PAT applications for melt extrusion and multivariate analysis of spectral data

Az SZTE Kutatóegyetemi Kiválósági Központ tudásbázisának kiszélesítése és hosszú távú szakmai fenntarthatóságának megalapozása

a kiváló tudományos utánpótlás biztosításával”

Gyógyszertudományok Doktori Iskola Ph.D. kurzus (GYTKDIE16)

„Introduction to melt extrusion and application of quality by design principles”

2012. 03. 27. – 03. 30.

„Solid dispersions: Types, production, characterization”

Prof. Dr. Peter Kleinebudde

TÁMOP‐4.2.2/

B‐10/1‐2010‐0012
projekt


Ins*tute
of
Pharmaceu*cs
and
Biopharmaceu*cs
 Heinrich‐Heine‐University


Düsseldorf,
Germany


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Peter
Kleinebudde


Introduc.on
to
melt
extrusion
and
applica.on
 of
quality
by
design
principles





Szeged,
March
26‐30,
2012



Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Content


•  Solid
dispersions:
Types,
produc*on,
characteriza*on


•  Introduc*on
to
melt
extrusion:
Equipment,
process,
 materials,
proper*es
of
extrudates,
downstream
 processing,
applica*ons


•  PAT
applica*ons
for
melt
extrusion

and
mul*variate
 analysis
of
spectral
data


•  Tools
for
risk
analysis
in
the
context
of
melt
extrusion


•  Approaches
to
develop
a
design
and
a
control
space


3


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


PAT
applica*ons


•  Defini*on



•  PAT
tools


•  Case
studies


4


QUALITY
IS
INVERSELY
PROPORTIONAL
TO
 VARIABILITY.



 
 
 
 
 D.C.
MONTGOMERY


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Quality


•  Level
1


Quality
is
a
simple
maYer
of
producing
goods
or
 delivering
services
whose
measurable
characteris*cs
 sa*sfy
a
fixed
set
of
specifica*ons
that
are
usually
 numerically
defined
(quality
of
performance,
 independent
of
the
customer).


•  Level
2


Quality
products
and
services
are
simply
those
that
 sa*sfy
customer
expecta*ons
for
their
use
or


consump*on
(quality
of
design,
dependent
on
the
 customer).



6
 Melt
extrusion
and
QbD
principles
I


Koriakan:
and
Rekkas
2010


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Quality
by
Design
approach


ICH
Quality
Implementa*on
Working
Group


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


ICH
Q8(R2):
Design
space


•  The
mul*dimensional
combina*on
and
interac*on
of
 input
variables
(e.g.,
material
aYributes)
and
process
 parameters
that
have
been
demonstrated
to
provide
 assurance
of
quality.


•  The
rela*onship
between
the
process
inputs
(material
 aYributes
and
process
parameters)
and
the
cri*cal
 quality
aYributes
can
be
described
in
the
design
space.



•  Working
within
the
design
space
is
not
considered
as
a
 change.


•  Movement
out
of
the
design
space
is
considered
to
be
a
 change
and
would
normally
ini*ate
a
regulatory


postapproval
change
process.



8
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Quality
Target
Product
Profile
(QTPP)


•  The
quality
target
product
profile
forms
the
basis
of
design
 for
the
development
of
the
product.
Considera*ons
for
the
 quality
target
product
profile
could
include:



– Intended
use
in
clinical
se_ng,
route
of
administra*on,
dosage
 form,
delivery
systems



– Dosage
strength(s)

 – Container
closure
system



– Therapeu*c
moiety
release
or
delivery
and
aYributes
affec*ng
 pharmacokine*c
characteris*cs
(e.g.,
dissolu*on,
aerodynamic
 performance)
appropriate
to
the
drug
product
dosage
form
being
 developed



– Drug
product
quality
criteria
(e.g.,
sterility,
purity,
stability,
and
drug
 release)
appropriate
for
the
intended
marketed
product



9
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Cri*cal
Quality
AYributes
(CQA)


•  A
physical,
chemical,
biological,
or
microbiological
property
or
 characteris*c
that
should
be
within
an
appropriate
limit,
range,
 or
distribu*on
to
ensure
the
desired
product
quality.



•  CQAs
are
generally
associated
with
the
drug
substance,
 excipients,
intermediates
(in‐process
materials),
and
drug
 product.



•  CQAs
of
solid
oral
dosage
forms
are
typically
those
aspects
 affec*ng
product
purity,
strength,
drug
release,
and
stability.


•  CQAs
for
other
delivery
systems
can
addi*onally
include
more
 product
specific
aspects,
such
as
aerodynamic
proper*es
for
 inhaled
products,
sterility
for
parenterals,
and
adhesion
 proper*es
for
transdermal
patches.



•  For
drug
substances,
raw
materials,
and
intermediates,
the
CQAs
 can
addi*onally
include
those
proper*es
(e.g.,
par*cle
size
 distribu*on,
bulk
density)
that
affect
drug
product
CQAs.



10
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Cri*cal
Process
Parameter
(CPP)



•  A
process
parameter
whose
variability
has
an
impact
on
 a
cri*cal
quality
aYribute
and
therefore
should
be


monitored
or
controlled
to
ensure
the
process
produces
 the
desired
quality.



11
 Melt
extrusion
and
QbD
principles
I


Flow
Chart
Quality
by
Design


Adam
et
al.
Eur
J
 Pharm
 Biopharm
42
 (2011)
106‐115


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


PAT
Guidance


•  Process Analysers have been used in most industries for well over 50 years

•  Released September 29, 2004

•  Scientific principles and tools supporting innovation

–  PAT Tools –  Process Understanding –  Risk-Based Approach –  Integrated Approach

Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


What
is
PAT?


A system for:

– designing, analyzing, and controlling manufacturing – timely measurements (i.e., during processing) – critical quality and performance attributes – raw and in-process materials

– processes

“Analytical“ includes:

– integrated chemical, physical, microbiological, mathematical, and risk analysis

Focus of PAT is Understanding and Controlling the manufacturing Process

Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


The
Four
Key
Aspects
of
PAT


 Mul*variate
Data
Analysis,
represen*ng
a
move
away
from
 current
one
variable
at
a
*me
approaches


 Process
Analysers,
monitoring
the
quality
of
products
as
 they
exist
in
the
process.


 Process
Automa*on
and
Control,
allowing
real
*me
quality
 decisions.


 Knowledge
Management,
modern
quality
systems
 approaches


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


PAT
=
Process
Understanding


•  A process is well understood when:

– all critical sources of variability are identified and explained

– variability is managed by the process

– product quality attributes can be accurately and reliably predicted

•  Accurate and Reliable predictions reflect process understanding

•  Process Understanding inversely proportional to risk

Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


PAT
Guidance
for
industry


•  Design
and
op*miza*on
of
drug
formula*ons
and
 manufacturing
processes
within
the
PAT
framework
can
 include
the
following
steps
(the
sequence
of
steps
can
vary):



– Iden*fy
and
measure
cri*cal
material
and
process
aYributes
 rela*ng
to
product
quality



– Design
a
process
measurement
system
to
allow
real
*me
or
near
 real
*me
(e.g.,
on‐,
in‐,
or
at‐line)
monitoring
of
all
cri*cal
aYributes

 – Design
process
controls
that
provide
adjustments
to
ensure
control


of
all
cri*cal
aYributes



– Develop
mathema*cal
rela*onships
between
product
quality
 aYributes
and
measurements
of
cri*cal
material
and
process
 aYributes



17
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Process
‘signature’


• 

Stages of the product manufacturing process can be characterized and then described based on the use of a variety of diverse measurement

techniques.

• 

This multi-dimensional profile can then be used to produce a process ‘signature’ which, in turn, offers a means of ensuring process reproducibility and robustness.

•  

The process ‘signature’ may also be viewed as an end-point to work towards during scale-up or after equipment changes or site changes, for example.

18
 Melt
extrusion
and
QbD
principles
I


David
Rudd,
Glaxo
Smithkline


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Process
specifica*on


• 

Perhaps the concept of the process ‘signature’

equates to the establishment of a process specification - that is, a series of requirements which need to be met if the process is to be considered ‘under control’?

•  

Just as parametric release implies the removal of critical end-product testing, perhaps the natural corollary is to transfer the critical specification from the product to the process?

19
 Melt
extrusion
and
QbD
principles
I


David
Rudd,
Glaxo
Smithkline


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Manufacturing process

Control function On-line monitoring

of critical process parameters

Process control

Process feed ^Process _output

Closed loop control (process parameters only)

Temperature Time Pressure

etc.

Future
control
philosophy


David
Rudd,
Glaxo
Smithkline


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


• 

Development of novel analytical monitoring

techniques (or novel applications of existing techniques) appropriate for the type of measurements required

• 

Emphasis on indicators of ‘change’ rather than necessarily quantitative measurement

• 

New data processing methods required (data reduction and/or combinations of data from diverse sources)

Implica*ons
and
new
research
areas


David
Rudd,
Glaxo
Smithkline


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


22
 Melt
extrusion
and
QbD
principles
I


objects,
samples
(rows)


• Analy*cal
samples


• Process
*me
observa*ons


• Repe**ons



proper*es,
variables
(columns)


• Spectroscopic
data
(NMR,
 IR,
UV,
MS,
Raman,…)


• Taste
Sensor–mV‐response


• Temperature,
pH,…


X



Prop.

.


.


.
K
 Obj


.
 .
 .
 N


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


23
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


24
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


25
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


27
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


28
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


•  R

²

expresses
the
quality
of
the
model


•  R

²

for
each
principal
component
explain
the
 part
of
the
total
variability,
which
is
explained
 by
this
PC


•  Cross‐Valida*on
expressed
by
Q

²




^ 
quality
of
predic*on



 
number
of
components



 

Q²
>
0.5
O.K. 

Q²
>
0.9
very
good


29
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Principal
Component
Analysis
‐
PCA


•  Principal
component
analysis
reduces
the
 dimensionality
of
the
space
to
describe
data


•  Scores
are
related
to
the
samples.


•  Loadings
are
related
to
the
proper*es
and
can
be
 used
to
interpret
the
scores.


30
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Mul*variate
Analysis
MVA


•  Mul*ple
linear
Regression
(MLR)








Syn.
Ordinary
Least
Square
Regression
(OLS)


•  Principal
Component
Regression
(PCR)


•  Par*al
Least
Square
Regression
(PLS‐R)



Quan*ta*ve
descrip*on
between
the
independent
x‐Variables
 and
the
dependent
y‐Variables


31
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


•  
Par*al
Least
Squares
is
a
linear
regression
method
that
 forms
components
(factors,
or
latent
variables)
as
new
 independent
variables
(explanatory
variables,
or


predictors)
in
a
regression
model.
The
components
in
 par*al
least
squares
are
determined
by
both
the
 response
variable(s)
and
the
predictor
variables.



•  
A
regression
model
from
par*al
least
squares
can
be
 expected
to
have
a
smaller
number
of
components
 without
an
appreciably
smaller
R‐square
value.


32
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


33
 Melt
extrusion
and
QbD
principles
I


X
 N


K


Y
 N

M N
Objects
e.g.
42
tablets


K
measured
proper*es,
e.g.
wavenumbers
 1200‐1400cm^‐1

M
number
of
Y
yield
variable,
e.g.
API
 concentra*on


star*ng
point:
Data
matrix
X
(Dimension
NxK)


For
each
object
i
(i_1...N)
a
number
of
yield
variables
j
(j=1...M)
 are
measured
which
result
in
the
Matrix
Y
(Dimension
NxM).
If
 only
one
yield
variable
is
determined
this
gives
the
vector
Y.


Pharmaceutical Application of Raman Spectroscopy

RAMANRXN2™


BFC
5
Lab
Scale
Coater


Raman Advantages and Disadvantages

Advantages Disadvantages

Rich information content (fundamental vibrational modes)

Fluorescence even with 785nm lasers

Suitable for liquids, solids und gases Costs Requires minimal if any sample

preparation Weak Raman scattering

Excellent for aqueous systems (H₂O is not a strong Raman scaterer)

Remote sampling capability Non-invasive Non-destructive

Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


36
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


37
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


38
 Melt
extrusion
and
QbD
principles
I


Raw
spectra


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


39
 Melt
extrusion
and
QbD
principles
I


Pretreated
spectra


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


40
 Melt
extrusion
and
QbD
principles
I


Score
plot
PC1


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Par*al
Least
Square
Regression
(PLS‐R)


41
 Melt
extrusion
and
QbD
principles
I


Loading
plot
PC1


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Preprocessing
of
spectral
data


•  to
enhance
the
predic*ve
power
of
mul*variate
models


•  varia*on
in
X
which
is
unrelated
to
Y
may
degrade
the
 predic*ve
ability


•  removing
undesired
systema*c
varia*on
in
the
data


– Baseline
drit


– Mul*plica*ve
scaYer
effects


– Wavelength
regions
of
low
informa*on
content


42
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Preprocessing
of
spectral
data


•  Orthogonal
signal
correc*on
(OSC)


•  Mul*plica*ve
signal
correc*on
(MSC)


•  Standard
normal
variate
correc*on
(SNV)


•  Savitzky‐Golay
smoothing


•  First
order
deriva*on



•  Second
order
deriva*on



different
cases
of
filtering


•  A
filter
is
a
mathema*cal
func*on
through
which
a
 signal
is
processed
in
order
to
“improve”
its
proper*es.


43
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Preprocessing
of
spectral
data


•  Filters
are
based
on
all
spectra
in
a
data
set
or
on
 individual
spectra.


•  Standard
normal
variate
(SNV)


– Calculate
the
mean
(a)
and
the
standard
devia*on
(b)
from
 the
x_i
values
of
one
individual
spectrum


– Calculate
the
normalised
x_i,corr
values
for
the
spectrum


– Each
corrected
spectrum
has
the
same
offset
and
amplitude.


44
 Melt
extrusion
and
QbD
principles
I


€

x_i,corr = x_i−a b

Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Preprocessing
of
spectral
data:
SNV


45
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Preprocessing
of
spectral
data:
SNV


46
 Melt
extrusion
and
QbD
principles
I


CASE
STUDIES


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


PAT
in
melt
extrusion


•  More
than
just
applying
a
NIR‐probe
or
a
Raman‐probe


•  Do
not
„measure
what
you
can“,
but
measure
what
is
 cri*cal.


•  power‐consump*on‐controlled
extruder


•  Use
conven*onal
signals
as
well
in
addi*on
to
new
 sensors.



48
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


49
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


•  API
quan*fica*on,
different
metoprolol
tartrate
(MPT)
–
 Eudragit
RL
PO
mixtures,
containing
10%,
20%,
30%
and
 40%
MPT


•  Two
different
polymer–drug
mixtures
were
prepared
to
 evaluate
the
suitability
of
Raman
spectroscopy
for
in‐

line
polymer–drug
solid
state
characteriza*on.
Mixture
 1
contained
90%
Eudragit
RS
PO
and
10%
MPT
and
was
 extruded
at
140
C,
hence
producing
a
solid
solu*on.


Mixture
2
contained
60%
Eudragit
RS
PO
and
40%
MPT
 and
was
extruded
at
105
C,
producing
a
solid
dispersion.


50
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


•  Mean
centering,
Savitzky‐Golay
and
SNV
pre‐processing
 were
applied
on
the
in‐line
collected
spectra
before
 principal
components
analysis
(PCA)
and
par*al
least
 squares
analysis
(PLS),
to
exclude
inter‐batch
varia*on
and
 varia*on
caused
by
baseline‐shits,
respec*vely.



•  For
PCA
and
PLS,
20
spectra
of
each
polymer–drug
mixture
 were
used
to
develop
the
models.


•  A
PLS
model
was
developed,
regressing
the
MPT


concentra*ons
(Y)
versus
the
corresponding
in‐line
collected
 Raman
spectra
(X).



•  This
model
was
validated
with
20
other
spectra
from
each
 polymer–drug
mixture,
which
were
not
used
to
develop
the
 PLS
model.


51
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


52
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


53
 Melt
extrusion
and
QbD
principles
I


R²(PC1)=0.97;
R²(PC2)=0.01


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Raman
in
melt
extrusion


54
 Melt
extrusion
and
QbD
principles
I


Q²=0.997


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


55
 Melt
extrusion
and
QbD
principles
I


T_m
=
120
C


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


56
 Melt
extrusion
and
QbD
principles
I


A
=
10%;
B
=
40%
MPT


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


57
 Melt
extrusion
and
QbD
principles
I


A
=
10%;
B
=
40%
MPT


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Conclusion
Raman
study


•  Raman
spectroscopy
was
evaluated
as
a
PAT‐tool
to
monitor
 the
API
concentra*on
and
polymer–drug
melt
solid
state
 during
pharmaceu*cal
hot‐melt
extrusion
processes.



•  Comparison
between
the
in‐line
collected
Raman
spectra
 and
the
offline
obtained
DSC
thermograms
demonstrated
 that
informa*on
about
the
solid
state
of
a
polymer–drug
 melt
can
be
obtained
from
the
Raman
spectra,
allowing
 monitoring
and
predic*on
of
the
polymer–drug
solid
state
 throughout
the
extrusion
process.



•  With
Raman
spectroscopy,
it
was
possible
to
detect
 differences
between
amorphous
and
crystalline
polymer–

drug
melts.
The
in‐line
collected
Raman
spectra
also
gave
an
 indica*on
of
the
occurring
interac*ons
during
the
hot‐melt
 extrusion
process,
which
leads
to
a
beYer
understanding
of
 the
process.


58
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Conclusion
Raman
study


•  A
PLS
model
was
developed
and
validated,
allowing
 drug
concentra*on

monitoring
of
unknown
samples
 during
hot‐melt
extrusion.
Raman
spectroscopy
was
 able
to
detect
varia*ons
in
drug
concentra*on
and
to
 predict
drug
concentra*ons
with
an
RMSEP
of
0.59%.


59
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


60
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


•  Kollidon
SR
was
extruded
with
varying
metoprolol
 tartrate
(MPT)
concentra*ons
(20%,
30%
and
40%)
and
 monitored
using
NIR
spectroscopy.



•  A
PLS
model
allowed
drug
concentra*on
determina*on.


The
correla*on
between
predicted
and
real
MPT
 concentra*ons
was
good
(R2
=
0.97).
The
predic*ve
 performance
of
the
model
was
evaluated
by
the
root
 mean
square
error
of
predic*on,
which
was
1.54%.



•  Kollidon
SR
with
40%
MPT
was
extruded
at
105
C
and
 135
C
to
evaluate
NIR
spectroscopy
for
in‐line
polymer–

drug
solid‐state
characterisa*on.


61
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


62
 Melt
extrusion
and
QbD
principles
I


8:2


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


63
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


•  Mul*plica*ve
signal
correc*on
(MSC)
was
used
before
 chemometric
analysis
of
the
spectra.
Using
MSC,
undesired
 scaYer
is
removed
from
the
raw
spectra
to
prevent
it
from
 domina*ng
over
the
chemical
informa*on
within
the
 spectra.
The
result
of
MSC
pre‐processing
is
that
each
 corrected
spectrum
has
the
same
offset
and
amplitude,
 elimina*ng
the
difference
in
light
scaYer
in
the
spectra
from
 the
different
samples,
before
developing
the
calibra*on
 model.



•  Furthermore,
second
deriva*ve
pre‐processing
was
done
 ater
MSC
correc*on.
Second
deriva*ves
of
NIR
spectra
 magnify
differences
in
spectral
features
provide
baseline
 normalisa*on
and
remove
data
offsets
due
to
scaYering
 effects
and
pathlength
varia*on.



64
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


•  For
principal
component
analysis
(PCA)
and
for
the
 development
of
the
par*al
least
squares
(PLS)
model,
 20
spectra
of
each
polymer–drug
mixture
(20%,
30%


and
40%
MPT)
were
used.



•  Prior
to
PCA
and
PLS,
spectra
were
mean
centred.
The
 PLS
model
was
developed
by
regressing
the
MPT
 concentra*ons
(Y)
versus
the
corresponding
in‐line
 collected
NIR
spectra
(X).



•  This
model
was
validated
using
five
new
NIR
spectra
 collected
during
new
extrusion
runs
of
each
polymer–


drug
mixture.
These
valida*on
spectra
were
used
to
 evaluate
the
predic*ve
performance
of
the
PLS
model.


65
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


66
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


NIR
in
melt
extrusion


67
 Melt
extrusion
and
QbD
principles
I


R²(PC1)=99%;
R²(PC2)=0.4$


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


68
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


69
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


70
 Melt
extrusion
and
QbD
principles
I


MPT





phys.
Mix


Kollidon
SR






extrudate


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Solid
state
analysis


71
 Melt
extrusion
and
QbD
principles
I


Inline
Raman


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Conclusion
I


•  With
NIR
spectroscopy,
it
was
possible
to
detect
 varia*ons
in
drug
concentra*ons.


•  A
PLS
model
was
developed
and
validated,
allowing
 con*nuous
drug
concentra*on
monitoring.
It
was
 possible
to
predict
drug
concentra*ons
with
an
RMSEP
 of
1.54%.


72
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Conclusion
II


•  With
respect
to
the
polymer–drug
behaviour
during
 extrusion,
in‐line
NIR
spectroscopy
was
able
to
detect
 changes
in
solid
state
of
the
extrudates,
as
well
as
in
 amount
and
strength
of
the
intermolecular
interac*ons
 during
processing.



•  Furthermore,
the
use
of
NIR
spectroscopy
allowed
the
 determina*on
of
the
type
of
interac*ons
occurring
 during
hot‐melt
extrusion.
These
interac*ons
are


manifested
as
hydrogen
bonds
between
Kollidon
SR
and
 MPT
molecules.


73
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Conclusion
III


•  In‐line
Raman
spectroscopy
confirmed
these
NIR
 observa*ons.


•  The
collected
spectra
displayed
similar
peak
shits
and
peak
 broadening,
demonstra*ng
the
equivalent
changes
in
solid
 state
and
interac*ons
during
melt
extrusion.



•  Comparison
of
the
in‐line
collected
NIR
spectra
and
the
off‐

line
DSC
analysis
and
off‐line
collected
ATR
FT‐IR
spectra
 showed
that
NIR
is
a
more
powerful
process
analyzer,
able
 to
differen*ate
between
extrudates
being
processed
under
 varying
condi*ons,
whereas
DSC
analysis
and
ATR
FT‐IR
 indicated
no
differences
in
occurring
interac*ons
between
 extrudates
produced
at
different
temperatures.


74
 Melt
extrusion
and
QbD
principles
I


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Düsseldorf


NASA,
2004


Contact


Prof.
Dr.
Peter
Kleinebudde


Ins*tute
of
Pharmaceu*cs
and
Biopharmaceu*cs
 Universitaetsstrasse
1


40225
Duesseldorf

 Germany


tel.:
+49‐211‐8114220
 fax:
+49‐211‐8114251
 e‐mail:
kleinebudde@hhu.de


Acknowledgements


Dr.
Iris
Ziegler
 Dr.
Markus
Thommes
 Markus
Wirges


Thank
you
for
your
kind
aYen*on!


Pharmaceu*cal
Solid
State



PSSRC
Research
Cluster


Zweizeilige

 Überschrit


76