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GHENT UNIVERSITY
FACULTY OF PHARMACEUTICAL SCIENCES
Department of Pharmaceutical Analysis
Laboratory of Pharmaceutical Process Analytical Technology
Academic year 2011-2012
Jeroen Van Renterghem
First Master in Drug development
Promoter
Prof. Dr. T. De Beer
Commissioners
Prof. C. Vervaet
Dr. K. Van Uytfanghe
ASSESSMENT OF TABLET PROPERTIES USING TRANSMISSION -AND
BACKSCATTERING RAMAN SPECTROSCOPY AND TRANSMISSION NIR
SPECTROSCOPY
GHENT UNIVERSITY
FACULTY OF PHARMACEUTICAL SCIENCES
Department of Pharmaceutical Analysis
Laboratory of Pharmaceutical Process Analytical Technology
Academic year 2011-2012
Jeroen Van Renterghem
First Master in Drug development
Promoter
Prof. Dr. T. De Beer
Commissioners
Prof. C. Vervaet
Dr. K. Van Uytfanghe
ASSESSMENT OF TABLET PROPERTIES USING TRANSMISSION -AND
BACKSCATTERING RAMAN SPECTROSCOPY AND TRANSMISSION NIR
SPECTROSCOPY
“De auteur en de promotor geven de toelating deze masterproef voor consultatie beschikbaar
te stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de
beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting
uitdrukkelijk de bron te vermelden bij het aanhalen van de resultaten uit deze masterproef.”
Gent, 30 mei 2012
Prof. Dr. T. De Beer Jeroen Van Renterghem
"The author and the promoters give the authorization to consult and to copy parts of this
thesis for personal use only. Any other use is limited by the laws of copyright, especially
concerning the obligation to refer to the source whenever results from this thesis are cited."
Ghent, May 30, 2012
Prof. Dr. T. De Beer Jeroen Van Renterghem
SUMMARY
The PAT-initiative (Process Analytical Technology) from the FDA (U.S. Food and
Drug Administration) suggest the implementation of fast, non-destructive techniques in
pharmaceutical continuous manufacturing processes for process control. The laboratory of
pharmaceutical process analytical technology has been doing innovative development
according to this initiative. The ConsigmaTM
-25 is a continuous granulation system that can
be coupled to a rotary press, making it a continuous line from powder to tablet. At first,
granules were made according to a full factorial design with the granulator module of the
ConsigmaTM-
25 and tableted. These tablets were then analyzed with transmission –and
backscattering Raman spectroscopy and transmission NIR (Near Infrared) spectroscopy. This
thesis study had two main objectives. The first goal of this study was to correlate the Raman
and NIR spectra from tablets with their tablet properties (API level, disintegration time,
friability, porosity and tensile strength). The second goal of this study was to understand the
influences of the granulator process parameters (API concentration, screw configuration,
barrel temperature and liquid feed rate) on the tablet properties (disintegration time, friability,
porosity and tensile strength). Partial least squared regression (PLS) was used to make
correlation models between the tablet properties and the spectra of the tablets. Only a good
model for the API level could be made. Influences of the granulation process parameters on
the tablet properties were found. Most of the physical property information was erased due to
the robust tableting process. No good PLS models for disintegration time, friability, porosity
and tensile strength could therefore be developed. The three spectroscopic techniques were
able to display the different solid states of theophylline (theophylline monohydrate and
theophylline anhydrate) and the API concentration (using PCA). It was also found that the
difference between backscattering and transmission Raman spectroscopy in the quantification
of the API content was insignificant. This might be due to the uniformity of the premixes,
resolving the sub-sampling problem of backscattering Raman. So far, these tools can only be
used for quantification purposes.
The next step towards innovation should be the implementation of PAT-tools in an in-
line, at-line or on-line set-up at the end of the tableting process. The ConsigmaTM
-25 could
also be implemented with these tools for moisture level and hydrate level determination after
the dryer unit. New tests with a fixed concentration or another API could give other results to
achieve correlation models for physical tablet properties. Better laser techniques could also
give faster results for timely measurements.
SAMENVATTING
Het PAT (Proces Analytische Technologie) initiatief van de FDA suggereert de
implementatie van snelle, niet-destructieve technieken in continue farmaceutische productie
processen voor proces controle. Op basis van dit initiatief werkt het laboratorium voor proces
analytische technologie aan innovatieve ontwikkelingen. De ConsigmaTM
-25 is een continu
granulatie systeem dat aan een rotatieve tabletpers gekoppeld kan worden zodat men één
continue lijn van poeder tot tablet bekomt. Eerst werden granules gemaakt volgens een full
factorial design met de granulator module van de ConsigmaTM
-25 en getabletteerd.
Vervolgens werden deze tabletten geanalyseerd met transmissie –en backscattering Raman
spectroscopie en NIR transmissie spectroscopie. Dit onderzoek had twee hoofddoelen. Het
eerste hoofddoel van deze studie was om de Raman –en NIR spectra van deze tabletten te
correleren met hun tablet eigenschappen (API gehalte, desintegratietijd, friabiliteit, porositeit
en treksterkte). Het tweede hoofddoel van deze studie was het begrijpen van de invloeden van
de granulator proces parameters (API level, schroefconfiguratie, barrel temperatuur en
watertoevoersnelheid) op de tableteigenschappen (desintegratietijd, friabiliteit, porositeit en
treksterkte). Partial least squared regression (PLS) werd gebruikt om correlaties te vinden
tussen de tablet eigenschappen en de spectra. Enkel goede regressie modellen voor de API
concentratie konden gemaakt worden. Er werden invloeden gevonden van de parameters van
het granulatieproces op de fysische tablet eigenschappen. De meeste fysische informatie werd
echter gewist door de robuustheid van het tabletteer proces. Voor de desintegratietijd,
friabiliteit, porositeit en treksterkte kon daardoor geen enkel goed PLS regressie model
gemaakt worden. De drie spectroscopische technieken konden wel de verschillende vaste fase
eigenschappen van theophylline en de API concentratie aantonen (door Principal Component
Analysis of PCA). Er werd ook vastgesteld dat het verschil tussen backscattering en
transmissie Raman spectroscopie in de kwantificatie van de API niet significant was. Dit kan
te wijten zijn aan de uniformiteit van de premixen, hierdoor wordt het sub-sampling probleem
van backscattering Raman opgelost. Tot hiertoe kunnen deze technieken enkel gebruikt
worden om te kwantificeren. De volgende stap in de richting van innovatie zou de
implementatie van deze PAT-tools in een in-line, at-line of on-line test opstelling na het
tabletteer proces kunnen zijn. De ConsigmaTM
-25 zou ook kunnen worden uitgerust met deze
tools voor de bepaling van de hydraatvorm of het vochtgehalte na de droogstap. Nieuwe tests
met een vaste concentratie of een andere API zouden andere correlatie modellen kunnen
opleveren. Betere laser technieken zouden ook sneller resultaat kunnen geven.
THANK YOU
I would like to use this page to thank everyone who helped on this thesis study.
I found it very interesting what these people are doing on a daily basis. From the beginning in
February until the end of May was a journey through innovation. There were some problems
along the way, but these people gave me answers. It was also a lesson in English writing,
which Prof. Dr. T. De Beer strongly advised me to do so.
Thank you,
Prof. Dr. T. De Beer for helping me to write this thesis by providing me with important
lecture and knowledge and a critical point of view.
Chemometrics was totally new to me, but very interesting towards a future career. Thank you
for this experience in the lab of process analytical technology.
Maunu Toiviainen, my tutor throughout this journey. He reviewing this thesis and
conducted all of the spectral measurements and gave me insight on PAT-tools.
Elisabeth Peeters, who learned me about tableting and reference analyses. She gave me
insight from a pharmaceutical and industrial point of view.
Jurgen Vercruysse for making granules and giving me insight on the ConsigmaTM
-25
granulation process.
These people are making the future of pharmaceutical science. Innovation should not be
abandoned by the government, so lets thank the
University of Ghent for this unique opportunity.
TABLE OF CONTENTS
LITERATURE ...................................................................................................... 1
1 INTRODUCTION & OBJECTIVES ......................................................................................... 1 1.1 CONTINUOUS MANUFACTRING .............................................................................. 1 1.2 RESEARCH OBJECTIVES ............................................................................................ 2 1.3 EARLIER ADVANCES ON THIS RESEARCH TOPIC............................................... 3
2 MATERIALS & METHODS ..................................................................................................... 4 2.1 GRANULATION ............................................................................................................ 4
2.1.1 Definition ................................................................................................................. 4 2.1.2 Granulation techniques ............................................................................................ 4 2.1.3 Apparatus ................................................................................................................. 6
2.2 TABLETING ................................................................................................................... 7 2.2.1 Introduction ............................................................................................................. 7 2.2.2 Apparatus ................................................................................................................. 7 2.2.3 Tablet properties ...................................................................................................... 9
2.2.3.1 Porosity .............................................................................................................. 9 2.2.3.2 Tensile strength ................................................................................................ 10 2.2.3.3 Disintegration time ........................................................................................... 11 2.2.3.4 Friability ........................................................................................................... 11 2.2.3.5 Active Pharmaceutical Ingredient concentration.............................................. 11
2.3 DESIGN OF EXPERIMENTS ...................................................................................... 13 2.4 PROCESS ANALYTICAL TECHNOLOGY ............................................................... 14
2.4.1 Introduction ........................................................................................................... 14 2.4.2 Raman spectroscopy .............................................................................................. 14
2.4.2.1 Backscattering Raman spectroscopy ................................................................ 17 2.4.2.2 Transmission Raman spectroscopy .................................................................. 17
2.4.3 Near-Infrared spectroscopy ................................................................................... 18 2.4.4 Interpretation of spectral baseline offset in NIR and Raman spectra .................... 20 2.4.5 Multivariate Data analysis ..................................................................................... 21
2.4.5.1 PCA .................................................................................................................. 21 2.4.5.2 PLS ................................................................................................................... 22 2.4.5.3 MLR ................................................................................................................. 22
EXPERIMENTAL .............................................................................................. 23
1 MATERIALS & METHODS ................................................................................................... 23 1.1 GRANULATION .......................................................................................................... 23
1.1.1 Design of experiments ........................................................................................... 23 1.2 TABLETING ................................................................................................................. 25 1.3 PROCESS ANALYTICAL TECHNOLOGIES ............................................................ 26
1.3.1 Raman spectroscopy .............................................................................................. 26 1.3.2 NIR spectroscopy .................................................................................................. 27
1.4 REFRENCE ANALYSIS .............................................................................................. 28
2 RESULTS AND DISCUSSION ............................................................................................... 29 2.1 DOE ANALYSYS OF GRANULE ATTRIBUTES ..................................................... 29
2.2 INFLUENCE OF GRANULATION PROCESS PARAMETERS ON TABLET
PROPERTIES, DOE ANALYSIS ................................................................................. 36 2.3 RAMAN SPECTROSCOPY ......................................................................................... 40
2.3.1 PCA of API concentration and hydrate level ........................................................ 40 2.4 NIR TRANSMISSION SPECTROSCOPY .................................................................. 43
2.4.1 PCA of API concentration and hydrate level ........................................................ 43 2.5 PREDICTING TABLET PROPERTIES FROM THE RAMAN AND NIR SPECTRA
USING PLS ................................................................................................................... 45
3 CONCLUSIONS ...................................................................................................................... 48
REFERENCES .................................................................................................... 50
LIST OF ABBREVIATIONS
API Active Pharmaceutical Ingredient
Bar Granulation barrel temperature
B-Raman Backscattering Raman spectroscopy
CCD Charged Coupled Device
CCP Critical Control Point
DoE Design of Experiments
ECM Exchangeable Compression Module
EMR Electromagnetic radiation
FDA Food and Drug Administration
HPLC High Performance liquid chromatography
Moi Moisture
N Newton
NIR Near-Infrared
PAT Process Analytical Technology
PC Principal Component
PCA Principal Component Analysis
PLS Partial Least Squares Regression
PVP Polyvinlypyrrolidone
Q² Goodness of Prediction
R² Coefficient of determination
RMSEE Root Mean Squared Error of Estimation
SNV Standard Normal Variate
Scr Screw configuration
T Temperature
TA Theophylline Anhydrate
TM Theophylline Monohydrate
T-NIR Transmission Near-Infrared spectroscopy
T-Raman Transmission Raman spectroscopy
TS Tensile strength
1
LITERATURE
1 INTRODUCTION & OBJECTIVES
1.1 CONTINUOUS MANUFACTRING
Batch processing has been the main pharmaceutical manufacturing method for the last
decades, but it has many drawbacks. It is time consuming, labour intensive and batches not
meeting the standards have to be destroyed. This is mainly because the batch processes are
not completely understood and controlled. After a batch has been produced the product is
analyzed off-line. From large amounts of product, only a small amount is analyzed. To control
the quality of the finished product there are many time consuming off-line tests used such as
liquid-phase UV-Vis spectroscopy, HPLC methods, etc...
Continuous manufacturing has been applied for a long time in many industries such as
the cosmetic industry, the chocolate industry and the car industry. The pharmaceutical
industry is catching up, but since it is very regulated, it is hard to do so. ‘Quality should not be
tested into the products; it should be built in or should be by design’ [1-2]
. The pharmaceutical
industry wants to provide the patient with a good quality product, produced with a continuous
line and controlled at the time of manufacturing.
GEA Pharma systems produced as one of the first companies a fully continuous
pharmaceutical manufacturing line: the ConsigmaTM
-25 consists of a wet high shear
granulator, dryer and a conditioning module, all-in-one continuous line. Built-in PAT-tools
could check critical aspects of the produced granules (e.g. the moisture level, particle size,
concentration -and hydrate level of the API). A big advantage is scaling up. Scaling up of a
batch process and all its regulations are no longer needed. If more product is needed, the
continuous manufacturing process just has to be run longer.
2
1.2 RESEARCH OBJECTIVES
Continuous granulation is a fast technique with many variables to control.
Understanding the continuous granulation process is still in its infancy. Variables such as
barrel temperature, screw configuration, liquid feed rate, powder feed rate have an influence
on granule attributes and tablet properties. In the guidance for industry [2]
, the FDA
recommends the use of PAT-tools for process monitoring and control. Raman -and NIR
spectroscopy are commonly used PAT-tools. They are fast and non-destructive and easy to
use and built-in into the process environment using probes. No sample preparations are
needed. These tools could provide important information to the manufacturer on each critical
process -and formulation parameter throughout the manufacturing process. The information
from these tools is available within seconds, hence allowing process adaptations (corrective
actions) when necessary. The influences of adjusting a process parameter must therefore be
fully understood. This research tries to understand the influence of the granulation process
parameters (API concentration, barrel temperature, screw configuration and liquid feed rate)
on the tablet properties (disintegration time, friability, porosity and tensile strength).
Furthermore, this thesis study is an application of PAT-tools in an off-line measuring
set-up for the prediction of tablet properties using Raman -and NIR transmission spectroscopy
and Raman backscattering spectroscopy. The tablets were manufactured from granules which
were made with the granulator unit of the ConsigmaTM
-25. It is the goal of this study to
answer some key questions towards future research, in the scope of the implementation of
PAT-tools in a continuous process environment. The objective questions are:
- Can Raman/NIR transmission and Raman back-scattering spectra be correlated
with the tablet properties such as tensile strength, API content, disintegration time,
porosity and friability?
- How do the granulation process parameters affect the tablet properties?
- Can Raman/NIR transmission and Raman back-scattering be used to see the API
hydrate level in tablets?
- How much more accurate is transmission Raman when compared to backscattering
Raman in the quantification of API content?
- Which optical measurement technique gives best results in the quantification of the
tablet properties?
3
1.3 EARLIER ADVANCES ON THIS RESEARCH TOPIC
This section gives an overview of the advances that have been done in previous studies
to answer the objective questions.
Different granules have been made in a previous study by varying four different
process parameters (API concentration, barrel temperature, screw configuration and liquid
feed rate) of the continuous granulator that is part of the ConsigmaTM
-25. The effects of the
granulator process parameters on the granule attributes (size distribution, friability, hausner
ratio and carr index) have already been studied. Next, granules were made on basis of these
four different process parameters and tableted. The main difference between the previous
study and this thesis study is the focus of the experiment. The focus in this thesis study is on
tablet properties instead of granule attributes. Since it is a goal to understand the effect of the
granulation process parameters on the tablet properties, the results of the influence of the
granulation process parameters on the granule attributes (amounts of fines and oversized
granules and friability) are crucial and can therefore be found in the result section
(Experimental, section 2.2). Furthermore, Raman and NIR spectra were measured from the
dried granules to see if there was any solid-state change of theophylline anhydrate after the
granulation and drying procedure.
Fonteyne et al. found that Raman spectroscopy is an adequate tool for determining the
solid state change in granules from theophylline anhydrate (TA) to theophylline monohydrate
(TM) during the granulation procedure. These granules were also made with the ConsigmaTM
-
25. They found out that the experiments with higher barrel temperature and higher liquid feed
rate resulted in more change from TA to TM [3]
. This thesis study attempts to show the
hydrate level of theophylline in tablets with Raman –and NIR transmission spectroscopy and
backscattering Raman spectroscopy.
4
2 MATERIALS & METHODS
This section will provide an overview and some basic knowledge of all methods and
equipment that were used in this study: granulation, tableting, DoE and process analytical
technology (PAT). Tabletting and the applied PAT-tools to analyze the tablets are discussed
in more detail as they are of highest importance to this study.
2.1 GRANULATION
2.1.1 Definition
Granulation is a technique used to enlarge the particle size of powders. These
permanent aggregates have sizes between 0.1 and 2 mm. Granulation is often a step prior to
tableting with the aim of giving better friability, better flowability, less fluffiness and a lower
bulk density to the starting material powders. To make these granules, there are several
techniques that can be used, as overviewed in 2.1.2. [4-5]
.
2.1.2 Granulation techniques
In granulation, two different techniques can be distinguished: dry granulation and wet
granulation. The applied technique depends on the powder properties and the properties of the
used API and excipients. Direct compression means that granulation is not applied. It is used
when powders can be compressed directly into a tablet without using any granulation
technique in advance. These formulations are very interesting for the industry as the costs for
granulation are no longer needed.
Dry granulation uses pressure to make the individual particles stick together. If the
powders are sensitive to liquids or heat, this is the easiest way to make granules. The first step
is to compress the powder, either on a rotary tablet into bigger blocks (slugging) or squeezed
between two rolls into briquettes or lints with a chilsonator. The next step is to mill the
pressed powder blocks or briquettes to have a more uniform size distribution of the produced
granules.
5
Wet granulation is the most common applied technology. This technology uses a
granulation fluid and a binder. Many different binders can be used: microcrystalline cellulose,
sucrose and polyvinylpyrrolidone are only some of them. The binder helps the individual
particles to make bonds and stick together. The different bonds are formed in two steps: (1)
nucleation of particles and (2) coalescence of agglomerates. The aggregation of particles (1) is
due to the formation of mobile liquid bridges. (2) The formation of the bond strength, due to
the collision of particles must be bigger than the separation forces. After the wet granulation
process, the granules must be dried to become granules with a desired moisture level.
Currently, a lot of equipment to perform wet granulation is commercially available [6]
.
Fluid bed granulators use compressed air to blow the particles in the air. These particles are
then sprayed on with a solution mostly containing the binder. The last step is to dry the
granules within the fluid bed. The drying process strengthens the solid bonds after
coalescence. The big advantage is that the fluid bed granulator therefore does not need any
other drying equipment. A single pot processor is a mixer/granulator used for batch
production which applies high shear force to make granules. This system dries the granules
within a heated bowl while under a vacuum. The mixer/granulator can also be coupled to a
fluid bed dryer. A continuous twin screw granulator (Fig. 2.1-1) uses kneading elements to
press the transported powders together with shear forces while a liquid is added, this way
inducing the agglomeration. It is a fast technique with many parameters to be controlled:
barrel temperature, rotary screw speed, liquid feed rate, amount of kneading elements, powder
feed rate etc... A fluid bed dryer is used to dry the granules after the granulation procedure.
Figure 2.1-1: Continuous twin screw granulator: (A) transport zones. (B) kneading zones existing of kneading
elements.
6
2.1.3 Apparatus
The system used in this research is the ConsigmaTM
-25 (Collette, GEA Pharma
Systems, Wommelgem, Belgium, Fig. 2.1-2). It is a continuous granulation production line
that consists of three units. Starting from a wet high shear twin screw granulator (1) going on
to the second unit which is a fluid bed dryer (4) and ending with a conditioning unit (5). The
production process starts with the feeder (2). The feeder must be filled by hand with a
premixed powder. A rotating screw prevents the powder from forming bridges in the feeder.
The powder is then transported on screws into the high shear granulator unit. The powder feed
rate and the liquid feed rate to this unit can be monitored and changed on the coupled
computer system. They are controlled by the change in mass of both the feeder (2) and the
recipient that contains the
granulation liquid (3).
The liquid is provided by
a pump with two tubes
and nozzles which can be
set in sync or out of sync.
The temperature around
the barrel can also be
adjusted. After
granulation, the wet
granules are transported
due to a vacuum, into the
dryer. Six segments in the
fluid bed dryer are filled one by one to give a maximal production efficiency. The process
parameters of the dryer unit can also be selected on the computer system. The humidity,
airflow and temperature can be controlled. After the granules have dried, they are transported
to the conditioning unit. In the conditioning unit an extra milling step can be performed to
produce granules with the right particle size. PAT-tools can be inserted to check if the
granules meet the wanted standards when they leave the dryer. This system can handle very
low (commonly R&D) quantities up till many tons of powder for real industrial production. If
this system is attached with a rotary press such as the ModulTM
P, it forms a continuous line,
from powder to tablet. In this research, only the high shear wet granulator was used. The other
modules were not attached.
FIGURE 2.1-2: ConsigmaTM
-25. (1) granulation unit, (2) feeder, (3)
granulation liquid, (4) fluid bed dryer, (5)
conditioning unit
7
2.2 TABLETING
2.2.1 Introduction
The tableting procedure consists of three main steps: filling the die with powder,
compressing it into a tablet and ejecting the tablet. There are many factors to be reckoned
with. One of the most important factors are the granule attributes. Understanding all of the
parameters and its influences are the key to a good tablet.
2.2.2 Apparatus
The system used in this study is the ModulTM
P (GEA
Pharma Systems, Wommelgem, Belgium)(Fig. 2.2-1). It is a
fully automated rotary tablet press mainly used for formulation
testing on a pilot scale. The ModulTM
S and modulTM
D are
larger rotary presses used in industrial settings. The main
innovation of the ModulTM
is the ECM concept. The
“Exchangeable Compression Module” can be removed within
minutes to clean the system. The pressure module is
completely separated from the mechanical and electric parts.
Because the powder only comes in contact with the ECM, it is
very easy to clean between operations. The ECM can simply be
taken out and cleaned in a separate room. While one ECM is
cleaned, another can be installed, making this system very efficient for the industrial
production. Another innovation is the DAAS-system (Data Acquisition and Analysis System).
It registers the different movements of the punches and the compression forces with highly
precision. The registered data can be accessed and analyzed while the press is running.
Figure 2.2-1: ModulTM P
8
The tableting process of a rotary press can be explained as follows (Fig. 2.2-2): The
powder (1) flows in the dies due to gravity as the rotating paddles in the feeder move the
powder over the dies. In the first station of the feeder, the lower punch is set at its lowest
point (2). This results in an overfill of the die (more powder in the die than is necessary to
make the tablet). In the next station (3), the lower punch is moved up until the die contains
the exact amount of powder to make the right tablet. The scraper (4) takes away the excess
powder, leading it to the middle of the rotating table into the recuperation groove. Next, the
lower punch is lowered down (5) (under filling) a little to prevent dust and loss of powder
when the upper punch enters the die to compress the powder in the pre-compression
compartment (6). After the first compression follows the main compression (7) in the main
compression compartment. The last compartment is the ejection compartment (8) where the
lower punch is moved up, pushing the tablet out of the die. The finished tablet is lead out of
the tableting table by an ejection finger (9).
Figure 2.2-2: Scheme of a rotary press.
9
2.2.3 Tablet properties
Tablet properties are very important because they have an influence on both
bioactivity and bioavailability. The goal of this thesis is to predict these tablet properties with
models. In order to set up a model, all properties should first be tested with the reference
analysis methods. The studied properties are: porosity, tensile strength, disintegration time,
friability and API concentration.
2.2.3.1 Porosity
Porosity is strongly correlated to the disintegration time of a tablet, mainly because the
water can access a larger surface of the tablet. Porosity is defined as the empty space between
materials, frequently expressed as a percentage of the total volume. It is a fraction between 0-
1. To calculate the porosity:
x 100
Where: ε: porosity (%)
ρapp: apparent density (g/cm³)
ρ: true density (g/cm³)
The true density (ρ) was measured from the granules with a helium pycnometer, prior
to tableting. The apparent density (ρapp) is the ratio of the mass (M) and the volume of the
tablet.
ρapp =
Where: ρapp: apparent density (g/cm³)
M: tablet mass (g)
V: tablet volume (cm³)
10
The mass (M) of the tablet was determined by weighing the tablet. To measure the
volume (V) of the tablet, the tablet can be seen as a combination of a cylinder plus two times
the volume of a sliced cone. To measure the dimensions of the tablet, a projection microscope
was used (Reickert, 96/0226, Vienna, Austrich).
Where: V: tablet volume (cm³)
D1: diameter cylinder (cm)
D2: diameter bottom of the sliced cone (cm)
D3: diameter top of the sliced cone (cm)
H1: height cylinder (cm)
H2: height sliced cone (cm)
2.2.3.2 Tensile strength
To calculate the tensile strength of the tablets, the mass, the hardness and the thickness
of the tablets must be measured. This is done by a hardness tester (Pharma Test, Hainburg,
Germany). This semi-automated machine first measures the thickness. Next, it gives a
controlled pressure onto the tablet. The system measures the force that is needed to break the
tablet diametrically (hardness, N). The results of the hardness test can be converted with the
equation for tensile strength, this makes it easier to compare the hardness between tablets:
Tensile strength (MPa) =
Where: F = force to break the tablet, hardness (N)
D = diameter of the tablet (mm)
T = thickness of the tablet (mm)
Figure 2.2-3: The dimensions of a tablet
11
2.2.3.3 Disintegration time
The disintegration of a tablet is defined as the time that is needed for the tablet to fall
into its primary particles. The primary particles are in this case the granules. The test is done
with a disintegration apparatus (PTZ E, PharmaTest). It consists of a cylinder with six glass
tubes. At the bottom of these tubes, there is a netting. The cylinder is moved up and down
with a certain frequency in a beaker, containing 900 ml of water at a temperature of 37 ± 2°C.
The right temperature is provided by circulating warm water in a bath that surrounds the
beakers. The temperature in the beakers is controlled before each test with a thermometer.
2.2.3.4 Friability
When tablets are transported, manually handled or during a coating process, the edges of
the tablets can wear off and the tablet loses weight. The friability is a measure of the
mechanical strength at the surface of the tablets. The test must be done with a friabilator
(Pharma Test, Hainburg, Germany). This machine consists of a rotating drum. The Ph. Eur.
says that the sample for tablets under 650 mg must be around 6.5 gram. For this research, 22
tablets from each batch were picked at random. The tablets were put between two sieves to
de-dust them with a vacuum cleaner. After this, the tablets were weighted and put in the drum
of the friabilator. This machine turns 100 times at a speed of 25 rpm, allowing the tablets to
fall, hereby hitting the walls and each other with every turn. After the test, the tablets were
picked out, de-dusted and weighed again.
2.2.3.5 Active Pharmaceutical Ingredient concentration
The API concentration in a tablet is important for the right dosage. The most common
technique to measure concentrations is UV-VIS spectroscopy (Shimadzu UV-1650 UV-VIS
spectrophotometer, France). This spectroscopic technique is based on absorption of visible
(VIS) or ultraviolet (UV) light to measure concentrations in a solution. The positive
relationship between the absorbance and the concentration is demonstrated by the Lamber-
Beer Law.
12
Where: E: extinction (absorbance)
ε: molair extinction coefficient
L: path length (cm)
c: concentration (mol/L)
The relationship between absorbance and transmission is given by the following
equation:
Where: A: absorbance
T: transmittance
I0: the intensity of the primary light
I: the intensity of light coming out of the sample
The transmittance has a value between 0 and 1. It is the fraction of the primary light
that goes through the sample. In order to calculate the concentration of the API, a calibration
curve must be made by preparing samples with various levels of API dissolved in them. The
absorbance of the excipients must be checked as well because the absorbance is the sum of
the individual absorbances from each substance in the sample. The interference must be taken
into account.
13
2.3 DESIGN OF EXPERIMENTS
The main goal of a DoE is to create representative and informative experiments [7]
.
Many processes are still under research with the aim of finding the optimal process
parameters. Rather than testing one parameter by one, it is better to follow a DoE with
varying parameters. This saves a lot of resources, time and money. It not only gives structure
to a problem, it is also more easy for the experimenter to analyze which factors have a
significant effect on a process and which can be varied without having any effect on the final
product. DoE is applicable for screening, optimization and robustness testing of a system.
Screening is done to find the significant factors. “Optimization” is to find the optimum system
parameters to have the best end product. The robustness testing wants to find out how
sensitive a small change in a parameter influences the end product. The current study tries to
find the relation between the granulation process parameters (factors: API concentration,
barrel temperature, screw configuration and liquid feed rate) and the tablet properties
(responses: disintegration time, porosity, friability and tensile strength) using Multiple Linear
Regression (MLR). The granulation process parameters were varied according to a full
factorial design (see experimental part 1.1.1).
14
2.4 PROCESS ANALYTICAL TECHNOLOGY
2.4.1 Introduction
The PAT initiative from the FDA states that timely measurements should be
performed for improving process understanding and for developing process control strategies
[2]. The need for fast and robust process analytical techniques to monitor and control fast
production lines by timely measurement is essential. Raman spectroscopy and NIR
spectroscopy are two techniques that are suitable for this purpose. They have many
monitoring applications starting from the powder blending procedure until the coating process
of the final oral dosage form [8-11]
. The amount of research being done on the implementation
of Raman and NIR spectroscopy as PAT-tools for the production processes of oral dosage
forms is increasing rapidly. In the next chapters, these two most promising PAT techniques
are explained in more detail.
2.4.2 Raman spectroscopy
The principles of Raman spectroscopy lie in the Raman effect that was first observed
by Sir C.V. Raman. The Raman effect is the inelastic scattering of electromagnetic radiation
(EMR) as a result of energy exchange between radiation and molecular vibrations. It is
different than other spectroscopic techniques because it is not based on absorbance of light
but on light scattering effects. In Raman spectroscopy, the samples are irradiated with
monochromatic laser light, often with wavelengths in the visible (e.g. 532nm) or near-infrared
(e.g. 785 nm) region. Because the frequencies of the EMR are in the visible or near-infrared
region, the term scattered “light” is often used. The energy of the irradiation light (laser light)
is enough to bring the sample molecules in a higher vibrational state, hence inducing the
Raman effect. Most of the scattered light has the same frequency as the irradiation light
(Raleigh radiation). Only a fraction of 10-8
is scattered inelastically. This inelastic scattering
can occur with a lower frequency than the irradiation light (Stokes radiation) or a higher
frequency (anti-Stokes radiation) than the irradiation light (see Fig. 2.4-1). In these cases,
energy has been exchanged between the incident light and the sample [1]
.
15
Figure 2.4-1: IR and NIR absorption, the Raman effect and fluorescence. Figure from De Beer et al., reference 1.
The Raman spectrum is displayed as the Raman shift in wavenumber units (cm-1
)
versus intensity. The Raman shift refers to the difference in the observed scattered frequency
from the frequency of the excitation radiation (see Fig. 2.4-2).
Δw (cm-1
)
x 10
7
Where: Δw: Raman shift (cm-1
)
λ0: Excitation wavelength
λ1: Raman spectrum wavelength
Figure 2.4-2: Example of Raman spectrum. Figure from De Beer et al., reference 1
16
Raman spectroscopy is a fast technique, based on transitions between vibrational
energy levels of the molecules. But not every molecule is Raman active. The selectivity of
this technique lies in the change in polarizabilty that should occur during the normal modes of
the analyzed molecule. The ease with which the electron cloud of a molecule can be distorted
after bringing the molecule into an electromagnetic field is defined as polarizabilty.
A molecule is not a stationary object, it is continuously in motion. The motion of
molecules can either be a rotation, a translation or a vibration. A molecule of N atoms has 3N
degrees of freedom: 3 translations, 3 rotations (2 for linear molecule) and 3N-6 normal modes
(vibration modes) (3N-5 for linear molecules). These normal modes have their own
frequencies and they can cause a change in polarizability of the molecule. The polarizability
depends on the bondstrength. As the bondstrength varies when the bondlength changes
because of a vibrational change, this results in a change of polarizability. For example: a
molecule is vibrating with a certain frequency vvib. If this molecule interacts with
electromagnetic radiation from a laser light, a change in polarizability can occur because of
bond stretches or a bond bending. If there is no change in polarizability, only Rayleigh
scattering will be observed. In order to observe Stokes and anti-Stokes scattering, there must
be a change in polarizability in the molecule in the course of vibration [12-13]
. Only these
molecules are Raman active. Aromatic molecules have been used mostly for Raman testing
because of the earlier mentioned selection rule.
Raman spectroscopy has many applications [8]
, both in quantitative and qualitative
measurements. The earlier mentioned theoretical approach makes this technique very useful
in making unique fingerprints for each Raman active molecule. Recent studies experimented
with some different geometry test settings of the Raman spectroscopic technique. Two
different measurement geometries of Raman spectroscopy, namely backscattering and
transmission modalities are illustrated in Fig. 2.4-3. The following sections discuss the
geometric set-ups and explains their advantages and drawbacks.
17
2.4.2.1 Backscattering Raman spectroscopy
In conventional Raman, the Raman spectra are collected at the same side as the
irradiation light comes from (see Fig 2.4-3). Back-scattering Raman is often plagued by sub-
sampling. This is due to the low penetration dept of the laser as only scattering from the top
layer of the sample is obtained. Besides this disadvantage, it has a larger Raman signal than
transmission Raman spectroscopy because the scattered light doesn’t have to travel through
the whole turbid sample before being collected as in transmission Raman spectroscopy.
Backscattering Raman is also easier to implement in process environments because no
transmission accessory is needed (less equipment).
2.4.2.2 Transmission Raman spectroscopy
In transmission Raman, the Raman spectra are collected at the opposite side as the
irradiation light came from (see Fig. 2.4-3). In this case, the laser light irradiates the sample
from below and the Raman signal is collected from above. Sub-sampling is no longer a mayor
issue due to the scattered light that passes through the whole turbid sample before collecting
the signal. Because the signal is smaller in transmission mode, it must be amplified. This can
be done by longer acquisition times or by using more powerful lasers. Matousek et al also
demonstrated a passive way of enhancing the Raman signal by placing a multilayer dielectric
optical element in front of the laser beam over the sample. This prevents loss of photons at the
point of impact that could increase the radiation in the sample [14]
.
Figure 2.4-3: Different geometry settings for Raman spectroscopy. Backscattering Raman and Transmission
Raman spectroscopy. R = Raman signal ; L = laser irradiation
18
The advantages of the transmission geometry over the backscattering geometry have
been studied. Matousek et al. demonstrated that transmission Raman was able to see different
layers of substances whereas backscattering Raman spectroscopy was not able to do so [15]
.
Johansson et al. demonstrated that the transmission Raman spectroscopy technique is capable
of providing information on the sample constitution [16]
. The experiments showed that Raman
transmission spectroscopy is more accurate than conventional Raman in back-scattering
mode. In another study, Aina et al demonstrated the ability of transmission Raman
spectroscopy to quantify the polymorphic content of pharmaceutical formulations [17]
. Their
experiments also showed that transmission mode is more accurate. This thesis study tries to
show if transmission Raman is more accurate than backscattering Raman in the quantification
of the API in tablets.
2.4.3 Near-Infrared spectroscopy
Near-Infrared spectroscopy contains the spectral area starting from 780nm up till
2500nm. This is the region between visible light and infrared light. In many aspects, this
technique has its similarities with Raman
spectroscopy. They both rely on transitions
between vibrational levels due to interaction
with EMR. Their greatest difference is the
selection rule of molecules to be Raman and
NIR active. Where Raman spectroscopy relies
on a change in polarizabilty during the normal
modes, NIR spectroscopy relies on the change
in dipole moment during the normal mode of
the molecule. For example: Fig. 2.4-4a shows
different vibrational states of a diatomic
molecule. Fig. 2.4-4b shows that there is no
change in dipole moment of an X2 molecule.
Fig. 2.4-4c shows that there is a change in dipole moment during the stretch vibration of an
XY molecule. Two-atomic molecules require a permanent dipole to be IR active, while more
Figure 2.4-4 (a) Vibration states of diatomic
molecule. (b) No change in dipole moment during the
stretch vibration of an X2 molecule. (c) Change in dipole
moment during the stretch vibration of an XY molecule.
Figure from De Beer et al., reference 1.
19
atomic molecules only require a dipole induced by the vibration [1]
. Bending and stretching
are the two vibrational modes that occur in molecules that absorb NIR energy.
Furthermore, NIR spectroscopy relies on absorption in the NIR region due to
overtones and combinations of fundamental vibrations of functional groups such as –OH, -
SH, -NH, -CH. The NIR absorption bands are wide and overlapping, often requiring
multivariate data analysis to extract the relevant information from the spectra [1, 18]
. The NIR
spectrum (see Fig. 2.4-4) in the transmission mode is shown in wavenumber units (cm-1
) or
wavelength (nm) versus the absorbance (log10(1/T), T is the transmittance). Since NIR
spectroscopy is based on absorbance and Raman spectroscopy is based on inelastic scattered
light, both techniques can be used complementary.
NIR spectra of pharmaceutical solids contain both physical information and chemical
information. The physical information is due to light scattering effects. This is displayed as
differences in spectral baseline level. The chemical information consists (e.g. different
chemical compounds) of the locations and the intensities of the absorption bands. Both of
these information types may be utilized in the analysis of the sample properties. The next
section explains how to interpret the baseline offset in NIR and Raman spectra.
20
2.4.4 Interpretation of spectral baseline offset in NIR and Raman spectra
The variations in the spectral
baseline offset are due scattering effects
(see Fig. 2.4-5). These scattering effects
can for example be related to the physical
properties of the tablets such as porosity,
tensile strength, smoothness of the surface
etc... When conducting pre-processing of
the spectra, one must be very careful.
Standard Normal Variate (SNV) and
Multiplicative Scatter Correction (MSC)
are pre-processing methods to remove
these light scattering effects. This
eliminates the baseline offset. They are
usually used when a chemical property is
examined (e.g. API concentration, API
hydrate level).
In the case of conventional Raman spectroscopy, the intensity of the Raman signal
increases with increasing backscattering from the surface of the tablet, thus when more
inelastic scattering returns to the detector (e.g. when the tablet is less porous). The explanation
for baseline offset of transmission Raman is different: the more light coming through the
sample, the higher the intensity/baseline offset. The interpretation of the baseline effects of
Raman spectra on the basis of the physical sample properties is therefore difficult and not
many studies have been conducted on this subject. In the case of transmission NIR spectra,
high baseline offset for log(1/T) spectra means that little light has been transmitted through
the sample, which indicates high scattering power and absorbance by the sample (see Fig. 2.4-
5).
Figure 2.4-5: Example of a transmission NIR
spectrum. The spectral baseline offset is evident. Colouring
according to API content (theophylline anhydrate). Lowest
(blue), medium (green) and highest (red) concentration.
21
2.4.5 Multivariate Data analysis
Spectral monitoring of a process gives rise to large data sets. For example: monitoring
a process with Raman spectroscopy gives rise to large amounts of Raman spectra in which
each Raman spectrum provides information about over 4000 Raman shifts. Variable reduction
techniques (e.g. PCA) are required to extract useful and relevant information from such large
datasets. The three multivariate techniques used in this study for data analysis are: Principal
Component Analysis (PCA), Partial Least Square Regression (PLS) and Multiple Linear
Regression (MLR). If the reader wants more information on these subjects, see reference [7]
.
2.4.5.1 PCA
PCA is a projection technique to reduce the amount of variables, often to a 2D or 3D
space. This makes it much easier to interpret. A multivariate dataset can be seen as a matrix of
N rows (observations) and K columns (variables). The PCA analysis transforms this matrix to
scores and loadings. At this point the variables of the original data sheet have been changed
into new variables: Principal Components (PC). The first PC (PC1) is a line that describes the
highest variance in a K-dimensional space. The second PC (PC2) describes the second highest
variance in this space. The PC1 and PC2 are in orthogonal position to each other. Each
original observation can be projected onto these lines, giving it a score for the PC1 and PC2.
These scores can be seen in a 2D dimension with a plot of the PC1 versus PC2 or in a 3D
space with PC1 versus PC2 versus PC3. The relation between the PC‘s and the original
variables can be seen in the loadings. The model of the scores and loadings are linear, hence
allowing only linear information to be extracted. Spectral data often contains non-linear
information which sometimes make the interpretation of the loadings difficult. PCA analysis
has been performed in this study to extract the relevant information from the tablet spectra.
.
22
2.4.5.2 PLS
This study tries to predict the tablet properties (responses) from
the tablet Raman and NIR spectra (factors). When there are a lot of
factors that are likely to be collinear, PLS can produce a good predictive
model. The idea of PLS analysis is to extract the factors that accounts
for the most variance (latent factors) and models the responses. The
extracted latent factors T from the X matrix are used to predict the
extracted U factors from the responses (Y matrix). The predicted Y-
scores are then used to construct a model to predict the responses (e.g.
tablet properties). Fig. 2.4-6 makes it clear [19]
. The latent factors are
actually new variables coming from projecting each observation on the
PLS components giving it a score ti for observation i. The first PLS
component is a line that approximates the point swarm and provides a good correlation with
the other line in the Y space. These lines interject with the average point. This average point is
due to pre-processing operations such as mean-centring and scaling to unit variance. PLS is
used in this study for predicting tablet properties from Raman and NIR tablet spectra.
2.4.5.3 MLR
MLR or Multiple Linear Regression is a method used to find the linear relationship
between factors and responses [7, 20]
. MLR is based on least squares: the model is fit such that
the sum-of-squares of differences of observed and predicted values is minimized. A problem
with MLR is: when the number of factors gets too large, it is likely to have an over-fitted
model. This type of model fits the samples very well, but won’t be able to predict new
observations. MLR does not work well with correlated data. It also assumes that the data have
no noise and it requires more observations than variables. It can only fit one response at a
time. The goal is predicting tablet properties from spectral data (which are collinear), hence
PLS is the best option. MLR has been used in this study to demonstrate the effect of the
granulation process parameters (factors) on the tablet properties (responses).
Figure 2.4-6: principle of
PLS modelling
23
EXPERIMENTAL
This section contains all materials and methods that were used during the experiments.
The answers on the objective questions can be found in the conclusion section.
1 MATERIALS & METHODS
1.1 GRANULATION
1.1.1 Design of experiments
This study started with the production of granules using the continuous granulation
unit of the ConsigmaTM
-25. Different granules were produced according to a DoE, resulting a
large variation in the properties of the granules. The raw materials that were used are:
- Theophylline anhydrous, API (Fagron Iberica, Barcelona, Spain)
- Lactose monohydrate 200M as filler (Caldic Belgium NV, Hemiksem, Belgium)
- Polyvinylpyrrolidone (PVP) as binder (Kollidon 30, BASF, Burgbernheim,
Germany)
- Water as granulation liquid
A full-factorial design was used to conduct the experiments. The four main parameters
(API concentration, screw configuration, barrel temperature and liquid feed rate) give a total
of 36 different set-ups. One set-up was repeated two times, batch code 2122 (see Table 1.1-1),
giving this DoE a total of 38 granule batches (3*2*3*2+2). Premixes of three formulations
were made to feed the granulator (see Table 1.1-1). They were mixed by hand and with a
tumbling mixer (20 minutes at a speed of 25 rpm). Two screw configuration were used: one
zone of four kneading elements and two zones of six kneading elements. The barrel
temperature was varied between 25°C or 35°C. The liquid feed rate was examined at 36,2 ,
41,2 or 46,3 g/min corresponding to moisture levels of 8, 9 and 10 % w/w, respectively. Other
parameters were kept constant during the production of the DoE granules: Screw speed, 950
rpm; powder feed rate, 25 kg/h. During granulation, the granulator was stabilized for 60
seconds before collecting 500 grams of granules. The granules were collected onto aluminium
foil and dried in the oven for 24 hours at 40°C. They were collected in plastic bags after
drying. The batches were given a specific name according to the used parameters (see Table
1.1-1, column FSTL, Formulation, Screw configuration, barrel Temperature and Liquid feed
24
rate). For example, batch 1111: lowest API concentration, one zone of four kneading
elements, 25°C, 36.2 g/min liquid feed rate. Batch 1222: lowest API concentration, two
kneading elements, 35°C, 41.2 g/min liquid feed rate etc...
Run
order
Batch code
(FSTL)
Formulation
(theo%/
lactose%/PVP%)
(w/w)
Screw
Configuration
Barrel
temperature
(°C)
Liquid Feed
rate (g/min)
1 1111 19.5/78/2.5 1x4 25 36,2
2 1112 19.5/78/2.5 1x4 25 41,2
3 1113 19.5/78/2.5 1x4 25 46,3
4 1121 19.5/78/2.5 1x4 35 36,2
5 1122 19.5/78/2.5 1x4 35 41,2
6 1123 19.5/78/2.5 1x4 35 46,3
7 1211 19.5/78/2.5 2x6 25 36,2
8 1212 19.5/78/2.5 2x6 25 41,2
9 1213 19.5/78/2.5 2x6 25 46,3
10 1221 19.5/78/2.5 2x6 35 36,2
11 1222 19.5/78/2.5 2x6 35 41,2
12 1223 19.5/78/2.5 2x6 35 46,3
13 2122R1 29.25/68.25/2.5 1x4 35 41,2
14 2111 29.25/68.25/2.5 1x4 25 36,2
15 2112 29.25/68.25/2.5 1x4 25 41,2
16 2113 29.25/68.25/2.5 1x4 25 46,3
17 2121 29.25/68.25/2.5 1x4 35 36,2
18 2122 29.25/68.25/2.5 1x4 35 41,2
19 2123 29.25/68.25/2.5 1x4 35 46,3
20 2211 29.25/68.25/2.5 2x6 25 36,2
21 2212 29.25/68.25/2.5 2x6 25 41,2
22 2213 29.25/68.25/2.5 2x6 25 46,3
23 2221 29.25/68.25/2.5 2x6 35 36,2
24 2222 29.25/68.25/2.5 2x6 35 41,2
25 2223 29.25/68.25/2.5 2x6 35 46,3
26 2122R2 29.25/68.25/2.5 1x4 35 41,2
27 3111 39/58.5/2.5 1x4 25 36,2
28 3112 39/58.5/2.5 1x4 25 41,2
29 3113 39/58.5/2.5 1x4 25 46,3
30 3121 39/58.5/2.5 1x4 35 36,2
31 3122 39/58.5/2.5 1x4 35 41,2
32 3123 39/58.5/2.5 1x4 35 46,3
33 3211 39/58.5/2.5 2x6 25 36,2
34 3212 39/58.5/2.5 2x6 25 41,2
35 3213 39/58.5/2.5 2x6 25 46,3
36 3221 39/58.5/2.5 2x6 35 36,2
37 3222 39/58.5/2.5 2x6 35 41,2
38 3223 39/58.5/2.5 2x6 35 46,3
Table 1.1-1: Full factorial design, DoE
25
1.2 TABLETING
The granules from all 38 DoE runs were tableted. The tablets were manufactured using
a high speed rotary tablet press (ModulTM
P, GEA Pharma Systems, Belgium). Ten punches
were used with a flat faced bevel edge and a diameter of 9 mm. The flat faced bevel edges
were used to keep the influence of a convex surface out of the equation. No pre-compression
was performed, only a main compression. Paddle speed 1: 10 rpm ; Paddle speed 2: 20 rpm ;
Tableting speed: 200 tablets per minute ; Main compression force: 40 kN.
These selected settings were similar for each batch of granules. As the tableting
process parameters were kept constant, observed differences in tablet properties can only be
attributed to differences in granule properties between the 38 DoE batches, such as size
distribution, friability, amount of fines, amount of oversized granules, etc… The diameter
(9mm) and the thickness (4mm) of all tablets were kept constant, hence ensuring that possible
differences after spectral analysis (transmission and backscattering Raman, NIR transmission)
are not caused by the tablet dimensions. Two hundred tablets from each DoE batch were
collected in plastic bags and they were given the granule batch code as identity and a run
number according to the tableting run order. The tablets with masses between 290-310 mg
were manually selected from this bag and put in plastic tablet holders for spectral
measurements. The tablets that were out of this range were put is a separate bag. The identity
of the tablets in the tablet holders was preserved through all further analysis: Raman
spectroscopy, NIR spectroscopy and reference analysis.
26
1.3 PROCESS ANALYTICAL TECHNOLOGIES
1.3.1 Raman spectroscopy
The system used for conventional Raman
spectroscopy was also used in transmission mode
using the transmission Raman accessory from
Kaiser Optical Systems. The Raman system used
for this study was the RamanRXN2TM
Analyzer
(Kaiser optical systems) with a 785 nm excitation
laser with a power of 400mW and a charge-
coupled device (CCD) detector. The laser light
was sent through an optical fiber, irradiating the
sample from underneath with the transmission
accessory (transmission mode, see Fig. 1.3-1) and
from above through the PhAT probe
(backscattering mode). The Raman signal was
collected by a PhAT probe which was installed at
25 cm from the sample tablets. Raman shifts
from 150cm-1
– 1890 cm-1
were collected with a
resolution of 0.3cm-1
. Seventy-two tablets (from
the plastic tablet holders) from each DoE batch were measured both in transmission geometry
with an acquisition time of 55 seconds and in backscattering geometry with an acquisition
time of 15 seconds. All measurements were taken in dark conditions by covering the rotary
tablet holder and PhAT probe with a black cover to attenuate background noise. These tablets
were given an identity according to the tray slot in the plastic tablet holders and the slots in
the automated Raman tablet holder (see Fig 1.3-1 and 1.3-2). Three tablets containing the pure
analyte (one theophylline anhydrous tablet, one lactose tablet and one PVP tablet) were also
measured (38 times over a period of 2 weeks) to monitor the electronic drift of the instrument.
PCA and PLS analysis were performed on the spectra with SIMCA-P+ 12.0.1 (Umetrics,
Sweden).
Figure 1.3-1: Transmission geometry
experimental set-up. (1) transmission accessory from
Kaiser Optical Systems with optical fiber (2) PhAT
probe (3) rotating tablet holder
27
Figure 1.3-2: Raman system in transmission mode. (1) Laptop controlling the rotation of the tablet
holder (2) tablet holder (3) PhAT probe (4) RamanRXN2TM
analyzer (5) optical fiber in transmission
mode.
1.3.2 NIR spectroscopy
The NIRFlex N-500 transmission FT-NIR
spectrometer (BUCHI, Switzerland) and the NIRWare
software were used for collecting the NIR spectra (see
Fig. 1.3-2). The tablet holder could contain 10 tablets at
once. The spectral region 11520 cm-1
– 6000 cm-1
was
collected for each tablet with 128 scans, a resolution of
4cm-1
and an acquisition time of 38 seconds. This spectral
region corresponds to the third and second overtones
region where R-X and O-H vibrations are expected. The
same seventy-two tablets from each Doe batch that were
analyzed with conventional Raman and transmission
Raman were measured with the NIR system. PCA and
PLS analysis were performed on the spectra with
SIMCA-P+ 12.0.1 (Umetrics, Sweden).
Figure 1.3-3 T-NIR APPARATUS:
(1) NIRFLEX N-500 transmission FT-NIR
spectrometer. (2) Computer with
NIRWare software.
28
1.4 REFRENCE ANALYSIS
The reference analyses were performed after the Raman/NIR measurements. The
qualitative and quantitative determinations of the tablets via UV-VIS spectroscopy,
disintegration, tensile strength and porosity tests were preserved to find out (via multivariate
data-analysis) whether there exists correlations between these analysis and the Raman/NIR
spectra of the tablets. Table 1.4-1 gives an overview of all performed reference analyses.
REFERENCE ANALYSIS AMOUNT OF TABLETS
PER BATCH
APPARATUS
UV-VIS spectroscopy 6 tablets Shimadzu UV-1650 UV-VIS,
France
Disintegration 6 tablets PTZ E, PharmaTest
Tensile strength 20 tablets PTB 311, PharmaTest
Friability 22 tablets PTFE, PharmaTest
Microscopic porosity test 10 tablets (Reickert, 96/0226, Vienna,
Austrich).
Table 1.4-1: Reference analysis
The disintegration time for all samples was measured at the time when all particles
were fallen through the netting of the sample holders. These measurements were taken by a
single operator to attenuate operator dependency. Tensile strength was calculated using the
hardness, diameter and thickness of the tablets measured with the hardness tester. Friability
was performed with 22 tablets which had a mass around 6.5g according to the European
Pharmacopoeia.
Prior to UV-Vis spectroscopy, each tablet was weighted and put in a volumetric flask
of 100 mL with a magnetic stirrer. The volumetric flask was filled with ±80 mL of distilled
water. The tablets were given the time to dissolve on a magnetic stir plate. Once the tablet was
dissolved, the volumetric flask was filled up to 100 mL with distilled water. 100 µL of this
solution was diluted to 10 mL in a volumetric flask of 10 mL. The absorbance at 272 nm of
this diluted solution was measured against a blank containing distilled water.
29
2 RESULTS AND DISCUSSION
2.1 DOE ANALYSYS OF GRANULE ATTRIBUTES
In a study prior to this one, the influences of the continuous granulation process
parameters (API level, screw configuration, barrel temperature and liquid feed rate) on the
granule attributes have been researched. Particle size, friability, Hausner ratio and Carr index
were the investigated granule attributes. The Hausner ratio (tapped density/bulk density) and
Carr index are often used to define the flowability of a powder. The most interesting attributes
are the amount of fines (<300µm), the amount of oversized granules (>2000µm) and the
friability (%) of granules. These attributes might explain some characteristics of the tablets
produced from these granules. Modde 9.1 (Umetrics, Sweden) was used to make interaction
models and coefficient plots.
-10
-8
-6
-4
-2
0
2
4
6
AP
I
Scr
Ba
r
Mo
i
AP
I*S
cr
AP
I*B
ar
AP
I*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for <300µm (%)
N=38 R2=0,939 RSD=2,579
DF=27 Q2=0,869 Conf. lev.=0,95
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 11:59:56 (UTC+1)
Figure 2.1-1: The influence and significance of process parameters/ interactions on the amount of fines:
coefficient plot. Scr = screw configuration, Moi = moisture, Bar = barrel temperature.
30
The amount of fines increases with an increase in API concentration (Fig. 2.1-1).
Theophylline anhydrate is less soluble than lactose monohydrate, resulting in less time to
make liquid bridges with other particles and hence a higher amount of fines (<300µm).
Decreasing moisture, amount of kneading elements and granulation barrel temperature
increases the amount of fines. Lesser liquid bridges are built with lower moisture, lower barrel
temperature and less shear, resulting in a higher amount of fines. More kneading elements
mixes the water better with the powder, which enhances liquid bridge formation, resulting in
less amounts of fines. Significant interactions between API*Scr and Scr*Bar can be seen in
Fig. 2.1-1. The interaction between API*Scr (Fig. 2.1-2) is explained as follows: less
kneading elements together with higher theophylline concentrations results in highest amount
of fines. The interaction between Scr*Bar (Fig. 2.1-3) is explained as follows: less kneading
elements together with lowest granulation barrel temperature results in highest amount of
fines.
Figure 2.1-2: Interaction plot API*Scr
14
16
18
20
22
24
26
28
30
1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0
<3
00
µm
(%
)
Screw configuration
Interaction Plot for API*Scr, resp. <300µm (%)
N=38 R2=0,924 RSD=2,694
DF=31 Q2=0,882
API (low )
API (high)
API (low )
API (low )
API (high)
API (high)
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 10:20:33 (UTC+1)
31
Figure 2.1-3: Interaction plot Scr*Bar
18
19
20
21
22
23
24
25
26
27
25 26 27 28 29 30 31 32 33 34 35
<3
00
µm
(%
)
Barrel temperature °C
Interaction Plot for Scr*Bar, resp. <300µm (%)
N=38 R2=0,924 RSD=2,694
DF=31 Q2=0,882
Scr (1)
Scr (2)
Scr (1)
Scr (1)
Scr (2)
Scr (2)
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 10:17:34 (UTC+1)
32
Figure 2.1-3: The influence and significance of process parameters/ interactions on the amount of oversized
granules: coefficient plot. Scr = screw configuration, Moi = moisture, Bar = barrel temperature.
The amount of oversized granules (>2000µm) increases with less theophylline, more
kneading elements, higher barrel temperature and more moisture (Fig. 2.1-3). This is
completely the opposite result as in the amount of fines. Less theophylline solves faster due to
a higher granulation barrel temperature and more moisture, resulting in better formation of
liquid bridges and agglomeration. More kneading elements mixes the powder better with
water, hence enhancing liquid bridge formation and thus more oversized granules. Three
significant interactions were found: API*Scr, API*Moi and Scr*Bar. The interaction between
API*Scr (Fig. 2.1-4) is explained as follows: decreasing theophylline concentrations with
increasing amount of kneading elements results in increasing amounts of oversized granules.
The interaction between API*Moi (Fig. 2.1-5) is explained as follows: decreasing
theophylline concentrations together with increasing moisture results in highest amount of
oversized granules. The interaction between Scr*Bar (Fig. 2.1-6) is explained as follows:
increasing mixing power with more kneading elements together with a higher barrel
temperature results in higher amounts of oversized granules due to better liquid bridge
formation.
-8
-6
-4
-2
0
2
4
6
8A
PI
Scr
Ba
r
Mo
i
AP
I*S
cr
AP
I*B
ar
AP
I*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for >2000µm (%)
N=38 R2=0,876 RSD=3,503
DF=27 Q2=0,748 Conf. lev.=0,95
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 12:00:40 (UTC+1)
33
Figure 2.1-4: Interaction plot API*Scr.
Figure 2.1-5: Interaction plot API*Moi
8
10
12
14
16
18
20
22
24
1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0
>2
00
0µ
m (
%)
Screw configuration
Interaction Plot for API*Scr, resp. >2000µm (%)
N=38 R2=0,869 RSD=3,409
DF=30 Q2=0,790
API (low )
API (high)
API (low )
API (low )
API (high)
API (high)
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 11:20:46 (UTC+1)
8
10
12
14
16
18
20
22
24
26
28
19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
>2
00
0µ
m (
%)
API % w/w
Interaction Plot for API*Moi, resp. >2000µm (%)
N=38 R2=0,869 RSD=3,409
DF=30 Q2=0,790
Moi (low )
Moi (high)
Moi (low )Moi (low )
Moi (high)
Moi (high)
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 11:26:55 (UTC+1)
34
Figure 2.1-6: Interaction plot Scr*Bar
14,0
15,0
16,0
17,0
18,0
19,0
1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0
>2
00
0µ
m (
%)
Screw configuration
Interaction Plot for Scr*Bar, resp. >2000µm (%)
N=38 R2=0,869 RSD=3,409
DF=30 Q2=0,790
Bar (low )
Bar (high)
Bar (low )
Bar (low )
Bar (high)
Bar (high)
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 11:37:26 (UTC+1)
35
Figure 2.1-7: The influence and significance of process parameters/ interactions on friability: coefficient plot.
API = theophylline anhydrate concentration, Scr = screw configuration, Moi = moisture, Bar = barrel temperature.
Fig. 2.1-7 shows the influences of the examined process parameters upon the friability
of the granules. Three factors are significant: API, Scr and Moi. Increasing theophylline
concentration and a decrease in moisture and amounts of kneading elements results in a
higher friability of granules. These parameters give a higher amount of fines. The higher
friability of granules is due to the high amount of fines that get lost during the granule
friability test.
-4,0
-3,0
-2,0
-1,0
0,0
1,0
2,0
3,0
4,0A
PI
Scr
Ba
r
Mo
i
AP
I*S
cr
AP
I*B
ar
AP
I*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for Friability %
N=38 R2=0,882 RSD=2,122
DF=27 Q2=0,791 Conf. lev.=0,95
Investigation: GranuleAttributesDoE (MLR)
MODDE 9.1 - 2012-05-26 11:44:17 (UTC+1)
36
2.2 INFLUENCE OF GRANULATION PROCESS PARAMETERS ON TABLET
PROPERTIES, DOE ANALYSIS
Modde 9.1 (Umetrics, Sweden) was used to develop interaction models correlating the
examined granulation process parameters (API, screw configuration, granulation barrel
temperature and moisture) and the examined tablet properties (disintegration time, friability,
porosity and tensile strength). Coefficient plots were calculated to find out which granulation
process parameters and interactions have a significant influence on the tablet properties.
An increase in TA concentration, kneading elements and moisture level increases the
disintegration time of the tablets. The theophylline concentration is the most significant factor
(Fig 2.2-1). As seen above, increasing theophylline concentration increases the amount of
fines. The specific surface area of fines is higher, and hence more Van der Waals interaction
forces are likely to occur when such granules are tableted [21-22]
. Tablets with more
theophylline are therefore stronger, which might explain the higher disintegration time.
Figure 2.2-1: Coefficient plot showing the effect/significance of process parameters/interactions on the
disintegration time of the tablets. The (theophylline anhydrate) , scr (screw configuration) , Bar (granulation barrel
temperature), Moi (moisture).
0
50
100
150
200
250
300
350
400
Th
e
Scr
Ba
r
Mo
i
Th
e*S
cr
Th
e*B
ar
Th
e*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for Disintegration time (s)
N=229 R2=0,783 RSD=170,2
DF=218 Q2=0,761 Conf. lev.=0,95
Investigation: CalibrationSets_disintegration (MLR)
MODDE 9.1 - 2012-05-05 15:08:13 (UTC+1)
37
Fig. 2.2-2 shows the coefficient plot for the response variable tensile strength.
The two most important factors are the theophylline concentration and moisture. As seen
above in section 2.1 (granule attributes), the amount of fines increases with increasing
amounts of theophylline and decreasing moisture. As the amount of fines increases, the
specific surface area also increases. When tableting granules with such characteristics, the
Van der Waals interactions between theophylline are stronger due to the higher specific
surface area [22]
. This results in harder tablets and thus a higher tensile strength.
Figure 2.2-2: Coefficient plot showing the effect/significance of process parameters/interactions on the tensile
strength of the tablets. The (theophylline) , scr (screw configuration) , Bar (granulation barrel temperature), Moi
(moisture).
-0,20
-0,10
-0,00
0,10
0,20
0,30
0,40
0,50
0,60
Th
e
Scr
Ba
r
Mo
i
Th
e*S
cr
Th
e*B
ar
Th
e*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for Tensile strength (MPa)
N=760 R2=0,518 RSD=0,4413
DF=749 Q2=0,505 Conf. lev.=0,95
Investigation: CalibrationSets_tensilestrenght (MLR)
MODDE 9.1 - 2012-05-05 15:08:42 (UTC+1)
38
The same reasoning can be adopted for porosity. Increasing theophylline
concentration, barrel temperature and moisture content results in larger granules. The specific
surface area of these granules is smaller. Tableting such granules will lead to lesser Van der
Waals interaction forces between theophylline and more porous tablets as seen in Fig. 2.2-3.
Figure 2.2-3: Coefficient plot showing the effect/significance of process parameters/interactions on the porosity
(%)of the tablets. The (theophylline) , scr (screw configuration) , Bar (granulation barrel temperature), Moi
(moisture).
-1,5
-1,0
-0,5
0,0
0,5
1,0
Th
e
Scr
Ba
r
Mo
i
Th
e*S
cr
Th
e*B
ar
Th
e*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for Porosity (%)
N=380 R2=0,172 RSD=2,957
DF=369 Q2=0,119 Conf. lev.=0,95
Investigation: CalibrationSets_porosity (MLR)
MODDE 9.1 - 2012-05-15 15:24:23 (UTC+1)
39
Fig. 2.2-4 shows that the theophylline concentration is the only significant factor that
influences friability. The same theory can be used to explain this result. The amount of
oversized granules is larger with lower theophylline concentrations. These oversized granules
have a lower specific surface area and thus Van der Waals interaction forces are less likely to
occur. Tableting such granules will result in weaker tablets, which will yield a higher
friability percentage.
Figure 2.2-4: Coefficient plot showing the effect/significance of process parameters/interactions on the friability
(%) of the tablets. The (theophylline) , scr (screw configuration) , Bar (granulation barrel temperature), Moi
(moisture).
-3,0
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
Th
e
Scr
Ba
r
Mo
i
Th
e*S
cr
Th
e*B
ar
Th
e*M
oi
Scr*
Ba
r
Scr*
Mo
i
Ba
r*M
oi
Scaled & Centered Coefficients for Friability (%)
N=38 R2=0,563 RSD=2,061
DF=27 Q2=-0,007 Conf. lev.=0,95
Investigation: CalibrationSets_friability (MLR)
MODDE 9.1 - 2012-05-15 16:04:12 (UTC+1)
40
2.3 RAMAN SPECTROSCOPY
2.3.1 PCA of API concentration and hydrate level
Simca-P+ 12.0.1 (Umetrics, Sweden) was used to make the PCA models. PCA was
performed on the backscattering and transmission Raman spectra of all Raman measured
tablets from 400.2cm-1
- 600cm-1
and from 1600.2cm-1
till 1800cm-1
(X-variables) with mean-
centering on the X variables and SNV pre-processing. The two spectral regions were
combined and two principal components accounted for 98.3% of the total spectral variability
in backscattering mode (PC1: 96.7% ; PC2: 1.61%) and 98.7% in transmission mode (PC1:
97.3% ; PC2: 1.49%). The PCA score plot (backscattering Raman) is shown in Fig. 2.3-1. The
score points are coloured according to the API concentration (blue squares: 39% , red squares:
29.25% and black squares: 19.5%) in which case a clear clustering is observed along the PC1.
Figure 2.3-1: PC scores from backscattering Raman coloured according to the API content (%). Black squares 19.5%,
red dods 29.25% and blue squares 39%.
41
The loadings are shown in Fig. 2.3-2. The PC1 loading looks like the difference
between the pure analyte spectra of theophylline anhydrate and lactose monohydrate (see Fig.
2.3-3). The lactose peak at 475 cm-1
is negative in the PC1 loading plot. The main loading
peak at 555cm-1
is positive, describing the theophylline anhydrate concentration. It is
positively correlated with the scores indicating an increase in the scores is an increase in
theophylline concentration and a decrease in lactose concentration. The PC2 loading shows a
positive peak at 1689.9 cm-1
which manifests TM and a negative peaks at 555 cm-1
which
manifests TA. TA is also manifested in the PC2 loading by the two negative peaks
surrounding the TM peak at 1689.9 cm-1
. The PC2 scores are therefore positively correlated
with the hydrate level. The principal components describe the same chemical properties in the
transmission mode.
-0,06
-0,04
-0,02
0,00
0,02
0,04
0,06
0,08
0,10
0,12
SN
V:4
00
,2
SN
V:4
29
,9
SN
V:4
59
,9
SN
V:4
89
,9
SN
V:5
19
,9
SN
V:5
49
,9
SN
V:5
79
,9
SN
V:1
60
9,8
SN
V:1
63
9,8
SN
V:1
66
9,8
SN
V:1
69
9,8
SN
V:1
72
9,8
SN
V:1
75
9,8
SN
V:1
78
9,8
Raman shift cm-1
PC1 and PC2 loading
R2X[1] = 0,966587 R2X[2] = 0,0161171
p[1]
p[2]
SIMCA-P+ 12.0.1 - 2012-05-05 15:22:26 (UTC+1)
Figure 2.3-2: PC1 loading (black) and PC2 loading (red).
42
In the 3D score plot (Fig. 2.3-1), the tablets from batches 1222, 2221 and 3212 show a
higher hydrate level (higher PC2 score). Raman and NIR spectra from the granules (collected
after granulation and after drying in an oven at 40°C) of these batches did not show any
appearance of theophylline monohydrate (experiments conducted in a previous study). The
higher hydrate level in these tablets is therefore not due to the granulation process in which
case the higher amount of kneading elements (better water mixing) might explain these
results. The tablets from batches 1222, 2221 and 3212 could have absorbed water due to bad
laboratory practices before they were analyzed with Raman and NIR spectroscopy
Raman backscattering -and transmission spectroscopy were both able to see the
different solid-states in the tablets. Two principal components accounted for more spectral
variability in transmission mode than in backscattering mode, which might be due to the
reduction of the sub-sampling problem.
0
50000
100000
150000
200000
250000
15
0
23
9,7
32
9,7
41
9,7
50
9,7
59
9,7
68
9,7
77
9,7
86
9,7
95
9,7
10
49
,7
11
39
,7
12
29
,7
13
19
,7
14
09
,7
14
99
,7
15
89
,7
16
79
,7
17
69
,7
18
59
,7
Ra
ma
n in
ten
sity
Raman shift (cm-1)
Backscattering Raman spectra of Pure AnalytesTheophylline anhydrous
Lactose monohydrate
PVP
SIMCA-P+ 12.0.1 - 2012-05-05 15:30:48 (UTC+1)
Figure 2.3-3: Backscattering Raman spectra of the pure analytes: theophylline anhydrous (black), Lactose
monohydrate (red), PVP (green).
43
2.4 NIR TRANSMISSION SPECTROSCOPY
2.4.1 PCA of API concentration and hydrate level
The NIR spectrum showed noise from 7500-6000 cm-1
, corresponding to the second
overtone region. This region was therefore not used in any data analysis. Only the third
overtone region was used for PCA and PLS analysis (9500cm-1
-8700cm-1
). PCA was
performed with SIMCA-P+ 12.0.1 (Umetrics, Sweden) on the (1/T) NIR spectra
(9500cm-1
-8700cm-1
) of all 72 tablets from each batch. Mean-centering and MSC-pre-
processing were performed on this spectral range. Two components account for 99.9% of the
variability (PC1: 99.6% ; PC2 0.343%).
Fig. 2.4-1 shows the PC1 versus PC2 scores plot where the score points are coloured
according to the API concentration level: blue (39%), red (29.25%) and black squares
(19.5%). By looking at the PC1 loading (see Fig. 2.4-2), it is possible to explain the clustering
along the PC1, which is caused by the API concentration level. The PC1 loading is negative at
8900 cm-1
, a peak that represents the pure analyte spectrum of theophylline. The scores are
negatively correlated with the API concentration. The higher the PC1 scores, the lower the
API concentration.
Figure 2.4-1: PC score plot
44
Fig. 2.4-2 also shows the PC2 loading. The PC2 loading clearly shows a positive peak
at 8936 cm-1
. This peak represents the API hydrate level. The PC2 scores are therefore
positively correlated with the API hydration level. The score plot shows that the batches 1222,
2221 and 3212 have a higher API hydrate level (see Fig. 2.4-1) which was also observed in
the Raman data.
Figure 2.4-2: PC loading plot. PC1 loading (black), PC2 loading (red).
-0,15
-0,10
-0,05
-0,00
0,05
0,10
0,15
0,20
0,25
MS
C:9
50
0
MS
C:9
46
4
MS
C:9
42
4
MS
C:9
38
4
MS
C:9
34
4
MS
C:9
30
4
MS
C:9
26
4
MS
C:9
22
4
MS
C:9
18
4
MS
C:9
14
4
MS
C:9
10
4
MS
C:9
06
4
MS
C:9
02
4
MS
C:8
98
4
MS
C:8
94
4
MS
C:8
90
4
MS
C:8
86
4
MS
C:8
82
4
MS
C:8
78
4
MS
C:8
74
4
MS
C:8
70
4Var ID (Primary)
PC loadings
R2X[1] = 0,995566 R2X[2] = 0,00343487
p[1]
p[2]
SIMCA-P+ 12.0.1 - 2012-05-08 15:45:42 (UTC+1)
45
2.5 PREDICTING TABLET PROPERTIES FROM THE RAMAN AND NIR
SPECTRA USING PLS
PLS analysis has been performed for transmission -and backscattering Raman
spectroscopy (200-1800 cm-1
) and NIR transmission spectroscopy (11500cm-1
-8900cm-1
) with
Simca-P+ 12.0.1 (Umetrics, Sweden). Mean-centring was done on both X (spectra) and Y
variables (tablet properties). SNV pre-processing was only performed on the spectra to
develop better correlation models between the spectra and the API concentration. SNV pre-
processing was not performed on the spectra to develop correlation models between de tablet
spectra and disintegration time, friability, porosity and tensile strength. Correlation models
between the individual tablet spectra and their reference analyses (API concentration,
disintegration time, porosity and tensile strength) were developed. The friability analyses
were correlated with the mean spectrum of the 72 tablets from their batch.
After a certain amount of PLS components, the model does not have a better
predictive power (Q²). The amount of PLS components after the highest raise in Q² was
chosen for PLS modeling. Table 2.5-1 shows the results.
Tablet property
(Y variables)
PAT tool
Amount of
PLS
components
R²Y
Q²Y
RMSEE
API
concentration
(%)
B-Raman
2
0.977
0.976
1.08212 %
T-Raman 2 0.979 0.979 1.02438 %
T-NIR 2 0.978 0.978 1.05234 %
Disintegration
(sec)
B-Raman 3 0.731 0.725 186.171 sec
T-Raman 3 0.751 0.744 179.192 sec
T-NIR 3 0.753 0.744 178.701 sec
Friability (%) B-Raman 1 0.384 0.31 2,12239 %
T-Raman 2 0.395 0.262 2.13093 %
T-NIR 5 0.763 0.537 1.39334 %
Porosity (%) B-Raman 3 0.477 0.447 2.32791 %
T-Raman 4 0.208 0.187 2.86803 %
T-NIR 8 0.408 0.315 2.49228 %
Tensile strength
(MPa)
B-Raman 1 0.474 0.472 0.458 MPa
T-Raman 2 0.479 0.472 0.457 MPa
T-NIR 5 0.579 0.576 0.411 MPa Table 2.5-1: PLS analysis results.
46
R²Y is the correlation coefficient of the correlation model between the spectra of the
tablets and the reference analyses. Q²Y is the goodness of prediction coefficient of the
correlation model between the spectra of the tablets and the reference analyses. RMSEE (Root
Mean Square Error of Estimation) is a value which indicates the difference between the
predicted -and the measured value.
A quick look at the results from table 2.5-1 shows that only good models for
predicting the API concentration (%) could be made. This is due to the most informative
signal: the theophylline concentration. A loss of physical information due to the robust
tableting process might explain the poor modeling for disintegration time, friability, porosity
and tensile strength. The results also show that the best technique for the quantification of the
API level is transmission Raman spectroscopy (with SNV pre-processing and mean-
centering). With 3 PLS components, the difference between T-Raman and B-Raman in
predicting power becomes larger (T-Raman: RMSEE= 0.8670% ; B-Raman: RMSEE =
0.9478%), due to the reduction of the sub-sampling problem. This reduction is not significant,
possibly due to a good premix blending. The PLS models fitting for the prediction of API
level are illustrated for the three optical measurement techniques in figures 2.5-1 – 2.5-3.
Figure 2.5-1: Transmission NIR observed vs predicted plot from a 2 PLS component model for predicting the
API concentration (%). RMSEE = 1.05234%
18
20
22
24
26
28
30
32
34
36
38
40
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
YV
ar(
AP
I (%
w/w
))
YPred[2](API (% w/w))
T-NIR API prediction PLS model2 PLS components
RMSEE = 1,05234
1111-551111-561111-571111-581111-591111-601112-551112-561112-571112-58
1112-591112-601113-611113-621113-63
1113-641113-651113-67
1121-611121-621121-631121-641121-651121-66
1122-611122-621122-641122-651122-661122-67
1123-611123-621123-631123-641123-65
1123-66
1211-611211-621211-63
1211-641211-651211-661212-611212-621212-631212-641212-651212-66
1213-611213-621213-63
1213-641213-651213-66
1221-551221-56
1221-57
1221-58
1221-591221-601222-61 1222-62
1222-631222-64
1222-65
1222-661223-611223-621223-631223-641223-651223-66
2111-612111-622111-632111-642111-65
2111-66
2112-612112-622112-632112-642112-65
2112-66
2113-612113-622113-632113-642113-652113-66
2121-61
2121-62
2121-63
2121-64
2121-652121-662122-612122-622122-632122-642122-66
2122-67
2122R1-61
2122R1-622122R1-632122R1-64
2122R1-652122R1-66
2122R2-612122R2-622122R2-632122R2-642122R2-652122R2-66
2123-612123-622123-632123-642123-652123-662211-55
2211-562211-57
2211-58
2211-592211-60
2212-612212-622212-632212-642212-65
2212-66
2213-612213-62
2213-632213-64
2213-652213-66
2221-612221-622221-63
2221-642221-65
2221-662222-552222-562222-572222-58
2222-59
2222-602223-612223-622223-632223-64
2223-652223-66
3111-61
3111-623111-633111-643111-65
3111-66
3112-613112-623112-633112-643112-653112-66
3113-61
3113-623113-63
3113-643113-653113-66
3121-613121-623121-63
3121-64
3121-65
3121-66
3122-613122-623122-633122-643122-65
3122-663123-613123-623123-633123-643123-65
3123-66
3211-613211-623211-633211-64
3211-65
3211-663212-553212-563212-57
3212-58
3212-593212-603213-55
3213-56
3213-573213-583213-593213-60
3221-613221-62
3221-63
3221-643221-65
3221-66
3222-613222-623222-633222-643222-65
3222-66
3223-613223-623223-63
3223-643223-65
3223-66
y=1*x-9,685e-007
R2=0,9779
SIMCA-P+ 12.0.1 - 2012-05-14 14:14:18 (UTC+1)
47
Figure 2.5-2: Backscattering Raman observed vs predicted plot from a 2 PLS component model for predicting
the API concentration (%). RMSEE = 1.08212%
Figure 2.5-3: Transmission Raman observed vs predicted plot from a 2 PLS component model for predicting the
API concentration (%). RMSEE = 1.02438 %
18
20
22
24
26
28
30
32
34
36
38
40
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
YV
ar(
AP
I (%
w/w
))
YPred[2](API (% w/w))
Backscattering Raman API prediction PLS model2 PLS components
RMSEE = 1,08212
1111-551111-561111-571111-581111-591111-601112-551112-561112-571112-58
1112-591112-60
1113-611113-621113-631113-641113-651113-67
1121-611121-621121-631121-641121-651121-66
1122-611122-621122-641122-651122-661122-67
1123-611123-621123-631123-641123-65
1123-66
1211-611211-621211-631211-641211-651211-66
1212-611212-621212-631212-641212-651212-66
1213-611213-621213-63
1213-641213-651213-661221-551221-56
1221-57
1221-58
1221-591221-601222-611222-62
1222-631222-64
1222-65
1222-661223-611223-621223-631223-641223-651223-66
2111-612111-622111-632111-642111-65
2111-66
2112-612112-622112-632112-642112-65
2112-66
2113-612113-622113-632113-642113-65
2113-66
2121-61
2121-62
2121-63
2121-64
2121-652121-662122-612122-622122-632122-642122-66
2122-67
2122R1-61
2122R1-622122R1-632122R1-64
2122R1-652122R1-66
2122R2-612122R2-622122R2-632122R2-642122R2-652122R2-66
2123-612123-622123-632123-642123-652123-662211-55
2211-562211-57
2211-58
2211-592211-60
2212-612212-622212-632212-642212-65
2212-66
2213-612213-62
2213-632213-642213-65
2213-66
2221-612221-622221-63
2221-642221-65
2221-662222-552222-562222-572222-58
2222-59
2222-602223-612223-622223-632223-642223-65
2223-66
3111-61
3111-623111-633111-643111-65
3111-66
3112-613112-623112-633112-64
3112-653112-663113-61
3113-623113-63
3113-643113-653113-66
3121-613121-623121-63
3121-64
3121-65
3121-66
3122-613122-623122-633122-643122-65
3122-663123-613123-623123-633123-64
3123-65
3123-66
3211-613211-623211-633211-64
3211-65
3211-663212-55
3212-563212-57
3212-58
3212-593212-603213-553213-56
3213-573213-583213-593213-60
3221-613221-62
3221-63
3221-643221-653221-66
3222-613222-623222-633222-643222-65
3222-66
3223-613223-623223-633223-643223-65
3223-66
y=1*x-2,893e-007
R2=0,9767
SIMCA-P+ 12.0.1 - 2012-05-14 14:33:06 (UTC+1)
18
20
22
24
26
28
30
32
34
36
38
40
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
YV
ar(
AP
I (%
w/w
))
YPred[2](API (% w/w))
Transmission Raman API prediction PLS model2 PLS components
RMSEE = 1,02438
1111-551111-561111-571111-581111-591111-601112-551112-561112-571112-581112-59
1112-601113-611113-621113-631113-641113-651113-67
1121-611121-621121-631121-641121-651121-66
1122-611122-621122-641122-65
1122-661122-67
1123-611123-621123-631123-641123-65
1123-66
1211-611211-621211-631211-641211-651211-66
1212-611212-621212-631212-641212-651212-66
1213-611213-621213-63
1213-641213-651213-661221-551221-56
1221-57
1221-58
1221-591221-601222-611222-62
1222-631222-64
1222-65
1222-661223-611223-621223-631223-641223-651223-66
2111-612111-622111-632111-642111-65
2111-66
2112-612112-622112-632112-642112-65
2112-66
2113-612113-622113-632113-642113-652113-66
2121-61
2121-62
2121-63
2121-64
2121-652121-662122-612122-622122-632122-642122-662122-67
2122R1-61
2122R1-622122R1-632122R1-64
2122R1-652122R1-66
2122R2-612122R2-622122R2-632122R2-642122R2-652122R2-66
2123-612123-622123-632123-642123-652123-662211-55
2211-562211-57
2211-58
2211-592211-60
2212-612212-622212-632212-642212-65
2212-66
2213-612213-62
2213-632213-64
2213-652213-66
2221-612221-622221-63
2221-642221-65
2221-662222-552222-562222-572222-58
2222-59
2222-602223-612223-622223-632223-642223-652223-66
3111-61
3111-623111-633111-643111-65
3111-66
3112-613112-623112-633112-643112-653112-663113-61
3113-623113-63
3113-643113-653113-66
3121-613121-623121-633121-64
3121-65
3121-66
3122-613122-623122-633122-643122-65
3122-663123-613123-623123-633123-643123-65
3123-66
3211-613211-623211-633211-64
3211-65
3211-663212-553212-56
3212-57
3212-58
3212-593212-603213-553213-56
3213-573213-583213-593213-60
3221-613221-62
3221-63
3221-643221-65
3221-66
3222-613222-623222-633222-643222-653222-66
3223-613223-623223-633223-643223-65
3223-66
y=1*x-4,135e-007
R2=0,9791
SIMCA-P+ 12.0.1 - 2012-05-14 15:02:09 (UTC+1)
48
3 CONCLUSIONS
The first main goal of this study was to predict tablet properties using non-invasive
spectroscopic measurements. Three spectroscopic techniques (T-NIR, T-Raman and B-
Raman) and multivariate data analysis (PLS) were used to achieve this goal. Only good PLS
models for API concentration (%) were developed. This was due to the overall most
informative signal, which is theophylline. The best technique in predicting the API
concentration was transmission Raman spectroscopy (RMSEE = 1.02438 %, 2 PLS
components). The difference between T-Raman and B-Raman in the quantification of the API
concentration was not significant. This might be due to a good premix uniformity in which
case the effect of non-representative sampling of backscattering Raman was not observed. For
these formulations, the speed of backscattering Raman (15 seconds) and the easier set-up for
this technique has the advantage over transmission Raman spectroscopy (55 seconds). No
good PLS models for tensile strength, friability, porosity and disintegration time were
developed. This might be due to the loss of physical information caused by the robust
tableting process. The PAT-tools used in this study might only be used for the quantification
of the API in pharmaceutical solid dosage forms.
The second main goal of this study was to find the influence of the granulation process
parameters on the tablet properties using MLR. The continuous granulation process
parameters also had an influence on the granule attributes. The granule attributes together
with the weak solubility of theophylline anhydrate in contrast to the higher solubility of
lactose monohydrate were linked to the tablet properties. Higher amounts of fines results in
stronger, less friable and less porous tablets due to stronger Van der Waals interaction forces.
Higher amounts of oversized granules results in more friable, more porous and weaker tablets.
The PCA models for T-Raman, B-Raman and T-NIR displayed a clear clustering
according to the chemical properties. PC1 described the theophylline concentration and PC2
described the hydrate level. The higher hydrate level in batches 1222, 2221 and 3212 was
observed by each technique. It was concluded that bad laboratory practice was the cause of
these results. T-Raman, B-Raman and T-NIR are therefore adequate tools to determine the
theophylline hydrate level in tablets.
New tests with a constant concentration or another API could give other correlation
models for disintegration, friability, porosity and tensile strength. Implementation of PAT-
49
tools after the tableting process for quantitative purpose and building in probes in the
ConsigmaTM
-25 line could be the next step in development of continuous processing.
Optimization of the spectral techniques might give better results. For example: the NIR
system could be optimized to have a clear signal in the second and first overtone region and
better lasers for the Raman measurements could give faster results, ideal for timely
measurements.
50
REFERENCES
1. De Beer, T., et al., Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes. International Journal of Pharmaceutics, 2011. 417(1-2): p. 32-47.
2. FDA. Guidance for Industry PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Asssurance. 2004 [cited 2004; Available from: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM070305.pdf.
3. Fonteyne, M., et al., Real-time assessment of critical quality attributes of a continuous granulation process. Pharm Dev Technol, 2011.
4. Tousey, M.D. The Granulation Process 101 Basic Technologies for Tablet Making. 2002; Available from: http://www.dipharma.com/The_Granulation_Process_101.pdf.
5. Tousey, M.D. (2011) The Manufacturing process Tablet and Capsule Manufacturing. 11, 12.
6. Vervaet, C. and J.P. Remon, Continuous granulation in the pharmaceutical industry. Chemical Engineering Science, 2005. 60(14): p. 3949-3957.
7. Eriksson, L., et al., Multi-and Megavariate Data Analysis Part I Basic Principles and Applications Second revised and enlarged edition. 2006: Umetrics AB. 425.
8. Buckley, K. and P. Matousek, Recent advances in the application of transmission Raman spectroscopy to pharmaceutical analysis. Journal of Pharmaceutical and Biomedical Analysis, 2011. 55(4): p. 645-652.
9. Hausman, D.S., R.T. Cambron, and A. Sakr, Application of Raman spectroscopy for on-line monitoring of low dose blend uniformity. International Journal of Pharmaceutics, 2005. 298(1): p. 80-90.
10. Muller, J., et al., Feasibility of Raman spectroscopy as PAT tool in active coating. Drug Development and Industrial Pharmacy, 2010. 36(2): p. 234-243.
11. Luukkonen, P., et al., Real-time assessment of granule and tablet properties using in-line data from a high-shear granulation process. Journal of Pharmaceutical Sciences, 2008. 97(2): p. 950-959.
12. Bugay, D.E. and H.G. Brittain, Spectroscopy of Pharmaceutical Solids in Chapter 9 Raman Spectroscopy, H.G. Brittain, Editor. 2006, Taylor & Francis Group: New York.
13. Unit 4 Raman Spectroscopy. Available from: http://vedyadhara.ignou.ac.in/wiki/images/8/8a/Unit_4_Raman_Spectroscopy.pdf.
14. Matousek, P., Raman signal enhancement in deep spectroscopy of turbid media. Applied Spectroscopy, 2007. 61(8): p. 845-854.
15. Matousek, P. and A.W. Parker, Bulk Raman analysis of pharmaceutical tablets. Applied Spectroscopy, 2006. 60(12): p. 1353-1357.
16. Johansson, J., et al., Quantitative transmission Raman spectroscopy of pharmaceutical tablets and capsules. Applied Spectroscopy, 2007. 61(11): p. 1211-1218.
17. Aina, A., et al., Transmission Raman spectroscopy as a tool for quantifying polymorphic content of pharmaceutical formulations. Analyst, 2010. 135(9): p. 2328-2333.
18. Reich, G., Near-infrared spectroscopy and imaging: Basic principles and pharmaceutical applications. Advanced Drug Delivery Reviews, 2005. 57(8): p. 1109-1143.
19. Tobias, R.D. An Introduction to Partial Least Squares Regression. Available from: http://www.ats.ucla.edu/stat/sas/library/pls.pdf.
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20. Mutliple Linear Regression. 2011; Available from: http://www.ltrr.arizona.edu/~dmeko/notes_11.pdf.
21. Dammer, S.M., J. Werth, and H. Hinrichsen. Electrostatically charged granular Matter. Available from: http://www.physik.uni-wuerzburg.de/~hinrichsen/publications/source/064.pdf.
22. Sun, C.Q. and M.W. Himmelspach, Reduced tabletability of roller compacted granules as a result of granule size enlargement. Journal of Pharmaceutical Sciences, 2006. 95(1): p. 200-206.
EVENING LECTURES
Access to quality medicines in resources limited setting
In many countries, the right for medical health and medicines is still not written in
their law. Therefore a lot of people don’t have access to essential medicines (India, Africa). A
list of these medicines have been made. They should be available within the context of
functioning health systems at all times in adequate amounts and in appropriate dosage forms
with assured quality (Quality Assurance) and adequate information and at a price the
individual and the community can afford. The problem with this definition is that it is difficult
in a practical set-up. The medicines are often very expensive, even in the public sector, where
prices reach up to 250% of the international reference price. The distribution centres are far
away from the people who need it. The reality is that the authorities don’t have the resources
for quality assurance. The consequences of poor regulated countries is a higher prevalence of
poor-quality medicines (HIV, malaria, Tuberculosis and antibiotics, etc...). The WHO has
come up with a plan by providing controls on the producers which cannot finance or organise
quality assurance. In my opinion, a health care system should be obligated by law. Private
donations or private initiatives have worked for hospital care. These hospitals could provide
better pharmaceutical care as distribution centres.
Pfizer Forensic Laboratory
Counterfeit medicines are non-authentic drugs or packaging that appears to be the
same as the authentic. It often doesn’t even contain the API. These products are a plague to
both the pharmaceutical industry (money lost) and the health care system. It is the goal of the
pharmaceutical industry to cure people. Public health is in danger because of those
counterfeits. On top of that, they are often expensive and can be purchased on the internet
without prescription (e.g. Viagra). Pfizers global security system has been working around the
clock to stop these practices. Their laboratories use IR, Raman spectroscopy (see thesis),
HPLC, GC methods to find these counterfeits. The people who make these products do it out
of financial considerations. It is easy to make and, -distribute and no legislation is needed.
Viagra is one of the most common counterfeit medicines. Blue paint is often used to colour
these products which is very dangerous for the public health. In my opinion, the people who
make these products are murderers. They don’t care about the health of the people. Another
interesting thing is that laboratories use Raman spectroscopy to counter the counterfeits.
Applications of Antibodies in the Analysis of Drugs, Disease Markers, Bacteria and Toxins
Antibodies are polypeptides which belong to the immunoglobulin group. They are
products of the immune system that protect our body against strange organisms. Their
strongest advantage is their great diversity and selectivity which enables them to distinguish
between many antigens. It is these characteristics that are exploited by humans through
recombinant DNA. The applications seem endless: the detection of heart disease markers
(troponin), analysis of drugs, blood samples, urine samples, ELISA tests etc... Selected
antibodies can enumerate whole Listeria cells. Huge systems and libraries for antibody
screening have been developed for fast detection of the specific antibody for the wanted
purpose. Another development are biochips. Biochips are small plastic or glass chips that
contain minute concentrations of antibodies, or DNA, immobilised onto its surface and is used
as part of a detection device for multiple and simultaneous analyses of biologically significant
molecules. Anticancer drugs are being tested which are basically radioactive substances or
enzymes attached to an antibody. In my opinion, these systems and applications are very
promising towards curing cancer. The other systems that are based on antibodies are also very
handy for rapid detection of diseases.
Everything depends on everything else
Viruses and bacteria contain more than 75% of all species. Microbial ecosystems seem
to have adopted a lifestyle of distribution through a metabolic network. Every organism has
its task within their own community. This is sometimes organised by a network of nanowires
which enables them to exchange molecules and even DNA. Quorum sensing is another way to
do so. They communicate with the “parvome language”. Antibiotics are molecules which are
made by micro-organisms, but when used in a higher concentration, they become lethal for
micro-organisms. Resistance is a big problem. The huge waste of antibiotics in the
environment by humans is the source of that problem. It gives micro-organisms time to
become resistant against many antibiotics. We are losing the fight. Pharmaceutical companies
are not eager to invest in new antibiotics. Universities are therefore the only hope for the
development of new antibiotic drugs. In my opinion, these new drugs need to be developed. If
the industry doesn’t want to do it, the universities most be up for the job. Much is to be
learned from micro-organisms. Especially the distribution of resources. To make a
comparison, the distribution of drugs should also be universal in the human community,
which brings us back to the topic of the first evening lecture.