Hi,
my
name
is
Tannaz
Tajsoleiman.
Today,
I'm
going
to
talk
about
an
application
of
JMP
data
analysis
platform
for
pharmaceutical
industry.
The
focus
of
this
presentation
is
going
to
be
on
how
we
can
get
more
cross-
level
insight
using
smarter
Design of Experiment
DoE
and
afterwards
how
we
can
analyze
it
better
in
order
to
find
optimum
process
settings
for
specifically,
for
example,
pharmaceutical
industries.
But
what
do
I
mean
of
cross-
level
insight?
To
get
to
that,
let
me
explain
the
case
story
that
we
have
done
with
one
of
our
customers.
That
they
were
in
the
phase
of
process
development of
a
vaccine
production,
and
they
wanted
to
characterize
this
process.
If
we're
going
to
divide
the
different
phase
of
vaccine
production
and
the
process.
We
can
split
it
into
two
main
phases
of
Upstream
and
Downstream.
The
Upstream
phase
is
the
phase
that
they
infect
the
host
cells
with
a
limited
number
of
virus
and
then
keep
it
under
a
controlled
environment
and
under
specific
process
condition
for
the
virus
to
grow
and
the
population
reaches
the
target
level.
After
that,
they
have
to
move
the
materials
to
the
next
unit.
We
call
it
Downstream
Unit
for
purification
and
the
formulation
to
make
the
vaccine
ready
for
the
injection
and
going
out
to
the
market.
The
customer
were
focusing
on
the
process
development
of
these
two
phases,
meaning
that
they
needed
to
characterize
the
process.
In
order
to
do
that,
they
needed
to
identify
what's
the
most
significant
parameters
for
each
of
these
units,
both
Upstream
and
Downstream
using
Design of Experiments.
T
hen
after
that,
collect
the
data
to
be
able
to
model
the
process
for
each
individual
unit,
and
then
using
the
model
to
find
the
optimum
robust
setting
for
each
of
them.
It's
a
very
typical
task
within
the
bio-industries
to
split
the
process
into
main
units
and
then
characterize
each
unit
individually.
M
ost
of
the
cases,
this
characterization
are
not
influenced
by
each
other.
So
they
are
completely
independent
task
and
Design of Experiments.
Keeping
this
in
mind,
the
agenda
of
this
talk
is
going
to
be
on
how
we
can
make
these
DOEs
more
descriptive
and
more
comprehensive
to
have
the
highest
or
more
information
about
different
levels
of
the
process,
and
then
afterwards,
how
we
can
use
JMP
data
analysis
platform
to
first
model
the
data
and
then
use
the
model
to
optimize
the
processes.
Let's
start
with
the
Design of Experiment
for
this
specific
case
study. W
e
started
with
actually
running
several
workshops
with
the
teams
coming
from
both
Upstream
and
also
Downstream.
T
hen
we
had
several
workshops
to
just
figuring
out
what
are
the
most
important
factors
or
parameters
in
their
processes.
We
could
easily
see,
in
the
first
glance,
that
we
are
ending
up
to
a
very
high
number
of
factors
that
practically
it's
impossible
or
it's
out
of
the
budget
to
run
such
a
big
Design of Experiments.
W
e
had
to
narrow
it
down
with
starting
scoring
each
of
these
parameters
based
on
the
importance
and
how
much
they
vary
within
a
normal
production
and
then
how
it
would
be
the
ease
of
change
for
each
of
these
factors.
W
e
could
score
it
first
and
then
narrow
the
number
of
parameter
to
eight
for
each
of
these
units.
After
that,
we
could
easily
also
within
that
workshop,
could
see
the
high
influence
of,
for
example,
Upstream
outcome
on
the
Downstream
Unit
just
because
these
two
units
are
highly
connected
together
and
they
can
easily
influence
each
other.
Meaning
that
if
they
wanted
to
characterize
a
Downstream
separately
and
finding
the
optimum
setting,
most
probably
it
cannot
work
as
optimum
as
they
expected
because
it
can
highly
influence
by
the
outcome
of
Upstream
Unit.
It's
better
to
have
it
instead
of
two
individual
or
independent
Design of Experiment,
having
a
joint
DoE,
cover
both
sides
together
and
also
covers
the
interaction.
That
gives
us
a
very
good
cross-
factor
overview
within
our
Design of Experiments
and
help
also
at
the
same
time
to
minimize
the
number
of
runs
that
they
make.
A
lso
getting
too
much
better
randomization
factor
for
our
runs,
of
course,
under
practical
supervision.
How
we
have
done
this
theory?
Before
I
jump
to
the
demo
that
I
wanted
to
show
you,
let
me
give
you
some
information
about
the
limitation
that
we
had
in
this
theory.
F
irst
of
all,
each
Upstream
batch
could
directly
go
to
one
Downstream
batch.
But
in
the
Downstream,
they
had
the
possibility
to
run
two
reactors
in
parallel
to
increase
the
capacity
of
Downstream.
But
the
biggest
limitation
they
had
in
the
Downstream
was
that
when
they
were
running
those
parallel
reactors,
two
of
the
design
factors
had
to
be
set
the
same
for
those
two
parallel
reactors.
For
example,
in
this
case,
they
had
to
keep
the
Enzyme
time
and
Hold
up
time
on
the
Downstream
constant
on
both
parallel
reactors.
Then
also
it
was
very
important
to
have
a
proper
time
planning
between
Upstream
and
Downstream,
meaning
that
they
couldn't
have
a
big
lag
from
the
finishing
of
the
Upstream
batch
until
it
reaches
to
the
Downstream
batch.
Also
between
these
two
phases,
we
could
identify
a
factor
called
initial
cell
density
that
is
mostly
coming
from
Upstream,
that
it
was
a
common
factor
between
these
two
units.
W
e
call
it
as
a
pair
factor
to
join
these
two
DoEs
and
connect
these
two
DoEs
into
one
complex
one.
To
start
that,
we
also
needed
to
understand,
okay,
what
is
the
minimum
number
of
Upstream
batches
or
Downstream
batches
we
need?
S
ince,
as
you
can
see
here,
they
both
had
eight
design
factors,
but
since
the
Downstream
process
had
the
extra
limitation
on
our
design,
that
would
be
the
one
controlling
the
minimum
number
of
experiments.
That's
why
I
start
the
DoE
by
looking
at
the
Downstream
process.
Let's
have
a
look
at
the
demo.
As
you
can
see
here,
these
are
the
design
factor
that
we
had
for
the
Downstream
Unit.
I
want
to
know
how
many
minimum,
how
many
experiments
or
badges
do
I
need
from
Upstream
to
be
able
to
cover
all
the
required
badges
or
experiments
I
run
in
my
DoEs.
Starting
with
custom
design,
then
I
can
load
my
factor.
These
are
all
factors
that
I
have
for
Downstream.
Plus
I
need
to
add
my
initial
cell
density,
the
part
factor
that
comes
from
Upstream
batches.
As
you
can
see
here,
almost
all
of
these
factors,
the
changes
are
set
as
easy
except
the
Hold up
time
and
also
Enzyme
time,
which
are
the
two
factors
that
I
want
to
keep
them
similar
for
the
two
running
batches
in
parallel.
With
this
setting,
I
can
be
sure
that
those
batches
are
set
to
have
the
same
Hold
up
time
and
Enzyme
time.
Moreover,
I'm
interested
in
the
main
effect
in
my
model
and
the
interactions.
Let's
have
a
look
here.
As
you
can
see,
some
of
them,
the
interactions
are
set
at
if
possible,
which
I
want
to
have
them
as
necessary.
And
we
are
good
here.
As
you
can
see
here,
JMP
suggests
me
that
you
need
minimum
19
experiments
in
a
Downstream
to
cover
for
these
interactions.
T
o
be
safe,
we
always
want
to
have
one
minimum
extra
on
top.
T
hat
gives
me
20
experiments
or
20
Upstream
batch
as
the
minimum
starting
point
for
Upstream.
Okay,
then
let's
have
a
look
at
my
Upstream.
These
are
my
Upstream
factors
and
I
can
load
it
in
my
custom
design.
I
am
also
here
interested
in
two
interactions.
S
ince
we
said
that
we
want
to
have
20
Upstream
experiments,
I
can
specify
as
a
user
how
many
experiments
I
want.
T
hen
I
can
make
the
design
by
pressing
this
one.
To
save
the
time,
I
already
saved
the
design
and
let
me
bring
it
up
here.
This
is
the
experimental
design
for
my
Upstream
in
the
first
level.
Now,
I
want
to
use
this
design
to
design
my
Downstream
experiment.
To
do
that,
I
go
back
to
my
design
factor
for
Downstream.
A
gain,
custom
design.
I
will
load
my
factor
as
I
want
them
to
be.
N
ow,
I
want
to
include
this
design
experiment
for
Upstream
in
my
experiment.
How
I
do
that
is
that
I
first
select
that
design
window
and
then
I
can
call it.
I
can
select
the
coherent
factors,
and
then
import
that
experiment
also
in
my
design.
A
s
you
can
see
here,
these
are
the
experiments
that
was
designed
for
the
Upstream,
and
they
are
set
as
a
covariate
.
More
than
step
is
that
I
want
to
have
all
my
Downstream
parameters
plus
my
initial
set
density.
I
want
to
look
at
them
also
in
interaction
mode.
But
the
factors
that
comes
from
Upstream,
I
keep
them
as
a
main
effect.
As
you
can
see
here,
there
are
some
still
at
if
possible,
I
need
to
be
sure
that
they
are
set
as
a
necessary
one.
Then
since
I
already
included
a
design
experiment,
then
I
can
exclude
the
factor
that
I
have
from
Upstream.
Maybe
I
can
just
remove
them
all
here
again.
Add
the
main
factor,
the
second
order,
and
then
the
factors
from
Upstream
only
as
a
main
factor.
Then
I
can
choose
them.
If
I
go
up
here
and
say
perfusion
rate,
this
one,
this
one,
and
this
one,
these
are
the
factors
are
coming
from
my
Upstream,
and
I
can
set
them
as
if
possible.
Just
to
be
sure
that
I
have
everything
included
in
this
one
and
this
one
has
to
be
also
if
possible.
I
don't
need
to
force
it
in
this
design
anymore
because
it's
already
included
there.
Now,
I
can
set
it
to
have
10
whole
plots
and
20
runs
here
and
make
the
design.
To
save
the
time
again,
I
have
made
the
design
and
this
would
be
the
outcome.
Now
here
I
have
a
combined
DoE
both
for Upstream
and
Downstream.
The
nice
part
about
it
is
that
now
I
have
a
column
called
Whole
Plot
that
it
says
that
for
the
same
number,
meaning
that
it
says
that
each
parallel
unit,
I
have
a
fixed
Hold up
time
and
the
Enzyme
time.
As
you
can
see
here
that
it
changes,
it
keeps
constant
between
each
two
pairs.
I'm
combining
both
designs
Upstream
and
Downstream
now
into
one
unit.
Then
I
can
also
prioritize
which
Upstream
Unit
has
to
be
run
first
to
be
able
to
keep
the
time
balance
between
the
Upstream and
Downstream,
and
also
have
so
much
flexibility
in
keeping
the
condition
fixed
between
the
parallel
expert.
This
could
give
me
a
very
good
overview
of
both
level
and
then
both
phases
together.
Let's
go
back
to
the
slides.
N
ow
we
have
a
good
Design of Experiment
covering
both
phase.
Now
let's
have
a
look
how
we
can
do
the
analysis
together.
But
to
save
the
time,
I'm
going
to
only
look
at
the
Upstream
process
and
look
at
the
data.
T
he
aim
is
that
now
I
want
to
model
the
growth
phase
of
the
virus
in
the
Upstream
and
use
the
Kinetic
Modeling.
Then
after
I
find
that
model,
then
I
can
find
the
optimal
process
conditions
for
Upstream.
How
can
use
the
functionality
build
up
in
JMP?
T
his
is
my
data
set
at
the
moment,
it's
a
normalized
data
set
that
we
have.
A
s
you
can
see
here,
this
is
the
data
that
we
collected
during
this
experiment.
It
was
30
different
batches.
T
hen
we
ran
different
processes
and
we
monitored
the
growth
phase
of
virus
over
different
days
and
different
conditions.
And
then
we
could
see
how
the
virus
population
builds
up.
U
nder
some
conditions
after
a
while,
the
virus
population
starts
to
degrade
or
go
to
the
dead
space.
N
ow
we
want
to
characterize or model
this
profile
or
this
growth
phase
of
the
virus
to
be
able
to
predict,
okay,
what
would
be
the
best
combination
of
cell,
virus,
the
environmental
condition,
and
also
how
many
days
do
we
need
to
run
in
the
Upstream?
To
do
that,
JMP
has
a
very
nice
feature
in
the
specialized
modeling
and
fit
curve.
I
can
put
Y,
the
yield
is
the
virus
concentration
or
population
over
time,
as
Y.
X
as
my
day
and
the
batch
number
and
the
factors
that
is
controlling
my
process,
which
is
the
incubator,
the
temperature
and
the
initial
virus
concentration
in
the
logarithmic
scale.
The fit curve
gives
me
a
very
nice
initial
overview
of
how
each
of
these
batches
build
up
over
time
individually.
But
then
now
I
want
to
model
these
batches
or
these
characteristic,
this
Kinetic
model.
We
have
a
very
nice
option
here
in
JMP
that's
called
Exponential
Growth
and
Decay
and
then
Fit
Cell
Growth.
So
it's
a
built-
in
library
that
you
can
easily
choose
and
then
you
can
fit
a
logistic
growth
model
to
your
profile.
As
you
can
see
here,
this
cell
growth
models
tries
to
fit
this
functionality
to
each
of
my
curves,
covering
of
Y0
,
which
is
initial
virus
concentration,
YM
ax
is
the
maximum
reached
virus
concentration,
and
then
division
rate
and
then
mortality
rate.
T
hen
it
gives
you
a
nice
summary
of
each
of
these
batches,
and
this
is
the
valid
development
of
each
of
these
parameter
for
each
of
the
batches.
But
then
I
want
to
have
one
extra
further,
just
figuring
out
how
is
this
process
parameter
affecting
each
of
my
factors
here.
T
hen
in
Pro
version
of
JMP,
there
is
a
very
nice
option
called
Curve
DoE
A
nalysis
that
gives
you
this
option
to
analyze
what
factors
impacting
each
of
my
process
parameter
of
YM
ax,
for
example,
division
ratio
or
mortality
rate
or
so
on.
As
you
can
see
here,
it
gives
you
a
combined
window
that
for
each
of
these
four
parameters,
it
gives
you
a
possibility
to
use
the
Generalized
Regression and
then
try
to
analyze
or
model
each
of
them
individually.
As
an
example,
you
see
that
for
YM
ax, I
can
see
the
temperature
and
initial
virus
concentration
has
a
significant
effect
and
incubators
are
not
working
similar.
You
really
get
a
good
overview
of
the
different
parameters
and
stuff.
Or
if
I
go
for
mortality,
I
look
at
the
Profiler,
I
see
also
how
would
be
the
effect
of
these
two
factors
on
each
of
the
incubators,
for
example.
It
has
a
very
good
user
interface
to
combine
all
these
analysis
together
and
gives
you
a
very
nice
overview
of
different
factors
and
their
effect
on
my
process.
But
also
gives
you
a
nice
Profiler
at
the
bottom
here
that
gives
you
good
information
about,
okay,
if
I
change
my
incubator
and
temperature
and
my
initial
virus
concentration,
how
does
it
affect
my
yield?
T
hen
if
I'm going to, for
example,
reach
to
my
target
within
half
a
day,
how
should
be
my
process
condition?
T
hen
I
can
easily
use
the
Desirability
Function
here
and
maximize
it.
What
I
can
see
here,
if
I
want
to
reach
my
target
and
get
the
maximum
virus
concentration
within
half
a
day,
I
have
to
start
with
the
highest
initial
virus
concentration
and
highest
temperature. While i
f
I
give
it
more
time
and,
for
example,
say
that
it's
fine,
I
can
run
my
Upstream
with
two
and
a
half
days,
then
the
situation
would
be
different.
You
would
say
that,
okay,
to
get
the
higher
yield,
you
need
to
start
with
the
lower
cell
density
or
virus
density,
and
then
viruses
that
like
that
high
temperature.
That's
why
you
should
go
with
a
lower
temperature.
It's
a
very
nice
intuitive
way
to
just
play
around
with
different
factors
and
then
based
on
the
visibility
of
your
system
and
also
the
practicality
that
you
have
in
your
process,
then
you
can
fine
tune
and
find
the
optimum
robust
process
condition.
This
is
fantastic.
But
the
biggest
problem
that
I
have
with
this
Curve
DoE
Analysis
is
that
I'm
missing
some
very
nice
functionality
in
the
standard
modeling
part
of
it,
which
if
I
go
for
diagnostic
plot,
for
example,
I'm
missing
this
digitized
residual
plot
that
helped
me
to
find
outli
er,
which
I
cannot
identify
if
I
do
have
outli
er
here
or not.
Or
if
I
do
need
to
apply
some
transformation
like
p
lots
transformation
or
so.
I
really
missed
those
functionality
in
this
part,
but
I
still
have
an
option
to
compensate
that
missing
with
extracting
this
summary
out
and
then
try
to
model
each
of
these
parameters
individually.
But
let's
do
that.
I
extracted
that
parameter,
as
you
can
see
here,
the
group
summary
parameter,
and
I
extracted
them,
and
then
I
added
to
my
data
table.
Now
I
can
have
a
look
at
each
of
them
individually
and
make
the
analysis
and the
to
just
figuring
out.
For
example,
I
will
go
for
Y0
,
and
then
you
have
the
option,
if
I
go
here,
to
just
apply
your
own
routine
of
modeling
and
then
include
all
the
factors
and
use
Generalized
Regression
and
then
move
on
with
figuring
out
if
you
need,
for
example,
to
apply
it
in
transformation
or
remove
any
outliers.
Then,
for
example,
if
I
look
at
this
one,
if
I
look
at
YMax
model
and
I
try
to
model
it
in
a
normal
routine,
I
can
see
that,
for
example,
I
do
have
one
out
lier
that
I
have
to
remove
it.
Or
for
example,
I
need
to
apply
some
transformation
on
my
data
to
clear
it
out
first
and
also
to
make
it
more
normalized
and
then
include
it
in
my...
Then
move
on
with
my
rest
of
the
analysis.
But
then
if
I
compare
here,
for
example,
now
I
want
to
remove
the
outlier
here.
Now
I'm
removing
the
outlier
here.
Then
I
see
that
I
don't
need
any
more
plots
curves
transformation
after
removing
the
outlier.
What
I
see
here,
this
is
going
to
be
the
profile
from
a
normal
or
standard
approach
of
modeling
of
each
of
the
parameters.
While
if
I
come
back
to
this
section,
I
was
missing
that
analysis
and
therefore,
what
I
could
get
out
of
it
was
such
a
Profiler
because
I
still
had
my
outlier
in
this
data
set
and
I
couldn't
see
that. T
hen
it
was
coming
with
something
else.
If
I
do
this
first,
I
do
my
initial
inspection
and
then
I
can
come
back
here
in
my
picture
and
then,
for
example,
I
can
apply...
I
first
need
to
remove
this
again,
Re
move
Fit.
And
then,
for
example,
I
can
apply
my
local
data
filter
and
exclude
my
outlier
and
then
do
the
modeling
part
of
it.
And
then,
for
example,
and
move
with
the
other
part.
I
have
this
option
to
do
this.
And
of
course,
I
do
have
the
option
to
force
factor
in
model
launch,
as
that's
controlled
and
force
term,
to
force
some
factors
that
I
could
see
in
my
previous
analysis
that
I
have
to
have
it.
To
start
with.
I
have
these
two
options
to
compensate
for
the
missing
diagnostic
part
in
this
analysis.
But
in
general,
that's
a
perfect
tool
to
have
a
very
good
overview
about
what
could
be
the
potential
factors
affecting
on
my
process.
Okay.
To
summarize
how
we
can
use
this
functionality,
as
you
can
see
here,
we
started
with
fitting
a
curve,
and
then
we
extracted
the
model
parameter,
and
then
we
could
investigate
each
of
those
parameter
individually,
and
then
either
we
force
the
significant
factor
in
the
fit
curve,
or
exclude
the
outliers,
and
also
just
beforehand
apply
any
transformation
that
is
needed,
and
then
you
can
get
a
combined
overview
as
such
in
the
fit
model.
This
going
to
be
a
very
nice
analysis and
a
very
useful
package
of
functionality
for
our
case.
To
summarize,
what
we
have
done
is
that
I
really
wanted
to
emphasize on,
first
of
all,
it's
very
important
to
know
the
complexity
of
the
system,
especially
when
we
have
interact
with
some
different
level
of
processes
that
they
are
interacting
partly
together and
before
moving
on
for
several
DoEs
that
they
are
independently
working
and
they
are
independently
used
to
characterize
the
system.
Try
to
combine
these
DoEs
into
one
coherent
one
because
it
really
gives
you
a
good
overview
of
multi-
layer
interactions
and
also
gives
you
a
lot
of
information
that
you
miss
in
practice
if
you
run
several
independent
DOEs.
Then
JMP
also
gives
you
a
lot
of
flexibility
on
building
up
these
DoEs
and
also
implement
a
lot
of
practicality
information
in
your
DoEs.
T
hen
at
the
end
also,
we
could
see
that
how
different
data
analysis
functionality and
also
built
in
libraries
could
help
to
characterize
the
different
processes,
specifically,
for
example,
in
this
case,
it
was
the
self-
growth
process,
and
how
we
can
use
some
very
powerful
tools,
especially
in
JMP
Pro,
and
also
how
we
can
interact
with
other
standard applications
that
you
have
in
JMP
to
compensate
for
some
missing
features
in
the
JMP
Pro
Advanced
Analysis
Toolbox.
With
that,
I
want
to
thank
you
so
much
for
your
attention
and
I
hope
you
like
the
presentation.
