Document ID: EPA-HQ-OPPT-2002-0066-0004
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2002-12-05T05:00Z

EVALUATION
OF
SAR
PREDICTIONS
OF
ESTROGEN
RECEPTOR
BINDING
AFFINITY
EPA
CONTRACT
NUMBER
68­
W­
01­
023
WORK
ASSIGNMENT
2­
3
August
1,
2002
[
chemical
IDs
removed
from
this
version
 
jpk]

PREPARED
FOR
Jim
Kariya
WORK
ASSIGNMENT
MANAGER
U.
S.
ENVIRONMENTAL
PROTECTION
AGENCY
ENDOCRINE
DISRUPTOR
SCREENING
PROGRAM
WASHINGTON,
D.
C.

BATTELLE
505
KING
AVENUE
COLUMBUS,
OH
43201
Battelle
Report
1
August
1,
2002
Evaluation
of
SAR
Predictions
of
Estrogen
Receptor
Binding
Activity
Work
Assignment
2­
3
I.
0
Introduction
and
Background
The
Environmental
Protection
Agency
(
EPA)
is
attempting
to
validate
Structure
Activity
Relation
(
SAR)
models
to
predict
the
extent
of
binding
affinity
of
chemicals
with
estrogen
receptor
sites.
Two
models
are
considered
in
this
report,
referred
to
as
"
Model
A"
and
"
Model
B".

The
validation
experiment
was
carried
out
by
comparing
the
model
predictions
of
Relative
Binding
Affinity
(
RBA)
for
a
sample
of
chemicals
with
estrogen
receptor
(
ER)
binding
affinity
laboratory
assay
results.
The
laboratory
assay
results
are
taken
to
be
the
authority
measure.

This
report
discusses
the
results
of
a
statistical
comparison
of
the
laboratory
assay
results
with
the
model
predictions.
Estimates
of
sensitivity,
specificity,
positive
predictive
probability,
and
negative
predictive
probability
of
each
of
the
models
are
determined
for
various
portions
of
the
data
and
are
compared
among
one
another.
These
measures
are
defined
and
discussed
in
some
detail
in
the
report
"
Issues
Related
to
Sampling
Chemicals
for
Verifying
Predictiveness
of
Endocrine
Binding
Activity
QSAR
Models",
August
17,
2001.
The
effects
of
using
both
models
jointly
for
prediction
of
positive
ER
activity
is
discussed.

Section
II
discusses
the
sources
of
data
underlying
the
comparisons
and
data
decisions
that
were
made
concerning
which
portions
of
the
data
to
include
in
the
comparisons
and
which
portions
to
exclude.
Section
III
discusses
the
results
of
the
detailed
comparisons
that
were
carried
out.
The
section
is
divided
into
subsections
in
accordance
with
items
1
to
7
in
the
"
Detailed
description
of
statistical
analyses
being
requested"
section
of
EPA's
QSAR
model
evaluation
Work
Assignment
dated
June
19,
2002.
The
discussion
in
Section
III
is
based
on
and
refers
to
the
tables,
figures,
and
detailed
calculations
included
in
Appendix
A.

2.0
DATA
The
EPA
selected
a
set
of
9,067
chemicals
for
which
it
needs
to
set
priorities
for
testing
for
endocrine
receptor
binding
activity.
Model
A
and
Model
B
made
predictions
of
RBA
on
a
subset
of
6,649
chemical
from
this
set,
for
which
CAS
numbers
(
and
therefore
chemical
structure
specification)
exist.
Each
of
the
models
divided
these
chemicals
into
six
strata
(
differing
for
each
model)
depending
on
the
order
of
magnitude
of
the
predicted
RBA.
Model
A
predicted
319
of
the
6,649
chemicals
(
4.8%)
to
be
positive
endocrine
receptor
binders.
Model
B
predicted
304
of
the
6,649
chemicals
(
4.6%)
to
be
binders.
These
was
an
overlap
of
78
chemicals
(
1.2%)
between
the
positive
predictions
from
each
model.
Battelle
Report
2
August
1,
2002
Three
samples
of
chemicals
were
selected
from
the
subset
of
6,649
chemicals
for
evaluation
by
laboratory
assay.

The
first
sample
was
(
ideally)
a
simple
random
sample
of
(
nominally)
200
chemicals
from
the
subset
of
6,649.
Actually
197
chemicals
were
selected.
Several
deviations
from
the
random
sampling
scheme
were
necessitated
by
difficulties
in
acquiring
some
of
the
chemicals
or
in
acquiring
them
at
the
purity
required
for
the
assay.
Other
chemicals
from
the
randomly
generated
list
were
substituted
until
a
set
of
nearly
200
acquirable
chemicals
of
the
desired
purity
was
obtained.
The
first
sample
was
designed
to
permit
comparisons
between
the
laboratory
assay
results
and
predictions
from
each
of
the
models.
This
sample
is
referred
to
as
the
"
Random
200"
chemicals.

The
second
and
third
samples
were
(
ideally)
stratified
random
samples
of
(
nominally)
50
chemicals
each
from
among
the
chemicals
that
Model
A
predicted
to
be
positive
(
Model
A
sample)
and
from
among
the
chemicals
that
Model
B
predicted
to
be
positive
(
Model
B
sample).
Actually
49
chemicals
were
selected
in
the
Model
A
sample
and
43
chemicals
were
selected
in
the
Model
B
sample.
The
Model
A
sample
was
designed
to
provide
an
enhanced
positive
predictive
probability
sample
for
Model
A.
The
Model
B
sample
was
designed
to
provide
an
enhanced
positive
predictive
probability
sample
for
Model
B.
These
samples
are
referred
to
as
the
"
50
Model
A"
chemicals
and
the
"
50
Model
B"
chemicals
respectively.

The
laboratory
assay
classified
each
of
the
197
+
49
+
43
sampled
chemicals
as
"
Binders"
(
B),
"
Extrapolated"
(
E),
"
Activity"
(
A),
or
"
Non­
Binder"
(
N)
based
on
the
maximum
extent
of
displacement
of
the
radiolabeled
estradiol
by
the
test
chemical.
The
criteria
used
for
this
classification
is
discussed
in
the
"
Estrogen
Receptor
Binding
Assay
Overview
Report"
for
EPA
Work
Assignment
3­
04
Task
4,
April
2002.

Among
the
"
Random
200"
chemicals,
25
of
the
197
chemicals
were
classified
by
the
laboratory
assay
as
B
or
E.
The
remainder
were
classified
as
A
or
N.
Among
the
"
50
Model
A"
chemicals,
18
of
the
49
chemicals
were
classified
as
B
or
E.
Among
the
"
50
Model
B"
chemicals,
12
of
the
43
chemicals
were
classified
as
B
or
E.

The
chemicals
classified
as
A
or
N
were
treated
as
negatives
for
the
purposes
of
the
statistical
analyses.
The
chemicals
classified
as
B
were
treated
as
positives.
The
chemicals
classified
as
E
were
treated
as
positives
in
one
analysis
and
as
negatives
in
another.
Ideally
it
was
desired
in
the
second
analysis
to
only
classify
those
Es
as
positive
for
which
the
lower
95
percent
confidence
bound
on
percent
bound
at
the
highest
test
chemical
concentration
fell
below
the
50
percent
level.
However
the
analysis
would
be
simplified
if
the
first
analysis
excluded
all
of
the
Es
and
the
second
analysis
included
all
of
the
Es.
These
are
referred
to
as
the
"
bookend"
analyses.
If
there
were
no
qualitative
differences
in
results
between
the
"
bookend"
analyses
it
would
not
be
necessary
to
carry
out
the
intermediate
analysis.
Battelle
Report
3
August
1,
2002
Several
chemicals
were
excluded
from
the
analyses
because
the
laboratory
assays
produced
steep
or
erratic
curves,
as
discussed
in
the
"
Estrogen
Receptor
Binding
Assay
Overview
Report".
Dr.
Susan
Laws,
EPA/
RTP,
reviewed
the
laboratory
assay
results
and
specified
which
chemicals
should
be
excluded
from
the
comparisons
and
which
should
be
retained.
Dr.
Laws'
assessments
are
summarized
in
the
Excel
file
"
ER
Binding
Summary
Data
(
Task
6)".
The
chemicals
that
were
omitted
from
the
analyses
based
on
Dr.
Laws'
assessments
and
supplemented
by
the
recommendations
in
the
"
Estrogen
Receptor
Binding
Assay
Overview
Report"
were
omitted
from
both
the
numerators
and
the
denominators
of
the
calculations
of
model
performance.

The
CAS
Numbers
of
the
chemicals
that
were
omitted
are
summarized
below.

Table
2­
1.
Battelle
M
Numbers
and
CAS
Numbers
of
the
Chemicals
That
Were
Omitted
from
the
Analyses
[
deleted
from
this
copy,
jpk,
8­
2­
02]

In
addition
chemical
[
deleted,
jpk
8­
2­
02]
("
Random
200"
Chemicals,
non­
binder)
was
omitted
from
the
analysis
based
on
the
"
Estrogen
Receptor
Binding
Assay
Overview
Report",
Section
5,
where
it
was
stated
that
this
chemical
exhibited
erratic
binding
behavior.

Thus
the
analyses
in
this
report
were
based
on:

1.
189
chemicals
from
the
"
Random
200"
chemicals
2.
48
chemicals
from
the
"
50
Model
A"
Positive
predicted
chemicals
3.
40
chemicals
from
the
"
50
Model
B"
positive
Predicted
chemicals
Battelle
Report
4
August
1,
2002
The
CAS
numbers
associated
with
these
three
samples
are
contained
in
Appendix
B,
along
with
which
CAS
numbers
were
predicted
to
be
binders
or
extrapolateds
by
the
laboratory
assay
and
which
were
predicted
to
be
positives
by
each
of
the
models.

Among
these
chemicals
there
were:

4.
11
binders
and
7
extrapolateds
from
the
"
Random
200"
chemicals
5.
16
binders
and
1
extrapolated
from
the
"
50
Positive
Predicted"
Model
A
chemicals
6.
9
binders
and
0
extrapolated
from
the
"
50
Positive
Predicted"
Model
B
chemicals
The
CAS
numbers
corresponding
to
these
chemicals
are
summarized
in
Table
2.2.

Table
2­
2.
CAS
Numbers
of
the
Chemicals
That
Were
Included
in
the
Analyses
[
deleted
from
this
copy
 
jpk,
8­
2­
02]
Battelle
Report
5
August
1,
2002
Model
A
`
s
predictions
of
positive
chemicals
included
6
chemicals
among
the
"
Random
200"
chemicals
sample.
None
coincided
with
the
11
chemicals
classified
by
the
laboratory
assay
as
binders
and
2
coincided
the
7
chemicals
classified
by
the
laboratory
assay
as
extrapolateds.

Model
B
`
s
predictions
of
positive
chemicals
included
no
chemicals
among
the
"
Random
200"
chemicals
sample.

3.0
RESULTS
AND
DISCUSSION
This
section
discusses
the
results
of
the
calculations
to
assess
the
relations
between
the
model
predictions
of
Relative
Binding
Affinity
with
the
laboratory
assay
results
on
the
same
chemicals.
The
results
are
divided
into
subsections,
numbered
1
to
7.
These
subsections
correspond
to
the
items
enumerated
in
the
Work
Assignment
section
"
Detailed
description
of
statistical
analyses
being
requested".

The
tables,
calculations,
and
figures
that
present
the
detailed
results
are
included
in
Appendix
A.
The
discussion
in
this
section
refers
to
those
exhibits.
Note
that
the
confidence
bounds
shown
in
Appendix
A
are
upper
and
lower
95%
bounds.
Thus
the
confidence
intervals
are
90%
intervals.

1.,
2.
Sensitivity,
Specificity,
Positive
Predicitivity,
Negative
Predicitivity
of
Model
A
and
Model
B
Proportion
of
True
Positives.
Comparison
of
the
Positive
Predictivities
Estimated
from
the
"
Random
200"
Chemicals
and
from
the
"
50
Model
A"
or
"
50
Model
B"
Samples.

The
detailed
analysis
results
are
displayed
in
Tables
A­
1
to
A­
7
of
Appendix
A.
The
tables
based
on
the
"
Random
200"
chemicals
sample
provide
estimates
of
sensitivity,
specificity,
PPP,
and
NPP.
Those
based
on
the
"
50
Model
A"
and
"
50
Model
B"
predicted
positives
samples
provide
estimates
of
positive
predictive
probability
only.

The
estimated
proportion
of
true
positive
endocrine
disruptors
based
on
the
laboratory
assay
results
of
the
"
Random
200"
chemicals
is
J
=
5.8%
(
Table
A­
1)
if
only
the
binders
are
included
and
is
J=
9.5%
(
Table
A­
3)
if
the
extrapolateds
are
added.

The
sensitivities
of
both
models
are
very
low.
The
upper
95%
confidence
bound
on
sensitivity
for
Model
A
is
at
most
31.0%
(
Table
A­
3)
and
that
for
Model
B,
is
at
most
23.8%
(
Table
A­
5).

The
specificities
of
both
models
are
in
the
mid
to
upper
90%
range.
The
lower
95%
confidence
bound
on
specificity
for
Model
A
exceeds
93.5%
(
Tables
A­
1,
A­
3)
and
that
for
Model
B
exceeds
98.3%
(
Tables
A­
5,
A­
7).

The
positive
predictive
probabilities
of
both
models
are
low.
For
model
A,
the
estimated
positive
predictive
probabilities
are
33.3%
based
on
the
"
Random
200"
chemicals
(
Table
A­
3)
Battelle
Report
6
August
1,
2002
and
35.4%
based
on
the
"
50
Model
A"
predicted
positives
(
Table
A­
4),
even
when
the
extrapolateds
are
included
in
the
calculation.
The
results
based
on
the
"
Random
200"
chemicals
and
on
the
"
50
Model
A"
predicted
positives
are
not
significantly
different
(
p=
0.16,
p=
1.00)
whether
or
not
the
extrapolateds
are
included.
For
Model
B
the
positive
predictive
probability
is
undefined
based
on
the
"
Random
200"
chemicals
since
none
of
the
chemicals
in
this
sample
were
predicted
by
the
model
to
be
positive.
Based
on
the
"
50
Model
B"
predicted
positive
chemicals
the
positive
predictive
probability
is
22.5%
(
Table
A­
6).

The
negative
predictive
probabilities
of
both
models
are
in
the
90
percent
range.
For
model
A
the
estimated
NPP
is
93.9%
if
the
extrapolateds
are
not
included
(
Table
A­
1)
and
is
91.3%
if
the
extrapolateds
are
included
(
Table
A­
3).
This
is
approximately
what
one
would
expect
from
choosing
chemicals
at
random,
based
on
the
estimated
values
of
the
proportion,
J,
of
true
positive
endocrine
disruptors.
The
NPP
for
Model
B
is
about
the
same
as
that
for
Model
A,
94.2%
if
the
extrapolateds
are
not
included
(
Table
A­
5)
and
90.5%
if
the
extrapolateds
are
included
(
Table
A­
7).
This
is
again
approximately
what
one
would
expect
from
choosing
chemicals
at
random,
based
on
the
estimated
values
of
the
proportion,
J,
of
true
positive
endocrine
disruptors.

3.
Quantify
the
Sensitivity,
Specificity,
Positive
Predictivity,
and
Negative
Predictivity
of
Joint
Predictions
by
the
Two
Models
The
detailed
results
are
displayed
in
Tables
A­
8
to
A­
12
of
the
Appendix.

Meaningful
joint
model
predictions
cannot
be
based
on
the
"
Random
200"
chemicals
(
Tables
A­
8,
A­
9)
sample
since
model
B
has
no
predicted
positives
among
that
sample.

Only
positive
predictive
probability
can
be
estimated
from
the
"
50
Model
A"
and
the
"
50
Model
B"
positive
predictive
samples.
If
both
models
are
required
to
be
positive
to
infer
that
a
chemical
is
positive
the
estimated
positive
predicted
probability
is
62.5%
(
Tables
A­
11,
A­
12).
However
just
8
chemicals
of
the
80
positive
predictive
chemicals
would
be
jointly
inferred
to
be
positive.
If
just
one
model
is
required
to
infer
that
a
chemical
is
positive
the
estimated
positive
predicted
probability
is
26%
to
27%,
depending
on
whether
or
not
the
extrapolateds
are
included
(
Tables
A­
11,
A­
12).
The
upper
95%
confidence
bound
is
approximately
35%.
This
is
not
an
improvement
over
the
individual
model
predictions..

4.
Efficiency
of
Model
A
and
Model
B
in
Concentrating
the
True
Positives
in
the
Predicted
Positive
Set
and
in
Diluting
the
True
Positives
in
the
Predicted
Negative
Set.

Criteria
for
assessing
the
efficiency
of
a
model
are
discussed
in
Appendix
A.
These
are
referred
to
as
7.
Positive
Prediction
Concentration
Efficiency
/
PPP/
J
8.
Negative
Prediction
Dilution
Efficiency
/
(
1­
NPP)/
J
Battelle
Report
7
August
1,
2002
One
would
expect
PPP/
J
to
be
greater
than
1
and
(
1­
NPP)/
J
to
be
less
than
1.

Based
on
the
"
Random
200"
chemical
sample
the
estimate
of
the
Positive
Prediction
Concentration
Efficiency
is
based
on
6
chemicals
for
Model
A
and
0
chemicals
for
Model
B.
Thus
it
is
undefined
for
Model
B
and
has
a
confidence
interval
ranging
from
approximately
0
to
7
for
Model
A.
Thus
inferences
about
Prediction
Concentration
Efficiency
cannot
be
based
on
this
sample.

Based
on
the
"
50
Model
A"
and
the
"
50
Model
B"
positive
predictive
samples
the
Prediction
Concentration
Efficiency
is
estimated
to
be
5.7
for
Model
A
with
binders
only,
with
lower
and
upper
confidence
bounds
(
3.8,
8.0).
If
the
extrapolated
chemicals
are
included
the
estimates
diminish
a
bit,
but
still
substantially
exceed
1.
For
Model
B
the
Prediction
Concentration
Efficiency
is
estimated
to
be
3.9,
with
lower
and
upper
confidence
bounds
(
2.1,
6.2).
Both
models
have
at
least
moderate
Positive
Prediction
Concentration
Efficiency.

Based
on
the
"
Random
200"
chemical
sample
the
estimate
of
the
Negative
Prediction
Dilution
Efficiency
is
1.0
or
0.9
for
Model
A
(
depending
on
whether
extrapolateds
are
included)
and
1.0
for
Model
B.
Confidence
bounds
range
from
about
0.6
at
the
lower
end
to
about
1.4
to
1.7
at
the
upper
end.
Thus
neither
model
decreases
the
probability
that
a
chemical
is,
in
fact,
positive,
conditional
on
the
model
predicting
the
chemical
to
be
negative.

5.
Relationship
Between
Predicted
Binding
Strength
and
Positive
Predictivity
The
relationship
is
displayed
in
Appendix
A,
Tables
A­
13
to
A­
15.
The
relationship
cannot
be
assessed
based
on
the
"
Random
200"
chemical
sample
because
the
six
positive
predictions
by
Model
A
all
fall
within
the
weakest
stratum,
log10(
RBA)
0
[­
3,
­
2].
For
Model
B
there
were
no
positive
predictions.

For
the
"
50
Model
A"
and
"
50
Model
B"
positive
prediction
chemical
samples
the
trend
in
PPP
with
RBA
is
nonsignificant
for
Model
A
(
p=
1.0)
(
Table
A­
14)
and
marginally
significant
for
Model
B
(
p=
0.09)
(
Table
A­
15).
Note
however
that
the
trend
in
Model
B
is
opposite
to
what
one
would
expect.
The
highest
positive
predictive
probability
occurs
in
the
weakest
RBA
stratum.

We
thus
conclude
that
these
models
do
not
demonstrate
association
between
predicted
binding
strength
and
positive
predictivity.

6.
Degree
of
Overlap
of
Positive
Predictions
Between
Model
A
and
Model
B
The
degree
of
overlap
between
the
positive
predictions
for
Model
A
and
Model
B
is
displayed
in
Appendix
A,
Tables
A­
16
to
A­
18,
particularly
Table
A­
18.
.
Each
model
predicted
about
300
of
the
6,649
chemicals
to
be
positive.
There
were
78
chemicals
that
were
predicted
to
be
positive
by
both
models.
This
is
approximately
25%
of
each
model's
predictions.
Battelle
Report
8
August
1,
2002
When
dividing
predictions
into
RBA
strata
Table
A­
18
demonstrates
that
Model
B
generally
predicted
greater
RBA
than
Model
A.
21
of
the
78
predictions
were
in
the
same
stratum;
10
of
the
21
in
the
weakest
stratum,
[­
3,
­
2].
While
there
were
just
nine
chemicals
for
which
Model
A
predicted
a
higher
stratum
than
Model
B,
there
were
48
chemicals
for
which
Model
B
predicted
a
higher
stratum
than
Model
A.

There
is
not
a
great
deal
of
stratum
overlap
of
positive
predictions
between
Model
A
and
Model
B.

7.
Relationship
Between
Measured
Binding
Strength
and
Standard
Error
of
the
RBA.

The
results
in
this
section
are
based
on
the
laboratory
results
only.
Standard
errors
of
log10
RBA
estimates
were
available
only
for
the
binders
and
for
the
extrapolated
chemicals.
Those
chemicals
that
were
excluded
from
the
previous
analyses
because
of
steep
or
erratic
binding
curves
are
also
excluded
from
the
analysis
in
this
section.

The
relationship
between
log10
RBA
and
standard
error
of
log10
RBA
is
displayed
in
Appendix
A,
Figures
A­
1
to
A­
4.
Figure
A­
1
pertains
to
the
"
200
Random"
chemical
sample.
Figures
A­
2
and
A­
3
pertain
to
the
"
50
Model
A"
and
"
50
Model
B"
positive
prediction
chemical
samples
respectively.
Figure
A­
4
displays
chemicals
from
all
three
samples
superimposed
to
assess
whether
there
were
any
differences
in
the
relationships.
In
Figures
A­
1
to
A­
3
the
binders
and
extrapolateds
were
plotted
using
symbols
"
B"
and
"
E"
respectively.
The
"
E"
s
are
seen
to
have
lower
RBAs
than
the
"
B"
s,
as
would
be
expected.

Correlation
coefficients
and
associated
p­
values
between
average
log10
RBA
and
average
standard
error
log10
RBA
are
shown
below
in
Table
2.3.

Table
2.3
Correlation
Coefficients
and
Associated
P­
values
Between
Average
Log10
RBA
and
Average
Standard
Error
Log10
RBA
"
Random
200"
Chemicals
"
50
Positive
Predicted"
Model
A
Chemicals
"
50
Positive
Predicted"
Model
B
Chemicals
0.36
(
0.14)
Figure
A­
1
­
0.34
(
0.18)
Figure
A­
2
­
0.52
(
0.15)
Figure
A­
3
These
is
no
significant
association
between
average
log10
RBA
and
average
standard
error
log10
RBA
for
any
of
the
samples
(
Figures
A­
1
to
A­
3).
The
relationships
in
all
three
samples
coincide
(
Figure
A­
4).
Battelle
Report
9
August
1,
2002
4.0
REFERENCES
1.
"
Issues
Related
to
Sampling
Chemicals
for
Verifying
Predictiveness
of
Endocrine
Binding
Activity
QSAR
Models",
August
17,
2001.
Battelle.
Endocrine
Disruptor
Screening
Program
Work
Assignment
1­
5,
Task
4.
Contract
No.
68­
W­
01­
023.

2.
"
Estrogen
Receptor
Binding
Assay
Overview
Report",
April,
2002.
Battelle.
OPPT
Statistical
and
Technical
Support
for
the
Assessment
of
Toxic
Substances
Work
Assignment
3­
04
Task
6.
2001.
Contract
No.
68­
W­
99­
033.

3.
"
ER
Binding
Summary
Data
(
Task
6)".
Excel
file.
Communication
from
USEPA/
OSCP/
OPPTS.
June,
2002.
Battelle
Report
A­
1
August,
2002
APPENDIX
A
Tables,
Figures,
Detailed
Calculations
Battelle
Report
A­
2
August,
2002
1.,
2.
Sensitivity,
Specificity,
Positive
Predicitivity,
Negative
Predicitivity
of
Model
A
and
Model
B.
Proportion
of
True
Positives.
Comparison
of
the
Positive
Predictivities
Estimated
from
the
"
200"
Chemicals
and
from
the
Model
A
or
Model
B
Samples.

A.
Model
A.
Binders
Only
"
200"
Chemicals
Table
A­
1
Model
A
Prediction
Positive
Negative
Total
Lab
Result
Positive
0
11
11
Negative
6
172
178
Total
6
183
189
Sensitivity:
(
0/
11)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
23.8%)
Specificity:(
172/
178)
=
96.6%
Lower
and
Upper
95%
Confidence
Bounds
(
93.5%,
98.5%)
PPP:
(
0/
6)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
39.3%)
NPP:
(
172/
183)
=
93.9%
Lower
and
Upper
95%
Confidence
Bounds
(
90.2%,
96.6%)
J
=
P(
True
Positive):
(
11/
189)
=
5.8%
Lower
and
Upper
95%
Confidence
Bounds
(
3.3%,
9.5%)

"
50"
Model
A
Positive
Prediction
Chemicals
Table
A­
2
Model
A
Prediction
Positive
Negative
Total
Lab
Result
Positive
16
0
16
Negative
32
0
32
Total
48
0
48
PPP:
(
16/
48)
=
33.3%
Lower
and
Upper
95%
Confidence
Bounds
(
22.2%,
46.1%)

Two­
sided
comparison
between
PPPs
(
0%
and
33.3%):
p=
0.16,
not
significant.
Battelle
Report
A­
3
August,
2002
B.
Model
A.
Binders
Plus
Extrapolated
"
200"
Chemicals
Table
A­
3
Model
A
Prediction
Positive
Negative
Total
Lab
Result
Positive
2
16
18
Negative
4
167
171
Total
6
183
189
Sensitivity:
(
2/
18)
=
11.1%
Lower
and
Upper
95%
Confidence
Bounds
(
2.0%,
31.0%)
Specificity:
(
167/
171)
=
97.7%
Lower
and
Upper
95%
Confidence
Bounds
(
94.7%,
99.2%)
PPP:
(
2/
6)
=
33.3%
Lower
and
Upper
95%
Confidence
Bounds
(
6.3%,
72.9%)
NPP:
(
167/
183)
=
91.3%
Lower
and
Upper
95%
Confidence
Bounds
(
87.0%,
94.4%)
J
=
P(
True
Positive):
(
18/
189)
=
9.5%
Lower
and
Upper
95%
Confidence
Bounds
(
6.2%,
13.8%)

"
50"
Model
A
Positive
Prediction
Chemicals
Table
A­
4
Model
A
Prediction
Positive
Negative
Total
Lab
Result
Positive
17
0
17
Negative
31
0
31
Total
48
0
48
PPP:
(
17/
48)
=
35.4%
Lower
and
Upper
95%
Confidence
Bounds
(
24.0%,
48.3%)

Two­
sided
comparison
between
PPPs
(
33.3%
and
35.4%):
p=
1.00,
not
significant.
Battelle
Report
A­
4
August,
2002
C.
Model
B.
Binders
Only
"
200"
Chemicals
Table
A­
5
Model
B
Prediction
Positive
Negative
Total
Lab
Result
Positive
0
11
11
Negative
0
178
178
Total
0
189
189
Sensitivity:
(
0/
11)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
23.8%)
Specificity:
(
178/
178)
=
100
%
Lower
and
Upper
95%
Confidence
Bounds
(
98.3%,
100
%)
PPP:
(
0/
0)
Undefined
NPP:
(
178/
189)
=
94.2%
Lower
and
Upper
95%
Confidence
Bounds
(
90.6%,
96.7%)
J
=
P(
True
Positive):
(
11/
189)
=
5.8%
Lower
and
Upper
95%
Confidence
Bounds
(
3.3%,
9.5%)

"
50"
Model
B
Positive
Prediction
Chemicals
Table
A­
6
Model
B
Prediction
Positive
Negative
Total
Lab
Result
Positive
9
0
9
Negative
31
0
31
Total
40
0
40
PPP:
(
9
/
40)
=
22.5%
Lower
and
Upper
95%
Confidence
Bounds
(
12.3%,
36.0%)

D.
Model
B.
Binders
Plus
Extrapolated
"
200"
Chemicals
Battelle
Report
A­
5
August,
2002
Table
A­
7
Model
B
Prediction
Positive
Negative
Total
Lab
Result
Positive
0
18
18
Negative
0
171
171
Total
0
189
189
Sensitivity:
(
0/
18)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
15.3%)
Specificity:
(
171/
171)
=
100
%
Lower
and
Upper
95%
Confidence
Bounds
(
98.3%,
100
%)
PPP:
(
0/
0)
Undefined
NPP:
(
171/
189)
=
90.5%
Lower
and
Upper
95%
Confidence
Bounds
(
86.2
93.8%)
J
=
P(
True
Positive):
(
18/
189)
=
9.5%
Lower
and
Upper
95%
Confidence
Bounds
(
6.2%,
13.8%)

"
50"
Model
B
Positive
Prediction
Chemicals
(
See
Section
C.
There
were
no
extrapolated
Model
B
chemicals).

3.
Quantify
the
Sensitivity,
Specificity,
Positive
Predictivity,
and
Negative
Predicitivity
of
Joint
Predictions
by
the
Two
Models
Rule
1.
A+
and
B+
implies
Chemical
Positive
Rule
2.
A+
or
B+
implies
Chemical
Positive
A.
"
200"
Samples.
Binders
Only
Table
A­
8
Lab
Model
A+,
B+
A+,
B­
A­,
B+
A­,

BPositive
0
0
0
11
11
Negative
0
6
0
172
178
Total
0
6
0
183
189
Battelle
Report
A­
6
August,
2002
Rule
1.
A+
and
B+
implies
Chemical
Positive
Sensitivity:
(
0/
11)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
23.8%)
Specificity:
(
178/
178)
=
100%
Lower
and
Upper
95%
Confidence
Bounds
(
98.3%,
100%)
PPP:
(
0/
0)
Undefined
NPP:
(
178/
189)
=
94.2%
Lower
and
Upper
95%
Confidence
Bounds
(
90.6%,
96.7%)

Rule
2.
A+
or
B+
implies
Chemical
Positive
Sensitivity:
(
0/
11)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
23.8%)
Specificity:
(
172/
178)
=
96.6%
Lower
and
Upper
95%
Confidence
Bounds
(
93.5%,
98.5%)
PPP:
(
0/
6)=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
39.3%)
NPP:
(
172/
183)
=
94.0%
Lower
and
Upper
95%
Confidence
Bounds
(
90.2%,
96.6%)

B.
"
200"
Samples.
Binders
and
Extrapolated
Table
A­
9
Lab
Model
A+,
B+
A+,
B­
A­,
B+
A­,

BPositive
0
2
0
16
18
Negative
0
4
0
167
171
Total
0
6
0
183
189
Rule
1.
A+
and
B+
implies
Chemical
Positive
Sensitivity:
(
0/
18)
=
0%
Lower
and
Upper
95%
Confidence
Bounds
(
0%,
23.8%)
Specificity:
(
171/
171)
=
100%
Lower
and
Upper
95%
Confidence
Bounds
(
98.3%,
100%)
PPP:
(
0/
0)
Undefined
NPP:
(
171/
189)
=
90.5%
Lower
and
Upper
95%
Confidence
Bounds
(
86.2%,
93.6%)

Rule
2.
A+
or
B+
implies
Chemical
Positive
Sensitivity:
(
2/
18)
=
11.1%
Lower
and
Upper
95%
Confidence
Bounds
(
2.0%,
31.0%)
Specificity:
(
167/
171)
=
97.7%
Lower
and
Upper
95%
Confidence
Bounds
(
94.7%,
99.2%)
PPP:
(
2/
6)=
33.3%
Lower
and
Upper
95%
Confidence
Bounds
(
6.3%,
72.9%)
NPP:
(
167/
183)
=
91.3%
Lower
and
Upper
95%
Confidence
Bounds
(
87.0%,
94.4%)
Battelle
Report
A­
7
August,
2002
C.
Chemicals
Predicted
Positive
by
One
or
Both
Models.
Binders
Only
Based
on
the
performance
of
the
models
on
the
(
sub)
population
of
6,649
TSCA
chemicals,
we
calculate
the
following
joint
probabilities
on
Model
A
and
Model
B
results,
conditional
on
Model
A
being
positive
for
a
chemical
or
Model
B
being
positive.

Table
A­
10
Model
A
Positive
Negative
Total
Model
B
Positive
78
226
304
Negative
241
Total
319
545
Therefore
P(
A+,
B+
|
A+
or
B+)
=
78/
545
=
0.143
=
B++
P(
A+,
B­
|
A+
or
B+)
=
241/
545
=
0.442
=
B+­
P(
A­,
B+
|
A+
or
B+)
=
226/
545
=
0.415
=
B­+
1.000
Table
A­
11
SAR
Model
Predictions
A
­
pos.
B
­
pos.
A
­
pos.
B
­
neg.
A
­
neg.
B
­
pos.
A
­
neg.
B
­
neg.
Total
Lab
Result
Positive
5
11
4
0
Negative
3
29
28
0
Total
8
40
32
0
80
Note
that
only
PPP
can
be
estimated
from
this
table.
Battelle
Report
A­
8
August,
2002
Rule
1.
A+
and
B+
implies
Chemical
Positive
PPP
=
P(
Lab+|
A+,
B+)
=
5/
8
=
62.5%
Lower
and
Upper
95%
Confidence
Bounds
(
28.9%,
88.9%).

Rule
2.
A+
or
B+
implies
Chemical
Positive
PPP
=
P(
Lab+|
A+
or
B+)
=
P(
Lab+|
A+,
B+)
B++
+
P(
Lab+|
A+,
B­)
B+­
+
P(
Lab+|
A­,
B+)
B­+
=

(
5/
8)(
0.143)
+
(
11/
40)(
0.442)
+
(
4/
32)(
0.415)
=
26.3%.

This
is
approximately
the
(
right)
marginal
ratio
20/
80
=
0.25.
95
percent
confidence
bounds
on
this
"
probability"
are
(
17.2%,
34.2%).
We
use
these
as
approximate
confidence
bounds
on
the
positive
predictive
probability.

D.
Chemicals
Predicted
Positive
by
One
or
Both
Models.
Binders
and
Extrapolated
Table
A­
12
SAR
Model
Predictions
A
­
pos.
B
­
pos.
A
­
pos.
B
­
neg.
A
­
neg.
B
­
pos.
A
­
neg.
B
­
neg.
Total
Lab
Result
Positive
5
12
4
0
Negative
3
28
28
0
Total
8
40
32
0
80
Rule
1.
A+
and
B+
implies
Chemical
Positive
PPP
=
P(
Lab+|
A+,
B+)
=
5/
8
=
62.5%
Lower
and
Upper
95%
Confidence
Bounds
(
28.9%,
88.9%).

Rule
2.
A+
or
B+
implies
Chemical
Positive
PPP
=
P(
Lab+|
A+
or
B+)
=
P(
Lab+|
A+,
B+)
B++
+
P(
Lab+|
A+,
B­)
B+­
P(
Lab+|
A­,
B+)
B­+
=

(
5/
8)(
0.143)
+
(
12/
40)(
0.442)
+
(
4/
32)(
0.415)
=
27.4%.
Battelle
Report
A­
9
August,
2002
This
is
approximately
the
(
right)
marginal
ratio
21/
80
=
0.263.
95
percent
confidence
bounds
on
this
"
probability"
are
(
18.3%,
35.6%).
We
use
these
as
approximate
confidence
bounds
on
the
positive
predictive
probability.

4.
Efficiency
of
Model
A
and
Model
B
in
Concentrating
the
True
Positives
in
the
Predicted
Positive
Set
and
in
Diluting
the
True
Positives
in
the
Predicted
Negative
Set.

This
section
discusses
the
performance
of
each
model
with
respect
to
modifying
the
probability
that
a
chemical
is
truly
positive,
conditional
on
the
model
predicting
that
the
chemical
is
positive
or
conditional
on
it
predicting
that
the
chemical
is
negative.
If
the
model
performs
well
it
would
be
expected
that:

°
The
probability
that
a
chemical
is
positive
conditional
on
the
model
predicting
it
positive
should
be
greater
than
the
unconditional
probability
that
a
randomly
chosen
chemical
is
positive.

°
The
probability
that
a
chemical
is
positive,
conditional
on
the
model
predicting
it
negative
should
be
smaller
than
the
unconditional
probability
that
a
randomly
chosen
chemical
is
positive.

These
two
criteria
suggest
the
following
measures
of
model
efficiency.

1.
P(
True
Positive|
Model
Predicts
Positive)/
J
/
PPP/
J
2.
P(
True
Positive|
Model
Predicts
Negative)/
J
/
(
1­
NPP)/
J
For
a
model
that
performs
well
it
would
be
expected
that
PPP/
J>>
1
and
(
1­
NPP)/
J<<
1.
Ideally
these
values
would
be
1/
J
and
0
respectively.

Based
on
the
"
200"
chemicals
data
set
J
is
estimated
as
J
=
5.8%
(
3.3%,
9.5%)
based
on
the
eleven
binder
chemicals
only
J
=
9.5%
(
6.2%,
13.8%)
based
on
the
eighteen
binder
and
extrapolated
chemicals
For
purposes
of
the
calculations
in
the
section
these
estimates
of
J
will
be
regarded
as
approximately
the
true
population
values,
without
variation.
They
are
based
on
a
relatively
large
sample
size,
n=
189
chemicals.
Battelle
Report
A­
10
August,
2002
Model
A.
Positive
Prediction
Concentration
Efficiency
1.
Based
on
"
200"
Chemicals
Sample
Binders
Only
PPP/
J
=
(
0/
6)/
0.058
=
0
(
0/
0.058,
.393/
0.058)
=
(
0,
6.78)

Binders
Plus
Extrapolated
PPP/
J
=
(
2/
6)/
0.095
=
3.51
(
0.063/
0.095,
0.729/
0.095)
=
(
0.663,
7.67)

2.
Based
on
"
50"
Chemicals
Positive
Prediction
Sample
Binders
Only
PPP/
J
=
(
16/
48)/
0.058
=
5.74
(
0.222/
0.058,
.0.461/
0.058)
=
(
3.82,
7.95)

Binders
Plus
Extrapolated
PPP/
J
=
(
17/
48)/
0.095
=
3.73
(
0.240/
0.095,
0.483/
0.095)
=
(
2.53,
5.08)

Model
A.
Negative
Prediction
Dilution
Efficiency
Based
on
"
200"
Chemicals
Sample
Binders
Only
(
1­
NPP)/
J
=
(
1
­
172/
183)/
0.058
=
1.04
((
1
­
0.966)/
0.058,
(
1­
0.902)/
0.058)
=
(
0.59,
1.69)

Binders
Plus
Extrapolated
(
1
­
NPP)/
J
=
(
1
­
167/
183)/
0.095
=
0.92
((
1
­
0.944)/
0.095,
(
1
­
0.870)/
0.095)
=
(
0.59,
1.37)

Model
B.
Positive
Prediction
Concentration
Efficiency
1.
Based
on
"
200"
Chemicals
Sample
Binders
Only
PPP/
J
=
(
0/
0)/
0.058
undefined
Binders
Plus
Extrapolated
PPP/
J
=
(
0/
0)/
0.095
undefined
2.
Based
on
"
50"
Chemicals
Positive
Prediction
Sample
Binders
Only
Battelle
Report
A­
11
August,
2002
PPP/
J
=
(
9/
40)/
0.058
=
3.88
(
0.123/
0.058,
.0.360/
0.058)
=
(
2.12,
6.21)

Binders
Plus
Extrapolated
Same
as
binders
only.
There
were
no
extrapolated
chemicals
in
the
model
B
positive
prediction
sample.

Model
B.
Negative
Prediction
Dilution
Efficiency
Based
on
"
200"
Chemicals
Sample
Binders
Only
(
1­
NPP)/
J
=
(
1
­
178/
189)/
0.058
=
1.00
((
1
­
0.967)/
0.058,
(
1­
0.906)/
0.058)
=
(
0.57,
1.62)

Binders
Plus
Extrapolated
(
1
­
NPP)/
J
=
(
1
­
171/
189)/
0.095
=
1.00
((
1
­
0.938)/
0.095,
(
1
­
0.862)/
0.095)
=
(
0.65,
1.45)

5.
Relationship
Between
Predicted
Binding
Strength
and
Positive
Predictivity
Model
A.

1.
Based
on
"
200"
Chemicals
Sample
Binders
Only
Table
A­
13
Model
A
Prediction
Positive
(
logR10
BA)
Neg
Total
>
2
(
1,2]
(
0,1]
(­
1,0]
(­
2,­
1]
[­
3,­
2]

Lab
Result
Positive
0
0
0
0
0
0
11
11
Negative
0
0
0
0
0
6
172
178
Total
0
0
0
0
0
6
183
189
Just
6
chemicals
within
the
random
sample
of
189
chemicals
were
predicted
to
be
positive.
All
6
Battelle
Report
A­
12
August,
2002
fall
within
the
weakest
binding
stratum
[­
3,
­
2].
Therefore
no
trend
can
be
assessed.

The
same
situation
occurs
for
binders
plus
extrapolated.

2.
Based
on
"
50"
Chemicals
Positive
Prediction
Sample
Binders
Only
Table
A­
14
Model
A
Prediction
Positive
(
log10
RBA)
Neg
Total
>
2
(
1,2]
(
0,1]
(­
1,0]
(­
2,­
1]
[­
3,­
2]

Lab
Result
Positive
0
0
0
(
0%)
2
(
40%)
5
(
31.3%)
9
(
34.6%)
0
16
Negative
0
0
1
3
11
17
0
32
Total
0
0
1
5
16
26
0
48
Nearly
all
of
the
chemicals
are
in
the
two
weakest
strata,
(­
2,­
1]
and
[­
3,
­
2].
An
exact
contingency
table
test
of
homogeneity
of
positive
predictive
probabilities
across
strata
shows
no
significant
differences
(
p=
1.0).

There
is
just
one
extrapolated
positive
chemical
in
the
positive
predicted
sample.
It
fall
in
the
[­
3,
­
2]
stratum.
Thus
the
positive
predictive
probability
in
that
stratum
becomes
10/
26
=
38.5%.
The
exact
contingency
table
test
of
homogeneity
of
positive
predictive
probabilities
across
strata
remains
nonsignificant
(
p=
0.9).

Model
B
1.
Based
on
"
200"
Chemicals
Sample
No
chemicals
among
those
that
Model
B
predicted
to
be
positive
fall
among
the
"
200"
chemicals
random
sample.
Therefore
no
trend
can
be
assessed.
The
same
situation
occurs
for
binders
plus
extrapolated.

2.
Based
on
"
50"
Chemicals
Positive
Prediction
Sample
Binders
Only
Battelle
Report
A­
13
August,
2002
Table
A­
15
Model
B
Prediction
Positive
(
log10
RBA)
Neg
Total
>
2
(
1,2]
(
0,1]
(­
1,0]
(­
2,­
1]
[­
3,­
2]

Lab
Result
Positive
0
0
2
(
20%)
0
(
0%)
1
(
10%)
6
(
46.2%)
0
9
Negative
0
0
8
7
9
7
0
31
Total
0
0
10
7
10
13
0
40
An
exact
contingency
table
test
of
homogeneity
of
positive
predictive
probabilities
across
strata
is
marginally
significant
(
p=
0.09).
A
Mantel­
Haenszel
test
for
trend
across
strata
is
marginally
significant
(
p=
0.10).
Note
however
that
the
trend
is
in
the
opposite
direction
from
what
one
would
expect.
The
largest
positive
predictive
probability,
46.2%,
is
in
the
weakest
binding
stratum
[­
3,
­
2].

There
are
no
extrapolated
positive
chemicals
in
the
positive
predicted
sample.

6.
Degree
of
Overlap
of
Positive
Predictions
Between
Model
A
and
Model
B
Each
model
predicted
the
relative
binding
affinity
(
RBA)
for
the
same
set
of
6,649
chemicals.
RBAs
greater
than
10­
3
%
were
defined
as
positive.

1.
The
marginal
distributions
of
the
positive
predictions
for
each
model
by
RBA
strata
are
as
follows:
Battelle
Report
A­
14
August,
2002
Table
A­
16
Stratum
(
log10RBA)
>
2
(
1,
2]
(
0,
1]
(­
1,
0]
(­
2,
­
1]
[­
3,
­
2]
Total
Model
A
0
1
7
36
121
154
319
Model
B
1
23
71
63
90
56
304
The
distribution
of
model
B's
RBA
predictions
is
shifted
toward
higher
RBAs
as
compared
to
Model
A's
predictions.

2.
The
joint
frequency
of
positive
and
negative
predictions
is
as
follows:

Table
A­
17
Model
B
Positive
Negative
Total
Model
A
Positive
78
241
319
Negative
226
6,104
6,330
Total
304
6,345
6,649
Each
model
predicted
approximately
300
of
6,649
chemicals
(
4.6%,
average)
to
be
positive.
Of
these
positive
prediction
chemicals
78
(
approximately
25%)
were
predicted
to
be
positive
by
both
models.

2.
The
breakdown
of
the
chemicals
that
were
predicted
to
be
positive
by
each
model
into
RBA
strata
is
as
follows:
Battelle
Report
A­
15
August,
2002
Table
A­
18
Model
B
Prediction
Log10
RBA
Strata
(
1,
2]
(
0,
1]
(­
1,
0]
(­
2,
­
1]
[­
3,
­
2]
Total
Model
A
Prediction
(
0,
1]
1
0
1
0
0
2
(­
1,
0]
1
2
6
1
1
11
(­
2,
­
1]
4
14
7
5
6
36
[­
3,
­
2]
2
6
3
8
10
29
Total
8
22
17
14
17
78
21
of
the
78
jointly
positive
predictions
overlap
strata,
10
of
the
21
being
in
the
[­
3,­
2]
stratum.
For
48
of
the
78
overlap
chemicals
Model
B
predicts
a
higher
stratum
than
Model
A.

7.
Relationship
Between
Measured
Binding
Strength
and
Standard
Error
of
the
RBA
Battelle
Report
A­
16
August,
2002
Figure
A­
1
Battelle
Report
A­
17
August,
2002
Figure
A­
2
Battelle
Report
A­
18
August,
2002
Figure
A­
3
Battelle
Report
A­
19
August,
2002
Figure
A­
4
Battelle
Report
B­
1
August,
2002
APPENDIX
B
CAS
NUMBERS
INCLUDED
IN
"
Random
200"
Sample
"
50
Model
A"
Positive
Prediction
Sample
"
50
Model
B"
Positive
Prediction
Sample
[
deleted
from
this
copy
 
jpk,
8­
2­
02]