GHENT UNIVERSITY

FACULTY OF PHARMACEUTICAL SCIENCES

Department of Pharmaceutical Analysis

Laboratory of Pharmaceutical Process Analytical Technology

Academic year 2013-2014

DEVELOPMENT OF REGIME MAP FOR CONTINUOUS HIGH SHEAR

WET GRANULATION USING TWIN SCREW GRANULATOR

Jens DHONDT

First Master of Drug Development

Promoter

Prof. Dr. T. De Beer

Commissioners

Prof. Dr. B. De Spiegeleer

Dr. S. Mortier

GHENT UNIVERSITY

FACULTY OF PHARMACEUTICAL SCIENCES

Department of Pharmaceutical Analysis

Laboratory of Pharmaceutical Process Analytical Technology

Academic year 2013-2014

DEVELOPMENT OF REGIME MAP FOR CONTINUOUS HIGH SHEAR

WET GRANULATION USING TWIN SCREW GRANULATOR

Jens DHONDT

First Master of Drug Development

Promoter

Prof. Dr. T. De Beer

Commissioners

Prof. Dr. B. De Spiegeleer

Dr. S. Mortier

COPYRIGHT

"The author and the promoter 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."

May 16, 2014

Promoter Author

Prof. Dr. T. De Beer Jens Dhondt

Resume

Twin-screw granulation is an emerging continuous granulation technique in the

pharmaceutical industry. Due to several advantages over batch-to-batch granulation. The

twin-screw granulator, present in the ConsiGmaTM-25 production line, allows easy

modifications of multiple parameters making twin-screw granulation an interesting

technique to study relating possibilities. At this moment, the available information about the

use of this technique is limited. The equipment allows the manipulation of size and

properties of the granules by adjusting binder addition method, screw configuration, stagger

angle of the kneading elements, granulation barrel temperature, liquid addition, powder

feed rate and screw speed. For each configuration, there is a different torque requirement

for the rotation of the screws and hence resulting different particle size distribution. By using

the torque information generated through the system, it is possible to calculate the specific

mechanical energy applied on the granules during each run. This data can be used to

compose a granulation regime map, creating the possibility to use this information as a

guidance for process understanding and optimization in a scale-independent manner.

To enable utilization of this technique on a large scale, there is a need for an in-line

quality monitoring and control considering the fact that taking samples out of a closed

production line is difficult. For particle size distribution analysis the EyeconTM has been

developed using 3D imaging techniques. The use of this device looks promising, although

tests revealed some problems concerning detection of large amounts of small particles.

The screening test and regime map experiments showed that wet binder addition,

2x6 screw configuration, 30° stagger angle (for this formulation), intermediate SME

(resulting from intermediate powder feed rate and -screw speed) and approximately 9%

liquid addition should be optimal condition to get a high yield of granules suited for

tabletting. The friability using this kind of configuration also appears to be satisfying. Further

experiments are desired in the near future such as testing other screw configurations, the

effect of the stagger angle and use of higher barrel temperatures. This should also be tested

for other formulation using different excipients, binders and active pharmaceutical

ingredients.

Samenvatting

Twin-screw granulatie is een relatief nieuwe techniek betreffende continue

granulatie in de farmaceutische industrie. Wegens meerdere voordelen tegenover batch-to-

batch granulatie is er hiervoor een groeiende interesse. De twin-screw granulator aanwezig

in de ConsiGmaTM-25 productielijn, laat het toe om makkelijke meerdere parameters aan te

passen wat hiervan een interessante techniek maakt om te bestuderen. Momenteel is de

beschikbare informatie betreffende deze techniek echter beperkt. Dit toestel laat toe om de

grootte en eigenschappen van de granules te manipuleren door het aanpassen van de

methode van binder toevoeging, schroef configuratie, hoek tussen de kneedelementen,

temperatuur van het granulatievat, vloeistofadditie, toevoersnelheid van poeder en

schroefsnelheid. Voor elke configuratie is er een ander krachtmoment nodig voor de rotatie

van de schroeven, dit levert verschillende deeltjesgroottedistributies op. Door gebruik te

maken van de informatie over het krachtmoment, die door het systeem bijgehouden werd,

tijdens elke test is het mogelijk om de specifiek mechanische energie te berekenen. Deze

data kan gebruikt worden om een granulatie "regime map" samen te stellen, dit creëert de

mogelijkheid om deze informatie te gebruiken als hulpmiddel om het proces beter te

begrijpen en te optimaliseren gebruik makende van een schaalonafhankelijke methode.

Om deze techniek op grote schaal toe te passen is er nood aan continue

kwaliteitscontrole aangezien staalname uit gesloten productielijnen moeilijk is. Voor de

analyse van de deeltjesgroottedistributie werd de EyeconTM ontwikkeld die gebruik maakt

van 3D beeldvorming. Het gebruik van dit toestel lijkt veelbelovend alhoewel de tests

problemen blootlegden bij de detectie van grote hoeveelheden kleine deeltjes.

De screening test en regime map experimenten toonden aan dat natte toevoeging

van binder, 2x6 schroef configuratie, 30° hoek tussen kneedelementen (voor deze

formulatie), gemiddelde SME (afkomstig van gemiddelde toevoersnelheid van poeder en

schroefsnelheid) en ongeveer 9% toevoeging van vloeistof optimale condities zouden zijn om

hoge hoeveelheden bruikbare granulaten te produceren. De friabiliteit lijkt ook te voldoen

bij deze condities. Extra experimenten zijn gewenst om het effect te bestuderen van andere

schroefconfiguraties, de hoek tussen de kneedelementen en het gebruik van hogere

temperaturen tijdens granulatie. Dit zou ook getest moeten worden voor andere formulaties

gebruik makend van andere excipiënten, binders en actieve bestanddelen.

Acknowledgements

I would like to thank Professor De Beer for the opportunity to work at his laboratory and

valuable tips during the design of the experiments. Special thanks to my supervisor Ashish

Kumar for the pleasant cooperation during my thesis period and expanding my

interdisciplinary knowledge! Thanks to Fien, Jurgen and Valerie from the laboratory of

pharmaceutical process analytical technology and the laboratory of pharmaceutical

technology for the help during experimental set-ups. Thanks to the staff of BIOMATH for the

warm welcome during my modelling work at their department. Thanks to family and friends

for support, and occasional distractions during this period.

TABLE OF CONTENTS

1. INTRODUCTION .................................................................................................................. 1

1.1. DEFINITION OF GRANULATION .................................................................................... 1

1.2. APPLICATIONS .............................................................................................................. 2

1.3. CURRENT GRANULATION TECHNIQUES ...................................................................... 3

1.4. GRANULE GROWTH DURING WET GRANULATION PROCESS ...................................... 4

1.4.1. Wetting and Nucleation ....................................................................................... 5

1.4.2. Growth and Consolidation ................................................................................... 6

1.4.3. Attrition and Breakage ......................................................................................... 6

1.5. CONTINUOUS GRANULATION ..................................................................................... 6

1.6. TWIN-SCREW GRANULATION ...................................................................................... 7

1.6.1. ConsiGmaTM-25 system ........................................................................................ 8

1.6.2. Screw configuration ............................................................................................. 9

1.6.3. Granule growth during twin screw granulation ................................................... 9

1.6.4. Influence of equipment parameters on granule quality .................................... 10

1.6.5. Quality by Design ................................................................................................ 11

1.7. REGIME MAP THEORY ............................................................................................... 11

2. OBJECTIVES ....................................................................................................................... 13

3. MATERIALS AND METHODS ............................................................................................. 14

3.1. PREPARATION OF GRANULES .................................................................................... 14

3.1.1. Granulation process ........................................................................................... 14

3.1.2. Experimental design ........................................................................................... 15

3.2. GRANULE EVALUTION ................................................................................................ 16

3.2.1. Real-time PSD analysis ....................................................................................... 16

3.2.2. Determination of torque and specific mechanical energy ................................. 17

3.2.3. Sieve analysis ...................................................................................................... 18

3.2.4. Friability .............................................................................................................. 18

3.2.5. Flowability .......................................................................................................... 19

3.3. EVALUATION OF RESPONSES ..................................................................................... 20

4. RESULTS AND DISCUSSION ............................................................................................... 21

4.1. USE OF EYECONTM DATA ............................................................................................ 21

4.2. INFLUENCE OF PROCESS VARIABLES ON PSD ............................................................ 23

4.2.1. Fines (<150µm) ................................................................................................... 24

4.2.2. Oversized fraction (>1400µm) ........................................................................... 26

4.2.3. Yield fraction (150-1400µm) .............................................................................. 29

4.2.4. Overview............................................................................................................. 30

4.3. GRANULE PROPERTIES ............................................................................................... 33

4.3.1. Friability .............................................................................................................. 33

4.4. GRANULATION REGIME MAP .................................................................................... 35

5. CONCLUSIONS .................................................................................................................. 39

6. REFERENCES ..................................................................................................................... 40

LIST OF ABBREVIATIONS

API Active Pharmaceutical Ingredient

DOE Design of Experiments

ICH International Conference on Harmonization

LED Light-Emitting Diode

L/S-ratio Liquid-to-Solid ratio

NIR Near Infrared

PAT Process Analytical Technology

PLS Partial Least Squares

PSD Particle Size Distribution

PVP Polyvinylpyrrolidone

QbD Quality by Design

RGB Red Green Blue

rpm Rotations per minute

1

1. INTRODUCTION

1.1. DEFINITION OF GRANULATION

Granules can be described as grains of agglomerated powder particles. Granule

preparations consist of dry powder particles that are combined into larger units in which the

primary particles are still distinguishable. The granules consist of the active substance and

the excipients. When applying wet granulation, granulation liquid is used to bond the

individual particles. The use of dry granulation is also possible, in this case the granules are

formed through heat treatment, pressurisation or the cohesive characteristics of the

materials itself. After the granulation process, the obtained granules should provide

sufficient resistance to possible segregation of ingredients in further processing. Granules

can be used as such or as an intermediate to make tablets or capsules. [1,2]

This report will mainly discuss twin-screw granulation which is a wet granulation

technique, so granulation liquid is used. Wet granules exist in a number of different liquid

saturation states. Depending on the granule structure and liquid addition, there are five

saturation states that can be differentiated (Figure 1.1): pendular-, funicular-, capillary-,

droplet- and pseudo-droplet state. [3] When liquid bridges hold the particles together they

are in the pendular state. If more liquid is added, the granule voids become saturated and a

capillary state occurs. The funicular state occurs between pendular- and capillary state. The

droplet state describes a state where the particles are held within a droplet without air being

entrapped. If air is entrapped it is called the pseudo-droplet state. [2,3]

Figure 1.1: Saturation states of wet granules [3]

2

1.2. APPLICATIONS

As mentioned above, granules are often used as an intermediate product before

tabletting. Wet granulation techniques are applied in various industries like the

pharmaceutical industry, food manufacturing, agricultural product manufacturing and

plastic industry. [4] Some of the advantages that granulation provides are listed below:

Improved flow properties:

When using granulation as an intermediate step in tabletting, a homogenous

distribution of the particles is very important. In that way it improves the mass

distribution of the tablets. [5]

Less segregation:

In continuous processes, a flow of powder occurs through the production transfer

lines. A segregation pattern is often observed when using a free-flowing powder mixture

somewhere in the production line. Agglomeration prevents segregation of a mixture by

creating approximately equally sized particles.[6]

Increased porosity of particles:

This results into a better dissolution rate.

Reduced dustiness

Dust causes problems in mass distribution and wastage of material. There is also a

risk for the operators at the production line. The product can be inhaled and there is

even a dust explosion risk. Granulation "captures" the dust into larger particles. [2]

Thus, granulation is a key step in solid-dosage manufacturing process in the

pharmaceutical industry. In a way it can be viewed as a product design. By using a

combination of particle design and process design, the properties of the granules can be

modified. [3] This makes it possible to control different characteristics (as mentioned above),

and hence require a more accurate manufacturing.[2]

The granulation process also shows some disadvantages. The production process is

rather complex. Even with the knowledge of all the variables during this process, it is still

impossible to predict the behaviour of the granule in an accurate way. Developing a granule

formulation and production process mainly starts with the trial and error approach. For

3

example, some formulations request a high amount of excipients, which can influence the

granulation process. Binder viscosity, powder wetting and liquid distribution are some of the

parameters that have an important influence on the process. When scaling up to an

industrial scale, this leads to a significant failure rate. [6] During granulation, optimal

conditions are required. For instance, when the moisture content of the granules is too

high, they may end up sticking to the punch tip faces in the tabletting machine. If the

granules are too dry, there may occur problems after tabletting. [7] Also, agglomerates are

relatively big particles that can cause problems when processing formulations of low-dosed

drugs. In such cases, the accuracy of the dosing of the granules is extremely important and

sometimes it can be better to use a powder mixture instead of granules. [5] When the

granulation process is not conducted properly there can be problems in down-stream

processes, such as caking, segregation and poor tabletting performance. [3]

1.3. CURRENT GRANULATION TECHNIQUES

There are three main classes of granulation: wet-, dry- and melt granulation. The

most suitable technique for granulation depends on the physicochemical properties of the

active pharmaceutical ingredient (API) in the formulation. Properties such as hygroscopicity,

melting point, glass transition temperature, etc. will determine which granulation technique

needs to be applied.

During wet granulation, liquid is added to the powder mixture, to form agglomerates.

After drying, granule formation is achieved. The most common equipment used for wet

granulation are fluidized bed mixers and high shear mixers. Not all API are resistant to high

moisture levels and due to the high work rate of these machines the temperature can rise,

which may affect certain heat sensitive drug substances. Therefore, alternative methods are

also necessary.

The most popular technique for dry granulation is roller compaction. Granulation

occurs by inducing a high pressure to the powder mixture which will deform, fragment,

densify and bond the material. However, some problems may occur using this technique.

Because of the lack of binder liquid, the particles will be dry, thus leading to a higher amount

4

of fines and dust. Also, the compressibility of the material will decrease due to the

compaction process. Therefore, it is better to avoid roller compaction when the desired

formulation contains high proportions of the drug substance. [8]

Melt granulation methods are commonly used for hygroscopic API. During the

process, the temperature stays below the melting point of the API but above the glass

transition temperature of the binder. The powder mixture with low-melting binders typically

melts or softens at relatively low temperatures (50-80°C). [9] Due to the melting of the

materials in the powder mixture, the agglomerates are formed when temperature decreases

again. Otherwise, water of crystallization is exempted out of the powder bed and serves as a

kind of binder liquid. For melt granulation fluidized bed granulators, high shear mixers and

twin-screw extruders can be used. Extrusion is the most promising melt granulation

technique because the rate of energy input can be easily adjusted. Hot melt extrusion has

proven to be useful to improve the bioavailability of poorly soluble drugs by formation of

amorphous states. [8]

1.4. GRANULE GROWTH DURING WET GRANULATION PROCESS

Figure 1.2: Modern approach on wet granulation process [3]

5

During the wet granulation process particles are bound together by capillary and

viscous forces into agglomerates. After drying or sintering, more permanent bonds are

formed. When determining wet granulation behaviour three processes play an important

role: wetting and nucleation, consolidation and growth, breakage and attrition (Figure 1.2).

1.4.1. Wetting and Nucleation

This is an important stage in the granulation process but cannot be seen separately

from other effects like aggregation and attrition. During this process, the liquid binder is

brought into contact with the powder mixture, and distributed homogenously in optimal

conditions. The area where the binder and powder surface makes first contact is called the

nucleation zone.

Nucleation can occur by two different mechanisms: distribution or immersion (Figure

1.3). When the primary powder particles are bigger than the binder droplets the binder is

distributed among them. As soon as the binder droplets are bigger than the powder particles

they are immersed by the droplet. [2]

Figure 1.3: Nucleation mechanisms. a: Distribution; b: Immersion [3]

Binder dispersion is an indication of the mixing quality between the powder mixture

and granulation fluid. This has a great effect on the growth rate of the granules. When the

granulation liquid is distributed homogenously, the produced granules have the similar

physical properties and a narrow particle size distribution (PSD). The quality of liquid

dispersion mostly depends on the used combination of powder mixture and granulation

liquid, and the method of binder delivery. A bimodal nuclei size distribution is inevitable,

when an instantaneous uniform binder dispersion cannot be achieved by the binder delivery

method. [3]

6

1.4.2. Growth and Consolidation

Granule growth starts when liquid is added to the powder mixture, and can continue

for some time after liquid addition has been completed. It can occur by a series of different

mechanisms. When two granules collide they won't necessarily be linked together. The

mechanical properties of the granules and availability of granulation liquid determine

whether or not two particles will form a larger agglomerate. Aggregation is the process

when two larger granules collide and stick together. When small particles stick to the surface

of a large granule it is called layering. As discussed in 1.1. wet granules can occur in different

states. [3]

Consolidation is the process during which granules collide mutually and with the

equipment surfaces. This will reduce their size and porosity by squeezing the entrapped air

and granulation liquid to the surface. When the porosity decreases, the granules will be

firmer, thus less dust will be generated and the handling will be better during downstream

processing. The dissolution rate will also decrease which is undesirable in certain cases. [3] A

proper management of these variables is necessary to maintain optimal granule properties.

1.4.3. Attrition and Breakage

Granule breakage can be divided into two phenomena: attrition of dried granules and

breakage of wet granules. Breakage can be useful in cases where maximum granule size is

limited. Attrition on the other hand generates dust derived from the dry granules.[3]

1.5. CONTINUOUS GRANULATION

Continuous processing is still in its initial phase, in the pharmaceutical industry. These

techniques require new machines, employee training, process validation and regulatory

documents. So, it will be a big investment for most companies. [10] On the other hand, it can

be said that continuous granulation is the better one for the future, compared with batch

granulation, so it is probably worth investing in it.

Continuous twin-screw granulation offers various advantages over batch-to-batch

granulation. As it is possible to continue the process 24/7 the production capacity will grow

eliminating the scale up requirement and intermediate storage. [11] Also, the process

7

operates at steady-state which results in more uniform granule properties. [12] Specific to

twin-screw granulators, they can be put in a continuous line with dryer, product control

hopper and tabletting machine. This allows an on-line product quality measurement and

real-time product release. The two screws in the granulator are self-cleaning minimising

accumulation of material and possible degradation. [4] And finally from an economic point

of view, a continuous process offers a reduction in costs and time. [13] Overall, a shorter

time-to-market period can be achieved and the stock can be minimized. [14]

By implementing continuous granulation into the pharmaceutical industry, the

"Quality by Design" (QbD) approach will play an important role. These guidelines are meant

to enable a manufacturing process to continuously deliver a high quality product. QbD is

defined by the International Conference on Harmonization (ICH) in the Q8 guidelines. By

following these, it will become possible to reduce the costs of production, improve process

efficiency and still guarantee a high quality standard. The goal is to make possible real-time

release of pharmaceutical products. [10,15] Process Analytical Technology (PAT) strategies

should already be implemented during process development rather than only on the

manufacturing sites. Such data analysis approaches would enhance the product knowledge

and process understanding. [16]

1.6. TWIN-SCREW GRANULATION

In this report a high-shear twin-screw granulator, which is part of the ConsiGmaTM-25

continuous solid dosage tablet manufacturing line by GEA Pharma Systems, was used. Using

this equipment it is possible to change several input factors, which can affect the granule

properties and further downstream processing. This technique has not been studied very

extensively yet, so the effect of the equipment parameters on the quality of the final

product was studied using the "Design of Experiments" (DOE) approach.

The TSG is interesting to use due to the possibility to change multiple variables such

as screw design, screw speed, powder feed rate, liquid addition, etc. to optimise the

performance. Besides, it is also possible to perform melt granulation with the same

equipment. [17]

8

1.6.1. ConsiGmaTM-25 system

Figure 1.4: ConsiGmaTM-25 (1) Continuous feeder (2) Continuous twin-screw high-shear granulator

(3) Granulation liquid vessel (4) Six parallel cell fluid bed dryer (5) Product control hopper [10]

The ConsiGmaTM-25 unit (Figure 1.4) is a continuous manufacturing line which

consists of three major parts: a continuous twin-screw high-shear granulator (2), a six

parallel cell fluid-bed dryer (4) and a discharge system (5). [10] The discharge system is

equipped with a mill which is used to remove the oversized granule fraction. [11] It is also

possible to connect this system to an in-line tabletting machine, which makes a continuous

'from powder to tablet' manufacturing system. [18] The granules and tablets made by this

system show highly repeatable results. [12]

However, twin-screw granulation requires a stabilization period before reaching a

steady-state condition. This state is defined by measuring the torque, temperature and

granule quality. [19] Due to this stabilisation period it is not possible to change the

equipment variables to other values and have the desired granulation yield immediately.

However, the granules produced during this stabilization period were observed to be in

compliance with the specifications. [12]

9

1.6.2. Screw configuration

Figure 1.5 shows the two identical screws, used in the TSG, consisting of multiple

elements. The biggest part of the screws are the conveying sections that will transport the

material forward. During this transport, the granulation liquid is added to the powder. The

powder and granulation liquid are mixed in mixing sections containing kneading elements.

These elements can be placed at stagger angles of 30°, 60° or 90° and their number can vary.

It is also possible to have several mixing sections in the screws. The composition of the

screws can be adapted by the operator to obtain the most efficient screw configuration for a

certain formulation and process condition.

Figure 1.5: Screw configuration of co-rotating screws in twin-screw granulator [10]

1.6.3. Granule growth during twin screw granulation

High shear mixer and fluidized bed granulator are closed batch systems in which

granulation rate processes, as discussed before in section 1.3., occur simultaneously. Twin

screw granulation is a continuous process where certain granulation mechanisms may be

dominant within a region along the length of the screw. Nucleation, growth and breakage

occurs in succession only with respect time, but also with space. The screws used in this

experiment consisted of a conveying section, kneading section, second conveying section,

second kneading section and a third conveying section that discharges the granules. In the

first part of the screws wetting occurs resulting in over-wetted bigger sized agglomerates.

The kneading section in downstream of the screw creates a high shear zone and hence

causes distributive mixing of the wetted and not-yet wetted powder. This leads to "squeeze-

out" of granulation liquid from the over-wetted agglomerates which results in size reduction

by consolidation and breakage of granules. Aggregation and breakage are observed in the

next conveying section. In the second kneading section aggregation and consolidation

occurs. And in the last conveying section breakage determines the final granule size. Thus,

the granule growth in a TSG is regime separated, which means that wetting and nucleation

10

are physically separated from consolidation and growth allowing the operator to control

granule properties better. [20] Along the length of the screws the granules became stronger.

Their size was limited by the free space between the screws and granulation barrel.

1.6.4. Influence of equipment parameters on granule quality

Granules with good flow properties, large size, less fines and narrow PSD, can be

achieved with more granulation liquid addition, higher screw speed and a screw

configuration that provides sufficient mixing. [2,20] The liquid-to-solid ratio (L/S) showed to

have a significant influence on the granule properties. Changing the powder feed rate and

the L/S-ratio results to changes in size, shape, structure, porosity, strength and dissolution

time of the granules. This resulted in higher torque values during the process as did a higher

number of kneading elements. This also caused an increase in temperature of the

granulation barrel. A granule mixture with less fines and larger agglomerates was achieved

by increasing the barrel temperature in a controlled way. [17] When increasing the screw

speed and decreasing the moisture content a reduction in residence time was observed. [18]

Using different starting materials, both the granule and tablet characteristics are influenced.

[14] The solid state of wet granules is influenced by the temperature of the granulation

barrel and the amount of granulation liquid added. The powder feed rate also showed some

correlation with the solid state, but was found to be less significant. [10]

A bimodal size distribution is observed when using TSG. [14] This should be avoided

to reduce the risk of segregation in downstream processing. When granulating using TSG,

the granule mixture shows a broad PSD containing large amounts of fines (<250 µm) or large

amounts of oversized granules (>1400 µm). This bimodal size-distribution could possibly be

explained by the presence of binder-rich zones. Due to the short residence time in the

granulation barrel, the powder mixture and binder liquid do not have much time to interact,

which could lead to an inhomogeneous spread of binder. In earlier experiments no binder-

rich zones could be detected. This is an indication that binder liquid is distributed

homogenously amongst the granules. [21] The bimodal size distribution has shown to be

more dependent on the granulation technique itself rather than the liquid distribution. [18]

11

1.6.5. Quality by Design

As discussed in section 1.5. QbD is critical in continuous granulation techniques. For

example, it is important to know the solid state of the API during processing and storage to

assure a safe product. Raman- and NIR-spectroscopy (780-2500 nm) can be used to study

solid state behaviour while wet granulation and the in-line particle probe can be used for

PSD measurement. [10]

Another PAT-tool making it possible to control PSD during continuous granulation is

the EyeconTM 3D particle characterizer that will be used during the experiments in this study.

This device works on the principle of 3D imaging that can be used for particle size-, shape-,

and surface analysis. Studies showed the PSD to be consistent with sieve analysis. The

camera is sensitive to variations during the process such as granule size and particle count.

[22]

Process analytical technology can be useful for in-line measurement of the granule's

critical quality attributes making it possible to obtain a real-time release of a product. [18]

Data-driven and mechanistic modelling and understanding of the process shows

perspectives in systematic process design and quality control instead of trial and error

approach. [23] However, this requires a highly reliable method to provide sufficient control.

For measurements to be sufficiently representative, a rigorous calibration and validation of

the twin-screw granulation process model is needed. [19]

1.7. REGIME MAP THEORY

Granulation mechanisms in twin-screw granulators are not as well studied as those in

batch-wise equipment. A regime map semi-quantitatively explains the variation in

granulation behaviour. By developing a regime map, it becomes possible to compare the

input variables of the process and equipment with the granule properties. Earlier regime

maps for co-rotating twin screw granulation have been developed based on those from high

shear granulators [2]. The studied parameters were L/S-ratio, screw speed and mixing

geometry of the screw. The screw speed had a major influence on the fill level of the

granulation barrel. An increase in fill level showed a direct correlation with an increase in

friction force and torque. L/S-ratio also showed a correlation with fill level and torque. As the

12

L/S-ratio increased, so did the fill level and torque. After reaching a critical value the mixture

became over-wetted and the torque decreased again. Depending on the screw configuration

three regimes were suggested: granulation regime, extrudate regime and blocked regime.

Granules with a large size and narrow PSD were obtained with more binder addition, a

higher screw speed and screw configuration that provided sufficient mixing. [2,24]

This approach can have economic benefits by making it possible to predict granule

growth behaviour for a given formulation with a limited set of data. It can also be a tool for

the comparison of interaction between different powder mixtures and liquid binders. [6]

A regime map approach also shows possibilities for QbD applications in granulation

processes. The mechanistic basis derived from a regime map is better than data-driven

models, and the granulation design space can be determined more effectively and with

fewer experiments. [15]

13

2. OBJECTIVES

Granulation is a crucial intermediate step in pharmaceutical preparations such as

tabletting, and can also be used as an end product. This technique improves product

properties making it easier to handle in industrial processes. [24] Currently, continuous

manufacturing in pharmaceutical industries is still in its initial phase. The wet granulation

process step in continuous manufacturing can be achieved by a twin-screw granulator. With

this equipment it is possible to make a continuous 'from powder to tablet' manufacturing

system. [18] This offers multiple advantages over classical batch-to-batch granulation.

[4,13,14] TSG is a recent developed technique and so far not much research has been done.

In the TSG (ConsiGmaTM-25), it is possible to manipulate multiple factors which

provides an opportunity to easily adjust the equipment for different formulations. By using

the regime map theory, it is possible to semi-quantitatively explain variations in granulation

behaviour when using TSG. In this study, a regime map has been developed to semi-

quantitatively explain the influence of multiple input factors on the granulation behaviour.

The factors studied are powder feed rate, liquid addition, screw configuration (number of

kneading elements), screw speed, stagger angle of kneading elements, barrel temperature

and method of binder addition. When using a continuous manufacturing system in

pharmaceutical processes, it is necessary to take samples out of the system and perform

quality analysis on them. However, a transfer line in continuous manufacturing systems is

closed, so it is difficult to take samples. Thus, the EyeconTM system based in-line solution for

PSD analysis was developed, which uses 3D imaging to calculate the diameter of the

granules in real-time. The performance of this equipment has also been evaluated.

Screening tests have been performed to find optimal granulation conditions for the

used formulation. The full ConsiGmaTM-25 manufacturing line has been used equipped with

the EyeconTM system. Afterwards the use of sieve analysis allowed a comparison with the in-

line PSD data. Also the friability, bulk- and tapped density will be determined to analyse the

granule quality. In a second series of tests, the regime map has been developed based on

the acquired data of the screening experiment. This allows a closer look into a certain region

within the design space.

14

3. MATERIALS AND METHODS

3.1. PREPARATION OF GRANULES

3.1.1. Granulation process

The ingredients used for production of the granules were α-Lactose monohydrate

200M (Pharmatose® 200M, DFE-Pharma, Caldic, Hemiksem, Belgium) as a filler and

polyvinylpyrrolidone (PVP) (Kollidon® 30, BASF, Ludwigshafen, Germany) as a binder.

Distilled water was used as granulation liquid. Granulation experiments were performed

using the ConsiGmaTM-25 system (GEA Pharma Systems, ColletteTM, Wommelgem, Belgium).

After preliminary tests, the liquid addition was kept between 8 and 9%. The liquid addition is

a measurement for the L/S-ratio. Lower liquid addition resulted into a very high amount of

powder that did not granulate, higher liquid addition was possible but the granules were too

big and clotted between the EyeconTM-slide and wet transfer line. This restricts the ability of

the experimental design to study the influence of the liquid addition. In this thesis, the terms

powder feed rate and throughput will be used as synonyms. 3 to 4 kg of sample was

collected per batch. The air flow of the dryer was 420 m/s and the filling time of each drying

cell was 270 s for 10 kg/h and 180 s for 25 kg/h. The drying time was 380 s. When using dry

addition of PVP in the pre-blend powder mixture, it was mixed using a tumbling mixer

(Inversina, Bioengineering, Wald, Switzerland) for 15 min at 25 rpm. PVP-solutions for wet

binder addition were prepared using a rotor-stator mixer (Silverson L4R, USA)

The TSG had two segments: a feed segment to transport the powder, and a work

segment in which the powder was mixed, wetted and granulated. The powder dosing unit

worked on a loss-in-weight principle. This system used a dual-port injection of granulation

liquid from the injection nozzles into the granulation barrel, leading to lower and more

stable torque and better distribution of the granulation liquid. This has proven to be more

effective than single port injection. [18] The liquid addition was done using a peristaltic

pump, controlling the flow according to a loss-in-weight principle. The temperature of the

granulation process was controlled with a temperature control unit connected to the

granulator jacket. The screw in the granulation barrel had a length-to-diameter ratio of 25:1.

The wet granules are discharged into a vacuum wet transfer line and transported to the six-

segmented fluid-bed dryer. The granules were dried by hot air, for which temperature and

15

flow rates were controlled. The dryer is rather semi-continuous due to which the granules

were dried in six "mini-batches", and were sequentially discharged into the dry transfer line.

In the product transfer hopper, the granules were unloaded gravitationally. During

granulation, process parameters like torque and jacket temperature were recorded by the

equipment.

3.1.2. Experimental design

The experimental range for the screening design was determined by carrying out

preliminary tests, especially considering liquid addition. A D-optimal experimental design

was used to evaluate the influence of process variables on the granulation process. D-

optimality is a commonly used principle for screening and optimization of the process. The

design attempts to span an area as big as possible within the design space to maximise the

obtained information with a minimum of experiments. This is useful to save time and costs.

The experiments are typically conducted on the boundaries of the experimental range. [25]

The process variables studied were: powder feed rate (10 - 25 kg/h), liquid addition

(8; 8,5 and 9 %), screw configuration (1x2-, 1x6- and 2x6 kneading elements), screw speed

(500-; 900 rpm), stagger angle of the kneading elements (30 - 60°), barrel temperature (25 -

40 °C) and binder addition (Dry or Wet). When the 2x6 screw configuration was used, the

two kneading zones were separated by a conveying zone with the same length as the

kneading zones to limit the accumulation of the material. During all the experiments, liquid

addition occurred just in front of the first kneading zone.

DOE was made using the Modde software (Umetrics, Umeå, Sweden). A 46 run

experimental design (with 3 centre points) was used for these experiments, and selected

based on G-efficiency out of a series of designs proposed by the software. G-efficiency is a

value which is an indication for the performance of experiments and predictive value. The

exact experimental design can be found in Annex 1.

Based on the results of the screening design and a theoretical point of view a second

series of regime map experiments was set up. Here the binder addition was always wet and

several other variables were fixed at a constant value. The temperature was kept at 25°C and

a 60° stagger angle was used. The 1x6 and 2x6 screw configuration were tested, liquid

16

addition was varied over 8-, 9- and 10%, powder feed rate over 10-; 17,5- and 25 kg/h and

the screw speed over 500-, 700- and 900 rpm. The range for liquid addition could be

expanded to 10% given the fact that the dryer unit was not used. After collecting the

granules, they were dried for 24h in an oven at 40°C.

3.2. GRANULE EVALUTION

3.2.1. Real-time PSD analysis

During the granulation process, an in-line analysis of the EyeconTM 3D Particle

Characterizer (Innopharma Labs, Dublin, Ireland) was used for analysing the PSD of the

granules. This device was installed in-line between the granulation barrel and wet transfer

line and captured images every 2 s. The granules were photographed by the EyeconTM using

an in-house developed slide making the granules visible.

Figure 3.1: Working principle of the EyeconTM equipment [26]

The 3D-imaging system captures sharp images of the moving granules using 1 µs

illumination pulses which enable capturing moving particles up to 10 m/s. It is possible to

use this equipment either in-line (as done during these tests) or off-line. The LED lamps are

located around the circumference of the EyeconTM camera and are emitting RGB light to

illuminate the sample which creates a 3D surface map. The colour distribution provides

topological information about particle edges for size determination. By using the principle of

17

photometric stereo imaging, it is possible to extract 3D information from 2D images. This

should also detect overlapping particles. The size of the particles was then estimated by

calculating the maximum and minimum diameter after fitting an ellipse on the particle edges

as shown in Figure 3.1.

The software used by the EyeconTM calculates the volume-based size distribution of

all the granules measured during each run. The D10, D25, D50, D75 and D90 values for each run

were derived based on this distribution. Because of the transformation from number based

to volume based values the larger particles have a relatively big influence on the complete

distribution. This indicates that D10 values are better to examine PSD than D50 values. [22]

Using the raw data collected by the EyeconTM, it is possible to look at all the granules

measured by the equipment. For a high number of size fractions, the number of granules

observed in that fraction is displayed, making it possible to build in virtual sieves and

compare these results with data from sieve analysis.

3.2.2. Determination of torque and specific mechanical energy

The ConsiGmaTM-25 system keeps track of certain parameters during the granulation

process. Torque values were extracted from each run and the average value for the run

(after steady state conditions were reached) was calculated. The torque value represents the

energy required to rotate the screws in the barrel, which experiences the frictional forces by

the material during the process. Specific mechanical energy is a scale-independent measure,

used to capture information about energy introduced to the system. It is based on the

mechanical torque, screw speed and material throughput during the granulation process, as

shown in equation 3.1. [27]

(Equation 3.1)

Where: SME = Specific Mechanical Energy (J/kg)

T = motor Torque (Nm)

N = screw speed (rpm)

Q = powder feed rate (kg/min)

18

3.2.3. Sieve analysis

PSD was determined off-line using sieve analysis performed with the Retsch VE 1000

sieve shaker (Haan, Germany). 100,0 g of granules were sieved for 5 min at an amplitude of 2

mm using a series of sieves (75, 150, 250, 500, 710, 1000, 1400 and 2000 µm). After sieving,

the mass of the granules was determined gravitationally. All these tests were performed in

duplicate. Afterwards, the PSD was calculated. [28] For sieve analysis as well as in-line

analysis of PSD, the results were expressed as a fraction. Three fractions were considered:

Fines (<150µm), Yield (150-1400µm) and Oversized granules (>1400µm).

During examination of these results it was important to note that the granules were

not spherical (Figure 3.2). Thus, the orientation of a particle could determine whether or not

it can pass a certain sieve. Although a lengthwise orientation seems rather improbable.

Figure 3.2: Picture of granules produced during regime map tests after drying

3.2.4. Friability

The friability of the granules is a value for expressing breakage through stress. The

result is maximum 30% for good granules. A low value is an indication for stronger granules.

Granules with a high friability tend to break easier and are more difficult to handle. Friability

was determined using a friabilator (PTF E Pharma Test, Hainburg, Germany) at a speed of 25

rpm for 10 min. 10 g of sample was subjected to the analysis together with 200 glass beads

(mean diameter 4 mm) to falling shocks. This was conducted in triplicate. Prior to the

determination, the granule fraction < 150 µm was removed to assure the same starting

conditions. Friability was calculated using equation 3.2. [29]

19

(Equation 3.2)

Where: F = Friability

Iwt = Initial weight

Fwt = Weight after friability test

3.2.5. Flowability

The bulk volume of 30,0 g granules was recorded in a 100 mL measuring cylinder.

After 1250 taps with a tapping machine (J. Englesman, Ludwigshafen, Germany) the volume

was recorded again. 3 repetitions per batch were made. Bulk- and tapped density were

calculated using equation 3.3 and 3.4. The compressibility index was calculated using

equation 3.5. [30] Calculation of compressibility index gives us an idea on how easy it is to

handle and pour the powder. A smaller compressibility index indicates a free-flowing batch

of granules which is easy to handle and will produce less dust. A high index is typical for a

poor-flowing granule batch.

(Equation 3.3)

Where: ρi = bulk density

V0= volume before tapping

(Equation 3.4)

Where: ρf = tapped density

V1250= volume after 1250 taps

(Equation 3.5)

Where: C% = compressibility index

ρi = bulk density

ρf = tapped density

20

3.3. EVALUATION OF RESPONSES

Due to the D-optimal design used for DOE, an interaction model was used to estimate

the influence of process parameters on the granule properties. Using the Modde software

summary of fit- , coefficient- , effect- and 4D contour plots were made to evaluate the

experiments. The summary of fit plot is a tool to evaluate if the model is statistically correct.

The R2 value measures if the model fits to the experimental data. For a good model, a

minimum R2 value of 0,5 is required. Q2 is related to further predictions by the model. If Q2

is higher than 0,1 the model is suitable to predict responses between two conducted

experiments. The model validity should be at least 0,25 for the model to be significant.

Reproducibility shows the variation on a model, a value of 0,5 or higher should be achieved.

[25]

A coefficient plot shows the regression coefficients with confidence intervals. Here it

is possible to see if the process variable is correlated with a certain response. A coefficient is

significant when the confidence interval does not cross zero. The effect plots display the

change in response when a factor varies from its low level to its high level, keeping all other

factors at their averages. Coefficient plots and effect plots often show the same results, so

most of the time only the effect plot has been considered in this study as these are ranked

by decreasing significance of the effect. In these plots, it is also possible to visualise

interactions (indicated by an *) between different parameters on the same response. [25]

For example it could be possible that screw configuration*powder feed rate show a

positive correlation for a certain response. This means that when the screw configuration

increases (more kneading elements) and the powder feed rate increases the response will be

lower. Contour plots are the visualisation of a response that is plotted against multiple

factors.

21

4. RESULTS AND DISCUSSION

4.1. USE OF EYECONTM DATA

The D-values (D10, D25, D50, D75, and D90) calculated by the inline PSD analyser were

introduced as a response in the D-optimal design and data shown in Annex 1 was fitted by

means of the partial least squares (PLS) method. However, the summary of fit plot showed

poor predictive results as indicated by low Q2 value which is a measure of the percentage of

the variation of the response predicted by the model according to cross-validation (Figure

4.1). Only the predictive value for D10 and D90 looked reliable. Therefore, a fitted model for

this data could not be used for further investigation.

Figure 4.1: Summary of fit plot - EyeconTM data

According to the data generated by the inline measurements of PSD, the D10

diameter varied from 211,5µm to 888,5µm. This suggests that less than 10% of the granules

produced in each batch were fines (<150µm), which is a rather high result. Given the fact

that a volumetric method was used to calculate these values it could be a possible

explanation for the large D10 values. The volumetric method transforms a number

distribution into a volume distribution and hence causing a big influence of the larger

particles on the PSD compared with small particles. [22] However, this assumption could not

22

fully explain the reason behind the large differences with the expected data (large amounts

of fines for runs with low L/S). A method making it possible to compare the EyeconTM data

with a more reliable method was required.

By using the data generated by the EyeconTM, it is possible to build in virtual sieves

and compare these results with actual sieve analysis. Here the amount of fines (f<150µm)

varied from 0,07-3,07%. Annex 2 contains the calculated sieve fractions after the sieve

analysis performed with the same batches. This data was used to validate the results

obtained by the EyeconTM. Here the fraction of fines varied from 13,34-73,61%. These values

are much higher and more likely to be correct compared with those generated by the

EyeconTM, which indicates a problem concerning the detection of small granules and fines. In

an earlier paper, this equipment showed to be reliable [22], so multiple variables could

cause problems.

Some possible explanations could be that the image analysis algorithm of the

equipment recognized a high amount of fines as "background surface" instead of individual

particles, multiple small particles could be mistaken for one big particle, the velocity of the

fines was too high, and there were too many particles in one picture, etc. There could also

be a problem with the focus of the EyeconTM that allows to detect particles at a certain

distance, but in an industrial process not all particles fall down right next to each other. The

variation may also occur due to the in-house developed PSD measurement slide (using

Plexiglas) for the EyeconTM. As an example, three images taken by the EyeconTM are shown in

Figure 4.2 each with different particle sizes and amount of particles.

Figure 4.2: Images taken by EyeconTM during experimental run N45 (Left: high amount of fines;

Middle: multiple small particle; Right: large particle)

23

A possible source of deviation outside the EyeconTM device could be the placement of

the equipment. In the tests performed during granule production, the device was placed

between the granulation barrel and the wet transfer line. After drying, it is normal that the

granules lose a little volume due to evaporation of the granulation liquid, so the device will

detect more big granules than the sieve analysis results show. Also, the dryer from the

ConsiGmaTM-25 system uses hot air to dry the granules, the air flow causes collision of the

granules with each other and with the inner wall of the dryer. This possibly causes additional

breakage of the granules. However, the difference should not be as big as detected.

Therefore, extra tests should be performed placing the EyeconTM after the dryer at the dry

transfer line.

4.2. INFLUENCE OF PROCESS VARIABLES ON PSD

Figure 4.3: Summary of fit plot - Sieve analysis (Columns from left to right: <75µm, 75-150µm, 150-

250µm, 250-500µm, 500-710µm, 1000-1400µm, 1400-2000µm, >2000µm, Fines <150µm, Yield 150-

1400µm and Oversized >1400µm)

For evaluation of process variables on PSD, the raw data from sieve analysis was

used. To reduce the number of fractions to be discussed only Fines (<150µm), Yield (150-

1400µm) and Oversized (>1400µm) were considered. These fractions contain information

out of all the fractions measured using sieve test. The calculated data for these fractions are

represented in Annex 2.

24

The summary of fit plot based on the sieve analysis (Figure 4.3) shows that all R2

values are above 0,5 so the model is significant. Q2 values are all above 0,1 indicating a good

predictive value of the model. The high model validity i.e., always higher than 0,25 suggests

that there is no lack of fit of the model. The variation of the values shown by the

reproducibility is above 0,5 except for the 150-250µm interval which also shows rather low

R2 and Q2 values. By calculation of Fines, Yield and Oversized fractions the model validity

increases due to more consistent results. This increases the predictive value of the model.

4.2.1. Fines (<150µm)

The effect plot (Figure 4.4) shows a negative correlation between screw

configuration, powder feed rate and screw speed*temperature. Positive correlations are

found for stagger angle, screw configuration*binder addition, screw speed*stagger angle,

powder feed rate*screw configuration and liquid addition* binder addition. Dry binder

addition is left out of the effect plots as the effect is always exactly the opposite of wet

binder addition.

Figure 4.4: Effect plot for the Fines (% <150µm) fraction based on the sieve analysis data.

(PFR= powder feed rate; Liq= liquid addition; Screw = screw configuration; Speed = screw speed;

Angle= stagger angle; Temp = temperature; Bind= binder addition)

25

The contour plots visualize the effect of several factors on the amount of fines. To

analyse the influence of liquid addition, powder feed rate, screw speed and screw

configuration, the stagger angle, barrel temperature and binder addition were kept constant

at 60°, 25°C and Wet respectively. These constants are most commonly used during the

production of granules with this equipment. To evaluate the effect of the constant factors,

the stagger angle was later adjusted to 30°, temperature was increased to 40°C and binder

addition was changed to Dry one at a time to see the effect of each factor individually.

Figure 4.5: 4D contour plot Fines (%<150µm) - Sieve Analysis.

(A colour variation from blue over green to red shows an increase in % fines)

Figure 4.5 shows an increasing number of kneading elements, which is reflected in

the screw configuration from 1x2 over 1x6 to 2x6, reduces the amount of fines produced. A

1x2 screw configuration provides insufficient mixing compared with 2x6 so the granules

cannot grow enough. Temperature had an insignificant effect as shown by the effects plot

(Figure 4.4). However the contour plots show a decrease in the percentage of fines (Annex

3.3) especially at 2x6 screw configuration and high screw speed. An increased solubility of

the binder can be an explanation together with better mixing and low fill level (with low

26

friction forces as a result) for these results. Besides, dry binder addition also leads to an

increase in fines (Annex 3.4), when using wet binder addition the binder is pre-solved into

the granulation liquid which probably provides better binder dispersion.

Further, although an increase in liquid addition had a positive effect in terms of

reduction in fines fraction, this effect was not significant probably due to a small liquid

addition range (8-9%) which could not provide sufficient information. Lower screw speed led

to a reduction in the amount of fines as more time was available for mixing of the powder

and granulation liquid. An increase in the powder feed rate also caused a reduction in the

amount of fines. When looking into the effect plot (Figure 4.4), more fines were produced at

an increased stagger angle. This was also reflected in the contour plots. A higher stagger

angle of 60° led to higher friction forces compared to a 30° angle, which caused more

breakage and hence resulted in more fines. Figure 4.5 with additional contour plots to

further explain these results can be found in Annex 3.1-3.4.

Overall, more fines were produced with a lower number of kneading elements, at

higher screw speed, with low liquid addition, low powder feed rate, 60° stagger angle, at low

temperature (25°C) and with dry binder addition. Among all the parameters, screw speed,

powder feed rate and stagger angle are the most significant factors.

4.2.2. Oversized fraction (>1400µm)

For the oversized fraction, the effect plot and contour plots were made the same as

earlier that for the fines-fraction. The effect plot for oversized fraction (Figure 4.6) showed a

positive correlation for screw configuration, powder feed rate, wet binder addition and

powder feed rate*stagger angle. A negative correlation was found for stagger angle and

screw speed. Again no significant effects were observed for temperature and liquid addition

27

Figure 4.6: Effect plot - Oversized fraction (%>1400µm) - Sieve analysis

The contour plots (Figure 4.7) show that the powder feed rate is positively correlated

with the amount of oversized granules. The liquid addition does not show a significant

effect, but a higher liquid addition seems to favour production of oversized granules. A 2x6

screw configuration resulted in highest, whereas a 1x2 configuration resulted in the smallest

amount of oversized granules fraction. The granules from 1x6 screw configuration stays in

between. A lower screw speed increased the amount of oversized granules. When increasing

the stagger angle from 30° to 60° the percentage of oversized granules produced decreases.

Although a lower temperature seems to favour oversized granules (Figure 4.7 and Annex

4.3), no significant effect was observed in the effect plot. Wet binder addition led to more

oversized granules(Figure 4.7 and Annex 4.4). Additional contour plots can be found in

Annex 4.1 - 4.4.

28

Figure 4.7: 4D contour plot oversized fraction (%>1400µm) - Sieve analysis

The oversized fraction showed a significant positive correlation with powder feed

rate, screw configuration, and wet binder addition. A higher screw configuration (2x6

kneading elements) provides better mixing, so production of bigger granules is possible. Wet

binder addition has the advantage of presolved binder in the granulation liquid, so better

binder dispersion can be achieved. It is better to take a look into the powder feed rate

together with screw speed expressed as SME in the regime map as the ratio between these

two values determine the fill level. A lower stagger angle provides less friction force, hence

production of bigger granules is possible. Here a lower temperature seems to favour

production of oversized granules (Annex 4.3) which does not seem to add up as the same

effect was observed for fines.

29

4.2.3. Yield fraction (150-1400µm)

Figure 4.8: Effect plot - Yield (150µm < % < 1400µm) - Sieve analysis

The yield fraction should be looked at slightly different given the fact that factors

both for production of fines and oversized granules influence the yield fraction. The effect

plot (Figure 4.8) shows positive correlations for screw configuration and screw

configuration*angle. The positive correlation for screw configuration is less distinct as the

one for oversized granules. Negative correlation is observed for stagger angle, screw

configuration*binder (wet), screw speed*stagger angle and powder feed rate*screw

configuration. Again the negative correlation for stagger angle is less strong than the one

observed for oversized granules.

If the 4D contour plot in Figure 4.9 is considered, high screw configuration (2x6), low

screw speed, low powder feed rate and intermediate liquid addition (despite small

experimental range) led to higher yield fractions. However, based on the 30° stagger angle

contour plot, at high screw speed the yield fraction increased (Annex 5.1-5.4). Similar to the

30° stagger angle, the yield fraction also increased at higher temperature. In this case, dry

binder addition influenced the yield fraction positively at 2x6 screw configuration and

negatively at 1x2 screw configuration. The 2x6 screw configuration compensates for the less

effective binder addition with better mixing.

30

Figure 4.9: 4D contour plot yield fraction (% 150-1400µm) - Sieve analysis

In order to determine the optimal conditions to achieve a high yield fraction, it is

more likely to consider the process parameters both for high yield fraction and oversized

fraction together. There is a resemblance between these two fractions when considering the

most influential factors. And by using a mill it is possible to reduce the oversized fraction to

get a bigger yield fraction.

4.2.4. Overview

After examining the results for production of fines and oversized granules, it is clear

that the production variables have opposite effects on the two responses. Table 4.1 shows

this in an overview.

Summarising the information about Fines-, Oversized- and Yield fractions, a high number of

kneading elements i.e., a 2x6 screw configuration is favoured for the yield fraction. The

stagger angle has a negative effect on both yield and oversized fraction and hence a 30°

stagger angle looks most promising to achieve a high amount of usable granules. A high

powder feed rate in combination with high screw speed supports increased yield fraction.

Wet binder addition produced the biggest granules, so the highest yield fraction was

achieved through this method. Higher liquid addition and higher temperature both favoured

production of usable granules but their effects were insignificant.

31

Table 4.1: Effect of process variables on fines and oversized granules. (- indicates a negative

correlation; + indicates a positive correlation; * indicates an insignificant effect)

Fines (%<150µm) Oversized granules (%>1400µm)

Powder feed rate (kg/h) - +

Liquid addition (%) * * Screw configuration (#kneading elements)

- +

Screw speed (rpm) + -

Stagger angle (°) + -

Barrel temperature (°C) * *

Binder addition (Wet/Dry) Dry Wet

Figures 4.10 and 4.11 show contour plots for these conditions for yield fraction and

oversized fraction. A yield fraction of 44-54% can be achieved using a 30° stagger angle, 25°C

barrel temperature (this is used over 40°C because the difference is too small and 25°C

should provide economic advantages in theory), wet binder addition and a 2x6 screw

configuration. The screw speed, powder feed rate and liquid addition can be varied to

estimate the optimal conditions. The conditions described above will also produce an

oversized fraction of roughly 15-30%. The yield fraction could be increased and oversized

fraction was decreased by using a mill. This can limit the granule size to 1400µm, increasing

the yield fraction.

All these results are in accordance with Vercruysse et al. [17] and Tu et al. [2]. Note

that studies used different conditions compared with this study, and Tu et al. also used

different equipment.

32

Figure 4.10: 4D response contour plot yield fraction (30° stagger angle, 25°C barrel temperature

and Wet binder addition)

Figure 4.11: 4D response contour plot oversized fraction (30° stagger angle, 25°C barrel

temperature and Wet binder addition)

33

4.3. GRANULE PROPERTIES

Figure 4.12: Summary of fit plot - granule properties

Properties measured after drying were friability, bulk density, tapped density,

compressibility index and Hausner ratio. These properties were visualized the same way as

earlier with PSD. The summary of fit plot (Figure 4.12) only shows good results concerning

the friability tests. The other properties show poor predictive values as reflected by the low

Q2. This is due to a small spread in results after analysis of the granules. Based on these

results only the model for friability can be trusted. The low model validity for the other

results is probably due to the small variation in these results. All data points are close to

each other so no big differences could be observed.

4.3.1. Friability

The effect plot (Figure 4.13) shows negative correlations for screw configuration and

PFR. Positive correlations are observed for screw configuration*wet binder addition, liquid

addition*wet binder addition and stagger angle. As lower friability is desired, so screw

configuration (high number of kneading elements) and powder feed rate influence the

friability in a positive way. A high stagger angle leads to less firm granules.

34

Figure 4.13: Effect plot - Friability

The contour plots show better (low) friability values when using more kneading

elements (2x6 gives best results), a high screw speed and high powder feed rate. A lower

L/S-ratio results into firmer granules, however, the range is too small to draw conclusions

(Figure 4.14). A 30° stagger angle also improves friability properties. (Annex 6.1-6.4)

To produce granules resistant to product handling, it is recommended to use a screw

configuration with more kneading elements. The 2x6 screw configuration provides better

mixing so binder dispersion increased, leading to firmer granules. A higher powder feed rate

also influences the friability in a positive way, due to increased possibility of consolidation of

granules. A higher fill level will provide better mixing thus a higher quality of granules. The

same conclusion could be made for screw speed. The best results are obtained with a 30°

stagger angle, the 60° stagger angle shows more breakage of granules due to higher friction

forces. Wet binder addition again is preferred. The 2x6 screw configuration does not show a

big difference compared to dry binder addition, but 1x6 and 1x2 screw configurations do.

Liquid addition does not show a big influence because the range is too small, also

temperature does not seem to have a significant effect.

35

Figure 4.14: 4D contour plot - Friability (%)

4.4. GRANULATION REGIME MAP

For the granulation regime map, a second series of tests were performed. Based on

the previous results, a number of variables were kept at a constant level to assure optimal

granulation conditions. The temperature was fixed at 25°C and all the tests were performed

with wet binder addition. The stagger angle of the kneading elements was kept constant at

60°. Previous tests suggested slightly better results at 30° stagger angle, but this was

probably due to the fact that no API was used. The experimental design can be found in

Annex 7.

Four input variables were considered in this design. Two screw configurations: 1x6

and 2x6 kneading elements, the liquid addition 8-, 9-, and 10%, powder feed rate 10, 17,5,

and 25kg/h, and screw speed was also varied over three levels: 500-, 700- and 900 rpm were

used. Afterwards, the PSD of the granules from TSG was determined based on sieve test and

the torque values were extracted from the ConsiGmaTM computer. Torque throughput and

screw speed were used to calculate the SME. The SME has been used to expand the use of

this regime map as it is a scale-independent value and expands the applicability to multiple

scales TSG (i.e. different screw diameter). Figure 4.15 shows the resulting Regime Map.

36

Figure 4.15: Regime Map for α-Lactose monohydrate and PVP granules (Wet binder addition,

barrel temperature 25°C, 60° stagger angle kneading elements)

37

The regime map for the D25, D50 and D75 for 1x6 and 2x6 screw configuration have

been shown in Figure 4.15. Less amount of fines was produced compared with the first

series of screening tests. This was probably due to the fact, for this part of the study the

dryer was not used, which minimized the chance of breaking the granules. In this regard, the

fraction of granules suited for tabletting can be found in the D25 and D50 plots. The 2x6 screw

configuration led to bigger granules and thus a higher yield fraction (% between 150 - 1400

µm).

Besides, a high L/S-ratio yielded bigger granules. This complied with the results from

the previous screening study and after extension of the liquid addition range the effect was

clearer. At 10% liquid addition, a minimum of 525 µm in the D25 plot and a maximum of 2475

µm in the D75 plot was observed. Such an increase in granule size indicates that it is better to

use a lower liquid addition. In this regard, 9% liquid addition was most optimum, with a

range from 525- to 2025 µm. The amount of oversized granules can still be reduced by using

the dryer and a mill. The same trends are observed when using 1x6 screw configuration.

Regarding the SME, the granules look to be the biggest at an intermediate level. They

can be observed around 7 kJ/kg. This value can be obtained using for example the following

conditions: Wet binder addition, Temperature 25°C, Stagger angle 60°, Screw configuration

2x6, Liquid addition 9%, Throughput 10 kg/h and screw speed 500 rpm (Experiment 37 in

Annex 7). These conditions had an average torque value of 2,51. Calculating SME at this

condition: SME = (2,51 Nm x 500 rpm) / 0,167 kg/min = 7515 J/kg or 7,515 kJ/kg. Here a low

throughput and low screw speed yielded to good results due to an ideal fill level for mixing

properties.

Lower SME values also led to a high yield at high throughputs and screw speeds

appear. Experiment 45 in Annex 7: Liquid addition 9%, Throughput 25 kg/h, Screw Speed 900

rpm and Torque 2,50 Nm gives a SME of 5,40 kJ/kg. The same torque as experiment 37 is

achieved, this suggests that high throughput in combination with high screw speed prevents

extra load on the equipment. Working at high speed showed advantages earlier during the

screening tests, which has been confirmed here. This also gives the pharmaceutical industry

the opportunity to work at high throughput to achieve higher production output.

38

Based on these results, it is clear that the throughput and screw speed should be

increased together for optimal results. By this, a favourable fill level can be achieved and the

torque values are also at an optimum level, so the energy input to the TSG is at desired level.

The liquid addition should be kept at an intermediate level as a low liquid addition produces

a low amount of usable granules and high liquid addition causes higher torque values.

The regime map together with the experimental design in Annex 7, can be used as a

guidance for process optimization or up-scaling of new formulations. For other formulations

more regime maps are necessary and more process variables could be investigated.

39

5. CONCLUSIONS

Based on the results of the first screening experiments, an optimal granulation

condition for the specific formulation was determined. A high number of kneading elements

favours production of granules usable for tabletting, so a 2x6 screw configuration is desired.

The 30° stagger angle of the kneading elements produced the highest amount of good

quality granules although in theory 60° should provide better mixing properties. Further, a

high powder feed rate (25 kg/h) in combination with high screw speed (700-900 rpm) and

wet binder addition also yield good results. The powder feed rate and screw speed

determine the fill level of the granulation barrel. An optimal ratio between the two variables

provides good mixing properties, and good results for PSD and friability. Last, both liquid

addition and barrel temperature were insignificant but the contour plots showed slightly

more positive results at the higher values. During the regime map experiments the range for

liquid addition was expanded to better evaluate these results.

The data acquired from the EyeconTM seemed to be unusable for the regime map

experiments. A comparison between this data and results after sieve analysis revealed a

problem in the detection of fines (fraction < 150 µm). Some possible problems were

discussed. The problem could be that the software cannot detect fines, this can be

investigated by off-line measurement of samples with high amounts of fines. There is also a

possibility that the equipment works fine but PSD changed after the measurement during

the drying process. This can be tested by placing the EyeconTM equipment between the dryer

and dry transfer line of the ConsiGmaTM in the future.

The granulation regime map revealed that a relative high amount of yield fraction

(granules 150-1400 µm) can be achieved by using an intermediate liquid addition (9%). The

investigation of SME showed a favourable results for SME values in the range of 5-8 kJ/kg,

which was derived from throughput, torque and screw speed values that are tuned in to

each other (if the throughput is high than the screw speed should also be high). For the

further knowledge regarding the TSG, more variables can be varied in order to determine

the influence of the stagger angle of the kneading elements, higher temperature, the

possibilities of different screw configurations, and the effect of different formulations.

40

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Annex 1: Experimental design

Exp Name Powder Feed Rate (kg/h) Liquid addition (%) Screw Configuration Screw Speed (rpm) Stagger Angle (°) Barrel Temperature (°C) Binder Addition

N1 25 9 1x2 500 60 25 Dry

N2 10 8 1x2 900 60 25 Dry

N3 25 8 1x2 500 60 40 Dry

N4 10 9 1x2 500 60 40 Dry

N5 10 8 1x6 500 60 25 Dry

N6 25 8 1x6 900 60 25 Dry

N7 25 9 1x6 500 60 40 Dry

N8 10 9 1x6 900 60 40 Dry

N9 25 8 2x6 500 60 25 Dry

N10 10 9 2x6 900 60 25 Dry

N11 10 8 2x6 900 60 40 Dry

N12 25 9 2x6 900 60 40 Dry

N13 10 8 1x2 500 30 25 Dry

N14 25 9 1x2 900 30 40 Dry

N15 10 9 1x6 500 30 25 Dry

N16 25 9 1x6 900 30 25 Dry

N17 25 8 1x6 500 30 40 Dry

N18 10 8 1x6 900 30 40 Dry

N19 25 9 2x6 500 30 25 Dry

N20 10 8 2x6 900 30 25 Dry

N21 10 9 2x6 500 30 40 Dry

N22 25 8 2x6 900 30 40 Dry

N23 10 8 1x2 500 60 25 Wet

N24 25 9 1x2 900 60 40 Wet

N25 25 9 1x6 500 60 25 Wet

N26 10 9 1x6 900 60 25 Wet

N27 10 8 1x6 500 60 40 Wet

N28 25 8 1x6 900 60 40 Wet

N29 10 8 2x6 900 60 25 Wet

N30 25 9 2x6 900 60 25 Wet

N31 25 8 2x6 500 60 40 Wet

N32 10 9 2x6 500 60 40 Wet

N33 25 8 1x2 900 30 25 Wet

N34 10 9 1x2 900 30 25 Wet

N35 25 9 1x2 500 30 40 Wet

N36 10 8 1x2 900 30 40 Wet

N37 25 8 1x6 500 30 25 Wet

N38 10 8 1x6 900 30 25 Wet

N39 10 9 1x6 500 30 40 Wet

N40 25 9 1x6 900 30 40 Wet

N41 10 9 2x6 500 30 25 Wet

N42 10 8 2x6 500 30 40 Wet

N43 10 9 2x6 900 30 40 Wet

N44 17,5 8,5 2x6 700 30 32,5 Wet

N45 17,5 8,5 2x6 700 30 32,5 Wet

N46 17,5 8,5 2x6 700 30 32,5 Wet

Annex 2: Calculated fractions after sieve analysis

Exp Name Fines (<150µm) (%) Yield (150-1400µm) (%) Oversized (>1400µm) (%)

Exp Name Fines (<150µm) (%) Yield (150-1400µm) (%) Oversized (>1400µm) (%)

N1 40,37 41,83 17,80

N24 41,21 42,81 15,98

N2 73,61 25,44 0,95

N25 29,61 52,09 18,30

N3 60,34 26,26 13,40

N26 56,45 40,42 3,14

N4 63,79 33,82 2,40

N27 46,94 46,94 6,12

N5 65,20 33,51 1,29

N28 36,00 53,55 10,44

N6 61,82 35,44 2,74

N29 44,36 42,88 12,76

N7 28,54 57,97 13,49

N30 34,02 42,69 23,29

N8 66,57 32,69 0,74

N31 26,20 43,31 30,49

N9 13,24 53,15 33,62

N32 35,40 53,62 10,98

N10 23,48 57,11 19,40

N33 28,51 52,09 19,39

N11 29,75 54,67 15,58

N34 35,12 52,37 12,51

N12 23,05 53,98 22,97

N35 36,90 41,48 21,61

N13 58,81 38,30 2,89

N36 40,51 48,26 11,23

N14 26,16 64,40 9,44

N37 30,71 47,24 22,05

N15 33,07 49,65 17,28

N38 29,89 52,11 18,00

N16 26,48 57,18 16,34

N39 51,76 39,65 8,58

N17 40,77 41,77 17,47

N40 27,77 54,45 17,78

N18 36,42 54,03 9,54

N41 18,16 50,22 31,62

N19 16,35 59,34 24,30

N42 24,25 51,49 24,25

N20 24,65 59,03 16,32

N43 25,94 49,67 24,39

N21 25,73 47,38 26,89

N44 22,31 50,20 27,50

N22 24,93 52,31 22,75

N45 24,03 45,71 30,25

N23 43,56 45,80 10,64

N46 27,80 45,24 26,96

Annex 3.1: Contour plots fines

Annex 3.2: Contour plots fines (30° stagger angle)

Annex 3.3: Contour plots fines (Temperature 40°C)

Annex 3.4: Contour plots fines (Dry binder addition)

Annex 4.1: Contour plots oversized fraction

Annex 4.2: Contour plots oversized fraction (30° stagger angle)

Annex 4.3: Contour plots oversized fraction (Temperature 40°C)

Annex 4.4: Contour plots oversized fraction (Dry binder addition)

Annex 5.1: Contour plots yield fraction

Annex 5.2: Contour plots yield fraction (30° stagger angle)

Annex 5.3: Contour plots yield fraction (Temperature 40°C)

Annex 5.4: Contour plots yield fraction (Dry binder addition)

Annex 6.1: Contour plots friability

Annex 6.2: Contour plots friability (30° stagger angle)

Annex 6.3: Contour plots friability (40°C barrel temperature)

Annex 6.4: Contour plots friability (Dry binder addition)

Annex 7: Regime map design

Experiment Temperature (°C) Stagger angle (°) Screw configuration Liquid addition (%) Throughput (kg/h) Screw Speed (rpm) Torque (Nm) SME (kJ/kg)

E1 25 60 1x6 8 10 500 1,40 4,21

E2 25 60 1x6 8 10 700 1,53 6,43

E3 25 60 1x6 8 10 900 1,56 8,45

E4 25 60 1x6 8 17,5 500 2,02 3,47

E5 25 60 1x6 8 17,5 700 1,52 3,66

E6 25 60 1x6 8 17,5 900 1,61 4,97

E7 25 60 1x6 8 25 500 3,18 3,81

E8 25 60 1x6 8 25 700 1,10 1,86

E9 25 60 1x6 8 25 900 1,16 2,51

E10 25 60 1x6 9 10 500 1,60 4,80

E11 25 60 1x6 9 10 700 1,85 7,77

E12 25 60 1x6 9 10 900 1,87 10,12

E13 25 60 1x6 9 17,5 500 2,46 4,21

E14 25 60 1x6 9 17,5 700 1,83 4,40

E15 25 60 1x6 9 17,5 900 1,82 5,61

E16 25 60 1x6 9 25 500 2,98 3,58

E17 25 60 1x6 9 25 700 1,40 2,35

E18 25 60 1x6 9 25 900 1,28 2,77

E19 25 60 1x6 10 10 500 1,93 5,78

E20 25 60 1x6 10 10 700 1,82 7,66

E21 25 60 1x6 10 10 900 1,68 9,09

E22 25 60 1x6 10 17,5 500 2,45 4,20

E23 25 60 1x6 10 17,5 700 1,84 4,43

E24 25 60 1x6 10 17,5 900 1,55 4,78

E25 25 60 1x6 10 25 500 3,46 4,15

E26 25 60 1x6 10 25 700 1,86 3,12

E27 25 60 1x6 10 25 900 1,24 2,69

E28 25 60 2x6 8 10 500 2,39 7,17

E29 25 60 2x6 8 10 700 2,43 10,21

E30 25 60 2x6 8 10 900 2,39 12,90

E31 25 60 2x6 8 17,5 500 0,30 0,51

E32 25 60 2x6 8 17,5 700 0,93 2,24

E33 25 60 2x6 8 17,5 900 1,09 3,36

E34 25 60 2x6 8 25 500 3,90 4,68

E35 25 60 2x6 8 25 700 2,20 3,69

E36 25 60 2x6 8 25 900 2,61 5,63

E37 25 60 2x6 9 10 500 2,51 7,54

E38 25 60 2x6 9 10 700 2,33 9,77

E39 25 60 2x6 9 10 900 2,18 11,78

E40 25 60 2x6 9 17,5 500 3,80 6,52

E41 25 60 2x6 9 17,5 700 2,26 5,43

E42 25 60 2x6 9 17,5 900 2,18 6,73

E43 25 60 2x6 9 25 500 8,06 9,67

E44 25 60 2x6 9 25 700 3,09 5,19

E45 25 60 2x6 9 25 900 2,50 5,40

E46 25 60 2x6 10 10 500 2,48 7,45

E47 25 60 2x6 10 10 700 2,21 9,27

E48 25 60 2x6 10 10 900 2,07 11,16

E49 25 60 2x6 10 17,5 500 4,84 8,29

E50 25 60 2x6 10 17,5 700 2,78 6,67

E51 25 60 2x6 10 17,5 900 2,35 7,27

E52 25 60 2x6 10 25 500 6,22 7,46

E53 25 60 2x6 10 25 700 3,63 6,10

E54 25 60 2x6 10 25 900 2,18 4,72

Annex 8: Report Francqui-Lectures Prof. Daan J.A. Crommelin

Inaugural lecture (March 7)

The impact of the pharmaceutical sciences on health care starts from discovery of

new medicines and goes all the way over manufacturing processes to distribution and

support in community pharmacies. From a historical point of view the pharmaceutical

sciences sometimes had a bad connotation. Treatments with unclear working mechanisms,

side effects and ineffective drugs are some of the numerous examples that can be

mentioned. Over the years great leaps forward were made. The understanding of working

mechanisms, drug dosage, quality control etc. all improved dramatically the last decades.

One of the newest challenges are personalized medicines, given the fact that all people are

genetically unique medicines will have a different effect on all of them. Also molecular

biology, information towards patients, pharmaceutical business models and lots of other

fields are challenges for the future. In my opinion interdisciplinary fields of work (like the

Google smart contact lenses for glucose levels), product knowledge both in the industry and

community pharmacies and follow-up of patients are only some of the fields in which young

pharmacists can and should play an important role in the future.

Biotech takes over (March 10)

More and more newly approved medicines are developed on a biotechnological

basis. Most of them are relatively big proteins which causes significant problems concerning

the quality of those products. A key aspect in quality control is the process itself (Quality by

Design) which should be controlled perfectly, this is one of the big challenges in upscaling of

this type of products. The bioavailability of biologicals also has to be studied further,

although these drugs are designed to interact with certain specific receptors or endogenous

compounds, some of them still show side effects.

The generic paradigm revisited (March 10)

In the same way that generic medicines are copies of original medicines we could say

that biosimilars are copies of biological medicines. However some remarks should be made.

Generic medicines are in fact the same as the reference product, biosimilars can differ in the

raw material due to slightly different processes, although the clinical effect should be the

same as the reference product. Even over time the process of a certain product can be

changed without changing the label of the product. Keeping this in mind we can distinguish

biosimilars (which have, as the name suggests, similar effects as the reference product) and

bioquistionables which show deviating clinical results compared with the reference product.

It is the role of the industrial pharmacists and regulatory bodies to keep these in control.

Drug targeting (April 7)

Several techniques have been developed to target drugs such as use of liposomes,

polymers and antibodies in combination with nanomedicines but no big breakthroughs yet.

These passive drug targeting strategies look promising in theory but practical use shows

multiple obstacles such as drug release in the wrong places and problems in penetrations of

the blood vessels. Active targeting relies on methods like triggered drug release and local

drug delivery. Triggered drug release for example can be used to treat tumors by distributing

the drug delivery system in big parts of the body and releasing the drug only in the right

place with radiation. Other new techniques are based on nanotechnology and gene therapy.

Problem is that on this moment Glybera is the only medicine in this class that has been

approved. The most popular technique in this field is the artificial virus approach which uses

empty viral envelopes to target certain tissues and fill it with "healing" DNA for the clinical

effect. A further developed method is stem cell therapy, this is used on a relatively large

scale in for example the treatment of leukaemia. Again the challenge is the quality control of

these product.

Animal experiments (April 7)

Due to ethical problems, animal experiments are questioned more and more. These

are popular for toxicity testing, although more and more studies show big differences in the

biological profile of animals and humans. Even the predictive value of monkey studies can be

questioned. A big problem is the inbred of animals which leads to small genetical

differences, humans on the other hand show great interindividual differences. We should

probably try to limit animal experiments but the search for high quality alternatives is

necessary.