver
Carbaryl
to
5,6­
diOH
carbaryl
Cytochrome
P450
isozymes
liver
Carbaryl
to
1­
naphthol
hydrolases
liver
4­
OH
carbaryl
to
4­
OH
carbaryl
glucuronide
UDPGA
transferase
liver
4­
OH
carbaryl
to
4­
OH
carbaryl
sulfate
sulfotransferase
liver
3,4­
diOH
carbaryl
to
3,4­
diOH
1­
naphthol
hydrolases
liver
3,4­
diOH
carbaryl
to
3,4­
diOH
carbaryl
glucuronide
UDPGA
transferase
liver
3,4­
diOH
carbaryl
to
3,4­
diOH
carbaryl
sulfate
sulfotransferase
liver
5,6­
diOH
carbaryl
to
5,6­
diOH
carbaryl
glucuronide
UDPGA
transferase
liver
1­
Naphthol
to
3,4­
diOH
1­
naphthol
Cytochrome
P450
isozymes
liver
1­
Naphthol
to
1­
naphthyl
glucuronide
UDPGA
transferase
liver
1­
Naphthol
to
1­
naphthyl
sulfate
sulfotransferase
liver
3,4­
diOH
1­
naphthol
to
3,4­
diOH
1­
naphthyl
glucuronide
UDPGA
transferase
liver
3,4­
diOH
1­
naphthol
to
3,4­
diOH
1­
naphthyl
sulfate
sulfotransferase
liver
A
saturable
Michaelis­
Menten
form
of
metabolism
was
assumed:

dA
dt
V
C
K
C
LV
M
i
j
LV
i
j
LV
i
m
LV
i
j
LV
i
,
,
,
max,
,
,
,

,
,,
,
=
+

where
the
formation
rate
of
the
jth
metabolite
from
the
ith
chemical
in
the
liver
is
determined
from
the
maximum
velocity
of
metabolism
(
Vmax),
the
Michaelis­
Menten
constant
(
Km)
and
the
concentration
of
the
ith
chemical
in
the
liver.
II.
B.
6
­
Page
25
of
162
The
parameters
Vmax
and
Km,
were
determined
using
ERDEM
by
adjusting
these
values
to
fit
to
the
available
data
(
see
Results).
The
Vmax
was
scaled
as
to
body
weight
at
the
0.7
power
for
the
different
rat
body
weights.
Vmax
was
further
scaled
from
the
rat
to
the
human
according
to
age
(
3
and
9
years
of
age).

V
V
V
V
LV
i
j
LV
US
i
j
BW
BW
f
max,
,
,
max,
,
,
,
,
Re
.
(
)
=
0
7
where
the
subscript
US
represents
the
unscaled
quantity.
The
Vmax
and
Km
values,
obtained
after
a
number
of
iterations
of
comparing
model
runs
with
multiple
sets
of
experimental
data
for
the
rat,
were
then
used
in
the
modeling
of
the
human.
This
is
an
important
quality
assurance
step
(
see
Appendix
B)
for
establishing
model
parameters
based
on
experimental
data.

The
total
metabolism
rate
for
carbaryl
is
expected
to
scale
from
rat
to
human,
but
the
ratio
of
products
based
on
the
relative
rates
of
each
specific
pathway
may
not
be
representative
of
actual
human
metabolism.
The
few
human
studies
show
evidence
of
different
ratios
of
enzyme
activity,
resulting
in
a
different
metabolite
profile
between
the
rat
and
man
(
Knaak
et
al.
1965).
The
model
would
benefit
from
additional
metabolism
data
in
humans
from
which
to
determine
appropriate
values
for
rate
constant
ratios
in
man.

2.1.2.4
Excretion
Model
in
ERDEM
The
urine
elimination
rate
for
the
ith
chemical
was
modeled
as
saturable,
determined
from
the
concentration
of
the
ith
chemical
in
the
kidney
and
the
urine
elimination
rate
constants
(
Vm
and
KmM)
for
the
ith
chemical.

dA
dt
V
C
K
C
Urine
i
mUrine
i
KD
i
mM
Urine
i
KD
i
,

,
,
,

,
,
,
=
+

Elimination
rate
constants
were
determined
for
the
rat
by
fitting
the
available
data
(
see
Results)
to
the
model.
The
scaling
for
the
rat
Vmax
was
set
by
body
weight
to
the
0.7
power.

V
V
V
V
mUrine
i
mUrine
US
i
BW
BW
f
,
,
,
,
,
,
Re
.
(
)
=
0
7
where
the
subscript
US
represents
the
unscaled
quantity.
The
human
values
for
the
urine
elimination
rate
constant
specific
to
each
age
group
were
scaled
from
the
rat
to
the
human
based
on
body
weight.
II.
B.
6
­
Page
26
of
162
2.1.2.5
Acetylcholinesterase
Inhibition
Model
in
ERDEM
The
following
outlines
the
basic
process
of
ChE­
inhibition
for
a
single
N­
methyl
carbamate
pesticide.

1.
There
is
a
certain
amount
of
ChE
in
each
tissue
and
a
certain
amount
is
synthesized
to
keep
this
level
at
a
near
physiological
steady­
state
(
Ks).
This
is
a
basic
physiological
process
independent
of
any
foreign
chemicals
entering
the
system.

2.
A
certain
amount
of
enzyme
is
degraded
(
Kd).
This
also
reduces
the
amount
of
free
enzyme
available
to
perform
its
normal
physiological
function.
When
no
inhibitor
is
present
this
degradation
process
is
balanced
by
the
synthesis
described
above.
However
in
the
presence
of
inhibitor,
the
formation
of
the
complex
can
be
thought
of
as
another
stress
that
reduces
the
amount
of
enzyme
available
for
normal
physiological
function.
This
reduces
the
activity
of
the
enzyme
on
its
normal
physiological
substrate,
acetylcholine
at
the
neurological
site.

3.
Inhibitors,
such
as
the
N­
methyl
carbamates,
enter
the
system
and
reduce
the
amount
of
free
enzyme
by
forming
a
complex
with
the
enzyme.
The
enzyme
that
is
complexed
with
the
ChE­
inhibiting
chemical
is
no
longer
available
to
perform
its
normal
physiological
activity
leading
to
the
build
up
of
acetylcholine.
(
Each
N­
methyl
carbamate
pesticide
has
a
unique
rate
constant
for
the
formation
of
the
complex
with
the
enzyme,
Ki).

4.
The
enzyme­
inhibitor
complex
in
turn
reacts
to
result
in
a
break
down
of
the
ChE­
inhibiting
chemical
and
a
return
or
regeneration
of
free
enzyme.
This
process
is
also
governed
by
a
chemical
specific
rate
constant,
Kr.
The
period
of
inhibition
varies
for
different
compounds
and
is
generally
dependent
upon
the
rate
of
regeneration.
Because
the
period
of
inhibition
is
often
brief
(
due
to
rapid
regeneration),
the
whole
process
has
been
dubbed
as
`
reversible'.

Figure
6
summarizes
this
process.
The
"
released
metabolite"
represents
the
Nmethyl
carbamate
that
is
broken
down.
Each
carbamate
has
its
own
specific
rate
constants
for
the
process.
Any
number
of
N­
methyl
carbamates
can
interact
at
same
time
or
at
any
time
with
the
free
AChE.
II.
B.
6
­
Page
27
of
162
Figure
6.
Schematic
diagram
of
N­
methyl
carbamates
binding
to
AChE.

The
following
differential
equations
represent
the
mass
balance
for
the
Figure
6:

j
rj
jx
ij
x
xj
j
j
rj
jx
j
ij
d
x
s
x
INce
K
C
K
Ace
dt
dINce
INce
K
C
K
K
Ace
K
dt
dAce
×
 
×
×
=
×
+









×
+
×
 
=


where
Acex
is
the
amount
of
AChE
(
µ
mol)
in
compartment
x,
INcexj
is
the
amount
(
µ
mol)
complex
of
AChE
and
inhibitor
j
in
compartment
x,
Ks
is
zero­
order
rate
of
enzyme
synthesis,
Kd
is
the
first­
order
rate
of
enzyme
degradation
(
hr­
1),
Kij
is
the
bimolecular
rate
of
inhibition
for
jth
inhibitor,
Krj
is
the
first­
order
rate
of
regeneration
for
jth
complex,
subscript
x
indicates
tissue
compartment,
and
subscript
j
indicates
the
identity
of
the
inhibiting
chemical.
II.
B.
6
­
Page
28
of
162
Thus,
the
total
amount
of
active
enzyme
is
equal
to
the
amount
present
in
the
system
minus
the
amount
degraded
minus
the
amount
forming
a
complex
with
the
inhibitor
plus
the
amount
regenerated
after
the
enzyme
breaks
down
or
metabolizes
the
inhibitor.

This
assessment
considers
only
a
single
carbamate,
carbaryl.
The
PD
model
integrated
with
the
PBPK
model
in
the
compartments
where
acetylcholinesterase
inhibition
occurs
enables
the
evaluation
of
the
relevant
health
effects.
The
carbaryl
model
provides
a
basis
for
the
consideration
of
multiple
compounds
acting
in
combination
at
any
of
the
steps
outlined
above.
The
simplest
interaction
would
be
simply
adding
the
inhibition
caused
by
each
compound.
In
such
cases,
depending
upon
the
specific
rate
constants,
different
chemical
molecules
would
each
contribute
to
enzyme
inhibition.
It
might
be
possible
however
that
interaction
would
involve
competition
between
the
various
chemicals
for
binding
with
the
enzyme.
If
data
suggest
that
interactions
between
the
N­
methyl
carbamates
other
than
dose­
additive
ones
are
observed,
these
will
be
included
in
future
modeling
efforts.

2.2
PBPK/
PD
Model
Parameters
Parameters
for
PBPK
models
include
three
distinct
types
of
data:
physiological,
physiochemical,
and
biochemical.
The
physiological
data
are
independent
of
the
chemical
being
modeled
and
refer
to
such
things
as
organ
volumes
and
blood
flows.
Distribution
within,
between
and
among
organs,
tissues,
and
fluid
is
modeled
according
to
compartmental
volumes,
blood
flow
rates,
and
blood
tissue
partitioning.
Compartments
are
modeled
in
ERDEM
based
on
the
information
available
for
the
exposure
to
a
particular
chemical
or
chemicals
and
the
metabolites.
The
compartments
used
for
a
metabolite
may
be
a
subset
of
those
used
for
the
parent
chemical.

2.2.1
Physiological
Parameters
The
body
volume
is
determined
for
each
species,
where
the
rat
values
were
measured
in
the
experimental
studies,
and
human
values
are
based
on
age.
The
compartment
volumes
are
expressed
as
a
percentage
of
the
body
volume
(
Table
3).
These
compartments
are
the
components
that
are
active
in
the
carbaryl
model
in
ERDEM
(
Figure
5).
II.
B.
6
­
Page
29
of
162
Table
3.
Volumes
of
Compartments
for
Humans
by
Percentage
for
PBPK
Modeling
with
ERDEM
rat
human
Ages
adult
3
9
Volume
of
the
Body
(
kg)
a
as
measured
15
20.4
Compartments
(%
of
Volume
of
the
Body)

Arterial
Blood
1.75
1.75
1.75
Brain
f
1.21
2.1
2.1
Dermis
b
5.1
5.1
5.1
Fat
c,
g
6
11.5
11.5
GI
Tract
Walls
h
(
not
including
stomach)
1.7
1.7
1.7
Kidney
b,
h
0.9
0.4
0.4
Liver
d,
h
3.55
2.6
2.6
Portal
Blood
0.5
0.5
0.5
Rapidly
Perfused
Tissue
d
1.24
3
3
Slowly
Perfused
Tissue
e
73.9
67.2
67.2
Stomach
Wall
h
0.4
0.4
0.4
Venous
Blood
3.75
3.75
3.75
a.
Assume
density
of
1
L/
kg
b.
Value
from
Corley
et
al.
(
1990)
c.
Boot
et
al.,
1997,
the
age
range
is
4­
21
years
in
both
males
and
females
d.
Fisher
et
al.
(
1998)
Adjusted
based
on
fat
volume
percentage.
e.
Value
estimated
from
the
Fat
content
using
Fisher
et
al.
(
1998)
f.
Estimated
from
many
sources,
Milner
(
1990)
g.
Fisher
et
al.
1991
h.
ILSI
1994
II.
B.
6
­
Page
30
of
162
Cardiac
output
is
determined
for
each
species
and
human
demographic
group.
The
compartment
blood
flows
are
specified
as
a
percentage
of
the
cardiac
output.
The
compartments
requiring
blood
flow
input
are
the
brain,
liver,
kidney,
fat,
dermis,
slowly
perfused
tissue
(
muscle),
rapidly
perfused
tissue,
and
the
walls
of
the
GI
tract.
The
same
blood
flow
percentages
are
used
for
each
age
group
(
Table
4).

Table
4.
Blood
Flows
(
Percentage
of
Cardiac
Output)

rat
human
Age
adult
3
Years
9
Years
Cardiac
Output
(
L/
hr)
a,
c
5.66
303
337
Compartment
Blood
Flow
by
Compartment­
Percentage
of
Cardiac
Output
Brain
b,
d
3
10
10
Dermis
b
7.4
7.4
7.4
Fat
b,
c
9
5
5
GI
Tract
Wall
e
(
not
including
stomach)
9.4
9.4
9.4
Kidney
b,
d
17.4
13.5
13.5
Liver
b,
c
20
20.8
20.8
Rapidly
Perfused
Tissue
b,
c
17.7
17.8
17.8
Slowly
Perfused
Tissue
b,
c
15
15
15
Stomach
Wall
e
1.1
1.1
1.1
a.
Agata
et
al
(
1994),
Schmitz
et
al,
(
1998),
See
Appendix
C
b.
Fisher
et
al.
(
1998).
No
age
adjustment
was
made
for
human
blood
flow
percentages.
c.
Fisher
et
al.
1991
d.
Keys
et
al.
2003
e.
ICRP
2002
2.2.2
Physiochemical
Parameters
The
distribution
of
carbamate
pesticides
and
their
metabolites
between
body
tissues
and
sub­
cellular
organelles
is
largely
dependent
upon
the
manner
in
which
they
partition
between
water
and
lipids
(
Poulin
and
Theil
2000).
The
partitioning
behavior
has
been
associated
with
the
chemical
structure,
enabling
parameter
estimates
from
quantitative
structure­
activity
relationships
(
QSAR).
The
derivation
of
carbaryl
specific
tissues
to
blood
partition
coefficients
is
II.
B.
6
­
Page
31
of
162
examined
in
Appendix
D.
A
summary
of
partition
coefficients
for
each
physiological
compartment
in
relation
to
the
blood
flow
are
presented
in
Table
5.

Table
5.
Partition
Coefficients
for
Carbaryl
and
Metabolites
Chemical
Compartment
to
Venous
Blood
Carbaryl
4­
OH
carbaryl
3,4
DIOH
carbaryl
5,6
DIOH
carbaryl
Naphthol
3,4
DIOH
Naphthol
Brain/
Blood
b
1
1
1
0.1
1
0.3
Dermis/
Blood
5.45
a
n/
a
n/
a
n/
a
n/
a
n/
a
Fat/
Blood
5.5
b
4.81
a
5.07
a
0.17
a
10
b
0.31
a
GI/
Blood
c
4.11
2.12
2.18
0.84
1
0.9
Kidney/
Blood
4.11
a
2.12
a
2.18
a
0.84
a
1
b
0.9
a
Liver/
Blood
4.41
a
2.2
a
2.27
a
0.78
a
1
b
0.84
a
Rapidly
Perfused
Tissue/
Blood
1.5
b
2.12
c
2.18
c
0.84
c
1
b
0.9
c
Slowly
Perfused
Tissue/
Blood
1
b
1.55
a
1.59
a
0.81
a
1
b
0.84
a
a.
Calculated
using
QSAR
techniques
of
Poulin
and
Theil
(
2000),
see
Appendix
4.
b.
Based
on
consistency
with
pharmacokinetic
data
(
Table
5)
and
QSAR
considerations
c.
Same
as
kidney
(
other
rapidly
perfused
tissue)
n/
a.
The
Dermal
compartment
is
not
active
for
this
chemical.

The
tissue:
blood
partition
coefficients
for
the
other
metabolites
and
tissues
were
assumed
to
be
1,
except
that
the
brain:
blood
value
was
set
to
a
low
value
(
0.1)
for
the
glucuronides.
The
partition
coefficients
for
the
downstream
metabolites
have
a
minor
impact
on
the
simulation
results
relative
to
the
14C
measurements.

The
QSAR
predictions
based
on
tissue
composition
are
typically
within
a
factor
of
3
of
the
in
vitro
measured
value
(
Poulin
and
Theil
2000,
Payne
and
Kenny
2002).
The
values
in
Table
5
that
are
based
on
the
pharmacokinetic
data
are
within
a
factor
of
3
of
the
QSAR
predicted
value,
except
for
the
brain:
blood
partition
coefficients
that
are
significantly
less
than
the
QSAR
predicted
values.
Large
disagreements
between
the
actual
and
QSAR
values
have
been
attributed
to
active
transport
mechanisms
at
the
blood:
barrier
(
Liu
et
al.
2004),
but
this
phenomenon
has
not
been
studied
for
carbaryl
or
its
metabolites.
II.
B.
6
­
Page
32
of
162
2.2.3
Biochemical
Parameters
The
biochemical
parameters
include
the
rate
constants
that
describe
the
rate
at
which
chemical
ADME
occurs.
These
parameters
have
been
fit
to
available
pharmacokinetic
studies
in
rats,
and
scaled
to
humans
as
described
previously
(
Section
2.1.2).
The
studies
are
listed
in
Table
1.

Chemical
absorption
parameters
were
estimated
based
on
the
rate
of
appearance
of
14C
or
carbaryl
in
blood.
The
fast
appearance
after
oral
dosing
(
Figure
1)
indicates
that
a
significant
amount
of
the
carbaryl
dose
is
absorbed
in
the
stomach.
The
data
from
bile
cannulated
rats
(
Marshall
and
Dorough
1979)
indicate
that
reabsorption
of
the
glucuronides
occurs
more
slowly
than
the
parent
compound.
Dermal
absorption
occurs
on
a
slower
time­
scale
(
Figure
2),
and
for
modeling
purposes,
is
assumed
to
occur
through
a
depth
of
1
cm
of
skin.
The
parameters
associated
with
absorption
are
shown
in
Table
6.

Table
6.
Carbaryl
absorption
parameters
Parameter
Value
Units
Carbaryl
absorption
rate
(
stomach)
27.3
1/(
h­
kg0.25)

Food
flow,
stomach
to
remaining
GI
0.0436
L/(
h­
kg0.74)

Carbaryl
absorption
rate
(
GI)
10
1/(
h­
kg0.25)

Glucuronide
recirculation
rate,
liver
to
GI
through
bile
1.5
1/(
h­
kg0.25)

Glucuronide
absorption
rate
(
GI)
1
1/(
h­
kg0.25)

Carbaryl
dermal
permeation
coefficient
0.012
cm/
h
Effective
skin
depth
1
cm
The
metabolism
processes
are
defined
by
saturable
Michaelis­
Menten
kinetics.
The
parameters
are
the
maximum
velocity
of
metabolism
(
Vmax)
and
the
Michaelis­
Menten
constant,
Km.

The
total
metabolism
rate
of
the
parent
compound
was
found
to
be
constrained
by
the
plasma
14C
data,
and
carbaryl­
specific
data
where
available.
The
metabolism
to
specific
chemicals
was
constrained
by
the
species
specific
data
in
tissues,
blood,
and
urine.
The
reported
values
in
Table
7
reasonably
simulate
the
study
results
in
Table
1.
An
emphasis
was
placed
on
the
low­
dose
studies
since
those
are
expected
to
be
closer
to
the
exposures
resulting
from
the
turf
application.
II.
B.
6
­
Page
33
of
162
Table
7.
Metabolism
Rates
for
carbaryl
in
Rats
and
Humans
Metabolic
Reaction
Enzyme
Compartment
Vmax
mM/(
hr­
kg
0.7)
Km
(
mM)

Carbaryl
to
4­
OH
carbaryl
Cytochrome
P450
isozymes
liver
0.021
0.05
Carbaryl
to
3,4­
diOH
carbaryl
Cytochrome
P450
isozymes
liver
0.034
0.04
Carbaryl
to
5,6­
diOH
carbaryl
Cytochrome
P450
isozymes
liver
0.016
0.04
Carbaryl
to
1­
naphthol
hydrolases
liver
0.071
0.048
4­
OH
carbaryl
to
4­
OH
carbaryl
glucuronide
UDPGA
transferase
liver
0.012
0.01
4­
OH
carbaryl
to
4­
OH
carbaryl
sulfate
sulfotransferase
liver
0.002
0.01
3,4­
diOH
carbaryl
to
3,4­
diOH
1­
naphthol
hydrolases
liver
0.009
0.01
3,4­
diOH
carbaryl
to
3,4­
diOH
carbaryl
glucuronide
UDPGA
transferase
liver
0.009
0.01
3,4­
diOH
carbaryl
to
3,4­
diOH
carbaryl
sulfate
sulfotransferase
liver
0.007
0.01
5,6­
diOH
carbaryl
to
5,6­
diOH
carbaryl
glucuronide
UDPGA
transferase
liver
0.02
0.01
1­
Naphthol
to
3,4­
diOH
1­
naphthol
Cytochrome
P450
isozymes
liver
0.073
0.013
1­
Naphthol
to
1­
naphthyl
glucuronide
UDPGA
transferase
liver
0.09
0.01
1­
Naphthol
to
1­
naphthyl
sulfate
sulfotransferase
liver
0.05
0.01
3,4­
diOH
1­
naphthol
to
3,4­
diOH
1­
naphthyl
glucuronide
UDPGA
transferase
liver
0.01
0.01
3,4­
diOH
1­
naphthol
to
3,4­
diOH
1­
naphthyl
sulfate
sulfotransferase
liver
0.005
0.01
In
vitro
assays
exist
to
estimate
the
metabolism
rates
(
Tang
et
al.
2002,
Lipscomb
et
al.
1998),
but
the
appropriate
methods
and
models
by
which
to
scale
up
to
the
in
vivo
system
require
further
research
(
Lipscomb
et
al.
1998).

Urinary
elimination
is
also
modeled
as
a
saturable
process.
The
rate
constants
were
initially
set
relative
to
the
metabolism
rates,
so
that
the
glucuronidated
and
sulfated
conjugates
would
be
rapidly
eliminated
after
formation.
The
specific
values
were
constrained
by
the
14C
profiles
in
urine,
as
well
as
by
the
specific
metabolite
data
in
urine.
Also,
the
elimination
constant
values
are
weakly
constrained
by
the
tissue
and
blood
profiles.
The
elimination
parameters
for
metabolites
modeled
by
ERDEM
are
presented
in
Table
8.
II.
B.
6
­
Page
34
of
162
Table
8.
Parameters
for
Urine
Elimination
Metabolite
Vmax
mM/(
hr­
kg0.7)
Km
(
mM)

4­
OH
carbaryl
0.0001
0.005
3,4
DIOH
carbaryl
0.0001
0.005
5,6
DIOH
carbaryl
0.0001
0.005
Naphthol
0.0002
0.005
3,4
DIOH
naphthol
0.001
0.005
Naphthyl
sulfate
0.004
0.005
Naphthyl
glucuronide
0.004
0.005
3,4
DIOH
naphthyl
sulfate
0.002
0.005
3,4
DIOH
naphthyl
glucuronide
0.002
0.005
4­
OH
carbaryl
sulfate
0.002
0.005
4­
OH
carbaryl
glucuronide
0.002
0.005
3,4
DIOH
carbaryl
sulfate
0.002
0.005
3,4
DIOH
carbaryl
glucuronide
0.002
0.005
5,6
DIOH
carbaryl
glucuronide
0.002
0.005
2.2.4
Pharmacodynamic
Parameters
The
pharmacodynamic
parameters
for
acetylcholinesterase
inhibition
are
based
on
the
kinetic
profile
of
inhibition
and
recovery.
Initial
values
were
based
on
in
vitro
experiments
(
Hetnarski
and
O'Brien
1975),
and
then
refined
based
on
the
rat
experiments
(
Table
1).
The
parameter
values
must
also
be
consistent
with
the
concentration
profiles
in
the
compartments
where
inhibition
occurs.
The
values
for
the
acetylcholinesterase
inhibition
parameters
(
Section
2.1.2.5)
are
shown
in
Table
9.
II.
B.
6
­
Page
35
of
162
Table
9.
Parameters
for
Acetylcholinesterase
Inhibition
Parameter
Value
Units
Brain
Baseline
concentration
3.74
×
10­
5
mM
Inhibition
Rate
250
1/(
h­
mM)
Regeneration
Rate
1
1/
h
Synthesis
Rate
0.01
mM/
h
Degradation
Rate
0.001
1/
h
Blood
Baseline
concentration
1.1
×
10­
6
mM
Inhibition
Rate
200
1/(
h­
mM)
Regeneration
Rate
1
1/
h
Synthesis
Rate
0.01
mM/
h
Degradation
Rate
0.001
1/
h
2.3
Exposure
Pathways
Following
Turf
Application
Oral
exposure
through
hand­
to­
mouth
activities
and
dermal
exposure
were
simulated
as
representative
activities
following
turf
application.
Hand­
to­
mouth
activities
are
characteristic
of
toddlers,
and
a
3­
year­
old
subject
was
implemented.
An
exposure
frequency
of
20
events/
hour
over
two
hours
was
simulated,
based
on
the
Phase
5
ORE
Risk
Assessment
(
U.
S.
EPA
2003).
Hand­
to­
mouth
activities
are
not
expected
to
be
significant
for
older
children,
where
exposure
is
expected
to
occur
through
the
dermal
pathway.
A
9­
year­
old
subject
was
implemented
to
be
representative
of
older
children.
A
skin
area
of
1000
cm2
was
assumed
to
be
exposed
over
3.55
hours
of
contact
time
following
turf
application.
The
contact
time
is
based
on
reported
values
from
a
biomonitoring
study
(
Bayer
2004c).

In
the
Bayer
2004
biomonitoring
study,
the
potential
absorbed
doses
of
carbaryl
to
homeowners
and
residents
were
evaluated
during
and
following
the
residential
application
of
carbaryl
by
measuring
urinary
pesticide
metabolite
levels
in
the
applicator,
spouse,
and
children
of
representative
families
that
use
pesticides.
Non­
professional
adult
and
child
volunteers
were
used
to
measure
carbaryl
absorbed
doses
in
homeowners
and
their
families
during
and
after
application
of
Sevin
®
GardenTech
Ready­
To­
Spray,
a
formulation
of
carbaryl.
Sites
were
selected
in
Missouri
and
California.
Ten
families
were
monitored
in
Missouri
and
13
families
were
monitored
in
California.
The
focus
for
this
assessment
was
the
exposure
of
the
children.

The
simulated
absorbed
doses
in
this
assessment
were
based
on
the
Bayer
2004
biomonitoring
study.
Based
on
the
reported
values
of
urinary
1­
naphthol
in
the
various
children's
age
groups,
distributions
were
created
to
establish
likely
ranges
of
metabolite
excretion
that
would
correspond
to
ranges
of
carbaryl
absorbed
dose
(
Appendix
C).
The
data
analysis
is
based
on
the
Missouri
cohort
II.
B.
6
­
Page
36
of
162
since
their
application
behavior
was
consistent
with
the
label
instructions
of
4
lbs
a.
i./
acre.
The
Missouri
group
applied
2.3­
6.5
lbs
a.
i./
acre,
compared
to
2.0
to
163
lbs
a.
i./
acre
in
California.

After
4
days,
the
excreted
mass
for
the
99.9%
ile
of
the
4­
5
year
old
group
is
1.8
mg
carbaryl
equivalents,
and
2.5
mg
carbaryl
equivalents
for
the
9­
12
year
old
group.
The
4­
5
year
old
group
is
assumed
to
be
representative
of
toddler
exposure,
including
age
3.
For
the
simulations,
carbaryl
doses
that
resulted
in
similar
excretion
levels
were
applied.

The
proposed
simulations
are
intentionally
conservative
for
the
dose
metric
of
brain
cholinesterase
inhibition.
By
assuming
continuous
turf
exposure
over
the
estimated
duration,
as
opposed
to
distributing
the
exposure
duration
over
several
periods
during
the
day,
higher
peak
carbaryl
concentrations
are
possible.
The
excretion
data
actually
imply
that
the
exposure
is
spread
over
the
observation
period
since
a
plateau
in
metabolite
levels
is
not
clearly
reached
after
4
days
(
Appendix
C);
the
excretion
of
carbaryl
from
a
single
event
is
expected
to
occur
within
1­
3
days
(
Knaak
et
al.
1965,
Benson
and
Dorough
1984).
Also,
the
source
of
all
metabolites
is
assumed
to
be
from
exposure
events
associated
with
activities
on
the
carbaryl­
treated
turf,
but
non­
zero
metabolite
levels
on
day
0
(
pre­
treatment)
indicate
that
other
exposures,
including
to
non­
carbaryl
sources
of
1­
naphthol,
may
be
occurring
(
Appendix
C).
These
other
exposures
would
contribute
to
the
total
amount
of
carbaryl
absorbed,
but
the
peak
carbaryl
values
in
brain,
and
thus
the
maximum
acetylcholinesterase
inhibition,
would
be
reduced.
II.
B.
6
­
Page
37
of
162
3.0
RESULTS
3.1
Overview
A
robust
PBPK
model
enables
the
estimation
of
relevant
dose
metrics
for
a
variety
of
exposure
scenarios
in
different
populations.
For
carbaryl,
the
brain
and
blood
concentrations,
and
degree
of
acetylcholinesterase
inhibition
in
those
compartments
are
the
metrics
of
interest.
The
PBPK
model
was
designed
to
predict
those
values
for
scenarios
associated
with
turf
post­
application
activities
for
children.

The
PBPK
model
was
first
developed
to
be
consistent
with
the
rat
data
(
Table
1).
The
physiologic
parameters
were
implemented
for
the
rat,
and
the
kinetic
parameters
were
estimated
based
on
the
experimental
data.

The
human
scenarios
were
then
implemented
by
substituting
human
physiologic
parameters
in
the
PBPK
model.
The
kinetic
parameters
were
appropriately
scaled
as
described
in
Section
2.1.2.
The
oral
hand­
to­
mouth
and
dermal
exposure
scenarios
were
implemented
for
children,
and
the
degree
of
acetylcholinesterase
inhibition
and
tissue
concentrations
were
evaluated.

3.2
Rat
Model
The
PBPK
model
simulates
results
consistent
with
the
available
data.
These
data
include
studies
published
in
the
literature
and
those
delivered
to
the
U.
S.
EPA
by
registrants
(
Table
1).
Priority
was
given
to
the
studies
at
lower
doses,
since
exposures
following
turf
application
will
likely
be
low
relative
to
the
laboratory
setting.

Intravenous
(
IV)
dosing
experiments
enable
the
evaluation
of
the
model
distribution,
metabolism,
and
excretion
behaviors
independent
of
the
absorption
characteristics.
IV
doses
of
approximately
1
and
10
mg/
kg
of
14C
carbaryl
(
ring
label)
were
administered
to
rats,
and
radioactive
residues
were
measured
in
a
variety
of
tissues
(
Bayer
2004a).

Representative
comparisons
of
the
PBPK
model
to
the
experimental
data
are
shown
in
Figures
7
and
8.
The
complete
set
of
simulation
charts
is
in
Appendix
E.
II.
B.
6
­
Page
38
of
162
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
7.
14C
concentration
in
brain
after
IV
dose
of
0.8
mg/
kg
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

0
0.5
1
1.5
2
2.5
3
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
8.
14C
concentration
in
blood
after
IV
dose
of
0.8
mg/
kg
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

The
14C
data
were
reported
in
ppm
(
Bayer
2004a).
The
PBPK
model
tracks
14C
based
on
chemical
species,
resulting
in
units
of
mmol/
L.
The
model
results
were
converted
to
ppm
by
multiplying
by
the
molecular
weight
of
carbaryl
for
comparison
to
the
data.

The
rat
PBPK
model
shows
similar
consistency
with
oral
studies,
conducted
near
the
NOAEL
level
of
1
mg/
kg
(
U.
S.
EPA)
and
also
at
8.45
mg/
kg.
One
exception
is
that
brain
14C
concentrations
are
over­
predicted
(
Figure
9).
II.
B.
6
­
Page
39
of
162
0
0.1
0.2
0.3
0.4
0.5
­
5
0
5
10
15
20
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
9.
Brain
14C
concentration
after
oral
dose
of
1
mg/
kg
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

By
reducing
the
partition
coefficient
for
carbaryl
and
its
major
metabolites
to
less
than
or
equal
to
one
(
Table
5),
the
low
dose
data
were
better
simulated.
The
equal
partitioning
between
blood
and
brain
(
partition
coefficient
=
1)
is
inconsistent
with
the
known
physical
properties
of
carbaryl,
so
the
partition
coefficient
values
were
not
reduced
to
less
than
1.

Carbaryl
was
measured
for
the
higher
dose
oral
study,
where
brain
concentrations
of
the
parent
compound
were
reasonably
simulated
(
Figure
10),
while
blood
concentrations
were
predicted
but
not
detected
(
Figure
11).

0
0.5
1
1.5
2
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
10.
Brain
carbaryl
concentration
after
oral
dose
of
8.45
mg/
kg
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.
II.
B.
6
­
Page
40
of
162
0
0.5
1
1.5
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

PBPK
model
Figure
11.
Blood
carbaryl
concentration
after
oral
dose
of
8.45
mg/
kg
carbaryl
in
rats.
Carbaryl
was
not
detected
in
blood
at
15
minutes,
approximately
the
time
of
the
PBPK
model
predicted
peak
(
Bayer
2004a).

Dermal
exposure
studies
in
rats
of
1.74
mg/
20
cm2
(
Knaak
et
al.
1984)
and
0.793
mg/
12.5
cm2
(
Cheng
1994)
were
evaluated
to
assess
the
dermal
permeability
of
carbaryl.
Representative
comparisons
to
the
data
show
that
the
model
predicts
the
kinetics
of
absorption
(
Figures
12
and
13).
Similar
fits
are
observed
with
data
from
other
dermal
studies
and
for
other
compartments.
Complete
comparisons
are
in
Appendix
E.
II.
B.
6
­
Page
41
of
162
0
0.00005
0.0001
0.00015
0.0002
0.00025
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
12.
Blood
14C
concentrations
following
1.74
mg/
20
cm2
carbaryl
dermal
application
on
rats.
Data
from
Knaak
et
al.
1984,
average
of
3
animals
at
each
sample
time.

0
0.0005
0.001
0.0015
0.002
0
50
100
150
200
Time
(
hours)
Cumulative
residue
(
mmol)

data
PBPK
data
Figure
13.
Cumulative
14C
residues
in
urine
following
1.74
mg/
20
cm2
carbaryl
dermal
application
on
rats.
Data
from
Knaak
et
al.
1984,
pooled
urine
from
3
animals.

A
metabolism
study
in
rats
measured
concentrations
of
specific
metabolites
in
urine
after
intraperitoneal
(
IP)
dosing
of
rats
at
20
mg/
kg
(
Knaak
et
al.
1965).
The
model
simulates
the
results
for
the
identified
metabolites
(
Figure
14).
The
excretion
of
glucuronides
is
slower
than
sulfates
due
to
enterohepatic
recirculation.
II.
B.
6
­
Page
42
of
162
0
2
4
6
8
10
12
14
16
18
0
5
10
15
20
25
30
Time
(
hours)
%
dose
in
urine
naphthol
sulfate
PBPK
model
naphthol
glucuronide
PBPK
model
5,6­
DIOH
carbaryl
glucuronide
PBPK
model
4­
OH
carbaryl
glucuronide
PBPK
model
4­
OH
carbaryl
sulfate
PBPK
model
Figure
14.
Excretion
of
metabolites
following
20
mg/
kg
IP
dosing
of
carbaryl
in
rats.
Data
from
Knaak
et
al
1965,
pooled
urine
from
3
animals.

Oral
studies
in
rats,
separate
from
the
ones
discussed
above,
measured
the
degree
of
cholinesterase
inhibition
in
blood
and
brain
(
Brooks
and
Broxup
1995a,
b).
The
lowest
dose
tested
was
10
mg/
kg,
which
resulted
in
measurable
inhibition
followed
by
recovery
within
8
hours.
These
dynamics
were
reproduced
by
the
model
(
Figures
15
and
16).
The
data
were
given
by
inhibition
in
each
brain
region,
and
were
averaged
for
comparison
to
the
PBPK
model.
II.
B.
6
­
Page
43
of
162
0
20
40
60
80
100
120
0
5
10
Time
(
hours)
%
control
Brooks
1995a
Brooks
1995b
PBPK
model
Figure
15.
Blood
acetylcholinesterase
inhibition
after
10
mg/
kg
carbaryl
oral
dose
in
rats.
Data
are
average
of
12
animals
(
6
male,
6
female)
at
each
sample
time.

0
20
40
60
80
100
120
0
5
10
Time
(
hours)
%
control
Brooks
1995a
Brooks
1995b
PBPK
model
Figure
16.
Brain
acetylcholinesterase
inhibition
after
10
mg/
kg
carbaryl
oral
dose
in
rats.
Data
are
average
of
12
animals
(
6
male,
6
female)
at
each
sample
time.

A
combined
low
dose
oral
and
dermal
exposure
study
in
rats
was
conducted
to
simulate
conditions
corresponding
to
hypothesized
repeated
hand­
to­
mouth
activities
in
toddlers
(
Bayer
2004a).
Two
oral
doses
of
0.075
mg/
kg
were
given
an
hour
apart,
while
a
0.75
mg/
kg
dermal
dose
was
applied
for
hours
0
to
2.
The
II.
B.
6
­
Page
44
of
162
model
simulations
are
consistent
with
the
data,
showing
modest
under­
prediction
of
the
blood
14C
data,
and
over­
prediction
of
the
brain
14C
and
carbaryl
concentrations
(
Figures
17­
19).

0
0.05
0.1
0.15
0.2
0
2
4
6
8
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
17.
Blood
14C
concentration
following
dermal
and
repeated
oral
dosing
of
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

0
0.01
0.02
0.03
0.04
0.05
0
2
4
6
8
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
18.
Brain
14C
concentration
following
dermal
and
repeated
oral
dosing
of
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.
II.
B.
6
­
Page
45
of
162
0
0.002
0.004
0.006
0.008
0.01
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
19.
Brain
carbaryl
concentration
following
dermal
and
repeated
oral
dosing
of
carbaryl
in
rats.
Data
from
Bayer
2004a,
average
of
4
animals
at
each
sample
time.

Additional
simulations
(
not
shown)
without
the
dermal
exposure
showed
results
nearly
identical
to
Figures
17
to
19.
This
indicates
that
in
the
hand­
to­
mouth
exposure
scenario,
the
oral
route
is
the
dominant
route
of
exposure.

The
rat
model
sufficiently
captures
the
behavior
of
carbaryl,
including
the
important
dose
metrics
for
evaluation
of
the
post­
turf
application
exposure
scenario.
The
simulations
are
consistent
with
the
available
data
across
studies
and
measurements.
Additional
comparisons
to
data
may
be
found
in
Appendix
E.
Exact
matches
of
simulations
to
data
would
not
be
expected
based
on
the
diverse
studies
and
experimental
and
animal
variability.
One
unexpected
difference
is
the
non­
detection
of
carbaryl
in
blood
15
minutes
after
an
oral
dose
of
8.45
mg
(
Figure
11).
The
non­
detection
of
carbaryl
in
the
experimental
study
appears
to
be
inconsistent
with
the
corresponding
brain
carbaryl
concentrations
(
Figure
10),
and
expected
blood
acetylcholinesterase
inhibition
predicted
by
the
PBPK
model
and
observed
in
a
separate
study
under
similar
dosing
conditions
(
Figure
16).
Aside
from
that
particular
measurement,
the
model
describes
the
absorption
and
distribution
of
carbaryl
to
the
brain,
where
acetylcholinesterase
inhibition
 
the
dose
metric
of
interest
 
occurs,
and
simulates
the
subsequent
elimination
of
carbaryl
and
its
metabolites.

3.3
Oral
Exposure
by
Hand­
to­
Mouth
Activities
An
oral
exposure
scenario
by
hand­
to­
mouth
activities
was
simulated
for
a
3­
year­
old
child.
The
rat
model
was
converted
to
an
age­
specific
human
model
by
implementing
the
appropriate
physiological
parameters
(
Section
2.2.1)
and
scaling
the
kinetic
constants
(
Section
2.2.3)
for
a
3­
year­
old
child.
II.
B.
6
­
Page
46
of
162
Starting
at
time
0,
two
hours
of
hand­
to­
mouth
activity
occurred
at
20
events/
hour.
The
simulation
duration
was
for
4
days
from
the
beginning
of
the
activity,
over
which
the
cumulative
urine
excretion
and
tissue
concentrations
were
tracked.

3.3.1
Estimates
of
absorbed
dose
from
oral
exposure
The
absorbed
dose
associated
with
the
oral
hand­
to­
mouth
exposure
is
based
on
the
biomonitoring
study
(
Bayer
2004b,
Appendix
C).
For
the
assumed
scenario,
a
magnitude
of
exposure
was
chosen
to
be
consistent
with
the
observed
excreted
mass
of
carbaryl
equivalents.

A
total
dose
of
0.08
mg/
kg
for
the
hand­
to­
mouth
events
resulted
in
urine
metabolite
levels
consistent
with
the
99.9%
ile
of
excreted
mass
of
carbaryl
equivalents
(
Figure
20).
Similar
to
the
oral
bolus
studies,
the
urinary
profiles
of
metabolites
indicate
significant
elimination
within
a
day.

0
0.5
1
1.5
2
0
20
40
60
80
100
Time
(
hours)
Cumulative
mass
(
mg)

data
PBPK
model
Figure
20.
Cumulative
excretion
of
carbaryl
equivalents
in
urine
from
3
year
olds
following
turf
application.
The
error
bars
represent
the
99.9%
range.
The
PBPK
model
assumes
oral
exposure
through
hand­
to­
mouth
activities.

The
plateau
in
the
metabolite
levels
is
reached
earlier
than
in
the
biomonitoring
study
(
Figure
20).
As
discussed
in
section
2.3,
this
simulation
is
a
conservative
scenario.
By
assuming
that
the
entire
absorbed
dose
occurs
in
a
single
2
hour
activity
period,
the
peak
tissue
concentrations
will
be
highest.
II.
B.
6
­
Page
47
of
162
3.3.2
Estimates
of
blood
and
tissue
concentrations
following
oral
exposure
The
concentration
of
carbaryl
in
blood
increases
over
the
exposure
duration,
then
rapidly
decreases,
and
a
similar
profile
is
observed
in
other
tissues
(
Figure
21).

0
0.002
0.004
0.006
0.008
0
5
10
15
20
25
Time
(
hours)
Concentration
(
mg/
L)

PBPK
model
Figure
21.
Carbaryl
concentration
in
venous
blood
in
3
year
olds
following
turf
application.
The
PBPK
model
assumes
oral
exposure
through
hand­
to­
mouth
activities.

The
peak
concentrations
of
carbaryl
in
brain
and
blood
determine
the
magnitude
of
acetylcholinesterase
inhibition
(
Figure
22).
The
scale
in
Figure
22
only
spans
from
90­
100%
so
that
the
dynamics
of
the
slight
change
can
be
visualized.
The
level
of
inhibition
for
the
oral
hand­
to­
mouth
scenario
is
minimal
(
Table
10).
II.
B.
6
­
Page
48
of
162
90
95
100
0
2
4
6
8
10
Time
(
hours)
Activity
(%
baseline)

brain
blood
Figure
22.
Acetylcholinesterase
activity
in
brain
and
blood
in
3
year
olds
following
turf
application.
The
PBPK
model
assumes
oral
exposure
through
hand­
to­
mouth
activities.

Table
10.
Blood
and
brain
carbaryl
concentrations
and
acetylcholinesterase
activity.
Compartment
Peak
carbaryl
concentration
(
mg/
L)
Acetylcholinesterase
activity
(%
baseline)
Blood
0.0061
99.4
Brain
0.0061
99.5
3.4
Dermal
Exposure
A
dermal
exposure
scenario
was
simulated
for
a
9­
year­
old
child.
The
rat
model
was
converted
to
an
age­
specific
human
model
by
implementing
the
appropriate
physiological
parameters
(
Section
2.2.1)
and
scaling
the
kinetic
constants
(
Section
2.2.3)
for
a
9­
year­
old
child.

At
time
0,
a
mass
of
carbaryl
is
applied
on
the
exposed
skin
surface
of
1000
cm2,
and
is
washed
off
after
3.55
hours.
This
is
intended
to
simulate
the
average
of
transfer
and
rub­
off
activities
to
the
lower
arms
and
legs
(
including
hands
and
feet)
over
the
duration
of
activity
on
the
treated
turf.
The
simulation
duration
was
for
4
days
from
the
beginning
of
the
activity,
over
which
the
cumulative
urine
excretion
and
tissue
concentrations
were
tracked.

The
profile
of
the
amount
of
carbaryl
on
skin
over
the
exposure
period
shows
slow
absorption
until
it
is
washed­
off
at
the
end
of
the
activity
(
Figure
23).
Since
the
kinetics
of
absorption
are
slow,
the
representation
of
average
dermal
II.
B.
6
­
Page
49
of
162
exposure
through
an
initial
application
is
a
suitable
approximation
of
the
expected
variable
profile
due
to
transfer
and
removal
events.

0
10
20
30
40
50
­
1
1
3
5
7
9
Time
(
hours)
Mass
(
mg)

Figure
23.
Modeled
carbaryl
mass
on
skin
over
exposure
period.

3.4.1
Estimates
of
absorbed
dose
from
dermal
exposure
The
absorbed
dose
associated
with
the
dermal
exposure
scenario
is
based
on
the
biomonitoring
study
(
Bayer
2004b,
Appendix
C).
For
the
assumed
scenario,
a
magnitude
of
exposure
was
chosen
to
be
consistent
with
the
observed
excreted
mass
of
carbaryl
equivalents.

A
dose
of
2
mg/
kg
was
applied
on
the
skin
surface,
and
the
total
amount
absorbed
before
wash­
off
is
1.69
mg,
or
a
dose
of
0.083
mg/
kg.
This
dose
resulted
in
urine
metabolite
levels
consistent
with
the
99.9%
ile
of
excreted
mass
of
carbaryl
equivalents
(
Figure
24).
Similar
to
the
oral
bolus
studies,
the
urinary
profiles
of
metabolites
indicate
significant
elimination
within
a
day.
II.
B.
6
­
Page
50
of
162
0
0.5
1
1.5
2
2.5
3
0
20
40
60
80
100
Time
(
hours)
Cumulative
mass
(
mg)

data
PBPK
model
Figure
24.
Cumulative
excretion
of
carbaryl
equivalents
in
urine
from
9
year
olds
following
turf
application.
The
error
bars
represent
the
99.9%
range.
The
PBPK
model
assumes
dermal
exposure
by
contact
with
treated
surfaces.

The
plateau
in
the
metabolite
levels
is
reached
earlier
than
in
the
biomonitoring
study
(
Figure
24).
As
discussed
in
section
2.3,
this
simulation
is
a
conservative
scenario.
By
assuming
that
all
of
the
absorbed
dose
occurs
in
a
single
activity
period,
the
peak
tissue
concentrations
will
be
highest.

3.4.2
Estimates
of
blood
and
tissue
concentrations
following
dermal
exposure
The
concentration
of
carbaryl
in
blood
increases
over
the
exposure
duration,
then
rapidly
decreases,
and
a
similar
profile
is
observed
in
other
tissues
(
Figure
25).
II.
B.
6
­
Page
51
of
162
0
0.002
0.004
0.006
0.008
0.01
0
5
10
15
20
25
Time
(
hours)
Concentration
(
mg/
L)

PBPK
model
Figure
25.
Carbaryl
concentration
in
venous
blood
in
9
year
olds
following
turf
application.
The
PBPK
model
assumes
dermal
exposure
by
contact
with
treated
surfaces.

The
peak
concentrations
of
carbaryl
in
brain
and
blood
determine
the
magnitude
of
acetylcholinesterase
inhibition
(
Figure
26).
The
scale
in
Figure
26
only
spans
from
90­
100%
so
that
the
dynamics
of
the
slight
change
can
be
visualized.
The
level
of
inhibition
due
to
the
dermal
exposure
scenario
is
minimal
(
Table
11).

90
95
100
0
2
4
6
8
10
Time
(
hours)
Activity
(%
baseline)

brain
blood
Figure
26.
Acetylcholinesterase
activity
in
brain
and
blood
in
9
year
olds
following
turf
application.
The
PBPK
model
assumes
dermal
exposure
by
contact
with
treated
surfaces.
II.
B.
6
­
Page
52
of
162
Table
11.
Blood
and
brain
carbaryl
concentrations
and
acetylcholinesterase
activity.
Compartment
Peak
carbaryl
concentration
(
mg/
L)
Acetylcholinesterase
activity
(%
baseline)
Blood
0.0084
99.1
Brain
0.0084
99.2
4.0
DISCUSSION
Note:
The
results
contained
in
this
report
are
preliminary
in
nature
and
do
not
reflect
current
OPP
and
EPA
policies
regarding
evaluation
of
inter­
and
intraspecies
extrapolation,
aggregate
risk,
and
application
of
the
FQPA
safety
factor
for
potential
sensitivity
of
infants
and
children.

For
the
carbaryl
exposure
simulation,
the
risk
metric
of
interest
was
brain
acetylcholinesterase
inhibition,
which
is
dependent
on
the
peak
brain
carbaryl
concentration.
The
exposure
scenarios
were
for
children
following
turf
application
of
carbaryl.
For
both
the
oral
hand­
to­
mouth
scenario,
and
dermal
scenario,
minimal
cholinesterase
inhibition
was
predicted.
This
is
not
surprising,
based
on
the
comparison
of
the
peak
brain
concentrations
after
exposure
relative
to
the
peak
brain
concentration
at
the
rat
no
observed
adverse
effect
level
(
NOAEL)
of
1
mg/
kg
oral
(
Table
12).

Table
12.
Comparison
of
peak
concentrations
after
turf
exposure
scenarios
to
the
rat
NOAEL
level
Rat
1
mg/
kg
oral
Oral
exposure
estimate
(
3
yr
old
human)
Dermal
exposure
estimate
(
9
yr
old
human)
Peak
brain
concentration
(
mg/
L)
0.18
0.0061
0.0084
The
confidence
in
the
model
predictions,
and
applicability
to
the
population
in
general
is
dependent
on
the
behavior
of
the
outcome
metrics
across
model
parameter
ranges.
The
relevant
range
of
values
may
span
the
uncertainty
or
variability
space.
The
model
parameters
that
affect
the
brain
dose
metrics
are
expected
to
be
those
associated
with
the
kinetics
of
acetylcholinesterase
inhibition,
clearance
of
carbaryl,
and
transport
to
the
brain.
Parameters
that
fall
into
this
category
include
the
rate
of
carbaryl
metabolism,
blood:
brain
partition
coefficient,
blood
flow
rate
to
brain,
and
the
rate
of
acetylcholinesterase
inhibition.
A
formal
sensitivity
analysis
was
not
performed
and
may
reveal
other
important
variables.
II.
B.
6
­
Page
53
of
162
The
range
of
parameter
values
was
based
on
the
likely
variability
in
the
human
population.
Where
statistical
distributions
for
the
parameters
could
be
established
from
the
literature
data,
the
coefficient
of
variation
was
applied
relative
to
the
best
fit
value
used
in
the
exposure
scenarios
(
Sections
2.3
and
2.4)
to
calculate
a
low
and
high
value.
Otherwise,
reported
limits
were
used
for
the
investigation.
The
investigated
ranges
of
parameter
values
are
listed
in
Table
13,
followed
by
discussion
of
how
each
range
was
established.

Table
13.
Investigated
parameters
and
values
Parameter
Best
fit
low
high
Variability
basis
Blood
flow
to
brain
(%
Cardiac
Output)
10
6.7
12.9
Reference
range
Blood:
brain
partition
coefficient
1
0.48
1.52
Observed
variability
for
rat
blood:
brain,
compared
with
human
fat:
blood
and
rat
fat:
blood
Inhibition
rate
(
mmol/
hr)
250
120
530
Observed
variability
for
rat
acetylcholinesterase
inhibition
Hydrolysis
rate
(
Vmax,
mmol/
hr/
kg)
0.071
0.022
0.22
Observed
malathion
variability
for
carboxylesterase
Total
oxidation
rate
(
Vmax,
mmol/
hr/
kg)
0.071
0.043
0.12
Observed
in
vitro,
and
variability
for
4­
OH
formation
and
3A4
drugs
For
the
blood
flow
to
the
brain,
the
range
is
based
on
cited
limits
reported
for
adults
(
Altman
and
Dittmer
1974).
The
ratio
of
the
high
and
low
values
relative
to
the
mean
was
then
applied
to
the
mean
values
used
in
each
of
the
child
scenarios.
For
example,
the
high
limit
for
adults
was
cited
as
67
ml/
g/
min
with
a
mean
of
52
ml/
g/
min
for
a
high/
mean
ratio
of
1.29.
The
average
%
cardiac
output
to
brain
for
children
is
10%
(
Table
4),
so
the
high
investigated
value
was
12.9%.
The
additional
blood
flow
was
subtracted
from
the
flow
to
rapidly
perfused
tissues
so
the
blood
flows
to
the
various
compartments
add
to
100%.
The
low
value
investigated
was
6.7%
of
cardiac
output.

Brain:
blood
partition
coefficients
have
not
been
measured
for
carbaryl
in
humans
or
other
species,
so
a
distribution
in
humans
is
not
available.
Another
chemical
where
similar
data
exists
was
used
to
estimate
a
realistic
value
for
the
coefficient
of
variation
(
COV)
in
brain:
blood
partition
coefficients.
Sato
et
al.
1977
and
Simmons
et
al.
2002
measured
the
brain:
blood
partition
coefficients
in
rats
for
trichloroethylene,
and
reported
the
means
and
standard
deviations.
The
COV
was
applied
relative
to
the
fit
value
used
in
the
initial
model
(
Table
13)
to
II.
B.
6
­
Page
54
of
162
establish
the
low
and
high
values
to
be
investigated.
Although
the
data
are
not
human,
when
the
COV
in
the
fat:
blood
partition
coefficients
for
trichloroethylene
are
compared
between
rats
(
Sato
et
al.
1977,
Simmons
et
al.
2002,
Koizumi
1989,
Barton
et
al.
1995)
and
humans
(
Sato
et
al.
1977),
the
rat
COV
is
larger
providing
some
evidence
that
the
values
based
on
the
rat
experiments
may
include
the
likely
human
values.
The
dissimilarity
between
trichloroethylene
and
carbaryl,
and
the
low
number
of
subjects
in
the
partition
coefficient
studies
(
N=
5
for
the
human
study,
up
to
10
in
the
rat
studies)
lowers
the
confidence
in
this
analysis.
However,
simulations
of
this
initial
range
illustrate
the
importance
of
this
value
and
related
assumptions.

The
kinetics
of
acetylcholinesterase
inhibition
in
the
brain
were
measured
in
rats
by
the
hydrolysis
of
acetylthiocholine
iodide
(
Mortensen
et
al.
1998).
They
modeled
the
process
as
saturable
(
Michaelis­
Menten)
and
reported
the
Vmax
and
Km
values.
Inhibition
is
modeled
as
a
linear
process
for
lower
environmental
exposures,
so
the
variability
in
the
quotient
of
Vmax/
Km
was
calculated
as
an
estimate
of
the
variability
of
the
inhibition
rate
constant,
ki.

The
metabolism
of
carbaryl
occurs
through
two
pathways,
hydrolysis
and
hydroxylation
(
Table
7).
Hydroxylation
primarily
occurs
through
P450
CYP3A4,
and
the
kinetics
were
measured
in
vitro
(
Tang
et
al.
2002).
The
standard
deviation
of
the
log
distribution
was
applied
to
the
model
in
vivo
value
(
Table
7)
to
establish
the
investigation
range.
This
range
is
consistent
with
the
metabolism
rate
ranges
(
Hattis,
and
cited
references)
of
pharmaceuticals
known
to
be
metabolized
through
the
CYP3A4
pathway
(
Flockhart),
including
alfentanil,
diltiazem,
fentanyl,
haloperidol,
midazolam,
nifedipine,
and
triazolam.
Carboxylesterase
in
involved
in
the
hydrolysis
pathway
(
Sogorb
et
al.
2002),
so
kinetic
data
from
the
metabolism
of
malathion
were
used
to
estimate
a
reasonable
range.
The
importance
of
carboxylesterase
may
be
inferred
from
the
metabolic
interactions
for
mixtures
of
carbaryl
and
malathion
(
Lechner
and
Abdel­
Rahman
1986).
Talcott
et
al.
1977,
and
Sams
and
Mason
1999
reported
the
malathion
metabolic
activity
from
individual
human
liver
and
serum
samples,
respectively.
The
geometric
standard
deviation
is
approximately
twice
as
large
for
metabolism
by
serum
than
by
liver
carboxylesterase
(
0.25
vs.
0.11),
so
that
value
was
used
to
establish
the
range
to
be
investigated
for
the
hydrolysis
pathway
for
carbaryl
(
Table
13).

The
dependence
of
the
results
on
a
particular
parameter
value
can
be
evaluated
by
the
change
in
a
dose
metric
as
that
value
is
changed.
Each
parameter
was
changed
from
the
best
fit
value
to
the
low
value
or
high
value,
while
the
other
parameters
were
held
at
the
best
fit
value
(`
low'
and
`
high'
in
Figures
27­
30).
The
dose
metrics
of
acetylcholinesterase
inhibition
and
peak
brain
carbaryl
concentrations
were
compared
to
the
case
where
all
the
parameters
were
held
at
their
best
fit
values
(`
all'
in
Figures
27­
30).
II.
B.
6
­
Page
55
of
162
The
metabolism
rate
and
brain:
blood
barrier
partition
coefficient
values
impact
the
brain
carbaryl
peak
concentration.
The
implemented
changes
to
the
brain
blood
flow
and
acetylcholinesterase
inhibition
rates
do
not
affect
the
peak
concentrations
for
either
the
hand­
to­
mouth
or
dermal
exposure
scenarios
(
Figures
27
and
28).

0
0.005
0.01
0.015
all
blood:
brain
brain
blood
flow
met
abolis
m
enzyme
ki
brain
concentration
(
mg/
L)

low
high
fit
Figure
27.
Brain
concentrations
for
different
parameter
values
in
3
year
olds
following
turf
application.
The
PBPK
model
assumes
oral
exposure
due
to
handto
mouth
activity.

0
0.005
0.01
0.015
all
blood:
brain
brain
blood
flow
met
abolis
m
enzyme
ki
brain
concentration
(
mg/
L)

low
high
fit
Figure
28.
Brain
concentrations
for
different
parameter
values
in
9
year
olds
following
turf
application.
The
PBPK
model
assumes
dermal
exposure
due
to
contact
with
treated
surfaces.

Although
there
are
modest
changes
to
the
brain
concentrations,
the
maximum
brain
acetylcholinesterase
inhibition
levels
are
not
affected.
Effectively
no
II.
B.
6
­
Page
56
of
162
inhibition
occurs
for
either
exposure
scenario
when
the
various
parameter
values
are
applied
(
Figures
29
and
30).

90
92
94
96
98
100
al
l
bloo
d:
brain
brain
blood
fl
ow
metaboli
s
m
en
zy
me
ki
%
basal
activity
low
high
fit
Figure
29.
Maximum
acetylcholinesterase
inhibition
for
different
parameter
values
in
3
year
olds
following
turf
application.
The
PBPK
model
assumes
oral
exposure
due
to
hand­
to­
mouth
activity.

90
92
94
96
98
100
al
l
bloo
d:
brain
brain
blood
fl
ow
metaboli
s
m
en
zy
me
ki
%
basal
activity
low
high
fit
Figure
30.
Maximum
acetylcholinesterase
inhibition
for
different
parameter
values
in
9
year
olds
following
turf
application.
The
PBPK
model
assumes
dermal
exposure
due
to
contact
with
treated
surfaces.

The
parameter
investigations
illustrate
the
uncertainty
in
and
variability
captured
by
the
model
for
the
results
in
Section
3.
The
parameters
that
affect
the
metrics
of
interest
also
highlight
important
experimental
measurements
that
would
increase
confidence
in
the
model.
Measurements
and
accurate
representation
of
II.
B.
6
­
Page
57
of
162
distribution
of
carbaryl
to
the
brain,
and
of
its
metabolism
in
humans,
and
the
associated
variability,
would
reduce
uncertainty
in
the
model
predictions.

Established
methods
exist
for
measuring
tissue:
blood
partition
coefficients
for
non­
volatile
chemicals
(
Jepson
et
al.
1994).
However,
if
the
active
transport
mechanisms
of
the
blood­
brain
barrier
are
the
cause
of
the
low
effective
partition
coefficient
value,
the
model
structure
would
need
to
be
expanded
to
include
them;
a
perfusion
limited
description
of
distribution
may
not
capture
the
important
distribution
behavior.
The
involvement
of
active
transporters
may
also
affect
the
assumptions
for
extrapolating
the
model
to
children.
The
partitioning
behavior
is
assumed
to
be
constant
for
children
3
years
and
older,
but
the
development
of
the
active
transport
systems
of
the
blood:
brain
barrier
would
need
to
be
considered.

Metabolism
in
humans
remains
difficult
to
estimate,
although
the
in
vitro
studies
with
specific
enzymes
provide
a
basis
(
Tang
et
al.
2002).
The
extrapolation
to
the
human
in
vivo
scenario
remains
an
area
of
research
(
Lipscomb
et
al.
1998).
Metabolic
studies
in
adults
will
likely
be
applicable,
since
the
P450
metabolic
enzyme
profile,
and
metabolism
of
the
few
studied
substrates,
in
children
older
than
1
year
is
similar
to
adults
(
Ginsberg
et
al.
2004).

PBPK
modeling
techniques
offer
the
promise
of
interpreting
this
type
of
exposure.
However,
the
reliability
of
the
output
from
such
a
model
is
dependent
on
the
necessary
input
information.
Continued
development
and
testing
of
the
model
with
quality
data
is
necessary
to
refine
the
input
parameters
and
values.
In
this
regard,
physiological
and
pharmacokinetic
(
PK)
data
are
gleaned
from
in
vivo
and
in
vitro
studies
as
reported
in
the
literature
to
establish
key
values
for
PK
parameters
and
the
time
course
for
disposition
of
the
compounds
in
the
body.
This
is
an
iterative
process.
It
is
anticipated
that
further
refinements
will
continue
to
improve
and
evolve
as
more
reliable
data
become
available.
Therefore,
the
results
reported
herein
reflect
the
current
state
of
reliability
with
the
understanding
that
further
anticipated
refinements
may
influence
the
conclusions.
II.
B.
6
­
Page
58
of
162
5.0
REFERENCES
Agata
Y,
Hiraishi
S,
Misawa
H,
Hirota
H,
Nowatari
M,
Hiura
K,
Fujino
N,
Oguchi
K,
and
Horiguchi
Y.
(
1994)
Regional
Blood
Flow
Distribution
and
Left
Ventricular
Output
during
Early
Neonatal
Life:
A
Quantitative
Ultrasonographic
Assessment.
Pediatric
Research.
Vol
36,
pp.
805­
810.

Ahdaya,
S.,
Guthrie,
F.
E.
Stomach
absorption
of
intubated
insecticides
in
fasted
mice,
Toxicol.
22,
311­
317
(
1982).

Altman,
P.
L,
Dittmer,
D.
S.
Biology
Data
Book,
2nd
ed.
Vol.
III.
Federation
of
American
Societies
for
Experimental
Biology,
Washington,
DC
(
1974).

Barton,
H.
A.,
J.
R.
Creech,
C.
S.
Godin,
G.
M.
Randall,
C.
S.
Seckel
(
1995).
"
Chloroethylene
mixtures:
pharmacokinetic
modeling
and
in
vitro
metabolism
of
vinyl
chloride,
trichloroethylene,
and
trans­
1,2­
dichloroethylene
in
rat."
Toxicol.
Appl.
Pharmacol.
130:
237­
247.

Bayer
2004a:
Metabolism
and
Pharmacokinetics
of
[
14C]
Carbaryl
in
Rats.
Bayer
CropScience.
Bayer
reports
201025
and
201026.

Bayer
2004b:
Application
of
carbaryl
pharmacokinetic
data
in
the
estimation
of
potential
post­
application
health
risk
associated
with
broadcast
lawn
care
products.
Infoscientific,
Inc.

Bayer
2004c.
Measurement
of
Pesticide
Exposure
of
Suburban
Residents
Associated
with
the
Residential
Use
of
Carbaryl.
ABC
Laboratories.

Benson,
W.
H.,
Dorough,
H.
W.
Comparitive
Ester
Hydrolysis
of
Carbaryl
and
Ethiofencarb
in
Four
Mammalian
Species,
Pesticide
Biochem.
Physiol.
21,
199­
206
(
1984).

Boot,
AM,
Bouquet,
J,
de
Ridder
MAJ,
Krenning,
EP,
and
de
Muicnk
Keizer­
Schrama,
S.
(
1997).
Determinants
of
body
composition
measured
by
dual­
energy
X­
ray
absorptiometry
in
Dutch
children
and
adolescents.
Am
J
Clin
Nutr.
66:
232­
8.

Brooks,
W.,
and
Broxup,
B.
(
1995a).
An
acute
study
of
the
time
course
cholinesterase
inhibition
by
orally
administered
carbaryl
technical.
MRID
438452­
02.

Brooks,
W.,
and
Broxup,
B.
(
1995b).
A
time
of
peak
effects
study
of
a
single
orally
administered
dose
of
carbaryl,
technical
grade,
in
rats.
MRID
438452­
03.
II.
B.
6
­
Page
59
of
162
Casper,
H.
H.,
Peaks,
J.
C.,
Dinusson,
W.
E.
Gastric
Absorption
of
a
Pesticide
(
1­
Naphthyl
N­
Methylcarbamate)
in
the
Fasted
Rat,
Pesticide
Biochem
Physiol.
2,
391­
396
(
1973).

Cheng,
T.
(
1994).
Dermal
Absorption
of
14C­
Carbaryl
(
80S)
in
Male
Rats
(
Preliminary
and
Definitite
Phases).
MRID
433297­
01.

Cool,
M.,
Jankowski,
K.
A
Survey
of
the
Metabolism
of
Carbamate
Insecticides
in
Insecticides,
Hutson,
D.
H,
Roberts,
T.
R.
Eds.
John
Wiley
and
Sons,
Ltd.,
New
York,
NY,
1985.

Corley,
RA,
Mendrala,
AL,
Smith,
FA,
Staats,
DA,
Gargas,
M.
L.,
Conolly,
R.
B.,
Andersen,
M.
E.,
and
Reitz,
R.
H.
(
1990)
Development
of
a
Physiologically
Based
Pharmacokinetic
Model
for
Chloroform.
Toxicol
Appl
Pharmacol.
103:
512­
527
Fisher,
J.
W.,
M.
L.
Gargas,
B.
C.
Allen
and
M.
E.
Andersen
(
1991).
"
Physiologically
based
pharmacokinetic
modeling
with
trichloroethylene
and
its
metabolite,
trichloroacetic
acid,
in
the
rat
and
mouse."
Toxicol.
Appl.
Pharmacol.
109:
183­
195.

Fisher,
JW,
Mahle,
D.,
and
Abbas,
R.
1998.
A
Human
Physiologically
Based
Pharmacokinetic
Model
for
Trichloroethylene
and
Its
Metabolites,
Trichloroacetic
Acid
and
Free
Trichloroethanol.
Toxicol.
Appl.
Pharmacol.
152:
339­
359.

Flockhart
Table,
Cytochrome
P450
Drug­
Interaction
Table,
http://
medicine.
iupui.
edu/
flockhart/
table.
htm
Ginsberg,
G.,
Hattis,
D.,
Sonawane,
B.
Incorporating
Pharmacokinetic
Differences
between
Children
and
Adults
in
Assessing
Children's
Risk
to
Environmental
Toxicants.
Toxicol.
Appl.
Pharmacol.
198,
164­
183
(
2004).

Hassan,
A.,
Zayed,
S.
M.
A.
D.,
Abdel­
Hamid,
F.
M.
Metabolism
of
Carbamate
Drugs
I:
Metabolism
of
1­
Naphthyl­
N­
Methyl
Carbamate
(
Sevin)
in
the
Rat,
Biochem.
Pharamacol.
15,
2045­
2055
(
1966).

Hattis,
D.
Human
Interindividual
Variability
in
Parameters
Related
to
Susceptibility
for
Toxic
Effects,
http://
www2.
clarku.
edu/
faculty/
dhattis/

Hetnarski,
B.;
O'Brien,
R.
D.
Electron­
donor
and
affinity
constants
and
their
application
to
the
inhibition
of
acetylcholinesterase
by
carbamates.
Journal
of
Agricultural
and
Food
Chemistry
1975,
23,
709­
713.

International
Commission
on
Radiological
Protection
(
ICRP)
"
Annals
of
the
ICRP:
Basic
Anatomical
and
Physiological
Data
for
Use
in
Radiological
Protection:
Reference
Values"
Volume
32(
3­
4),
Publication
89.
Elsevier
Science
Ltd.
New
York,
NY
(
2002).
II.
B.
6
­
Page
60
of
162
International
Life
Science
Institute,
Risk
Science
Institute
(
ILSI­
RSI)
(
1994)
"
Physiological
Parameters
for
PBPK
Models"
Report.
December.

Jepson,
G.
W.,
Hoover,
D.
K.,
Black,
R.
K.,
McCafferty,
J.
D.,
Mahle,
D.
A.,
Gearhart,
J.
M.
A
Partition
Coefficient
Determination
Method
for
Nonvolatile
Chemicals
in
Biological
Tissues.
Fund.
Appl.
Toxicol.
22,
519­
524
(
1994).

Keys,
D.
A.,
J.
V.
Bruckner,
S.
Muralidhara
and
J.
W.
Fisher
(
2003).
"
Tissue
dosimetry
expansion
and
cross­
validation
of
rat
and
mouse
physiologically
based
pharmacokinetic
models
for
trichloroethylene."
Toxicol
Sci
76(
1):
35­
50.

Knaak,
J.
B.,
Tallant,
M.
J.,
Bartley,
W.
J.,
Sullivan,
L.
J.
The
Metabolism
of
Carbaryl
in
the
Rat,
Guinea
Pig,
and
Man,
J.
Agr.
Food
Chem.
13,
537­
543
(
1965).

Knaak,
J.
B.,
Yee,
K.,
Ackerman,
C.
R.,
Zweig,
G.,
Fry,
D.
M.,
Wilson,
B.
W.
Percutaneous
Absorption
and
Dermal
Dose­
Cholinesterase
Response
Studies
with
Parathion
and
Carbaryl
in
the
Rat,
Toxicol.
Appl.
Pharmacol.
76,
252­
263
(
1984).

Koizumi
A.
(
1989)
"
Potential
of
physiologically
based
pharmacokinetics
to
amalgamate
kinetic
data
of
trichloroethylene
and
tetrachloroethylene
obtained
in
rats
and
man."
Br
J
Ind
Med.
46(
4):
239­
49.

Krishna,
J.
G.,
Casida,
J.
E.
Fate
in
Rats
of
the
Radiocarbon
from
Ten
Variously
Labeled
Methyl­
and
Dimethyl­
Carbamate­
C14
Insecticide
Chemicals
and
Their
Hydrolysis
Products,
J.
Agr.
Good
Chem.
14,
98­
105
(
1966).

Kuhr,
R.
J.,
Dorough,
H.
W.
Carbamate
Insecticides:
Chemistry,
Biochemistry,
and
Toxicology.
CRC
Press,
Cleveland,
OH,
1976.

Lechner,
D.
W.,
Abdel­
Rahman,
M.
S.
Kinetics
of
Carbaryl
and
Malathion
in
Combination
in
the
Rat.
J
Toxicol.
Environ.
Health
18,
241­
256
(
1986).

Lipscomb,
J.
C.,
J.
W.
Fisher,
P.
D.
Confer,
J.
Z.
Byczkowski
(
1998).
"
In
vitro
to
in
vivo
extrapolation
for
trichloroethylene
metabolism
in
humans."
Toxicol.
Appl.
Pharmacol.
152:
376­
387.

Liu,
X.,
Tu,
M.,
Kelly,
R.
S.,
Chen,
C.,
Smith,
B.
J.
Development
of
a
Computational
Approach
to
Predict
Blood­
Brain
Barrier
Permeability.
Drug
Metab.
Dispos.
32:
132­
139
(
2004).

Marshall,
T.
C.,
Dorough,
H.
W.
Biliary
Excretion
of
Carbamate
Insecticides
in
the
Rat,
Pesticide
Biochem.
Physiol.
11,
56­
63
(
1979).
II.
B.
6
­
Page
61
of
162
Milner,
R.,
"
Cranial
Capacity."
The
Encyclopedia
of
Evolution:
Humanity's
Search
For
Its
Origins.
New
York:
Holt,
1990:
98
Mortensen,
S.
R.,
Hooper,
M.
J.,
Padilla,
S.
Rat
Brain
Acetylcholinesterase
Activity:
Developmental
Profile
and
Maturational
Sensitivity
to
Carbamate
and
Organophosphorous
Inhibitors.
Toxicol.
125:
13­
19
(
1998).

Payne,
M.
P.,
Kenny,
L.
C.
Comparison
of
Models
for
the
Estimation
of
Biological
Partition
Coefficients.
J
Toxicol.
Environ.
Health,
Part
A.
65:
897­
931
(
2002).

Poulin
P,
Theil
FP.
A
priori
prediction
of
tissue:
plasma
partition
coefficients
of
drugs
to
facilitate
the
use
of
physiologically
based
pharmacokinetic
models
in
drug
discovery.
J
Pharm
Sci
89:
16­
35
(
2000).

Poulin
P,
Theil
FP.
Prediction
of
pharmacokinetics
prior
to
in
vivo
studies.
1.
Mechanism­
based
prediction
of
volume
of
distribution.
J
Pharm
Sci
91:
129­
156
(
2002).

Poulin
P,
Krishnan,
K.
A
mechanistic
algorithm
for
predicting
blood:
air
partition
coefficients
of
organic
chemicals
with
the
consideration
of
reversible
binding
in
hemoglobin.
Toxicol
Appl
Pharmacol
136:
131­
137
(
1996).

Roberts
TR.
Hutson
DH
(
1999).
Metabolic
Pathways
of
Agrochemicals,
Part
2:
Insecticides
and
Fungicides.
The
Royal
Society
of
Chemistry,
15­
24.

Sams,
C.,
Mason,
H.
J.
Detoxification
of
Organophosphates
by
A­
Esterases
in
Human
Serum.
Human
Experimental
Toxicol.
18,
653­
658
(
1999).

Sato
A.,
T.
Nakajima,
Y.
Fujiwara,
and
N.
Murayama
(
1977)
"
A
pharmacokinetic
model
to
study
the
excretion
of
trichloroethylene
and
its
metabolites
after
an
inhalation
exposure."
Br
J
Ind
Med.
34(
1):
56­
63.

Schmitz
L,
Koch
H,
Bein
G,
and
Brockmeier
K.
(
1998).
Left
Ventricular
Diastolic
Function
in
Infants,
Children,
and
Adolescents.
Reference
Values
and
Analysis
of
Morphologic
and
Physiologic
Determinants
of
Echocardiographic
Doppler
Flow
Signals
During
Growth
and
Maturation.
Journal
of
the
American
College
of
Cardiology.
Vol
32,
pp.
1441­
1448
Shah,
P.
V.,
Monroe,
R.
J.,
Guthrie,
F.
E.
Comparative
Rates
of
Dermal
Penetration
of
Insecticides
in
Mice,
Toxicol.
Appl.
Pharmacol.
59,
414­
423
(
1981).

Simmons
J.
E.,
W.
K.
Boyes,
P.
J.
Bushnell,
J.
H.
Raymer,
T.
Limsakun,
A.
McDonald,
Y.
M.
Sey,
and
M.
V.
Evans
(
2002).
"
A
physiologically
based
pharmacokinetic
model
for
trichloroethylene
in
the
male
long­
evans
rat."
Toxicol
Sci.
69(
1):
3­
15.
II.
B.
6
­
Page
62
of
162
Sogorb,
M.
A.,
Carrera,
V.,
Benabent,
M.,
Vilanova,
E.
Rabbit
Serum
Albumin
Hydrolyzes
the
Carbamate
Carbaryl,
Chem.
Res.
Toxicol.
15,
520­
526
(
2002).

Sullivan,
L.
J.,
Eldridge,
J.
M.,
Knaak,
J.
B.,
Tallant,
M.
J.
5,6­
Dihydro­
5,6­
dihidroxycarbaryl
glucuronide
as
a
significant
metabolite
of
carbaryl
in
the
rat,
J.
Agric.
Food
Chem.
20,
980­
985
(
1972).

Struble,
C.
(
1994).
Metabolism
of
14C­
carbaryl
in
rats
(
preliminary
and
definitive
phases).
MRID
43332101,
Rhone
Poulenc.

Talcott,
R.
E.,
Mallipudi,
N.
M.,
Fukuto,
T.
R.
Malathion
Carboxylesterase
Titer
and
Its
Relationship
to
Malathion
Toxicity.
Toxicol.
Appl.
Pharmacol.
50,
501­
504
(
1977).

Tang,
J.,
Cao,
Y.,
Rose,
R.
L.,
Hodgson,
E.
In
vitro
Metabolism
of
Carbaryl
by
Human
Cytochrome
P450
and
its
Inhibition
by
Chlorpyrifos,
Chemico­
Bio.
Interact.
141,
229­
241
(
2002).

U.
S.
EPA
2002.
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessing
Human
Exposure
and
Dose.
EPA/
600/
X­
04/
060.

U.
S.
EPA
2003.
HED
Phase
5
Occupational
and
Residential
Exposure
Assessment.
II.
B.
6
­
Page
63
of
162
Appendix
A.
Model
1.0
Descriptions
of
Exposure
Exposure
occurs
at
the
boundary
of
the
body
or
test
system.
It
is
of
considerable
interest
to
the
U.
S.
EPA
to
limit,
reduce
and
in
specific
instances
eliminate
exposure.
Humans
become
exposed
to
chemical
and
biological
substances,
physical
energy
and
radiation
through
the
activities
they
perform
routinely
in
everyday
life
or
occupationally
as
part
of
certain
policies,
practices
or
procedures.
Exposure
can
occur
accidentally
as
a
random
event,
or
during
an
occupationally
related
task
or
as
a
result
of
a
purposeful
action
such
as
a
(
terrorist)
attack.

Humans
may
become
incidentally
and
unknowingly
exposed.
Exposure
to
particles
and
gasses
in
the
air
we
breathe
may
be
unavoidable.
Dermal
contact
with
surfaces
residues
may
be
unforseen
and
unrecognizable.
Ingestion
of
particles
and
residues
in
food
may
be
unintended
and
unsuspected.
Under
certain
conditions,
exposure
can
be
limited
or
reduced
through
education,
managerial
oversight,
regulatory
responsiveness,
and
use
of
proper
personal
protection
devices
(
Ness,
1994).

When
exposure
is
perceived
as
unavoidable,
we
may
wish
to
describe
exposure
events
in
time
and
space
under
certain
recognizable
exposure
scenarios.
This
may
be
accomplished
more
easily
for
occupationally
related
exposures
where
policies,
practices
and
procedures
have
been
established
than
for
those
that
occur
randomly
or
incidentally.
However,
regardless
of
the
nature
of
the
exposure,
exposure
follows
along
recognized
pathways,
e.
g.,
inhalation,
ingestion,
and
dermal,
and
routes,
respiratory,
oral,
percutaneous.

ERDEM
was
designed
to
examine
three
pathways
of
exposure,
inhalation,
ingestion
and
dermal,
and
eight
routes
of
entry
into
the
in
silico
test
system.
Experimental
pathways
and
routes
of
entry
were
included
(
Appendix
A
Section
1.1)
along
with
what
might
be
perceived
as
naturally
occurring
unscheduled
or
not
experimentally
controlled
pathways
and
routes
(
Appendix
A
Section
1.2).
This
approach
greatly
enhanced
the
database
to
include
laboratory
animal
and
clinical
studies
in
addition
to
environmental
field
studies.
For
example,
enteral
administration
is
represented
by
intraperitoneal
injection
(
IP)
of
chemical
into
the
GI
tract
via
the
Portal
Blood
(
Liver
for
the
Stomach/
Intestine
Gastro­
Intestinal
model).

1.1
Experimental
Pathways
and
Routes
of
Entry
1.1.1
Intraperitoneal
Injection
Intraperitoneal
Injections
into
the
Portal
Blood
may
be
given
for
multiple
chemicals
for
up
to
nine
scenarios
starting
at
time
TINPj
,
and
repeated
at
the
II.
B.
6
­
Page
64
of
162
interval
TINP
TTj
,
.
The
amount
of
chemical
to
be
injected
is
calculated
from
the
concentration
of
the
chemical
times
the
Body
Volume.
The
amount
injected
decreases
at
an
exponential
rate.
All
injections
start
before
the
simulation
start
time
(
T0).
When
the
scheduled
event
occurs
to
start
the
injection,
the
amount
is
calculated
as:
A
A
C
V
INP
J
INP
CUR
J
INP
J
B
i
j
i
j
i
j
,
,
,
,,

,
,
,
=
+

where
A
INP
CUR
Ji
j
,
,
,
is
the
amount
remaining
to
be
absorbed
from
the
previous
interval.
The
amount
of
the
ith
chemical
from
the
jth
exposure
remaining
to
be
absorbed
is:

A
A
e
INP
CUR
J
INP
J
MIN
K
T
K
i
j
i
j
INP
ABS
Ji
j
INP
LIMi
,
,
,
(
,

.
,
,
,
,
,
)

=
 
 

where
 
T
T
T
INP
STL
J
j
=
 
,
,
,
T
INP
STL
J
j
,
,
is
the
start
time
for
the
last
IP
Injection
for
the
jth
exposure,
and
MIN
is
the
mean
minimum
of
the
two
terms.
The
amount
of
the
ith
chemical
remaining
to
be
absorbed
for
all
exposures
is:

A
A
INP
CUR
INP
CUR
J
j
N
i
ij
INP
EXP
,
,,,
,

=
=

1
and
the
rate
of
change
of
the
amount
of
chemical
injected
into
the
Portal
Blood
is
given
by:

dA
dt
K
A
INP
INP
ABS
J
INP
CUR
J
j
N
i
i
j
i
j
INP
EXP
=
=

,
,
,
,
,
,
,

1
At
the
start
of
the
next
injection
interval,
the
IP
amount
remaining
to
be
absorbed
is
accumulated
for
each
chemical;
the
elapsed
IP
simulation
time
is
reset
to
zero,
and
the
next
injection
occurrence
is
scheduled.

1.1.2
Intramuscular
Administration
Parenteral
administration
is
represented
by
intramuscular
injection
(
IM)
in
the
muscle
(
Slowly
Perfused
Tissue).
Intramuscular
Injections
may
be
given
for
multiple
chemicals
and
up
to
nine
scenarios
starting
at
time
T
INMj
,
and
repeat
at
the
interval
T
INM
TTj
,
.
The
amount
of
chemical
to
be
injected
is
calculated
from
the
concentration
of
the
chemical
times
the
Body
Volume.
The
amount
injected
decreases
at
an
exponential
rate.
All
injections
that
start
before
the
simulation
start
time
(
T0)
and
before
the
simulation
end
time
are
scheduled.
When
the
scheduled
event
occurs,
to
start
the
injection
the
amount
is
calculated
as:
A
A
C
V
INM
J
INM
CUR
J
INM
J
B
i
j
i
j
i
j
,
,
,
,
.
,
,
.

=
+

where
A
INM
CUR
Ji
j
,
,
,
is
the
amount
remaining
to
be
absorbed
from
the
previous
interval.
The
amount
of
the
ith
chemical
from
the
jth
exposure
remaining
to
be
absorbed
is:

(
)
A
A
e
INM
CUR
Ji
j
INM
J
MIN
K
T
K
i
j
INM
ABS
Ji
j
INM
LIMi
,
,
,
,
,

,
,
,
,
,

=
 
 
II.
B.
6
­
Page
65
of
162
where
 
T
T
T
INM
STL
J
j
=
 
,
,
,
T
INM
STL
J
j
,
,
is
the
start
time
for
the
last
IM
Injection
for
the
jth
exposure,
and
MIN
means
find
the
minimum
of
the
two
terms.
The
amount
of
the
ith
chemical
remaining
to
be
absorbed
for
all
exposures
is:

A
A
INM
CUR
INM
CUR
J
j
N
i
ij
INM
EXP
,
,,,
,

=
=

1
and
the
rate
of
change
of
the
amount
of
chemical
injected
into
the
Slowly
Perfused
Tissue
(
muscle)
is
given
by:

dA
dt
K
A
INM
INM
ABS
J
INM
CUR
J
i
N
i
i
j
i
j
INM
EXP
=
=

,
,
,
,
,
,
,

1
At
the
start
of
the
next
injection
interval,
the
IM
amount
remaining
to
be
absorbed
for
each
chemical
is
reset
to
zero
and
scheduled
for
the
next
injection
occurrence.

1.1.3
Intravascular
Administration
There
are
two
forms
of
intravascular
administration
into
the
Venous
Blood,
Bolus
Intravenous
Injection
or
Infusion.

1.1.3.1
Infusion
into
the
Venous
Blood
Infusion
is
the
direct
insertion
of
chemical
into
the
Venous
Blood
at
time
TINFj
for
a
period
of
time,
T
INF
Dj
,
,
which
can
be
repeated
at
the
interval,
T
INF
TTj
,
.
There
can
be
many
chemicals
in
each
infusion,
each
with
its
own
concentration.
The
rate
of
change
of
the
amount
of
the
ith
chemical
infused
into
the
Venous
Blood
versus
time
is
given
by:
dA
dt
C
Q
INF
VB
INF
j
N
INF
i
i
j
INF
j
,

,
.
=
=

1
The
flow
rate,
Q
INFj
,
is
independent
of
the
chemical.
There
is
one
flow
rate
for
each
exposure.
However,
the
concentration
of
the
ith
chemical
in
the
jth
exposure,
C
INFi
j
,
,
can
be
different
for
each
chemical.
The
total
amount
of
the
ith
chemical
passed
to
the
Venous
Blood
by
Infusion
is:

A
dA
dt
dt
INF
VB
INF
VB
T
t
Ti
i
,
,
.
=

0
1.1.3.1
Bolus
Intravenous
Injections
Bolus
Dose
Intravenous
(
IV)
Injections
start
at
a
given
time,
TBIVj
,
and
may
be
repeated
at
an
input
interval,
T
RIG
TTj
,
.
Bolus
Dose
Intravenous
Injections
(
IVs)

injected
before
simulation
start
time
are
not
modeled.
Those
that
occur
at
simulation
start
time
(
T0)
are
modeled
as
true
Bolus
Doses
into
the
Venous
II.
B.
6
­
Page
66
of
162
Blood.
The
equation
for
the
initial
values
for
the
amount
of
chemical
in
the
Bolus
IV
Dose
and
in
the
Venous
Blood
are
given
by:

A
A
BIV
BIV
j
N
T
i
i
j
BIG
0
1
=
=

,

A
A
VB
BIV
T
i
T
i
0
0
=
for
the
exposures
that
start
at
simulation
start
time.
A
Bolus
Dose
IV
that
occurs
after
the
simulation
start
time
is
simulated
with
a
rate
input
normally
having
a
time
duration
of
one­
quarter
of
a
communication
interval
or
one­
quarter
of
a
maximum
integration
step,
whichever
is
less.
The
equation
for
the
jth
exposure
for
the
ith
chemical
then
takes
the
form:
dA
dt
A
T
T
BIV
BIV
BIV
BIV
i
j
i
j
E
B
,
,

=
 
and
for
all
exposures
to
the
ith
chemical
at
time
t:
dA
dt
dA
dt
BIV
BIV
j
N
i
i
j
BIV
=
=

,

.
1
A
dA
dt
dt
A
BIV
BIV
T
t
BIV
Ti
i
T
i
=
+

0
0
1.1.4
Inhalation
Administration
There
are
two
types
of
inhalation,
Open
or
Closed
Chamber
Inhalation.
Open
Chamber
Inhalation
is
assumed.

The
subjects
in
each
simulation
are
in
a
closed
chamber
or
an
open
chamber.
They
cannot
be
mixed.
If
the
simulation
uses
an
open
chamber
then:

A.
If
no
exposure
is
defined
then
the
simulation
starts
with
an
open
chamber
with
no
concentration
of
chemical.
B.
There
is
no
change
in
the
concentration
of
chemical
in
an
open
chamber
due
to
exhaled
air.
C.
If
one
or
more
open
chamber
exposures
are
defined
and
none
are
designated
the
starting
exposure
then
exposure
number
one
is
the
exposure
starting
the
simulation.
D.
Any
number
of
chemicals
can
have
a
concentration
in
an
Open
Chamber
exposure.
E.
The
simulation
cannot
switch
in
the
middle
from
Open
to
Closed
Chamber
Inhalation.

If
the
simulation
uses
a
closed
chamber
then:

F.
There
must
be
a
Closed
Chamber
exposure
defined
to
start
the
simulation.
G.
Only
one
Closed
Chamber
exposure
can
be
active
at
once.
II.
B.
6
­
Page
67
of
162
H.
Any
number
of
chemicals
can
be
assigned
a
concentration
for
a
Closed
Chamber
exposure.
I.
The
chemicals
in
the
exhaled
air
change
the
concentration
of
each
chemical
in
the
closed
chamber.
J.
If
Closed
Chamber
Inhalation
is
chosen
then
the
whole
simulation
will
be
with
a
closed
chamber.
K.
Open
Chamber
Inhalation
can
be
approximated
with
an
extremely
large
closed
chamber.

The
input
concentration
for
an
Open
Chamber
is
in
units
of
parts
per
million.
The
input
for
Closed
Chamber
Inhalation
can
be
the
amount
(
mass
units),
or
the
concentration
(
units
of
parts
per
million).
The
Open
Chamber
concentration
for
the
ith
chemical
in
mass
per
unit
volume
is
calculated
from:

C
C
C
INHi
AIR
J
j
N
AIR
PPM
i
j
INH
EXP
i
=
=

,
,
,
,

1
1
,

For
Closed
Chamber
Inhalation
the
volume
of
the
chamber
is
required.
The
volume
of
the
air
in
the
chamber
is
calculated
by
subtracting
the
volume
of
the
number
of
subjects:
V
V
N
V
CC
GAS
CC
SBJ
B
j
j
,
=
 

If
the
input
into
the
Closed
Chamber
is
a
concentration
then
it
is
given
in
parts
per
million
and
converted
to
mass
per
unit
volume
units.
But,
if
the
input
is
an
amount,
then
the
amount
is
converted
to
concentration
by:

C
A
V
INH
CC
J
CC
GAS
i
i
j
j
=
,

,
,

.

There
are
two
types
of
lung
included
in
ERDEM,
Static
Lung
and
Breathing
Lung.
The
inhalation
pathway
involves
entry
through
the
Open
or
Closed
Chamber
Static
Lung
or
the
Breathing
Lung.
The
lung
equations
are
not
included
in
this
report
since
inhalation
exposures
were
not
considered.
Details
may
be
found
in
the
ORD\
NERL
APM
report
"
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessing
Human
Exposure
and
Dose"
(
U.
S.
EPA
2002).

1.2
Implementation
of
the
Exposure
Time
Histories
for
Rate
Ingestion,
Inhalation,
and
Skin
Surface
Exposures.

Exposure
time
histories
have
been
implemented
in
ERDEM
for
rate
ingestion,
open
chamber
Inhalation,
and
skin
surface
exposure
time
histories.
There
can
be
up
to
nine
time
histories
for
each
exposure
type
(
except
for
skin
surface
exposure
which
can
have
up
to
five
exposures),
but
only
one
for
each
chemical.
The
time
histories
may
be
repeated
periodically.
Each
time
history
will
have
a
start
time
and
duration
interval.
Any
exposure
can
be
expressed
as
an
exposure
time
history.

Each
exposure
route
has
most
of
these
variables:
II.
B.
6
­
Page
68
of
162
I.
Concentration
of
chemical
in
a
volume
of
food,
water,
or
air;
II.
Volume
of
the
food,
water
or
air;
III.
Flow
rate
­
volume
per
unit
time;
IV.
Start
time
of
exposure,
duration
of
exposure,
and
interval
between
exposures.

If
an
exposure
starts
on
or
before
the
simulation
start
time,
then
the
simulation
starts
with
the
exposure
in
effect.
Otherwise,
there
is
an
event
to
start
and
one
to
terminate
the
exposure.
There
can
be
overlapping
exposures
of
the
same
type
in
most
cases
(
not
for
Closed
Chamber
Inhalation
exposures).
If
the
exposure
is
an
exposure
time
history,
then
only
one
chemical
can
be
modeled
and
there
can
be
only
one
exposure
time
history
of
a
particular
type
in
any
one
simulation.

1.2.1
Ingestion
Into
the
Stomach
and
the
Stomach
Lumen
Rate
ingestion
input
is
a
time
history
of
the
time
and
the
amount
per
unit
time
(
concentration
times
flow
rate)
of
the
chemical.
Linear
interpolation
is
used
to
obtain
intermediate
values.

1.2.1.1
Bolus
Dose
Ingestion
Bolus
dose
ingestion
occurs
when
chemical
is
taken
into
the
Gastro­
Intestinal
tract
very
rapidly;
for
instance
in
one
big
bite
or
drink.
Bolus
dose
inputs
that
occur
at
simulation
start
time
(
T0)
are
modeled
as
true
bolus
doses.
The
initial
value
for
the
integration
in
the
stomach
or
stomach
lumen
is
the
sum
of
all
exposures
that
start
at
simulation
start
time.
The
equation
is:

A
A
C
V
BIG
BIG
BIG
BIG
j
N
T
i
T
i
i
j
j
Big
0
0
1
=
+
=

,

for
all
exposures
to
the
ith
chemical
at
time
T0.

Bolus
dose
inputs
with
start
time
TBIGj
that
are
greater
than
the
simulation
start
time
and
before
the
simulation
stop
time
are
simulated
by
rate
inputs
that
start
at
the
scheduled
bolus
dose
start
time
with
a
duration
of
1/
4
of
a
communication
interval,
or
1/
4
of
a
maximum
integration
interval,
whichever
is
less.
The
bolus
dose
for
the
jth
exposure
can
be
repeated
at
the
input
interval,
T
BIG
TTj
,
.
The
approximation
of
a
bolus
dose
input
via
a
rate
input
of
a
relatively
short
duration
produces
results
very
similar
to
those
achieved
with
an
actual
bolus
dose
while
allowing
a
more
accurate
evaluation
of
amounts
and
concentrations
via
numerical
integration.
The
equation
for
the
jth
exposure
for
the
ith
chemical
then
takes
the
form:
dA
dt
C
V
T
T
BIG
BIG
BIG
BIG
BIG
i,
j
i,
j
j
E
B
=
 
and
for
all
exposures
to
the
ith
chemical
at
time
t:
II.
B.
6
­
Page
69
of
162
dA
dt
dA
dt
BIG
BIG
j
N
i
i
j
BIG
=
=

,

.
1
The
variable
A
BIGT
i
0
is
the
initial
value
in
the
numerical
integrations
for
the
total
amount
of
the
ith
chemical
in
the
bolus
dose
ingestion,
and
the
amount
in
the
stomach
or
stomach
lumen:

A
dA
dt
dt
A
BIG
BIG
T
t
BIG
Ti
i
T
i
=
+

0
0
1.2.1.2
Rate
Ingestion
The
Rate
Ingestion
for
each
exposure
starts
at
a
given
time,
TRIGj
,
occurs
over
a
duration
of
time,
T
RIG
Dj
,
,
and
may
be
repeated
at
an
input
interval,
T
RIG
TTj
,
.
The
concentration
of
the
chemical
in
the
food
or
drink
and
the
flow
rate
are
required
inputs.
The
product
results
in
the
rate
of
change
of
chemical
in
the
stomach
or
stomach
lumen
versus
time.
Overlapping
exposures
are
allowed.
The
rate
of
change
of
the
ith
chemical
in
rate
ingestion
versus
time
is
given
by:
dA
dt
C
Q
RIG
RIG
j
N
RIG
i
i
j
RIG
j
=
=

,
.
1
Thus
there
is
one
flow
rate
for
each
exposure.
But
the
concentration
of
each
chemical
in
the
jth
exposure
may
be
different.
The
numerical
integration
to
obtain
the
total
amount
of
the
ith
chemical
passed
to
the
stomach
by
Rate
Ingestion
is:

A
dA
dt
dt
A
RIG
ST
RIG
T
t
RIG
Ti
i
i
,
=
+

0
0
1.2.2
Inhalation
Exposure
Inhalation
exposure
input
follows
a
time
history
and
concentration
in
Parts
Per
Million
(
PPM)
of
chemical.
Linear
interpolation
is
used
to
obtain
intermediate
values.
The
inhalation
pathway
involves
entry
through
the
Open
or
Closed
Chamber
Static
Lung
or
the
Breathing
Lung.
The
lung
equations
are
not
included
in
this
report
since
inhalation
exposures
were
not
considered.
Details
may
be
found
in
the
ORD\
NERL
APM
report
"
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessing
Human
Exposure
and
Dose"
(
U.
S.
EPA
2002).

1.2.3
Dermal
Exposure
There
are
two
types
of
dermal
exposure
modeled
in
ERDEM,
one
for
chemicals
in
an
aqueous
vehicle,
most
often
a
water
based
diluent,
and
chemicals
as
a
dried
residue
or
adsorbed
onto
particles
as
a
dry
source.
II.
B.
6
­
Page
70
of
162
1.2.3.1
Skin
Surface
Exposure
to
a
Chemical
in
an
Aqueous
Vehicle
Skin
Surface
exposure
input
follows
a
time
history
where
a
surface
area
of
the
skin
(
square
centimeters)
becomes
exposed
to
a
chemical
in
an
aqueous
vehicle
as
a
concentration
(
mass
per
centimeter
cubed).
This
concentration
and
area
of
the
skin
are
used
to
compute
the
rate
of
change
of
the
amount
of
chemical
absorbed.
Linear
interpolation
is
used
to
obtain
intermediate
values.
The
skin
surface
is
exposed
to
chemical
in
an
aqueous
vehicle
(
water)
at
time
T
SKWj
for
a
period
of
time,
T
SKW
Dj
,
,
which
can
be
repeated
at
the
interval,
T
SKW
TTj
,
.
Skin
surface
(
water)
exposures
progress
from
a
simulation
start
time
and
end
at
a
scheduled
termination
time
point.
The
concentration
of
the
ith
chemical
at
the
skin
surface
is
found
from
summing
the
concentrations
from
each
of
the
up
to
five
exposure
scenarios:

C
C
SKS
SKW
J
j
N
i
ij
SKW
EXP
=
=

,
,
,

1
The
rate
of
change
of
chemical
in
the
epidermis
due
to
the
concentrationC
SKSi
on
the
skin
surface
is
given
by:
dA
dt
C
K
A
SKW
DR
SKS
SKS
DR
PRM
SK
i
i
i
,

,
,
=
.

1.2.3.2
Skin
Surface
Exposure
to
Transfer
from
a
Dry
Surface
A
chemical
exists
on
a
surface
represented
as
a
mass
per
unit
area.
It
is
transferred
to
the
skin
of
a
subject
represented
by
a
transfer
coefficient.
A
short
exposure
period
would
represent
a
bolus.

The
rate
of
change
of
chemical
on
the
dermis
due
to
a
dry
exposure
is:
dA
sks
ex
i
dt
A
surf
i
K
sks
rt
i
,

,
=

Integrating
this
equation
gives
the
total
applied
dose.

The
rate
of
loss
of
chemical
from
the
skin
surface
due
to
evaporation
is
given
by:
dA
sks
ev
i
dt
wof
A
sks
i
ev
K
sks
ev
i
ev
K
sks
ev
i
,
(
.
)
(
,
,
)
=
 
+
10
1
1
2
2
 
 
 
where
 
wof
=
1
if
a
wash­
off
is
in
progress,
and
zero
otherwise,

 
ev1
1
=
if
the
first
evaporation
rate
constant
is
active,
and
zero
otherwise,

 
ev2
1
=
if
the
second
evaporation
rate
constant
is
active
and
zero
otherwise.

The
rate
that
the
ith
chemical
moves
from
the
skin
surface
into
the
dermis
is
given
by:
II.
B.
6
­
Page
71
of
162
dA
sks
dr
i
dt
K
sks
dr
prm
i
A
sk
C
sks
i
,

,
,
=

where
C
A
V
sks
sks
sk
i
i
=

If
no
wash­
off
is
in
progress,
then
the
rate
of
change
of
the
amount
of
the
ith
chemical
on
the
skin
is
given
by
the
rate
of
application
minus
the
rate
of
chemical
moving
into
the
dermis
minus
the
rate
of
loss
due
to
evaporation:
dA
sks
i
dt
dA
sks
ex
i
dt
dA
sks
dr
i
dt
dA
sks
ev
i
dt
=
 
 
,
,

,.

If
a
wash­
off
is
in
progress,
then:
dA
sks
i
dt
dA
sks
wof
i
dt
=
 
,

where
the
wash­
off
is
scheduled
at
time
t
wof
for
one
time
step,
 
t,
to
remove
all
chemical
on
the
dermis:
dA
sks
wof
i
dt
t
wof
A
sks
i
t
t
wof
,
(
)
(
)
.
=
 

1.3
Variable
Definitions
Bolus
Dose
Ingestions:

A
BIGi
=
The
amount
of
the
ith
chemical
in
all
of
the
Bolus
Dose
Ingestions
at
time
t,
A
BIGT
i
0
=
The
total
amount
of
the
ith
chemical
in
the
Bolus
Dose
at
simulation
start
time,
C
BIGi
j
,
=
The
concentration
of
the
ith
chemical
in
the
jth
Bolus
Dose,

N
BIG
=
The
number
of
Bolus
Dose
Ingestion
exposures,

T
BIGB
=
The
time
that
the
Bolus
Dose
Ingestion
starts,

T
BIGE
=
The
time
that
the
Bolus
Dose
Ingestion
ends,
and
V
BIGj
=
The
volume
of
the
jth
Bolus
Dose.

Rate
Ingestions:

A
RIG
i
0
=
The
initial
value
for
the
ith
chemical
in
the
Rate
Ingestions,

A
RIG
STTi
,
=
The
total
amount
of
the
ith
chemical
passing
from
Rate
Ingestions
to
the
Stomach
at
time
t,
II.
B.
6
­
Page
72
of
162
C
RIGi
j
,
=
The
concentration
of
the
ith
chemical
in
the
jth
Rate
Ingestion
exposure,
dA
dt
RIGi
=
The
rate
of
change
of
the
ith
chemical
in
the
Rate
Ingestions
at
time
t,

N
RIG
=
The
number
of
Rate
Ingestion
exposures,
and
Q
RIGj
=
The
flow
rate
for
the
jth
Rate
Ingestion.

Infusions:

A
INF
VBTi
,
=
The
total
amount
of
the
ith
chemical
in
Infusions
to
Venous
Blood
at
time
t,
dA
dt
INF
VBi
,
=
The
rate
of
change
of
the
ith
chemical
in
Infusions
versus
time
at
time
t,
C
INFi
j
,
=
The
concentration
of
the
ith
chemical
in
the
jth
Infusion,

Q
INFj
=
The
Infusion
flow
rate
for
the
jth
exposure.

Bolus
Dose
Intravenous
Injection
(
Bolus
IV):

j
i
BIV
A
,
=
The
amount
of
the
ith
chemical
in
the
jth
Bolus
Dose
IV,

A
BIVT
i
0
=
The
total
amount
of
the
ith
chemical
in
the
Bolus
Dose
IV
at
simulation
start
time,
N
BIV
=
The
number
of
Bolus
Dose
IV
exposures,

T
BIVB
=
The
time
that
the
Bolus
Dose
IV
starts,

T
BIVE
=
The
time
that
the
Bolus
Dose
IV
ends.

Intraperitoneal
Injection:

A
INP
CUR
Ji
j
,
,
,
=
The
amount
of
the
ith
chemical
remaining
to
be
absorbed
from
the
previous
interval
for
the
jth
IP
scenario,

A
INP
Ji
j
,
,
=
The
amount
of
the
ith
chemical
currently
in
the
IP
Injection
for
the
jth
scenario,

A
INPi
=
The
amount
of
the
ith
chemical
currently
in
the
IP
Injection,

C
INP
Ji
j
,
,
=
The
concentration
of
the
ith
chemical
in
the
IP
Injection
for
the
jth
scenario,
II.
B.
6
­
Page
73
of
162
dA
dt
INPi
=
The
rate
of
change
of
the
amount
of
the
ith
chemical
in
the
IP
Injection,

K
INP
ABS
Ji
j
,
,
,
=
The
first
order
absorption
rate
constant
for
the
jth
set
of
IP
Injections
of
the
ith
chemical,

K
INP
LIMi
,
=
The
factor
to
limit
the
minimum
amount
of
the
ith
chemical
from
IP
Injections
remaining
to
be
absorbed.

V
B
=
Volume
of
the
Body
of
each
subject.

Intramuscular
Injection:

A
INM
CUR
Ji
j
,
,
,
=
The
amount
of
the
ith
chemical
remaining
to
be
absorbed
from
the
previous
interval
for
the
jth
IM
Injection
scenario,

A
INM
Ji
j
,
,
=
The
amount
of
the
ith
chemical
currently
in
the
IM
Injection
for
the
jth
scenario,

A
INMi
=
The
amount
of
the
ith
chemical
currently
in
the
IM
Injection,

C
INM
Ji
j
,
,
=
The
concentration
of
the
ith
chemical
in
the
IM
Injection
for
the
jth
scenario,

dA
dt
INMi
=
The
rate
of
change
of
the
amount
of
the
ith
chemical
in
the
IM
Injection,

K
INM
ABS
Ji
j
,
,
,
=
The
first
order
absorption
rate
constant
for
the
jth
set
of
IM
Injections
and
the
ith
chemical,

K
INM
LIMi
,
=
The
factor
to
limit
the
minimum
amount
of
the
ith
chemical
from
IM
Injections
remaining
to
be
absorbed.

Skin
Surface
Exposure
(
Water):

A
SK
=
Area
of
the
skin
covered
by
the
solution
containing
the
chemical,

A
SKW
DR
,
=
The
amount
of
the
ith
chemical
that
has
moved
from
the
skin
surface
to
the
Dermis,
II.
B.
6
­
Page
74
of
162
dA
dt
SKW
DRi
,
=
The
rate
of
change
in
the
amount
of
the
ith
chemical
moving
from
the
skin
surface
to
the
Dermis,

C
SKS
=
The
concentration
of
the
ith
chemical
on
the
skin
surface
due
to
all
overlapping
exposures,

C
SKW
Ji
j
,
,
=
The
concentration
of
the
ith
chemical
for
the
jth
exposure
on
the
skin
surface,

K
SKS
DR
PRMi
,
,
=
The
permeation
coefficient
for
the
ith
chemical
from
Skin
Surface
to
Dermis.

Inhalation:

A
CC
Ji
j
.
,
=
The
amount
of
the
ith
chemical
in
the
jth
Closed
Chamber,

C
AIR
PPMi
,1
=
The
concentration
of
ith
chemical
in
air
for
one
part
per
million
at
one
atmosphere
and
25
º
C.
This
is
used
to
convert
concentration
in
PPM
to
mass
per
unit
volume.

C
AIR
Ji
j
,
,
=
The
concentration
of
the
ith
chemical
in
the
jth
exposure
in
parts
per
million.

C
INH
=
The
concentration
of
the
ith
chemical
in
inhaled
air,
units
of
mass
per
unit
volume.

N
SBJ
=
The
number
of
subjects
in
the
jth
Closed
Chamber,

V
CCj
=
The
volume
of
the
Closed
Chamber
for
the
jth
inhalation
exposure,

V
CC
GASi
,
=
The
volume
of
the
gas
in
the
chamber
adjusted
for
the
volume
of
the
subjects.

2.0
Chemical
Disposition
In
Silico
Absorption
involves
entry
of
a
drug
or
chemical
into
the
body.
We
have
observed
that
a
chemical
may
enter
directly
into
the
GI
tract
from
intraperitoneal
injection
(
Appendix
A
Section
1.1.1)
or
more
naturally
from
ingestion
of
food
or
from
purposeful
(
pica
or
geophagia)
or
accidental
non­
dietary
ingestion
of
filth
and
extraneous
matter.
II.
B.
6
­
Page
75
of
162
Intravascular
parenteral
administration
directly
into
the
blood
stream,
intravenously
or
intra­
arterially,
was
considered
as
an
exposure
route
because
this
route
of
administration
is
important
for
laboratory
or
clinical
testing
(
Appendix
A
Section
1.1.1).
This
approach
was
also
developed
for
intramuscular
injection
(
Appendix
A
Section
1.1.2)
as
an
avenue
of
comparison
with
other
parenteral
routes
of
exposure,
especially
dermal.

Once
the
drug
or
chemical
enters
the
blood
stream,
its
disposition
in
blood
and
other
fluids,
e.
g.
cerebrospinal
fluid
(
CSF),
organs
and
tissues
determines
its
access
to
the
site
or
sites
of
action.
Drug
and
chemical
disposition
involves
distribution
from
blood
and
fluids
to
tissues
and
organs,
metabolism
in
liver
and
other
organs
of
metabolism,
and
elimination
in
exhaled
breath,
fluids,
e.
g.
milk,
and
excreta.

2.1
Distribution
of
Chemical
from
Blood
to
Tissues,
Organs
and
in
Fluids
2.1.1.1
Binding
in
the
Arterial
Blood
The
binding
in
the
arterial
blood
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is
A
K
C
V
K
ABSC
AB
B
AB
MxB
AB
AB
AB
DB
AB
i
i
i
i
i
,
,
,
(
(
))
=
+
2.1.1.2
Calculation
of
Free
Chemical
in
the
Arterial
Blood
The
free
chemical
in
the
arterial
blood
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
AB
F
AB
AB
B
i
i
i
,
,
=
 

2.1.2
The
Venous
Blood
The
venous
blood
contains
chemical
output
from
the
compartments
and
input
to
the
static
lung
or
the
breathing
lung.
The
chemical
output
to
blood
from
the
GI
walls
is
passed
to
the
portal
blood.

2.1.2.1
Binding
in
the
Venous
Blood
The
binding
in
the
venous
blood
is
of
the
Michaelis­
Menten
form
but
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
VB
B
VB
MxB
VB
VB
VB
DB
VB
i
i
i
i
i
,
,
,
(
(
))
=
+
II.
B.
6
­
Page
76
of
162
2.1.2.2
Calculation
of
Free
Chemical
in
the
Venous
Blood
The
free
chemical
in
the
venous
blood
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
VB
F
VB
VB
B
i
i
i
,
,
=
 

2.1.3
Distribution
in
Tissues
2.1.3.1
Distribution
in
the
Residual
Carcass
The
rate
of
change
of
the
ith
chemical
in
the
carcass
is
given
by
the
rate
that
chemical
enters
from
the
arterial
blood,
and
in
the
chylomicrons
from
the
lymph
pool
(
when
the
four
walled
GI
model
is
used),
and
exits
via
the
venous
blood.
Elimination
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
carcass.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
CR
CR
B
CR
AB
F
LP
CR
LP
B
CR
CR
F
CR
VB
CR
E
CR,
M
j=
N
CR
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.

2.1.3.1.1
Binding
in
the
Carcass
The
binding
in
the
carcass
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is
A
K
C
V
K
ABSC
CR
Bi
CR
MxB
CR
CR
CR
DB
CR
i
i
i
i
,
,
,
(
(
))
=
+

2.1.3.1.3
Calculation
of
Free
Chemical
in
the
Carcass
The
free
chemical
in
the
carcass
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:
II.
B.
6
­
Page
77
of
162
A
A
A
CR
F
CR
CR
B
i
i
i
,
,
=
 

2.1.3.1.4
Elimination
in
the
Carcass
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Carcass,
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is
dA
dt
K
A
CR
E
CR
E
CR
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:

dA
dt
V
C
K
ABSC
CR
E
m
CR
E
CR
F
mM
CR
E
CR
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.3.2
Distribution
in
Fat
Tissue
The
rate
of
change
of
the
ith
chemical
in
the
fat
tissue
is
given
by
the
rate
that
chemical
enters
from
the
arterial
blood
and
the
chylomicrons
from
the
lymph
pool
and
exits
via
the
venous
blood.
Elimination
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
fat
tissue.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
FT
FT
B
FT
AB
F
LP
FT
LP
B
FT
FT
F
FT
VB
FT
E
FT,
M
j=
N
FT
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
Section
2.2
in
Appendix
A.
Binding
and
elimination
equations
are
presented
below.

2.1.3.2.1
Binding
in
Fat
Tissue
The
binding
in
the
fat
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:
II.
B.
6
­
Page
78
of
162
A
K
C
V
K
ABSC
FT
Bi
FT
MxB
FT
CR
FT
DB
FT
i
i
i
i
,
,
,
(
(
))
=
+

2.1.3.2.2
Calculation
of
Free
Chemical
in
Fat
Tissue
The
free
chemical
in
the
fat
tissue
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
FT
F
FT
FT
B
i
i
i
,
,
=
 

2.1.3.2.3
Elimination
in
Fat
Tissue
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Fat,
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
FT
E
FT
E
FT
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:

dA
dt
V
C
K
ABSC
FT
E
m
FT
E
FT
F
mM
FT
E
FT
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.3.3
Distribution
in
Slowly
Perfused
Tissue
The
rate
of
change
of
the
ith
chemical
in
the
slowly
perfused
tissue
is
given
by
the
rate
that
the
chemical
is
input
from
intramuscular
injections,
from
the
lymph
pool
as
chylomicrons
and
as
input
from
the
arterial
blood
that
exits
via
the
venous
blood.
Elimination
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
slowly
perfused
tissue.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
dA
dt
Q
C
R
dA
dt
dA
dt
dA
dt
SL
SL
B
SL
AB
F
LP
SL
LP
INM
B
SL
SL
F
SL
VB
SL
E
SL,
M
j=
N
SL
M
I
i
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
+
 
 

 
 


=
,
,
,
,
,

,

,
,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
II.
B.
6
­
Page
79
of
162
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.

2.1.3.3.1
Binding
in
the
Slowly
Perfused
Tissue
The
binding
in
the
slowly
perfused
tissue
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
SL
Bi
SL
MxB
SL
SL
SL
DB
SL
i
i
i
i
,
,
,
(
(
))
=
+

2.1.3.3.2
Calculation
of
Free
Chemical
in
the
Slowly
Perfused
Tissue
The
free
chemical
in
the
slowly
perfused
tissue
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
SL
F
SL
SL
B
i
i
i
,
,
=
 

2.1.3.3.3
Elimination
in
the
Slowly
Perfused
Tissue
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Slowly
Perfused
Tissue,
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
SL
E
SL
E
SL
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:

dA
dt
V
C
K
ABSC
SL
E
m
SL
E
SL
F
mM
SL
E
SL
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.3.4
Distribution
in
Rapidly
Perfused
Tissue
The
rate
of
change
of
the
ith
chemical
in
the
rapidly
perfused
tissue
is
given
by
the
rate
that
chemical
enters
from
the
lymph
pool
as
chylomicrons
and
from
the
arterial
blood
and
exits
via
the
venous
blood.
Elimination
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
rapidly
perfused
tissue.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:
II.
B.
6
­
Page
80
of
162
V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
RP
RP
B
RP
AB
F
LP
RP
LP
B
RP
RP
F
RP
VB
RP
E
RP,
M
j=
N
RP
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.

2.1.3.4.1
Binding
in
the
Rapidly
Perfused
Tissue
The
binding
in
the
rapidly
perfused
tissue
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
RP
Bi
RP
MxB
RP
RP
RP
DB
RP
i
i
i
i
,
,
,
(
(
))
=
+

2.1.3.4.2
Calculation
of
Free
Chemical
in
the
Rapidly
Perfused
Tissue
The
free
chemical
in
the
Rapidly
Perfused
Tissue
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
RP
F
RP
RP
B
i
i
i
,
,
=
 

2.1.3.4.3
Elimination
in
the
Rapidly
Perfused
Tissue
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Rapidly
Perfused
Tissue
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
RP
E
RP
E
RP
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:
dA
dt
V
C
K
ABSC
RP
E
m
RP
E
RP
F
mM
RP
E
RP
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+
II.
B.
6
­
Page
81
of
162
2.1.4
Distribution
of
Chemical
in
Organs
2.1.4.1
Distribution
of
Chemical
from
Blood
to
the
Brain
The
rate
of
change
of
the
ith
chemical
in
the
brain
is
given
by
the
rate
that
chemical
enters
from
the
arterial
blood
and
in
the
chylomicrons
from
the
lymph
pool
(
when
the
four
walled
gastro­
intestinal
model
is
used),
and
exits
via
the
venous
blood.
The
blood/
brain
barrier
is
modeled
by
properly
choosing
the
partition
coefficients.
Elimination
of
chemical
from
brain
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
brain.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
BN
BN
B
BN
AB
F
LP
BN
LP
B
BN
BN
F
BN
VB
BN
E
BN,
M
j=
N
BN
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
index
to
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.

2.1.4.1.1
Binding
in
the
Brain
The
binding
in
the
Brain
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
BN
Bi
BN
MxB
BN
BN
BN
DB
BN
i
i
i
i
,
,
,
(
(
))
=
+

2.1.4.1.2
Calculation
of
Free
Chemical
in
the
Brain
The
free
chemical
in
the
Brain
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
BN
F
BN
BN
B
i
i
i
,
,
=
 
II.
B.
6
­
Page
82
of
162
2.1.4.1.3
Elimination
from
the
Brain
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Brain
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:
dA
dt
K
A
BN
E
BN
E
BN
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:
dA
dt
V
C
K
ABSC
BN
E
m
BN
E
BN
F
mM
BN
E
BN
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.4.2
Distribution
of
Chemical
to
the
Liver
2.1.4.2.1
Stomach/
Intestine
Model
of
Distribution
to
the
Liver
The
liver
compartment
has
the
ith
chemical
input
from
the
stomach
and
intestine
following
intraperitoneal
injections.
Input
from
the
arterial
blood
is
also
included.
The
ith
chemical
is
moved
from
the
liver
to
venous
blood
where
it
may
be
lost
due
to
elimination.
Additional
chemical
is
bound
in
the
liver
using
an
equilibrium
process.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
Liver.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
dA
dt
dA
dt
dA
dt
Q
C
Q
C
R
dA
dt
dA
dt
dA
dt
LV
LV
ST
PB
IN
PB
INP
B
LV
AB
F
B
LV
LV
F
LV
VB
LV
E
LV
M
j
N
LV
M
I
i
i
i
i
i
i
i
i
i
i
j
M
j
l
m
C
l
m
=
+
+
+
 
 

 
 
=
=


,
,

,
,
,
,

,

,
,
,
,
,

,
,
,
1
where
the
equations
for
the
input
to
portal
blood
from
the
stomach
and
the
intestine
are
respectively:
dA
dt
K
A
ST
PB
ABS
ST
PB
ST
i
i
i
,

,
,
,
=

and
dA
dt
K
A
IN
PB
ABS
IN
PB
IN
i
i
i
,

,
,
,
=

where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
the
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.
II.
B.
6
­
Page
83
of
162
2.1.4.2.2
Gastro­
Intestinal
Model
of
Distribution
to
the
Liver
The
liver
compartment
for
the
complete
GI
tract
has
the
ith
chemical
input
from
the
portal
blood
(
from
intraperitoneal
injections)
and
lymph
pool
as
chylomicrons
in
addition
to
the
input
from
the
Arterial
Blood.
The
ith
chemical
is
moved
from
the
liver
to
the
venous
blood
to
the
bile
which
is
passed
to
the
Duodenum
Lumen,
and
may
be
lost
due
to
elimination.
Additional
chemical
is
bound
in
the
Liver
using
an
equilibrium
process.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
Liver.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
C
R
Q
C
C
R
dA
dt
K
A
Q
C
R
dA
dt
dA
dt
LV
LV
B
LV
AB
F
LV
F
LV
VB
PB
LV
PB
LV
F
LV
VB
LV
E
LP
LV
LP
BL
LV
F
BL
DUL
LV
M
j
N
LV
M
I
i
i
i
i
i
i
i
i
i
i
i
i
i
j
M
j
l
m
C
l
m
=
 
+
 
 

+
 
 
 
=
=


,
,
,

,
,
,

,

,

,
,

,
,
,
(
)
(
)

,
,

,
,
1
The
intraperitoneal
injection
in
this
case
is
passed
to
the
portal
blood.

2.1.4.2.3
Binding
in
the
Liver
The
binding
in
the
liver
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
LV
Bi
LV
MxB
LV
LV
LV
DB
LV
i
i
i
i
,
,
,
(
(
))
=
+

2.1.4.2.4
Calculation
of
Free
Chemical
in
the
Liver
The
free
chemical
in
the
liver
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
LV
F
LV
LV
B
i
i
i
,
,
=
 

2.1.4.2.5
Elimination
in
the
Liver
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Liver
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
LV
E
LV
E
LV
F
i
i
i
,

,
,
=
II.
B.
6
­
Page
84
of
162
and
the
saturable
form
for
elimination
is:
dA
dt
V
C
K
ABSC
LV
E
m
LV
E
LV
F
mM
LV
E
LV
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.4.3
Absorption
and
Distribution
in
the
Stomach
The
stomach
has
the
ith
chemical
input
by
bolus
ingestion
(
a
plug
of
food
or
drink)
and
rate
ingestion
(
food
or
drink
input
over
time),
with
chemical
output
to
portal
blood
via
the
liver
to
the
intestine.
The
equation
for
the
rate
of
change
of
ith
chemical
in
the
stomach
is:
dA
dt
dA
dt
dA
dt
K
A
K
A
ST
BIG
RIG
ABS
ST
PB
ST
ST
IN
ST
i
i
i
i
i
i
i
=
+
 
 
,
,
,
,

where
the
bolus
ingestion
and
the
rate
ingestion
exposures
are
discussed
in
Section
1.2.1
of
Appendix
A.

2.1.4.4
The
Intestine
The
rate
of
change
of
the
ith
chemical
in
the
intestine
is
given
by
the
rate
of
input
from
the
stomach
and
the
rate
of
output
to
the
portal
blood
via
the
liver
to
feces.
The
equation
is:

dA
dt
K
A
K
A
K
A
IN
ST
IN
ST
ABS
IN
PB
IN
IN
FEC
IN
i
i
i
i
i
i
i
=
 
 
,
,,
,

2.1.4.5
The
Kidney
The
rate
of
change
of
the
ith
chemical
in
the
kidney
is
given
by
the
rate
that
chemical
enters
from
the
arterial
blood
and
in
the
chylomicrons
from
the
lymph
pool
(
when
the
four
walled
GI
model
is
used),
and
exits
via
the
venous
blood
and
the
urine.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
kidney.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
KD
KD
B
KD
AB
F
LP
KD
LP
B
KD
KD
F
KD
VB
KD
URN
KD,
M
j=
N
KD
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
II.
B.
6
­
Page
85
of
162
2.1.4.5.2
Calculation
of
Free
Chemical
in
the
Kidney
The
free
chemical
in
the
kidney
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
KD
F
KD
KD
B
i
i
i
,
,
=
 

2.1.4.5.3
Elimination
in
the
Kidney
There
are
two
types
of
urine
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Kidney
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
KD
URN
KD
URN
KD
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:

dA
dt
V
C
K
ABSC
KD
URN
m
KDURN
KD
F
mM
KD
URN
KD
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.4.6
The
Spleen
The
rate
of
change
of
the
ith
chemical
in
the
spleen
is
given
by
the
rate
that
chemical
enters
from
the
arterial
blood,
and
in
the
chylomicrons
from
the
lymph
pool
(
when
the
four
walled
GI
model
is
used),
and
exits
via
the
portal
blood
(
or
into
the
liver
if
the
stomach/
intestine
GI
is
used).
Elimination
is
modeled
and
the
rate
of
elimination
of
the
ith
chemical
is
subtracted.
Chemical
may
be
metabolized
and
the
rate
of
metabolism
further
reduces
the
rate
of
increase
of
the
chemical
in
the
spleen.
Other
metabolites
may
metabolize
to
the
ith
chemical
and
their
rate
of
formation
is
added.
The
equation
is:

V
dC
dt
Q
C
K
A
Q
C
R
dA
dt
dA
dt
dA
dt
SP
SP
B
SP
AB
F
LP
SP
LP
B
SP
SP
F
SP
PB
SP
E
SP,
M
j=
N
SP
M
I
i
i
i
i
i
i
i
i
i,
j
M
j
l
m
C
l
m
=
+
 
 
 

 


=
,
,
,
,
,

,
,

,
,

,
,
,
1
where
the
variable
IC,
l,
m
is
the
circulating
compound
that
is
the
mth
metabolite
of
the
lth
circulating
compound.
The
equations
for
metabolism
are
presented
in
Section
2.2
of
Appendix
A.
Binding
and
elimination
equations
are
presented
below.
II.
B.
6
­
Page
86
of
162
2.1.4.6.1
Binding
in
the
Spleen
The
binding
in
the
spleen
is
of
the
Michaelis­
Menten
form
but
is
an
equilibrium
relationship
so
that
the
amount
of
the
ith
chemical
that
is
bound
is
calculated
rather
than
the
rate.
The
equation
is:

A
K
C
V
K
ABSC
SP
Bi
SP
MxB
SP
SP
SP
DB
SP
i
i
i
i
,
,
,
(
(
))
=
+

2.1.4.6.2
Calculation
of
Free
Chemical
in
the
Spleen
The
free
chemical
in
the
spleen
is
calculated
by
subtracting
the
amount
bound
from
the
total
amount
as
follows:

A
A
A
SP
F
SP
SP
B
i
i
i
,
,
=
 

2.1.4.6.3
Elimination
in
the
Spleen
There
are
two
types
of
elimination
currently
implemented
in
ERDEM:
a
linear
form
in
which
the
rate
of
elimination
is
proportional
to
the
amount
of
the
free
ith
chemical
in
the
Spleen
and
a
saturable
Michaelis­
Menten
form.
The
linear
form
is:

dA
dt
K
A
SP
E
SP
E
SP
F
i
i
i
,

,
,
=

and
the
saturable
form
for
elimination
is:

dA
dt
V
C
K
ABSC
SP
E
m
SP
E
SP
F
mM
SP
E
SP
F
i
i
i
i
i
,

,
,
,

,
,
,
(
(
))
=
+

2.1.4.7
The
Dermal
Tissue
The
dermal
dissue
receives
the
ith
chemical
by
permeation
through
the
skin
and
from
the
arterial
blood
and
is
released
to
the
venous
blood
according
to
the
equation:

V
dC
dt
dA
dt
K
A
Q
C
Q
C
R
DR
DR
SKS
DR
LP
DR
LP
B
DR
AB
F
B
DR
DR
DR
VB
i
i
i
i
i
i
i
=
+
+
 
,

,
,
,
,
,

where
dA
dt
C
K
AREA
SKS
DR
SKS
PRM
SKS
DR
SK
i
i
i
,

,
,
=
II.
B.
6
­
Page
87
of
162
2.2
Metabolism
in
Selected
Tissues
and
Organs
The
term
metabolism
refers
to
any
reaction
that
produces
a
new
compound.
The
term
Metabolite
could
be
replaced
with
a
term
such
as
Reactant.
ERDEM
has
been
designed
to
handle
multiple
circulating
compounds.
It
is
assumed
that
all
metabolites
are
circulating
and
the
metabolism
structure
is
the
same
in
all
compartments.
The
metabolism
parameters,
however,
can
be
different
in
each
compartment.
The
equations
implemented
in
ERDEM
are
presented
for
the
following
areas:

°
Enzyme
Destruction
and
Re­
synthesis
Maximum
rate
of
change
of
metabolite
formation,
taking
enzyme
destruction
and
re­
synthesis
into
consideration,
is
calculated.

°
Maximum
Rate
of
Metabolite
Formation
The
maximum
rate
of
formation
of
the
metabolite
is
found
for
the
liver
by
scaling
for
species
and
body
volume.
The
maximum
rate
for
other
compartments
is
scaled
from
the
Liver
value.

°
Saturable
and
Linear
Metabolites
Equations
and
parameters
are
presented
for
calculating
the
rate
of
metabolite
formation.

°
Inhibition
A
metabolite
or
circulating
compound
may
work
in
such
a
manner
as
to
inhibit
the
formation
of
another
metabolite.
There
are
four
types
of
inhibition
modeled
here,
competitive
inhibition,
mixed
inhibition,
strictly
non­
competitive
inhibition,
and
uncompetitive
inhibition.

Equations
are
presented
for
the
liver
metabolism
with
circulating
metabolites
in
section
2.2.6
of
Appendix
A.
The
other
compartments
use
similar
equations.
The
metabolism
parameters,
maximum
velocity
(
Vmax)
and
the
Michaelis­
Menten
constant
(
Km)
could
be
different
in
each
compartment,
or
the
Vmax
for
metabolism
in
other
compartments
may
be
calculated
relative
to
that
used
in
the
Liver.
If
the
units
of
volume
are
changed,
the
units
of
the
input
Vmax
cannot
be
changed.
Also,
the
units
of
the
volume
of
the
body
used
for
the
scaling
conversion
cannot
be
changed.
In
other
words,
there
can
be
no
volume
unit
conversion
before
the
calculation
of
the
scaled
version
of
the
Vmax.

An
input
reference
body
volume
is
assumed
(
currently
one
unit)
and
the
Vmax
input
is
assumed
to
be
in
units
of
amount
per
unit
time.
The
calculation
of
Vmax
then
always
works.
A
volume
unit
change
is
applied
both
to
the
reference
body
II.
B.
6
­
Page
88
of
162
volume
as
well
as
the
current
body
volume.
This
then
would
be
consistent
for
the
scaling
of
the
Vmax
for
elimination
as
will
the
maximum
binding
value
in
the
calculation
of
the
amount
bound.
This
ratio
of
body
volumes
will
be
used
throughout
the
scaling
processes
in
ERDEM.

2.2.1
Implementation
Outline
If
a
circulating
compound
is
metabolized,
then
one
or
more
metabolites
are
defined.
These
may
be
linear,
saturable,
or
be
affected
by
one
of
four
types
on
inhibition.
Each
of
these
metabolites
is
itself
considered
to
be
circulating.

The
user
will
input
the
circulating
compound
number
for
each
metabolite.
The
number
of
metabolites
for
each
circulating
compound
is
used
as
the
input.
These
metabolites
are
also
assumed
to
be
circulating.
The
user
will
input
metabolism
parameters
using
i,
j
with
"
i"
being
the
index
to
the
circulating
compound
and
"
j"
being
the
metabolite
counter
for
the
metabolites
of
circulating
compound
i.
The
user
will
need
to
input
set,
print,
display
and
plot
statements
using
the
index
i.

The
individual
metabolite
amounts
are
calculated
in
the
compartmental
calculations
for
the
individual
chemical.
The
metabolism
section
for
each
compartment
calculates
two
sums.
The
first
is
the
sum
of
all
rates
of
metabolite
formation
of
the
ith
circulating
compound.
The
second
is
the
sum
of
the
rate
of
formation
of
all
metabolites
that
are
the
same
as
the
ith
circulating
compound.
These
rate
sums
are
integrated
in
the
circulating
compound
section
for
each
compartment.

2.2.2
Variable
Names
for
Metabolism
Parameters
Table
2.1
presents
variable
names,
with
a
short
description,
that
are
used
globally
in
all
compartments.
The
variables
used
in
the
metabolism
calculations
are
shown
in
Table
2.2
(
the
liver
compartment
for
example).
Those
variables
that
now
have
one
or
two
indices
but
have
unchanged
names
are
not
listed.
II.
B.
6
­
Page
89
of
162
Table
2.2.2:
Metabolism
Variables
Used
in
All
Compartments.

Variable
Name
Variable
Description
Notes
CH_
NM_
SH(
I)
Chemical
Short
Name
for
ith
circulating
compound.
In
SET
statements
use
CH_
NM_
SH(
1,
I)
Eight
characters,
used
in
error
statements.

CH_
NM_
LG(
I)
Chemical
Long
Name
for
ith
circulating
compound.
Thirty
characters
for
use
in
descriptive
text.

N_
M(
I)
Number
of
metabolites
of
the
ith
circulating
compound.
A
two
digit
integer.
Maximum
value
is
six.
NMi
I_
CMPD(
I,
J)
Number
of
the
circulating
compound
that
is
the
jth
metabolite
of
the
ith
circulating
compound.
For
j=
1
to
N_
M(
i)
In
eqns:
Ic,
i,
j
TYPE_
M(
I,
J)
Type
of
the
jth
metabolite
(
equation(
s)
to
use)
of
the
ith
circulating
compound.
In
SET
statements
use
TYPE_
M(
1,
I,
J).
Up
to
three
characters
to
specify
equation(
s)
to
use.

Table
2.2.3:
Variables
Used
in
Metabolism
Calculations
(
Liver
Example)

Variable
Name
(
Used
in
Program)
Variable
Description
Variable
Name
(
in
Documents)

A_
LV_
F(
I)
Amount
of
the
ith
chemical
that
is
free.
A
LV
Fi
,

C_
LV_
F(
I)
Concentration
of
the
ith
circulating
compound
that
is
free.
C
LF
Fi
,

A_
LV_
M_
SUM(
I)
Sum
of
the
amounts
of
Liver
metabolite
of
the
ith
chemical.
(
mg)
A
M
LV
SUM
i
,
,

A_
LV_
MC_
SUM(
I)
Sum
of
amounts
of
Liver
metabolite
that
are
the
same
as
the
ith
chemical.
(
mg)
A
MC
LV
SUMi
,
,

DA_
LV_
M(
I,
J)
Rate
of
formation
for
the
kth
Liver
metabolite
for
the
ith
chemical.
(
mg/
H)
dA
dt
M
LVi
j
,
,

DA_
LV_
M_
SUM(
I)
Sum
of
rates
of
formation
for
all
Liver
metabolites
of
the
ith
chemical.
(
mg/
H).
dA
dt
M
LV
SUM
i
,
,

DA_
LV_
MC_
SUM(
I)
Sum
of
rates
of
formation
of
all
metabolites
that
are
the
same
chemical
as
the
ith
circulating
compound.
(
mg/
H)
dA
dt
MC
LV
SUMi
,
,
II.
B.
6
­
Page
90
of
162
Variable
Name
(
Used
in
Program)
Variable
Description
Variable
Name
(
in
Documents)

DCM_
M_
LV(
I,
J)
Maximum
rate
of
change
of
kth
Liver
metabolite
concentration
for
the
ith
chemical.
(
mg/
L/
H).
V
Mx
LVi
j
0
,
,

DRM_
LV_
MEDR(
I,
J)
Rate
of
change
of
the
maximum
jth
Liver
metabolite
metabolic
rate
including
enzyme
destruction
and
resynthesis
for
the
ith
chemical.
dV
dt
Mx
LV
edri
j
,
,
,

K_
LV_
ML(
I,
J)
The
rate
constant
for
the
Linear
form
of
the
metabolism
calculation.
K
ML
LVi
j
,
,

K_
MD1_
LV(
I,
J)
First
dissociation
constant
for
the
inhibitor
to
formation
of
the
jth
Liver
metabolite
of
the
ith
chemical.
K
MD
LVi
j
1,
,

K_
MD2_
LV(
I,
J)
Second
dissociation
constant
for
the
inhibitor
to
formation
of
the
jth
Liver
metabolite
of
the
ith
chemical.
K
MD
LVi
j
2,
,

K_
MM_
LV(
I,
J)
Michaelis­
Menten
constant
for
jth
Liver
metabolite
of
ith
chemical.
(
mg/
L)
K
mm
LVi
j
,
,

K1_
MER(
I,
J)
First
order
rate
of
jth
Liver
metabolite
enzyme
resynthesis
for
ith
chemical.
(
1/
H)
K
M
eri
j
1
,
,

K2_
MED(
I,
J)
Second
order
rate
of
jth
Liver
metabolite
enzyme
destruction
for
the
ith
chemical.
(
L/
MG)
K
M
edi
j
2
,
,

VM_
M_
LV(
I,
J)
Maximum
rate
of
jth
Liver
metabolite
metabolism
for
the
ith
chemical.
(
MG/
H)
V
Mx
LVi
j
,
,

VM_
MEDR_
LV(
I,
J)
Maximum
rate
of
jth
Liver
metabolite
metabolism
after
taking
enzyme
change
into
account
for
the
ith
chemical.
(
MG/
H)
V
Mx
LV
edri
j
,
,
,

2.2.3
Calculation
of
Maximum
Rate
of
Change
of
Metabolism
The
equation
for
the
maximum
rate
of
change
of
metabolism
in
the
Liver
for
the
jth
metabolite
of
the
ith
chemical
is
given
by
V
V
V
B
V
ref
r
Mx
LV
M
LV
m
i
j
x
i
j(
)
,
,
,
,
=
0
where
V
B
=
volume
of
the
body,

V
ref
=
reference
volume
for
j
i
LV
Mx
V
,
0
,

r
m
=
power
of
the
volume
of
the
body
for
interspecies
scaling.
II.
B.
6
­
Page
91
of
162
2.2.4
Calculations
when
including
Enzyme
Destruction
and
Re­
synthesis
The
equation
for
the
rate
of
change
of
maximum
metabolic
rate
in
the
liver
including
enzyme
destruction
and
re­
synthesis
for
the
jth
metabolite
of
the
ith
circulating
compound
is
given
by:

dV
dt
K
V
V
K
V
dA
dt
V
Mx
LV
edr
M
er
Mx
LV
Mx
LV
edr
M
ed
Mx,
LV,
edr
M,
LV
LV
i
j
i
j
i
j
i
j
i
j
i,
j
i
j
,
,

,
,
,
,

,
,

,
,
,

,
,
(
)

/
=
 
 
1
2
where
the
variable
definitions
are
given
in
Table
2.2.3,
and
VLV
=
the
volume
of
the
Liver.
The
value
of
the
maximum
metabolic
rate
taking
enzyme
destruction
and
re­
synthesis
into
consideration
is
obtained
by
integration
as:

V
dV
dt
dt
V
Mx
LV
edr
Mx
LV
edr
Mx
LV
i
j
i
j
i
j
.
,
,
,

,
,
,

,
.
=
+

2.2.5
The
Rate
of
Formation
of
Saturable
and
Linear
Metabolite
in
the
Liver
The
rate
of
formation
of
theth
metabolite,
when
saturable,
in
the
liver
from
the
ith
circulating
compound
is
given
by:

dA
dt
V
C
K
C
M
LV
Mx
LV
edr
LV
F
mm
LV
LV
F
i
j
i
j
i
i
j
i
,

,
,
,

,
,
,

,

,
(
)
=
+

where
the
indices
i,
and
j
are
defined
above
and
parameters
are
defined
in
Table
2.2.3.
For
those
metabolites
which
the
user
wants
to
be
strictly
linear,
then
the
linear
form
of
the
equation
would
apply.
The
rate
of
formation
of
a
linear
metabolite
in
the
liver
is:

dA
dt
K
A
M
LV
ML
LV
LV
F
i
j
i
j
i
,

,
,
,

,
.
=

The
sum
of
the
rates
of
formation
of
the
metabolites
for
the
ith
circulating
compound
can
be
calculated
according
to
where
the
rate
of
formation
of
metabolites
determines
the
loss
in
the
rate
of
increase
of
amount
in
the
liver
for
the
ith
circulating
compound:

dA
dt
dA
dt
M
LV
SUM
M
LV
j
N
i
i
j
M
,
,
,
,

,
=
=

1
II.
B.
6
­
Page
92
of
162
2.2.6
Circulating
Compounds
which
are
Metabolites
The
rates
of
formation
of
metabolites,
in
this
case
in
the
liver,
which
are
the
same
as
one
of
the
circulating
compounds
are
summed
and
then
added
to
the
rate
of
increase
of
the
amount
of
the
circulating
compound.
This
is
accomplished
by
assuming
that
every
metabolite
could
be
any
of
the
circulating
compounds.
An
index
Ic,
i,
j
is
saved
for
each
metabolite.
If
the
jth
metabolite
of
the
ith
circulating
compound
is
the
same
compound
as
the
kth
circulating
compound,
then
the
index
k
for
the
circulating
compound
is
saved
in
Ic,
i,
j
otherwise
the
index
Ic,
i,
j
is
set
to
zero.
If
the
index
is
non­
zero,
then
the
rate
of
formation
of
that
metabolite
is
added
to
a
sum
for
that
circulating
compound.
The
rate
of
formation
of
a
circulating
metabolite
may
be
linear
or
saturated
(
with
inhibition
if
applicable)
as
described
in
Section
2.2.5
of
Appendix
A.
The
rate
equation
the
becomes
dA
dt
dA
dt
MC
LV
SUM
M
LV
I
k
k
i
j
c
i
j
,
,
,
,

,
,
=
=

where
dA
dt
M
LVi
j
,
,

=
the
contribution
to
the
rate
of
change
of
the
kth
chemical
in
the
Liver
from
the
rate
of
formation
of
the
jth
metabolite
of
the
ith
chemical.

2.2.7
Inhibition
in
the
Metabolism
Process
Compounds
elsewhere
in
the
metabolism
chains
for
any
of
the
circulating
compounds
may
inhibit
the
formation
of
a
given
metabolite.
There
are
four
kinds
of
inhibition
addressed
here.
They
are
defined
by
the
formulas
for
an
apparent
Vmax,
and
an
apparent
Michaelis­
Menten
constant
Kmm
(
See
Table
2.2.7).

dA
dt
V
C
K
C
M
LV
App
LV
F
mm
App
LV
F
i
J
i
J
i
i
j
i
,

max,
,

,
,
,

,

,
(
|
|)
,

=
+

where
Vmax,
App
and
Kmm,
App
are
taken
from
Table
2.2.7
for
the
inhibition
case
that
applies.
II.
B.
6
­
Page
93
of
162
Table
2.2.7
Parameter
Formulas
for
Four
Types
of
Inhibition
of
Metabolism
Type
of
Inhibition
Vmax,
App
Kmm,
App
Competitive
Inhibition
V
Mx
LV
edri
j
,
,
,
K
C
K
mm
LV
LV
F
MD
LV
i
j
Ii
j
i
j
,
,
,
,
,
,
(
/
)
1
1
+

Mixed
Inhibition
V
C
K
Mx
LV
edr
LV
F
MD
LV
i
j
Ii
j
i
j
,
,

,
,
,

,
,
(
/
)
1
2
+
K
C
K
C
K
mm
LV
LV
F
MD
LV
LV
F
MD
LV
i
j
Ii
j
i
j
Ii
j
i
j
,
,
,

,
,
,
,
,

,
,
(
/
)

(
/
)
1
1
1
2
+

+

Pure
noncompetitive
Inhibition
V
C
K
Mx
LV
edr
LV
F
MD
LV
i
j
Ii
j
i
j
,
,

,
,
,

,
,
(
/
)
1
2
+
K
mm
LVi
j
,
,

Uncompetitive
Inhibition
V
C
K
Mx
LV
edr
LV
F
MD
LV
i
j
Ii
j
i
j
,
,

,
,
,

,
,
(
/
)
1
2
+
K
C
K
mm
LV
LV
F
MD
LV
I
i
j
Ii
j
i
j
,

,
,
,
,

,
,
(
/
)
1
2
+

where
Ii,
j
=
zero
or
the
index
to
the
chemical
that
is
the
inhibitor
to
the
jth
metabolite
of
the
ith
circulating
compound.

2.2.8
Metabolism
in
the
other
Organs
and
Tissues
2.2.8.1
Metabolism
in
the
Brain
The
brain
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
Liver
value
from
the
equation:

V
V
R
V
V
Mx
BN
Mx
LV
M
BN
LV
BN
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
BN
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
Brain
for
the
jth
metabolite
of
the
ith
chemical.

2.2.8.2
Metabolism
in
the
Kidney
The
kidney
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
liver
value
from
the
equation:

V
V
R
V
V
Mx
KD
Mx
LV
M
KD
LV
KD
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
KD
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
Kidney
for
the
jth
metabolite
of
the
ith
chemical.
II.
B.
6
­
Page
94
of
162
2.2.8.3
Metabolism
in
the
Carcass
The
carcass
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
Liver
value
from
the
equation:

V
V
R
V
V
Mx
CR
Mx
LV
M
CR
LV
CR
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
CR
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
Carcass
for
the
jth
metabolite
of
the
ith
chemical.

2.2.8.4
Metabolism
in
the
Fat
The
fat
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
liver
value
from
the
equation:

V
V
R
V
V
Mx
FT
Mx
LV
M
FT
LV
FT
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
FT
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
Fat
for
the
jth
metabolite
of
the
ith
chemical.

2.2.8.5
Metabolism
in
the
Slowly
Perfused
Tissue
The
slowly
perfused
tissue
metabolism
equations
are
the
same
as
those
for
the
Liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
Liver
value
from
the
equation:

V
V
R
V
V
Mx
SL
Mx
LV
M
SL
LV
SL
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
SL
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
slowly
perfused
tissue
for
the
jth
metabolite
of
the
ith
chemical.

2.2.8.6
Metabolism
in
the
Rapidly
Perfused
Tissue
The
rapidly
perfused
tissue
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
liver
value
from
the
equation:

V
V
R
V
V
Mx
RP
Mx
LV
M
RP
LV
RP
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=
II.
B.
6
­
Page
95
of
162
where
R
M
RP
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
rapidly
perfused
tissue
for
the
jth
metabolite
of
the
ith
chemical.

2.2.8.7
Metabolism
in
the
Spleen
The
spleen
metabolism
equations
are
the
same
as
those
for
the
liver
except
that
the
equation
for
the
Vmax
is
given
as
a
function
of
the
liver
value
from
the
equation:

V
V
R
V
V
Mx
SP
Mx
LV
M
SP
LV
SP
LV
i
j
i
j
i
j
,
,
,,
,
,
,
=

where
R
M
SP
LVi
j
,
,
,
is
a
scaling
factor
for
Vmax
in
the
spleen
for
the
jth
metabolite
of
the
ith
chemical.

2.3
References
U.
S.
EPA
2002.
Exposure
Related
Dose
Estimating
Model
(
ERDEM)
for
Assessing
Human
Exposure
and
Dose.
EPA/
600/
X­
04/
060.
II.
B.
6
­
Page
96
of
162
Appendix
B.
QA
1.0
Quality
Assurance
for
Data
and
Compartment
Models
The
purpose
of
this
section
is
to
identify
the
sources
and
quality
of
input
data.
The
data
fall
into
7
general
categories,
as
described
in
Table
B1.
Data
deemed
of
the
highest
quality
were
gleaned
from
publications
in
peer
reviewed
journals
available
in
the
open
literature
or
from
reports
conducted
under
Good
Laboratory
Practices
(
GLP)
protocols
(
Category
I).
These
data
were
used
in
accordance
with
the
purpose
intended
by
the
measurement
such
as
urinary
metabolite
data
from
studies
designed
to
quantify
and
identify
urinary
metabolites
according
to
specific
protocols
involving
methods
for
which
quality
assurance
requirements
were
set
a
priori.
Secondary
data
(
Category
II)
may
be
defined
as
environmental,
exposure,
or
health
data
developed
for
another
purpose
such
as
dermal
absorption
parameters
involving
structurally
related
chemicals.
Secondary
data
may
be
viewed
as
inputs
to
the
ERDEM
model
for
the
purpose
of
estimating
absorbed
dose,
tissue
concentrations,
and
urine
eliminations.

Many
diverse
types
of
data,
including
physical
data,
chemical
data,
and
physiological
data
may
also
be
used
for
PBPK
modeling
(
Categories
III
and
IV).
These
data
are
taken
from
a
variety
of
sources
including
databases,
peerreviewed
publications,
and
estimation
techniques.
For
example,
these
data
might
include
organ
volumes
modeled
as
compartments
designed
to
reasonably
represent
the
flow
of
chemical
within
the
blood
as
well
as
clearance
from
these
compartments.
Data
from
non
peer
reviewed
sources,
such
as
government
documents
or
internal
reports
(
Category
V),
are
evaluated
against
peer
reviewed
data.
These
data
may
be
chemical
specific
for
a
single
purpose
such
as
the
clinical
use
of
congeneric
compound
having
similar
physicochemical
properties
as
the
target
chemical.
Estimates
may
also
be
gleaned
or
inferred
from
a
method
or
statistical
process
(
Categories
VI
or
VII).
The
method
or
process
may
be
standardized
(
ASTM)
but
the
resultant
data
presented
to
support
the
method
may
not
be
intended
for
any
other
purpose
other
than
to
explain
the
accuracy
and
precision
of
the
method.
Estimates
gleaned
from
statistical
processes,
such
as
Quantitative
Structure
Activity
Relationships
(
QSAR),
may
also
represent
a
means
to
test
a
mechanism
rather
than
predict
biological
activity.
In
all
of
these
cases,
however,
estimates
must
be
accompanied
by
supporting
statistics
that
express
the
level
of
uncertainty
surrounding
the
method
or
process.

The
sources
of
all
data
contained
within
this
report
have
been
documented
by
reference
or
footnote
describing
the
source
of
the
data.
Chemical
reactions
are
modeled
as
metabolism.
A
general
summary
of
the
models
and
data
utilized
in
PBPK
modeling
are
presented
in
the
following
tables.
The
data
fall
into
7
general
categories,
as
described
in
Table
B1.
The
sources
of
the
major
data
utilized
are
categorized
and
described
in
Table
B2.
The
compartment
models
utilized
are
categorized
and
described
in
Tables
B3
and
B4.
II.
B.
6
­
Page
97
of
162
Table
B1.
Categories
of
Data
Sources
and
Models
Category
Description
I
Taken
from
peer
reviewed
literature
or
GLP
report,
used
for
the
purpose
intended
by
the
measurement
II
Taken
from
peer
reviewed
literature
or
GLP
report,
used
for
the
purpose
other
than
intended
by
the
measurement
III
Taken
from
peer­
reviewed
database
compiled
for
the
purposes
in
which
it
is
being
used
IV
Taken
from
non
peer­
reviewed
database
compiled
for
the
purposes
other
than
those
for
which
it
is
being
used
V
Taken
from
other
non
peer­
reviewed
source
VI
Estimated
based
on
peer­
reviewed
method
or
data
VII
Estimated
based
on
non
peer­
reviewed
method
Table
B2:
Quality
and
Sources
of
Data
Used
in
the
ERDEM
Human
Carbaryl
Model
Variables
Category
Description
Citation
Cardiac
Output
VI
The
data
from
Agata
et
al
(
1994)
and
Schmitz
et
al
(
1998)
and
a
curve
was
fitted
to
it
(
Appendix
D)
Agata,
et
al
(
1994),
Schmitz,
et
al
(
1998),
Rosenthal
and
Bush
(
1998)

Body
Weight
II
Studies
to
determine
the
distribution
of
body
weights
by
age
for
humans
Burmaster
and
Crouch
(
1997)
and
Exposure
Factors
Handbook,
Tables
7.2
Body
Compartment
Blood
Flow
percentages
I
Values
of
percentages
are
modified
with
the
addition
of
the
skin
model.
Fisher
et
al.
(
1998).

Dermis
V
Value
used
for
PBPK
modeling
of
chloroform
Corley
et
al.
(
1990)

Fat
II
Values
for
children
aged
4­
21
years
Boot,
et
al.,
(
1997)

Liver
Kidney
I
Values
taken
from
Reference
Man
ICRP
2002
Body
Compartment
Volumes
GI
Tract
I
Values
taken
from
reference
ILSI
1994
II.
B.
6
­
Page
98
of
162
Variables
Category
Description
Citation
Rapidly
Perf
VI
Modified
as
necessary
for
additional
of
dermal
model
Fisher
et
al.
(
1998)

Slowly
Perf
III
Estimated
from
the
fat
content
Fisher
et
al.
(
1998)

Brain
II
Measurements
Milner
(
1990)

Skin
Permeation
Coefficients
VI
Utilized
experimental
results
for
rats
Table
1
Gastrointestinal
Absorption
Rates
VI
Utilized
experimental
results
for
rats;
scaled
by
body
weight
for
rat
and
human
Table
1
Tissue
to
Blood
Partition
Coefficients
VI
Combination
of
QSAR
(
Appendix
D)
and
experimental
results
Poulin
and
Theil
(
2000),
Table
1
Metabolism
Constants
for
Saturable
Metabolism
VI
Utilized
experimental
results
for
rats;
scaled
by
body
weight
for
rat
and
human.
Table
1
Urine
Elimination
Rate
Constants
VI
Utilized
experimental
results
for
rats;
scaled
by
body
weight
for
rat
and
human.
Table
1
Acetylcholinesterase
inhibition
VI
Based
on
in
vitro
study;
refined
from
rat
in
vivo
experiments
Hetnarski
and
O'Brien
1975;
Table
1
Table
B3:
Categories
of
Compartment
Model
Approaches
Category
Description
A
Widely
accepted
modeling
approach
B
Approach
similar
to
commonly
used
and
accepted
approaches,
but
adapted
to
satisfy
project
specific
requirements
C
Novel
approach
addressing
specific
requirements
of
estimating
absorption
and
dose.
II.
B.
6
­
Page
99
of
162
Table
B4:
Quality
of
Compartment
Models
Model
Cat.
Description
Brain
A
The
blood­
brain
barrier
is
not
modeled.
A
permeation
coefficient
determines
the
amount
of
chemical
remaining
in
the
brain
and
that
passed
to
the
venous
blood.
Metabolism
is
modeled
as
a
saturable
Michaelis­
Menten
process.

Dermis
B
Chemical
is
placed
on
the
skin
over
a
short
application
period
and
then
a
permeation
coefficient
is
used
to
determine
absorption
into
the
skin
Kidney
A
A
permeation
coefficient
determines
the
amount
of
chemical
remaining
in
the
kidney
and
that
passed
to
the
venous
blood.
Urine
elimination
is
modeled
with
a
urine
rate
constant,
or
saturable
Michaelis­
Menten
constants.

Fat,
Liver,
Rapidly
Perfused
Slowly
Perfused
A
A
permeation
coefficient
determines
the
amount
of
chemical
remaining
in
the
compartment
and
that
passed
to
the
venous
blood.
Metabolism
in
the
liver
is
modeled
as
a
saturable
Michaelis­
Menten
process
Blood
A
The
arterial
blood
enters
a
compartment,
a
permeation
coefficient
determines
the
amount
of
chemical
remaining
in
the
compartment
and
that
passed
to
the
venous
blood.
Metabolism
in
the
blood
is
modeled
as
a
saturable
Michaelis­
Menten
process
Stomach/

Intestine
A
Modeled
with
rate
constants
from
stomach
to
intestine,
stomach
to
portal
blood,
intestine
to
portal
blood,
and
intestine
to
feces.

2.0
Quality
Assurance
for
the
Exposure
Related
Dose
Estimating
Model
(
ERDEM)

2.1
Quality
Assurance
of
the
ERDEM
Models
There
are
many
different
methods
of
checking
the
quality
of
the
ERDEM
Model.
The
key
is
to
never
assume
that
everything
is
working
properly.
Continuous
checking
and
testing
are
required.
The
inputs
to
the
model
must
be
checked
and
rechecked.
The
outputs
from
model
runs
must
be
carefully
checked.
.
2.2
Code
review
The
model
code
is
written
in
the
Advanced
Continuous
Simulation
Language
(
ACSL).
It
is
reviewed
periodically,
when
any
problems
occur,
or
when
changes
II.
B.
6
­
Page
100
of
162
are
to
be
made.
The
input
variable
initializations
are
checked,
those
variables
that
are
sent
from
one
compartment
to
the
next
are
checked,
the
warnings
of
improper
data
input
are
checked,
the
initialization
and
setting
of
exposure
events,
and
the
equations
in
the
derivative
section
are
checked.

2.3
Mass
balance
checks.

During
any
model
run,
mass
balance
checks
are
automatically
performed
by
compartment,
for
each
chemical,
and
an
overall
mass
balance
ratio
check
is
performed.
Failure
of
these
checks
can
be
due
to
improper
coding
of
an
equation,
or
could
be
the
result
of
lack
of
input
exposures,
or
faulty
inputs.
The
following
is
an
example
of
the
mass
balance
ratios
by
compartment
for
trichloroacetic
acid.
Values
on
the
order
of
10­
14
are
expected
because
the
model
is
run
in
double
precision:

MASS
BALANCE
DIFFERENCE
RATIOS
(
ADIF/
AINPUT):
R_
AB_
DIF(
2)=
0.2371175E­
15
R_
AL_
DIF=
0.000000
R_
BN_
DIF=­
0.1746669E­
15
R_
CC_
DIF=
0.000000
R_
CN_
DIF=
0.000000
R_
CNL_
DIF=
0.000000
R_
CP_
DIF=
0.9002710E­
17
R_
CR_
DIF=
0.000000
R_
DR_
DIF=­
0.1091668E­
15
R_
DU_
DIF=
0.000000
R_
DUL_
DIF=
0.000000
R_
FT_
DIF=
0.000000
R_
IN_
DIF=
0.000000
R_
KD_
DIF=­
0.1164446E­
15
R_
LD_
DIF=
0.000000
R_
LG_
DIF=
0.1035248E­
15
R_
LP_
DIF=
0.000000
R_
LV_
DIF=­
0.1064165E­
17
R_
OV_
DIF=
0.000000
R_
PB_
DIF=
0.000000
R_
PU_
DIF=
0.000000
R_
RP_
DIF=
0.000000
R_
SI_
DIF=
0.000000
R_
SIL_
DIF=
0.000000
R_
SL_
DIF=
0.000000
R_
SP_
DIF=
0.000000
R_
ST_
DIF=
0.000000
R_
STL_
DIF=
0.000000
R_
SW_
DIF=
0.000000
R_
TS_
DIF=
0.000000
R_
UD_
DIF=
0.000000
R_
VB_
DIF=
0.000000
2.4
Proper
Operation
with
Inputs
for
Known
Chemicals
and
Their
Known
Metabolism
Paths
An
example
of
proper
operation
with
inputs
for
MTBE
is
shown
in
Figure
1.
Plots
of
the
concentration
of
MTBE
in
the
liver,
kidney,
and
venous
blood
are
shown
for
a
continuous
inhalation
in
an
environment
with
400
ppb
of
MTBE.
A
basic
set
of
such
model
runs
are
performed
that
are
repeatable
so
that
if
the
model
is
changed,
then
these
sets
of
data
can
be
rerun
and
output
results
checked
against
earlier
results.
Compartments
are
checked
for
proper
circulation
of
chemicals
and
proper
metabolism
and
elimination.
II.
B.
6
­
Page
101
of
162
Figure
B1:
Concentrations
of
MTBE
in
Kidney,
Liver,
and
Venous
Blood
for
Continuous
Exposure
at
400
ppm.

2.5
Comparisons
of
Model
Runs
with
Experimental
Results
The
results
of
a
PBPK/
PD
model
mean
very
little
unless
some
measure
of
confidence
in
the
results
may
be
obtained.
One
way
to
do
this
is
to
test
the
model
against
experimental
data.
The
ERDEM
model
has
been
compared
with
results
of
at
least
three
different
sets
of
chemicals
and
experimental
data
sets
other
than
carbaryl:

°
Fisher
et
al,
1998,
with
trichloroethylene
on
human
volunteers.
°
Comparison
of
ERDEM
runs
with
methyl
tertiary­
butyl
ether
(
MTBE)
experimental
results
for
humans,
rats,
and
mice.
°
Comparison
with
radio­
labeled
isofenphos
and
parathion.

These
model
runs
with
ERDEM
validate
the
model
for
each
special
case.
Each
new
chemical
and
demographic
group
require
separate
comparison
with
experimental
data.
II.
B.
6
­
Page
102
of
162
2.6
Proper
Use
of
an
ERDEM
Model
In
order
to
gain
confidence
in
the
results
of
model
runs,
the
user
of
an
ERDEM
model
must
consider
the
following:

°
The
source
for
the
values
of
input
parameters
to
the
ERDEM
model
must
be
specified,
even
if
an
approximation.
If
the
model
is
functioning
as
designed
but
the
input
values
are
improper,
then
it
is
the
same
as
if
the
model
itself
did
not
work
properly.

°
Mistakes
in
the
setting
of
input
values,
even
though
they
are
well
chosen,
will
cause
improper
model
operation.

°
Part
of
quality
assurance
for
given
sets
of
model
runs
is
review
of
the
outputs
for
various
organs
and
determining
that
they
are
reasonable.

°
The
user
must
check
for
error
messages
output
by
the
model
engine
when
processing
improper
inputs.

°
The
ERDEM
model
checks
input
values
and
outputs
warnings
and
even
may
abort
the
model
run
when
illegal
values
are
input.
The
user
should
check
all
warnings.

°
The
user
must
check
output
results
for
reasonableness.

°
The
absorbed
dose
from
the
various
exposures,
the
amounts
eliminated,
the
amounts
metabolized,
and
the
amount
of
chemical
eliminated
due
to
enzyme
inhibition
should
all
be
checked.
These
are
supplied
as
part
of
the
output
printed
at
the
completion
of
a
model
run.

°
The
relative
and
absolute
error
limits
must
be
set
according
to
guidelines
available
in
the
Front
End.

The
mass
balance
ratios
for
each
compartment,
each
chemical
and
total
mass
balance
must
be
on
the
order
of
10­
14
or
less.
II.
B.
6
­
Page
103
of
162
Appendix
C.
Biomonitoring
Study
Data
Analysis
1.1
Study
Description
The
Bayer
biomonitoring
study
is
described
elsewhere
(
Bayer
2004c).
The
following
is
taken
directly
from
the
abstract
of
Bayer
2004c:

"
The
purpose
of
this
study
was
to
characterize
the
potential
absorbed
doses
of
carbaryl
to
homeowners
and
residents
during
and
following
the
residential
application
of
carbaryl
by
measuring
urinary
pesticide
metabolite
levels
in
the
applicator,
spouse,
and
children
of
representative
families
that
use
pesticides.
Non­
professional
adult
and
child
volunteers
were
used
to
measure
carbaryl
absorbed
doses
in
homeowners
and
their
families
during
and
after
application
of
Sevin
®
GardenTech
Ready­
To­
Spray,
a
formulation
of
carbaryl.
Sites
were
selected
in
Missouri
and
California.
Ten
families
were
monitored
in
Missouri
and
13
families
were
monitored
in
California.

"
The
formulated
product,
Sevin
®
GardenTech
Ready­
To­
Spray
(
22.5%
a.
i.
wt/
wt),
is
a
liquid
formulation
of
Sevin
®
,
for
use
on
lawns,
gardens,
and
ornamentals
for
insect
control.

"
Application
of
the
test
substance
was
performed
by
an
adult
at
each
site.
The
applicator
sprayed
approximately
4,000
to
10,000
ft2
of
lawn
and/
or
approximately
400
to
1,000
ft2
of
garden
or
ornamentals
on
the
property
or
residence.
The
test
substance
was
applied
at
the
recommended
label
rate
at
the
Missouri
sites
(
average
of
4.8
lb
a.
i./
A
with
a
range
of
2.5
to
6.5
lb
a.
i./
A).
The
test
substance
was
applied
at
an
average
rate
of
30.0
lb
a.
i./
A
(
range
of
2.0
to
160
lb
a.
i./
A)
for
the
California
sites.
The
method
of
application
was
a
hose
end
sprayer.
With
two
exceptions,
the
families
consisted
of
the
applicator,
spouse,
and
a
minimum
of
one
child
between
the
ages
of
4
to
17
years
old.
The
Missouri
Site
10
family
consisted
of
a
3.5
years­
old
child.
The
California
Site
8
family
consisted
of
an
applicator
and
one
child.
The
total
number
of
participants
in
the
study
was
106
(
23
applicators,
22
spouses,
6
adult
residents,
42
children
4­
12
years­
old,
and
13
children
13­
17
years­
old).

"
The
average
time
required
to
apply
the
test
substance
and
clean­
up
for
the
Missouri
sites
was
42
minutes
(
range
of
30
to
80
min).
The
average
amount
of
GardenTech
Ready­
To­
Spray
applied
was
45
oz
which
is
equivalent
to
451
g
a.
i
(
range
of
332
to
613
g
a.
i.).
The
average
time
required
to
apply
the
test
substance
and
clean­
up
for
the
California
sites
was
25
minutes
(
range
of
5
to
60
min).
The
amount
of
GardenTech
Ready­
To­
Spray
applied
was
32
oz
at
each
site,
which
is
equivalent
to
255
g
a.
i.
The
application
of
the
liquid
formulation
to
the
lawn,
vegetable
garden,
and
ornamental
flowers
was
selected
to
represent
an
upper­
bound
exposure
potential
for
carbaryl
residential
uses.
II.
B.
6
­
Page
104
of
162
"
Activities
outside
the
residence
in
or
near
the
treated
areas
for
the
applicator,
spouse,
and
children
were
monitored
the
day
of
application
(
day
0),
and
days
1,
2,
and
3
post­
application.
Activities
such
as
mowing,
trimming,
weeding,
planting,
applying
fertilizer,
working
in
gardens,
working
on
buildings,
hanging
clothes,
and
playing
were
documented
for
each
individual
participant
and
the
approximate
time
of
potential
exposure.

"
Urine
samples
were
collected
from
participants
beginning
two
days
before
(
day
2)
the
application
through
three
days
after
application
(
day
3).
Each
urine
sample
was
a
24­
hour
composite,
resulting
in
six
24­
hour
urine
samples
from
each
participant
(
days
­
2,
­
1,
0,
1,
2,
and
3).
The
day
0
sample
collection
period
began
with
the
first
void
after
the
application
and
ended
24
hours
later.
The
presence
of
1­
naphthol
in
the
urine
can
result
from
non­
carbaryl
sources
of
exposure.
The
two
days
of
pre­
application
monitoring
were
intended
to
provide
information
on
background
levels
of
1­
naphthol
among
the
study
participants.
No
correction
has
been
made
for
pre­
application
1­
naphthol
levels.
The
conversion
of
urinary
1­
naphthol
to
carbaryl
based
on
carbaryl
pharmacokinetic
data
inherently
assumes
that
all
1­
naphthol
present
in
the
urine
derived
from
carbaryl
and
is
an
inherent
source
of
overestimation
of
the
carbaryl­
specific
levels
of
1­
naphthol.

"
Urine
samples
were
analyzed
for
1­
naphthol
using
"
Method
for
the
HPLC
Analysis
of
1­
Naphthol,
Caffeine,
and
Cotinine
in
Urine."
The
LOQ
for
1
naphthol
was
determined
to
be
0.01
ppm.
The
average
recovery
for
the
0.01
ppm
level
was
71.5
±
0.14%.
The
average
recovery
for
the
0.1
ppm
level
was
80.9
±
0.89%.
The
average
recovery
for
the
1
ppm
level
was
84.3
±
0.93%.

"
Method
recoveries
were
corrected
for
any
analyte
determined
in
the
corresponding
controls.

"
In
both
the
Missouri
and
California
participants
there
were
levels
of
1­
naphthol
detected
in
the
Days
 
2
and
 
1
urine
samples.
The
1­
naphthol
likely
resulted
from
exposure
to
non­
carbaryl
sources
of
1­
naphthol
and
potentially
from
dietary
residues
of
carbaryl.
For
the
Missouri
sites
the
pre­
application
1­
naphthol
levels
(
corrected
to
carbaryl
residues)
found
in
the
urine
ranged
from
4.5
to
0.005
µ
g/
kg
body
weight
in
the
applicator,
5.8
to
0.005
µ
g/
kg
body
weight
in
the
spouses,
1.2
to
0.005
µ
g/
kg
body
weight
in
children
13
to
17
years
old,
and
12.5
to
0.005
µ
g/
kg
body
weight
in
children
4
to
12
years
old.
For
California
the
pre­
application
1­
naphthol
levels
(
corrected
to
carbaryl
residues)
found
in
the
urine
ranged
from
1.5
to
0.005
µ
g/
kg
body
weight
in
the
applicator,
3.7
to
0.005
µ
g/
kg
body
weight
in
the
spouses,
2.0
to
0.005
µ
g/
kg
body
weight
in
adult
residents,
1.4
to
0.005
µ
g/
kg
body
weight
in
children
13
to
17
years
old,
and
2.2
to
0.005
µ
g/
kg
body
weight
in
children
4
to
12
years
old.

"
For
the
Missouri
sites,
carbaryl
residue
levels
found
in
urine
from
applicators
on
day­
0
ranged
from
21.7
to
0.26
µ
g/
kg
body
weight.
Carbaryl
residue
levels
found
II.
B.
6
­
Page
105
of
162
in
urine
from
spouses
on
day
0
ranged
from
4.9
to
0.005
µ
g/
kg
body
weight.
Carbaryl
residue
levels
found
in
urine
from
children
4
­
12
years
old
on
day­
0
ranged
from
61
to
0.15
µ
g/
kg
body
weight.
Carbaryl
residue
levels
found
in
urine
from
children
13
­
17
years
old
on
day
0
ranged
from
12.6
to
0.25
µ
g/
kg
body
weight.
Maximum
carbaryl
residue
levels
found
were
21.7
µ
g
in
applicators,
4.9
µ
g
in
spouses,
61
µ
g
in
children
4
­
12,
and
12.6
µ
g
in
children
13
­
17."

1.2
Data
Analysis
As
mentioned
in
section
2.3
of
the
main
text,
only
the
Missouri
data
were
considered
for
this
assessment
based
on
the
inconsistencies
with
the
labeling
instructions
at
the
California
sites.
The
1­
naphthol
levels
were
converted
to
carbaryl
equivalents
based
on
the
following:
 
Molecular
weight
conversion
(
201.2/
144.2=
1.4)
 
Metabolic
selectivity,
where
40%
of
carbaryl
is
assumed
to
be
excreted
as
1­
naphthol
species
Therefore,
the
mass
of
1­
naphthol
in
urine
was
multiplied
by
3.5
(
1.4
/
0.40)
to
estimate
the
equivalent
mass
of
carbaryl
absorbed.

Repeated
measurement
analysis
of
variance,
rm­
ANOVA
(
with
a
preferable
balance
design),
was
selected
for
the
analysis
of
study
data
because
of
dependence
of
each
subsequent
within­
subjects
measurement
on
the
previous
measurements
(
as
an
individual­
subject
variation
constraint,
see
Winer,
1976;
BMDP,
1979;
and
Kleinbaum
et
al.,
1998).
In
the
Bayer
and
EPA
studies,
the
data
were
nested
with
states
by
sites;
the
dependent
variables
were
the
repeated
measures
of
urine
volume
(
mL),
creatinine
concentration
(
g/
L),
corrected
excretion
of
1­
naphthol
(
µ
g/
mL),
and
total
excretion
of
1­
naphthol
(
µ
g).
Their
children
age
grouping
critera
produced
an
unbalance
rm­
ANOVA
design.
The
rationale
for
both
age
groupings
was
not
explained
as
shown
in
the
following:

1.
Bayer
Age­
Grouping
 
applicator,
others,
and
(
2­
level)
children
such
as
4­
12;
13­
17
2.
EPA
Age­
Grouping
 
applicator,
others,
and
(
4­
level)
children
such
as
4­
5;
6­
10;
11­
15;
16­
17
Consideration
was
given
to
the
unbalanced
nature
of
Bayer
and
EPA
age
grouping,
and
adjustments
were
made
according
to
child
development
and
learning
(
Bredekamp,
1983;
American
Academy
of
Pediatrics
and
the
American
Red
Cross,
1990).
This
adjustment
produced
four
groups:
4­
5
years
of
age
described
as
preschoolers
(
n=
7),
6­
8
years
for
elementary
school
aged
(
n=
7),
9­
12
years
for
older
elementary
school
aged
(
n=
9)
and
13­
16
for
junior
high
school
and
high
school
aged
(
n=
7)
for
the
Missouri
data
(
Table
C1).
This
balance
sample
size
improvement
allows
us
to
perform
the
traditional
rm­
ANOVA
with
a
II.
B.
6
­
Page
106
of
162
better
suitability
for
the
balance
design
assumption.
Then,
the
balance
rm­
ANOVA
was
considered
the
more
appropriate
design
for
the
Missouri
data.

Table
C1.
Children
sample
size
distributions
in
three
groupings,
Bayer,
EPA,
and
Current
Age
group
Study
I
II
III
IV
Bayer
Age:
4­
12
Age:
13­
17
n=
23
n=
7
EPA
Age:
4­
5
Age:
6­
10
Age:
11­
15
Age:
16­
17
n=
7
n=
13
n=
8
n=
2
Current
Age:
3­
5
Age:
6­
8
Age:
9­
12
Age:
13­
17
n=
7
n=
7
n=
9
n=
7
The
results
generated
by
SPSS
software
were
summarized
into
the
upper
confidence
interval
plots
produced
by
R
software
for
the
cumulative
excreted
mass
of
carbaryl
equivalents
(
Figure
C1).
II.
B.
6
­
Page
107
of
162
Days
MO
­
TPC
Classification:
Total
Excreted
[
ug]

0
500
1000
1500
2000
2500
­
2
­
1
0
1
2
3
3:
ChildLE5
4:
ChildLE8
5:
ChildLE12
0
500
1000
1500
2000
2500
6:
ChildLE17
­
2
­
1
0
1
2
3
­­

­
90%
C.
I.
95%
C.
I.
99.9%
C.
I.

Figure
C1.
Confidence
interval
plot
by
age
group
of
cumulative
excreted
mass
of
carbaryl
equivalents.
II.
B.
6
­
Page
108
of
162
Table
C2.
Confidence
Intervals
by
Age
Group
STATE
Confidence
FACTOR
Days
Mean
StdError
Lower
Bound
Upper
Bound
MO
95%
C.
I.
3:
ChildLE5
­
2
19.624
34.733
­
51.771
91.02
MO
95%
C.
I.
3:
ChildLE5
­
1
33.259
34.91
­
38.501
105.018
MO
95%
C.
I.
3:
ChildLE5
0
98.071
185.091
­
282.388
478.531
MO
95%
C.
I.
3:
ChildLE5
1
204.743
207.305
­
221.379
630.864
MO
95%
C.
I.
3:
ChildLE5
2
270.686
211.814
­
164.705
706.077
MO
95%
C.
I.
3:
ChildLE5
3
359.876
348.674
­
356.834
1076.585
MO
95%
C.
I.
4:
ChildLE8
­
2
28.014
34.733
­
43.381
99.41
MO
95%
C.
I.
4:
ChildLE8
­
1
32.376
34.91
­
39.384
104.135
MO
95%
C.
I.
4:
ChildLE8
0
114.589
185.091
­
265.871
495.048
MO
95%
C.
I.
4:
ChildLE8
1
311.174
207.305
­
114.947
737.296
MO
95%
C.
I.
4:
ChildLE8
2
391.65
211.814
­
43.741
827.041
MO
95%
C.
I.
4:
ChildLE8
3
439.743
348.674
­
276.967
1156.452
MO
95%
C.
I.
5:
ChildLE12
­
2
101.478
30.632
38.513
164.443
MO
95%
C.
I.
5:
ChildLE12
­
1
116.189
30.788
52.903
179.475
MO
95%
C.
I.
5:
ChildLE12
0
473.983
163.235
138.449
809.517
MO
95%
C.
I.
5:
ChildLE12
1
639.794
182.826
263.991
1015.598
MO
95%
C.
I.
5:
ChildLE12
2
807.406
186.803
423.427
1191.384
MO
95%
C.
I.
5:
ChildLE12
3
1196.506
307.501
564.427
1828.584
MO
95%
C.
I.
6:
ChildLE17
­
2
13.457
34.733
­
57.938
84.853
MO
95%
C.
I.
6:
ChildLE17
­
1
32.971
34.91
­
38.788
104.731
MO
95%
C.
I.
6:
ChildLE17
0
250.114
185.091
­
130.346
630.574
MO
95%
C.
I.
6:
ChildLE17
1
412.171
207.305
­
13.95
838.293
MO
95%
C.
I.
6:
ChildLE17
2
463.029
211.814
27.638
898.419
MO
95%
C.
I.
6:
ChildLE17
3
570.257
348.674
­
146.452
1286.967
The
assessment
focused
on
evaluating
conservative
scenarios,
so
the
99.9%
ile
was
used
to
establish
the
magnitude
of
exposure.
The
group
LE5
was
simulated
to
evaluate
hand­
to­
mouth
exposure,
and
the
LE12
group
was
used
since
they
showed
the
highest
biomarker
levels.
The
older
group
is
assumed
to
be
exposed
by
the
dermal
route.

1.3
References
American
Red
Cross.
Safely
Checklist
from
The
American
Red
Cross
Child
Care
Course,
Health
&
Safety
Units.
The
American
Red
Cross,
1990.

BMDP
Statistical
Software
Manual
1979.
Berkeley:
University
of
California
Press.

Bredekamp
S.
Guide
to
accreditation
by
the
National
Academy
of
Early
Childhood
Programs.
National
Association
for
the
Education
of
Young
Children,
Washington,
DC,
1985.

Kleinbaum,
D.
G.,
Kupper,
L.
L.,
Muller,
K.
E.,
and
Nizam,
A.
(
1998)
"
Applied
Regression
Analysis
and
Other
Multivariable
Methods,"
3rd
Ed.
Pacific
Grove:
Duxbury
Press.
II.
B.
6
­
Page
109
of
162
Winer,
B.
J.(
1971).
"
Statistical
Principles
in
Experimental
Design,"
2nd
Ed.
New
York:
McGraw­
Hill.
II.
B.
6
­
Page
110
of
162
Appendix
D.
Cardiac
Output
and
Partition
Coefficients
1.0
Calculation
of
Cardiac
Output
for
Children
as
a
function
of
age
and
growth
Cardiac
output
measurements
used
in
the
simulations
were
obtained
from
Rosenthal
and
Bush
(
1998).
Pulmonary
blood
flow
was
determined
in
a
small
number
of
boys
and
girls
of
different
ages.
The
neonatal
values
were
obtained
from
Agata
et
al,
(
1994).
Values
for
children
0.5
to
19
years
of
age
were
obtained
from
Schmitz
et
al,
(
1998).
These
final
two
studies
were
selected
because
they
combined
information
for
both
males
and
females
and
the
total
number
of
subjects
was
larger
than
in
other
studies
surveyed.
The
values
used
for
the
simulations
represent
averages
for
each
age
group
and
gender.

1.1
Methods
and
Results
The
average
value
of
96
hours
after
birth
was
selected
as
the
starting
value
for
neonates.
This
value
was
selected
because
there
was
a
steady
decline
in
cardiac
output
after
birth,
reaching
a
steady
state
value
at
96
hours.
These
data
were
plotted
with
the
data
from
Schmitz
et
al,
(
1998)
and
a
regression
equation
was
obtained
(
Figure
D1)

Cardiac
output
(
liters/
hour)
=
174.64
x0.2989
,
where
x
is
age
in
years.

This
equation
was
used
to
calculate
the
average
cardiac
output
for
children
ages
3,
9,
and
18
years.
The
calculation
is
the
same
for
both
boys
and
girls.
After
the
calculation,
cardiac
outputs
were
rounded
to
3
significant
digits
(
Table
D1).
These
values
were
used
in
the
simulations
for
boys
and
girls.

Table
D1.
Cardiac
Output
for
Children
by
Age
Age
(
Years)
Cardiac
Output
(
Liters/
Hour)

3
242
9
337
18
414
II.
B.
6
­
Page
111
of
162
Cardiac
Output
in
children
y
=
174.64x0.2989
R2
=
0.9945
0
50
100
150
200
250
300
350
400
450
500
0
5
10
15
20
Age
in
years
Cardiac
Output
l/
hr
Figure
D1.
Cardiac
Output
Data
and
the
Fitted
Equation
for
Children
1.2
References
Agata
Y,
Hiraishi
S,
Misawa
H,
Hirota
H,
Nowatari
M,
Hiura
K,
Fujino
N,
Oguchi
K,
and
Horiguchi
Y.
(
1994)
Regional
Blood
Flow
Distribution
and
Left
Ventricular
Output
during
Early
Neonatal
Life:
A
Quantitative
Ultrasonographic
Assessment.
Pediatric
Research.
Vol
36,
pp.
805­
810.

Rosenthal
M.
and
Bush
A.
(
1998)
.
Haemodynamics
in
children
during
rest
and
exercise:
methods
and
normal
values.
Eur
Respir
J.
Vol
11:
pp.
854­
865.

Schmitz
L,
Koch
H,
Bein
G,
and
Brockmeier
K.
(
1998).
Left
Ventricular
Diastolic
Function
in
Infants,
Children,
and
Adolescents.
Reference
Values
and
Analysis
of
Morphologic
and
Physiologic
Determinants
of
Echocardiographic
Doppler
Flow
Signals
During
Growth
and
Maturation.
Journal
of
the
American
College
of
Cardiology.
Vol
32,
pp.
1441­
1448.
II.
B.
6
­
Page
112
of
162
2.0
Estimation
of
human
tissue:
plasma
partition
coefficients
(
Pt:
p)
for
carbaryl
and
metabolites
Tissue:
plasma
partition
coefficients
(
Pt:
p)
are
physiochemical
input
parameters.
The
Pt:
p
represent
the
relative
distribution
of
a
chemical
between
tissues
and
plasma
at
equilibrium.
In
vitro
and
In
vivo
methods
are
available
for
direct
estimation
of
the
Pt:
ps;
however,
these
methods
are
often
resource
and
time
intensive,
requiring
equilibrium
conditions
and
the
use
of
appropriate
analytical
methods.
In
recent
years,
algorithms
have
been
developed
for
predicting
Pt:
p
based
on
the
chemical's
n­
octanol­
water
partition
coefficient
(
Kow)
and
the
relative
distribution
of
lipids
in
tissue
and
plasma
(
Poulin
and
Theil
2002,
Poulin
and
Krishnan
1995,
Haddad
et
al.
2000;
).

The
basis
of
this
approach
is
that
the
solubility
of
a
chemical
in
tissue
(
e.
g.,
plasma)
may
be
approximated
by
its
additive
solubility
in
lipid
and
water
(
Poulin
and
Krishnan
1995).
Lipid
solubility
is
approximated
by
the
Kow.
The
quantitative
relation
between
Kow
and
Pt:
p
may
be
used
to
predict
tissue
distribution
based
on
the
following
equation:

Pt:
p,
nonadipose*
=
Kow
(
Vnlt
+
0.3Vpht)
+
[
Vwt
+
0.7Vpht]
(
D1)
Kow
(
Vnlp
+
0.3Vphp)
+
[
Vwt
+
0.7Vphp]

where
V
is
the
fractional
tissue
volume
content
of
neutral
lipids
(
nl),
phospholipids
(
ph)
and
water
(
w),
t
is
tissue,
p
is
plasma
(
Table
D2).
II.
B.
6
­
Page
113
of
162
Table
D2.
Human
physiological
parameters
for
volumes
used
in
estimating
Pt:
p
(
Poulin
and
Theil
2002)

Tissue
Tissue
(
Vt)
a
Water
(
Vw)
Vnl
Vpl
Adipose
0.120
0.180
0.790
0.002
Bone
0.086
0.439
0.074
0.001
Brain
0.020
0.770
0.051
0.057
Gut
0.017
0.718
0.049
0.016
Heart
0.005
0758
0.012
0.017
Kidney
0.004
0.783
0.021
0.016
Liver
0.026
0.751
0.035
0.025
Lung
0.008
0.811
0.003
0.009
Muscle
0.400
0.760
0.024
0.007
Skin
0.037
0.718
0.028
0.011
Spleen
0.003
0.788
0.020
0.020
Plasma
0.042
0.945
0.004
0.002
a.
Fraction
of
Body
weight
(
L/
Kg)
for
70
kg
human
Equation
D1
is
limited
to
non­
adipose
tissue
because
Kow
does
not
properly
estimate
the
hydrophobic
interactions
of
chemicals
and
ionized
lipids
found
in
adipose
tissue
(
Poulin
and
Theil
2002).
Alternatively,
Kob,
based
on
the
olive
oil:
buffer
partition
coefficient,
takes
into
account
partitioning
of
nonionized
and
ionized
species:

Pt:
p,
adipose*
=
Kob
(
Vnlt
+
0.3Vpht)
+
[
Vwt
+
0.7Vpht]
(
D2)
Kob
(
Vnlp
+
0.3Vphp)
+
[
Vwt
+
0.7Vphp]

However,
experimental
data
for
Kob
are
much
more
limited
than
for
Kow.
It
is
therefore
convenient
to
compute
this
parameter
from
Kow
(
Leo
et
al.
1971)
:

Log
Kob
=
1.115
x
log
Kow
­
1.35,
n=
104,
r
=
0.99
(
D3)

Both
equations
D1
and
D2
tend
to
overestimate
the
Pt:
p
to
the
extent
that
protein
binding
in
tissue
and
plasma
is
not
taken
into
consideration.
Protein
binding
may
be
considered
in
the
PBPK
model
as
an
equilibrium
reaction,
distinguishing
the
relative
amount
of
bound
compound
from
free.
On
the
other
hand,
if
not
large,
it
may
be
taken
directly
into
account
in
the
estimated
partition
coefficient.
Poulin
and
Theil
(
2002)
indicate
that
equation
D1
may
be
multiplied
by
the
ratio
of
the
II.
B.
6
­
Page
114
of
162
fraction
of
unbound
protein
the
plasma
to
the
fraction
of
unbound
protein
in
tissue
(
fup/
fut).

Pt:
p,
nonadipose=
Pt:
p,
nonadipose*
x
(
fup/
fut)
(
D4)

The
fraction
unbound
in
tissue
(
fut)
may
be
estimated
from
fup,
based
on
assumptions
of
the
relative
distribution
of
typical
binding
proteins
between
plasma
and
tissue
(
ie.,
albumin,
globulins,
and
lipoproteins).
According
to
Poulin
and
Theil
(
2000),
mammalian
systems
tend
toward
a
value
of
0.5;
hence,

fut
=
1/(
1+{[(
1­
fup)/
fup]
x
0.5})
(
D5)

A
10%
protein
binding
in
plasma
(
fup=
0.9)
is
assumed
to
obtain
a
ratio
where
fup/
fut
=
0.95.
Thus,
the
bias
in
Pt:
p
is
­
5%
for
10%
protein
binding.
However,
the
error
for
adipose
tissue
is
1:
1
since
the
adjustment
is
greater
(
fup/
1)
with
the
assumption
of
no
protein
binding
in
adipose
tissue:

Pt:
p,
adipose=
Pt:
p,
adipose*
x
fup
(
D6)

The
predicted
Pt:
p
may
be
checked
for
plausibility,
provided
that
experimental
data
exist
for
the
steady
state
volume
of
distribution.
Each
tissue
contributes
to
the
total
experimental
volume
of
distribution
as
follows:
Vt
x
Pt:
p.
Hence,
the
predicted
volume
of
distribution
(
Vdss)
may
be
computed
as
follows:

Predicted
Vdss
=
 
(
Vt
x
Pt:
p)
+
Vp
(
D7)

All
estimates
of
Pt:
p
assume
no
protein
binding,
hence
fup/
fut=
1
and
fup=
1.
Assuming
20%
protein
binding
in
the
plasma,
these
methods
overestimate
Pt:
p
by
10%
in
non­
adipose
tissue.

2.1
Estimation
of
LogP
(
Log10[
Kow])
for
carbaryl
and
metabolites
Experimental
values
for
LogP
were
used
for
carbaryl
and
naphthol
(
Table
D3).
Because
experimental
LogP
were
not
available
for
the
other
metabolites
of
carbaryl,
LogP
were
estimated
using
the
semi­
empirical
neural
network
approach
(
see
http://
www.
logp.
com./)
and
are
listed
in
Table
D3.
The
prediction
for
carabyl
using
this
method
was
logP=
2.35,
consistent
with
the
experimental
value
of
2.36.

2.2
Estimation
of
LogD
(
Log10[
Kob])
for
carbaryl
and
metabolites
Log
D
was
estimated
for
carbaryl
using
equation
D3
giving
a
value
of
Log10[
Kob]=
1.284.
Equation
D3
assumes
that
the
compound
is
neutral.
For
acid
or
base
compounds,
ionizability
was
taken
into
account.
Common
metabolites
of
carbaryl
tend
to
be
acidic.
Adjustments
based
on
Hendersen­
Hasselbalch
II.
B.
6
­
Page
115
of
162
equations
take
into
consideration
ionized
species.
Equation
D8
employs
the
neutral
LogD
and
the
pKa
to
estimate
LogD
for
monoprotic
acids
(
see
Table
D3).

LogD(
monoprotic
acid)
=
LogD(
neutral)
­
log(
1
+
10
pH­
pKa)
(
D8)

The
pKa
for
acid
metabolites
were
predicted
using
(
http://
ibmlc2.
chem.
uga.
edu/
sparc/
index.
cfm)
and
physiological
pH
was
employed.
The
pKa
values
of
the
compounds
are
predicted
using
approximately
300
Hammett
and
Taft
equations.

Table
D3.
Physiochemical
properties
of
carbaryl
and
its
metabolites
Compound
MW
Structure
pKa
Log(
Kow),
experiment
Log(
Kow),
predicted
Log(
Kob),
pH
7.4
Carbaryl
201.2
12.02
2.36
2.3484
1.284
4­
OH
carbaryl
217.2
9.2
1.8682
0.73
3,4
DIOH
carbaryl
235.2
12.66
0.2516
­
1.0694
5,6
DIOH
carbaryl
235.2
12.16
­
0.007
­
1.357805
Naphthol
144.2
9.4
2.85
2.6888
1.82775
3,4
DIOH
naphthol
178.2
9.92
0.1867
­
1.1418
NH
O
O
OH
OH
O
H
OH
O
H
NH
O
O
OH
OH
NH
O
O
OH
OH
NH
O
O
II.
B.
6
­
Page
116
of
162
Compound
MW
Structure
pKa
Log(
Kow),
experiment
Log(
Kow),
predicted
Log(
Kob),
pH
7.4
Naphthyl
sulfate
224.2
­
0.4554
­
9.0745
Naphthyl
glucuronide
320.3
2.78
0.8421
­
4.3046
3,4
DIOH
naphthyl
sulfate
258.2
­
3.3424
­
12.1069
3,4
DIOH
naphthyl
glucuronide
354.3
­
1.5013
­
7.115
4­
OH
carbaryl
sulfate
297.3
­
1.276
­
10.05
4­
OH
carbaryl
glucuronide
393.3
2.75
0.0215
­
5.2775
3,4
DIOH
carbaryl
sulfate
315.3
­
3.1608
­
12.1781
N
H
O
O
OH
O
S
O
O
OH
O
O
H
O
H
OH
O
OH
O
O
O
N
H
O
S
O
O
OH
O
O
NH
O
OH
O
H
O
H
O
OH
O
OH
OH
O
H
S
O
O
O
O
H
OH
O
O
H
OH
OH
O
OH
O
O
S
O
O
OH
II.
B.
6
­
Page
117
of
162
Compound
MW
Structure
pKa
Log(
Kow),
experiment
Log(
Kow),
predicted
Log(
Kob),
pH
7.4
3,4
DIOH
carbaryl
glucuronide
411.4
­
0.913
­
6.5872
5,6
DIOH
carbaryl
glucuronide
411.4
­
1.1719
­
6.8687
2.3
Calculation
of
partition
coefficients
As
mentioned
in
section
2.2.2,
partition
coefficients
were
calculated
only
for
carbaryl
and
its
primary
metabolites,
along
with
3,4
DIOH
naphthol.
As
seen
in
Table
D3,
complete
chemical
descriptions
were
available
for
these
compounds,
but
the
other
metabolites
have
not
been
completely
characterized.

To
calculate
the
Pt:
p
for
any
non­
adipose
tissue,
the
calc
entry
for
any
tissue
is
divided
by
the
calc
entry
for
plasma.
To
calculate
the
Pt:
p
for
adipose
tissue,
the
calc
entry
for
adipose
is
divided
by
the
calc
entry
for
plasma(
D*
vo:
w).
In
the
following
tables,
`
calc'
refers
to
the
numerator
of
equation1
for
all
tissues
except
the
adipose
tissue,
where
the
numerator
of
equation
D2
is
employed.
For
plasma
tissue,
the
denominator
of
equation
D1
is
employed
except
for
`
plasma
(
D*
vo:
w),
where
the
denominator
of
equation
D2
is
employed.
O
OH
OH
OH
O
O
H
O
OH
O
O
N
H
NH
O
O
O
H
O
OH
OH
O
H
O
OH
O
II.
B.
6
­
Page
118
of
162
Table
D4.
Partition
coefficient
calculations
for
carbaryl
HUMAN
Neutral
Lipid
Phospholipids
Carbaryl
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
19.116
15.2946
17.107
14.90
1.782
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
229.087
Bone
0.086
0.439
0.0740
0.0011
229.087
17.4678
10.32
9.18
0.786
Brain
0.020
0.770
0.0510
0.0565
229.087
16.3760
9.68
8.61
0.172
Gut
0.017
0.718
0.0487
0.0163
229.087
13.0062
7.69
6.83
0.117
Heart
0.005
0.758
0.0115
0.0166
229.087
4.5450
2.69
2.39
0.011
Kidney
0.004
0.783
0.0207
0.0162
229.087
6.6512
3.93
3.50
0.015
Liver
0.026
0.751
0.0348
0.0252
229.087
10.4728
6.19
5.50
0.143
Lung
0.008
0.811
0.0030
0.0090
229.087
2.1231
1.25
1.12
0.008
Muscle
0.400
0.760
0.0238
0.0072
229.087
6.7121
3.97
3.53
1.411
Skin
0.037
0.718
0.0284
0.0111
229.087
7.9947
4.73
4.20
0.156
Spleen
0.003
0.788
0.0201
0.0198
229.087
6.7673
4.00
3.56
0.009
Plasma
0.042
0.945
0.0035
0.0023
229.087
1.9030
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
19.116
1.0264
Whole
blood
0.077
0.820
0.0032
0.0020
229.087
1.6919
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
19.116
0.8940
Erythrocytes
0.035
Vdss
4.653
II.
B.
6
­
Page
119
of
162
Table
D5.
Partition
coefficient
calculations
for
3,4­
dihydroxy
carbaryl
HUMAN
Neutral
Lipid
Phospholipids
3,4­
DiOH
Carbaryl
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.085
0.2488
0.303
0.26
0.031
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
1.785
Bone
0.086
0.439
0.0740
0.0011
1.785
0.5724
0.69
0.60
0.051
Brain
0.020
0.770
0.0510
0.0565
1.785
0.9308
1.12
0.98
0.020
Gut
0.017
0.718
0.0487
0.0163
1.785
0.8251
1.00
0.86
0.015
Heart
0.005
0.758
0.0115
0.0166
1.785
0.7990
0.96
0.84
0.004
Kidney
0.004
0.783
0.0207
0.0162
1.785
0.8400
1.01
0.88
0.004
Liver
0.026
0.751
0.0348
0.0252
1.785
0.8442
1.02
0.88
0.023
Lung
0.008
0.811
0.0030
0.0090
1.785
0.8275
1.00
0.87
0.007
Muscle
0.400
0.760
0.0238
0.0072
1.785
0.8114
0.98
0.85
0.340
Skin
0.037
0.718
0.0284
0.0111
1.785
0.7824
0.94
0.82
0.030
Spleen
0.003
0.788
0.0201
0.0198
1.785
0.8483
1.02
0.89
0.002
Plasma
0.042
0.945
0.0035
0.0023
1.785
0.9540
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.085
0.9469
Whole
blood
0.077
0.820
0.0032
0.0020
1.785
0.8282
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.085
0.8217
Erythrocytes
0.035
Vdss
0.570
II.
B.
6
­
Page
120
of
162
Table
D6.
Partition
coefficient
calculations
for
3,4­
dihydroxy
carbaryl
glucuronide
HUMAN
Neutral
Lipid
Phospholipids
3,4­
DiOH
Carbaryl
Glucuronide
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.122
Bone
0.086
0.439
0.0740
0.0011
0.122
0.4488
0.55
0.47
0.041
Brain
0.020
0.770
0.0510
0.0565
0.122
0.8178
1.00
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.122
0.7360
0.90
0.78
0.013
Heart
0.005
0.758
0.0115
0.0166
0.122
0.7716
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.122
0.7975
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.122
0.7738
0.94
0.82
0.021
Lung
0.008
0.811
0.0030
0.0090
0.122
0.8180
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.122
0.7682
0.93
0.81
0.324
Skin
0.037
0.718
0.0284
0.0111
0.122
0.7296
0.89
0.77
0.029
Spleen
0.003
0.788
0.0201
0.0198
0.122
0.8050
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.122
0.9471
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.122
0.8219
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.527
II.
B.
6
­
Page
121
of
162
Table
D7.
Partition
coefficient
calculations
for
3,4­
dihydroxy
carbaryl
sulfate
HUMAN
Neutral
Lipid
Phospholipids
3,4­
DiOH
Carbaryl
sulfate
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.001
Bone
0.086
0.439
0.0740
0.0011
0.001
0.4398
0.54
0.46
0.040
Brain
0.020
0.770
0.0510
0.0565
0.001
0.8096
0.99
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.001
0.7294
0.89
0.77
0.013
Heart
0.005
0.758
0.0115
0.0166
0.001
0.7696
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.001
0.7944
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.001
0.7687
0.94
0.81
0.021
Lung
0.008
0.811
0.0030
0.0090
0.001
0.8173
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.001
0.7651
0.93
0.81
0.323
Skin
0.037
0.718
0.0284
0.0111
0.001
0.7258
0.88
0.77
0.028
Spleen
0.003
0.788
0.0201
0.0198
0.001
0.8019
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.001
0.9466
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.001
0.8214
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Vdss
0.525
II.
B.
6
­
Page
122
of
162
Table
D8.
Partition
coefficient
calculations
for
5,6­
dihydroxy
carbaryl
HUMAN
Neutral
Lipid
Phospholipids
5,6­
DiOH
Carbaryl
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.044
0.2161
0.263
0.23
0.027
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.984
Bone
0.086
0.439
0.0740
0.0011
0.984
0.5129
0.62
0.54
0.046
Brain
0.020
0.770
0.0510
0.0565
0.984
0.8764
1.06
0.92
0.018
Gut
0.017
0.718
0.0487
0.0163
0.984
0.7821
0.95
0.82
0.014
Heart
0.005
0.758
0.0115
0.0166
0.984
0.7858
0.95
0.83
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.984
0.8195
0.99
0.86
0.004
Liver
0.026
0.751
0.0348
0.0252
0.984
0.8103
0.98
0.85
0.022
Lung
0.008
0.811
0.0030
0.0090
0.984
0.8229
1.00
0.87
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.984
0.7906
0.96
0.83
0.333
Skin
0.037
0.718
0.0284
0.0111
0.984
0.7570
0.92
0.80
0.030
Spleen
0.003
0.788
0.0201
0.0198
0.984
0.8275
1.00
0.87
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.984
0.9507
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.044
0.9468
Whole
blood
0.077
0.820
0.0032
0.0020
0.984
0.8251
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.044
0.8216
Erythrocytes
0.035
Vdss
0.549
II.
B.
6
­
Page
123
of
162
Table
D9.
Partition
coefficient
calculations
for
5,6­
dihydroxy
carbaryl
glucuronide
HUMAN
Neutral
Lipid
Phospholipids
5,6­
DiOH
Carbaryl
glucuronide
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.067
Bone
0.086
0.439
0.0740
0.0011
0.067
0.4448
0.54
0.47
0.040
Brain
0.020
0.770
0.0510
0.0565
0.067
0.8141
0.99
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.067
0.7330
0.89
0.77
0.013
Heart
0.005
0.758
0.0115
0.0166
0.067
0.7707
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.067
0.7961
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.067
0.7715
0.94
0.81
0.021
Lung
0.008
0.811
0.0030
0.0090
0.067
0.8177
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.067
0.7668
0.93
0.81
0.324
Skin
0.037
0.718
0.0284
0.0111
0.067
0.7279
0.89
0.77
0.029
Spleen
0.003
0.788
0.0201
0.0198
0.067
0.8036
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.067
0.9469
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.067
0.8217
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.526
II.
B.
6
­
Page
124
of
162
Table
D10.
Partition
coefficient
calculations
for
naphthol
HUMAN
Neutral
Lipid
Phospholipids
Naphthol
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
67.259
53.3563
49.542
43.47
5.198
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
707.946
Bone
0.086
0.439
0.0740
0.0011
707.946
53.0614
15.11
13.60
1.164
Brain
0.020
0.770
0.0510
0.0565
707.946
48.9145
13.93
12.53
0.251
Gut
0.017
0.718
0.0487
0.0163
707.946
38.6682
11.01
9.91
0.169
Heart
0.005
0.758
0.0115
0.0166
707.946
12.4366
3.54
3.19
0.015
Kidney
0.004
0.783
0.0207
0.0162
707.946
18.8937
5.38
4.84
0.021
Liver
0.026
0.751
0.0348
0.0252
707.946
30.7572
8.76
7.88
0.205
Lung
0.008
0.811
0.0030
0.0090
707.946
4.8526
1.38
1.24
0.009
Muscle
0.400
0.760
0.0238
0.0072
707.946
19.1433
5.45
4.91
1.962
Skin
0.037
0.718
0.0284
0.0111
707.946
23.1889
6.60
5.94
0.220
Spleen
0.003
0.788
0.0201
0.0198
707.946
19.2368
5.48
4.93
0.013
Plasma
0.042
0.945
0.0035
0.0023
707.946
3.9022
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
67.259
1.2274
Whole
blood
0.077
0.820
0.0032
0.0020
707.946
3.5116
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
67.259
1.0770
Erythrocytes
0.035
Vdss
9.271
II.
B.
6
­
Page
125
of
162
Table
D11.
Partition
coefficient
calculations
for
naphthyl
sulfate
HUMAN
Neutral
Lipid
Phospholipids
Naphthyl
sulfate
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.350
Bone
0.086
0.439
0.0740
0.0011
0.350
0.4658
0.57
0.49
0.042
Brain
0.020
0.770
0.0510
0.0565
0.350
0.8334
1.01
0.88
0.018
Gut
0.017
0.718
0.0487
0.0163
0.350
0.7482
0.91
0.79
0.013
Heart
0.005
0.758
0.0115
0.0166
0.350
0.7754
0.94
0.82
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.350
0.8033
0.98
0.85
0.004
Liver
0.026
0.751
0.0348
0.0252
0.350
0.7835
0.95
0.83
0.021
Lung
0.008
0.811
0.0030
0.0090
0.350
0.8193
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.350
0.7741
0.94
0.82
0.327
Skin
0.037
0.718
0.0284
0.0111
0.350
0.7369
0.90
0.78
0.029
Spleen
0.003
0.788
0.0201
0.0198
0.350
0.8110
0.99
0.86
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.350
0.9480
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.350
0.8227
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.532
II.
B.
6
­
Page
126
of
162
Table
D12.
Partition
coefficient
calculations
for
naphthyl
glucuronide
HUMAN
Neutral
Lipid
Phospholipids
Naphthyl
glucuronide
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
6.952
Bone
0.086
0.439
0.0740
0.0011
6.952
0.9565
1.13
0.98
0.084
Brain
0.020
0.770
0.0510
0.0565
6.952
1.2819
1.51
1.31
0.026
Gut
0.017
0.718
0.0487
0.0163
6.952
1.1020
1.30
1.13
0.019
Heart
0.005
0.758
0.0115
0.0166
6.952
0.8842
1.04
0.91
0.004
Kidney
0.004
0.783
0.0207
0.0162
6.952
0.9721
1.15
1.00
0.004
Liver
0.026
0.751
0.0348
0.0252
6.952
1.0631
1.25
1.09
0.028
Lung
0.008
0.811
0.0030
0.0090
6.952
0.8569
1.01
0.88
0.007
Muscle
0.400
0.760
0.0238
0.0072
6.952
0.9455
1.12
0.97
0.388
Skin
0.037
0.718
0.0284
0.0111
6.952
0.9464
1.12
0.97
0.036
Spleen
0.003
0.788
0.0201
0.0198
6.952
0.9829
1.16
1.01
0.003
Plasma
0.042
0.945
0.0035
0.0023
6.952
0.9756
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
6.952
0.8478
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Vdss
0.665
II.
B.
6
­
Page
127
of
162
Table
D13.
Partition
coefficient
calculations
for
3,4­
dihydroxy
naphthol
HUMAN
Neutral
Lipid
Phospholipids
3.4
DiHydroxy
Naphthol
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.072
0.2385
0.290
0.25
0.030
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
1.538
Bone
0.086
0.439
0.0740
0.0011
1.538
0.5541
0.67
0.58
0.050
Brain
0.020
0.770
0.0510
0.0565
1.538
0.9141
1.10
0.96
0.019
Gut
0.017
0.718
0.0487
0.0163
1.538
0.8118
0.98
0.85
0.015
Heart
0.005
0.758
0.0115
0.0166
1.538
0.7950
0.96
0.83
0.004
Kidney
0.004
0.783
0.0207
0.0162
1.538
0.8337
1.01
0.87
0.004
Liver
0.026
0.751
0.0348
0.0252
1.538
0.8338
1.01
0.87
0.023
Lung
0.008
0.811
0.0030
0.0090
1.538
0.8261
1.00
0.87
0.007
Muscle
0.400
0.760
0.0238
0.0072
1.538
0.8050
0.97
0.84
0.338
Skin
0.037
0.718
0.0284
0.0111
1.538
0.7746
0.94
0.81
0.030
Spleen
0.003
0.788
0.0201
0.0198
1.538
0.8419
1.02
0.88
0.002
Plasma
0.042
0.945
0.0035
0.0023
1.538
0.9530
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.072
0.9469
Whole
blood
0.077
0.820
0.0032
0.0020
1.538
0.8272
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.072
0.8217
Vdss
0.563
II.
B.
6
­
Page
128
of
162
Table
D14.
Partition
coefficient
calculations
for
3,4­
dihydroxy
naphthyl
glucuronide
HUMAN
Neutral
Lipid
Phospholipids
3.4
DiHydroxy
naphthyl
glucuronide
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.032
Bone
0.086
0.439
0.0740
0.0011
0.032
0.4421
0.54
0.47
0.040
Brain
0.020
0.770
0.0510
0.0565
0.032
0.8117
0.99
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.032
0.7311
0.89
0.77
0.013
Heart
0.005
0.758
0.0115
0.0166
0.032
0.7701
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.032
0.7952
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.032
0.7700
0.94
0.81
0.021
Lung
0.008
0.811
0.0030
0.0090
0.032
0.8175
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.032
0.7659
0.93
0.81
0.324
Skin
0.037
0.718
0.0284
0.0111
0.032
0.7268
0.88
0.77
0.028
Spleen
0.003
0.788
0.0201
0.0198
0.032
0.8027
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.032
0.9467
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.032
0.8215
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.525
II.
B.
6
­
Page
129
of
162
Table
D15.
Partition
coefficient
calculations
for
3,4­
dihydroxy
naphthyl
sulfate
HUMAN
Neutral
Lipid
Phospholipids
3.4
DiHydroxy
naphthyl
sulfate
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.000
Bone
0.086
0.439
0.0740
0.0011
0.000
0.4398
0.54
0.46
0.040
Brain
0.020
0.770
0.0510
0.0565
0.000
0.8096
0.99
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.000
0.7294
0.89
0.77
0.013
Heart
0.005
0.758
0.0115
0.0166
0.000
0.7696
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.000
0.7944
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.000
0.7687
0.94
0.81
0.021
Lung
0.008
0.811
0.0030
0.0090
0.000
0.8173
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.000
0.7651
0.93
0.81
0.323
Skin
0.037
0.718
0.0284
0.0111
0.000
0.7258
0.88
0.77
0.028
Spleen
0.003
0.788
0.0201
0.0198
0.000
0.8019
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.000
0.9466
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.000
0.8214
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.525
II.
B.
6
­
Page
130
of
162
Table
D16.
Partition
coefficient
calculations
for
4­
OH
carbaryl
HUMAN
Neutral
Lipid
Phospholipids
4­
OH
Carbaryl
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
5.323
4.3895
5.216
4.53
0.542
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
72.778
Bone
0.086
0.439
0.0740
0.0011
72.778
5.8494
5.33
4.68
0.401
Brain
0.020
0.770
0.0510
0.0565
72.778
5.7548
5.24
4.60
0.092
Gut
0.017
0.718
0.0487
0.0163
72.778
4.6296
4.22
3.70
0.063
Heart
0.005
0.758
0.0115
0.0166
72.778
1.9690
1.79
1.57
0.007
Kidney
0.004
0.783
0.0207
0.0162
72.778
2.6550
2.42
2.12
0.009
Liver
0.026
0.751
0.0348
0.0252
72.778
3.8515
3.51
3.08
0.080
Lung
0.008
0.811
0.0030
0.0090
72.778
1.2321
1.12
0.99
0.007
Muscle
0.400
0.760
0.0238
0.0072
72.778
2.6544
2.42
2.12
0.849
Skin
0.037
0.718
0.0284
0.0111
72.778
3.0350
2.76
2.43
0.090
Spleen
0.003
0.788
0.0201
0.0198
72.778
2.6970
2.46
2.16
0.006
Plasma
0.042
0.945
0.0035
0.0023
72.778
1.2504
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
5.323
0.9688
Whole
blood
0.077
0.820
0.0032
0.0020
72.778
1.0980
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
5.323
0.8416
Erythrocytes
0.035
Vdss
2.189
II.
B.
6
­
Page
131
of
162
Table
D17.
Partition
coefficient
calculations
for
4­
OH
carbaryl
glucuronide
HUMAN
Neutral
Lipid
Phospholipids
4­
OH
Carbaryl
glucuronide
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
1.051
Bone
0.086
0.439
0.0740
0.0011
1.051
0.5179
0.63
0.54
0.047
Brain
0.020
0.770
0.0510
0.0565
1.051
0.8809
1.07
0.93
0.019
Gut
0.017
0.718
0.0487
0.0163
1.051
0.7857
0.95
0.83
0.014
Heart
0.005
0.758
0.0115
0.0166
1.051
0.7869
0.95
0.83
0.004
Kidney
0.004
0.783
0.0207
0.0162
1.051
0.8212
0.99
0.86
0.004
Liver
0.026
0.751
0.0348
0.0252
1.051
0.8131
0.99
0.86
0.022
Lung
0.008
0.811
0.0030
0.0090
1.051
0.8233
1.00
0.87
0.007
Muscle
0.400
0.760
0.0238
0.0072
1.051
0.7923
0.96
0.83
0.333
Skin
0.037
0.718
0.0284
0.0111
1.051
0.7591
0.92
0.80
0.030
Spleen
0.003
0.788
0.0201
0.0198
1.051
0.8292
1.00
0.87
0.002
Plasma
0.042
0.945
0.0035
0.0023
1.051
0.9510
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
1.051
0.8254
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.546
II.
B.
6
­
Page
132
of
162
Table
D18.
Partition
coefficient
calculations
for
4­
OH
carbaryl
sulfate
HUMAN
Neutral
Lipid
Phospholipids
4­
OH
Carbaryl
sulfate
Tissue
Tissue
(
Vt)
Water
(
Vw)
(
Vnl)
(
Vpl)
Ko:
w
­
num­
Pt:
b
Pt:
p
Vt*
Pt:
p
Adipose
(
D*
vo:
w)
0.120
0.180
0.7900
0.0020
0.000
0.1814
0.221
0.19
0.023
Adipose
(
Ko:
w)
0.120
0.180
0.7900
0.0020
0.053
Bone
0.086
0.439
0.0740
0.0011
0.053
0.4437
0.54
0.47
0.040
Brain
0.020
0.770
0.0510
0.0565
0.053
0.8131
0.99
0.86
0.017
Gut
0.017
0.718
0.0487
0.0163
0.053
0.7322
0.89
0.77
0.013
Heart
0.005
0.758
0.0115
0.0166
0.053
0.7705
0.94
0.81
0.004
Kidney
0.004
0.783
0.0207
0.0162
0.053
0.7957
0.97
0.84
0.004
Liver
0.026
0.751
0.0348
0.0252
0.053
0.7709
0.94
0.81
0.021
Lung
0.008
0.811
0.0030
0.0090
0.053
0.8176
1.00
0.86
0.007
Muscle
0.400
0.760
0.0238
0.0072
0.053
0.7664
0.93
0.81
0.324
Skin
0.037
0.718
0.0284
0.0111
0.053
0.7275
0.89
0.77
0.029
Spleen
0.003
0.788
0.0201
0.0198
0.053
0.8032
0.98
0.85
0.002
Plasma
0.042
0.945
0.0035
0.0023
0.053
0.9468
Plasma
(
D*
vo:
w)
0.042
0.945
0.0035
0.0023
0.000
0.9466
Whole
blood
0.077
0.820
0.0032
0.0020
0.053
0.8216
Whole
blood
(
D*
vo/
w)
0.077
0.820
0.0032
0.0020
0.000
0.8214
Erythrocytes
0.035
Vdss
0.526
II.
B.
6
­
Page
133
of
162
2.4
References
Haddad
S,
Poulin
P,
Krishnan
K.(
2000).
Relative
lipid
content
as
the
sole
mechanistic
determinant
of
the
adipose
tissue:
blood
partition
coefficients
of
highly
lipophilic
organic
chemicals.
Chemosphere
839­
843.

Laskowski,
DA
(
2002)
Physical
and
Chemical
Properties
of
Pyrethroids.
Rev
Environ
Contam
Toxicol
174:
49­
170
Leo
A,
Hansh
C,
Elkins
D.(
1971).
Partition
coefficients
and
their
uses.
Chem
Rev
71:
525­
615.
.

Meylen
WM,
Howard
PH.
(
2001).
Log
n­
octanol:
water
partition
coefficients.
KOWWIN
database.
Syracuse
Research
Corporation,
Environmental
Science
Center,
Syracuse,
NY
13210
Poulin
P,
Theil
FP.
(
2000).
A
priori
prediction
of
tissue:
plasma
partition
coefficients
of
drugs
to
facilitate
the
use
of
physiologically
based
pharmacokinetic
models
in
drug
discovery.
J
Pharm
Sci
89:
16­
35
Poulin
P,
Theil
FP.(
2002).
Prediction
of
pharmacokinetics
prior
to
in
vivo
studies.
1.
Mechanism­
based
prediction
of
volume
of
distribution.
J
Pharm
Sci
91:
129­
156.

Poulin
P,
Krishnan
K.
(
1996)
A
mechanistic
algorithm
for
predicting
blood:
air
partition
coefficients
of
organic
chemicals
with
the
consideration
of
reversible
binding
in
hemoglobin.
Toxicol
Appl
Pharmacol
136:
131­
137.

Verschueren,
K.
Handbook
of
Environmental
Data
on
Organic
Chemicals.
2nd
Ed.
Van
Nostrand
Reinhold.
New
York.
1983.
pp.
799­
803.
II.
B.
6
­
Page
134
of
162
Appendix
E.
Additional
Model
Simulation
Results
The
comparisons
of
the
results
from
the
PBPK
model
used
in
this
assessment
to
the
available
data
(
Table
1)
are
shown
here,
organized
by
study.
The
parameter
estimates
focused
on
the
low
dose
experiments,
since
the
environmental
exposure
is
expected
to
be
below
most
levels
simulated
in
the
laboratory.
The
parameter
values
are
intended
to
be
representative
across
studies,
not
exact
to
only
a
single
study,
due
to
variations
across
data
sets.

1.0
Bayer
Metabolism
Studies
(
Bayer
2004a)

These
studies
were
conducted
in
Sprague­
Dawley
rats.
The
PBPK
model
predictions
of
naphthol
concentrations
in
tissues
only
reflect
the
concentration
of
1­
naphthol,
not
its
subsequent
metabolites
or
conjugates.

1.1
Oral
dose
of
approximately
1
mg/
kg
The
administered
dose
was
1.05
mg/
kg
in
Srague­
Dawley
rats.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
­
5
5
15
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E1.
Blood
concentration
14C.
II.
B.
6
­
Page
135
of
162
0
0.1
0.2
0.3
0.4
0.5
­
5
0
5
10
15
20
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E2.
Brain
concentration
14C.

1.2
Oral
dose
of
approximately
10
mg/
kg
The
actual
administered
dose
was
8.45
mg/
kg
to
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.

0
1
2
3
4
5
6
7
8
9
­
5
0
5
10
15
20
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E3.
Blood
concentration
14C.
II.
B.
6
­
Page
136
of
162
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
­
5
5
15
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E4.
Brain
concentration
14C.

0
0.5
1
1.5
2
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E5.
Brain
carbaryl
concentration.
II.
B.
6
­
Page
137
of
162
0
0.05
0.1
0.15
0.2
0.25
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E6.
Brain
naphthol
concentration.

0
2
4
6
8
10
12
14
16
18
20
­
5
5
15
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E7.
Fat
concentration
14C.
II.
B.
6
­
Page
138
of
162
0
5
10
15
20
25
­
5
0
5
10
15
20
25
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E8.
Liver
concentration
14C.

0
0.5
1
1.5
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

PBPK
model
Figure
E9.
Blood
carbaryl
concentration.
None
was
detected
at
15
minutes.
II.
B.
6
­
Page
139
of
162
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E10.
Blood
naphthol
concentration.

0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E11.
Blood
naphthol
sulfate
concentration.

1.3
IV
dose
of
approximately
1
mg/
kg
The
administered
dose
was
0.8
mg/
kg
in
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.
II.
B.
6
­
Page
140
of
162
0
0.5
1
1.5
2
2.5
3
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E12.
Blood
concentration
14C.

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E13.
Brain
concentration
14C.

1.4
IV
dose
of
approximately
10
mg/
kg
The
administered
dose
was
9.2
mg/
kg
in
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.
II.
B.
6
­
Page
141
of
162
0
2
4
6
8
10
12
14
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E14.
Brain
concentration
14C.

0
5
10
15
20
25
30
35
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E15.
Fat
concentration
14C.
II.
B.
6
­
Page
142
of
162
0
5
10
15
20
25
30
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E16.
Liver
concentration
14C.

­
1
1
3
5
7
9
11
13
15
0
2
4
6
8
10
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E17.
Blood
concentration
14C.
II.
B.
6
­
Page
143
of
162
0
2
4
6
8
10
12
14
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E18.
Brain
carbaryl
concentration.

0
5
10
15
20
25
30
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E19.
Fat
carbaryl
concentration.
II.
B.
6
­
Page
144
of
162
0
2
4
6
8
10
12
14
16
18
20
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E20.
Liver
carbaryl
concentration.

0
1
2
3
4
5
6
7
8
9
10
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E21.
Blood
carbaryl
concentration.
II.
B.
6
­
Page
145
of
162
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E22.
Brain
naphthol
concentration.

0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E23.
Fat
naphthol
concentration.
II.
B.
6
­
Page
146
of
162
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E24.
Liver
naphthol
concentration.

0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E25.
Blood
naphthol
concentration.
II.
B.
6
­
Page
147
of
162
0
1
2
3
4
5
6
7
8
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E26.
Blood
naphthol
sulfate
concentration.

1.5
Mixed
oral
and
dermal
dose
Two
oral
doses
of
0.075
mg/
kg
were
administered
to
Sprague­
Dawley
rats
at
hour
0
and
hour
1
respectively.
A
dermal
dose
of
0.75
mg/
kg
was
also
applied
during
this
period,
although
the
simulation
results
are
nearly
identical
with
or
without
the
dermal
contribution.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.

0
0.01
0.02
0.03
0.04
0.05
0
2
4
6
8
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E27.
Brain
concentration
14C.
II.
B.
6
­
Page
148
of
162
0
0.05
0.1
0.15
0.2
0
2
4
6
8
Time
(
hours)
ppm
14C
residue
data
PBPK
model
Figure
E28.
Blood
concentration
14C.

0
0.002
0.004
0.006
0.008
0.01
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E29.
Brain
carbaryl
concentration.
II.
B.
6
­
Page
149
of
162
0
0.002
0.004
0
2
4
6
8
10
Time
(
hours)
Concentration
(
mg/
L)

data
PBPK
model
Figure
E30.
Brain
naphthol
concentration.

2.0
Cholinesterase
inhibition
studies
Carbaryl
at
10
to
125
mg/
kg
was
administered
to
Sprague­
Dawley
rats
(
Brooks
and
Broxup
1995a,
b).
Only
the
10
mg/
kg
data
were
considered
for
parameter
estimation.
The
data
points
are
the
average
value
for
12
animals
at
each
time
point
(
6
male,
6
female).

0
20
40
60
80
100
120
0
5
10
Time
(
hours)
%
control
Brooks
1995a
Brooks
1995b
PBPK
model
Figure
E31.
Blood
acetylcholinesterase
inhibition
after
administration
of
10
mg/
kg.
II.
B.
6
­
Page
150
of
162
0
20
40
60
80
100
120
0
5
10
Time
(
hours)
%
control
Brooks
1995a
Brooks
1995b
PBPK
model
Figure
E32.
Brain
acetylcholinesterase
inhibition
after
administration
of
10
mg/
kg.

0
20
40
60
80
100
120
0
20
40
60
Time
(
hours)
%
control
data
PBPK
model
Figure
E33.
Brain
acetylcholinesterase
inhibition
after
administration
of
30
mg/
kg.
II.
B.
6
­
Page
151
of
162
0
20
40
60
80
100
120
0
20
40
60
Time
(
hours)
%
control
data
PBPK
model
Figure
E34.
Blood
acetylcholinesterase
inhibition
after
administration
of
30
mg/
kg.

0
20
40
60
80
100
120
0
5
10
15
20
Time
(
hours)
%
control
data
PBPK
model
Figure
E35.
Brain
acetylcholinesterase
inhibition
after
administration
of
50
mg/
kg.
II.
B.
6
­
Page
152
of
162
0
20
40
60
80
100
120
0
5
10
15
20
Time
(
hours)
%
control
data
PBPK
model
Figure
E36.
Blood
acetylcholinesterase
inhibition
after
administration
of
50
mg/
kg.

0
20
40
60
80
100
120
0
20
40
60
Time
(
hours)
%
control
data
PBPK
model
Figure
E37.
Brain
acetylcholinesterase
inhibition
after
administration
of
90
mg/
kg.
II.
B.
6
­
Page
153
of
162
0
20
40
60
80
100
120
0
20
40
60
Time
(
hours)
%
control
data
PBPK
model
Figure
E38.
Blood
acetylcholinesterase
inhibition
after
administration
of
90
mg/
kg.

­
20
0
20
40
60
80
100
120
0
5
10
15
20
Time
(
hours)
%
control
data
PBPK
model
Figure
E39.
Brain
acetylcholinesterase
inhibition
after
administration
of
125
mg/
kg.
II.
B.
6
­
Page
154
of
162
0
20
40
60
80
100
120
0
5
10
15
20
Time
(
hours)
%
control
data
PBPK
model
Figure
E40.
Blood
acetylcholinesterase
inhibition
after
administration
of
125
mg/
kg.

3.0
Dermal
study
at
0.793
mg/
12.5
cm2
(
Cheng
1994)

Carbaryl
was
applied
to
the
skin
of
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
4
animals
at
each
sample
time.

0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0.00035
0
10
20
30
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E
41.
Blood
concentration
14C.
II.
B.
6
­
Page
155
of
162
0
0.00001
0.00002
0.00003
0.00004
0.00005
0.00006
0.00007
0.00008
0.00009
0.0001
0
10
20
30
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E42.
Carcass
concentration
14C.

0
0.0001
0.0002
0.0003
0.0004
0.0005
0.0006
0.0007
0.0008
0.0009
0
10
20
30
Time
(
hours)
Cumulative
residue
(
mmol)

data
PBPK
model
Figure
E43.
Cumulative
14C
residues
in
urine.
II.
B.
6
­
Page
156
of
162
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
10
20
30
Time
(
hours)
Mass
(
mg)

data
PBPK
model
Figure
E44.
Carbaryl
mass
on
skin.

0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0
10
20
30
Time
(
hours)
Mass
(
mg)

data
PBPK
model
Figure
E45.
Carbaryl
mass
in
skin.

4.0
Dermal
study
1.74
mg/
20cm2
(
Knaak
et
al.
1984)

Carbaryl
was
applied
to
the
skin
of
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
3
animals
at
each
sample
time.
II.
B.
6
­
Page
157
of
162
0
0.00005
0.0001
0.00015
0.0002
0.00025
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E46.
Blood
concentration
14C.

0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E47.
Liver
concentration
14C.
II.
B.
6
­
Page
158
of
162
0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E48.
Slowly
perfused
concentration
14C.

0
0.00005
0.0001
0.00015
0.0002
0.00025
0.0003
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E49.
Rapidly
perfused
concentration
14C.
II.
B.
6
­
Page
159
of
162
0
0.0005
0.001
0.0015
0.002
0.0025
0
50
100
150
200
Time
(
hours)
Concentration
(
mmol/
L)

data
PBPK
model
Figure
E50.
Fat
concentration
14C.

0
0.0005
0.001
0.0015
0.002
0
50
100
150
200
Time
(
hours)
Cumulative
residue
(
mmol)

data
PBPK
data
Figure
E51.
Cumulative
14C
residues
in
urine.
II.
B.
6
­
Page
160
of
162
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
50
100
150
200
Time
(
hours)
Mass
(
mg)

data
PBPK
model
Figure
E52.
Carbaryl
mass
on
skin.

0
0.05
0.1
0.15
0.2
0.25
0.3
0
50
100
150
200
Time
(
hours)
Mass
(
mg)

data
PBPK
model
Figure
E53.
Evaporated
carbaryl.
II.
B.
6
­
Page
161
of
162
0.000001
0.00001
0.0001
0.001
0.01
0
50
100
150
200
Time
(
hours)
Mass
(
mg)

data
PBPK
model
Figure
E54.
Carbaryl
in
skin.

5.0
IP
dose
of
20
mg/
kg
(
Knaak
et
al.
1965)

Carbaryl
was
given
IP
to
Sprague­
Dawley
rats
for
the
purpose
of
studying
the
metabolism
of
carbaryl.
The
data
points
are
for
pooled
urine
samples
from
3
animals.

0
2
4
6
8
10
12
14
16
18
0
5
10
15
20
25
30
Time
(
hours)
%
dose
in
urine
naphthol
sulfate
PBPK
model
naphthol
glucuronide
PBPK
model
5,6­
DIOH
carbaryl
glucuronide
PBPK
model
4­
OH
carbaryl
glucuronide
PBPK
model
4­
OH
carbaryl
sulfate
PBPK
model
Figure
E55.
Carbaryl
metabolites
in
urine.

6.0
Bile
cannulation
experiments
II.
B.
6
­
Page
162
of
162
Oral
doses
of
0.01
mg/
kg
carbaryl
were
administered
to
Sprague­
Dawley
rats.
The
data
points
are
the
average
values
for
2
animals.

0
0.000001
0.000002
0.000003
0.000004
0.000005
0.000006
0.000007
0.000008
0.000009
0.00001
0
20
40
60
Time
(
hours)
Cumulative
residue
(
mmol)

data
PBPK
model
Figure
E56.
Cumulative
14C
residue
in
bile.

0
0.000002
0.000004
0.000006
0.000008
0.00001
0.000012
0
20
40
60
Time
(
hours)
Cumulative
residue
(
mmol)

data
PBPK
model
Figure
E57.
Cumulative
14C
residue
in
urine.
