Hello,
everyone.
My
name
is
Tom
Witkos,
and
today
we're
together
with
Hadley
Myers from
JMP.
We'll
be
presenting
to
you
about
speeding
up
assay
development
with
automated
workflow
for
assessing
plate
bias
of
microtiter
plate
assays.
First,
I
would
like
to
start
with
acknowledgements
of
several
team
members
from
AstraZeneca,
Deepika,
and
Barbara,
who
has
helped
us
with
explaining
certain
concepts
and
also
testing
the
JMP
plugin.
During
the
presentation,
I
would
briefly
introduce
to
you
what
the
potency
testing
of
biologics
mean,
how
we
use
microtiter plates
in
our
work,
and
also
statistical
analysis
such
as
plate
bias
and
uniformity
that
are
very
important
for
assay
development.
U
ltimately,
we
will
present
to
you
the
JMP
Add-in
that
Hadley
and
I
have
written
that
allows
us
to
perform
this
analysis
in
a
much
more
streamlined
and
more
powerful
manner.
First,
I
would
like
to
put
potency
assays
into
drug
R&D
perspective.
There
are
a
number
of
biological
assays
used
both
at
the
pre-
clinical,
clinical,
and
commercial
stages.
First,
we
typically
have
screening
and
selection
assays
at
the
pre-
clinical
stage.
Those
types
of type
of
assays
are
trying
to
answer
the
question,
which
molecule
has
a
biological
activity? I
dentify
candidates
for
drugs.
When
the
candidate
drug
has
been
already
identified,
we
would
move
to
something
which
we
would
call
potency
assays.
Here
we
only
work
with
a
certain
drug
candidate,
only
one.
We
manufacture
many
different
lots
of
this
molecule
that
are
going
to
be
used
in
clinical
trials
and
ultimately
as
well,
lots
that
would
be
made
post-
approval
of
the
drug
in
the
commercial
setting.
H
ere,
the
question
that
we
are asking
is,
is
the
biological
activity
at
a
similar
level
between
lots?
A
ll
we
care
about
is
the
lot-
to-
lot
consistency,
whether
it's
preserved
or
not.
A
s
such,
potency
testing
is
a
critical
property
of
the
drug,
and
it's
part
of
something
which
we'll
call
CMC,
Chemistry
Manufacturing
Process
and
Controls.
Analytical
testing,
such
as
the
biological
activity
tests
that
our
team
is
in
charge
of
developing,
are
quite
important
to
feed
into
the
optimization
of
the
manufacturing
process
at
pretty
much
every
stage.
B
oth
at
the
upstream
and
downstream
stages.
W
hen
the
biologics
are
being
manufactured
and
then
further
purified,
as
well
as
at
the
stages
of
developing
formulation
and
also
packaging
for
these
drug
molecules.
Screening
and
potency
assays
both
measure
biological
activity,
but
they
do
it
in
a
slightly
different
manner.
Within
the
screening
bio
assays,
we
have
quite
a
few
compounds
that
are
not
going
to
have
any
biological
activity
or
very
little.
Some
with
moderate
and
also
a
few
compounds
with
quite
high
biological
activity.
Because
we
are
focusing
on
identification
of
candidate
drug
molecules,
the
differences
that
we
see
in
the
biological
activity
are
going
to
be
very
high.
Basically,
our
assays
need
to
resolve
lock
scale
differences
between
different
drugs
regarding
their
biological
activity.
For
the
CMC
potency
assays,
we
already
have
the
candidate
drug
nominated.
W
hile
making
different
lots
of
the
same
molecule,
we
would
expect
when
the
manufacturing
process
is
well
optimized
and
controlled,
that
the
differences
that
we
observe
in
the
biological
activity
are
going
to
be
small.
W
e
need
to
quite
precisely
resolve
small
differences
within
the
twofold
change
between
different
lots
of
the
same
drug.
There
are
a
number
of
different
potency
assays
set
ups.
Without
going
into
too
big
details,
ultimately,
what
we
try
to
do
is
measure
biological
activity
by
quantifying
the
target
binding, m
ainly
the
protein
that
our
drug
interacts
with.
T
hat
can
be
either
as
part
of
the
recombinant
protein
or
being
presented
by
the
cells
that
express
a
certain
target
on
the
surface,
as
well
as
quantification
of
effect
of
that
target
binding. I
n
this
case,
that
will
be
ultimately
cell-
based
report
essays
where
we
will
be
looking
at
the
activation
or
incubation
of
certain
signalling
pathways
due
to
the
interaction
of
the
drug
with
a
certain
receptor.
A
s
you
can
see,
regardless
of
the
assay
types,
in
all
of
these
settings,
we
use
micro
titer
plates.
Most
commonly,
we
use
96-
well
plates,
but
a
higher
number
of
well
plates
are
also
possible
to
be
used.
Within
one
assay
run,
so
within
one
experiment,
we
use
either
one
or
multiple
plates,
and
that
would
depend
on
in-plate
and
place-
to-
plate
variability.
Ultimately,
within
one
microtiter
plate,
every
single
well
can
be
seen
as
a
separate
mini
reaction.
In
this
particular
case,
we
have
a
very
common
protein
target
where
our
drug
interacts
to.
T
hen
the
amount
of
the
bound
drug
is
detected
with
the
secondary
antibody.
In
this
example
as
well,
every
component
is
the
same
across
all
the
wells.
The
only
difference
being
a
gradient
in
the
concentration
of
our
given
molecule.
T
hat
will
be
delivered
in
something
which
we
call
dilution
series.
O
n
each
plate,
we'll
have
dilution
series
of
reference
material
and
also
of
our
tested
samples
of
unknown
biological
activity.
By
having
a
dilution
series,
we
would
expect
to
see
a
dose-
response
curve.
W
hat
you
can
see
here
in
case
of
the
binding,
what
we
would
expect
is
that
the
more
drug
we
put,
the
higher
the
response
we
should
be
able
to
observe.
We
would
then
fit
that
data
into
a
model.
Most
likely
it's
going
to
be
a
non-linear-
4PL
model.
T
hen
we
would
work
with
these
fits,
and
we
do
a
pairwise
comparison
of
fits
between
the
reference
standard
and
our
tested
molecules,
where
we
would
share
the
lower
and
upper
asymptotes
and
the
growth
rate,
so
the
slope
of
those
curves.
Given
that
the
curves
look
similar
to
each
other,
we
would
be
able
to
calculate
the
inflection
point.
In
this
case,
it's
going
to
be
either
EC
50
or
IC
50,
depending
whether
we
are
looking
at
the
activation
or
inhibition.
And
that
difference
in
the
EC
50
or
IC
50
between
these
two
curves
would
then
can
be
mathematically
converted
into
something
we
term
%
relative
potency.
T
hat's
our
ultimate
readout.
Within
potency
assay
development,
there
are
a
number
of
experiments
that
we
need
to
perform
in
order
to
achieve
an
assay
that
can
be
used
for
biologics
testing
and
can
ultimately
be
validated.
For
example,
here
we
would
start
with
screening
reagents
and
assay
conditions,
both
done
in
the
one
factor
at
the
time
and
the
Screening
DoE
fashion.
Then
we
would
establish
a
proof
of
concept
dose-
response
curve.
We
would
look
at
the
plate
layout
and
then
lock
it
together
with
its
experimental
conditions.
A
ll
of
these
steps
can
be
analyzed
in
JMP.
F
or
today's
presentation,
I'm
going
to
focus
on
quite
crucial
part
of
this
assay
development
step,
which
is
establishing
the
plate
layout.
L
ooking
at
some
time
through
the
term
plate
uniformity
and
plate
bias.
First,
let's
talk
about
plate
bias.
Let's
step
back
for
a
moment
and
think
of
a
situation
where
we
have
have
every
single
well
being
identical.
Meaning
that
we
even
have
a
constant
concentration
of
our
drugs
across
both
rows
and
columns.
I
n
ideal
world,
we
would
expect
to
see
no
variability
between
the
wells,
as
you
can
see
here
on
the
plate
map
generated
by
JMP.
In
reality,
we
know
that
some
variability
is
inevitable.
However,
we
really
need
to
understand
its
sources
and
the
variability
needs
to
be
controlled
in
order
to
have
a
precise
and
accurate
measurement
of
the
biological
activity.
Scale
really
matters
in
these
types
of
plate
maps
that
can
be
very
easily
generated
using
JMP.
H
ere
I
created
two
different
data
sets
and
then
visualize
them
using
JMP.
A
s
you
can
see,
just
by
looking
at
the
plate
maps,
you
could
see
a
very
random
pattern
in
response
both
across
columns
and
wells.
Here
I'm
showing
you
an
analysis
by
the
plate
row,
but
then
the
moment
you
scale
it
up,
you
can
clearly
see
that
the
upper
essay
is
much
more
accurate
and
precise
and
have
lower
variability
compared
to
what
we
could
see
in
the
lower
part
example.
JMP
offers
really
this
great
graphical
and
statistical
tools
that
when
combined
together
are
very
useful
for
this
type
of
analysis.
Patterns
of
variability
can
really
be
non-
random
as
well.
Here,
following
in
our
example,
we
still
have
a
constant
concentration
of
our
drug,
and
we
can
see
the
changes
in
both
either
average
responses
or
in
variability.
In
the
top
example,
you
can
see
a
drift
in
the
average
response
when
we
move
across
plate
rows.
I
n
the
bottom
example,
you
see
that
even
though
the
average
variability
across
the
plate,
row
wise,
is
still
the
same,
we
can
clearly
see
that
with
the
plate
row
changes,
we
also
see
a
magnitude
of
the
changes
in
terms
of
the
variability
within
the
row. T
hat's
something
that
we
really
need
to
pay
a
big
attention
to
because
ultimately
on
our
plates,
we
always
have
to
have
certain
wells
where
we
are
going
to
deliver
our
dilution
series
of
reference
material
and
our
tested
samples.
F
or
the
plate
uniformity,
I'm
going
to
present
you
briefly
a
case
study
with
a
non-
cell
based
binding
assay
where
we
have
a
recombinant
target.
T
hat's
a
recombinant
protein
which
is
coded
on
the
microtiter
plates.
W
e
come
up
with
our
drug,
again,
in
the
form
of
dilution
series.
I
n
this
case,
the
biologic
is
a
monoclonal
antibody.
W
e
are
able
to
use
a
detection
reagent
coupled
with
an
enzyme,
where
after
addition
of
the
clear
substrate,
the
substrate
gets
catalyzed
by
the
enzyme
in
presence
of
our
drug
in
order
to
yield
a
colorful
product.
A
s
you
can
see,
there
are
multiple
steps
and
multiple
washes
alongside.
Many
binding
assays
would
have
similar
set
up,
but
they
can
differ.
T
hese
differences
can
be,
for
example,
in
liquid
volumes,
incubation
times,
buffers,
reagent
conditions,
and
plate
types
used.
B
ecause
of
that,
plate
uniformity
has
really
been
conducted
for
every
assay
we
develop.
Because
there
are
many
potential
sources
of
variability,
including
the
operator.
We
already
had
a
workflow
in
place
in
our
team
to
do
that,
but
that
was
really
cumbersome,
which
I
hope
you
would
be
quite
clear
when
we
dive
into
this
slide.
What
we
had
to
do
is
manually
export
the
plate
response
data
from
our
plate
reader
into
the
Excel
sheet.
We
had
to
manually
rearrange
the
data
into
dilution
series
and
then
break
it
down
per
plate.
We
were
calculating
EC
50
or
IC
50
in
a
different
third
party
software.
Those
measured
inflection
points
had
to
be
then
manually
re-exported
from
that
software
back
into
Excel.
We
had
to
manually
arrange
the
data
back
in
the
Excel
in
a
format
that
would
be
digestible
for
a
simple
Fit
Y
by
x
analysis
in
JMP.
T
ogether
with
Hadley,
we
were
able
to
develop
a
JMP
plugin,
which
is
able
to
do
everything
and
even
more
than
that
in
just
one
step
without
the
need
of
using
any
pieces
of
software
whatsoever.
A
s
you
can
see
here,
the
whole
workflow
is
broken
down
into
three
steps.
First
is
the
data
import
into
JMP.
We
are
able
to
import
multiple
plates
at
once.
We
are
able
to
generate
plate
heat
maps
just
to
visually
export
the
data
and
potentially
detect
and
eliminate
outliers
that
would
otherwise
skew
our
data
analysis
and
potentially
lead
to
misconclusions.
T
hen
we
are
able
to
look
at
the
curve
bias
analysis
going
by
plate
rows
or
plate
columns.
I
f
anything
looks
potentially
suspicious
or
we
want
to
do
a
deeper
dive,
we
fully
employ
JMP
interactiveness.
Just
by
hovering
over
a
certain
data
point,
we're
able
to
look
at
the
curves
and
that
particular
infliction
point
calculation
comes
from.
I
hope
it
will
be
much
clearer
in
a
moment
when
Hadley
is
going
to
demonstrate
the
JMP
plugin
in
JMP.
Hadley,
the
floor
is
yours.
All
right,
thank
you
very
much.
Thomas
and
hello,
everyone.
I'm
going
to
go
ahead
and
share
my
screen.
Before
I
jump
directly
into
the
ad,
I'd
like
to
show
you
that
the
ad
takes
advantage
of
a
number
of
features
in
JMP.
First
one
is
the
Fit
Curve
platform
available
from
Specialized
Modeling.
How
the
Fit
Curve
works.
Actually,
maybe
I'll
show
it
in
bio
assays
a
bit
better.
How
Fit
Curve
works
is
that
it
allows
you
to
calculate
information
about
your
known
curve
shapes.
For
example,
the
growth
rate,
or
in
our
case,
the
one
that
we
were
interested
in
was
the
inflection
point,
fit
a
variety
of
different
types
of
curves.
For
example,
the
sigmoidal
curves,
which
is
what
we
were
using
here.
The
other
one
that
it
makes
use
of
is
the
map
shapes.
Map
shapes
tells
the
software
to
recognize
a
certain
value
in
a
column
as
a
figure
on
a
map
and
to
reproduce
that
figure
in
Graph
Builder.
You
can
see
that
here.
JMP
comes
preloaded
with
many
default
shapes,
for
example,
country
names,
and
there
are
some
others
as
well.
The
microtiter
wells
are
not
included
with
JMP
by
default.
If
anybody
is
interested
in
using
them,
you
can
find
them
over
on
the
community.
I
think
that
there's
a
link
to
this
location
where
you
can
download
these
well shapes, these map shapes
in
our
presentation
on
the
same
page.
Without
further
ado,
let's
jump
into
some
of
what
the
Add-in
does.
If
I
were
to
open
up
this
example
here
where
you
could
see
the
name
of
the
cell,
the
plate
that
it
came
from,
the
data,
and
then
the
plate
row
column,
and
then
the
concentration
value.
The
first
thing
we'd
like
to
do
is
to
visualize
this.
The
Add-in
allows
us
to
do
that
by
clicking
on
the
Generate
Cell
Maps
button
where
I
can
put
in
the
data,
the
plate
name,
and
the
cell
name, press OK.
Here
you
can
see
all
the
different
map
shapes
and
use
these
to
very
quickly
identify
outliers
or
any
special
thing
that
may
be
happening
over
the
plate
that
you
can
recognize
visually.
Right
away,
we've
gotten
some
value
of
this.
What
I'd
like
to
say,
though,
is
that
the
data,
when
it
comes
out
of
the
machine
at
a
day's
end,
I
was
told
this
in
quite
such
a
nice
format,
it
actually
looks
like
this.
It's
these
CSV
files,
and
you
can
see
all
of
that
information
here.
The
very
first
step
is
to
take
all
of
this
data
and
to
get
it
into
JMP.
Of
course,
JMP
can
read
CSV
files,
so
that's
not
such
a
big
problem.
There
it
is.
Once
we've
done
that,
we
need
to
grab
the
individual
data
from
the
plates
themselves.
There's
a
number
of
plates
here.
Here
we
can
use
the
Add-in
for
some
help.
I'm
going
to
grab
this.
By
the
way,
for
anyone
watching
this,
if
you
have
similar
data
or
similar
needs
and
are
interested
in
developing
these
Add-ins
yourself,
you
can
always
reach
out
to
your
local
JMP
team
for
help.
They'd
be
happy
to
show
you
how
to
do
these
and
work
with
you
to
do
them
as
well.
N
ow
that
I've
highlighted
all
of
these,
I've
selected
them
in
the
table,
I'm
going
to
go
ahead
and
import
this
data.
N
ow
it's
in
the
right
format
and
I
can
go
ahead
and
generate
my
map
shapes.
The
other
thing
that
I
can
do
is
calculate
all
my
EC
50
values
or
anything
else
that
I'd
like
to
calculate.
In
order
to
do
that,
I
need
to
grab
the
concentrations.
As
I
understand
that
the
concentrations
that
are
used
change
from
example
to
example,
so
there
wasn't
an
option
to
automate
this.
But
the
people
running
these
know
what
they
are
for
their
specific
example,
so
they
can
grab
them
and
then
import
them
like
that.
N
ow
what
we'll
do
is
we'll
go
ahead
and
generate
all
of
our
EC
50
values.
Generate
inflection
point
table.
H
ere's
my
data.
Here's
my
concentration.
Here's
my
plate,
and
here's
my
row
information.
I'll
press
OK.
Now
I've
got
a
table
of
all
of
the
EC
50
values.
This
is
very
nice.
I
can
do
some
analysis
JMP if
I
want
to.
The
other
thing
you'll
notice
is
that
it's
created
a
Fit
Group
script
here,
which
I
can
use
to
explore
the
estimates
by
plate
row,
and
by
plate
themselves.
This
is
plate
row.
If
we're
looking
at
it
by
row,
if
we're
looking
at
by
column,
it
would
be
plate
column.
This
is
very
nice,
but
what
it
does
is
it
takes
advantage
of
another
JMP
feature
called
Graphlets,
which
allows
you
to
generate
or
open
up
and
run
any
any
platform
in
JMP
from
any
platform
in
JMP,
either
that
platform
or
another
platform.
In
this
case,
we'd
like
to
run
Fit
curve
from
our
Fit
Y
by
x
Fit
Group
report.
We
can
do
that
simply
by
hovering
over
any
point
that
captures
our
interest.
When
we
do
that,
we
generate
the
inflection
point.
The
whole
curve,
we
can
look
at
it,
or
we
can
dive
in
a
little
closer
by
clicking
that
button.
That
takes
us
right
into
Fit
curve
with
the
sample
that
generated
that
shape.
Different
things
that
we
can
do
with
this
data.
Perhaps
one
other
thing
that
I'll
show
you
that
the
Add-in
can
do
is
to
explore
this
a
little
bit
more
closely
to
explore
the
variability
from
sample
to
sample.
We
can
do
that
by
pressing
this
button
and
then
exploring
the
standard
deviation
of
all
of
the
samples
that
in
theory
would
be
the
same,
but
there's
going
to
be
some
variability.
We
could
see
what
that
looks
like
over
here.
I
hope
you
found
that
interesting.
I
hope
that's
given
you
some
ideas
about
how
you
can
build
similar
Add-ins
yourself
or
automate
processes
that
typically
take
a
long
time.
Thomas,
how
long
would
this
have
taken
without
the
use
of
the
Add-in?
Probably
something
around
3-5
hours.
I
think
the
conclusions
that
could
be
drawn
may
not
necessarily
reflect
the
reality.
Simply
because
certain
tools
were
not
available
for
us.
Definitely
much
of
a
drastic
change
in
the
ways
how
we
work.
Thank
you
very
much,
Hadley,
for
that
presentation.
Just
to
finish
off,
I
will
just
summarize
everything.
Hopefully,
you
can
see
my
slides
now.
I
would
just
want
to
reiterate
the
benefits
of
the
new
tools.
It
is
really
a
one
stop-
shop
for
plate
bias
and
uniformity
analysis
with
most
models
which
are
already
chosen
for
users.
That
was
quite
important
for
us
as
well.
Just
to
ensure
that
we
have
this
full
compliance
within
our
team
that
the
best
models
are
being
used.
The
plate
heat
map generation
option
allowed
us
to
remove
any
of
the
obvious
data
outliers
in
order
to
focus
on
the
true
variability
analysis.
Let's
think
for
a
moment
that
we
have
an
outlier
in
one
of
the
five
plates
in
one
of
the
wells
in
row
H
that
not
necessarily
means
that
the
variability
of
the
whole
row
H
across
all
the
assay
runs
would
always
be
much
higher
compared
to
the
other
rows.
As
reiterated
also
at
the
very
beginning
through
that
question
that
Hadley
has
asked,
we
really
were
able
to
remove
the
manual
and
error
prone
copy
paste
of
the
data.
What
I
really
personally
very
liked
is
the
interconnected
connectivity
of
curve
analysis
with
plate
raw
and
column
variability
analysis.
T
hat's
these
types
of
graphs
that
Hadley
has
demonstrated
to
you.
More
in
depth
analysis
of
uncertainty
in
the
calculation
of
the
inflection
point
is
possible.
That's
something
that
we
are
exploring
further
as
well
to
perhaps
extend
that
JMP
Add-in
even
further.
A
ll
in
all,
it
definitely
aids
and
speeds
up
development
of
robust
potency
assays in
our
team
going
forward.
T
hank
you
very
much
for
your
attention.
Both
Hadley
and
I
would
be
willing
to
take
any
questions
just
by
contacting
us
at
JMP
User
forum.
Thank
you
very
much.
