Hello,
everyone.
Thank
you
for
joining
my
talk.
I
am
Giulia
Lambiase,
I'm
a
Senior
Scientist
at
AstraZeneca.
I
work
in
biopharmaceutical
development
in
the
analytical
science
team.
Today,
I
want
to
talk
to you
about
the
use
of
DoE
for
the
development
of
analytical
characterization
methods,
most
especially
chromatography
methods.
In
today's
talk,
I'm
going
to
talk
about
therapeutic
proteins,
what
they
are,
and
why
they
are
challenging
for
analytical
testing,
and
introduce
you
to
the
use
of
design
of
experiment
for
analytical
method
development
and
the
application
of
DoE
for
the
development
of
charge
variance
method,
specifically cation
exchange
chromatography
method.
To
start
off,
protein
therapeutics
are
inherently
very
complex
due
to
their
larger
size
and
the
presence
of
the post-
translational
modification,
and
also
chemical
modifications
that
can
the
protein
undergo
through
during
the
processes
of
expression
in
cells,
purification,
and
storage.
Monoclonal
antibodies
dominate
the
biopharmaceutical
market,
representing
about
70
%
of
the
total
sales
of
biopharmaceutical
products.
However,
recently,
there
is
a
push
for
new
products,
next
generation
biopharmaceuticals,
which
are
bispecific
antibodies,
antibody
fragment,
fusion
proteins,
and
many
other
formats.
All
of
them
come
with
unique
challenges
due
to
their
complex
structure
and
presence
of
higher
order
structure,
glyco
forms,
charge
variants,
the
sulfate
bonds,
oxidized
deamidated
species,
isomerization,
aggregation,
fragmentation.
A
ll
of
these
modification,
chemical
process
[inaudible 00:02:37]
modification,
can
impact
on
potency,
safety,
quality
of
the
final
drug
product.
This
is
why
thorough
analytical
characterization
and
analytical
testing
throughout
all
the
stages
of
product
life
cycle
is
key
to
meet
regulatory
standards,
to
be
enabled
to
deliver
a
product
that
meets
regulatory
quality
profile.
We
use
a
plethora
of
analytical
techniques
for
analyzing
proteins,
and
these
are
based
mostly
on
chromatography
methods,
electrophoretic
methods,
and
[inaudible 00:03:25]
.
Due
to
the
inherent
structural
complexity
of
proteins,
analytical
method
development
can
be
quite
challenging.
In
today's
talk,
I'm
going
to
specifically
talk
about
chromatography
methods
and
the
use
of
design
of
experiment
to
help
the
development
of
chromatography
separation.
Chromatography
method
can
be
quite
complex,
especially
if
you
have
a
complex
analyte
like
a
protein.
This
is
because
the
separation
depends
on
the
interplay
of
several
variables
such
as
mobile
phase
composition,
buffer
pH,
flow
rate,
column
chemistry,
temperature,
the
type
of
detector
that
you
decide
to
use
for
the
analysis.
All
of
these
parameters
need
to
be
fine-
tuned
and
controlled
during
the
separation
process
in
order
to
achieve
the
desired
separation.
DoE
can
be
very
useful
versus
one
factor
at
a
time
approach.
One
factor
at
a
time
approach
involved
the
variation
of
one
parameter
at
a
time,
maintaining
the
other
constant.
This
may
lead
to
a
large
experimental
run,
lack
of
information
because
there's
lack
of
investigation
on
factors
interactions.
Lack
of
information
also
leads
to
additional
experiments
during
method
validation,
which
may
lengthen
even
more
the
method
development
process
and
finally
retard
the
overall
product
development.
DoE,
in
comparison
to
one
factor
at
a
time
approaches,
DoE
enables
the
variation
of
multiple
parameters
at
a
time.
This
allow,
with
a
reduced
number
of
experiments,
to
investigate
a
large
number
of
factors,
including
the
interactions
between
them.
Also
the
development
of
mathematical
models
that
allow
the
assessment
of
relevance
and
the
statistical
significance
to
facilitate
all
the
steps
required
during
method
validation.
DoE
enables
really
to
investigate
a
wide
design
space
with
less
resources,
so
in
a
more
efficient
way.
In
fact,
I
like
saying
DoE
enables
faster,
cheaper,
and
smarter
experiments to
deliver
stronger
and
better
analytical
methods.
In today's
talk,
I'm
going
to
talk
you
through
a
split
DoE
approach
for
the
development
of
a
cation
exchange
chromatography
method.
Cation
exchange
chromatography
is
used
for
the
analysis
of
charge
variants.
Specifically,
if
you
see
here
on
the
left
hand
side
of
this
slide,
you
can
see
a
chromatogram
of
a
protein
where
you
can
see
some
acidic
species
here
on
the
left,
[inaudible 00:07:17]
on
the
left
of
a
main
species
peak,
and
some
basic
species
peak.
All
these
acidic
basic
species
can
be
formed
due
to
the
presence
of
chemical
modification
that
can
lead
to
superficial
charge
distribution
variation
in
the
protein.
Cation
exchange
chromatography
methods
are
quite
complex
chromatography
methods
because
the
separation
efficiency
is
affected
by
a
number
of
factors
and
quite
sensitive
to
small
changes
of
these
factors
such
as
column
chemistry,
mobile
phase
pH,
temperature,
flow
rate,
content
of
salt,
time
of
the
separation.
In
this
approach,
I'm
going
to
talk
you
through
an
efficient
way
to
develop
cation
exchange
chromatography
method
using
DoE.
If
you
are
familiar
with
DoE,
you
may
know
that
often
requires
a
sequential
approach.
In
this
experiment,
I
performed
a
main
effects
screening
design
for
enabling
the
selection
of
the
best
column
chemistry
and
the
mobile
phase
pH
for
the
charge
variance
separation
of
this specific
mAb
molecule.
During
the
second
DoE,
I
use
response
surface
methodology,
particularly
a
central
composite
design
DoE,
to
optimize
the
chromatography
separation
by
changing
the
flow
rate
and
[inaudible 00:09:36]
.
Let's
take
into
more
detail
in
the
first
DoE
experiment.
This
was
a
main
effects
screening
design
where
I
screened
four
column
chemistry
bought
by
four
different
providers,
Agilent,
Sepax, Phenomenex,
and
Waters, and
I
screened
a
range
of
pH
from
5.5
to
6.5.
My
response
was
the
experimental
peak
capacity,
which
is
a
parameter
that
tells
you
the
efficiency
of
a
chromatographic
separation,
precisely
the
number
of
peaks
that
can
be
separated
within
the
chromatogram,
the
chromatography
time
that
you
set.
Other
parameters
such
as
concentration
of
buffer,
concentration
of
salt at
the
start
of
the
chromatography
gradient,
flow
rate,
gradient
time,
shape,
temperature,
injection
volume,
concentration,
and
the
UV
absorbance
were
kept
constant.
These
are
the
results
for
the
first
DoE.
On
the
left
hand
side,
you
can
see
the
four
different
column
results.
You
can
see
how
the
experimental
peak
capacity
changes versus
the
pH
change
in
the
mobile
phase
in
all
the
four
different
columns.
You
can
see
that
we
aim
to
have
high
experimental
peak
capacity
values.
You
can
see
that
the
Phenomenex
column
performed
best.
In
all
of
these
three
columns,
we
can
see
that
pH
of
6.5
enables
greater
experimental
peak
capacities.
But
the
Phenomenex
column
allowed
for
better
separation
results.
It
is
also
visible
on
the
right
hand
side
of
this
slide
in
the
panel
A.
You
can
see
at
pH
6.5,
how
the
separation
differs
when
using
different
chromatography
columns.
We
have
Agilent
,
Waters
,
Phenomenex
,
and
Sepax
.
Definitely,
the
separation
of
the
charge
variants
using
the
Phenomenex
column
is
much
better
than
in
the
others
because
these
acidic
peaks
are
very
well
separated
as
well
as
these
basic
species
here
from
the
main
product
peak.
Panel
B,
we
have
isolated
only
the
results
of
the
Phenomenex
column.
How
the
chromatography
separation
was
that
with
the
mobile
phase
or
with
pH
5.5, 6.0, and
6.5.
We
can
see
how
the
separation
improves
with
the
increase
in pH.
Obviously,
the
mobile
phase
pH
is
dictated
by
the
intrinsic
molecule
pI.
We
could
only
investigate
this
range.
Otherwise,
the
molecule
would
have
struggled
to
find
its
own
column.
Based
on
our
fundamental
knowledge
of
chromatography
separation
with
cation
exchange
columns,
we
decided
that
this
parameter,
so
this
Phenomenex
column
and
pH
of
6.5,
were
optimal
to
carry
on
development.
We
carried
on
with
the
second
DoE
using
a
central
composite
design.
Central
composite
design
is
a
type
of
DoE
falling
within
the
umbrella
of
response
surface
methodology,
which
is
used
for
optimized
conditions
for investigating
the
presence
of
curvature,
for
instance,
and
extrapolate
optimal
values.
In
this
case,
we
use
our
Phenomenex
column
and
mobile
phase
pH
of
6.5, and
started
to
play
with
other
parameters
such
as
buffer
concentration,
concentration
of
the
salt
at
the
start
of
the
gradient,
and
flow
rate
to
investigate
optimal
conditions.
Central
composite
design
enabled
to
very
efficiently,
with
a
few
number
of
runs,
to
identify
optimal
separation
conditions,
optimal
method
conditions.
In
fact,
at
the
very
end
of
the
split
DoE
approach,
we
could
say
that
with
the
investigation
of
four
column,
mobile
phase,
pH
range,
salt
composition,
gradient
flow
rate.
With
only
27
experimental
runs,
we
could
optimize
a
method
for
a monoclonal
antibody.
This
method
is
very
useful
because
it
is
now
used
as
a
quick,
high
throughput
screening
experiment.
In
a
quick,
high
throughput
analytical
method
for
screening
differences
in
the
charge
variance
profile
of
these
specific
molecules
expressed
in
different
conditions
and
compare
it
to
a
standard.
You
can
see
here
that
the
blue
line
is
our
reference
standard
and
the
red
line
is
a
stress
material
of
the
same
molecule.
You
can
see
how
the
charge
variance
profile
changed
as
a
consequence
of
the
stress
condition
applied
to
this
molecule.
This
was
achieved
thanks
to
this
analytical
method
which
was
developed
and
optimized
with
a
DoE
approach.
We
also
decided
to
implement
this
DoE
approach
as
a
platform
workflow
for
analytical
method
development
for
new
products,
new
bio
pharmaceuticals, and
we
screened
a
number
of
products.
For
all
of
them,
we
applied
first
the
first
main
effect
screen
design,
and
we
identified
the
best
column
and
mobile
phase
pH
to
use.
Secondly,
we
applied
the
central
composite
design
to
optimize
the
separation.
Now,
we
have
identified
a
platform
column
and
a
mobile
phase
composition
for
this
class
of
therapeutics.
When
new
molecules
comes
into
the
pipeline,
we
can
very
quickly,
just
by
using
a
central
composite
design,
which
involves
actually
just
12
runs,
optimize
the
chromatography
profile
and
deliver
an
optimal
cation
exchange
method
for
a
specific
product.
The
key
take-
home
messages
from
my
talk
today
is
that
DoE
system
method
development
followed
by
appropriate
statistical
analysis
enables
to
plan
experiment
based
on
time,
cost,
and
analytical
resources
available
very
efficiently,
and
schedule
the
execution
of
experiments
with
adequate
sample
type
and
size
to
extrapolate
the
maximum
amount
of
information
from
our
chemical
data and
efficiently
address
the
challenges
and
goals
of
the
intended
research.
It
definitely
saves
time
and
cost
for
experiment
execution
in
comparison
to
one
factor
at
a
time
approaches.
Most
especially,
it
allows
the
complexity
of
analytical
method
development,
but
still
interrogating
several
factor
at
a
time
and
studying
the
effect
of
both
individual
method
parameters
and
the
interaction
on
the
dependent
variable.
With
today's
talk,
I
hope
I
inspired
you
to
apply
more
DoE
in
your
experiments.
Thank
you
very
much,
everyone,
for
your
attention.
If
you
have
any
questions,
feel
free
to
reach
out
to
me.
Thank
you.
