Hello, and
welcome
to
my
presentation
about
JMP
in
qualification
and
validation
of
biological
assays.
I've
divided
this
presentation
into
five
parts.
At
first,
I
want
to
give
you
a
small
introduction
about
my
person
and
the
company
I'm
working
for,
VelaLabs.
The
second
part
is
a
general
introduction
about
method
qualification
and
method
validation
like
we
perform
it
at
VelaLabs
often.
The
third
part
is
how
we
collect
and
summarize
the
data.
Then
I
will
continue
with
the
JMP
data
table
where
I've
created
some
scripts
to
evaluate
the
data
generated
during
qualification
and
validation.
The
last
part,
I
will
talk
about
some
additional
robustness
parameters
where
different
functions
of
JMP
are
used.
My
name
is
Alexander
Gill,
and
I'm
at
VelaLabs
since
2019.
I'm
a
laboratory
expert
in
the
l
igand binding assay
group
.
I'm
mostly
responsible
for
method
development,
qualification,
and
validation
for
Biacore assays
and
ELI assays.
VelaLabs is
a
contract
laboratory
for
quality
control u
nder
GMP
conditions.
We
have
four
operational
departments:
the
ligand binding assay
group,
the
physico-
chemical
group,
and
the
cell-
based
assay
group,
and the
microbiological
group.
Method
qualification
and
validation
is
important
in
the
life
cycle
of
pharmaceuticals
and
biologicals.
Here,
the
life
cycle
of
such
drugs
is
shown
from
the
pre-
clinical
phase
over
the
clinical
phases
and
the
application.
During
the
pre-
clinical
phase,
developed
methods
are
suitable
which
are
on
the
scientifically
sound.
Afterwards,
for
the
clinical
trials
phase
1
and
phase
2,
we
use
mostly
qualified
methods.
For
method
qualification,
we
show
with
some
suitable
parameters
the
performance
of
the
assay.
If
the
assay
is
then
validated,
derived
from
the
data
generated
during
qualification,
we
create
limits
which
must
be
reached
during
method
validation.
The
validated
method
afterwards
is
used
for
clinical
trials
phase
3,
new
drug
application,
and
also
for
batch
release
in
post-marketing
afterwards.
Here,
I've
shown
some
examples
for
the
performance
parameters.
The
accuracy
shows
if
the
method
has
any
bias
or
shift,
or
especially
it
lacks
bias
or
shift/
the
intermediate
precision
is
the
variability
between
runs
where
we
show
that
different
operators
and
different
devices
on
different
days
do
not
influence
the
result.
The
repeatability
is
the
variability
within
one
run
where
we
try
to
keep
the
differences
between
the
reported
values
as
small
as
possible.
The
linearity
shows
the
dose
response
of
the
assay
over
the
whole
assay
range.
During
the
robustness,
we
show
that
different
parameters
can
or
cannot
influence
the
result.
For
example,
different
ligand
lots
or
different
models
of
devices.
Then
the
sensitivity
to
detect
stability-
indicating
changes,
there
we
use
mostly
stress
samples
to
show
that
they
can
be
easily
distinguished
to
non-
stress
samples.
Specificity
is,
for
example,
a
blank
subtraction
or
positive
or
negative
controls.
The
data
collection
is
mostly
performed
in
Microsoft
Excel
because
it's
more
accessible
within
our
company.
I
will
come
later
to
this.
We
also
collect
the
reported
value,
which
is
the
final
outcome
of
the
assay.
The
reported
value
is
calculated
using
a
validated
software
like
PLA,
SoftMax
Pro,
or
the
Biacore
Software.
This
is
to
ensure
the
data
integrity.
Every
step
where
a
human
is
involved
in
the
evaluation
has
to
be
checked
by
a
second
operator.
As
I
use
a
relative
potency
assay
as
example
for
this
presentation,
I've
also
shown
here
what's
the
reported
value
for
this
assay.
It's
the
relative
potency
with
the
95
%
confidence
interval
as
a
quality
parameter.
Here
are
the
reasons
why
we
use
Microsoft
Excel
for
the
data
collection
because
it's
available
on
every
PC
within
our
company
and
every
employee
has
basic
knowledge
about
it.
The
raw
data
from
the
validated
softwares
are
also
often
exported
in
Excel.
What
is
really
important
that
the
data
in
Excel
are
organized
in
datasets,
so
they
can
be
transferred
to
JMP
more
easily.
Here
is
a
basic
experimental
design
for
a
method
qualification
or
validation.
The
first
six
runs
are
basically
designed
around
the
intermediate
precision
where
we
use
50%,
100%,
and
200
%
sample
mimics
in
each
of
these
six
runs.
These
runs
are
spread
above
two
devices,
two
operators,
and
performed
on
three
different
days.
We
report
the
mean
relative
potency
for
each
of
these
dosage
points,
the
standard
deviation,
the
coefficient
of
variation,
and
the
95
%
confidence
interval.
For
accuracy,
we
use
the
same
dataset
as
for
intermediate
precision,
but
we
calculate
the
mean
recovery,
and
therefore
standard
deviation,
C
V,
and
95
%
confidence
interval
both
for
all
18
datasets
together
and
also
for
each
dosage
point
separate.
The
seventh
run
is
for
the
determination
of
repeatability
where
we
use
six
100
%
sample
mimics
within
one
run
and
also
report
the
mean
relative
potency,
standard
deviations,
CV,
and
the
95
%
confidence
interval.
Then
for
linearity,
which
is
here
in
run
1,
we
use
the
sample
mimics
for
intermediate
precision
and
additionally
use
75
%
and
150
%
sample
mimic
within
this
one
run
to
show
that
the
results
are
linear
over
the
whole
assay
range.
Therefore,
we
report
the
correlation
coefficient,
the
slope,
Y-intercept,
and
residual
sum
of
squares.
For
robustness,
in
this
case,
we
show
a
lower
and
a
higher
immobilization
level
and
also
use
two
different
lots
of
the
ligand.
Then
now,
I'll
show
you
the
Excel
table
where
we
can
see
here
in
the
first
few
columns
the
metadata
for
each
data
set,
then
the
reported
value
with
the
95
%
confidence
interval,
the
slope
ratio,
which
is
additional
quality
parameter
and
shows
afterwards
if
the
analyte
is
comparable
to
the
reference.
The
column
for
recovery
is
empty
because
the
recovery
will
be
calculated
in
the
JMP
software.
Here,
the
matrix
where
it's
defined
which
datasets
are
used
for
which
parameters.
Then
there
are
two
different
possibilities
to
transfer
this
data
into
the
JMP
software.
One
is
with
this
function
where
a
data
table
can
directly
be
created
out
of
this
table.
But
in
this
case,
I
won't
use
this
function
because
I
have
already
created
a
JMP
table
with
all
the
scripts
I
need.
I
just
copy
all
the
data.
But
for
this
procedure,
it's
important
to
show
all
available
digits
of
the
reported
values
because
only
the
shown
digits
are
pasted
afterwards
into
the
JMP
software.
I
now
copy
with
CTRL+
C
all
this
data
and
then
go
to
the
JMP
data
table
where
I
can
paste
all
this
data.
Then
we
get
here
an
alert
because
in
the
column
Recovery,
I
created
a
formula
to
calculate
the
recovery.
I
don't
want
to
paste
the
data
in
here,
but
the
Excel
table
does
not
contain
data
in
this
column.
We
click
Okay,
and
everything
is
pasted
as
we
wanted.
For
what
purposes
JMP
can
be
used
under
GMP
conditions?
We
use
it
during
the
method
development
phase
for
design
of
experiments,
for
example,
to
investigate
more
different
parameters
of
the
method
within
one
set
of
experiments.
Then
use
it
for
the
statistical
data
analysis
and
also
for
comparability
studies.
For
example
, if
a
customer
wants
to
compare
a
biosimilar
with
the
originator.
During
qualification
and
validation,
JMP
can
also
be
used
for
the
design
of
experiments.
For
example,
for
the
intermediate
precision
parameters
or
to
spread
the
robustness
parameters
over
the
qualification
runs.
Then
I
will
show
afterwards
for
the
determination
of
assay
performance
in
qualification
and
for
the
check
of
the
assay
performance
during
validation.
But
for
this,
an additional
QC
check
is
required
afterwards
if
all
the
calculations
are
performed
in
the
right
way.
This
is
very
important
that
JMP
is
not
really
usable
for
the
determination
of
reported
values.
Therefore,
as
I
mentioned
before,
we
used
mostly
validated
softwares.
Now
we
go
to
the
JMP
data
table
where
I
will
first
show
you
how
I
create
most
of
the
script.
Therefore,
I
use
distribution.
For
example,
if
I
create
the
accuracy
at
50
%,
I
select
the
Recovery
and
choose
it
for
the
Y
columns.
Then
I
click
Okay.
Then
we
have
here
all
available
datasets.
To
limit
these
datas
ets,
I
create
a
local
data
filter
and
use
Accuracy
and
edit.
If
it
then
choose
all
the
columns
indicated
with
an
X,
we
have
reduced
the
data
sets
to
18.
To
reduce
it
further
for
only
the
50
%
sample
mimics,
I
add
with
the
AND
function
an
additional
filter
for
the
nominal
potency,
which
I
then
limit
to
the
sample
mimics
with
about
50
%
nominal
potency.
Then
you
see
we
have
only
six
datasets
left
with
mean
recovery
of
99
%
and
a
coefficient
of
variation
of
about
6
%
and
the
confidence
interval.
To
save
this
script,
I
go
again
to
the
red
triangle
here
and
save
the
script
to
data
table.
For
example,
as
accuracy
50
%
2,
because
I've
already
created
a
similar
script
here.
The
difference
for
the
intermediate
position,
if
we
open,
for
example,
here
the
intermediate
position
at
100
%
is
only
that
we
not
use
the
recovery
here,
but
the
relative
potency
and
have
also
again
the
same
parameters
reported.
For
repeatability, w
e
choose
only
one
run
with
the
six
100
%
sample
mimics.
We
report
also
the
same
data
like
the
mean
relative
potency,
the
standard
deviation,
the
95
%
confidence
interval,
and
also
the
coefficient
of
variation.
What's
also
very
interesting
here
is
the
linearity
where
we
use
a
different
function.
I
created
this
using
a
Y
by
X
plot
and
plotted
the
relative
potency
by
the
nominal
potency
and
created
a
linear
fit
through
all
these
data
points.
Then
we
report
the
Y-intercept,
the
slope
of
the
linear
fit,
the
RS
quare
or
coefficient
of
correlation,
and
also
the
sum
of
squares
error
or
residual
sum
of
squares.
Then
we
go
back
to
the
presentation.
For
additional
robustness
parameters,
we,
for
example,
show
the
performance
of
the
assay using
different
material
lots.
For
them,
we
show
if
they
have
equal
variances.
If
the
variances
are
equal,
we
use
the
T-t
est.
If
not,
we
use
the
Welch-
test.
For
example,
for
ELISA
methods, w
e
also
measure
sometimes
the
plates
on
two
different
models
of
plate
readers
to
show
if
both
models
can
be
used.
This
is
then
analyzed
using
a
paired
T-
test.
At
the
end,
I
want
to
thank
you
for
your
attention.
If
you
have
any
further
questions,
you
can
type
it
into
the
Q&A
or
contact
me
directly.
