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.