exploration of the intensified vibratory mill as viable particle ...

KU Leuven Biomedical Sciences Group Faculty of Pharmaceutical Sciences Department of Pharmaceutical and Pharmacological sciences

EXPLORATION OF THE INTENSIFIED

VIBRATORY MILL AS VIABLE

PARTICLE SIZE REDUCTION

TECHNOLOGY FOR THE PRODUCTION

OF NANO- AND MICROSUSPENSIONS

Elene DE CLEYN

Dissertation presented in

partial fulfilment of the

requirements for the

degree of Doctor in

Pharmaceutical Sciences

March 2021

Jury:

Promoter: Prof. dr. Guy Van den Mooter

Co-promoter: Prof. dr. René Holm

Chair: Prof. dr. Jef Rozenski

Jury members: Prof. dr. Erwin Adams

Prof. dr. Christian Clasen

Prof. dr. Brigitte Evrard

Dr. Bernard Van Eerdenbrugh

“It is not the critic who counts; not the man who points out how the strong man

stumbles, or where the doer of deeds could have done them better.

The credit belongs to the man who is actually in the arena, whose face is marred by

dust and sweat and blood; who strives valiantly; who errs, who comes short again

and again, because there is no effort without error and shortcoming;

but who does actually strive to do the deeds; who knows great enthusiasms, the

great devotions; who spends himself in a worthy cause; who at the best knows in the

end the triumph of high achievement, and who at the worst, if he fails,

at least fails while daring greatly, so that his place shall never be with those cold and

timid souls who neither know victory nor defeat.”

(Theodore Roosevelt)

i

Acknowledgements

At the end of these four years of joy and tears, hopes and fears and overall the

rollercoaster this PhD was – and as you might know, I am not the kind of theme park-

person – I cannot more profoundly state that this booklet, this PhD, this research

project was never started nor finished, if it was not by the endless support of many.

First of all, I want to express my deep appreciation to my promoter Guy Van den Mooter

for granting me the opportunity of joining his team. Based on my not so overwhelming

grades, but supported by the positive feedback from TCD Dublin, I think you might

have made a jump in the dark over there. Therefore, I am more than grateful that you

dared to do so. I am grateful for your keen motivation and passion, how you could

motivate and push me to that next limit and how you taught me to improve my scientific

and time management skills. In this context I would like to convey my deep appreciation

to my co-promoter René Holm as well. In addition to your strong scientific and industrial

input, you taught me how to improve my interpersonal skills, to be loyal to myself and

certainly, to be more resilient. Aside from these soft skills, your scientific interest has

built many bridges at Janssen Pharmaceutica, for which I am more than grateful. In

this regard, I would like to thank Janssen Pharmaceutica for making this research

possible.

I wish to thank all jury members, Professor Jef Rozenski, Professor Erwin Adams,

Professor Christian Clasen, Professor Brigitte Evrard and Doctor Bernard Van

Eerdenbrugh to meticulously assess my thesis and for the constructive feedback,

which enhanced the overall quality of the presented work.

Another radiant spotlight must be shed on my wonderful colleagues. I was not only

blessed by a tremendous team in my laboratory at the KULeuven but by a marvellous

team at JnJ as well. I cannot fully grasp how profoundly grateful I am for the fact that

you all decided to welcome me and to include me in your group, as the whirlwind I can

be. Feeling as if you could be yourself at your workspace is not granted to many, and

hereby, I was blessed twice. Listing all these people would take a while but to give you

a couple; Maarten, Annelies, Timothy, Sien, Melissa end Eline at the KULeuven, and

Jasmine, Famke, Eddy, Marleen, Tom, Sanket and Nathalie at JnJ but also the many

others in my team at the KUL and at JnJ and also the people from the other teams at

ii

JnJ with who I collaborated such as Tanya, Linda, Jasper, Alain, Brecht, Christopher;

I owe you big time! My warmest thanks.

I am not joking when I say that my thesis would literally not exist without the warm,

fuzzy feelings of drinking a well-caffeinated cup of coffee and retrieving focus when

listening to ASMR Weekly on YouTube. A big ‘thank you’ to that!

I would like to express my heartfelt appreciation to my wonderful friends as well. I am

certainly not going to list them all. But really, even though you, my dear friend, were

not fully aware of it, your support, your kindness, all the motivational cards and gifts I

received, I deeply appreciate it! Imagine one moment in your life, you had that special

moment with me… Thank you for that! I love you all!

At the end of this lengthy list there are still seven, important people I would like to refer

to; two women - three men - two women.

First of all, two women, who shaped my life, but could not be here today. My dearest

and sweetest Lauren and oma Luce, I do miss you. This one is for you…

Secondly, I want to thank the three strongest and bravest men of my life. Papa, I am

so grateful for who you are, how you always try to make me laugh, how you believed

in me and how you supported me which led me this far. Alec, I am blessed to have

such a protective though massively proud and supporting brother like you. How you

wanted to be updated, how you believed in me, how you stood by my side… It radiated

in me. Mattias, though you ended up in the middle of this bumpy ride, even at the start

of the rockiest patch, you were at the end the sturdy rock I could hold on to. I cannot

express how grateful I am for who you are. Really, Mattias, this one is also for you!

Last but not least, I wanted to express my warmest feelings, extend my profoundest

gratitude and send my deepest love to the two strongest women in my life: Karin Thijs

(my mother) and Margaretha Jozephina Heylen (my grandmother). I hope you realise

how you were an immense support during my PhD and even broader, in my life.

Though, without your awareness, you did perform a second roll as well. You were and

are an important role model in my life. I would not be this social, this caring, this

assertive, this keen on being independent, this (pro-)active and this strong, as it was

not without your input and overall being. My warmest and most grateful ‘Thank you’ to

my mama and my oma, as they were both an important basis and trigger for this work.

iii

List of Abbreviations

αGMmax: Maximum contact pressure

a: Frequency of particle compressions

AFM: Atomic force microscopy

API: Active pharmaceutical ingredient

BCS: Biopharmaceutics Classification System

CMC: Critical micellar concentration

CV: Coefficient of variation

DCS: Developability Classification System

DoE: Design of experiments

Ekin: Kinetic energy

FDA: Food and Drug Administration

GI: Gastro-intestinal

GM: Grinding media

HPH: High-pressure homogenisation

HPMC: Hydroxypropyl methyl cellulose

IM: Intramuscular

iRI: Imaginary part of the complex refractive index

IV: Intravenous

IVM: Intensified vibratory milling

LAI: Long-acting injectables

LD: Laser diffraction

mDSC: Modulated differential scanning calorimetry

MPS: Mononuclear phagocytic system

iv

NC: Number of contact moments between beads

Obsc: Red light obscuration

Obsc. blue.: Blue light obscuration

OVAT: One-variable-at-a-time

PSD: Particle size distribution

R2: Determination coefficient

Radj2: Adjusted determination coefficient

RAM: Resonant Acoustic® Mixing

REML: Restricted maximum likelihood

Res.: Residuals

Res. weight.: Residuals weighted

RI: Refractive index

RMSE: Root mean square error

rRI: Real part of the complex refractive index

SC: Subcutaneous

SDS: Sodium dodecyl sulphate

SEM: Scanning electron microscopy

SF: Stress frequency

SI: Stress intensity

SN: Stress number

Sodium CMC: Sodium carboxymethylcellulose

SThM: Scanning thermal microscopy

TEM: Transmission electron microscopy

TPGS: d-α-Tocopherol polyethylene glycol succinate

v

UWL: Unstirred water layer

WBM: Wet bead milling

XRPD: X-ray powder diffraction

vii

Table of Contents

Acknowledgements i

List of Abbreviations iii

Table of Contents vii

Graphical abstract 1

Introduction 5

NANO-AND MICROSUSPENSIONS: FROM INTEREST TO NEED 7

THE DISADVANTAGE OF POORLY WATER SOLUBLE COMPOUNDS 9

Hurdles 9

Increasing the solubility and the dissolution rate 11

Nano- and microsuspensions: Small but significant 13

DELIVERY DEPENDENT DELIVERABLES: NANO- AND MICROSUSPENSIONS IN ACTION 15

Oral delivery 15

Parenteral delivery 16

1.3.2.1. Intravenous delivery 16

1.3.2.2. Long-acting injectables: The advantage of poorly water soluble compounds 18

1.3.2.3. Other routes of administration 19

FROM PRODUCTION TO PATIENT: HOW TO PRODUCE, STABILISE AND CHARACTERISE

NANO– AND MICROSUSPENSIONS? 20

Production 20

Stabilisation 21

Characterisation 24

TOP-DOWN PRODUCTION 25

High-pressure homogenisation 25

Wet bead milling 26

Modelling 28

New technologies: Intensified vibratory milling 30

Objectives 35

Size analysis of small particles in wet dispersions by laser diffractometry 39

ABSTRACT 41

INTRODUCTION 42

THEORETICAL BACKGROUND OF LASER DIFFRACTOMETRY 44

MATERIALS AND METHODS 48

viii

Materials 48

Methods 48

3.4.2.1. Retrieving the real part of the complex refractive index 48

3.4.2.2. Production of suspensions 49

3.4.2.3. Size determination by laser diffractometry 50

RESULTS AND DISCUSSION 51

Optical parameters 51

3.5.1.1. The influence of the imaginary part of the complex refractive index on the final

particle size distribution 51

3.5.1.2. Techniques to find the real part of the complex refractive index 52

Background signal 53

Data analysis: Fitting 55

Data analysis: Obscuration 58

Data analysis: Stability of the sample within the hydro-unit 60

Flow chart 61

CONCLUSIONS 64

SUPPLEMENTARY INFORMATION 64

Exploration of the heat generation within the intensified vibratory mill 65

GRAPHICAL ABSTRACT 67

ABSTRACT 67

INTRODUCTION 68


Materials 71

Methods 71

4.4.2.1. Production of suspensions 71

4.4.2.2. Temperature measurement 72

4.4.2.3. Laser diffractometry 73


The effect of acceleration, bead-suspension ratio and milling time. 74

The effect of acceleration 78

The effect of API concentration and milling time 80

CONCLUSIONS 86

Picking up good vibrations: Exploration of the intensified vibratory mill via a modern design of

experiments 87


ABSTRACT 89

ix

INTRODUCTION 90


Materials 93

Methods 93

5.4.2.1. Preparation of suspensions 93

5.4.2.2. Experimental design 94

5.4.2.3. Temperature measurement 94


RESULTS 96

Design of experiments computation 96

Statistical analysis of the dv50 101

Statistical analysis of the temperature after milling 103

Statistical analysis of the particle size distribution (dv90, span) 105

DISCUSSION 108

Application of the stress model 108

The optimal bead size 109

Cooling the system 111

Method optimisation 112

CONCLUSIONS 116


Stability trends of micron and submicron suspensions manufactured by the intensified vibratory

mill 117


ABSTRACT 119

INTRODUCTION 120


Materials 122

Methods 122

6.4.2.1. Preparation of suspensions 122


6.4.2.3. Differential centrifugal sedimentation 123

6.4.2.4. Scanning electron microscopy 123

6.4.2.5. Caking test 123

6.4.2.6. Stability study 124


Post-milling stability trends 126

x

Confirmation of the new trend 131

CONCLUSIONS 139


General discussion and future outlook 141

QUESTIONS ARISING FROM LASER DIFFRACTION AND INTENSIFIED VIBRATORY MILLING 143

POSITION IN THE LASER DIFFRACTION AND INTENSIFIED VIBRATORY MILLING LANDSCAPE

144

Guidance to quality laser diffraction data 144

Filling the knowledge gaps in the field of intensified vibratory milling 145

The peculiar aftermath of intensified vibratory milling 147

TOWARDS THE FUTURE 148

The challenges and opportunities of the Resodyn® Acoustic Mixers 148

Embarking future research 149

7.3.2.1. Consolidation of the predictive models 149

7.3.2.2. Investigation of the peculiar stability trend 150

Summary - Samenvatting 153

Summary 155

Samenvatting 159

Supplementary information 163

SUPPLEMENTARY INFORMATION TO CHAPTER 3 165



A POEM ON CHAPTER 5 180

References 185

Contributions 199

Curriculum Vitae 201

Graphical abstract

Introduction

7

NANO-AND MICROSUSPENSIONS: FROM INTEREST TO NEED

Modern advances in drug discovery programs have led to an increasing number of

chemical compounds having a poor water solubility and/or dissolution rate in aqueous

media, which impedes their oral bioavailability. Recent numbers proposed values of

36% to 40% of the marketed drugs and nearly 90% of the developmental pipeline drugs

presenting these hampering conditions.1, 2 These numbers have sparked the scientist’

interest in the search for enabling formulation strategies. Among these enabling

strategies, micronisation and nanonisation have widely present their merit, enhancing

bioavailability, safety and patient compliance.3, 4 In light of particle size reduction, drug

particles were on a first attempt micronised whereby an increase in bioavailability was

observed, but results were overall poor. With the advent of nanonisation, the

formulation of poorly soluble compounds into acceptable orally bioavailable immediate-

release formulations succeeded.5 In addition, these nano- and microsuspensions were

exploited as parenteral formulation, and especially microsuspensions found renewed

virtue as a sustained delivery platform in the form of long-acting injectables (LAIs).

Beside its use as an injectable, these drug particles were further explored for a variety

of other administration routes such as the ocular, brain, topical, buccal, nasal and

transdermal route. Hence, this enabling platform may tackle many formulation and

pharmacokinetic challenges.1, 6; 7 As a result, the microsuspensions, -particles and -

crystals and nanosuspensions, -particles and -crystals have since the sixties and

nineties respectively, been vastly explored and even to this day, the research on both

topics is still booming and blooming (Figure 1.1).

8

Figure 1.1. Increasing interest in the field of nano- and microsuspensions. Results of the search (Third of November

2020) on PubMed with search queries; (((Microcrystal) OR (microsuspension)) AND (drug)) OR (API), and,

(((Nanocrystal) OR (nanosuspension)) AND (drug)) OR (API)

Not only in the academia, but also from an industrial perspective, have nano- and

microparticles attracted considerable research interest. Marketed drugs with

suboptimal drug delivery are often reformulated to improve efficacy, safety and therapy

adherence and, even in more economical outcomes such as marketing exclusivity and

patent protection, these drug delivery technologies can play a crucial role.1

Furthermore, already marketed active pharmaceutical ingredients (APIs) can be

reformulated and remarketed as LAIs, leading to a strategically beneficial patent life

extension.8 This increasing industrial interest is reflected in the global markets. In this

regard, nanoparticles are part of the wider nanotechnology-enabled drug delivery

platforms to which other formulation platforms such as nano-emulsions and nanogels

contribute as well. The overall market of these platforms has been forecasted to

account for US$ 136 billion by 2021 where nanocrystals will be the number one player

with a 60% share.9 Further forecast, even amid the current COVID crisis, project a US$

164.1 billion market by 2027. Over the same analysis period, nanocrystals on itself

were projected to record an impressive global US$ 83.1 billion market.10

Considering this growth, it is noteworthy that the practical distinction between nano-

and micro particles is still ambiguous and that there is currently no agreement on what

the prefix “nano” addresses to. The origin of this prefix, however, can be found directly

in the Greek νανοσ meaning “dwarf” and was within the scientific literature firstly

applied on very small organisms whose dimensions would now be estimated around

200 nm.11 Today, the use of the prefix “nano” has vastly increased, and the prefix can

9

be found in diverse scientific disciplines, where a different terminology is commonly

applied.11 In colloid chemistry and material sciences the threshold of nanoparticles has

been placed at 100 nm. As materials at this scale exhibit distinguishable quantum

electronic properties, the photographic and semiconductor field commonly apply this

limit as well.7 The theory that materials smaller than 100 nm can, de facto, exhibit new

or enhanced properties that might be of chemical, optical, mechanical or electrical

nature, even advanced in the ISO-guidelines.12 Such a discontinuity in particle

properties can be merely found in APIs as well, nevertheless, the pharmaceutical field

uses a more metric unit nomenclature and considers nanoparticles and therefore

nanosuspensions as having sizes below 1000 nm.5, 7 Worse still, this aberrant

terminology is in practice only loosely applicable, as suspensions always display a

certain degree of polydispersity, meaning that suspensions with a median particle size

of 1000 nm or below can still include an important number of microparticles and vice

versa. Even more, the question rises what a particle size defines, as it can be

measured via various techniques and dependent upon, will be expressed by various

terms such as the hydrodynamic radius and the volumetric median diameter.13 Within

this dissertation the term nano- and microsuspension will be variably used and

interpreted based on the particle size distribution (PSD). The main particle size

measurement technique was laser diffraction (LD) and therefore the volumetric median

diameter will be standard-wise employed. A volumetric median particle size of 1000

nm could be set as threshold; however, one has to keep in mind that particle size is

only an attribute in defining the desired, final pharmacokinetic profiles and should

always be considered within this context.

THE DISADVANTAGE OF POORLY WATER SOLUBLE COMPOUNDS

Hurdles

In the middle of the twentieth century, drug discovery shifted from its early traditional

and serendipitous discovery in merely mother nature, to the roots of rational drug

design. First steps were found in the exploration of the enzymatic interactions and the

drug-receptor interactions in the 1960s. Twenty years later, the advancements in

molecular biology, gene research and technology directed rational drug design towards

two remarkable discovery platforms: combinatorial chemistry and high-throughput

screening. Within combinatorial chemistry, chemical building blocks are combined and

permuted to create vast compound libraries, which are in a next step high-throughput

10

screened for biological or pharmacological activity, leading to vast arrays of potential

drug leads.14 Despite these platforms proving their high efficacy, they nonetheless did

not result in an increasing number of marketing authorisations.15

While many dosage forms exist, the easy-to-use and less invasive oral formulation is

generally preferred by patients and manufacturers.16 Fundamental parameters

controlling rate and extent of drug exposure after oral administration, are the drug

dissolution and permeability in and through the gastro-intestinal (GI) tract.17 Prior to

permeation the drug must, in most cases, dissolve in the GI fluids (Figure 1.2.).

Solubility is therefore one of the key drivers in the oral bioavailability of APIs.2, 17

Figure 1.2. Fate of soluble drugs passing through the GI tract. Figure adapted from Lipp, 20162.

The highly potent and highly selective drugs derived from the aforementioned

platforms tend to be larger, more lipophilic and less water-soluble than their

predecessors.14 Despite their superb in vitro results, poor absorption, distribution,

metabolism and excretion properties such as dose-limiting solubility will in most cases

eventually lead to a poor oral bioavailability and thus, poor in vivo results.14, 15, 17 Hence

these compounds will likely fail during clinical trials and cause a costly late stage

attrition, which is one of the reasons why the development of a novel treatment can

range to 1.8 billion dollars and can take on an average 13.5 years.2, 14, 15

11

Increasing the solubility and the dissolution rate

To establish a firm grip on these issues, the terminology of poor and good solubility

and permeability should be well defined. In need of this classification and overall

harmonisation, Amidon and co-workers crafted the Biopharmaceutics Classification

System (BCS), which correlates the in vitro drug product dissolution to the in vivo

bioavailability and categorises all APIs in four classes dependent on their solubility and

permeability; The most desired Class I drugs with a high solubility and permeability;

Class II drugs are poorly soluble, but highly permeable; Class III drugs are poorly

permeable, but highly soluble; Class IV drugs combine a poor solubility and poor

permeability.17 A drug is regarded as highly soluble if its highest dose can be

solubilised in one glass (250 mL) or less of an aqueous medium over a specific pH

range at 37 °C.17, 18 Depending if the Food and Drug Administration (FDA), World

Health Organisation or European Medicine Agency guidelines are followed, this pH

range is 1 to 7.5, 1.2 to 6.8 or 1 to 8, respectively. High permeability on other hand, is

demonstrated if at least 85% of the administered dose is absorbed, as compared to an

intravenous (IV) reference bolus or values obtained via mass balance determination.

Permeability determination via in vivo intestinal perfusion in humans is accepted as

well, if suitability of the methodology is demonstrated.18

Yet another determinant in an APIs’ bioavailability is its dissolution rate. If insufficient,

it can leave an API undissolved over its time-window for absorption in the GI tract and

thus limit the APIs’ bioavalability.19 Therefore, an improved classification system arose

from the BCS: the Developability Classification System (DCS) which implements the

dissolution rate, aside from the solubility and permeability. Thus, BCS class II

compounds are, within the DCS, further subdivided in the dissolution-rate limited and

the solubility limited compounds, DCS class IIa and DCS class IIb, respectively.20

Dependent on scientific advancements, the DCS is continuously optimised and

therefore remains highly applicable.21

12

Figure 1.3. Marketed versus pipeline drugs: trend toward low solubility. Figure adapted from Lipp, 20162.

As briefly stated before, 36% to 40% of the marketed drugs and nearly 90% of the

development drugs would nowadays be assorted to BSC class II and IV (Figure 1.3.).1,

2 These percentages reflect the critical need for enabling production and formulation

strategies.

Luckily, the past decades, formulation scientists abided to the request and designed

strategies that may confer new hope for promising drugs that would otherwise be

abandoned. Examples of these newly developed go-to strategies that address the

solubility and dissolution rate challenge are:

Amorphous solid dispersions: Formulations containing a drug molecularly

dispersed within an inert carrier matrix. Drug dissolution is enhanced via several

mechanisms including improved wetting, reduction or even elimination of the impact of

lattice energy and reduction in the effective particle size.19

Inclusion complexation with cyclodextrins: Cyclodextrins are macrocyclic

oligosaccharides enhancing the apparent solubility by inclusion of the poorly water

soluble compounds in their hydrophobic inner cavity, while keeping their hydrophilic

exterior in contact with the aqueous medium.19

Lipid based formulations: These formulations range from simple solutions or

suspensions of drug in lipids to the most complex combinations of different lipids,

13

surfactants and cosolvents. Oral bioavailability is enhanced via increased solubilisation

and dissolution rate, the stimulation of the intestinal lymphatic drug transport and the

inhibition of intestinal efflux and metabolism.19

Nano- and microsuspensions: A flexible formulation approach to parenteral

as well as oral administration, applicable on both bench level and on commercial scale.

Both dissolution rate and saturation solubility are increased via the decreased particle

size, the increased surface area and the decreased diffusional layer.19 The formulation

generally consists of drug particles homogenously dispersed in an aqueous or

nonaqueous medium (e.g. oils or polyethylene glycol) with a suitable mix of stabilisers.

These nano and microparticles can be in crystalline, partially amorphous or amorphous

state.13 Formulations comprising crystalline nano- and microsuspensions are the focus

of this PhD dissertation and will be discussed in more detail in following paragraphs.

Nano- and microsuspensions: Small but significant

Nano- and microsuspensions display distinctive properties as compared to their poorly

soluble bulk counterparts. Via number of ways, their solubility and dissolution rate are

increased leading to an enhanced oral bioavailability.

First, the reduced particles size entails a large surface area, thereby increasing the

dissolution rate, as depicted by the Nernst-Brunner or modified Noyes-Whitney

equation (Equation 1.1.):

𝑑𝑀

𝑑𝑡=

𝐷𝐴

ℎ𝐷 (𝐶𝑠 − 𝐶𝑡) (Equation. 1.1.)

where dM/dt is the dissolution rate, D is the diffusion coefficient, A is the surface area,

hD is the diffusion layer thickness, Cs the saturation solubility of the drug in the bulk

medium and Ct the amount of drug in solution at time t.22

Secondly, Bisrat and Nyström applied the Prandtl equation (Equation 1.2.) to evidence

how the particle size impacts the hydrodynamic boundary layer thickness and so the

diffusion boundary layer thickness:

ℎ𝐻 = 𝑘 (𝐿

12

𝑉 12

) (Equation. 1.2.)

where hH is the hydrodynamic boundary layer thickness, L is the length of the surface

in the direction of flow, k is a constant and v is the relative velocity of the flowing liquid

14

against the surface.23 The fraction of the hydrodynamic boundary layer (hH) where the

diffusion dominates, the diffusion boundary layer (hD), will probably vary between

materials, nonetheless, general trends can be depicted.23 As the particle becomes

smaller and more regularly shaped, the hH becomes smaller and hence hD becomes

smaller, which will eventually lead to an increased dissolution rate (Equation 1.1.). This

phenomenon is especially pronounced for particle sizes below 2 to 5 µm.5, 23, 24

Thirdly, the increased surface curvature of especially particles smaller than 100 nm, is

strongly correlated with a higher saturation solubility as described by the Ostwald-

Freundlich or Kelvin equation (Equation 1.3.):

𝐶𝑠 = 𝐶∞𝑒𝑥𝑝 (2𝜆Μ

𝑟𝜌𝑅𝑇) (Equation 1.3.)

where Cs is the saturation solubility of the API, C∞ is the saturation solubility of an

infinitely large drug crystal, λ is the interfacial tension between crystal and matrix, M is

the drug molecular weight, r is the particle radius, ρ is the particle density, R is the gas

constant and T is the absolute temperature.25 After decades of vast presence in the

scientific literature, the application of the given equation (Equation 1.3.) has

nonetheless been challenged.26 The Ostwald-Freundlich equation and so the Kelvin

equation seemed to contradict the thermodynamics of Gibbs, by an incorrect

application of the Laplace equation. Still, this publication acknowledged the

nanosuspensions’ increased saturation solubility but, the increased surface area, the

size dependence of the interfacial energy and the altered surface energies were

accounted for this trend.6, 26 Irrespective to its origin, this supersaturation will further

drive the dissolution process (Equation 1.1.).27 Last, the stabilisers present in nano-

and microsuspensions often contain surface-wetting capabilities. Improving the

wettability of the drug leads to less drug agglomeration and thus, an increase in the

‘effective’ surface area which, via the Noyes-Whitney equation (Equation 1.1.), further

enhances the dissolution process.24, 28

Even though these advantages already proved the great capabilities of the extremely

small particle size, the next paragraphs will substantiate more how nano- and

microsuspensions may be a large player per administration route.

15

DELIVERY DEPENDENT DELIVERABLES: NANO- AND

MICROSUSPENSIONS IN ACTION

Oral delivery

Beside its increased dissolution rate and saturation solubility, which drives the

permeation over the intestinal wall, orally administered nano- and microsuspensions

enclose a variety of other beneficial properties. Oral suspensions, in general, are

alluring dosage forms for the very young and old patients experiencing difficulties in

swallowing tablets or capsules, and for their superior taste-masking of the drug.29

Zooming in on the GI tract, suspensions on the micro and nano level can have

additional advantages. First, nanoparticles present improved mucoadhesive properties

to biological membranes and thus to the GI walls. Around the GI wall the API

concentration increases wherewith locally persisting, infectious micro-organisms such

as parasites can be targeted.30 This locally increased API concentration also drives the

passive permeation over the GI wall.27

Adjacent to the GI walls, a stagnant layer of water and mucus is present, better known

as the unstirred water layer (UWL). Permeation through this layer is needed for the

drug to reach the GI wall and further the blood stream. The installed concentration

gradient across the UWL may alter this drug permeation. However, not only free drug

molecules but also small colloidal structures such as micelles and nanoparticles seem

to be able to drift into the UWL and can therefore serve as drug shuttles over the UWL.

As a result, the drug concentration at the membrane surfaces increases, which will

reduce the UWL resistance and improve the UWL and GI wall permeation.24, 31

Surprisingly, even intact submicron particles may be able to reach the systemic blood

circulation by mechanisms involving the M-cells in the GI lymphoid Peyer’s patches.29

Surfactants present in the formulation may further impact the drug intake by the

inhibition of the efflux pump, the P-glycoprotein.29

Another challenge that nanosuspensions may encounter is the oral bioavailability

difference of many poorly soluble compounds when administration was performed in

fasted or fed state. Indeed, food intake will lead to an increased presence of fat and

bile salts in the GI tract which will improve the dissolution of the poorly soluble

compound. This positive food effect will alter the bioavailability of the drug and lead to

intraindividual variability, depending on what and when a patient has eaten.

16

Nanosuspensions’ remarkable increase in dissolution rate and their mucoadhesive

properties are not heavily affected by the patient’s nutritional state and as a result, this

positive food effect may be mitigated.24, 27

During toxicological studies, high drug doses are preferably administered via the same

route as the intended clinical use. Hence, formulators are often challenged to orally

administer highly concentrated formulations during the drug development process. To

solubilise these high doses, concentrated surfactant, co-solvent or lipid systems were

previously employed, however these can have vehicle effects in the GI tract. Nano-

and microsuspensions can accommodate these large drug amounts with minimal GI

concerns.24

Parenteral delivery

1.3.2.1. Intravenous delivery

The earlier mentioned solubilisation of poorly soluble compounds via excessive

amounts of surfactants, co-solvents or lipids, did not only facilitate their oral

administration but enabled their IV administration as well. This formulation approach,

however, provoked severe side effects such as anaphylactic reactions and pain upon

injection.29 Nanosuspensions on the other hand were capable to administer larger IV

quantities of drugs at importantly lower toxicity.7

The fate of these injected nanoparticles strongly depends on the particle morphology,

size, dissolution rate, surface morphology and surface modification.32 The dissolution

rate in particular will determine the drugs’ biodistribution since it will discriminate if the

nanoparticles will occur as a solid or if it will mimic a solution.33

To mimic such an injected solution and retrieve a fast onset of action, the nanoparticles

should, as a rule of thumb, be smaller than 100 nm.27 After injection these suspensions

are subject to an instantaneous sink condition and likely have a quick dissolution.32

Nanoparticles up to 150 nm may even extravasate and distribute as such over

surrounding tissues.7

The IV injected nanoparticles larger than 150 nm cannot extravasate and are naturally

targeted by the mononuclear phagocytic system (MPS) cells.7, 30 Recognised as being

foreign, the particles are phagocytosed and accumulate mainly in the Kupffer cells in

the liver and to a lesser extent in the spleen and in the lung macrophages. Due to this

17

process, nanoparticles may directly combat MPS infections. To this end, the particle

surface should be modulated to enhance the macrophages’ clearance.27, 30

Furthermore, these macrophages can act as a depot for sustained drug release.27

To target other organs, surface modulation can help to circumvent the recognition by

the MPS and enable longer recirculation times.27 As a result, nanoparticles will

disperse over the body and will preferentially diffuse into tissues with a leaky

vasculature such as tumours or sites of infection and inflammation.29, 32 A more active

approach to target tumour cells may be possible as well such as surface modification

of nanoparticles with folate via conjugation or coating since its receptor is generally

overexpressed in tumour cells.7

Even though the maximum acceptable particle size for IV nanoparticles is still

ambiguous, first results showed how surprisingly well this formulation platform was

tolerated (Table 1.1.). Consequently, the FDA granted in 2006 for the first time a

marketing authorisation to an IV administered nanoformulation, Abraxane.32

Table 1.1. Outcome of injecting particles. Table reproduced from Wong, J et al, 2008. 32

Protocol, particle dose/kg Particle size (µm) Outcome

Bolus, 6 x 109 1.3 Pharmacokinetic study

Bolus, 1.6 x1012 0.5 – 1.17 Pharmacokinetic study

Rats, bolus, 8 x 106 0.4, 4, 10 Well tolerated

Dogs, bolus, 1 x1010 3.4 Well tolerated

Dogs, repeated bolus, 2.4x 108 3.7 Well tolerated

Dogs, 2 min bolus, 8.9 x 107 3.4 Well tolerated

Humans, bolus, 9.9 x 107 2.0-4.5 Optison, approved product

Dogs, infusion, 1.3 x 1012 0.4 Well tolerated

Rats, bolus, 2.5 x 1012 0.4 Well tolerated

18

1.3.2.2. Long-acting injectables: The advantage of poorly water soluble

compounds

LAIs are weekly, monthly or less frequently administered formulations enabling a

controlled drug release and thus a prolonged and stable therapeutic exposure.4, 7 They

are mostly administered intramuscularly (IM) or subcutaneously (SC) as oil solutions,

liposomes, implants and micro- or nanosuspensions.34 The ease of self-administration,

the mild discomfort and large injection area, are strong motivators for SC use.

However, for larger injection volumes (2 – 5 mL) and for irritants, the IM route is

preferred.34 As a result, a high drug loading can be delivered via a fairly small injection

volume into a body compartment of limited fluid volume. The blood plasma

concentration profile following this LAI delivery is typically less variable over time which

reduces the number and extent of adverse effects.4, 35 Another clear advantage for the

patient is the prolonged exposure, which importantly enhances the patient compliance

(Figure 1.4.).7 29

For people suffering from dysphagia and in the field of chronic diseases where patient

adherence is detrimental for the treatment’s outcome, LAI can undoubtedly herald a

new era. Since the incidence of chronic diseases and of dysphagia is sharply

increasing in our aging population, it comes as no surprise that the LAIs currently

represent an expanding share of the drug products reaching the market.34, 36

From a strategical and economical perspective, LAIs may offer some other clear

advantages as they might be highly protected by complex regulations on their

intellectual property, comprising multi-layered patent protection.37 Additionally, the

reformulation of already marketed APIs to LAIs can, as previously alluded on, result in

an extended patent protection.8

The hallmark of the LAIs might be situated in the field of the antipsychotics, as it

encompasses some of the most successful LAI formulations. In the 1960s, their oil-

based parenteral depot formulations were introduced on the European, Canadian and

Australian market. However, the greatest breakthrough occured in 2003 when a LAI

suspension of risperidone (Risperdal Consta®, Janssen Pharmaceutica) was

introduced in the market. Before this date, the United States was reluctant to use

parenteral depot formulation of antipsychotics due to concerns regarding adverse

events, tolerability and the sense of disrespect for the patients voice.34, 38 In 2009 this

19

market expanded with a LAI containing paliperidone palmitate (Invega Sustenna®,

Janssen Pharmaceutica) and a LAI containing olanzapine pamoate (Zyprexa®

Relprevv™, Elli Lily).34 Remarkably, all these more recent LAIs are nano- and

microsuspensions. Even though critical research has nowadays put the first

overwhelmingly positive, clinical results in a better, realistic context, and stigma is still

ongoing, clinicians increasingly appraise the platform and warmly welcome advances

herein.38, 39, 40

Figure 1.4. Typical drug plasma concentration versus time profiles observed after the single parenteral

administration of a sustained release formulation (green curve) and the repeated administration of an immediate

release oral dosage form, a tablet (red curve) over one month (left) and one year (right). Figure reproduced from

Owen, A et al, 2016 35.

1.3.2.3. Other routes of administration

In addition to the IV, IM and SC delivery, nano- and microsuspensions find their utilities

in other routes of administration such as the ocular, brain, topical, buccal, nasal,

ophthalmic and transdermal applications. For the ophthalmic route, nanosuspensions

may offer increased drug dosing with extensive residence times while lowering the

formulations’ osmolality as compared to conventional dosing forms.6 The vaginal,

rectal and transdermal application of nanosuspensions exploit the particles’ increased

bioadhesiveness and membrane permeation. For inhalation, nanocrystals smaller than

500 to 1000 nm are exhaled, however, nebulisation of nanosuspensions will generate

20

droplets in the size of a few µm, which are suitable for successful pulmonary

administration.27 Even the brain can be targeted via nano- and microsuspensions for

which currently, more invasive ways are adapted, but future work aims to retrieve a

passive or active targeting of the brain via a less invasive route.29

FROM PRODUCTION TO PATIENT: HOW TO PRODUCE, STABILISE AND

CHARACTERISE NANO– AND MICROSUSPENSIONS?

Production

The existing technologies to produce micron and submicron suspensions can be

divided into the so-called top-down technologies and the bottom-up technologies. In

the top-down approach large particles are reduced in size, whereas in the bottom-up

approach molecules are precipitated in a controlled manner. The latter approach

usually involves extensive amounts of organic solvents which are, aside of their

economic and ecological burden, difficult to remove from the final formulation.

Moreover, the precipitation process is hard to control in terms of particle size and

morphology.41, 42 All these elements seem to reduce the success of the bottom-up

approaches, whilst the top-down approach already showed its ease-to-use and

commercial viability, leading to a vast array of commercialised products.3, 42 However,

the top-down approach is prone to flaws as well (Table 1.2.) and thus, is still open for

advancements and future research.

21

Table 1.2. Overview of the general advantages and disadvantages of the top-down approaches, the bottom-up

approaches and the hybrid methods. Modified from Fontana,F. et al. ,2018.42

Methodology Advantages Disadvantages

Top-down approaches Simple

Fast

Avoid organic solvents

High reproducibility

Easy of scale-up

Energy-intensive process

Potential instability induced by high shear and temperature

Product contamination from the grinding media

Bottom-up approaches Small particle size

Monodispersed particles

Difficult to scale-up

Time consuming to find the suitable conditions

Difficult to control the particle growth

Incomplete removal of toxic solvents

Hybrid method Remarkably smaller particle sizes Increase in cost and time for preparation

During the top-down production, the suspension particles are subjected to mechanical

attrition during wet bead milling (WBM) or to high pressure during high-pressure

homogenisation (HPH).42 Recent years hybrid methods have been developed where

different production technologies are combined, which merely consists of a bottom-up

approach to generate crystals that are in a next step reduced in size by a top-down

approach. As an example, these hybrid methods encompass the combination of

solvent/antisolvent precipitation with HPH (NANOEDGE®); spray drying with HPH (H42

technology), freeze drying with HPH (H96 technology) and WBM with HPH

(smartCrystal® technology).13, 42

In a next step, nano- and microsuspensions can be converted to the dried state via

conventional drying operations such as lyophilisation, spray-draying or fluid-bed

coating. To preserve the redispersibility upon reconstitution, redispersants such as

sucrose are commonly added. Reconstitution can happen with either water prior to

patient intake or directly with gastric fluids if the dried powder has been converted to a

conventional formulation such as a capsule or tablet.6, 43

Stabilisation

The long-term stabilisation of micron and submicron suspensions imposes an uphill

battle against the suspensions’ unfavourable thermodynamics.7 The most important

stability issues lead to a change in the suspensions’ PSD and as the particle size is

22

the key contributor to the drug products’ safety and efficacy, the formulator is

challenged to appropriately address these stability issues of which agglomeration,

sedimentation, Ostwald ripening and secondary nucleation will be dealt in more detail

below.44

First, the sharp decrease in particle size entails a large surface area which is, from a

thermodynamic point of view, correlated to an increase in the system’s Gibbs free

energy (Equation 1.4.) and is, thus, inherently unfavourable.29

∆𝐺 = 𝛾𝑠/𝑙 ∆𝐴 (Equation 1.4.)

where ΔG refers to the system’s Gibbs free energy increase, γs/l refers to the solid-

liquid interfacial tension, and ΔA refers to the surface increase of this solid-liquid

interface.29

As a reduction in Gibbs free energy will drive these metastable systems to a

thermodynamically stable state, these systems will spontaneously try to reduce their

interfacial surface area. Accordingly, the system will try to counterbalance the particle

size reduction by agglomeration.44 To impede this natural tendency, the formulator may

add excipients with stabilising properties such as a surfactant(s) and/or polymer(s),

which may stabilise the system by i) the reduction of the interfacial tension via wetting

properties, ii) steric hindrance (steric stabilisation), iii) the electrostatic repulsion of

(surface-)charged individual particles (electrostatic stabilisation), iv) the synergetic

combination of the steric and electrostatic stabilisation (electrosteric stabilisation).7, 29,

44, 45 Besides their impact on short- and long term storage, these stabilisers will also

contribute to the successful formation of submicron particles during production.44

Another natural occurring phenomenon that needs to be tackled is sedimentation. As

long as the resuspendability is adequate, this is not necessarily deleterious, simple

shaking will homogenise the system, but in most cases, the formulator needs to take

this in consideration.7 As indicated in Stokes’ law (Equation 1.5.) the rate of this

process is dependent on the particle size, medium viscosity and difference in medium

and API density.29, 44 Since smaller particles will compensate this sedimentation

process by Brownian motion, particle size reduction forms the most commonly applied

strategy to avoid severe particle settling. The addition of viscosity enhancers such as

carboxymethylcellulose may alleviate this process as well.7

23

𝑣 = 2𝑟2(𝜌𝑝−𝜌𝑚)𝑔

9𝜂 (Equation 1.5.)

where v is the settling rate of the particle, r is the radius of the particle, ρp is the density

of the particle, ρm is the density of the medium, g is the gravitational acceleration and

η is the viscosity of the medium.29

Finally, the shelf life of nano- and microsuspensions can be gravely reduced due to

secondary nucleation and Ostwald ripening (Figure 1.5.). Secondary nucleation is the

natural crystallisation of the supersaturated system where as the temperature

fluctuates drug can dissolve and crystallise from the seeded supersaturated matrix

upon cooling.7 Ostwald ripening, on other hand, is merely present in polydisperse

systems where the large particles will, at expense of the smaller particles, increase in

size. Smaller particles will have, as alluded on in Equation 1.3., a higher saturation

solubility than their larger counterparts, and will more easily dissolve, generating a

local, high API concentration. This local difference in API concentration will drive a flux

of dissolved API molecules to the larger particles where they will crystallise and cause

particle growth.7, 44, 46 A narrow PSD may mitigate the differences in the saturation

solubilities, and thus prevent the Ostwald ripening process. Stabilisers may also

reduce the interfacial tension and hence, tune the Ostwald ripening process as long as

they do not enhance the drug solubility. In this context, many authors suggest that an

excess of stabiliser may promote Ostwald ripening.44, 46 Current literature, however,

presents Ostwald ripening as a multi-step process where, depending on the rate-

controlling step, another optimal surfactant concentration may be adequate.47 Other

nanoparticle growth pathways such as digestive and intraparticle ripening may

occasionally occur in inorganic materials, but are only scarcely discussed in the

pharmaceutical context.48

Nowadays, the selection of a suitable stabiliser and stabiliser concentration is merely

based on trial and error and therefore there is considerable scope of improvement

herein. Few studies have attempted to develop a more rational approach and develop

predictive methods.45 Current studies do not try to resolve the matter but are devoted

to streamline high-throughput methods in order to save time and materials.46

24

Figure 1.5. A schematic representation of the Ostwald ripening process. As a difference in saturation solubility

between smaller and bigger particles occur, a flux of dissolved molecules flows from the smaller particles to the

larger ones. On the surface of the larger particles, the molecules recrystallise. As a result, the larger particles

increase in size, at expense of the smaller particles. Copied from Wu, L et al, 2011, with permission44.

Characterisation

For the characterisation of manufactured nano-and microsuspensions, a variety of

analytical methods is available which can be generalised in two subcategories. The

first category measures the attributes of single nanoparticles such as the particle size

and solid state. The second category deals with bulk or formulation properties such as

the viscosity and falls out of the scope of this PhD dissertation.43

Considering its remarkable influence on in vitro and in vivo performance, nano- and

microsuspensions’ key quality attribute is beyond doubt, the PSD which is defined by

its average particle size and its width or dispersity and can be measured by a multitude

of techniques including dynamic light scattering and LD.13 For the particle size, which

is such a vital characteristic for suspensions, measurement via at least two

complementary methods is highly recommended. Via microscopical techniques such

as scanning electron microscopy (SEM), the morphology and size of the particles can

be visualised.13 As the amorphous fraction can have an ambiguous effect - favourable

for the bioavailability, nonetheless detrimental for the long-term stability - on the

produced suspensions, solid-state analysis should be considered for which

calorimetric methods such as modulated differential scanning calorimetry (mDSC) and

scattering methods like X-ray powder diffraction (XRPD) can be used. With these

techniques, the presence of (pseudo)polymorphs can be evaluated as well.13, 44

25

As the focus of this dissertation is the investigation of the intensified vibratory mill (IVM)

as a viable nanonisation and micronisation technique, the analytical focus is placed on

the key characteristic: the PSD, and consequently, considerable research attention has

been devoted to LD as particle size measurement technique. In a next step, LD and

SEM were employed to evaluate the suspensions’ PSD and aggregation behaviour.

Other techniques such as mDSC and XRPD were only employed in a limited manner.

TOP-DOWN PRODUCTION

High-pressure homogenisation

The second most frequently used top-down production technique is HPH. The two

most common homogenisation principles that subdivide this category are

microfluidisation on the one hand and piston-gap homogenisation on the other hand.42,

49 Within the latter category, particle size reduction merely relies on the forces aroused

when a suspension is forced to pass a very narrow gap, where the extreme reduction

in diameter leads to a sharp increase in dynamic pressure and sharp decrease in static

pressure, which can even drop below the vapour pressure of the liquid, causing boiling

to occur.42, 49 As the suspension leaves the gap, the earlier formed gas pockets will

implode, generating a micro-jet and shock waves which crush on nearby solid

particles.49 This process, better known as cavitation, is mostly present in the

Dissocubes® technology where suspensions in aqueous medium are prepared. In the

Nanopure® technology on the other hand, a lower vapour pressure and temperature

are applied to create non-aqueous suspensions. As a result, the cavitation is reduced

or practically inexistent, but the high turbulence still causes extensive shear forces to

perform a particle size reduction.42, 49

In case of microfluidisation (patented as IDD-P® technology), the suspension is

accelerated through purpose-built homogenisation chambers, which are named after

the path’s geometry. Thus, the suspensions flow changes a few times in the Z

chamber, while in the Y chamber the suspension gets divided in two streams which

eventually frontally collide.42, 49 Particle size reduction occurs due to these frontal

collisions, collisions on impact valves and chamber walls, attrition forces and to a

limited extent, cavitation.41, 42, 49 For the production of drug nanocrystals, the most

common geometry is the Z-shaped chamber, whereas the Y-chamber is more often

applied to prepare liquid-liquid type dispersions.42 Microfluidisation seemed to be more

26

effective as compared to the commonly applied piston-gap method. For the

nanonisation of softer materials, the HPH seems to be preferred over WBM.50

Furthermore, HPH generate suspensions with a more uniform particle size and better

thermal stability as compared to WBM.51 Finally, HPH can cause cell lysis which

lessens the microbiological hurdles within the final drug formulations.50

Despite these various merits, HPH based nanoparticles are underrepresented on the

pharmaceutical market since the production technologies’ success is hampered by

various hurdles. Even though the contamination risk is reduced as compared to WBM,

wearing of the valves and hence product contamination may still occur.42 Particle size

reduction seems to be less effective compared to WBM as well.42, 49 The high number

of passages makes this technology less production friendly. To minimise this number

of passages, prior micronisation is often applied.7, 13, 42 As HPH miniaturisation is less

straightforward and these technologies come at a costly expense, pharmaceutical

companies may have the tendency to stick to the more universally applied WBM.51

Wet bead milling

As WBM forms the most notable top-down production technique, with demonstrated

efficiency, viability and cost-effectiveness on both bench level as production scale, the

technology has been detailed in numerous papers.33, 46 Summarising this large body

of scientific data in a one-pager would be preposterous. For further background

information, the interested reader is, hence, referred to excellent review papers

available in the field.

To stage the results of this dissertation, the following paragraphs will nonetheless

elaborate on the concept of WBM, the technologies’ strengths and flaws, a

summarising overview of how it can be modelled and how as a widely spread

production technology it may form a benchmark for comparison for upcoming milling

technologies.

As mentioned earlier, WBM delivery has been propelled to the forefront by researchers

from both academia and industry to effectively produce nano- and microsuspensions,

which comes as no surprise given the extensive list of merits that this method

encompasses. This technology is widely spread, in both experience and literature, is

commercially established, is simple and highly reproducible. WBM is from a practical

27

point of view easy-to scale up and continuous manufacturing by recirculation of a

mother suspensions boosts the production efficiency.50

As a nanonisation technology, it was patented as the Nanocrystal® technology in 1990

by Elan.30 Its success is reflected in the manifold of marketing authorisations granted

to formulations produced with this technique.6 Within WBM the API, excipients,

dispersant and milling beads, which can compose of various inert materials such as

highly cross-linked polystyrene, glass and yttrium stabilised zirconia, are charged into

the milling chamber.13, 52. Next, the milling beads are accelerated by the movement of

the complete container, or by agitators installed in the chamber.52 The movement of

API particles and beads generate forces such as impact, pressure and shear forces

whereby a particle size reduction of drugs to the nanoscale is achieved.30 However,

these highly energetic forces come at a cost. Inefficiently dissipated energy can pose

three different hurdles. First, it can generate uncontrolled heat. Heat may have

detrimental effects on the chemical and physical stability of the suspension.

Evaporation of the solvent may occur, and the stabilisers’ cloud point may be

surpassed, causing it to dehydrate leading to re-micellisation. Hence, less stabiliser

will be accessible for the newly formed high-energy surfaces. This ineffective

stabilisation may lead to aggregation. With the temperature, the solubility of the API

may rise, leading to a temporarily supersaturated state. As the suspensions cools down

after comminution, second nucleation and crystal growth will thereupon occur. As a

result, WBM technologies should be equipped with adequate water jackets. The

second hurdle is how the uncontrolled energy may cause solid state changes as

amorphisation and polymorphism. 7 Finally, these energetic conditions leave the beads

to wear, eventually contaminating the final drug formulation.46, 49 Despite these

extensive energetic conditions, nanonisation with WBM may still take hours to days.30

Another major disadvantage, not yet touched upon, is the cumbersome drug product

development process, which is merely based on trial and error rather than rationally

approached. It is currently not possible to predict a priori which stabilisers and process

parameters should be applied. Even though high-throughput platforms for WBM are

available, the investigation of all these variables makes an onerous and highly

inefficient process.53 This has led authors such as Kwade, Eskin and Afolabi to predict

milling outcomes, which will be the main topic of the next paragraph. In a further step,

new grinding platforms may be explored to address the WBM challenges.54, 55

28

Modelling

Considering the 44 different parameters that have been identified as affecting the WBM

process, the complexity of WBM cannot be underestimated.56 Nevertheless,

researchers try to predict milling kinetics and milling outcomes by process modelling

where the stress model, as suggested by Kwade54, and the microhydrodynamic model,

as proposed by Afolabi and co-workers55, are the most widely known.

In this respect, Kwade was the first to describe the milling process in a mechanistic

model that linked the process parameters of a stirred media mill to the stress applied

on the suspension’s particles via two central parameters, the stress number (SN) and

the stress intensity of the grinding media (SIGM). This simplification and the direct link

between process parameters and the stress applied on the suspension’s particles

made the model easy to apply.54 However, this simplification resulted in significant

caveats such as the absence of most parameter interactions and the absence of

important terms such as the formulations viscosity. Afolabi and co-workers resolved

these caveats in their advanced microhydrodynamic model, which was firstly

introduced by Eskin and co-workers.55, 57, 58, 59 This microhydrodynamic model was

based on the transformation of turbulent flow into kinetic energy, Hertzian contact angle

mechanics and particle compression probability to estimate the total energy spent on

solids deformation.55, 60 In this regard, the breaking kinetics were defined by the power

dissipation mechanisms which comprises energy dissipation by liquid-bead viscous

friction and lubrication, energy dissipation from inelastic bead collisions and the power

spent on suspension shearing.55

Even though the stress model presented by Kwade54 and the microhydrodynamic

model of Afolabi and co-workers55 were based on different principles, process

optimisation with respect to a certain particle size, could be realised with either models,

leading to the same optimal process parameters for a specific compound fineness.60

De facto, both models contain two central parameters which are remarkably similar

and define the final optimised process variables:

One central parameter describes the frequency of particle stressing, which is

stress frequency (SF) or frequency of particle compressions (a), in the model of

Kwade54 or in the model of Afolabi and co-workers55, respectively.

29

The second central parameter describes the stress on the particle during a

collision, which is SI and maximum contact pressure (αGMmax) , in the model of Kwade54

or in the model of Afolabi and co-workers55, respectively.

Still, due to its complexity and its broader content, the microhydrodynamic model

parameters proved to be more accurate.60 Interested readers are therefore strongly

advised to read through the microhydrodynamic modelling of Afolabi and co-workers.55,

57 As the scope of this thesis dissertation is to give a first mechanistic insight in the

milling by the IVM, the stress model of Kwade54 which links concrete operation

parameters to final particle sizes, was favoured and therefore will be further applied

and will be described in more detail below.

As previously mentioned, the stress model of Kwade contains two central parameters

of which the SIGM can be described by the bead size (dGM, m), the bead density (ρGM,

kg/m3) and the tip speed of the stirred media mill (vt, m/s), which in a broader context

can be interpreted as the speed generated by the driving system of the mill 54 (Equation.

1.6.).

𝑆𝐼𝐺𝑀 = 𝑑𝐺𝑀3𝜌𝐺𝑀 𝑣𝑡

2 (Equation 1.6.)

SN is determined by the number of contacts between the beads (NC), the probability

that a particle is caught between the beads and sufficiently stressed during the media

contact (PS) and by the overall number of API particles inside the mill (NP) (Equation

1.7.). NC can be assumed to be proportional to the number of stirrer revolutions (n, s-

1), which is comparable to the stirrer speed or in a broader context the speed generated

by the driving system of the mill, the grinding time (t, s) and the number of grinding

media (NGM) (Equation 1.8.).54

𝑆𝑁 =𝑁𝐶 𝑃𝑆

𝑁𝑃 (Equation 1.7.)

𝑁𝐶 ∝ 𝑛 𝑡 𝑁𝐺𝑀 ∝ 𝑛 𝑡 𝑉𝐺𝐶 𝜑𝐺𝑀 (1−𝜀)

𝜋

6 𝑑𝐺𝑀

3 (Equation. 1.8.)

where n (s-1) is the number of revolutions of the stirrer per unit time; t (s) is the milling

time, VGC (m3) is the volume of the grinding chamber, φGM is the filling ratio of the

grinding media, ε is the porosity of the bulk of grinding media (GM), dGM (m) is the

diameter of the grinding media.61 The product of SI and SN is proportional to the total

specific energy, which is the total energy input to the total mass of feed material, which

30

can, if under control, guarantee an efficient production of suspension with a certain

particle fineness.61

New technologies: Intensified vibratory milling

The vast interest in nano- and micronisation along with the drawbacks of conventional

WBM have sparked the scientist’ interest in and search for new milling platforms. In

this spirit, Leung and co-workers62 reported for the first time in 2013 on a new drug

sparing technology utilizing low shear acoustic mixing to quickly manufacture nano-

and microsuspensions, which was in a later publication of Li and co-workers referred

to as the IVM process.63 This process applies the Resonant Acoustic® Mixing (RAM)

platform (Figure 1.6.) which was originally commercialised as a dry mixing platform.64

Consequently, several studies have been conducted on RAMs application in dry

mixing, while very little is known about its application in wet milling.62, 63

Figure 1.6. Figure illustrating the LabRAM II equipment. As depicted in the picture right above, the recipient should

be fixed in-between two transducers. The recipient will be forcefully vibrated and as a result, the content of the

recipient will be mixed.

In this respect, there is considerable ambiguity with regard to the IVMs mixing and

milling regime. Previously, the mixing behaviour was introduced as micro-mixing

31

zones, but current knowledge seems to present a more complex mixing and milling

regime with multiple, integrated phenomena dependent on both process parameters

and product properties (manufacturer communication). Since the geometry and the

motion of IVMs recipients significantly differ from conventional mills, one can assume

a different bead motion and consequently composition of comminution forces which

will lead to different comminution output in terms of particle size, generated heat and

eventually stability propensities.50 The internal knowhow presumably falls under patent

restrictions and an early publication on the RAMs software even referred to it as

Resodyn ‘black box’ controller.65 Nonetheless, for the IVM is a fairly new concept, we

want, to the extent possible, shortly elaborate on the equipment, production

mechanism and recent literature.

The IVM (Figure 1.7.) consists of a platform with a vibrating container to which, as in

the case of conventional WBM, milling media, suspension matrix and API are charged.

Due to the vibration, the milling media start to move and collide, resulting in the desired

particle size reduction. This vibrating container could be well-plates or vials, wherefore

the IVM can serve as a drug sparing, high-throughput screening method.62 For the

bench level IVM, as in the case of the LabRAM II (Figure 1.6.), vacuum equipped, inline

temperature controlled and jacketed mixing vessels are commercially available, but

these vessels’ minimal content remains a considerable volume of 500 mL.

As a mechanical vibrating system, the IVM works as a spring-damper system, where

the springs, driven by the driving system, store the potential energy, that is further

transferred to the plate, recipient and mixing/milling content, which will cause the

damping by energy adsorption. The vibration of the mixing recipient could be plotted

in time as a sinusoidal wave with a frequency of around 60 Hertz to keep the system

at its optimal and safe conditions, namely the resonance. This resonance is a

parametric resonance, where the system is driven at twice its natural frequency and

will cause a maximum amplitude while the required power stays minimal. As the period

of oscillation is fixed, the only value that can be altered is the amplitude which differs

based on the milling content and the set acceleration level. As an example, an empty

container can reach in the LabRAM II at the maximal acceleration of 100 g a vertical

displacement of 1.4 cm. A certain level of mixing intensity, given by the software as a

power percentage (0 – 100%) will be needed to substantiate this acceleration for a

32

certain mixing/milling mass. Via an accelerometer on the baseplate and a back-track

system, the acceleration and frequency will be controlled within its installed range. If

an adaptation is needed, it will be tracked as a change in power or phase for the

amplitude or frequency, respectively. Sudden changes in the power and phase often

relate to changes in the mixing/milling regime whereas a more gradual change is linked

to changing material properties.

Figure 1.7. The concept of IVM. At the beginning (left), the recipient is charged with beads (grey spheres), API (dark

polygons), suspension matrix and other excipients. Parameters are installed on the IVM software and the process

is started. During the process, the transducer will make an upwards and downwards movement that may be

depicted as a sinusoidal wave over time. This movement is copied by the installed recipient and thus, the beads

start to accelerate (middle). During their movement, the beads can cause impact, shear forces and pressure, which

causes the particles to break. After the mixing and milling process (right) the recipient and transducer are

motioneless and the API particles are reduced in size.

Over the last decades, the RAM has been widely explored as a powder mixer, yet it

was only in 2013, that the group of Leung and co-workers applied it for the first time as

a milling platform.62 Their first investigation included a miniaturisation of the IVM

process via a high-through put platform for rapid evaluation of APIs millability and the

formulation stabilising propensities; and directly translated these high-throughput

results to scale-up possibilities. IVM was, within this article, marked for its fast

comminution which overcame WBMs extensive milling times. The slight temperature

increase within the IVM was attributed to the more efficient energy use.62 Later

33

publications noted this temperature increase as well, even though limited to a couple

degrees.64 A more considerable heat generation was observed by Hoang and co-

workers with final temperatures of around 30 to 60 degrees.66 Within this article the

impact of a set of process variables on the particle size reduction process of the IVM.

The study results were encouraging and well-found, nonetheless, created via a one-

variable-at-a-time (OVAT) approach and merely translated to guidance maps. Even

though these guidance maps are of great practical value, profound mechanistic

insights in the IVM were not provided.66 In a further investigation on the IVM, Li and

co-workers attempted to control the heat generation via the insertion of pauses where

the recipients were removed from the milling equipment and placed in a refrigerated

bath.63 This approach proved to be effective, but was not suitable to standardise or

scale-up. Furthermore, the influence of this quick cooling step on the aggregation of

particles and recrystallisation of dissolved API was not explored. In contradiction to

early findings, this article disagreed whether the IVM could outperform the conventional

WBM. These contradictory results in terms of milling capability and heat generation

highlight the current knowledge gaps in the intriguing field of IVM.

What we know on this less conventional, but auspicious nanonisation and

micronisation method, is solemnly based on this scarce body of data. Therefore, we

would like to take a new look at the IVM as milling technology and try to extend our

understanding of the underlying principles. Still in its infancy, this milling process can

be more in-depth explored in terms of the impact of process variables on the

suspension’s critical quality attributes. In this scope, the particle size reduction, heat

generation and the effect of the milling regime on the final stability propensities will be

more thoroughly investigated in this thesis project with, not surprisingly, the IVM as key

player.

Objectives

37

As IVM is still an underexplored technology, the general aim of this PhD project was to

gain fundamental knowledge on the IVM as a manufacturing method for nano- and

microsuspensions with bedaquiline as model API. The mechanical breakdown of the

API in the IVM is promoted by the simple concept of bead-to-bead and bead-to-wall

collisions. Despite the simplicity of this fundamental concept, IVM is a complex process

impacted by an intricate interplay of process and formulation variables. To tackle this

complexity, a step-by-step approach was developed composed of four different parts

which started with the evaluation of LD as particle size measurement technique and

ended with the investigation of the different stability trends as observed during the

storage of suspensions, manufactured with IVM, at 5 °C. In the end, this PhD project

allowed to predict milling outcomes upfront and to meet specific particle size and

temperature requirements via meticulous tuning of different process parameters.

To assess the final suspension’s quality, it is of utmost importance to have an adequate

particle size measurement technique. Amidst the various particle size measurement

techniques, LD is the most notable, however, the technique has recently been subject

to criticism and has been vigorously challenged for its accuracy. Consequently, LD is

explored in chapter 3 for its capabilities of producing high quality particle size data with

variables having a significant impact on the data quality. With the flow chart presented

at the end of this chapter, an LD method was optimised for the analysis of bedaquiline

suspensions which will be, endorsed by experience, employed throughout the PhD

project.

As a high-energy grinding technology, the IVM has been noted for its fast nanosizing

potential though the energy input can unfortunately be dissipated as heat as well. To

alter the balance between particle size reduction and heat generation, process and

formulation variables can be tuned, wherefore fundamental insights in the IVM are

compulsory. Consequently, the feasibility of using IVM as a nanosizing technique with

a controlled heat generation was addressed via an OVAT approach in chapter 4. This

chapter mapped the adequate working range which guaranteed safe and feasible

milling conditions, identified the most critical process parameters influencing particle

size reduction and heat generation and studied the optimisation of these variables to

control further heat generation.

Driven by the heat generation, a design of experiment (DoE) was applied in a next step

to explore how five key process parameters impact the IVMs process. As described in

38

chapter 5, this study generated predictive models that allow to forecast milling

outcomes in terms of particle size and temperature, facilitating the rational selection of

optimal process parameters.

Chapter 6 reported on the stability of the DoE’s suspensions stored at 5 °C. In one

specific condition, an unusual particle size reduction was observed during the cold

storage, which seems to contradict the stability trends described in chapter 1. To study

the repeatability of the given phenomenon, this specific sample was manufactured in

several-fold, kept on cold storage and tracked via LD. In a further step, the particle size

was tracked via an optimised LD system and an orthogonal particle size technique, the

differential centrifugal sedimentation. The physical chemistry behind this observation

still needs to be elucidated, however, we propose several hypotheses in the final

chapter.

In this final chapter 7, the results of chapter 3 to 6 are broadly discussed in light of

recent literature, general conclusions are provided, and future works are identified.

Size analysis of small particles in wet dispersions by

laser diffractometry

Results in this chapter are based on:

De Cleyn, E., Holm, R. & Van den Mooter, G. Size Analysis of Small Particles in Wet

Dispersions by Laser Diffractometry: A Guidance to Quality Data. J. Pharm. Sci. 108,

1905–1914 (2019).

41

ABSTRACT

In the present work, the question how to obtain high quality LD results is discussed by

investigating various hurdles that can be encountered during particle size

measurements in wet dispersions and the associated data interpretation. Following

this an effective troubleshooting is discussed based upon theoretical insight in the LD

measurement. As an important element of the Mie theory, the refractive indices (RIs)

of the model compounds, bedaquiline and cinnarizine, were quantified using the LD

software, the Becke Line technique and the single solvent technique, as described by

Saveyn et al.67 The influence of parameters such as obscuration level, background

quality and fitting of data was investigated, and a summarising flow chart has been

provided. Through this analysis the present work emphasizes the need for a systematic

approach when conducting LD measurements, including standard performance of an

obscuration titration and an extended method optimisation, in order to reach high

quality LD results.

42

INTRODUCTION

Extensive efforts have been made by the pharmaceutical industry with respect to target

identification and lead candidate generation through high-throughput screening and

combinatorial chemistry.33 Although these advanced technologies have offered vast

amounts of new chemical entities, a significant part of these compounds are poorly

water soluble. Given that aqueous solubility is a critical parameter for drug absorption

from conventional dosage forms, it has become a significant hurdle within the

pharmaceutical development process to define formulations that provide sufficient and

constant plasma concentrations of the administered compound.68, 69 New formulation

strategies have been introduced to tackle this hurdle, from which the generation of

nano- and microsuspensions has already demonstrated its value, utility and

commercial viability.33, 70 The stability of these suspensions is judged by critical

attributes such as particle size and PSD.70

Different techniques can be opted to quantify the particle size and PSD, from which

coulter counting, microscopy and dynamic light scattering are well-known, despite

having considerable technical disadvantages such as their narrow measuring range.

44, 68, 71 For suspensions it is important to detect the larger particles as well as the

smaller sized population, given that the long-term stability of suspensions may be

notably decreased by a broad PSD.50 As a result, LD, comprising a measurement

range of 0.01 µm to 3500 µm, is often used alone or in combination with dynamic light

scattering to characterise suspensions and thus forms the preferred method for size

analysis of sub-millimetre particles.44, 72 To be able to analyse particles of such a broad

size range the equipment has to accommodate and implement complementary

techniques. Particles scatter light in every direction and more information can be

gained if sideward and backward scattering are captured as well. Furthermore, diverse

scattering patterns can be obtained from light with different wavelengths.

Consequently, LD equipment manufacturers tend to incorporate multiple laser sources

with different wavelengths, as in the case of the Mastersizer 2000™ and Mastersizer

3000™ from Malvern Instruments®, or polarisation intensity differential scattering,

which is patented by Beckman Coulter Inc. Besides its broad range, LD is popular for

its ease of use and adequate reproducibility. Despite these clear advantages, the

trustworthiness and accuracy of LD data have been questioned by numerous

authors.73, 74, 75

43

Keck and Müller investigated the significant impact on the final PSD of choosing either

the Fraunhofer or Mie theory.73 This included investigations of the differences obtained

when using complex scattering pattern, along with the choice of optical parameters in

the case of the Mie theory. Although the work by Keck and Müller73 discussed some of

the most important parameters influencing the LD results, additional hurdles

concerning LD analysis and data interpretation are still present, both within the case of

dry and wet measurements. Obscuration parameters such as the red (obsc. red) and

blue light obscuration (obsc. blue) and fitting parameters such as residual (res.) and

residual weighted (res. weight.) in the case of the Mastersizer 2000™ or R and Chi-

squared in case of the L-950 of HORIBA76 offer a clear insight in the quality of the

results, however, they are still often overlooked. Understanding these attributes is the

only way to precisely develop and optimise a LD method. To limit the scope of this

work, the focus is placed on wet samples. Dry powders need different set-ups with

different hurdles to tackle, though the overall LD theory and above-mentioned

parameters can still be considered during method development and data interpretation.

The objective of the present work was therefore to provide the reader with a deeper

insight into the LD system and the factors that should be considered during

measurement, analysis and data interpretation to obtain meaningful wet LD

measurements.

44

THEORETICAL BACKGROUND OF LASER DIFFRACTOMETRY

Beyond the elements described by Keck and Müller73, which the interested reader is

referred to, other aspects of the LD system may have a tremendous effect on the final

PSD, which to the best of our knowledge have not yet been discussed. To complete

the theoretical background on LD and hence, making a correct interpretation of LD

deviations and results possible, a short summary of LD and the definition of

supplementary terms are provided below.

Figure 3.1. View on the scattering phenomena, which occur if electromagnetic radiation hits a particle

In general, LD is based on scattering of electromagnetic radiation. Besides diffraction,

the electromagnetic radiation will be reflected, refracted, absorbed and re-radiated

(Figure 3.1.), generating a complex scattering pattern, which can be assessed by the

front, side and backward scatter detectors in the LD equipment. Hence, the term LD,

although designated for the detection system as such, does not cover the whole set-

up.

The complex scattering pattern is highly dependent on the ratio of the wavelength of

the laser source and the particle size of the analysed sample. This explains why, laser

sources with different wavelengths are often used within LD to create deviating

scattering patterns and thereby to create a deeper insight into the particle size of the

sample. Overall, three standard scattering patterns can be detected i) if a particle is

45

ca. six times larger than the wavelength, Fraunhofer scattering can be expected,

wherein the forward scatter is prominently present compared to the backward

scattering, ii) Rayleigh scattering, the other extreme where the intensity of the forward

and backward scattered light is of the same range, will appear when the particle is ca.

ten times smaller than the wavelength of the incident electromagnetic radiation, and iii)

the intermediate region which is dominated by Mie scattering from which the final

particle size can be calculated using the Mie theory. Even the simplified version of the

Mie theory73 (Equation 3.1.) shows the impact of the complex refractive index (RI, m)

on the scattering pattern. This complex RI includes the real part of the complex

refractive index (rRI), which resembles the refraction of light and the imaginary part of

the complex refractive index (iRI), which embodies the absorption of light.

Consequently, it is of utmost importance that the RIs are determined correctly to

preserve an adequate interpretation of the final scattering pattern and hence to secure

the accurate deduction of the particle size. The Mie equation further contains the

intensity of the scattered light (I, W/sr), the flux per unit area of incident light (E, W/m2),

various constants (k,K), a first order Bessel function (J1) and the angle of scatter (W,°),

as a simplified version of the Mie Theory73 (Equation 3.1.) presents below;

𝐼 (𝑤) = 𝐸 {𝑘2𝐴4[𝐽1]2𝑊−1 + [𝐾1𝑊]1 + [𝐾2𝑊]3 + [𝐾3𝑊]5 + 𝑘4𝐴6(𝑚 − 1)2 𝑊6

8𝜋2 }

(Equation. 3.1.73)

Next to the scattering phenomena, a fraction of the light will be unaffected and will pass

straight through the sample cell. The amount of this unscattered light is predominately

based on the number of particles present within the sample cell. To capture this

unaltered light, an additional detector has been installed within the multi-element

detector plane, namely the obscuration detector (Figure S-3.1., Supplementary

Information, §9.1.). The name obscuration emerges as a synonym for sample

concentration and is expressed as a percentage within the Mastersizer 2000™. Within

this system, both a red light laser with a wavelength of 633 nm and a blue light-emitting

diode with a wavelength of 466 nm are present as electromagnetic radiation sources.

Hence, both obsc. red. and obsc. blue will be provided during LD measurements and

afterwards, as part of the results section. Since the blue light-emitting-diode light is

Fraunhofer Term Rayleigh Term

46

more prone to be scattered by smaller particles, the corresponding obscuration will

mostly represent the number of smaller particles present within the sample cell and

hence, within the original sample. Obsc. red is often envisaged as the standard

obscuration level. Within the equipment of HORIBA, multiple light sources are present

as well. The sample concentration is presented as the percentage transmission (%T),

the percentage of incident light not scattered by the particles.77

A part of the LD method development will enclose the search for the optimal

obscuration. Information offered by the manufacturer78 recommends obscuration

ranges based on the particle size of the sample, although this attribute is actually the

output of the LD measurement itself. Besides, the optimal obscuration is a balance

between recording sufficient light intensity and avoiding multiple light scattering. A too

low obscuration will allow background noise to affect the results. Multiple light

scattering on the other hand will lead to a reduction in the detected particle size. As

the term suggests, at excessive obscuration levels, the light will be scattered multiple

times (Figure 3.2.). The diffracted light may hit another particle and thereby be

scattered towards higher angle detectors. Since smaller particles scatter

electromagnetic radiation to these detectors, the presence of these particles will be

overestimated. Hence, finding the optimal obscuration by titration of the obscuration is

a critical step in gaining a precise LD method.79

Figure 3.2. Multiple light scattering.

Another point to consider is the fitting of the raw data. The raw data, as offered by the

LD equipment, enclose both the background signal and the complex scattering pattern

47

which is described by the intensity of the scattered light and the angle of the scattered

light, presented by the detector number. If the intensity of the background, also called

‘blank’, surpasses the measurement signal, so-called negative data are generated,

leading to an underestimation of the scattering signal and so, a deviation in the final

PSD. The raw data will be fitted to acquire the PSD. In the case of the Mastersizers™,

both the raw and the fitted data will be plotted, visualising the accuracy of the given fit.

If deviations are noted between detectors numbers 1 to 50 an inaccurate rRI may be

the cause. Inconsistency around the detectors capturing light extinction80, namely

detector number 51 and 52, can be induced by an inaccurate iRI. All manufacturers of

LD system will present their fit quality in a different way. In the case of Malvern, the fit

quality is indicated in two terms, the res. and res. weight. should be below 2% and in

the same order of magnitude to consolidate a reliable measurement. HORIBA on other

hand will present fit quality based on the Chi-squared, which presents the overall

quality of the experiment and the R parameter, showing the correctness of the chosen

optical parameters, which must be minimised to produce qualitative LD data.76

Deviations of fit can also be linked to poor background quality and poor data quality.

48

MATERIALS AND METHODS

Materials

Bedaquiline and hydroxypropyl methylcellulose (HPMC) were provided by Janssen

Pharmaceutica (Beerse, Belgium). d-α-Tocopherol polyethylene glycol 400 succinate

(TPGS 400) was provided by Eastman Chemical Company Kingsport (TN, USA).

Cinnarizine, polysorbate 20 and polysorbate 80 were obtained from Fagron Belgium

NV (Nazareth, Belgium). Sodium dodecyl sulphate (SDS) was obtained from Sigma

Aldrich Chemie GmbH (Steinheim, Germany). Sodium carboxymethylcellulose

(sodium CMC) and ethyl acetate were obtained from Merck KGaA (Darmstadt,

Germany). Isopropanol, acetone and 1-methyl-2-pyrrolidinone were obtained from

VWR (VWR international, Germany). Deionised water (R≥18 mΩ, Maxima Ultrapure

Water, Elga Ltd., Wycombe, England or Milli-Q® Advantage A10 system, Merck KGaA,

Darmstadt) was used for all the experiments.

Methods

3.4.2.1. Retrieving the real part of the complex refractive index

In the Becke line technique, the compound was consecutively dispersed in a broad set

of rRI matching liquids (Cargille-Sacher Laboratories Inc., NJ, USA) and placed

underneath a microscope (Nikon Eclipse E1000 Microscope with Film Camera System,

Nikon instruments Inc., NY, USA). By lowering the stage of the microscope and

observing in which direction the condensed Becke line moved, the material with the

highest rRI, particle or matrix, could be identified.81

In addition, the single solvent method, as described by Saveyn et al.67, was used. The

density of bedaquiline was measured, using a pycnometer (Beckman Instruments

INC., Fullerton, CA, USA). To simplify the calculations, the rRI of very low concentrated

(0.2, 0.4, 0.6, 0.8 and 1.0%(v/v)) solutions was computed, using an Abbé refractometer

(Atago NAR-1T Liquid, ATAGO U.S.A., Inc., WA, USA). Ultimately, extrapolation up to

100% solute gave the rRI of the analysed compound.67

Finally, the rRI were calculated based on the chemical structure of the compounds,

using the predictive software Chemsketch (Advanced Chemistry Development, Inc.,

Toronto, Canada).

49

The selected rRIs were further optimised within the Mastersizer 2000™ software

(Version 5.61, Malvern Instruments Ltd, Malvern, UK). One set of raw data was

subjected to a range of varying rRIs and iRIs. The tendency of the particle size and the

efficiency of the overlap of the raw and fitted distribution were evaluated. As a result,

the best fitted model and consequently the most appropriate res. and res. weight. were

retrieved. These RIs were used for further LD experiments.

3.4.2.2. Production of suspensions

All suspensions were prepared using wet media milling. 10 mL vials were filled with

bedaquiline (5 wt%), 3 mL of zirconia beads (ø 1mm, Nikkato Corporation, Sakai,

Osaka, Japan) and 8 mL of a stabiliser solution. Both surfactants and hydrophilic

polymers were used as stabilisers in this study.

When a surfactant was used, the concentration was set at 50 times the corresponding

critical micellar concentration (CMC), as obtained from literature. The concentration

TPGS 400 was based on the CMC of TPGS 1000. If needed, average values were

calculated (Table 3.1.). The surfactant solutions were produced one day in advance

and magnetically stirred overnight. To improve dissolution, the SDS and TPGS solution

were stored at approximately 60 °C overnight.

In case of the hydrophilic polymers HPMC and sodium CMC, a concentration of 0.125

%w/v was applied.

Later the vials were placed on an in-house made roller mill. The suspensions were

milled for 28 hours at a speed of ca. 327 rpm.

50

Table 3.1. Surfactants and their corresponding CMC

3.4.2.3. Size determination by laser diffractometry

Conducting laser diffraction experiments

LD measurements were performed on a Mastersizer 2000™ with hydro-unit. In all

experiments, a degassed 0.005 %w/v polysorbate 20 solution was used as dispersant.

As a further preparation of the LD experiments, the hydro-unit was shut down for 90 s

after addition of the dispersant after which a stirrer speed of 600 rpm was set and the

system was left to stabilise. Finally, the system was aligned, and the background was

evaluated.

The general-purpose model for irregularly shaped particles with normal calculation

sensitivity was applied within the Mastersizer 2000™ software. The set optical

parameters were a sample rRI of 1.595 (see below), a sample iRI of 0.001 (see below)

and a dispersant rRI of 1.333. The stirrer speed was set at 600 rpm and no sonication

was applied. An obscuration titration was performed for each sample to elucidate the

optimal obscuration level.

Analysis of Data

Overall, the size range was set at 2000 µm. If gas bubbles emerged as peaks within

the final PSD, the size range was decreased to 96.86 µm to exclude these peaks from

the PSD. Runs that even after size range reduction presented dv-values that were

affected by gas bubbles were removed and averages were recalculated. The

volumetric PSDs with complementary dv-values, residuals, obscuration levels and

volume-based median diameters were interpreted and background stability over time

was assessed.

Stabiliser CMC (%w/v) Applied CMC (%w/v) 50CMC (%w/v)

Polysorbate 20 0.007482 0.0074 0.37

Polysorbate 80 0.001683 0.0016 0.080

SDS 0.2016

as average of 0.1728 - 0.230482, 84

0.2016 10.08

TPGS 0.0285 0.02 1

51

RESULTS AND DISCUSSION

The optimisation of a LD method and further, finding the source of the deviations in LD

data can be complicated since the different aspects of the LD system are often

interconnected. Searching for the correct optical parameters, ensuring background

quality, fitting of raw data, the obscuration titration and ensuring the stability of the

sample within the hydro-unit are key steps in LD method optimisation that will be

discussed in this work. Numerous deviations that occurred during the analysis of fine

suspensions, and the ways how to tackle them, will be discussed as well. Finally, a

flow chart can be found at the end of this article, providing a guideline in LD method

optimisation.

Optical parameters

The implementation of the complex RI within the Mie theory and the associated

calculation model is critical in acquiring correct LD data. Consequently, the question

rises how to measure both the iRI and the rRI. Guidelines offer numerous techniques

to predict or to calculate the rRI, whereas standard methods for the measurement of

the iRI are still lacking. Retrieving the correct optical parameters forms the first step in

achieving qualitative LD results.

3.5.1.1. The influence of the imaginary part of the complex refractive index

on the final particle size distribution

Preliminary experiments showed that the iRI had only limited effect on the fitting of the

Mie model and thus on the final PSDs in the software supplied with the Mastersizer

2000™. Due to hardware and software development such as the increased number of

higher-angle detectors, the Mastersizer 3000™ on other hand implements the iRI more

accurately, which then evidently has a larger effect on the final PSDs.82

Information offered by the manufacturer mentioned two techniques wherewith the iRI

could be estimated.80 The iRI could be predicted based on microscopical images of the

compound or determined based on volume concentration measurements and Lambert-

Beer’s law. Overall, particles with chromophores, low transparency, aspherical habitus

and rough surface structure are prone to have a higher iRI. Within the LD software the

accuracy of this value has to be specified with an order of magnitude, hence making

fit optimisation within the software the most common way of finding an acceptable iRI.80

52

Fit optimisation of the Mie model of an SDS- and TPGS-stabilised suspension, by

comparison of the effect of the different iRIs on the final PSD, residual (res.) and

residuals weighted (res. weight.) with the goal to diminish the latter two, led to an

optimal value of 0.001 which was set within the software for further experiments (Table

S-3.1., Supplementary Information, §9.1.). Fit optimisation could be applied on LD

systems of different manufactures too. When using the LD-950, provided by Horiba,

the minimisation of the residual R-value has to be taken into account to verify the

correct refractive indices, as information offered by the manufacturer suggests.76

3.5.1.2. Techniques to find the real part of the complex refractive index

Numerous approaches to determine the rRI have been described in the literature and

guidelines.67, 86 Empirical methods based on the Clausius-Mossotti equation, the

Gladstone-Dale approximation and RI trend analysis could estimate rRIs, though more

accuracy is preferable.86

Predictive software estimated the rRI based on the compounds chemical structure.

The results were 1.666 ± 0.020 and 1.625 ± 0.020 for bedaquiline and cinnarizine,

respectively (Table 3.2). These outcomes were close to the values found with the

Becke line technique, which were 1.5920 - 1.5960 and 1.6880 - 1.6920 for bedaquiline

and cinnarizine, respectively. However, as these were based on experimental data,

they were presumed to be more accurate. The single solvent technique of Saveyn et

al.67 was applied as well. The density of the compounds must be measured to prepare

volumetric based sample solutions. For this reason, the density of bedaquiline was

experimentally determined to be 1.35 ± 0.00 g/mL and the density of cinnarizine, 1.13

± 0.00 g/mL, was obtained from the literature.87 The single solvent technique of Saveyn

et al. resulted in higher rRIs than the predictive software and the Becke line technique.

For bedaquiline the values ranged from 1.7678 to 1.8342, while values around 1.73

were obtained for cinnarizine. Elements such as the limited precision of the used

refractometer and the lack of a thermostabilising system could explain the deviating

data.

53

Table 3.2. Computed rRI results.

ChemSketch Becke Line Single solvent technique67

Compound Acetone Ethyl acetate 1-methyl-2-pyrrolidinone

Bedaquiline 1.666 ± 0.02 1.5920 - 1.5960

1.8342 1.7678 1.7749

Cinnarizine 1.625 ± 0.02 1.6880 - 1.6920

1.734 1.737

The Becke line data, i.e. values between 1.5920 and 1.5960 for bedaquiline, were used

as input values in the software for further refinement using fit optimisation.

Consequently, the fitting, the PSD and residuals of the SDS- and TPGS-stabilised

suspensions were evaluated while varying the rRI. Based upon this optimisation, a final

rRI of 1.595 for bedaquiline suspensions was found (Table S-3.2., Supplementary

Information, §9.1.).

The predicted value of 1.666, set as rRI during the analysis of the TPGS-stabilised

suspension, would lead to a slight residual increase from 0.501 to 0.532, compared to

the optimal value of 1.595. The dv50-value on the other hand increased to 0.400 µm.

When the same comparison was applied on the SDS-stabilised suspension, both

residual as residual weighted decreased slightly. In this specific case, the predicted

values with fit optimisation could be set instead of the Becke line data. Broadening this

conclusion to other suspensions could be false since the effect on the final fitting and

PSD is unclear. Albeit, predictive software can offer a fast, first idea of the rRI.

Background signal

Besides the correct RIs, a qualitative acceptable background forms one of the primary

steps in retrieving qualitatively and quantitatively correct PSDs. The background or

blank will be subtracted from the measurement signal and thus produces the final

complex scattering pattern from which the PSD will be derived. Hence, when deviations

such as gas bubbles, noise, contamination or misalignment present themselves during

background measurement, they will cause deviations in the final obscuration, fitting

and PSD.

An example of how poor background quality inferred with the LD results was found

during the measurement of an SDS-stabilised suspension (Figure 3.3.). Due to the

presence of background noise and gas bubbles, which were not captured during

background measurement, the baseline of the final PSD was raised and additional

54

peaks unrelated to particles, were observed. Hereby, the poor background quality

impacted the fit, the residuals and the final PSD. By repeated measurements, the

inconsistency of the signal and the accompanied peaks could be observed. The

background quality had to be extensively improved to allow adequate LD

measurements.

Figure 3.3. Poor background quality, including noise, the significant presence of gas bubbles and the resulting

baseline rise, affecting the fit and PSD of an SDS-stabilised suspension.

To secure good background quality, the instrument should be switched on at least 30

minutes before measurement. Further, the background should be checked before and

after every measurement and over a whole dataset. A checklist facilitating the

background assessment of the Mastersizer 2000™ includes the intensity of the

background at certain detectors and its overall shape. In the first place, the light

intensity of the background should fall below 100 units at detector 1 and below 20 units

at detector 20. This standard rule can be expanded to a limitation of 5 units at detector

30 and of 1 unit at detector 40 to increase the background quality, as this is essential

during analysis of submicron particles (Figure S-3.2., Supplementary Information,

§9.1.). Secondly, the average shape should be exponential. As a last step, the

background should be observed for a short period before each measurement. The

background should be low and stable. The measurement signal should be random.

The measurement can only be started when these prerequisites are met.

Deviations within the background can be associated with air bubbles, dissolution of

material or presence of impurities in the medium or on the cell windows. Refractive

index gradients and beam steering due to temperature gradients or mixing dispersants

55

can emerge as well as a disparity in the background. Misalignment can emerge but

can easily be overcome by an extra alignment step, where the laser will be repositioned

towards the centre of the detector pane. Misalignment often appears as castellation in

which the shape of the background function resembles a battlement. In case of

impurities or dissolving materials, the dispersant should be refreshed. Overall, a longer

stabilising period in-between dispersant addition and background measurement can

resolve most of the above-mentioned hurdles and is the best answer to negative data

and instability caused by RI gradients and beam steering. In view of this, the stabilising

time was increased up to eight minutes within this study.

In addition, the background should be evaluated over the whole measuring time. In this

way the cleanness of the cell windows can be assessed. Poorly soluble compounds

can adhere and accumulate at the surface of the cell windows and raise the

background signal. If for example smaller particles adhere, a rise in the background

signal of higher angle detectors will be observed. This can suppress the measurement

signal of these nanoparticles and thus their presence in the final PSD. If needed, the

cell windows can be removed from the measuring cell and manually cleaned. In

extreme cases repetition of the measurement is advised.

Within this study, background was stable and low (Figure S-3.2., Supplementary

Information, §9.1.), due to intensive cleaning with e.g. isopropanol. The slight

deviations in the background between detector 1 - 20 were most likely caused by gas

bubbles.

Data analysis: Fitting

The first point of reflection was the exactness of the set of optical parameters, since

wrong RI can cause fitting deviations. Nonetheless, the prospect of having elevated

fitting parameters in case of a suboptimal RI forms the basis of fit optimisation, which

was discussed earlier.

During analysis of low concentrated samples, sharp peaks occurred in the raw dataset,

captured at the first detectors. Smoothly fitting these peaks was troublesome and

caused a discrepancy between the fitted and raw data distribution. The background

surpassed the measurement signal and as a result, residuals, one of the fitting

parameters of the Mastersizer 2000™, increased even though the correct RIs were

applied. This background noise must in general be minimised during data acquisition

56

to preserve their high quality. In extreme cases, these sharp peaks raised the baseline.

A signal at the lower numbered detectors is often linked to the presence of bigger

particles. As a consequence, dv-values increased, in particular the dv90-value. A low

measurement signal and thus a low sample concentration within the measuring cell

provoked these sharp peaks. These peaks emphasise the need for a background of

utmost quality. The inconvenience can be overcome by increasing the sample

concentration, though multiple light scattering, which will be touched upon below,

should be taken into consideration as well.

A second element that needed consideration was the inconsistent presence of an extra

peak around detector 10, which caused the overall fit to deviate (Figure 3.4. and 3.5.).

This peak sharply affected the final dv-values (Figure 3.4.) and randomly reoccurred

in the PSD over consecutive runs, though it was not linked to the original sample.

These peaks were often present with a size of ca. 100 to 300 µm (Figure 3.5.). The

light intensity of the peaks was high and highly variable. To obtain a stable, dispersed

system, a surfactant was added to the dispersant in low concentration, which, however,

induced the risk of foaming. The inconsistency in the presence and the size of the peak

during consecutive runs and observation of foam strongly suggested that the peaks

originated from gas bubbles that would influence the final data.

Practical steps could be implemented to avoid gas bubbles and their influence on both

the fit and PSD. Dispersant was degassed and after its addition to the hydro-unit, the

unit was shut down for a few minutes, so the air bubbles could evacuate. Further, the

rotation speed of the stirrer was reduced to 600 rpm and stabilisation time was

enhanced up to eight minutes. If not sufficient, extra stabilisation time or short, sharp

surges in the stirrer speed may drive the bubbles to the surface. However, gas bubbles

could not always be completely avoided. If they appeared as additional peaks within

the PSD, the size measurement range was retrospectively limited to 96.86 µm, which

improved the dv-values (Figure 3.5.). Certainly, when lower obscuration levels were

analysed, gas bubbles had less resistance to appear in the volumetric distribution. If

size range reduction was not sufficient, the runs containing these gas bubbles were

excluded from the average calculation, leading to a better fitting quality. However, by

this latter step, the background noise impacted the distribution even more, envisaged

in the previously mentioned sharp peaks in the raw data (Figure 3.4.).

57

Figure 3.4. Example of the influence of a gas bubble and a low obscuration and the exclusion of the runs containing

these gas bubbles on the fitting of the raw data distribution and the PSD of a TPGS-stabilised suspension.

Figure 3.5. Example of how gas bubbles randomly appeared during analysis and were excluded by size range

reduction during the particle size measurement of a HPMC-stabilised suspension. When the measuring size range

was the original, broad range, deviating dv90-values, easily reaching above 150 µm could be found, referring to the

newly emerged micro range peak. The peak randomly appeared, and its size varied as well. By limitation of the

measuring range, the new micro range peak was excluded, and dv-values reduced, presenting the true data.

58

Data analysis: Obscuration

Consecutively increasing the obscuration level led the PSD to significantly shift with an

important decrease of the dv10-value, making it unclear which obscuration level

represented the correct LD data. This shift was observed when a monomodal PSD was

evaluated at different obscuration levels, such as the deviating PSD of a sodium CMC-

stabilised suspension (Figure 3.6.), showing a clear overestimation of the lower micron

and submicron particles, most likely caused by multiple light scattering. Performance

of repetitive measurements at different obscuration levels and correct data

interpretation may tackle this problem. The optimal obscuration can be identified by

evaluation of the dv10-value. This dv-value increased up to a plateau, where the

optimal obscuration could be found, which was followed by a decline. When analysing

smaller sized suspensions, which generally needed low obscuration levels, the rise of

the dv10-value and its plateau were sharply reduced up to the point where the dv10-

value immediately started to drop when the obscuration was further increased.

Figure 3.6. Example of multiple light scattering within a monomodal distribution, when a sodium-CMC stabilised

suspension was analysed with increasing obscuration level.

59

The obscuration titration of a polysorbate 20-stabilised suspension showed a

monomodal distribution in the micrometre range during the first measurement,

however, increasing obscuration led to the emerge of a submicron peak which

moderately extended during further obscuration enhancement (Figure 3.7.). This led

to a sudden drop of the dv10-value and the dv50-value. The lower obscuration levels

on the other hand showed a poorer fit than those of the higher obscuration levels.

Overall, depending on the obscuration, markedly different PSDs could be obtained,

emphasising the need of an obscuration titration

Figure 3.7. Example of multiple light scattering, occurring within the multimodal PSD of a polysorbate 20 stabilised

suspension.

60

Besides multiple light scattering and the minimum obscuration have to surpass the

background noise, additional factors influenced the effect of the obscuration level on a

multimodal PSD. The intensity of light scattered by nanoparticles was lower than the

intensity of light when scattered by microparticles. Thus, the obscuration has to be

significantly increased to visualize the nanoparticles when present within a multimodal

system. This phenomenon was enhanced by the volumetric display of the PSD, leading

to the dilemma of the need to measure nanocrystals while increasing the risk of multiple

light scattering. Generally, the lowest obscuration level, which was not affected by

noise, was selected as the optimal obscuration level. The low obscuration level will

offer the researcher the worst-case scenario where no or only limited multiple light

scattering can be expected. Also, the quality of the background related to the optimal

obscuration level was an important factor, as the measurement signal must be larger

than the background signal to give a meaningful read-out. For finer suspensions, the

lower obscuration limit was defined by the noise level in the system, i.e. requiring a

high background quality for the measurement.

Data analysis: Stability of the sample within the hydro-unit

Another hurdle that may be encountered is a gradual shift of the PSD during

consecutive runs of a measurement, making it hard to decide at which point

representative data are obtained. Overall, three different, time dependent phenomena

can take place within the LD system that reduce the representability of the sample

within the LD system, namely i) dissolution of the compound, ii) agglomeration of the

compound particles, or iii) an inhomogeneous dispersion of the sample.

Dissolution will appear as a gradual decrease of the median particle size when

microparticles are analysed. During the analysis of nanosuspensions, dissolution may

make these particles undetectable, leading to an increasing dv10-value. Dissolution

will always be accompanied by a decreasing obscuration. In case of agglomeration,

both red and blue light obscuration can increase due to the presence of bigger particles

that scatter the light more intensively. Alternatively, the red light obscuration can

increase, as a consequence of disappearance of smaller particles, leading to a

diminishing blue light obscuration. If not properly dispersed, the dv-values and

obscuration levels will change depending on the fraction of particles passing the laser.

To obtain representative data, the rotation speed of the stirrer and the time between

sample addition and sample measurement must be optimised. This speed should be

61

high enough for homogenisation, without promoting dissolution and foaming. Enough

time was required to secure an adequate sample dispersion in the measuring cell

before analysis. Although these phenomena were more difficult to identify, they can

co-occur as well.

The influence of these phenomena can be restricted through the use of a limited

number of runs in the calculation of the average, as has been done within this study. If

dissolution or agglomeration took place, the first three reliable runs were selected.

While in the case of an inadequate dispersion, the measurement was repeated or only

the last runs were accepted. When the measurement was repeated, the time in-

between sample addition and analysis was increased to secure a homogenously

dispersed sample. The variability in-between the particle sizes of the consecutive runs

should comply to the ISO 13320 guidelines.88 These guidelines state that the

coefficient of variation (CV) should be below 5% in case of dv10- and dv90-value, while

the dv50-value should stay below the limit of 3%. These values are doubled in the case

of a value below 10 µm and can be applied to assess the precision of the data and

hence the stability of the dispersed system within the LD system In the case of

dissolution, the dispersant could be replaced with one where the particles are less

soluble in.

Flow chart

To summarise all the information that has been provided in this work, a flow chart has

been offered, based on the Mastersizer 2000™ software but which can be extrapolated

to other LD systems as well. Although such a summarising chart cannot fully cover the

complexity of LD and its broad range of samples, it can form a basic guideline during

LD method optimisation and data analysis. Overall, due to the possible lower accuracy

and precision of the LD method, even after method optimisation, additional analysis

with an orthogonal technique or multiple sampling is advised.

When optimising a LD method (Figure 3.8.), the RIs must be set first. Different

techniques, as has been previously mentioned can be opted to find both the iRI and

the rRI. For the estimation of the rRI, the Becke line technique and further fit

optimisation in the software is advised. Additionally, the number of runs during one

analysis, the measurement time of one run and the delay in-between the different runs

have to be set, which is e.g. dependent on the stability of the sample within the hydro-

62

unit. Afterwards, the accessory parameters must be installed. Dispersant has to be

added to the hydro-unit. The choice of this dispersant is dependent on the solubility

and the stability of the sample in the dispersant during measurement. To mimic the

compounds matrix and to stabilise the sample during analysis, surfactant can be added

to the dispersant or the dispersant can be saturated with the compound. After addition,

the stirrer speed and sonication level will be set. Stirrer speed should be high enough

to homogenise the sample but should be reduced in case of foaming or sample fragility.

Sonication can be introduced to break agglomerates. After installation of the accessory

parameters, a background stabilisation time can be introduced before alignment and

background/blank measurement take place. The background signal and the random

measurement signal should be briefly checked and secured. The background must

accomplish the previously mentioned prerequisites. If not, additional cleaning steps or

background stabilisation time can usually omit the issue. Misalignment can present

itself as castellation and can be overcome by cleaning or replacement of the cell

windows. Alignment can be set manually as well but is not preferred. After the

background check, sample will be added. Depending on the state of the sample within

the hydro-unit, additional dispersion time can be introduced before the measurement

is started. After the measurement, the data should be first qualitatively checked before

conclusions based on the dv-values can be made. First the measuring range should

be expanded or rather reduced, though this is only the final resort in case of gas

bubbles, to cover the full PSD of the sample. ‘Data’ should be checked for the presence

of negative data. Further ‘Fit’ is reviewed to check fitting parameters such as res., res.

weight, R parameter or Chi-squared. and where possible discrepancy between the raw

data and the fitted data takes place. Only then, the ‘Records’ can be examined. Not

only the dv-values on itself are important but also trends during analysis, can indicate

instability of the sample during analysis or issues regarding impurities or gas bubbles

which should be taken into consideration during further result analysis. When runs are

excluded due to discrepancies, averages have to be recalculated. After cleaning, the

background must be shortly assessed and also over the whole experiment.

Accumulating impurities can be found and resolved by additional cleaning. Finally, the

overall quality of the method can be evaluated and if needed, further optimised by

variation of the different software parameters, accessory parameters and stabilisation

periods. Thus, the flow chart should be executed algorithmically to obtain a final

optimised method. When an optimised method is found, it is still important to execute

63

an obscuration titration every time samples are analysed. Overall, relying on standard

LD methods will not guarantee LD quality and so the quantity of the LD results could

be inaccurate.

Figure 3.8. Flowchart wherewith LD method optimisation can be conducted, starting with the installation of the

correct software parameters, leading to accessory parameters selection, sample measurement, analysis of results

and cleaning of system. Intermediate steps are presented in the diamond shaped boxes.

64

CONCLUSIONS

Over the last decades, nano- and microsuspensions have been well received within

the pharmaceutical community. For the analysis of one of their most critical quality

attributes, the particle size and PSD, different techniques have been described within

the literature. Of these LD is the most widely used technique, due to its broad

measuring range, its reproducibility, and its ease to use. However, the accuracy of LD

has been questioned by multiple authors.73, 74, 75 The present work showed that not

only the set of optical parameters, but also parameters such as the fit, obscuration,

stability of the dispersion within the LD system and background quality can have a

significant impact on the final PSD. An extensive insight in the LD system and effective

troubleshooting, such as suggested in the present work are needed during LD

measurements, if high quality LD data should be generated.

SUPPLEMENTARY INFORMATION

All supplementary material as denoted in the manuscript is provided in Chapter 9:

Supplementary Information, §9.1

Exploration of the heat generation within the intensified

vibratory mill


De Cleyn, E., Holm, R. & Van den Mooter, G. Exploration of the heat generation within

the intensified vibratory mill. Int. J. Pharm. 119644 (2020).

doi:10.1016/j.ijpharm.2020.119644

67

GRAPHICAL ABSTRACT

ABSTRACT

Recently, IVM has emerged as a viable milling technology known for its nanosizing

and high-throughput potential. The extent of the heat generation is ambiguously

discussed within the literature. For this reason, the impact of formulation and process

parameters for both particle size reduction and heat generation needs further

investigation to develop robust processes using the IVM technology. For this reason,

the impact on particle size and heat generation of three different process parameters:

bead-suspension ratio, milling time, and acceleration as well as one formulation

parameter, API concentration was investigated by an OVAT approach. Further the

effect of additional fixtures on the heat generation was noted. The data obtained in this

work showed that if the impact on heat generation was taken into account, the process

parameters could be fine-tuned to optimise the nanosizing potential while containing a

continuous milling process. A longer milling time and production of highly concentrated

formulations were proposed as gentle optimisation steps for the herein presented

comminution process.

68

INTRODUCTION

Over the past decades, the number of drug molecules in the pharmaceutical

development pipelines with poor aqueous solubility has steadily increased, leading to

low and variable oral bioavailability if administered in a conventional dosage form. To

address this challenge, both industry and academia have put significant effort into

finding enabling formulation and processing technologies that can facilitate the usage

of these molecules.33, 42 Among these, the production of micron and submicron

suspensions is a viable and cost-effective strategy, resulting in marketed drugs such

as Rapamune® (Pfizer (Wyeth), NY, USA), Emend® (Merck, NJ, USA) and Megace

ES® (Par Pharmaceutical, NY, USA). 3, 42

Micro- and certainly nanoparticle suspensions exhibit distinctive performance

properties when compared to their poorly soluble bulk counterparts, leading to

enhanced bioavailability after oral uptake, which have been described by numerous

authors.33, 42 Their large specific surface area enhances the dissolution rate, as

described by the Nernst- and Brunner equation (Equation 1.1.).22 In addition, the

diffusion layer thickness decreases with increasing curvature, as described by the

Prandtl equation (Equation 1.2.), further increasing the dissolution performance.5 The

greater surface curvature of particles <100 nm is strongly correlated with a higher

saturation solubility, as described by the Ostwald-Freundlich equation (Equation 1.3.).5

As a formulation system, nano- and microsuspensions can also be used to create LAIs,

a formulation route of utmost interest to combat chronic diseases where patient

compliance is essential for the treatment response.35

Nano- and microsuspensions can be produced in two ways overall; either by particle

size reduction of larger particles, the so-called top-down approach, or via the controlled

precipitation of dissolved molecules, the so-called bottom-up approach. The bottom-

up process usually demands significant volumes of organic solvents and is generally

hard to control with respect to particle size. With the top-down approach, no harsh

solvents are used, but the energy generated is often inefficiently dissipated, leading to

heat generation and potential mechanochemical activation. This may alter the

physicochemical state of the compound and cause instability of the produced drug

product.41, 42, 89 Currently, manufacturers are able to control the overall temperature

rise of the WBM process by the implementation of a cooled water jacket. Two other

69

hurdles for which solutions were implemented in the WBM, are the facts that the

formulation development of (sub)micron suspensions is still mainly based on trial error

and that there is often a limited API availability during the early phases of drug

development. To address these challenges, high-throughput WBM processes were

developed in which a versatile set of formulations can be tested with a limit amount of

API.53 By all these counteractions, most hurdles of WBM could be resolved. As a result,

WBM is the golden standard in the field with a demonstrated efficiency at production

scale and already delivered marketed formulations.46

Still, one major drawback of the WBM is its long milling times.41, 42 For this reason,

research is evolving and new production methods such as IVM have emerged, in which

the RAM platform is used.62, 63, 66 This technology is based on a mixing platform with

the addition of milling media to the recipient which causes a substantial particle size

reduction.62, 90 As a milling platform, the IVM is already identified for its nanosizing

potential, and its drug-sparing opportunities. By implementation of a 96-well plate,

likewise presented in the WBM system, as described by Van Eerdenbrugh et al.53 the

IVM can be applied as a high-throughput screening method.62 The energy build-up

caused by bead-to-bead and bead-to-wall collisions forms the basis of particle size

reduction during IVM. However, this energy build-up may cause a significant

temperature rise that may affect the chemical and physical stability of the suspension.

During heat exposure both excipient and API can degrade, and the solvent can

evaporate. The cloud point of a stabiliser could be surpassed, causing it to dehydrate

leading to re-micellisation.91 Hence, less stabiliser will be accessible for the

stabilisation of the high-energy surfaces of the (sub)micron particles, leading to their

aggregation. The API will normally be crystalline in a suspension, hence high-energy

milling could also lead to polymorphic transition and even amorphisation. Further, the

solubility of the API may increase with temperature, leading to a supersaturated state

followed by further crystallisation and Ostwald ripening, when the suspension cools to

room temperature after the comminution process. Since the pKa is temperature-

dependent pH shifts may also occur92, changing the protonated state of the API during

the process, which could influence its interaction with the micelles and its

solubilisation.50, 89, 93

The RAM platform has mostly been used for dry mixing applications, hence its

application in wet milling is less discussed in the literature and data on the heat

70

generation is underrepresented and ambiguous.62, 63, 66 The heat generation within the

IVM has been previously mentioned by Li and co-workers, but the publication did not

explore this phenomenon further. Li et al. interrupted the milling process every eight

minutes, when the samples were dipped in a refrigerated bath.63 Even though this

method was presented to be effective in small scale, it will be hard to standardise

according to good manufacturing practices or scale-up for clinical or commercial

production. Further, it is known that the final PSD of a milling process is based on the

counteracting trends of comminution and agglomeration.94 The effect of these

implemented breaks and sharp temperature fluctuations on the agglomeration and

thereby the final particle size hence the effective comminution of the milling process

was not explored. To strengthen our knowledge on the heat generation within the IVM

while keeping a continuous manufacturing process, the impact of different formulation

and process parameters on the final temperature should be explored. In this scope,

this article elaborates further on the limited temperature mapping within the IVM

presented by the group of Lu et al.66 Herein, we demonstrate the important heat

generation within the IVM, we report the impact of additional IVM fixtures on the final

temperature and we present the impact of different process and formulation

parameters on both particle size reduction and temperature rise using an OVAT

approach.

71


Materials

Bedaquiline, a poorly soluble model drug (BSC class II), was provided by Janssen

Pharmaceutica (Beerse, Belgium). The particle size of this starting material was 7.5

µm, 48.5 µm and 328 µm for dv10, dv50 and dv90, respectively. Polysorbate 20 was

obtained from Croda (Trappes, France). Deionised water (R≥18.2 mΩ, Milli-Q®

Advantage A10, Merck, Darmstadt, Germany) was used for all experiments.

Methods

4.4.2.1. Production of suspensions

All suspensions were prepared using IVM. Glass vials were filled with bedaquiline,

zirconia beads (Nikkato Corporation, Sakai, Osaka, Japan) and a polysorbate 20

solution. A specific bead-suspension ratio (mL/mL) was set and a bead size of 1000

µm was used. Polysorbate 20 was used in concentrations corresponding to 50, 250 or

500 times its CMC, respectively 0.37 %w/v, 1.85 %w/v and 3.70 %w/v.82 The vials were

placed on an in-house made platform within the LabRAM II (Resodyn Acoustic Mixers,

Butte, USA) and milled for a certain period of time (min), at a defined acceleration (g).

Within the IVM, the vials are shaken vigorously and must therefore be properly

secured. For this reason, a polyoxymethylene derivative-based in-house platform was

constructed (Figure 4.1.).

Figure 4.1. The in-house made platform for holding vials during IVM. An additional hole had to be made at the

bottom of the plate to reduce the mass of this additional feature.

An overview of the different processes and formulation parameters used is shown in

Table 4.1.

72

Table 4.1. Overview of the experiments and their varying formulation and process parameters

Formulation Beads Mill

Sample API conc (%)

Stabiliser conc (*CMC)

Bead size (µm)

Bead-suspension ratio (mL/mL)

Acceleration (g)

Milling time (min)

1 5 50 1000 1.125 75 5

2 5 50 1000 1.125 75 10

3 5 50 1000 1.125 75 15

4 5 50 1000 1.125 75 20

5 5 50 1000 1.125 75 25

6 5 50 1000 1.2 50 15

7 5 50 1000 1.2 75 15

8 5 50 1000 1.2 100 15

9 5 50 1000 0.692 100 15

10 5 50 1000 0.375 100 15

11 5 50 1000 0.774 50 20

12 5 50 1000 0.774 65 20

13 5 50 1000 0.774 80 20

14 1 50 1000 0.774 65 10

15 1 50 1000 0.774 65 20

16 1 50 1000 0.774 65 30

17 5 250 1000 0.774 65 10

18 5 250 1000 0.774 65 20

19 5 250 1000 0.774 65 30

20 10 500 1000 0.774 65 10

21 10 500 1000 0.774 65 20

22 10 500 1000 0.774 65 30

4.4.2.2. Temperature measurement

The temperature of the suspension was initially determined using a manual

thermometer (Sample 1 – 5, Table 4.2.) by measuring the suspensions temperature

over the period of 1min 10 s – 1 min 25 s after milling, as an indication of the final

suspension temperature. In later experiments (Sample 6 – 22, Table 4.2.), the

temperature was measured with an infrared thermometer (VWR® Traceable® Infrared

Thermometer, Radnor, PA, US) for which an emissivity of 0.69 was set. Temperature

was measured at fixed time points after milling. Extrapolation to zero could give an

estimate of the suspension’s temperature immediately after milling, as more deeply

discussed in ‘§4.5. Results and Discussion’.

73

4.4.2.3. Laser diffractometry

LD measurements were performed on a Mastersizer 2000™ (Malvern Instruments,

Worcestershire, UK) with a hydro-unit, using deionised water as dispersant. Stirring

speed was set to 600 rpm and the system was left to stabilise for five minutes. Finally,

the system was aligned, and the background was evaluated. The general-purpose

model for irregularly shaped particles with normal calculation sensitivity was applied.

The set optical parameters were a sample rRI of 1.595, a sample iRI of 0.001 and a

dispersant rRI of 1.333. A limited obscuration titration was performed for each sample

to determine the optimal obsc. and obsc. blue. The quality of the LD data was based

on the fitting, res. and res. weight., as described in chapter 3.95 The particle size was

described by the volumetric median, the dv50-value. Additionally, the PSD was

described by a volumetric distribution with the dv10-, dv50- and dv90-values.

74


The effect of acceleration, bead-suspension ratio and milling time.

Preliminary experiments showed that the maximum acceleration of 100 g at 30 minutes

milling time was not feasible. The impact of the beads and the resulting temperature

increase was to such an extent that vials broke, suspension medium evaporated and

surrounding rubber and plastics melted. For this reason, milling time and acceleration

were limited to feasible and safe conditions.

Further, an early stabiliser screening using WBM, presented polysorbate 20 as a viable

stabiliser for the production of micron-sized bedaquiline suspensions. Preliminary IVM

studies showed that foaming was under control when using polysorbate 20 during IVM

operation, while other stabilisers such as SDS failed.

Since a temperature sensor could not be applied inline, the suspension temperature

was at first measured over the period of 1min 10 s – 1 min 25 s after milling, as an

indication of the final suspension temperature. Milling time was investigated at first

(Sample 1 to 5, Table 4.1.). Longer milling times led to temperature increases, with

temperatures of 38.9 °C noted after 5 minutes of milling and 73.4 °C after 25 minutes

of milling. A significant particle size decrease was observed as well, with a dv50 of 48.5

µm to 5.41 µm after 5 minutes of milling, which decreased further to the nano-range

after 25 minutes of milling (Figure 4.2.). As previously reported by Peltonen56, the

milling time, apart from the acceleration, determined the overall energy input. A longer

milling time will lead to more monodisperse particles but will eventually lead to

aggregation of the particles as well. Nevertheless, increasing the milling time was

reasonable to increase the specific energy and thereby influencing the particle size

reduction, though the resulting temperature rise, contamination risk and aggregation

risk should be taken into consideration as well.

75

Figure 4.2. The effect of milling time on the particle size, given as the dv50-value and the temperature of the final

suspension. For these experiments, samples 1 – 5 of table 4.1. were investigated.

Other important factors determining the final particle size were acceleration (Figure.

4.3.) and bead-suspension ratio (Figure 4.4.). By an increase in acceleration, which in

the case of IVM is also noted in literature as the intensity (%)63, the vertical

displacement of the container becomes larger under the same, constant frequency of

around 60 Hz. If the movement of the container would be monitored over time, a

sinusoidal wave could be depicted in which a constant frequency of around 60 Hz is

set and in which the acceleration is specified by the amplitude of this sinusoidal wave.

This can be interpreted as an increased milling speed. As a result, the vials were

shaken more vigorously, leading to a more intense particle size reduction, with a dv50

value of 1.84 µm at 50 g, which was 680 nm at 100 g (Figure. 4.3.). This may be

explained by the enhanced kinetic energy of the beads, which intensified the shear

forces, attrition forces, compressive forces and impact on the API particles.

76

Figure 4.3. The influence of acceleration, given by sample 6 to 8 of Table 4.1., on both temperature and particle

size after milling.

Figure 4.4. The influence of bead-suspension ratio, given by sample 8 to 10 of Table 4.1., on particle size and

temperature of the suspension after milling.

77

Higher bead-suspension ratio means that more beads per API particle are present in

the vial. As a result, the probability for the particle to undergo collision will increase and

so a higher specific energy input will go in the system. As expected, final dv50 values

of about 2.5 µm were found, which further decreased to 680 nm, when increasing the

bead-suspension ratio from 0.375 mL/ mL to 1.2 mL/mL (Figure. 4.4.).

As presented above, the specific energy of the milling process increases when the

acceleration or the bead-suspension ratio are enhanced. On the other hand, this

increased specific energy is reflected in a sharp heat generation as well. To obtain a

more accurate estimation of the suspension’s final temperature, the temperature was

measured at fixed time points after milling, using an infrared thermometer. The

temperature values were fitted to Newton’s law of cooling (Equation. 4.1.) for which a

high determination coefficient could be reached, as presented in the case of sample 7

in Table 4.1. (Figure 4.5.). Extrapolation to zero could give an estimate of the

suspension’s temperature immediately after milling. In this way, heating was identified

when a high acceleration (Figure 4.3.) or bead-suspension ratio (Figure 4.4.) was set.

In the most extreme settings, 90 °C could be reached. This value approached the cloud

point of the stabiliser used (polysorbate 20), which is about 92-97 °C.91, 96 Parameters

such as acceleration and bead-suspension ratio should thus be taken into

consideration when controlling the temperature of the milling process.

(𝑇𝑡 − 𝑇𝑎) = (𝑇0 − 𝑇𝑎) 𝑒𝑥𝑝 (−𝑡

𝑦) (Equation 4.1)

where Tt is the temperature of the object at time t, Ta is the surrounding, ambient

temperature, T0 is the initial temperature of the object and y is a time constant.97

78

Figure 4.5. Application of Newton's law of cooling on Sample 7 of Table 4.1. Extrapolation to zero minutes indicates

the suspension’s temperature after milling

As alluded to above, the high temperatures can affect a wide array of the suspension’s

properties. To more precisely map the heat generation of the milling process, an effort

was made to improve the final temperature measurement by implementing an average

measurement by a threefold infrared temperature measurement.

The effect of acceleration

Using vials containing 5% (w/v) of API and 50*CMC of polysorbate 20, the effect of the

acceleration was further tested (Sample 11 to 13, Table 4.1.). A bead-suspension ratio

of 0.774 mL/mL and a milling time of 20 minutes were set. High-energy milling

introduced a sharp heat generation and particle size decrease over the three different

accelerations, leading to a final dv50-value of 1.28 µm, 1.00 µm and 0.88 µm and an

extrapolated temperature of 49.1 °C, 64.0 °C and 80 °C for accelerations of

respectively 50 g, 65 g and 80 g (Figure 4.6.).

79

Figure 4.6. The effect of acceleration on the particle size reduction and heat generation, as presented by samples

11 to 13 of Table 4.1. The computed end-of-milling temperatures were fitted as a function of acceleration (g). An

exponential trend with a determination coefficient of 1.000 could be noted.

Mathematical fitting of the extrapolated temperatures to the set acceleration suggested

an exponential trend (Figure 4.6.). By extrapolation of this function, the temperature

after milling can be estimated for other accelerations. It is worth mentioning that if an

acceleration of 100 g would be applied under these process conditions, an estimated

temperature of over 120 °C is computed.

Heat transfer from the vial to the polyoxymethylene platform was expected to be

limited, but the platform became hot, nonetheless. Temperatures of 30-40 °C were

often measured and depending on the process parameters, the resultant temperature

could be 60 °C or even 70 °C, leaving the platform as a cooking plate. Therefore, users

of the IVM should be aware that additional fixtures such as platforms will not only lead

to possible motor deterioration but could also cause heating of the samples. In this

study, the influence of the platform on the final temperature was further considered.

Based on first the short milling time and brief exposure to the platform and secondly

the poor thermal conductivity of the platform, the heat transfer was expected to be

80

limited within the earlier mentioned dataset. In this sense, the effect of acceleration on

temperature cannot be proven to be exponential but can still be assumed to be non-

linear.

The effect of API concentration and milling time

In a next set of experiments (Sample 14 to 22, Table 4.1.), the effect of a cooled

platform, API concentration and milling time were analysed. The stabiliser

concentration was increased to 50, 250 and 500 times the CMC for the 1%(w/v),

5%(w/v) and 10%(w/v) API suspensions, respectively. Platforms were cooled between

experiments. To investigate the influence of this adaptation, the 5%(w/v) API

suspension were milled within a cooled and a non-cooled platform. Variation on the

temperature measurement was considerably lower in the case of the cooled platform

and in the end, the final, extrapolated temperature was reduced (Figure 4.7.). Cooling

fixtures in-between milling experiments seemed to be a necessity to avoid additional

heating and to keep the variability on the final temperature under control.

Figure 4.7. The cooling profile of the 1%, 5% and 10% suspensions milled for 20 minutes at 65 g. in which the 1%

and 10% suspensions were milled on a cooled platform. In the case of the 5% suspension, the suspension was

milled on a platform that was not cooled (left) and that was cooled (right). Cooling the platform will lead to a decrease

in both the variability of the temperature measurement and the temperature measurement on itself.

In scope of the particle size reduction, the same trends were observed for the IVM as

discussed within the WBM system. Overall, a longer milling time and lower API

concentration will lead to smaller particles. Nonetheless, an interaction between milling

time and API concentration was found in the WBM, in which a higher API concentration

will lead to additional shear forces and so a more effective comminution process.55

This interaction could be extrapolated to IVM as well, if the dv90-value is taken into

consideration (Table 4.2.). Since the dv10-value and dv50-value of the 1%-suspension

nearly reached the detection limit of the Mastersizer 2000™, focus was placed on the

81

dv90-value as a more precise descriptor of this trend. Nonetheless, further research

would be interesting to underscore this trend and to investigate the extent of this

additional shear, as in comparison to its presence in WBM. A reduced presence could

be the reasoning why the WBM led to faster particle breakage as compared to IVM.63

Overall, this phenomenon was to the best of our knowledge never addressed in the

scope of the resultant temperature rise. Surprisingly, while the temperature increased

as a function of milling time, it appeared to be independent of the API concentration

(Figure 4.8., Table 4.2.). This was, to the best of our knowledge, not mentioned in

literature before and in this way, the usage of more concentrated formulations should

be even more favoured. Thus, the additional shear of the API particles has a limited

effect on heat generation during IVM. The movement of the beads could be

represented as cloud milling, as alluded on by Hagedorn et al.98. Their team found that

within the dual centrifugation system virtually all beads accelerate as one cloud and

clash at once to the bottom or top of the vial, which is one of the main movement paths

that can be found within the IVM as well. This in comparison to WBM, where an

important part of the beads is distributed to the wall of the chamber and is not active

within the milling process. By the cloud movement, a set of bead-wall collisions could

be prevented, whereby high temperatures could be prevented. Even though a solid

scientific background is still needed, this movement and the origin of the temperature

rise being the bead-wall collisions, could explain why the additional API particles did

not have a significant effect on the final temperatures measured.98 Since the trends in

particle size reduction found in WBM seem to be extrapolatable to IVM, it can be

anticipated that the same trend is also applicable for WBM. If not, the hypothesis of

cloud milling is more substantiated, but further research is for this reason needed.

82

Figure 4.8. The effect of milling time and API-concentration on the final temperature and particle size of the

suspensions. Independent of the set API concentration, the temperature rise over milling time stayed similar. Final

particle sizes of lower concentrated suspensions were smaller as well, though higher concentrated suspensions

presented more efficient milling process, with sharply decreasing functions.

In scope of the milling time, the mean temperatures over the different concentrated

suspensions were calculated (Table 4.2.). The low CV presented the low influence of

the API concentration on the final temperature rise. Mathematical fitting of the mean

temperatures to a polynomial function (Figure 4.9.) suggested that the incremental

temperature rise will decrease with increasing time and so that increasing the milling

time was a valid way of increasing the energy input of a milling system while keeping

a controlled temperature increase.

83

Figure 4.9. Fitting the temperature rise as a function of the milling time, using the average temperatures of the

different concentrated formulations on the different time points, as given in Table 4.2.

In this scope, initial steps for method optimisation can be suggested. Similar particle

sizes were found for the set-up with 20 min milling time and an acceleration of 80 g

and the set-up with 30 min and 65 g, but with a lower temperature in the latter case.

Thus, using a longer milling time seems to be advantageous if important to limit

temperature increase while keeping a continuous milling process. These outcomes are

in agreement with the suggested exponential increase in temperature versus

acceleration and the polynomial fit between temperature and milling time.

Further, the optimisation of the bead size can be assumed to be a valid step in the

optimisation of a milling process, but this parameter fell out of the scope of this study.

Thus, temperature increase as well as particle size reduction should be considered

when choosing a suitable nanosizing technology and corresponding process

parameters. Depending on the API and formulation properties, IVM might still be the

method of choice due to its efficient and rapid nanosizing, but this technology should

be avoided for thermolabile compounds if cooling cannot be guaranteed during

processing. On the other hand, the milling process can be modified by choosing

84

process parameters that limit temperature rise while substantiating the nanosizing

potential. Since Newton’s law of cooling describes an exponential relationship, short

pauses during milling can produce a temperature decrease to limit temperature

increase and is the focus of current investigation, albeit these breaks should be

standardised and upscalable. A more complex experimental design could offer the

insight in the complexity of this milling platform for which this study could already offer

a strong methodological basis.

85

Table 4.2. Final PSDs and computed temperatures, when the milling time and API concentration of the suspensions were varied.

Milling time

API conc.

Stabiliser conc. 10 min 20 min 30 min

dv10 (µm)

dv50 (µm)

dv90 (µm)

Temperature (°C)

dv10 (µm)

dv50 (µm)

dv90 (µm)

Temperature (°C)

dv10 (µm)

dv50 (µm)

dv90 (µm)

Temperature (°C)

1%(w/v)

50*CMC 0.126 1.545 4.576 47.5 0.098 0.586 4.091 60.4 0.092 0.379 2.901 67.3

5%(w/v)

250*CMC 0.149 1.423 3.626 46.2 0.12 1.083 3.58 59.5 0.098 0.47 2.451 66.7

10%(w/v)

500*CMC 1.052 2.353 5.279 44.4 0.137 1.137 3.242 59.9 0.111 0.7 2.685 67.3

Average 46.1 60.2 67.1

Standard deviation

1.6 0.3 0.4

CV (%) 3.4 0.6 0.5

86

CONCLUSIONS

Within the IVM, we found that similar formulation and process parameters as in WBM

will influence the particle size reduction, higher bead-suspension ratio, higher

acceleration, lower API concentration and longer milling time leading to a faster

comminution process. Nevertheless, these key formulation and process parameters

significantly impacted the heat generation as well, leading to temperatures of 100 °C

and above. Heat can gravely affect the (physico)chemical properties of the suspension,

hence, its generation should be under control. Due to the counteracting effect of the

different formulation and process parameters on particle size and generated heat, the

IVM seems to be as complex as the WBM process. Nevertheless, some first efforts

were made to optimise a continuous milling process while limiting heat generation.

Thus, to increase the specific energy of an IVM process an increased milling time is

favoured over acceleration. Only a modest temperature increase was found during

these optimisation steps. Furthermore, the production of more concentrated

formulations is favoured. During their production, additional shear by the API particles

boost the efficiency of the milling process but do not affect the generated heat. We

anticipate that this additional advantage of highly concentrated formulations, is

extrapolatable to WBM as well. Further, fixtures used to secure the vials within IVM,

can gravely impact the heat generated as well and should be cooled in-between

experiments. Since controlled heat generation is of utmost importance within the

milling process, these first steps can help in the optimisation of an IVM continuous

milling process and can open further research on its complex interplay with particle

size reduction.

Picking up good vibrations: Exploration of the intensified

vibratory mill via a modern design of experiments


De Cleyn, E., Holm, R., Khamiakova, T. & Van den Mooter, G. Picking up good

vibrations: Exploration of the intensified vibratory mill via a modern Design of

Experiments. Int. J. Pharm. 120367 (2021). doi:10.1016/j.ijpharm.2021.120367

With exception of: Supplementary information regarding the explanation of Kwades

stress model has been removed, as the Introduction (Introduction, §1.5.3.) broadly

describes the subject. The necessary references were provided instead.

89

GRAPHICAL ABSTRACT

ABSTRACT

The aim of this work was to strengthen the understanding of the IVM by unravelling the

milling process in terms of the particle size reduction and heat generation via a modern

DoE approach. Hence, the influence of five process parameters (acceleration, breaks

during milling, bead size, milling time and bead-suspension ratio) was investigated via

an I-optimal design. Particle size was measured via laser diffraction and the

temperature of the sample after milling was computed. To advance our understanding,

a mechanistic model for the set-up of wet-stirred media milling processes was applied

on the observed milling trends. A generic approach for the optimisation of the milling

process was retrieved and included the optimisation of the bead size and intermittent

pausing for effective cooling. To finetune the remaining process parameters, the

present work provides contour plots and strong predictive models. With these models,

the particle size and the temperature after milling of suspensions manufactured with

the IVM could be forecasted for the first time.

90

INTRODUCTION

Micronisation and nanonisation are commonly applied approaches to enable the

bioavailability of poorly water soluble compounds by enhancing their solubility and/or

dissolution rate.46 Formulated as a nano- or microsuspension, these poorly soluble

compounds may be delivered via a variety of administration routes including the oral,

ocular, brain, topical, buccal, nasal and transdermal routes.6 By virtue of their ease of

use, suspensions can be orally administered in wet state or after incorporation into

conventional dosage forms such as tablets or capsules.99 Injectable nano- and

microsuspensions have drawn increasing attention due to their potential use as

LAIs.100 This formulation type can offer great utility for chronic diseases, where lack of

medication compliance may be detrimental for the pharmacological response.35

Overall, advancements in the production and formulation of these suspensions can

solve numerous pharmacokinetic challenges and these topics are therefore still heavily

studied.6

Among all the usable production technologies, WBM is most widely applied with

demonstrated efficiency, viability and cost-effectiveness at production scale.33, 46

Milling media, dispersant, stabiliser, other excipients and the API are charged into the

milling chamber and by the movement of the milling media, shear forces, pressure and

impact are generated, leading to a particle size reduction. Either the milling beads are

accelerated by the movement of the complete container, or by agitators installed in the

chamber.52 Extensive ball milling to the nanorange was patented as the Nanocrystal®

Technology by Elan, in 199030, a technology that led to many (sub)micronised drug

products of poorly soluble APIs on the market.6 Opportunities for milling on bench-level

are numerous, such as the high-throughput platforms, streamlining formulation

screening and optimisation.53 The technologies’ major drawback is the heat generated

by inefficiently dissipated energy wherefore a water jacket is more often installed. Yet,

WBM is considered as the golden standard for nanonisation and micronisation.

However, this enabling platform still encounters some unresolved issues. These

include erosion of the milling media which may contaminate the final formulation46 and

extensive milling times from several hours to days30.

Consequently, new milling technologies such as the IVM in which the RAM platform is

used, are introduced in the field. Originally commercialised as a mixing platform, the

RAM platform has been mostly employed as a dry mixing process, whereas its

91

application in wet milling is less studied.62, 63 As a milling platform, it consists of a

closed vibrating container to which milling media are charged. The vibrating container

could be of various types of vessels, vials or well-plates. In this way, the IVM could

serve as a drug sparing, high-throughput screening method.62 The vibration can be

described as a sinusoidal wave with a frequency of around 60 Hz, as to keep the

system at its optimal and safe conditions, the parametric resonance. The amplitude of

the sinusoidal wave on the other hand differs, based on the milling content and the set

acceleration. Previously, the mixing behaviour was introduced as micro-mixing zones,

but current knowledge seems to present a more complex mixing and milling regime.101

Insights in milling regimes and the motion of grinding bodies in conventional mills have

already been broadly discussed in the literature.102, 103 Concerning later mill types,

Hagedorn and co-workers have discussed how virtually all beads accelerate as ‘one

cloud’ (cloud milling) in the dual centrifuge.98 Since the geometry and the motion of

IVMs milling chamber significantly differ from conventional and later mill types, one can

assume a different bead motion. Consequently, the extent and distribution of milling

forces will differ, leading to a different milling process in terms of particle size reduction

and heat generation.

In this viewpoint, IVM has been marked for its fast particle size reduction which may

overcome the extensive milling times, encountered in classical WBM. Yet

unacceptable high temperatures have been observed.104 First attempts to control this

temperature included the installation of milling breaks in which the containers were

removed from the milling equipment and placed in a refrigerated bath.63 This method

proved to be effective, but difficult to standardise or scale-up. Further, the influence of

this quick cooling step on the aggregation of particles and recrystallisation of dissolved

API was not explored. Consequently, the purpose of this study was to obtain a

comprehensive insight in the process of particle size reduction and heat generation as

it applies to IVM, with the usage of a DoE.

To the best of our knowledge, publications on the usage of DoE methodologies within

pharmaceutical milling are limited to classical designs such as the central composite

or (fractional) factorial designs.56 In order to reach predictive models of similar quality

and precision, modern DoE methodologies allow to simultaneously study many factors

in an irregular experimental domain in an economic fashion.105, 106 Surprisingly, the

application of modern DoE methodologies on pharmaceutical milling is so far

92

unexplored. Moreover, no previous study on the investigation of IVM via a DoE,

classical or modern, has, to the best of our knowledge, been published.

In this work, a novel DoE methodology was applied to generate deep insights in the

heat generation and nanosizing potential of the IVM. The selected I-optimal design and

consecutive statistical analysis ranked the investigated process parameters (bead

size, breaks during milling, acceleration, milling time and bead-suspension ratio),

elucidated interactions between these parameters and provided a predictive model for

important suspension attributes such as the dv50-value, dv90-value, span and

temperature after milling. To gain more mechanistic insight in the milling kinetics, the

stress model54 was applied on the milling trends and questions regarding the impact

of pausing on heat generation and particle size, were addressed.

93


Materials

Bedaquiline was provided by Janssen Pharmaceutica (Beerse, Belgium). Polysorbate

20 was obtained from Croda (Trappes, France). Deionised water (R≥18.2 mΩ, Mili-Q®

Advantage A10, Merck, Darmstadt, Germany) was used for all the experiments.

Methods

5.4.2.1. Preparation of suspensions

Glass vials were filled with bedaquiline (5%w/v), zirconia beads (Nikkato Corporation,

Sakai, Osaka, Japan) and polysorbate 20 solution (1.85%w/v). The vials were

thoroughly shaken on an in-house manufactured platform within the LabRAM II

(Resodyn Acoustic Mixers, Butte, USA). The investigated process parameters - the

acceleration, the bead size, the bead-suspension ratio, the milling time and the breaks

during milling - were varied as proposed in the DoE (Figure. 5.1.).

Figure 5.1. Experimental space of the custom designed DoE, including the investigated parameters; breaks during

milling (y-axis, left), milling time (y-axis, right), acceleration (x-axis below), bead-suspensions ratio (x-axis above) and

bead size (dot size). Every dot represents an experimental run of the DoE. The experimental design space seemed

widely covered by the installed experimental runs.

94

5.4.2.2. Experimental design

The acceleration, the bead size, the bead-suspension ratio and the milling time were,

as earlier demonstrated104, explorable parameters that could impact the output of the

milling process in terms of the suspension’s final particle size and temperature. In

attempt to control the temperature, an additional parameter, named ‘Breaks during

milling’, was included. This parameter encompassed the duration of the installed break

(0 minutes, 2.5 minutes or 5 minutes), implemented every 7.5 minutes of the milling

process.

Within the Jump software package (JMP® 13.0.0, SAS Institute Inc.), a custom DoE

was built in which the five process parameters were set as quantitative, continuous

parameters with an upper and lower limit based on historical data. To enhance the

predictive power and the robustness of the model, two centre points and four replicates

were included. Blocking was applied and experiments were randomised. Chosen

responses were the median particle size (dv50), the PSD (dv90, span) and the final

temperature of the suspension (Temp.). In order to broadly explore the complexity of

the milling process, 22 model parameters were selected for investigation. These

parameters included the intercept, all main effects, all two-way interactions, all

quadratic effects, and one three-way interaction (acceleration/bead-suspension

ratio/bead size). For a classical DoE with five factors, high-resolution designs would

be required to resolve such three-factor interaction. Nonetheless, the final

experimental design, generated with the JMP® software, was a three level I-optimal

design with 30 experimental runs, which covered the whole experimental domain

(Figure 5.1.).

5.4.2.3. Temperature measurement

After production, the temperature decline of the suspension was tracked with a

temperature gun (VWR® Traceable® Infrared Thermometer, Radnor, PA, US), as

presented previously104. The temperature directly after milling was obtained by

extrapolation of this tracked temperature trend.


LD measurements were performed on a Mastersizer™ 2000 (Malvern Instruments,

Worcestershire, UK) with hydro-unit, using miliQ water as the dispersant. Stirring

95

speed of 600 rpm was set, no sonication was applied, and the system was left to

stabilise for five minutes. Finally, the system was aligned, and the background was

evaluated. The general-purpose model for irregularly shaped particles with normal

calculation sensitivity was applied. The set optical parameters were a sample rRI of

1.595, a sample iRI of 0.001 and a dispersant rRI of 1.333. A limited obscuration

titration was performed for each sample to elucidate the optimal set of obsc. and obsc.

blue. Quality of data was given in terms of res. and res weight., as described in chapter

3.95 The final PSD was presented by the dv50-value, the dv90-value and the span

(Equation 5.1.).

𝑆𝑝𝑎𝑛 =𝑑𝑣90 − 𝑑𝑣10

𝑑𝑣50 (Equation 5.1.)

96

RESULTS

Design of experiments computation

To deconvolute the individual effects of, and the interactions between the various

parameters, the raw data (Table 5.1.) were analysed with the JMP® software. The data

were modelled by predictive models with restricted maximum likelihood (REML) to

account for the blocking nature of the experiment. REML with unbounded variance

components was used for the dv50-value, span and temperature after milling. As the

block variance was negligible, REML with bounded variance components was applied

on the dv90-value. To get valid statistical estimates, a Bayesian hierarchical model

was performed to compute the block variance for the dv90-value (Figure 5.2.) and to

test the significance of the different model parameters (Figure 5.3.). The block

variability approached zero and the same model parameters were significant for the

dv90-value, rationalizing the statistical adaptation to bounded variance components.

Predictive models are generally expressed as:

Y = β0 + β1X1 + β2X2 + β3X3 + … + β12X1X2 + β23X2X3 + β13X1X3 + … + β123X1X2X3 +

β11X12 + β22X2

2… + ε (Equation. 5.2.)

where Y is the dependent variable or response such as the temperature after milling;

X1, X2, X3… are the independent variables or factors such as milling time; β0 is the

intercept; β1, β2, β12, β23, β123, … are empirically estimated coefficients, better known

as the model parameters, which relate the main factors (if the parameters subscript is

one digit), interactions (if the parameters subscript is two or more digits) and quadratic

effects (if the parameters subscript is two times the same digit) to the forecasted

response Y; ε is the total error.

During the interpretation of the significance of the model parameters, the power of the

DoE for these model parameters (Table 5.2.) should be taken into consideration. The

power for a certain model parameter is the probability of the DoE to detect its

significance. The DoE was designed to have an optimal power in the evaluation of all

main effects and most two-way interactions. Since the DoE was restricted to 30

experiments, which was still an elaborated number, the power of the five quadratic

effects and of the intercept was rather modest (Table 5.2.). Notwithstanding this

modest power, the corresponding model estimates may still have an important effect

97

on the final responses, independent of statistical significance. For this reason, all model

parameters were included in the final predictive models (Model S-5.1., Model S-5.2.,

Model S-5.3.and Model S-5.4., Supplementary Information, §9.2). Overfitting was

evaluated by the adjusted determination coefficient (Radj2).

Figure 5.2. Block variance computed by Bayesian statistics for the response dv90 value. The normal distribution

clearly approaches the zero limit.

Figure 5.3. Bayesian output presenting the different parameters with their significance level. In comparison with

REML with bounded variance components, the same parameters proved to be statistically significant.

98

Table 5.1. Process parameters and raw data concerning PSD and temperature after milling, as have been input in the JMP® software

Sample Process Parameters

Results PSD

Quality of LD data

Bead size (µm)

Bead-suspension ratio (mL/mL)

Acceleration (g)

Total milling time (min)

Breaks during milling (min)

Temperature (°C)

Dv50 (µm)

Dv90 (µm) Span Obsc

Obsc blue Res.

Res. weight.

Block 1

Sample 1 200 0.375 50 10 2.5 22.5 21.125 64.279 2.879 9.72 7.63 0.366 0.378

Sample 2 200 1.200 50 10 0 29.5 3.97 40.469 10.109 8.22 9.96 0.442 0.527

Sample 3 1000 0.375 65 20 5 40.1 1.657 4.27 2.484 6.45 7.87 0.589 0.355

Sample 4 200 0.375 80 30 5 39.6 0.637 22.695 35.443 5.59 8.63 0.577 0.419

Sample 5 1000 0.774 80 30 2.5 89.6 0.376 1.746 4.388 5.20 8.21 1.878 1.066

Sample 6 1750 0.375 80 20 0 66.2 1.918 4.924 2.490 4.41 5.43 0.483 0.388

Block 2

Sample 1 1000 1.200 50 20 2.5 50.2 0.658 2.65 3.871 5.99 6.9 0.403 0.298

Sample 2 1750 0.774 65 10 2.5 50.1 2.046 4.929 2.330 4.64 6.17 0.499 0.386

Sample 3 1750 1.200 80 10 5 78.6 1.34 4.025 2.910 4.89 6.49 0.534 0.349

Sample 4 1750 1.200 80 10 5 78.8 1.283 3.807 2.866 4.64 5.56 0.516 0.485

Sample 5 1750 0.774 65 10 2.5 52.1 1.768 4.152 2.262 4.23 5.9 0.754 0.393

Sample 6 1000 1.200 50 20 2.5 51.9 0.855 2.885 3.240 6.09 8.84 0.703 0.573

Block 3

Sample 1 200 1.200 80 30 2.5 77.6 0.158 0.433 2.291 2.89 6.79 1.312 1.418

Sample 2 1000 1.200 80 10 0 85.0 0.897 3.088 3.315 4.02 5.66 0.543 0.372

Sample 3 1750 0.774 50 10 5 39.3 1.946 4.509 2.231 4.13 4.91 0.612 0.536

Sample 4 1750 0.375 50 30 2.5 43.8 1.736 4.13 2.300 3.45 4.21 0.69 0.438

Sample 5 1000 0.774 65 20 2.5 60.9 0.682 2.585 3.632 3.13 4.48 0.625 0.74

99

Sample 6 1750 0.375 50 10 0 32.9 2.077 4.378 1.699 5.41 6.15 0.491 0.342

Block 4

Sample 1 1750 1.200 80 30 0 134.7 1.241 2.881 2.227 5.87 7.56 0.635 0.526

Sample 2 1000 0.774 65 20 2.5 58.5 0.568 2.271 3.819 4.97 7.09 0.567 1.032

Sample 3 1750 1.200 50 30 0 71.0 0.516 2.287 4.229 2.33 3.54 0.768 0.87

Sample 4 1750 0.375 80 10 5 47.9 1.958 4.437 2.187 5.22 6.04 0.526 0.41

Sample 5 200 1.200 65 20 0 52.0 0.213 0.898 3.836 5.98 11.58 2.486 1.93

Sample 6 1000 0.375 65 30 0 55.0 1.184 3.286 2.662 4.00 5.42 0.849 0.943

Block 5

Sample 1 200 0.774 50 30 0 28.1 4.855 48.012 9.751 4.60 5.62 0.455 0.536

Sample 2 1750 0.774 65 30 5 72.7 0.908 3.225 3.426 4.00 5.68 0.487 0.402

Sample 3 200 0.774 50 30 0 31.2 2.651 18.444 6.781 3.08 3.79 0.439 0.455

Sample 4 200 0.774 80 10 5 41.6 0.414 4.081 9.614 4.87 7.85 0.565 0.645

Sample 5 200 1.200 50 30 5 31.4 1.777 18.073 10.075 6.17 8.12 0.601 0.622

Sample 6 200 0.375 80 10 0 33.2 3.099 25.094 7.781. 3.20 3.71 0.661 0.548

100

Table 5.2.: Power analysis of the investigated model parameters.

Parameter Power

Intercept 0.193

Acceleration (g) 0.932

Breaks during milling (min) 0.897

Milling time (min) 0.869

Bead-suspension ratio 0.776

Bead size (µm) 0.751

Acceleration (g)*Breaks during milling (min) 0.773

Acceleration (g)*Milling time (min) 0.692

Acceleration (g)*Bead-suspension ratio 0.639

Acceleration (g)*Bead size (µm) 0.721

Breaks during milling (min)*Milling time (min) 0.528

Breaks during milling (min)*Bead-suspension ratio 0.721

Breaks during milling (min)*Bead size (µm) 0.818

Milling time (min)*Bead-suspension ratio 0.567

Milling time (min)*Bead size (µm) 0.752

Bead-suspension ratio*Bead size (µm) 0.711

Acceleration (g)*Acceleration (g) 0.247

Breaks during milling (min)*Breaks during milling (min) 0.23

Milling time (min)*Milling time (min) 0.272

Bead-suspension ratio*Bead-suspension ratio 0.24

Bead size (µm)*Bead size (µm) 0.312

Acceleration (g)*Bead-suspension ratio*Bead size (µm) 0.601

A high power was installed for all main effects and for most two-way

interactions. The power to identify the significance of most quadratic effects

and of the intercept, was rather modest.

101

Statistical analysis of the dv50

Table 5.3. Investigated model parameters with their estimated parameter effects, their corresponding standard error

and borders of the 95%-confidence interval and computed p-values for the response dv50-value.

Term Estimate Std Error

Lower 95%

Upper 95% Prob>|t|

Intercept 0.41 0.49 -0.74 1.55 0.4349

Acceleration (g) (50,80) -1.97 0.24 -2.54 -1.40 <.0001

Breaks during milling (min) (0,5) 0.46 0.28 -0.18 1.11 0.1357

Milling time (min) (10,30) -1.16 0.23 -1.69 -0.64 0.0009

Bead-suspension ratio (0.375,1.2) -1.77 0.28 -2.50 -1.04 0.0014

Bead size (µm) (200,1750) -1.51 0.23 -2.05 -0.97 0.0003

Acceleration (g)*Breaks during milling (min) -0.48 0.31 -1.21 0.25 0.1622

Acceleration (g)*Milling time (min) 0.64 0.33 -0.43 1.70 0.1512

Acceleration (g)*Bead-suspension ratio 1.44 0.40 -0.09 2.98 0.0566

Acceleration (g)*Bead size (µm) 2.23 0.37 0.55 3.91 0.0301

Breaks during milling (min)*Milling time (min) 0.59 0.48 -0.78 1.96 0.2918

Breaks during milling (min)*Bead-suspension ratio 0.11 0.37 -0.75 0.97 0.7783

Breaks during milling (min)*Bead size (µm) -0.11 0.29 -0.80 0.57 0.7034

Milling time (min)*Bead-suspension ratio -0.02 0.42 -1.13 1.09 0.9566

Milling time (min)*Bead size (µm) 0.86 0.26 0.27 1.46 0.0102

Bead-suspension ratio*Bead size (µm) 1.92 0.39 0.98 2.85 0.0021

Acceleration (g)*Acceleration (g) 0.37 0.74 -2.21 2.96 0.6548

Breaks during milling (min)*Breaks during milling (min) -1.35 0.51 -2.66 -0.04 0.0455

Milling time (min)*Milling time (min) 1.21 0.63 -0.29 2.72 0.0978

Bead-suspension ratio*Bead-suspension ratio 1.45 0.53 -0.17 3.06 0.0660

Bead size (µm)*Bead size (µm) 1.69 0.61 0.24 3.13 0.0278

Acceleration (g)*Bead-suspension ratio*Bead size (µm) -2.11 0.38 -3.00 -1.21 0.0007

Statistically significant output is coloured in orange (|t| < 0.05) and red (|t| < 0.01).

In short, a broad set of parameters was statistically significant for the final median

particle size, the dv50-value (p-value < 0.05) (Table 5.3.). Milling time, acceleration,

bead-suspension ratio and bead size were critical factors, both as main factor as in

most of their two-way interactions (acceleration/bead size, milling time/bead size and

bead-suspension ratio/bead size), as illustrated in the broad set of crossing functions

in the interaction profiler (Figure 5.4.). Even with their modest power, some quadratic

interactions (breaks during milling and bead size) had a statistically significant

outcome. Also, the three-way interaction (acceleration/bead-suspension ratio/bead

size) had a statistically significant effect on the final dv50-value.

102

Acceleration was the most important factor governing the milling process, with a

statistically significant parameter estimate of -1.97 (± 0.24). Milling time showed a

statistically significant inverse dependence on the dv50-value with a parameter

estimate of -1.16 (± 0.23). Bead-suspension ratio and the bead size were other key

variables in the milling process with parameter estimates of -1.77 (± 0.28) and -1.51 (±

0.23), respectively. Surprisingly, the breaks during milling did not have in the provided

dataset a statistically significant effect on the final dv50-value.

Even with a modest power of 0.321 and 0.23, the quadratic effects of bead size and

breaks during milling were statistically significant with parameter estimates of 1.69 (±

0.61) and -1.35 (± 0.53), respectively. The quadratic term of milling time on the other

hand, did not show a statistically significant outcome. Albeit some factors were not

considered to be statistically significant, all model terms were included in the predictive

model (Model S-5.1., Supplementary Information, §9.2), as alluded to above. The

accuracy of the generated prediction model was described by the determination

coefficient (R2) and Radj2. As the final R2of 0.982 only decreased to a Radj

2 of 0.934, the

inclusion of all parameters was well-founded.

Figure 5.4. Interaction profiler depicting two-way interactions for dv50-value

103

Statistical analysis of the temperature after milling

Table 5.4. Investigated model parameters with their estimated parameter effects, their standard errors, the limits of

their 95%-confidence interval and computed p-values for the response temperature after milling.


Lower 95%

Upper 95% Prob>|t|

Intercept 58.71 1.14 55.89 61.53 <.0001

Acceleration (g) (50,80) 16.06 0.45 14.96 17.16 <.0001

Breaks during milling (min) (0,5) -3.33 0.48 -4.48 -2.18 0.0003

Milling time (min) (10,30) 8.56 0.46 7.48 9.64 <.0001

Bead-suspension ratio (0.375,1.2) 12.29 0.61 10.87 13.71 <.0001

Bead size (µm)(200,1750) 13.15 0.53 11.82 14.49 <.0001

Acceleration (g)*Breaks during milling (min) -1.68 0.57 -3.09 -0.28 0.0263

Acceleration (g)*Milling time (min) 4.55 0.72 2.87 6.22 0.0003

Acceleration (g)*Bead-suspension ratio 5.04 0.79 3.22 6.86 0.0002


Breaks during milling (min)*Milling time (min) -0.89 0.84 -2.82 1.05 0.3214

Breaks during milling (min)*Bead-suspension ratio -1.66 0.64 -3.18 -0.13 0.0372

Breaks during milling (min)*Bead size (µm) -0.70 0.53 -2.01 0.61 0.2338

Milling time (min)*Bead-suspension ratio 2.70 0.77 0.91 4.49 0.0083

Milling time (min)*Bead size (µm) 4.26 0.56 2.95 5.57 <.0001



Breaks during milling (min)*Breaks during milling (min) 0.14 1.18 -2.98 3.25 0.9139


Bead-suspension ratio*Bead-suspension ratio -2.07 1.22 -5.25 1.11 0.1522

Bead size (µm)*Bead size (µm) -7.56 1.05 -10.17 -4.95 0.0005

Acceleration (g)*Bead-suspension ratio*Bead size (µm) 0.93 0.71 -0.80 2.67 0.2357


Nearly all parameters demonstrated a significant effect on the temperature after milling

and thus on the heat generation during IVM (Table 5.4.). This high level of significance

is substantiated by the broad operational ranges and the high precision of the

temperature measurements. Albeit, it may also indicate the high complexity of the IVM

process.

Acceleration was a key contributor to the heat generation with a remarkable parameter

estimate of 16.06 (± 0.45). The milling time on the other hand only encompassed a

parameter estimate of 8.56 (± 0.46). Other significant key variables were bead size

104

and bead-suspension ratio, with parameter estimates of 13.15 (± 0.53) and 12.29 (±

0.61), respectively. Even in this case, bead size’ quadratic effect of -7.56 (± 1.05) was

statistically significant. As presumed, breaks during milling had a statistically significant

effect on the suspension’s final temperature. Albeit, the parameter estimate was only

-3.33 (± 0.48) implying that despite its significance, the model estimate on itself was

rather modest as compared to the model estimates of the remaining process

parameters. Thus, a temperature rise stayed, even with the inclusion of a periodical

five-minute break during milling, inevitable. Hence finetuning of all process parameters

remained important.

Almost all two-way interactions had a statistically significant outcome with parameter

estimates ranging from circa |2| to |5|. All parameters were included in the predictive

model (Model S-5.2., Supplementary Information, §9.2). With a R2 of 0.999 and a Radj2

of 0.996 (Figure 5.5.), temperature may be accurately forecasted.

Figure 5.5. Strong linear relationship (R2 of 0.999 and Radj2 of 0.996) between the predicted temperatures and the

actual temperatures of the suspensions after production.

105

Statistical analysis of the particle size distribution (dv90, span)


and borders of their 95%-confidence intervals including the computed p-value for the response dv90-value.


Lower 95%

Upper 95% Prob>|t|

Intercept -0.24 4.20 -9.93 9.44 0.9554

Acceleration (g) (50,80) -6.88 1.91 -11.28 -2.48 0.0069

Breaks during milling (min) (0,5) -0.85 1.98 -5.41 3.72 0.6804

Milling time (min) (10,30) -2.32 1.84 -6.58 1.93 0.2427


Bead size (µm) (200,1750) -9.13 1.94 -13.60 -4.66 0.0015

Acceleration (g)*Breaks during milling (min) -0.10 2.44 -5.72 5.53 0.9697

Acceleration (g)*Milling time (min) 1.84 2.11 -3.03 6.71 0.4095

Acceleration (g)*Bead-suspension ratio 0.54 2.33 -4.83 5.91 0.8231


Breaks during milling (min)*Milling time (min) 4.03 2.77 -2.36 10.42 0.1839

Breaks during milling (min)*Bead-suspension ratio -0.99 2.44 -6.62 4.63 0.6948

Breaks during milling (min)*Bead size (µm) 0.93 2.34 -4.46 6.33 0.7001

Milling time (min)*Bead-suspension ratio -1.98 2.62 -8.01 4.06 0.4717

Milling time (min)*Bead size (µm) 1.40 2.10 -3.45 6.25 0.5242



Breaks during milling (min)*Breaks during milling (min) -2.91 4.00 -12.13 6.30 0.4866


Bead-suspension ratio*Bead-suspension ratio 0.28 3.80 -8.47 9.03 0.9435

Bead size (µm)*Bead size (µm) 8.52 4.58 -2.05 19.08 0.1001

Acceleration (g)*Bead-suspension ratio*Bead size (µm) -3.05 2.95 -9.86 3.76 0.3323


In the analysis of the dv90-value, relatively high standard errors were observed and

only a handful of factors were statistically significant (Table 5.5). Due to this high

variation unaccounted by the model terms, the dv90 was only shortly described, though

the final predictive model (Model S-5.3., Supplementary Information, §9.2) was still

computed. As expected, the resulting R2 and Radj2 were the lowest so far with final

values of 0.928 and 0.739, respectively.

Acceleration had, as in the case of dv50, an important impact on the dv90, with a

parameter estimate of -6.88 (± 1.91). Other main effects such as bead-suspension ratio

106

and bead size and the two-way interactions acceleration/bead size and bead-

suspension ratio/bead size were also statistically significant. The effect of breaks

during milling on the other hand was limited.


and the borders of their 95%-confidence interval including the computed p-value for the response span.


Lower 95%

Upper 95% Prob>|t|

Intercept 2.10 1.11 -0.95 5.15 0.1291

Acceleration (g) (50,80) 0.65 0.26 -0.02 1.32 0.0541

Breaks during milling (min) (0,5) 1.16 0.30 0.44 1.88 0.0063

Milling time (min) (10,30) 1.26 0.41 0.24 2.28 0.0233


Bead size (µm) (200,1750) -3.15 0.54 -4.59 -1.72 0.0030

Acceleration (g)*Breaks during milling (min) 1.14 0.34 0.29 1.99 0.0175

Acceleration (g)*Milling time (min) 1.13 0.47 0.05 2.22 0.0423

Acceleration (g)*Bead-suspension ratio -3.12 0.45 -4.21 -2.03 0.0003

Acceleration (g)*Bead size (µm) 0.39 0.42 -0.61 1.39 0.3885

Breaks during milling (min)*Milling time (min) 2.74 0.54 1.49 3.99 0.0010

Breaks during milling (min)*Bead-suspension ratio -1.82 0.38 -2.75 -0.89 0.0027

Breaks during milling (min)*Bead size (µm) -2.38 0.32 -3.18 -1.59 0.0004

Milling time (min)*Bead-suspension ratio -3.91 0.54 -5.17 -2.66 0.0001

Milling time (min)*Bead size (µm) -1.98 0.48 -3.17 -0.79 0.0068

Bead-suspension ratio*Bead size (µm) 3.57 0.40 2.63 4.51 <.0001

Acceleration (g)*Acceleration (g) 2.82 0.85 0.83 4.81 0.0118

Breaks during milling (min)*Breaks during milling (min) 0.16 1.20 -3.12 3.44 0.8996

Milling time (min)*Milling time (min) -1.32 0.67 -3.01 0.37 0.1032

Bead-suspension ratio*Bead-suspension ratio -1.44 1.15 -4.50 1.62 0.2726

Bead size (µm)*Bead size (µm) 4.81 0.61 3.24 6.37 0.0005

Acceleration (g)*Bead-suspension ratio*Bead size (µm) 1.03 0.42 -0.02 2.08 0.0533


In contrast to dv90s limited set of statistically significant parameters, span produced

an extensive list of variables approaching and surpassing the statistical significance of

0.05 (Table 5.6.). Bead size critically impacted the span, indicated by the statistically

significant main effect, quadratic effect and two-way interaction with bead-suspension

ratio. For a limited set of model parameters, statistical significance could not be proven.

107

The resultant predictive model (Model S-5.4., Supplementary Information, §9.2.) had

a high R2 and Radj2 of 0.995 and 0.983, respectively.

108

DISCUSSION

Application of the stress model

Researchers attempt to predict WBM kinetics and WBM outcomes by process

modelling where the stress model suggested by Kwade54 and the microhydrodynamic

model proposed by Afolabi and co-workers55 are the most widely known. A brief

recapitulation of the stress model is provided below. A more detailed elaboration on

Kwades stress model, has been provided in the Introduction (Introduction, §1.5.3.)

Nonetheless, interested readers are referred to the original articles presenting these

pioneering mechanistic models54, 55.

In the stress model54 the process parameters of a stirred media mill are directly linked

to the stress applied on the suspension’s particles via two central parameters, SN

(Equation 1.7.) and SIGM (Equation 1.6.). The SN is a measure for the number of stress

events, and SIGM is the specific energy consumed by a single stress event. This

simplification resulted in important caveats. However, it made the model easy to apply

and offered easy to understand insights in the milling process.61 Accordingly, the

question rises if these principles, build upon a stirred media mill, are applicable on the

trends observed in the IVM.

Regarding the DoE, acceleration was the most important factor impacting the particle

size reduction in the IVM. As explained by the stress model (Introduction, §1.5.3), the

increased acceleration will lead to both an increased intensity of the stress during a

milling moment - an increased SI - and an increased number of stress moments overall

- an increased SN. Bead-suspension ratio was another key operation parameter.

Increasing the bead-suspension ratio, led to a higher NGM and thus a higher SN. This

increased number of collisions will naturally lead to a more intense particle size

reduction. In a similar manner, the milling time (t) led, via an increased NC, to an

increased SN. Thus, smaller particles were retrieved, when milling for a longer time.

To be more accurate, milling curves are within the literature described by a fast non-

linear decrease which eventually stabilise towards the (apparent) grinding limit where

the particle size will fluctuate based on the balancing phenomena of grinding,

aggregation and crystal growth.50, 94 In this study, the quadratic term of milling time,

which would indicate non-linearity, did not show a statistically significant outcome.

Nonetheless, the power of the quadratic term was low and may indicate that in the

109

case of a larger sample size, statistical significance would be evoked. This example

explains why power analysis is an important element to consider during DoE analysis.

Aside of its effect on the final particle size, milling time importantly impacted the span.

During the milling process the PSD evolves from a multimodal to a monomodal

distribution based on the interplay between particle size reduction, (re)aggregation and

recrystallisation94 and as a result, the span will differ in function of the grinding time.

The optimal bead size

The common rule, “the smaller the bead size, the smaller the final particle size”, has

already been variously challenged in the field of WBM.57, 56, 107 Depending on the

process parameters and the suspension properties, the impact of the bead size will

differ.56 The literature suggests that an optimal bead size exists within the IVM. This

bead size is dependent on the power density and hence dependent on the installed

acceleration and bead-suspension ratio.63 Nonetheless, this conclusion was based on

an OVAT approach which cannot capture interaction effects and hence present

conclusive results.

Within this DoE however the existence of the optimal bead size was confirmed. Aside

of the main effect, a wide array of two-way interactions proved to be statistically

significant. Furthermore, the quadratic effect was, even with a modest power of 0.312,

statistically significant indicating the non-linear impact of this process parameter on the

final dv50-value.

These previously mentioned two-way interactions could be mechanistically explained.

The statistically significant two-way interaction acceleration/bead size could be

rationalised by its similar effect on the kinetic energy (Ekin) (Equation 5.3.) of the

grinding media:

𝐸𝑘𝑖𝑛 = 𝑚 𝑣2

2 (Equation 5.3.)

where Ekin (J) is kinetic energy, m (kg) is the mass of the moving bead and v (m/s) is

the speed of the moving bead. If the acceleration increased, the velocity of the beads

rose, and more kinetic energy was created (Equation 5.3.). Since the velocity of the

beads is squared in Ekin’s equation as opposed to the mass, the acceleration could be

increased to such extent that the added value of the mass of the grinding media - and

so the bead size - to the final Ekin becomes negligible.

110

The statistically significant interaction bead-suspension ratio/bead size can be

depicted in the crossing functions in the interaction profiler (Figure 5.4.). The bead size

will influence the number of beads present in a fixed bead-suspension ratio. As the

bead size decreases, a higher number of beads may be present in the same bead-

suspension ratio but the generated kinetic energy per bead may be lower. This will

directly impact the NGM, SN and SI. As follows, the NGM and SN will increase whereas

the SI will most probably decrease (Introduction, §1.5.3). At a low bead-suspension

ratio and thus, low input energy, the bigger beads yielded the smallest particles, whilst

at a higher bead-suspension ratio, the smallest beads were more favourable (Figure

5.6.).The reason for this counteracting effect may be recognised in the voids in-

between the beads. At a larger bead size, the voids in-between the beads tend to be

larger. By settling herein, bigger API particles could avoid the milling process, leading

to an overall larger median particle size.

Figure 5.6. dv50-values (y-axis) in function of the bead size (x-axis) for an acceleration of 50 g (left) and 80 g (right)

at a bead-suspension ratio of 1.2.

Lastly, the optimal bead size was substantiated by the statistically significant effect of

the three-way interaction (bead-suspension ratio/acceleration/bead size). Normally

these more complex multiple-way-interactions are, based on the hierarchy-principle

and the sparsity-of-effects-principle omitted out of a DoE. Nonetheless, the

significance of this three-way interaction profoundly substantiated the literature

suggesting that the optimal bead size was highly correlated to the installed acceleration

and bead-suspension ratio.63 As visualised in Figure 5.6., at high specific energies

such as the acceleration of 80 g, smaller beads yielded smaller API particles. The fitting

111

of the function describing the impact of the bead size at low acceleration was

suboptimal. Nonetheless, it cannot be mistaken that at this lower specific energy, larger

beads were more advantageous.

Similarly, the dv90 was remarkably influenced by the acceleration as main effect and

the two-way interactions acceleration/bead size and bead-suspension ratio/bead size.

This could be explained by the interplay between the kinetic energy of the beads, the

number of beads and the size of the voids in-between the beads and their dependence

on the bead size.

These voids did not only impact the dv50 and dv90 but had an important influence on

the span as well. Within these voids, a fairly monodisperse PSD could evade further

milling. Consequently, the bead size as both main factor and quadratic effect produced

statistically significant parameter estimates of -3.15 (± 0.54) and 4.81 (± 0.61),

respectively.

Aside of its effect on the particle size reduction, the bead size critically determined the

heat generation in the IVM. Aside of the statistically significant main effect, the

statistically significant quadratic effect had a strong presence in the predictive model,

with a parameter estimate of -7.56 (± 1.05). Considering its important effect on both

particle size and generated heat, the optimisation of the bead size should be a standard

step during IVMs process optimisation.

Cooling the system

The breaks during milling had an important effect on the final temperature of the

suspension. Surprisingly, this main effect did not have in the provided dataset a

statistically significant effect on the final dv50 and dv90. Hence, every 7.5 minutes, a

break of five minutes may be included without constraining the milling process.

Intermittent pausing to cool the system was therefore an attractive option to control the

temperature during IVM, which would be easy to standardise and scale-up.

Nonetheless the model estimate of -3.33 (± 0.48) was quite modest as compared to

the model estimate of the other process parameters. Elevated temperatures may have

deteriorating effects on the (physico)chemical properties of the compound, the

stabiliser and other excipients. Besides, an increased temperature indicate that energy

was lost and thus indicate a suboptimal power consumption. To further limit the

generated heat, expected temperature may be computed, based on the set process

112

parameters, via the predictive model (Model S-5.2., Supplementary Information, §9.2).

Optimisation of the process parameters to limit this heat generation afterwards is

feasible and highly recommended. Another possibility is to lengthen the break.

However, this fell beyond the operational ranges studied in this DoE.

Method optimisation

Table 5.7. Parameter estimates of all main effects on the different process parameters and calculation of the ratio

of the estimated parameter effect on the temperature versus the estimated parameter effect on the dv50-value.

Model estimates

Acceleration (g) (50,80)

Breaks during milling (min) (0,5)

Milling time (min) (10,30)

Bead-suspension ratio (mL/mL) (0.375,1.2)

Bead size (µm) (200,1750)

dv50 -1.97 (± 0.24) 0.46 (± 0.28) -1.16 (± 0.23) -1.77 (± 0.28) -1.51 (± 0.23)

dv90 -6.88 (± 1.91) -0.85 (± 1.98) -2.32 (± 1.84) -6.43 (± 1.99) -9.13 (± 1.94)

Temp 16.06 (± 0.45) -3.33 (± 0.48) 8.56 (± 0.46) 12.29 (± 0.61) 13.15 (± 0.53)

Span 0.65 (± 0.26) 1.16 (± 0.30) 1.26 (± 0.41) -1.87 (± 0.44) -3.15 (± 0.54)

Temp / dv50 -8.15 -7.24 -7.38 -6.94 -8.71

In the present study, an imposing array of 22 model parameters and four responses

was extensively investigated. In general, all 22 investigated model parameters showed

a statistically significant effect on at least one of the four responses, with an exception

of the quadratic effects for milling time and bead-suspension ratio. Albeit, the power of

these factors was rather modest. Accordingly, they may still play a fair part in one of

the responses. All these statistically significant main effects, two-way interactions,

quadratic interactions and even three-way interaction marked the complexity of the

IVM process.

Nonetheless, the valuable and fast nanosizing potential of IVM and its potential high-

throughput screening was illustrated. After ten minutes of grinding, final dv50-values

in the lower micron range (Sample 4, block 2, Table 5.1.) and even submicron range

(Sample 2, block 3 and sample 4, block 5, Table 5.1.) could be detected. This particle

size reduction as presented by a decrease in dv50, dv90 and span, was strongly

associated with a temperature increase (Figure 5.7.). The large variability depicted at

the lowest dv50, dv90 and span, however, indicated that particle size reduction with a

controlled heat generation might be attainable.

113

In the provided datasets, nanonisation seemed to be the most attainable at the highest

acceleration, highest bead-suspension ratio and lowest bead size, which is

comprehendible as the high acceleration and bead-suspension ratio would install high

energy milling for which a smaller bead size is optimal. Even though these settings

would maximise the particle size reduction, they would enhance the heat generation

as well. In these cases, it might be advantageous to mill for a longer time than to

increase the acceleration. Even though they both lead to a temperature increase, their

trend towards this heat generation was different (Figure 5.8.). As suggested in chapter

4104, the temperature increase in function of acceleration seemed to be more

pronounced than the increase in function of milling time. The ratios of the parameter

estimate of temperature on dv50-value were calculated and seem to portray a similar

picture (Table 5.7.). While the ratio for acceleration presented a value of -8.15, this

change would only be limited to -7.38 in case of the milling time. In a similar manner,

an increase in bead-suspension ratio seemed to be a gentler approach for process

intensification than acceleration. At high bead-suspension ratios, even mild

acceleration led to appropriate particle size reduction, where dv50-values might reach

nanolevels and dv90-value lower microlevels (Sample 1 and 6, block 2, Table 5.1. and

sample 3, block 4, Table 5.1.). A further increase in grinding time would be interesting

to explore, but more experimentation is therefore required. Finally, an optimal bead

size may be chosen based on the installed acceleration and bead-suspension ratio.

In this regard, method optimisation may be supported by the determination of the

designs sweet spot via contour plots. The contour plot of the smaller bead size

displayed extreme values. At low acceleration and low bead-suspension ratio, the

small beads did not have the capability to compensate for the low kinetic energy and

a limited particle size reduction occurred. Whilst, at high accelerations and bead-

suspension ratios, the high number of small beads and the smaller voids in-between

the beads will enhance the particle size reduction (Figure 5.9., left). Independent of the

set bead size, the temperature showed the same trend. As the acceleration or bead-

suspension ratio rose, a higher temperature was attained. (Figure 5.9., right). In finding

a sweet spot, the two figures may be overlaid. Thus, to attain an extreme particle size

reduction with an acceptable temperature increase, a combination of for example a

relatively high bead-suspension-ratio, a relatively high acceleration and a relatively

small bead size would be advised.

114

Finally, for a more adequate optimisation, the herein described strong predictive

models may be utilised. With these models, JMP® may directly simulate data over the

full experimental domain. With flexible desirability functions, best possible conditions

may be obtained. Nonetheless, the model is currently further explored and validated

for other APIs and process parameters.

Figure 5.7. Measured temperatures in function of the dv90 (left), dv50 (middle) and span (right). Similar trends could

be noted where a decrease in particle size or PSD was importantly related to an increase in temperature. Nonetheless,

an important variability on the temperature could be detected by the bootstrap confidentiality intervals (coloured zone).

115

Figure 5.8. The temperature trend during continuous milling (breaks during milling = 0 min) when the milling time (left)

or the acceleration (right) were investigated. Even though the bootstrap confidence intervals (coloured zone) presented

a certain level of variability, the curve fitting acceleration seemed to be steeper than the curve fitting bead-suspension

ratio

Figure 5.9. Contour plots for the dv50 (left) and temperature after milling (right) with as independent variables: bead-

suspension ratio (x-axis, below), acceleration (y-axis, left) and bead size (x-axis, above). The settings of the other

parameters were variable. The response, dv50 and temperature after milling, was log-transformed and depicted by

colour. High values were coloured in red, whereas low values were coloured in blue. Generally, opposite trends were

observed.

116

CONCLUSIONS

In this work, an I-optimal design was applied to investigate how five critical process

parameters, namely bead size, bead-suspension ratio, milling time, breaks during

milling and acceleration, govern IVM in terms of heat generation and particle size

reduction. As a result, our understanding of the IVM was improved, which was further

strengthened by the application of Kwades stress model54. The complexity of the IVM

was demonstrated in the wide extent of statistically significant main effects, two-way

interactions, quadratic effects and even three-way interaction. The DoE confirmed the

existence of an optimal bead size. Intermitting pausing of 7.5 minutes proved to cool

down the system without constraining the particle size reduction. However, to keep

temperature under control, the remaining process parameters should be optimised as

well. In this regard, contour plots and accurate predictive models might be of value,

which were generated for the investigated API and process parameter ranges. With

these, both particle size and temperature may be for the first time accurately

forecasted. For other APIs and process parameters, the model may currently serve as

a rule of thumb. Further research is nonetheless required to substantiate these

extrapolations.



Supplementary Information, §9.2.

Stability trends of micron and submicron suspensions

manufactured by the intensified vibratory mill

119

GRAPHICAL ABSTRACT

ABSTRACT

IVM is a nanonisation and micronisation technology which can be used to enable the

oral bioavailability of poorly soluble compounds. The generated nano- and

microsuspensions entail a large surface area which enhances the compounds

solubility and dissolution rate, yet it may cause unfavourable effects and hence

spontaneous destabilisation as well. Stability studies on suspensions manufactured via

IVM have, to the best of our knowledge, not been reported in the literature before. An

extended stability study was, therefore, executed with 30 bedaquiline suspensions

milled with the intensified vibratory mill under various process settings. The PSD was

measured after production, after four weeks of storage at 5 °C and after eleven weeks

of storage at 5 °C with LD and SEM. In addition, a caking test was applied to scrutinise

the redispersibility of the prevailing sediments. One sample whose sediment proved to

be redisperible, demonstrated a peculiar trend during storage where a narrowing of the

PSD and a general particle size reduction was detected, which opposed the

conventional stability tendencies such as Ostwald ripening. This enigmatic trend was

further explored via a repetitive analysis with LD and in a further phase, with an

orthogonal particle sizing technique. Still, no matter the frequency nor technique, a

narrowing PSD was observed. To the best of our knowledge, this article reports for the

first time in the pharmaceutical literature on a narrowing PSD of a micronised

suspension. Inevitably, this trend might shed a fundamental new light on the stability

trends, exposed by suspensions post-micronisation.

120

INTRODUCTION

During the technological and scientific advances of the 1980s, two discovery platforms,

were generated that led to a wide range of potential drug leads: combinatorial

chemistry and high-throughput screening. Despite their high potential, most of these

potential drug leads exhibited a poor aqueous solubility and as a consequence, a poor

oral bioavailability.14 Nano- and microsuspensions have presented a strong

marketable value in the formulation of these poorly soluble compounds, as their

entailed large surface area enhances the compounds solubility and dissolution rate.

Nevertheless, these systems are, de facto, thermodynamically unfavourable.42, 44 The

stability of nano- and microsuspensions is therefore continuously challenged by a

multitude of destabilising phenomena, comprising sedimentation, Ostwald ripening,

secondary nucleation and aggregation.44 Sedimentation is the most abundant among

particles larger than 5 µm. Provided that the sediment can be homogeneously

redispersed, the settling process is not necessarily detrimental for the suspension’s

storage life. In this light, suspensions are often categorised in flocculated and

deflocculated systems, where flocculated systems contain loose aggregates.7 Their

sediment is easy to redisperse, but settles fast which restrains the time-window for

accurate sampling or accurate patient-dosing. Deflocculated systems gradually settle

but their particles create a sturdy, non-dispersible, solid cake. Formulation scientists

consequently aim for the midpoint of these two extremes, the partial or controlled

flocculation, where the loose aggregates are still resuspendable and settle at an

acceptable rate.108

By virtue of Brownian motion, particles smaller than 5 µm may evade the sedimentation

process but are, especially in the case of polydisperse systems, more prone to undergo

Ostwald ripening; a process wherein large particles grow at the expense of smaller

particles. A large body of literature suggests that an excess of stabiliser might promote

Ostwald ripening, however, a more recent study of Verma and co-workers identified

Ostwald ripening as a multi-step process whose rate controlling step will determine the

optimal stabiliser concentration.47, 109 Other nanoparticle growth pathways such as

digestive ripening and intraparticle ripening may occasionally occur in inorganic

materials but are only scarcely discussed in pharmaceutical literature.48 There are, to

the best of our knowledge, only two publications in the pharmaceutical literature

reporting a particle size reduction of an API during storage. Even though the API

121

appeared in the dry state in both publications, different reasons were provided for the

observed trend. In their seminal paper of 1985, Nyqvist and co-workers demonstrated

how the water molecules in the crystal lattice of zimeldine dihydrochloride, raclopride

and amiflamine would rearrange and cause crystal cracking and subsequent crystal

fracture.110 Such an anomalous decrease in particle size was also observed by Joshi

and co-workers during the storage of micronised budesonide. After air-jet milling, the

budesonide powder increased in specific surface area, increased in surface roughness

and in total pore volume. Stress relief by intraparticle crack formation, crack

propagation and particle fracture was suggested as the primary mechanism.108

It has been hypothesised that the manufacturing technology can impact the stability of

the manufactured suspension, nonetheless, in-depth research on the topic is lacking.50

In order to contribute to this neglected area, the samples prepared for the DoE

proposed in chapter 5 were stored at 5 °C and the particle size was assessed after

production, after four and after eleven weeks of storage. Anticipated stability trends

encompass Ostwald ripening and a stable suspension. Surprisingly, a peculiar particle

size reduction was observed for one sample which appeared to contradict these

conventional stability trends. This peculiar trend was further explored for its

reproducibility and its accuracy as it would shed a fundamental new light on post-milling

behaviour of suspensions.

122


Materials

Bedaquiline was provided by Janssen Pharmaceutica (Beerse, Belgium). Polysorbate

20 was obtained from Croda (Trappes, France). Poloxamer 338 was obtained from

BASF (Kolliphor® P338, BASF, USA). Deionised water (R≥18.2 MΩ, Mili-Q® Advantage

A10, Merck, Darmstadt, Germany) was used for all the experiments.

Methods

6.4.2.1. Preparation of suspensions

Glass vials were filled with bedaquiline (5%w/v), zirconia beads (Nikkato Corporation,

Sakai, Osaka, Japan) and polysorbate 20 solution (1.85%w/v). The vials were

thoroughly shaken on an in-house manufactured platform within the LabRAM II

(Resodyn™ Acoustic Mixers, Butte, USA). The process parameters - the acceleration,

the bead size, the bead-suspension ratio, the milling time and the breaks during milling

- were varied as proposed in the DoE (Chapter 5, §5.3.).


Mastersizer 2000™

In the Mastersizer 2000™ (Malvern Instruments, Worcestershire, UK) with hydro-unit,

miliQ water was used as the dispersant. A stirring speed of 600 rpm was set, no

sonication was applied, and the system was left to stabilise. The general-purpose


The set optical parameters were a rRI of 1.595 for the sample, iRI of 0.001 for the

sample and a dispersant rRI of 1.333. A limited obscuration titration was performed for

each sample to elucidate the optimal set of obsc. and obsc. blue. Quality of data was

given in terms of res. and res weight., as described in chapter 3.95 The final PSD was

described by the dv10-value, dv50-value and the dv90-value.

Mastersizer 3000™

In the Mastersizer 3000™ (Malvern Instruments, Worcestershire, UK) with hydro-unit,

a 1% dilution of poloxamer 338 in miliQ water was used as the dispersant. A stirring

speed of 2000 rpm was set, and no sonication was applied. The general-purpose


123

The set optical parameters were rRI of 1.61, a sample iRI of 0.01 and a dispersant rRI

of 1.333. As sample preparation, 0.1 mL of the suspension was diluted in 10 mL of

miliQ water. The final PSD was described by the dv10-value, the dv50-value and the

dv90-value.

6.4.2.3. Differential centrifugal sedimentation

The differential centrifugal sedimentation was performed on a CPS Instruments Disk

Centrifuge (CPS Instruments, Inc. Prairieville, LA, USA), model DC24000 equipped

with a normal density disc and detector at the wavelength of 405 nm, to obtain a weight-

average PSD, described by the dw10-value, the dw50-value and the dw90-value. The

disk centrifuge was operated at 20 000 rpm, and the spin fluid contained a sucrose

density gradient (4% to 12%) which was refreshed every four hours. A calibration and

a system suitability test with polyvinylchloride standards were executed prior to sample

analysis. For the sample analysis, the particle density was set at 1.31 g/mL and the

particle refractive index and particle absorption were set at 1.61 and 0.01, respectively.

The sample was diluted prior to analysis, by the dispersion of 70 drops of the

suspension, generated with a 23G needle, in 50 mL of miliQ water.

6.4.2.4. Scanning electron microscopy

Suspensions were diluted with miliQ water and consecutively dried on an Isopore filter

(ø 0.100 µm). The filters were adhered to the SEM stubs using double-sided carbon

tape. All samples were gold coated with a Quorum Q150 R S gold sputter (Quorum

Technologies Ltd, Laughton, East Sussex, England) and placed within the Phenom

Pro-X (ThermoFisher Scientific, MA, USA) to acquire the SEM images.

6.4.2.5. Caking test

For the caking test, the vials containing the samples were vigorously shaken to

homogeneously disperse the suspension and to redisperse possible sediment. If a

headspace was present, the vial was held upside down to control if a solid cake was

even after agitation, still attached at the bottom of the vial. If this in-situ control was not

attainable, the suspension was sacrificed and poured out to enable the solid cake

identification in the vial.

124

6.4.2.6. Stability study

Post-milling stability trends

All samples from the DoE, described in chapter 5, were stored after production at 5 °C.

To investigate their particle size and morphology, SEM was executed after production

and after four weeks of storage. Further, a Mastersizer 2000™ particle size

measurement was performed after production, after four weeks and after eleven weeks

of storage at 5 °C with exception of the samples of block 4 which were, due to practical

constraints, analysed after fifteen weeks instead of eleven weeks. The PSDs of each

sample were superimposed within the Mastersizer 2000™ software to identify the

stability trend of the sample, which subcategorised the samples in the following, four

categories: The category ‘Stable’ referred to the stable suspensions whose PSDs

minimally changed over time; the category ‘Ostwald’ referred to the suspensions

whose particle size clearly increased over time; for the suspensions of the category

‘Unclear’, the variability of the LD data did not permit a clear identification of the stability

trend; the suspensions of the category ‘New trend’ demonstrated a peculiar particle

size reduction during storage. The caking test was performed at the end of the stability

study, hence after 21 to 11 weeks of storage at 5 °C, for block 1 to block 5, respectively.

If caking was observed in a sample of block 1 to 3, inadequate sampling during particle

size measurements might have occurred which would have resulted in invalid data.

Thus, the results of the particle size measurement of the second stability point might

be invalid and were therefore highlighted in bold, red and italic. If a solid cake was

detected in a sample of block 4 or block 5, the second stability measurement was not

performed.

125

Confirmation of the new trend

In a second study, the reproducibility of the previously mentioned ‘New trend’ was

tested. The ‘New trend’ sample where no caking prevailed (Sample 2, Block 3, Table

6.2.), was, therefore, remanufactured in fourfold at two different time points, leading to

two different sample sets. These sample sets were investigated with LD, SEM and the

caking test on at least two different timepoints, as presented in Table 6.1.

Table 6.1. Overview of the timing and the analysis techniques applied to confirm the reproducibility of the new trend

observed in sample 2 of block 3 of the DoE

Sample set 1 Sample set 2

After production Caking, SEM LD, SEM

Timepoint 1 4 weeks: LD, Caking, SEM 3 weeks: LD, caking, SEM

Timepoint 2: 7 weeks: LD, Caking, SEM /

In order to verify this new trend, the ‘New trend’ sample (Sample 2, Block 3, Table 6.2.)

was remanufactured in sixfold and divided in sets of three where one set was studied

with the Mastersizer 2000™ and the other set was investigated with the Mastersizer

3000™ and differential centrifugal sedimentation with as analysis time points; after

production, after four weeks of storage at 5 °C and after ten weeks of storage at 5 °C.

126


Post-milling stability trends

Table 6.2. Overview of the stability trends of all the DoE samples. In the case of caking, the data of the data of the second stability measurement were highlighted in bold, red

and italic to signal its possible inaccuracy (Block 1, block 2 and block 3) or the data of the second stability measurement was not collected at all. (Block 4 and block 5).

Stability 0 After production

Stability 1 After 4 weeks

Stability 2 After 11 weeks

Sample Particle size

Particle size

Particle size

Stability trend

Block 1 dv10 (µm) dv50 (µm) dv90 (µm) dv10 (µm) dv50 (µm) dv90 (µm) dv10 (µm) dv50 (µm) dv90 (µm) Overlap PSD

Caking test

Sample 1 3.469 21.125 64.279 3.553 21.194 55.701 4.147 26.549 75.073 Stable No caking

Sample 2 0.335 3.97 40.469 0.849 3.699 37.467 1.137 4.332 43.17 Ostwald No caking




Sample 6 0.148 1.918 4.924 0.105 0.374 1.288 0.117 0.388 1.17 New trend Caking


Caking test


Sample 2 0.161 2.046 4.929 0.106 0.828 2.528 0.191 1.445 3.86 Unclear Caking


Sample 4 0.13 1.283 3.807 0.102 0.513 2.344 0.108 0.504 2.056 Unclear No caking



127


Caking test


Sample 2 0.114 0.897 3.088 0.103 0.552 2.187 0.105 0.457 1.882 New trend! No caking

Sample 3 0.167 1.946 4.509 0.273 1.62 3.494 0.357 2.048 4.916 Stable Caking


Sample 5 0.108 0.682 2.585 0.141 0.539 1.598 0.115 0.792 2.672 Stable Caking

Sample 6 0.85 2.077 4.378 0.769 1.852 4.629 1.322 3.349 9.369 Ostwald Caking

After 15 weeks

After 15 weeks


Caking test




Sample 4 0.154 1.958 4.437 0.226 1.669 6.157 Caking - - Ostwald Caking


Sample 6 0.134 1.184 3.286 0.113 0.486 1.724 Caking - - Unclear Caking


Caking test







128

The 30 samples did not only present different PSDs after production but demonstrated

different stability trends (Figure S-6.1., Figure S-6.2., Figure S-6.3., Figure S-6.4.,

Figure S-6.5, Supplementary Information, §9.3.) as well, leading to their categorisation

in four stability trends (Table 6.2.). Even though these PSDs and thus stability trends

were confirmed by SEM, they were merely defined by LD data and their accuracy was

therefore challenged by a caking test. The large volume of data extracted from this

stability study (Table 6.2.) impeded a broad, yet accurate discussion of each sample.

Nonetheless, some peculiar trends were noted which will be detailed below.

Despite their identical formulation, suspensions with comparable PSDs after

production could still demonstrate different stability trends, as displayed in sample 3

and sample 6 of block 1 of Table 6.2. They presented similar PSDs after production

with a dv10-, dv50- and dv90-value of 0.154 µm, 1.657 µm, 4.27 µm, and, 0.148 µm,

1.918 µm, 4.29 µm, respectively. Nonetheless, the PSDs evolved differently during

storage where sample 3 remained stable throughout the whole study whereas an

indispersible cake materialised in sample 6. The temperature after production,

retrieved as explained in chapter 5104 was distinct with a value of 40.1 °C and 66.2 °C

for sample 3 and sample 6, respectively. As an increased temperature might lead to

the (partial) desolvation of the hydrophilic part of the applied surfactant, it might have

impacted its stabilisation efficacy. As a result, the attraction energy between two

particles will prevail, and the particles will tend to aggregate. The higher temperature

that sample 6 faced could therefore explain the poor stability outcome as compared to

sample 3. From a stability point of view, these data suggested a preferred use of a

moderate acceleration, a smaller and thus, in this case, more performant bead size

and even intermittent pausing, as occurred during the production of sample 3, over

milling at a higher acceleration without intermittent pausing as occurred during the

manufacturing of sample 6. As proposed by Wang and co-workers50, these findings

suggest that the applied particle size reduction technology and even more, the applied

process parameters, might impact the stability of the manufactured suspension.

Furthermore, as formulation screenings are merely executed with fixed process

settings, these findings highlight how formulation scientists should take the production

technology and parameters in consideration when examining the results of such

formulation screenings.

129

The most striking result to emerge from this dataset was the anomalous trend of a

particle size reduction during storage, observed in a few samples of the stability study

(‘New trend’, Table 6.2.). This new trend was, as a first investigation, challenged via a

caking test. As described by Stokes’ law (Equation 1.5.), larger particles of a

multidisperse system will settle faster than their smaller counterparts.29, 44 To produce

an indispersible cake, smaller particles need to settle in-between these sedimented

larger particles, however, the smallest particles (mostly nanometer-range) will, by

virtue of the Brownian motion, hardly settle, if at all. The undispersible sediment will

therefore merely comprise of the larger particles of a multidisperse system. The

supernatans will as a consequence increasingly contain smaller particles. As sampling

occurs in the supernatans, the measured PSD will become less and less representative

for the suspensions aggregation behaviour and will falsly represent a particle size

reduction. Via the caking test, samples containing a solid, indispersible sediment and

thus, reflecting a false particle size reduction, were traced down (Table 6.2.). The last

stability data of the samples of block 1 to 3, were, as caking occurred, highlighted in

bold, italic and red to indicate their possible inaccuracy. In the case of the samples of

block 4 and 5, the particle size measurement was not executed if caking occured

(Sample 4 and sample 6, Block 4, Table 6.2.).

Suprisingly, sample 2 of block 3 did not demonstrate an indispersible cake, even after

17 weeks of storage. However, it did present the new trend during storage (Figure

6.1.). The change in the PSD was confirmed via SEM, where the size distribution of

the rather multidisperse particulate system seemed to narrow down as a function of

time (Figure 6.2.). The operating parameters to manufacture this suspension were an

acceleration of 80 g, a milling time of 10 min, a bead-suspension ratio of 1.2 mL/mL

and a bead size of 1000 µm. The installed bead-suspension ratio and acceleration

were rather extreme, though the milling time was limited to 10 minutes. With a final

suspension temperature of 85 °C, heat was generated to a high extent and the system

did approach the cloud point of the stabiliser polysorbate 20 (ca. 97 °C)91. Partial

dehydration could occur but aggregation was not observed in the SEM images. As this

unexpected behaviour would shed a new light on the behaviour of suspensions after

micronisation, an investigation of this particular trend was pursued.

130

Figure 6.1. PSDs of sample 2 of block 3 of the DoE, after production (red curve), after four weeks of storage at 5

°C (green curve) and after eleven weeks of storage at 5 °C (blue curve). A peculiar trend was observed where the

portion of nanoparticles, as compared to the microparticles, seemed to increase over time.

Figure 6.2. Change in particle size and morphology as observed in SEM for sample 2 of block 3. The PSD after

production (left) seemed to be wider and seemed to contain a larger portion of bigger particles than the PSD after

four weeks of storage at 5 °C (right). In both cases, a polyhedral morphology was detected.

131

Confirmation of the new trend

Table 6.3. Investigation of the reproducibility of the particle size reduction during storage observed in sample 2 of

block 3 of the DoE.

Sample set Stability point dv10 (µm) dv50 (µm) dv90 (µm)

2 0 0.109 0.939 3.568

2 1 0.122 0.541 1.99

1 1 0.13 0.573 2.175

1 2 0.118 0.796 3.095

The particle sizes presented in this summarising table are the average values,

measured with the Mastersizer 2000™. Sample set 2 was measured after

production and after three weeks of storage at 5 °C and sample set 1 was

measured after four weeks and after seven weeks of storage at 5 °C.

As a confirmation of the reproducibility of this newly found stability trend, the

investigated suspension (Sample 2, Block 3, Table 6.2.) was remanufactured in

manifold and tracked over time. The caking test remained negative throughout the

stability study, indicating that the suspension could be completely redispersed. The

summarising results (Table 6.3) of the raw data (Table S-6.1., Table S-6.2, Table S-

6.3, Table S-6.4., Supplementary Information, §9.3.), endorse the reproducibility of this

enigmatic behaviour. The dv50-value declined from a value of 0.939 µm to a value of

0.541 and 0.573 µm during the first three to four weeks of storage (Table 6.3.). This

particle size reduction was even more apparent in the dv90-value which sharply

reduced to approximately half its original value. The particle size change from week

four to week seven was opposite to what was observed during the previous stability

study. In the previous stability study the particle size continued to decrease over time,

whereas the PSDs in this stability study presented a mild particle growth. Despite this

difference in the second phase of the stability study, the net result remained the same.

In both stability studies the PSDs at the end of the storage time were still narrower and

had an importantly lower dv50-value than the PSDs after production. In order to

exclude that the trend was a reproducible flaw of the analysis technique, the

Mastersizer 2000™, the trend was further studied using the Mastersizer 3000™ and

an orthogonal particle size measurement technique namely differential centrifugal

sedimentation.

132

Table 6.4. Summarising table presenting the average values and standard deviation of the particle size, measured

with the Mastersizer 2000™, at three time points; after production, after four weeks of storage at 5 °C and after ten

weeks of storage at 5 °C.

Storage

dv10 (µm) dv50 (µm) dv90 (µm)

0 weeks Average 0.107 0.787 3.385

Standard deviation 0.007 0.113 0.103

4 weeks Average 0.148 0.626 2.194


10 weeks Average 0.157 0.757 2.748



with the Mastersizer 3000™, at three time points; after production, after four weeks of storage at 5 °C and after ten

weeks of storage at 5 °C.

Storage

dv10 (µm) dv50 (µm) dv90 (µm)

0 weeks Average 0.353 1.170 3.223


4 weeks Average 0.385 1.150 3.213


10 weeks Average 0.279 0.866 2.920



with the differential centrifugal sedimentation, at three time points; after production, after four weeks of storage at 5

°C and after ten weeks of storage at 5 °C.

Storage dw10 (nm) dw50 (nm) dw90 (nm)

0 weeks Average 208 592 1332

Standard deviation 11 17 110





The raw data of these three particle size measurement techniques might be found in

the supplementary information of this manuscript (Table S-6.5., Table S-6.6 and Table

S-6.7., Supplementary Information, §9.3.), though summarising tables are provided

above.

133

The PSDs observed with the Mastersizer 2000™ (Table 6.4.) were comparable to the

previously retrieved data. The Mastersizer 3000™ data (Figure 6.3. and Table 6.5.)

confirmed the particle size reduction from week zero to week four. However, in the

second phase of the stability study a particle size decrease was detected by the

Mastersizer 3000™ whereas a mild particle size increase was detected by the

Mastersizer 2000™. This difference may be attributed to the increased sensitivity of

the Mastersizer 3000™ to nanoparticles, hence the Mastersizer 3000™ data were

presumably more accurate. Still, the dv50-value in both the Mastersizer 2000™ and

the Mastersizer 3000™ reduced during the entire stability study. This trend was further

substantiated by the data acquired by differential centrifugal sedimentation (Figure 6.4.

and Table 6.6.). However, the particle sizes generated by differential centrifugal

sedimentation were considerably smaller than their counterparts measured with LD.

As the focus of the study was an early investigation of the observed new trend, an

extensive method optimisation of the differential centrifugal sedimentation method was

not pursued, resulting in the methods inferior accuracy. Yet, the dataset confirmed, the

peculiar narrowing of the PSD over time. Moreover, similar stability trends were

observed in other APIs as well (data non-disclosed). Even though this peculiar trend

seemed very contraindicative towards the widely known and accepted stability trends,

it was repeatedly observed and confirmed by an orthogonal particle sizing technique.

However, more particle size data with optimised (orthogonal) particle sizing techniques

are strongly required to fully confirm the perceived trend. Future studies might address

the explanation of the peculiar trend as well. Based on physicochemical principles, a

non-exhaustive list of plausible hypotheses is provided below, comprising digestive

ripening or reversed Ostwald ripening, the crystallisation of the amorphous (regions on

the) surface of the API particle, (surface) degradation and the Rehbinder effect.

Discarded hypotheses included intraparticle ripening and a stabiliser enabled

disaggregation.

134

Figure 6.3. Particle size reduction of sample F, as observed in the Mastersizer 3000™, illustrated by the PSD after

production (blue curve), after four weeks of storage (green curve) and after ten weeks of storage (red curve). The

PSD after production contain larger particles which evolves to smaller sized PSD.

Figure 6.4. Particle size reduction of sample F, as observed with differential centrifugal sedimentation. Larger

particles are present after production (green curve) which evolves to a smaller sized PSD after four weeks of storage

at 5 °C (red curve) and ten weeks of storage at 5 °C (blue curve).

135

Within inorganic chemistry, the process in which smaller particles grow at the expense

of larger particles is denoted as the process of digestive ripening or reversed Ostwald

ripening and has been the subject of extensive research.48, 111 How the size

modification during reversed Ostwald ripening precisely occurs still remains to be

elucidated and mainly descriptive insights are provided within inorganic chemistry.111

A current working hypothesis is the existence of a region in the parameter space of

component concentrations and interaction energies where smaller particles are more

stable than their larger counterparts. By the addition of a surface energy lowering

excipient, e.g. the wetting agent polysorbate 20, the effective surface energy of the

small particles might become negative. Mass would transfer from the larger particles

to these smaller particles and subsequently the PSD narrows to mono-sized

particles.48, 112, 113 Even though this theory appears particularly appealing to the

investigated system consisting of an API and the stabiliser polysorbate 20, extreme

caution must be paid in the direct translation of the given theorem. Contrary to the

research carried out in the field, the rate of the phenomenon presented itself over

weeks, whereas digestive ripening merely appears as a rapid process of maximum a

few hours. Another disagreement is the particle size range in which the phenomenon

takes place. While in the field of inorganic chemistry digestive ripening applies to a few

dozen of nanometres to quantum dots, the trend observed in our study was only

present at the higher nano- and lower microscale.48, 114 Albeit, the most intriguing

analogy of the surface energy lowering excipient, resuscitates digestive ripening as

potential hypothesis.112

Other interpretations for this intriguing trend, are related to the surface of the API

particle (Figure 6.5.). As the IVM is a high intensity milling process, it might

mechanically activate regions at the surface of the API and consequently reduce the

crystallinity of the milled compound.50

136

Figure 6.5. Surface phenomena that might occur during milling, illustrated by the circumstances before milling (left),

immediately after milling (middle) and the stabilised conditions after milling (right). Due to the high intensity milling,

surface domains might amorphisize which might crystallise during storage causing a volume reduction (green

arrows). Due to the high intensity milling, surface degradation may occur. The degradation product might have a

higher solubility than its poorly soluble parent compound and might consequently dissolve in the suspension matrix

(yellow arrows). Bedaquiline submicron suspensions are more prone to degradation than their macro-counterparts.

As a result, degradation in the suspension matrix might shift equilibria to an extent that particles start to dissolve

over time (yellow and blue arrows).

As a result, the crystal surface becomes disordered, which results in a highly defective

crystal phase that may spontaneously convert to localised amorphous regions.50, 115

As the amorphous compound is characterised by an enhanced solubility and a higher

free energy, reordering of these crystal defects and crystallisation of the amorphous

regions might spontaneously occur during storage. The high water content of the

suspension matrix will act as a plasticiser, which undoubtedly will trigger this

crystallisation process.50, 115 causing a reduction of the particle volume and hence, an

overall size reduction (Figure 6.5.).116 However, previous findings in our group

suggested that in the case of naproxen and cinnarizine amorphisation due to media

milling is highly unlikely in the suspended state and that the occasionally detected

amorphous fraction presumably originates form the drying process, required for solid-

state analysis. These findings were more specifically prevalent for APIs that had an

increased solubility in the stabiliser, which complies to the herein studied system of

bedaquiline and polysorbate 20.117

Further, these amorphous regions show a higher reactivity which in the presence of an

aqueous medium, may lead to chemical degradation, as observed for naproxen by

other authors.118 Furthermore, research in our group distinguished how cryomilling

might trigger polymer and subsequentially API degradation.119 Considering mechanical

activation might occur in the highly energetic IVM as well, surface API may degrade,

generating degradation products that might be more soluble than their poorly soluble

137

parent compound. In these circumstances, the degradation product might dissolve in

the aqueous environment, which drives particle size reduction (Figure 6.5.).

Despite the stability of bedaquiline in solid or in dispersed state, the submicron

suspension demonstrated an increased photodegradation (data non-disclosed).

Provided that the degradants are more soluble than to the parent compound

bedaquiline, photodegradation might shift the mass equilibria and drive a particle size

reduction (Figure 6.5.).

Another hypothesis is the propagation of the Rehbinder effect post-micronisation.

During nano- and micronisation, the stabiliser plays a major role in the mechanical

breakdown of the API via three different phenomena comprising the wetting and

stabilisation of the newly formed surfaces, its contribution to the microhydrodynamics

of the mixing and milling regime, and in the particle strength as described by the

Rehbinder effect.120 This Rehbinder effect explains how the stabiliser reduces the

compound hardness which eventually leads to crack propagation.3, 120, 121 The addition

of a stabiliser enhances the wet breakage rate via both the Rehbinder effect and the

wetting and stabilisation mechanism. However, as they coincidently occur, there is no

consensus on which mechanism is more dominant. Attempts of Biligili and co-workers

to address the question, led to two publications with contradictory findings.3, 120 The

increasing surface area of dry budesonide post-micronisation, was nonetheless

clarified by the theories of stress relief and crack propagation.108 Taken together, there

is still considerable controversy on the applicability of the Rehbinder effect in

pharmaceutical wet milling kinetics. Bearing in mind that the crack propagation would,

within the presented study, occur during storage, the hypothesis seemed highly

doubtful as compared to other well-reasoned hypotheses. Albeit, Monteiro and co-

workers described how the stabiliser sodium lauryl sulphate enabled the penetration

of the suspension medium into the aggregate pores. The aggregates consequently

disaggregated resulting in an apparent particle size reduction.120 This theory could be

transposed to our investigated system, but was discarded as the acquired SEM images

did not display any (dis)aggregation behaviour.

Another interesting hypothesis, which was eventually discarded by the SEM

observations and the orthogonal particle sizing technique, included intraparticle

ripening; a process where monomers diffuse along the surface of nanoparticles to

modify the particles habitus with time (Figure 6.6.). The working hypothesis of

138

intraparticle ripening is, as in the case of digestive ripening, based on the different

surface energies of the various facets of an API particle.48 Another reasoning may be

found in the Ostwald-Freundlich equation (Equation 1.3.) which denotes that the

saturation solubility of a particle is highly dependent on its curvature.25 Following LDs

assumption of particle sphericity, a change in habitus would importantly inflict LDs

accuracy and might be erroneously captured as a particle size reduction. Still, no

change in morphology was apparent on the SEM images (Figure 6.2.). Furthermore,

the enigmatic trend was confirmed via an orthogonal particle sizing technique.

Even more, the observed particle size reduction can originate from a complex interplay

of the previously described phenomena and processes.

Figure 6.6. Theoretical example of intraparticle dissolution, where the edges of a needle shaped particle dissolve

and recrystallise on other facets of the API particle, changing the particles morphology from needle shape to

spherical shape. From an LD perspective, this habitus change would be interpreted as a particle size reduction.

139

CONCLUSIONS

The herein provided stability study contributed to the observation that the selected

production technology and process parameters considerably affect the stability of the

produced suspensions. As a result, the applied process technology and process

parameters should be taken into consideration during a formulation screening as it is

conventionally executed with fixed process settings. Furthermore, one manufactured

suspension presented an unconventional narrowing of its PSD over time which was to

the best of our knowledge not reported in the pharmaceutical literature before. This

peculiar trend was further explored via a caking test, via repetitive measurements with

LD and via an orthogonal particle sizing technique which all confirmed this enigmatic

trend. More confirmatory experiments are, however, required. Due to time constraints,

this enigmatic behaviour was not further assessed. A non-exhaustive list of

hypothesises based on physicochemical principles provide the needed framework for

further exploration.



Supplementary Information, §9.3.

General discussion and future outlook

143

QUESTIONS ARISING FROM LASER DIFFRACTION AND INTENSIFIED

VIBRATORY MILLING

This PhD project revolved around the particle size analysis via LD, the nanonisation

and micronisation via IVM and the stability trends of the herewith manufactured micron

and submicron suspensions. Despite the simplicity of the LD and IVM set-up,

numerous questions arose from the ambiguous nature of the preliminary LD data and

from the intricate interplay of IVMs critical process parameters as which parameters

have an impact on the quality of LD data and how to treat them with care? How can

nano- and microsuspensions be manufactured with IVM and how can this

manufacturing process be finetuned? How do the manufactured (sub)micron

suspensions behave during storage?

Reviewing literature, these questions related to LD quality were nefariously all too often

ignored, whereas the questions concerning IVM are commonly addressed via the

cumbersome trial-and error approach. In adverse to these toilsome trends, this PhD

research project employed a step-by-step approach, as explained in chapter 2, which

followed the rational that before exploration of nanosizing technologies such as IVM, a

reliable particle size measurement method should be obtained. Consequently, one of

the most eminent particle sizing methods, LD, was studied for its capabilities of

producing high quality data. With the usage of the flow design provided at the end of

the first research project, an LD method was optimised for the analysis of (sub)micron

suspensions containing bedaquiline, the model compound throughout this PhD

dissertation. Furthermore, various polymers and surfactants were applied during this

first research project in order to stabilise the manufactured (sub)micron suspensions.

In the case of polysorbate 20, there were no aggregates detected on the SEM images,

the LD results indicated a short-term stability and the foam generated during milling

appeared negligible. Polysorbate 20 was therefore selected as a model stabiliser to be

considered for the subsequent research projects on IVM.

Endorsed by experience, this optimised LD method was transferred to the second

research project, which addressed the feasibility of IVM as nanosizing technique with

controlled heat generation via an assessment of various process parameters via an

OVAT approach. However, as temperature may have detrimental effects on the drug

product, a more thorough research was required. As a result, the early OVAT approach

paved the way for a profound DoE approach where the complex interplay of five

144

process variables was eventually unravelled. The fourth and last project of this PhD

dissertation studied the stability of the suspensions manufactured in the DoE (Chapter

5) during storage at 5 °C. Interestingly, few suspensions with similar PSD’s post-

manufacturing, did not show similar trends in particle size evolution during storage.

Moreover, a peculiar particle size reduction of a suspension post-micronisation was

observed, which to the best of our knowledge has not been reported in the

pharmaceutical literature before.

POSITION IN THE LASER DIFFRACTION AND INTENSIFIED VIBRATORY

MILLING LANDSCAPE

Guidance to quality laser diffraction data

Through its simplicity of use and its brief measurement time, LD has expeditiously been

incorporated in a manifold of projects in numerous academical and industrial

laboratories. In contrast to its wide application and the numerous papers emerging

hereof, 90% of the reported data has been stated to be false.73 As the reliability of LD

data is of utmost importance considering the harming impact of a false PSD on

conclusions with respect to the suspensions’ stability, suspensions’ syringeability and

more importantly the suspensions’ behaviour in the human body, a profound method

optimisation felt indispensable. During this method optimisation, issues concerning the

fit of the data, the obscuration, the samples stability within the LD system and the

background quality had to be tackled, before an optimised method could be installed.

When looking into the literature on LD accuracy, it shows that the critical impact of the

applied calculation model (Fraunhofer or Mie) and the applied refractive indices have

been repeatedly conveyed.73, 74, 75 A more recent investigation on this matter listed,

aside of the optical parameters, other important, yet questionable LD parameters such

as the technologies’ assumptions on the volume based results and on the spherical

equivalent diameter.74 Despite this prominent list of disputable parameters, these

articles failed to include the hurdles observed during our first research project. Even

more, as these articles highlighted the pitfalls of the LD platform, they did not seem to

propose solutions to resolve these hurdles and to advance in quality LD data.

Therefore, to aid formulation scientists in their search for an optimal LD method, the

previously identified critical variables were described and more importantly addressed

in a peer-reviewed publication.

145

Although the quality of LD results can be first-hand evaluated with the residuals and

residuals weighted, these parameters seemed to be rarely reported in scientific

literature. Even though the accuracy of this particle size technique was not proven in

this study and additional particle sizing with an orthogonal technique remained

recommended, peer reviewers are, nonetheless, advised to request these values as a

first assessment of one’s published LD data.

Filling the knowledge gaps in the field of intensified vibratory milling

As the first investigations on IVM were only conducted in 2013, it comes as no surprise

that only scant information is currently available on the topic.66 Even though these

former research efforts were undertaken to profoundly introduce IVM into the field of

pharmaceutical milling, most of them lacked (some) ingenuity to enable a thorough

understanding of this complex milling platform and consequently, there is still ample

room for investigation. This limited body of research considered IVMs critical process

parameters63, 66 or compared this novel milling technology with the golden standard

WBM62, 63, albeit contradictory conclusions were drawn on the latter subject. Various

authors confirmed the potential of IVM as drug-sparing nanonisation technique62, 63, 66,

122,, although there is still some critical disagreement to the extent (observable66, 63 to

barely or non62, 64) of IVMs heat build-up. Overall, current literature is dominated by

reports that do not consider heat generation as a fundamental determinant in IVMs

applicability.

As our preliminary data suggested, a critical portion of the power of this high-energy

grinding technology dissipated as heat, a second project was enrolled to investigate

the impact of five parameters, four process parameters and one formulation parameter,

on both the size reduction and the heat generation. Still, papers have been published

engaging the latter topic wherein Lu and co-workers have reported the highest

temperatures so far. In their study, temperatures of maximal 60 °C were reported.

Pursuant to the importantly higher temperatures observed in our study, which could

expand to 100 °C or above, this problematic heat generation felt underestimated. Even

more, Lu and co-workers continued milling for at least two hours without major issues

regarding heating or particle reaggregation. Even though our research suggested that

milling time presented a polynomial term towards its heat-build up, two hours of IVM

milling strongly feels, endorsed by our broader experience, as an excessive milling

time. However, the remark can be made that milling occurred in maximal 8 mL vials in

146

the study by Lu and co-workers, whereas 20 mL vials were used in our study, and as

pointed out by Lu et al., this could importantly influence the generated heat.66

Furthermore, the extreme temperatures detected during this second research project

did not appear to correlate with the observations of Leung and co-workers, who

outlined in their pioneering paper from 2013 how the mild temperature increases of the

IVM were enabled by a homogenous energy dissipation and effective energy

utilisation.66

Valuable conclusions were drawn in this second project, however, to tip the energy

balance further from heat generation to size reduction, an expert knowledge on IVM is

imperative. In order to unravel the complex interplay between IVMs process

parameters, a modern DoE was conducted in the third research project that deep dived

in the impact of five process parameters on the mechanical API breakdown and heat

generation. Through the complexity of the milling process and scarce publications on

the subject, direct comparison of results might be interesting, nonetheless

cumbersome and should therefore be interpreted with care. Our data seemed to merely

reinforce the recommendations towards process optimisation as described by Lu and

co-workers66 and by Li and co-workers63 which would in their case have been more

persuasive, if they were not solemnly drawn from an OVAT-perspective.63, 66 In this

respect, the considerable impact of acceleration and bead-suspension ratio on both

size reduction and heat generation was confirmed.

Regarding the bead size, our findings refute previous results of Lu and co-workers,

which stated that for nanonisation the smallest bead size is preferred, which was earlier

disproved by Li and co-workers.63, 66 The evidence in the third research project

highlighted the existence of an optimum bead size towards size reduction and heat

generation. Thus the selected media had an important impact on the heat generation

which was in contrast to the findings of Lu and co-workers who stated that the media

had only small effects on the operation temperature.66 Interestingly, this optimal value

was power dependent, as suggested by the group of Li and co-workers63, and therefore

related to the acceleration and bead-suspension ratio. However, the OVAT approach

used by Li and co-workers did not have the power to properly identify such multiple

way interactions, for which our DoE now provide sufficient credential. However, in

contrast to the performed optimisation steps of Li and co-workers63, our findings advise

to maximise the bead-suspensions ratio instead of acceleration in order to obtain high

147

power milling. Furthermore, the third research project confirmed how “breaks during

milling” contribute to an effective heat control without constraining the grinding process.

This scalable and standard parameter might be preferable to the effective, yet

laboursome milling interruption, described by Li and co-workers.63

The references made in these previous sections66, 63, 62, 122, pointed out that IVM is

undoubtfully an interesting and fast nanonisation and micronisation technology which

was captured in the findings of this third research project as well. Effective mechanical

breakdown to micron and submicron level persisted in a time frame of 10 to 30 minutes.

Within this DoE, multiple statistically significant interactions were identified which

displayed the complexity of IVM. This complexity translated in the major constraint of

this project; no generic recommendations could be determined. However, for the size

reduction the mechanistic model of Kwade54 was for the first time applied to gain some

mechanistic insights. Furthermore, the given project did provide predictive models that

may accurately predict milling outcomes based on the inserted process parameters.

The peculiar aftermath of intensified vibratory milling

Considering the overall dearth of information in the field of IVM, it came as no surprise

that no stability study on IVM was published so far. Therefore, post-production stability

was evaluated in the fourth research study (Chapter 6), utilising the samples of the

third research study (Chapter 5). During this stability study, samples with similar

particle sizes yet counteracting stability trends were detected; one suspension could

remain stable whereas the other underwent Ostwald ripening. In a formulation

screening, the first suspension would be accepted whereas the latter one would be

rejected. These findings sound a note of caution with the interpretation of a formulation

screening. As formulation screenings merely include a fixed set of process parameters,

they do not consider the impact of the process parameters on the stability of the

suspension.

The single most conspicuous observation to emerge from this dataset was undoubtely

the puzzling phenomenon of particle size reduction postmicronisation for which scarce

information is available in the pharmaceutical literature. There are, to the best of our

knowledge, only two published articles that discuss an API in crystalline state that

reduces in size during storage, however the API was in these both cases in the dry

148

state.108, 110 Unlike these two studies, we were surprised to confirm a similar trend in a

suspended, wet state. As these data were gathered in a preliminary, very exploratory

phase, the evidence seems highly valuable for future investigation.

TOWARDS THE FUTURE

The challenges and opportunities of the Resodyn® Acoustic Mixers

Today, IVM is facing key challenges making this technology unattractive for

commercialisation which consecutively hampered their industrial success. First, the

low production output is a major drawback, and IVM would indisputably be impossible

to use in such conditions in the pharmaceutical industry. For mixing purposes, the

LabRAM II might be upscaled to the commercially available versions with higher

production capacity such as the RAM 5 with a payload capacity of 35 kg and the RAM

55 with a payload capacity of 420 kg.90 An early investigation in milling upscaling by

Lu and co-workers, showed that the vessel size must be restrained to prevent

excessive heat build-up, so the upscaling may be more troublesome for aqueous

suspensions. As presented in chapter 4, highly concentrated formulations are

advantageous to mill as well.66 It is, nonetheless, highly doubtful if an increase in API

concentration and in vessel number per run could proceed to such an extent that a full-

scale industrial production would be realised.

This leads to the second major drawback, the extensive heat generation. Since the

RAM has mostly been used in mixing procedures, RAMs manufacturing company

appeared to merely invest their developments in this area. IVM could on the other hand

be an appealing, drug sparing, high-throughput screening platform to scope an APIs’

millability or to screen variable formulations. In these circumstances, the previous

recommended multivessel production might be employed, however a suiting water

jacket would be highly recommended. Therefore, RAMs manufacturing company

should enable the commercialisation of these additional IVM water jackets.

As the potential of the IVM is currently jeopardised by its rather low output and major

temperature build-up, the costly acquirement of the RAM is for milling purposes still

questionable. Interest could, nonetheless, be sparked by RAMs multitude of

applications, which encompass, in comparison to WBM, other formulation platforms as

well. Recent studies have shown that the RAM is highly applicable for nano-silica dry

coating of microcrystalline cellulose, where this technology outperformed two more

149

conventional technologies.123 Recently, the RAM presented great potential for the

development and scale-up of co-crystals in a practical and environmentally friendly

approach.124 In the more traditional approach, RAM is employed as a highly effective

mixing device, albeit, temperature increases were hereby detected as well.90

Embarking future research

7.3.2.1. Consolidation of the predictive models

A first approach for future research would be the consolidation of the predictive models

that were generated in the third research project (Chapter 5). Within the JMP®

software, experiments can randomly be generated with a forecasted dv50-value and

temperature after milling, which should comply to prior set specification limits. In order

to display the chance that the milling process would not comply to these prior set specs,

due to the process’ variability and natural occurring variability, an upper limit defect

rate, lower limit defect rate and total defect rate were calculated by the JMP® software.

Consequently, a formulation scientist may identify which dv50-value and temperature

after milling would be acceptable for a given formulation which can be input as specs

in the JMP® software to compute the required process parameters. These process

parameters are a well-founded starting point and thus, might highly accelerate the

production process development. In order to validate this application, confirmatory

experiments with bedaquiline have to be executed.

With respect to the stress model of Kwade54, as outlined in chapter 5, a milling process

is described by two important variables, the SE and the SN. Dependent on the process

parameters, a certain SN will be created wherein each collision will create a certain SE

on the particle, leading to particle fatigue or fracture. As observed throughout this PhD

project, the IVM may be categorised as a high-energy mill and therefore the

assumption can be made that the SE, accumulated in the samples of the DoE, is of a

rather high magnitude. This SE, developed in the DoEs working ranges, might be

capable to exceed the millability differences between different poorly soluble APIs. In

this context, the predictive model could be extrapolated to other APIs; without the

implementation of a millability factor, although with the assumption of a properly

stabilising formulation. Even if the model would be solemnly applied as a rule of thumb

where actual outputs slightly differ from the computed output, an extensive trial-and-

error research would still be eliminated.

150

7.3.2.2. Investigation of the peculiar stability trend

A second path for future research is to deep dive in the peculiar trend that was noted

in the stability study, provided in chapter 6, which may be scrutinised in twofold. First,

more information concerning this trend should be gathered as it may indicate which

hypothesis is worth further exploration. First of all, the employed differential centrifugal

sedimentation should be optimised for the investigated bedaquiline suspensions and

other orthogonal particle size measurement techniques which have measuring ranges

encompassing the suspensions PSD such as transmission electron microscopy (TEM)

or SEM, should be included.125, Since a multimodal PSD is involved, it is preferable to

employ TEM as literature indicates TEMs ability to distinguish smaller from larger

particles.125 In order to more precisely denote this enigmatic trend, the measurement

frequency may be increased and the study might be prolonged in time. As the caking

test in chapter 5 was executed a few weeks after the second stability measurement for

samples of block 1 to 3 and to have a benchmark for comparison, DoE samples with

similar particle sizes to the investigated sample (Table 6.2., Chapter 6) such as

samples 6 and 5 from block 2 and 3, respectively, might be included in future

assessment.

Secondly, hypotheses as provided at the end of chapter 6, may be evaluated in order

to rationalise this enigmatic trend. Revision of these hypotheses reduced the likelihood

of phenomena such as the Rehbinder effect, disaggregation or intraparticle dissolution

whilst phenomena, such as surface amorphisation and subsequent crystallisation,

surface degradation or reversed Ostwald ripening, are more likely to occur. As these

trends are mainly explained by surface changes such as altering surface energies or

transitions in solid-state, alternative techniques that can cope with nano- and

microparticle surface characteristics come into the picture.

In their seminal paper from 2014, Chen and co-workers have drawn our attention to

the use of synchrotron-based high-resolution total scattering pair distribution function

coupled with nanoindentation measurement as a valuable and effective tool for

investigation of subtle structural disorders in API crystals, a tool worth trying on this

peculiar sample as well.126 One year later, Maughan and co-workers achieved to

expand this nanoindentation method to (sub)micron crystals. In contrast to the

described production via crystallisation, it might be possible to apply nanoindentation

on the dried crystals to investigate if the peculiar sample concerns a distinct Young

151

modulus or a distinct hardness.127 For further surface assessment, inverse gas

chromatography may detect and localise surface amorphicity.128 In the more traditional

approach, inverse gas chromatography is employed for surface energy analysis, an

interesting output for our research as well since a change in surface energy is

correlated to inverse Ostwald ripening. These attachment energies might be ex-situ

computed by software packages as Materials studio, as well.126, 129 In quantitative

advanced imaging mode, atomic force microscopy (AFM) simultaneously provides the

surface topography and quantitative mechanical information such as stiffness,

adhesion and dissipation.128 At the end of the previous century, AFM was for the first

time linked to thermal analysis on a µm/nm scale. The resulting scanning thermal

microscopy (SThM) was able to provide simultaneous topographical and thermal

properties of a surface and near surface region. SThM might therefore serve as a

technique to discriminate between amorphous and crystalline regions on a sub-micron

scale and as highlighted by Bond and co-workers, the acquired data may be highly

comparable to the conventional differential scanning calorimetry data.130, 131 As a

result, the concept of local amorphous regions at the APIs surface, may be evaluated.

To investigate surface degradation, time-of-flight mass spectrometry would be advised,

as it might identify the surface chemistry.132

Even though these nano-and micro surface analysis techniques are still in their infancy,

it is not inconceivable that one of these techniques might be applied in our future work

to discover more on this peculiar trend, however, one should be aware that each

analysis technique has its own set of assumptions and limitations that fell out of the

scope of this discussion. Furthermore, these techniques are often limited by a poor

reproducibility and resolution, hence ideal background and matrix conditions are

merely required. Finally, sample preparation including drying seemed frequently

required, which may modify the surface characteristics of the sample. This PhD

dissertation was limited in time to test all hypotheses. Nonetheless, this discussion

provides a springboard for further exploration as results so far have been very

promising.

Overall, the findings presented in this thesis dissertation are substantial to the general

understanding of IVM, where the prospect of the prediction of IVMs milling outcomes,

serve as a spur to future research. This PhD dissertation led to the conclusion that IVM

is a feasible option to obtain nano- and microsuspensions in small scale for drug-

152

sparing high-throughput screening, despite all remaining challenges. Furthermore, the

subject of the detected particle size reduction post-micronisation is undoubtedly

reserved for future work.

Summary - Samenvatting

155

Summary

This thesis revolves around intensified vibratory milling (IVM) as a manufacturing

method for micron- and submicron suspensions, with the overall aim to profoundly

understand the main tenets governing the grinding process. A general introduction to

establish the framework for the subsequent research chapters is provided in the first

chapter. This introduction provides the prognosis why the interest in nano- and

microsuspensions is currently rapidly growing. In this context, the tendency towards

the manufacturing of poorly soluble compounds is placed in its historical context.

Subsequently, various enabling strategies that address this solubility hurdle are further

discussed. Two of these strategies, nanonisation and micronisation, were brought at

the foreground with an elaboration on their specific advantages per administration

route. Furthermore, the production, stabilisation and characterisation of these fine

particles are discussed whereupon a deep-dive in the top-down production of the nano-

and microsuspensions was provided, highlighting the commercial viability of one of the

top-down approaches, wet bead milling (WBM). As a consequence of the different

challenges still faced in WBM research, an alternative top-down production technology

was introduced in the form of the IVM. In regard to the frequent comparison of this

novel methodology with the golden standard WBM, not only the fundamentals of the

WBM are mentioned, but also the impact of its process parameters on the grinding

process as well as the attempts to mechanistically model this grinding process. The

introduction is concluded with a section on IVM and the scant information currently

available on the topic.

The second chapter defined the objectives and thus the framework of the presented

PhD dissertation.

In the third chapter, laser diffraction (LD) was assessed as a widely adopted particle

sizing technique to identify the critical parameters that infringe the data quality. Various

parameters attributing to poor data quality including poor background quality, instability

of the sample in the equipment, presence of gas bubbles and substandard obscuration

were addressed and tackled. Diverse techniques to retrieve a compound’s real part of

the refractive index were compared where a combination of the Becke line technique

and fit optimisation seemed to be most favourable. With the flow chart provided at the

end of this chapter, an optimal LD method was generated for the particle sizing of

156

(sub)micron suspensions composed of bedaquiline, the model active pharmaceutical

ingredient (API) throughout this PhD. This method was, endorsed by experience,

transposed to the subsequent research work on IVM.

The first work on IVM was provided in chapter four, where the impact of three process

parameters (bead-suspension ratio, milling time and acceleration) and one formulation

variable (API concentration) on size reduction and heat generation was explored via

an one-variable-at-a-time (OVAT) approach. While the nanosizing potential of IVM has

been investigated by previous authors62, not much was known regarding the heat

generation of this novel milling technique. It was observed that an increasing

acceleration restricted the process via its excessive heat build-up, whereas the milling

time presented a more acceptable heating. High drug loadings did not affect the final

temperature, whereas they create highly effective grinding processes, ergo, the

combination of concentrated formulations with prolonged milling times formed the most

favourable approach for process optimisation. Furthermore, these observations

facilitated a more rational selection of critical process parameters and feasible working

windows for the subsequent research project.

As particle size reduction seemed to go hand in hand with heat generation, the

feasibility to manufacture a (sub)micronised suspension with acceptable heat

generation with IVM had to be more profoundly explored. Via a Design of Experiments

(DoE) the impact of five process variables (bead-suspension ratio, milling time,

acceleration, bead size and breaks during milling) on the characteristics of the grinded

suspensions was therefore assessed, which led to the following conclusions: IVM is a

complex process defined by a wide extent of statistically significant main effects, two-

way interactions, quadratic effects and even a three-way interaction; the existence of

the optimal bead size, which is both acceleration and bead-suspension ratio

dependent; a generic approach for process optimisation constitutes the optimisation of

the bead size and intermittent breaks during the milling process. During this project,

an attempt was undertaken to tackle the excessive heat generation in a standard and

scalable fashion by the inclusion of a cooling parameter denoted as ‘break during

milling’, which proved to provide a substantial yet standardised cooling of the

suspension, without restraining the APIs mechanical breakdown. To advance our

understanding of the IVMs process, the stress model of Kwade54 was applied on the

observed grinding trends. In order to control the heat generation in the IVM, fine-tuning

157

of IVMs process parameters in a more confined manner appeared indispensable.

Therefore, the generated predictive models were applied to project the suspensions’

particle size distribution (PSD) and temperature, based on the installed process

parameters. From a statistical point of view, our data highlighted how modern DoEs

such as the I-optimal design can be successfully applied to elucidate the interplay of

process variables in complex milling platforms in a fast and economic fashion. In our

view these results constitute an excellent initial step towards a rational installation of

process parameters when grinding via IVM is required.

After the assessment of the nanonisation and micronisation potential via a DoE in

chapter five, the impact of these process parameters on stability prospects was studied

in the subsequent chapter six. Accordingly, the PSD of the manufactured suspensions

of described in chapter five were tracked during storage at 5 °C post nano- and

micronisation. Generally, the samples remained stable or experienced Ostwald

ripening, except for one sample, which presented the peculiar trend of PSD narrowing.

Concerning the stable suspensions and suspensions experiencing Ostwald ripening,

even suspensions with similar PSDs after production could follow opposite trends

during storage, a very important finding illustrating that the feasibility to produce a

stable microsuspension depends both on the formulation parameters as well as the

process parameters. Hence, these findings underline the importance to consider the

process technology and applied process parameters when examining the results of a

formulation screening. The peculiar trend of PSD narrowing, perceived in one

particular sample, requires a combination of surface and bulk characterisation to

elaborate on the samples’ solid-state, chemical and PSD behaviour. In a first attempt,

the trend was further explored for its reproducibility with the Mastersizer 2000™ and

explored with the Mastersizer 3000™ and an orthogonal particle sizing technology,

differential centrifugal sedimentation. Still, no matter the frequency nor technique, a

narrowing PSD was detected. Our observations are encouraging and should be further

validated by optimised orthogonal particle sizing techniques. Future studies on the

current topic are also required to verify which physicochemical principle might explain

this enigmatic trend.

In the final chapter, an overall discussion on all these results and conclusions is

presented, challenges ascribed to the heat generation and upscaling are discussed

and a prospective framework for future research is provided.

159

Samenvatting

Deze thesis behandelt intensief vibratorisch malen (IVM) als een productietechniek

voor micron en submicron suspensies, met als overkoepelend doel een grondig inzicht

te bekomen in de belangrijkste principes die dit maalproces beïnvloeden. Een

algemene introductie in het eerste hoofdstuk dient als kader voor de daaropvolgende

onderzoekshoofdstukken. Deze introductie biedt een inzicht in de snel groeiende

interesse in nano- en microsuspensies. Het stijgende aandeel van slecht oplosbare

geneesmiddelen wordt binnen zijn historische context geplaatst. Daarenboven wordt

de impact van deze slechte oplosbaarheid op de formulatie van orale

toediengingsvormen beschreven. Vervolgens worden de verschillende technieken

besproken die een slechte oplosbaarheid en lage oplossnelheid aanpakken. Een van

deze strategieën, nanonisering en micronisering, wordt naar de voorgrond gebracht

met een verdere analyse over de specifieke voordelen per toedieningsweg. Daarna

worden de productie, stabilisatie en eigenschappen van deze fijne partikels besproken

waarbij er dieper ingaan wordt op de top-down productie. De commerciële

haalbaarheid van een van deze top-down benaderingen wordt uitgelicht, namelijk het

natte kogelmalen (NKM). Aangezien het onderzoek op NKM nog steeds verschillende

uitdagingen met zich meebrengt wordt een alternatieve top-down productietechnologie

geïntroduceerd in de vorm van IVM. Omdat deze nieuwe methodologie vaak

vergeleken wordt met de gouden standaard die NKM bekracht, worden niet enkel de

basisprincipes van NKM vermeld maar ook de mechanistische modellen die NKM

omschrijven. De introductie wordt afgesloten met een sectie over IVM en de schaarse

informatie die momenteel beschikbaar is over deze techniek.

In het tweede hoofdstuk worden de objectieven van de vier experimentele

hoofdstukken en daardoor de omkadering van deze doctoraatsthesis omschreven.

In het derde hoofdstuk werd LD als deeltjesgrootte meettechniek geëvalueerd en

werden de kritische parameters geïdentificeerd die de datakwaliteit negatief

beïnvloedden. Verschillende parameters die leiden tot slechte datakwaliteit,

waaronder een inadequate achtergrond, labiliteit van het staal in het meettoestel, de

aanwezigheid van gasbubbels tijdens de meting en inadequate obscuratie werden

besproken en aangepakt. Diverse technieken waarmee het reeël gedeelte van de

complexe, refractieve index gegenereerd kon worden, werden vergeleken, waarbij een

160

combinatie van de Becke lijn-techniek en fit optimalisatie het meest wenselijke leek.

Met behulp van de flowchart, die aan het einde van dit hoofdstuk terug te vinden is,

vonden we een optimale LD methode voor de deeltjesgroottemeting van micron en

submicron suspensies die bedaquiline bevatten, het model actieve farmaceutische

ingredient (API) doorheen deze PhD. Deze methode werd verder toegepast in de

volgende onderzoekshoofdstukken.

Het eerste onderzoekswerk op IVM werd aangereikt in hoofdstuk vier, waarin de

impact van drie procesparameters (de kogelsuspensie ratio, productietijd en

acceleratie) en één formulatievariabele (de API concentratie) op de

deeltjesgroottereductie en warmteopwekking verkend werd door één parameter per

keer te wijzigen. Hoewel het nanonisatiepotentieel van IVM onderzocht werd door

eerdere auteurs62, was er weinig geweten omtrent de warmteopbouw van deze nieuwe

maaltechniek. In dit onderzoek bleek een stijgende acceleratie het process te

beperken door de exponentiële warmteopbouw, terwijl de productietijd een

aanvaardbaardere warmteopbouw veroorzaakte. Hoge API-concentraties hadden

geen invloed op de uiteindelijke temperatuur, terwijl ze wel uiterst efficiënte

maalprocessen genereren. Bijgevolg vormde de combinatie van sterk

geconcentreerde suspensies met verlengde productietijd de meest wenselijke

benadering voor procesoptimalisatie. Verder maakten deze observaties een rationele

selectie van de kritische processparameters voor het volgende researchproject

mogelijk.

Aangezien de deeltjesverkleining sterk leek te correleren met warmteopbouw, moest

verder nagegaan worden of het mogelijk was om met IVM een gemicroniseerde

suspensie met acceptabele warmteopwekking te bekomen. Via een statisisch

onderzoeksplanning werd de impact van vijf procesvariabelen (kogelsuspensieratio,

productietijd, acceleratie, kogelgrootte en periodieke onderbrekingen tijdens het

malen) op de eigenschappen van de gemaalde suspensies onderzocht, wat leidde tot

de volgende conclusies: IVM is een complex proces dat grotendeels gedefinieerd

wordt door statistisch significante hoofdeffecten, statistisch significante interacties

tussen twee factoren, statistisch significante quadratische effecten en zelfs een

statistisch significante interactie tussen drie factoren; het bestaan van de optimale

kogelgrootte, die afhankelijk is van zowel de acceleratie en kogelsuspensieratio; een

algemene benadering voor procesoptimalisatie omvat de optimalisatie van de

161

kogelgrootte en periodieke onderbrekingen tijdens het malen. Tijdens dit project werd

een poging ondernomen om de buitensporige warmteopwekking tegen te gaan door

een gestandaardiseerde en opschaalbare parameter omschreven als ‘periodieke

onderbreking tijdens het malen’ te implementeren, wat zorgt voor een substantiële

doch gestandaardiseerde afkoeling van de suspensie, zonder de deeltjesverkleining

te beperken. Om ons begrip van het IVM-proces te verbeteren werd het stress model

van Kwade54 toegepast op de geobserveerde maaltrends. Om de warmteopwekking

verder te beheersen, moesten de overige procesparameters van de IVM ook worden

afgesteld. Daarvoor werden de gegenereerde predictieve modellen toegepast om de

partikelgrootte en de temperatuur van de suspensie na het malen te voorspellen.

Vanuit een statistisch standpunt benadrukten onze data hoe een moderne statistische

onderzoeksplanning zoals het I-optimale design gehanteerd kan worden om op een

snelle en economische manier de interacties tussen procesvariabelen in complexe

maalplatformen te ontrafelen. Volgens ons bieden deze resultaten een uitstekende

eerste stap richting een rationele implementatie van procesparameters wanneer malen

via IVM vereist is.

Na het beoordelen van het nanonisatie- en micronisatiepotentieel via de statistische

onderzoekstplanning in hoofdstuk vijf werd de impact van deze procesparameters op

de stabiliteitsverwachtingen bestudeerd in hoofdstuk zes. Overeenkomstig met

hoofdstuk vijf werd de partikelgroottedistributie (PGD) van de gemaakte suspensies

gemeten na het malen en nadat zij vier en elf weken bewaard waren bij een

temperatuur van 5 °C. De stalen bleven grotendeels stabiel of ondergingen Ostwald

rijping, met uitzondering van één staal waarbij een bijzondere versmalling van de PGD

optrad. Wat betreft de stabiele suspensies en suspensies die Ostwald rijping

ondergingen konden suspensies met gelijkaardige PGD na de productie

tegenovergestelde stabiliteitstrends volgen, wat een zeer belangrijke vaststelling was

die illustreerde dat de slaagkans om stabiele microsuspensies te produceren

afhankelijk is van zowel de gebruikte formulatie als de gebruikte procesvariabelen. Bij

het bestuderen van formulatiescreeningen is het dan ook van belang om de gebruikte

procestechnologie en ingestelde procesparameters mee in consideratie te nemen.

De opmerkelijke trend van PGD-versmalling die ontdekt werd in één staal in hoofdstuk

zes vereist bij verder onderzoek een combinatie van oppervlakte- en

bulkkarakterisatietechnieken om inzicht te krijgen in het staal zijn kristalliniteit, zijn

162

PGD, zijn gehalte en zijn afbraakproducten. Bij een eerste poging werd de trend verder

onderzocht op vlak van reproduceerbaarheid met de Mastersizer 2000™. Om de trend

te verifiëren werd de partikelgrootte verder onderzocht met de Mastersizer 3000™ en

de differentieel centrifugale sedimentatie. Ongeacht de frequentie of gehanteerde

techniek werd een vernauwde PGD gedetecteerd. Onze observaties zijn bemoedigend

en zouden verder gevalideerd moeten worden met geoptimaliseerde

deeltjesgroottemetingen. Verdere studies op dit onderwerp zijn ook vereist om te

verifiëren welke fysicochemische principes deze merkwaardige trend kunnen

verklaren.

Het laatste hoofdstuk omvat een algemene discussie van al deze resultaten. Tot slot

worden de uitdagingen aangaande warmteopwekking en opschaling besproken en

wordt een kader voor toekomstig onderzoek voorgesteld.

Supplementary information

165

SUPPLEMENTARY INFORMATION TO CHAPTER 3

Tables

Table S-3.1. Fit optimisation of the iRI within the Mastersizer2000™ software, using a TPGS- and an SDS-stabilised

suspension.

Table S-3.2. Fit optimisation of the rRI within the Mastersizer 2000™ software, using an SDS-stabilised suspension

and a TPGS-stabilised suspension.

rRI iRI D[4,3] D[3,2] dv10

(µm) dv50 (µm)

dv90 (µm)

Obsc red

Obsc blue

Res. Res. weight.

SDS 1.595 0 0.72 0.43 0.25 0.47 1.135 4.2 7.4 1.47 2.291

SDS 1.595 0.001 0.73 0.43 0.24 0.46 1.149 4.2 7.4 1.41 2.259

SDS 1.595 0.1 1.7 0.44 0.24 0.48 1.672 4.2 7.4 3.49 5.186

TPGS 1.595 0 4.54 0.44 0.12 3.87 10.32 2.1 3.16 0.51 0.522

TPGS 1.595 0.001 4.85 0.48 0.13 4.07 10.39 2.1 3.16 0.5 0.471

TPGS 1.595 0.1 5.51 2.59 1.03 4.98 10.41 2.1 3.16 1.2 1.216

rRI iRI Res. Res. weight.

SDS 1.55 0.001 1.38 2.663

SDS 1.592 0.001 1.41 2.284

SDS 1.595 0.001 1.41 2.259

SDS 1.596 0.001 1.4 2.25

SDS 1.6 0.001 1.42 2.212

TPGS 1.55 0.001 0.39 0.468

TPGS 1.592 0.001 0.5 0.472

TPGS 1.595 0.001 0.5 0.471

TPGS 1.596 0.001 0.5 0.47

TPGS 1.6 0.001 0.51 0.467

166

Figures

Figure S-3.1. A basic LD set-up, containing a laser light source, a sample cell, a Fourier lens and a multi-element

detection pane, including the obscuration detector.

0 10 20 30 40 50

0

20

40

60

80

Lig

ht e

ne

rgy

Detector number

Measurement 1

Measurement 2

Measurement 3

Measurement 4

Measurement 5

Measurement 6

Figure S-3.2. Example of a background overlap conducted with the Mastersizer2000™ software; showing only

variability in-between detector 1 and 20 which can be explained by the presence of gas bubbles.

167


Predictive models

Model S-5.1. Predictive model for dv50-value

.

168

Model S-5.2. Predictive model for temperature after milling

169

Model S-5.3. Predictive model for dv90-value

170

Model S-5.4. Predictive model for span

171


Tables

Table S-6.1. Results of the particle size measurement of sample set one after four weeks of storage at 5 °C.

Table S-6.2. Results of the particle size measurement of sample set one after seven weeks of storage at 5 °C.

Sample

dv10 (µm)

dv50 (µm)

dv90 (µm)

Obsc Obsc blue Res. Res. weight.

D - Sampling 1

0.11 1.112 3.697 7.17 9.26 1.249 0.288

D - Sampling 2

0.114 0.892 3.301 4.85 6.43 0.624 0.236

E - Sampling 1

0.119 0.593 2.362 6.4 9.21 1.104 1.078

E - Sampling 2

0.129 0.769 2.87 3.67 5.24 0.529 0.697

Average 0.118 0.842 3.058

Standard deviation

0.008 0.218 0.574

CV (%) 6.954 25.920 18.761

Average (Mastersizer 2000™)

0.118 0.796 3.095 5.52 7.53 0.876 0.575

Sample

dv10 (µm)

dv50 (µm)

dv90 (µm)


A - Sampling 1

0.119 0.602 2.307 6.01 8.66 0.657 1.155

A - Sampling 2

0.11 0.62 2.704 7.97 11.06 0.447 1.166

D - Sampling 1

0.122 0.504 1.768 3.87 5.97 3.095 0.88

D - Sampling 2

0.161 0.672 2.322 1.36 2.1 0.841 1.331

E - Sampling 1

0.141 0.534 1.983 2.75 4.32 1.072 1.458

E - Sampling 2

0.137 0.557 1.927 2.4 3.63 1.923 1.301

Average 0.132 0.582 2.169


CV (%) 14.002 10.588 15.742


0.13 0.573 2.175 4.06 5.96 1.339 1.215

172

Table S-6.3. Results of the particle size measurement of sample set two after production.

Sample

dv10 (µm)

dv50 (µm)

dv90 (µm)


A - Sampling 1

0.107 1.1 3.899 5.19 6.92 1.675 0.291

A - Sampling 2

0.111 1.326 4.256 3.79 4.98 0.692 0.326

B - Sampling 1

0.106 0.767 3.185 5.04 6.97 0.912 0.299

B - Sampling 2

0.109 0.884 3.342 4.04 5.51 3.256 0.272

C - Sampling 1

0.116 0.735 3.07 3.21 4.65 0.449 0.562

C - Sampling 2

0.109 0.91 3.54 4.37 5.85 1.341 0.249

Average 0.110 0.954 3.549


CV (%) 2.963 21.381 11.660


0.109 0.939 3.568 4.27 5.81 1.388 0.333

Table S-6.4. Results of the particle size measurement of sample set two after three weeks of storage at 5 °C.

Sample

dv10 (µm)

dv50 (µm)

dv90 (µm)


A

0.143 0.72 2.471 2.56 3.82 0.559 0.992

B

0.109 0.439 1.612 7.68 12.04 1.699 1.749

C

0.123 0.522 1.779 5.26 8.15 2.789 1.218

Average 0.125 0.560 1.954


CV (%) 13.670 25.765 23.309


0.122 0.541 1.99 5.17 8 1.682 1.319

173

Table S-6.5. Results of the Mastersizer 2000™ particle size measurement at three different time points; after


Sample Timepoint dv10 (µm) dv50 (µm) dv90 (µm)

A After production 0.105 0.871 3.502

After 4 weeks 0.134 0.733 2.650

After 10 weeks 0.175 0.841 2.707

B After production 0.114 0.832 3.344

After 4 weeks 0.153 0.616 2.147

After 10 weeks 0.130 0.820 2.993

C After production 0.101 0.658 3.308

After 4 weeks 0.157 0.528 1.784

After 10 weeks 0.166 0.610 2.545

Table S-6.6. Results of the Mastersizer 3000™ particle size measurement at three different time points: after


Sample Timepoint dv10 (µm) dv50 (µm) dv90 (µm)

D After production 0.365 1.210 3.320

After 4 weeks 0.376 1.150 3.310

After 10 weeks 0.274 0.852 3.000

E After production 0.333 1.050 2.940

After 4 weeks 0.406 1.200 3.310

After 10 weeks 0.283 0.880 2.840

F After production 0.361 1.250 3.410

After 4 weeks 0.372 1.100 3.020

After 10 weeks 0.250 0.792 2.550

174

Table S-6.7. Results of the differential centrifugal sedimentation particle size measurement at three different time

points; after production, after four weeks of storage at 5 °C and after ten weeks of storage at 5 °C.

Sample Timepoint dw10 (nm) dw50 (nm) dw90 (nm)

D After production 220 576 1211

After 4 weeks 203 467 859

After 10 weeks 200 481 1095

E After production 202 591 1358

After 4 weeks 194 529 1130

After 10 weeks 192 530 1182

F After production 201 609 1427

After 4 weeks 190 531 1145

175

Figures

Figure S-6.1. Overlay of the PSDs of all six samples of block 1 of the DoE (sample one to six; from left to right, top to bottom). Each figure displays the overlay of the PSDs of

one sample, which includes the PSD after production (red curve), after four weeks of storage at 5 °C (green curve) and after eleven weeks of storage at 5 °C (blue curve). Based

on these overlays, the final stability trend per sample is depicted.

176




177




.

178

Figure S-6.4. Overlay of the PSDs of all six samples of block 4 of the DoE (sample one to six: from left to right, top to bottom). Each figure displays the overlay of the PSDs of



.

179



on these overlays, the final stability trend per sample is depicted

180

A POEM ON CHAPTER 5

As science shines in a PhD, we thought that

research should be well communicated

The data of the fifth chapter

were therefore in a poem captivated

Within this poem,

We present a rather new technique

The intensified vibratory mill

A mixing platform in milling streak

By addition of beads in the recipient,

and vertical displacement, labelled as acceleration,

mixing and milling can be achieved,

with as basis: the acoustic vibration

Since earlier research showed its nanosizing potential

though immense heat was generated

We decided to explore

and a design of experiments (DoE) was therefore created

Since heat generation can lead quite quick

to thousand and one physicochemical changes,

and might lead to chemical degradation

181

we start our search for the optimal process parameter ranges

And so were milling time,

bead-suspension ratio, acceleration,

bead size and breaks during milling

the parameters under investigation

As a result, a complex DoE was created,

With which two-way interactions, one three-way interaction

and quadratic effects were going to be rated

Power had to be diminished for some terms.

Nonetheless, we expect

To optimally find the parameters with a significant effect

One finding was the existence of the optimal bead size,

not only for particle size reduction and for contamination,

but also, for the aforementioned,

heat generation

Another conclusion was the next:

To have the API gently reduced,

Increasing milling time is preferred,

as increasing acceleration granted a heat boost

182

Another conclusion is the complexity of the system

which is clearly a fact,

With almost all parameters having

A statistically significant impact

In the prediction expression, is where

All the parameters went,

But, in the end,

a complex mathematical function was generated

with a high (adjusted) determination coefficient

With these prediction expressions,

A new possibility rose: the simulation,

Reducing time, energy and money

are clear advantages in this occasion

Simulated results as a rule of thumb,

will avoid unnecessary experimentation

And can challenge the models’ prediction capacity

as an important evaluation

Finally, visuals extracted out of the DoE

might be handy

183

to find the designs’ sweet spot

Within this rhyme,

no lucky shot,

but only stating that with this DoE,

The possibilities are a lot

Overall, the explosivity of the intensified vibratory mill can so,

Be further explored though not underrated,

But capitulated:

Opportunities such as optimisation and simulation are shown

Even though research is needed for the further unknown

185

References

1. Kalepu, S. & Nekkanti, V. Insoluble drug delivery strategies: Review of recent

advances and business prospects. Acta Pharmaceutica Sinica B 5, 442–453

(2015).

2. Lipp, R. The innovator pipeline: Bioavailability challenges and advanced oral

drug delivery opportunities. Am. Pharm. Rev. 16, (2013).

3. Bhakay, A., Merwade, M., Bilgili, E. & Dave, R. N. Novel aspects of wet milling

for the production of microsuspensions and nanosuspensions of poorly water-

soluble drugs. Drug Dev. Ind. Pharm. 37, 963–976 (2011).

4. Burgess, D. J. & Wright, J. C. An Introduction to Long Acting Injections and

Implants. in Long Acting Injections and Implants 1–9 (Springer US, 2012).

doi:10.1007/978-1-4614-0554-2_1

5. Morales, J. O., Watts, A. B. & McConville, J. T. Mechanical Particle-Size

Reduction Techniques. in 133–170 (Springer, New York, NY, 2012).

doi:10.1007/978-1-4614-1144-4_4

6. Jacob, S., Nair, A. B. & Shah, J. Emerging role of nanosuspensions in drug

delivery systems. Biomaterials Research 24, (2020).

7. Kipp, J. E. E. The role of solid nanoparticle technology in the parenteral

delivery of poorly water-soluble drugs. Int. J. Pharm. 284, 109–122 (2004).

8. Remenar, J. F. Making the leap from daily oral dosing to long-acting

injectables: Lessons from the antipsychotics. Mol. Pharm. 11, 1739–1749

(2014).

9. Nanotechnology for Drug Delivery: Global Market for Nanocrystals. Available

at:

https://www.researchandmarkets.com/reports/2124025/nanotechnology_for_dr

ug_delivery_global_market. (Accessed: 27th October 2020)

10. Nanotechnology in Drug Delivery - Global Market Trajectory & Analytics.

Available at:

https://www.researchandmarkets.com/reports/2769238/nanotechnology_in_dru

186

g_delivery_global_market. (Accessed: 27th October 2020)

11. Joachim, C. To be nano or not to be nano? Nature Materials 4, 107–109

(2005).

12. ISO/TR 18401:2017(en), Nanotechnologies — Plain language explanation of

selected terms from the ISO/IEC 80004 series. Available at:

https://www.iso.org/obp/ui/#iso:std:iso:tr:18401:ed-1:v1:en. (Accessed: 4th

November 2020)

13. Shah, S., Date, A. & Holm, R. Strategies for the Formulation Development of

Poorly Soluble Drugs via Oral Route. in 49–89 (John Wiley & Sons, Ltd, 2019).

doi:10.1002/9783527812172.ch2

14. Van Hijfte, L., Marciniak, G. & Froloff, N. Combinatorial chemistry, automation

and molecular diversity: New trends in the pharmaceutical industry. Journal of

Chromatography B: Biomedical Sciences and Applications 725, 3–15 (1999).

15. Bleicher, K. H., Böhm, H.-J., Müller, K. & Alanine, A. I. A guide to drug

discovery: Hit and lead generation: beyond high-throughput screening. Nat.

Rev. Drug Discov. 2, 369–378 (2003).

16. Stewart, K. D. et al. Preference for pharmaceutical formulation and treatment

process attributes. Patient Preference and Adherence 10, 1385–1399 (2016).

17. Amidon, G. L., Lennernäs, H., Shah, V. P. & Crison, J. R. A Theoretical Basis

for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug

Product Dissolution and in Vivo Bioavailability. Pharm. Res. An Off. J. Am.

Assoc. Pharm. Sci. 12, 413–420 (1995).

18. Chavda, H. V., Patel, C. N. & Anand, I. S. Biopharmaceutics classification

system. Systematic Reviews in Pharmacy 1, 62–69 (2010).

19. Williams, H. D. et al. Strategies to address low drug solubility in discovery and

development. Pharmacological Reviews 65, 315–499 (2013).

20. Butler, J. M. & Dressman, J. B. The developability classification system:

Application of biopharmaceutics concepts to formulation development. J.

Pharm. Sci. 99, 4940–4954 (2010).

187

21. Rosenberger, J., Butler, J., Muenster, U. & Dressman, J. Application of a

Refined Developability Classification System. J. Pharm. Sci. 108, 1090–1100

(2019).

22. Bosselmann, S. & Williams, R. O. Route-Specific Challenges in the Delivery of

Poorly Water-Soluble Drugs. in 1–26 (Springer, New York, NY, 2012).

doi:10.1007/978-1-4614-1144-4_1

23. Bisrat, M. & Nyström, C. Physicochemical aspects of drug release. VIII. The

relation between particle size and surface specific dissolution rate in agitated

suspensions. International Journal of Pharmaceutics 47, (1988).

24. Kesisoglou, F. & Mitra, A. Crystalline nanosuspensions as potential toxicology

and clinical oral formulations for BCS II/IV Compounds. AAPS J. 14, 677–687

(2012).

25. Sandri, G., Bonferoni, M. C., Rossi, S., Caramella, C. M. & Ferrari, F. Effects of

particle size, surface nature and crystal type on dissolution rate. in AAPS

Advances in the Pharmaceutical Sciences Series 29, 303–328 (Springer

Verlag, 2018).

26. Kaptay, G. On the size and shape dependence of the solubility of nano-

particles in solutions. Int. J. Pharm. 430, 253–257 (2012).

27. Shegokar, R. & Müller, R. H. Nanocrystals: Industrially feasible multifunctional

formulation technology for poorly soluble actives. International Journal of

Pharmaceutics 399, 129–139 (2010).

28. Lippold, B. C. & Ohm, A. Correlation between wettability and dissolution rate of

pharmaceutical powders. Int. J. Pharm. 28, 67–74 (1986).

29. Rabinow, B. E. Nanosuspensions in drug delivery. Nature Reviews Drug

Discovery 3, 785–796 (2004).

30. Müller, R. H., Jacobs, C. & Kayser, O. Nanosuspensions as particulate drug

formulations in therapy: Rationale for development and what we can expect for

the future. Adv. Drug Deliv. Rev. 47, 3–19 (2001).

31. Imono, M. et al. The elucidation of key factors for oral absorption enhancement

of nanocrystal formulations: In vitro–in vivo correlation of nanocrystals. Eur. J.

188

Pharm. Biopharm. 146, 84–92 (2020).

32. Wong, J. et al. Suspensions for intravenous (IV) injection: A review of

development, preclinical and clinical aspects. Advanced Drug Delivery Reviews

60, 939–954 (2008).

33. Merisko-Liversidge, E. & Liversidge, G. G. Nanosizing for oral and parenteral

drug delivery: A perspective on formulating poorly-water soluble compounds

using wet media milling technology. Adv. Drug Deliv. Rev. 63, 427–440 (2011).

34. DeLuca, P.P.; Mansour, H.M.; Rhee, Y.S.; Park, C. W. Sustained-Release

Injectable Drug Delivery. Pharm. Technol. 2010, (2010).

35. Owen, A. & Rannard, S. Strengths, weaknesses, opportunities and challenges

for long acting injectable therapies: Insights for applications in HIV therapy.

Advanced Drug Delivery Reviews 103, 144–156 (2016).

36. Maresova, P. et al. Consequences of chronic diseases and other limitations

associated with old age - A scoping review. BMC Public Health 19, 1431

(2019).

37. Marmora, L. & Gore, C. Intelectual Property Report on Long Acting

Technologies - November 2018. (2018).

38. Brissos, S., Veguilla, M. R., Taylor, D. & Balanzá-Martinez, V. The role of long-

acting injectable antipsychotics in schizophrenia: A critical appraisal.

Therapeutic Advances in Psychopharmacology 4, 198–219 (2014).

39. Rubio, J. M. et al. Psychosis relapse during treatment with long-acting

injectable antipsychotics in individuals with schizophrenia-spectrum disorders:

an individual participant data meta-analysis. The Lancet Psychiatry 7, 749–761

(2020).

40. Morris, M. T. & Tarpada, S. P. Long-acting injectable paliperidone palmitate: A

review of efficacy and safety. Psychopharmacol. Bull. 47, 42–52 (2017).

41. Verma, S., Gokhale, R. & Burgess, D. J. A comparative study of top-down and

bottom-up approaches for the preparation of micro/nanosuspensions. Int. J.

Pharm. 380, 216–222 (2009).

189

42. Fontana, F. et al. Production of pure drug nanocrystals and nano co-crystals by

confinement methods. Adv. Drug Deliv. Rev. 131, 3–21 (2018).

43. Kesisoglou, F., Panmai, S. & Wu, Y. Nanosizing — Oral formulation

development and biopharmaceutical evaluation. Adv. Drug Deliv. Rev. 59,

631–644 (2007).

44. Wu, L., Zhang, J. & Watanabe, W. Physical and chemical stability of drug

nanoparticles. Adv. Drug Deliv. Rev. 63, 456–469 (2011).

45. Malamatari, M., Taylor, K. M. G., Malamataris, S., Douroumis, D. &

Kachrimanis, K. Pharmaceutical nanocrystals: production by wet milling and

applications. Drug Discov. Today 23, 534–547 (2018).

46. Li, M., Azad, M., Davé, R. & Bilgili, E. Nanomilling of Drugs for Bioavailability

Enhancement: A Holistic Formulation-Process Perspective. Pharmaceutics 8,

17 (2016).

47. Verma, S., Kumar, S., Gokhale, R. & Burgess, D. J. Physical stability of

nanosuspensions: Investigation of the role of stabilizers on Ostwald ripening.

Int. J. Pharm. 406, 145–152 (2011).

48. Thanh, N. T. K., Maclean, N. & Mahiddine, S. Mechanisms of Nucleation and

Growth of Nanoparticles in Solution. Chem. Rev. 114, 7610–7630 (2014).

49. Keck, C. M. & Müller, R. H. Drug nanocrystals of poorly soluble drugs produced

by high pressure homogenisation. Eur. J. Pharm. Biopharm. 62, 3–16 (2006).

50. Wang, Y., Zheng, Y., Zhang, L., Wang, Q. & Zhang, D. Stability of

nanosuspensions in drug delivery. J. Control. Release 172, 1126–1141 (2013).

51. Nakach, M., Authelin, J.-R. R., Perrin, M.-A. A. & Lakkireddy, H. R. Comparison

of high pressure homogenization and stirred bead milling for the production of

nano-crystalline suspensions. Int. J. Pharm. 547, 61–71 (2018).

52. Müller, R. & Junghanns. Nanocrystal technology, drug delivery and clinical

applications. Int. J. Nanomedicine 3, 295 (2008).

53. Van Eerdenbrugh, B. et al. Downscaling drug nanosuspension production:

processing aspects and physicochemical characterization. AAPS

190

PharmSciTech 10, 44–53 (2009).

54. Kwade, A. Wet Comminution In Stirred Media Mills - research and its practical

application. Powder Technol. 105, 14–20 (1992).

55. Afolabi, A., Akinlabi, O. & Bilgili, E. Impact of process parameters on the

breakage kinetics of poorly water-soluble drugs during wet stirred media

milling: A microhydrodynamic view. Eur. J. Pharm. Sci. 51, 75–86 (2014).

56. Peltonen, L. Design space and QbD approach for production of drug

nanocrystals by wet media milling techniques. Pharmaceutics 10, 104 (2018).

57. Li, M., Alvarez, P. & Bilgili, E. A microhydrodynamic rationale for selection of

bead size in preparation of drug nanosuspensions via wet stirred media milling.

Int. J. Pharm. 524, 178–192 (2017).

58. Eskin, D., Zhupanska, O., Hamey, R., Moudgil, B. & Scarlett, B.

Microhydrodynamic analysis of nanogrinding in stirred media mills. AIChE J.

51, 1346–1358 (2005).

59. Eskin, D., Zhupanska, O., Hamey, R., Moudgil, B. & Scarlett, B.

Microhydrodynamics of stirred media milling. in Powder Technology 156, 95–

102 (2005).

60. Flach, F., Breitung-Faes, S. & Kwade, A. Model based process optimization of

nanosuspension preparation via wet stirred media milling. Powder Technol.

331, 146–154 (2018).

61. Kwade, A. & Schwedes, J. Breaking characteristics of different materials and

their effect on stress intensity and stress number in stirred media mills. Powder

Technol. 122, (2002).

62. Leung, D. H. et al. A new and improved method for the preparation of drug

nanosuspension formulations using acoustic mixing technology. Int. J. Pharm.

473, 10–19 (2014).

63. Li, M., Zhang, L., Davé, R. N. & Bilgili, E. An Intensified Vibratory Milling

Process for Enhancing the Breakage Kinetics during the Preparation of Drug

Nanosuspensions. AAPS PharmSciTech 17, 389–399 (2016).

191

64. Kotter, L. N. & Groven, L. J. Milling of Energetic Crystals with the LabRAM.

Propellants, Explos. Pyrotech. 44, 908–914 (2019).

65. Rexroth Corporation, B. Bosch Rexroth Resodyn RAM 5 success story.

66. Lu, H. D., D Aquino, J. A., Caruso, J., Ghosh, A. & Lohani, S. Application of

Acoustic- Vibratory Milling For Screening and Scale-Up of Micro/

Nanosuspensions in Early Drug Discovery and Development. J. Pharm. Drug

Deliv. Res. 07, (2018).

67. Saveyn, H., Mermuys, D., Thas, O. & van der Meeren, P. Determination of the

Refractive Index of Water-dispersible Granules for Use in Laser Diffraction

Experiments. Part. Part. Syst. Charact. 19, 426–432 (2002).

68. Zhang, X., Li, L. C. & Mao, S. Nanosuspensions of Poorly Water Soluble Drugs

Prepared by Top-down Technologies. Curr. Pharm. Des. 20, 388–407 (2014).

69. Lakshmi, P. & Kumar, G. A. Nano-suspension technology: A review. Int. J.

Pharm. Pharm. Sci. 2, 35–40 (2010).

70. Lestari, M. L. A. D., Müller, R. H. & Möschwitzer, J. P. Systematic Screening of

Different Surface Modifiers for the Production of Physically Stable

Nanosuspensions. J. Pharm. Sci. 104, 1128–1140 (2015).

71. Rawle, A. Basic principles of particle size analysis. Available at:

http://www.rci.rutgers.edu/~moghe/PSD Basics.pdf. (Accessed: 21st January

2018)

72. Erdoǧan, S. T. et al. Shape and size of microfine aggregates: X-ray

microcomputed tomography vs. laser diffraction. Powder Technol. 177, 53–63

(2007).

73. Keck, C. M. & Müller, R. H. Size analysis of submicron particles by laser

diffractometry—90% of the published measurements are false. Int. J. Pharm.

355, 150–163 (2008).

74. Kelly, R. N. & Etzler, F. M. What is wrong with laser diffraction? A Critical

Review of Current Laser Diffraction Methods for Particle Size Analysis.

Available at: http://www.donner-tech.com/whats_wrong_with_ld.pdf.

(Accessed: 22nd January 2018)

192

75. Beekman, A. et al. Micrometer-Scale Particle Sizing by Laser Diffraction:

Critical Impact of the Imaginary Component of Refractive Index. Pharm. Res.

22, 518–522 (2005).

76. Horiba. Technical Note: LA-950 Series Particle Size Distribution Analyzer

Understanding the Chi Square and R Parameter Calculations in the LA-950

Software.

http://www.horiba.com/fileadmin/uploads/Scientific/Documents/PSA/TN153.pdf

Available at:

http://www.horiba.com/fileadmin/uploads/Scientific/Documents/PSA/TN153.pdf.

(Accessed: 27th November 2018)

77. Horiba. Technical note: Effect of concentration on laser diffraction

measurements. (2010). Available at: www.horibalab.com. (Accessed: 27th

November 2018)

78. Malvern Instruments Ltd. Wet or liquid dispersion method development for

laser diffrraction particle size measurements. Available at:

https://warwick.ac.uk/fac/cross_fac/sciencecity/programmes/internal/themes/a

m2/booking/particlesize/wet_dispersion_application_note.pdf. (Accessed: 23rd

January 2018)

79. Malvern Instruments Ltd. Transferring methods from the Mastersizer 2000 to

the Mastersizer 3000 laser diffraction particle size analyzer. Available at:

https://www.malvern.com/en/support/resource-center/technical-

notes/TN130912TransferingMethodsMastersizer.html. (Accessed: 23rd

January 2018)

80. Malvern Instruments Ltd. Technical note: Selecting an appropiate particle

absorption for laser diffraction particle size calculations. Available at:

https://www.malvern.com/en/support/resource-center/technical-

notes/TN101104SelectingParticleAbsorbtionLaserDiffractio.html. (Accessed:

23rd January 2018)

81. Storey, R. A. & Ymen, I. Solid state characterization of pharmaceuticals. in

Solid state characterization of pharmaceuticals: 9.4.5 Measuring the Refractive

Indices of Solids (New Jersey; John Wiley & Sons, 2011).

193

82. Evans, M., Han, L., Ratcliffe, I. & Williams, P. A. Synthesis, characterisation

and properties of novel biosurfactants based on hydrophobically-modified

inulins. in Gums and Stabilisers for the Food Industry 18: Hydrocolloid

Functionality for Affordable and Sustainable Global Food Solutions (eds. A.

Williams, P. & O. Phillip, G.) 129 (2016).

83. Singh, M. S. & Lamprecht, A. P-glycoprotein inhibition of drug resistant cell

lines by nanoparticles. Drug Dev. Ind. Pharm. 42, 325–331 (2016).

84. Thermo Fisher Scientific. Sodium Dodecyl Sulfate (SDS), Lauryl - Safety data

sheet. Available at: https://www.thermofisher.com/order/catalog/product/28364.

(Accessed: 23rd January 2018)

85. Guo, Y., Luo, J., Tan, S., Otieno, B. O. & Zhang, Z. The applications of Vitamin

E TPGS in drug delivery. Eur. J. Pharm. Sci. 49, 175–186 (2013).

86. Malvern Instruments Ltd. Technical notes: Empirical methods for selecting an

appropiate refractive index for laser diffraction particle size calculations. (2007).

Available at: https://www.malvern.com/en/support/resource-center/technical-

notes/in-progress/TN101104SelectingRefractiveIndexParticleSize.html.

(Accessed: 30th January 2018)

87. Van Eerdenbrugh, B. et al. Drying of crystalline drug nanosuspensions—The

importance of surface hydrophobicity on dissolution behavior upon

redispersion. Eur. J. Pharm. Sci. 35, 127–135 (2008).

88. Virden, A. Method Development for Laser-Diffraction Particle-Size Analysis.

PharmTech 34, 100–106 (2010).

89. Michalchuk, A. A. L., Tumanov, I. A. & Boldyreva, E. V. The effect of ball mass

on the mechanochemical transformation of a single-component organic

system: anhydrous caffeine. J. Mater. Sci. 53, 13380–13389 (2018).

90. Osorio, J. G. & Muzzio, F. J. Evaluation of resonant acoustic mixing

performance. Powder Technol. 278, 46–56 (2015).

91. Na, G. C. et al. Cloud point of nonionic surfactants: modulation with

pharmaceutical excipients. Pharm. Res. 16, 562–568 (1999).

92. Reijenga, J., van Hoof, A., van Loon, A. & Teunissen, B. Development of

194

methods for the determination of pKa values. Analytical Chemistry Insights 8,

53–71 (2013).

93. Colombo, M., Minussi, C., Orthmann, S., Staufenbiel, S. & Bodmeier, R.

Preparation of amorphous indomethacin nanoparticles by aqueous wet bead

milling and in situ measurement of their increased saturation solubility. Eur. J.

Pharm. Biopharm. 125, 159–168 (2018).

94. Sommer, M., Stenger, F., Peukert, W. & Wagner, N. J. Agglomeration and

breakage of nanoparticles in stirred media mills-a comparison of different

methods and models. Chem. Eng. Sci. 61, 135–148 (2006).

95. De Cleyn, E., Holm, R. & Van den Mooter, G. Size Analysis of Small Particles

in Wet Dispersions by Laser Diffractometry: A Guidance to Quality Data. J.

Pharm. Sci. 108, 1905–1914 (2019).

96. Durga Maheswari, P., Rambhau, D. & Narasu, M. L. Micellar Solubilization in

the Formulation Development of Poorly Soluble Naproxen. Pharm. Regul. Aff.

Open Access 02, 1–12 (2013).

97. Besson, U. The History of the Cooling Law: When the Search for Simplicity can

be an Obstacle. Sci. Educ. 21, 1085–1110 (2012).

98. Hagedorn, M., Bögershausen, A., Rischer, M., Schubert, R. & Massing, U. Dual

centrifugation – A new technique for nanomilling of poorly soluble drugs and

formulation screening by an DoE-approach. Int. J. Pharm. 530, 79–88 (2017).

99. Malamatari, M., Somavarapu, S., Taylor, K. M. G. & Buckton, G. Solidification

of nanosuspensions for the production of solid oral dosage forms and inhalable

dry powders. Expert Opinion on Drug Delivery 13, 435–450 (2016).

100. Sigfridsson, K., Rydberg, H. & Strimfors, M. Nano- and microcrystals of

griseofulvin subcutaneously administered to rats resulted in improved

bioavailability and sustained release. Drug Dev. Ind. Pharm. 45, 1477–1486

(2019).

101. Resodyn acoustic mixers. No Title. in Technical InterChange 2018 (2018).

102. Raasch, J. Trajectories and impact velocities of grinding bodies in planetary

ball mills. Chem. Eng. Technol. 15, 245–253 (1992).

195

103. Blecher, L., Kwade, A. & Schwedes, J. Motion and stress intensity of grinding

beads in a stirred media mill. Part 1 : Energy density distribution and motion of

single grinding beads. Powder Technol. 86, 59–68 (1996).

104. De Cleyn, E., Holm, R. & Van den Mooter, G. Exploration of the heat

generation within the intensified vibratory mill. Int. J. Pharm. 587, 119644

(2020).

105. de Aguiar, P. F., Bourguignon, B., Khots, M. S., Massart, D. L. & Phan-Than-

Luu, R. D-optimal designs. Chemom. Intell. Lab. Syst. 30, 199–210 (1995).

106. Jensen, W. A. Open problems and issues in optimal design. Qual. Eng. 30,

583–593 (2018).

107. Ghosh, I., Bose, S., Vippagunta, R. & Harmon, F. Nanosuspension for

improving the bioavailability of a poorly soluble drug and screening of

stabilizing agents to inhibit crystal growth. Int. J. Pharm. 409, 260–268 (2011).

108. Joshi, V., Dwivedi, S. & Ward, G. H. Increase in the specific surface area of

budesonide during storage postmicronization. Pharm. Res. 19, 7–12 (2002).

109. Ariyaprakai, S. & Dungan, S. R. Influence of surfactant structure on the

contribution of micelles to Ostwald ripening in oil-in-water emulsions. J. Colloid

Interface Sci. 343, 102–108 (2009).

110. Nyqvist, H. & Wadsten, T. Can ageing effects in drugs be measured and

interpreted by ta-techniques? Thermochim. Acta 93, 125–127 (1985).

111. Shimpi, J. R., Sidhaye, D. S. & Prasad, B. L. V. Digestive Ripening: A Fine

Chemical Machining Process on the Nanoscale. Langmuir 33, 9491–9507

(2017).

112. Burlakov, V. M. et al. Reversing Ostwald Ripening. arxiv.org (2014).

113. Burlakov, V. M. & Goriely, A. Reverse coarsening and the control of particle

size distribution through surfactant. Appl. Sci. 10, 5359 (2020).

114. Lee, D. K., Park, S. Il, Lee, J. K. & Hwang, N. M. A theoretical model for

digestive ripening. Acta Mater. 55, 5281–5288 (2007).

115. Shah, U. V. et al. Effect of milling temperatures on surface area, surface

196

energy and cohesion of pharmaceutical powders. Int. J. Pharm. 495, 234–240

(2015).

116. Chu, J. J. & Steeves, C. A. Thermal expansion and recrystallization of

amorphous Al and Ti: A molecular dynamics study. J. Non. Cryst. Solids 357,

3765–3773 (2011).

117. Kayaert, P. & Van Den Mooter, G. Is the amorphous fraction of a dried

nanosuspension caused by milling or by drying? A case study with Naproxen

and Cinnarizine. Eur. J. Pharm. Biopharm. 81, 650–656 (2012).

118. Kumar, S. & Burgess, D. J. Wet milling induced physical and chemical

instabilities of naproxen nano-crystalline suspensions. Int. J. Pharm. 466, 223–

232 (2014).

119. Pas, T. et al. Mechanodegradation of Polymers: A Limiting Factor of

Mechanochemical Activation in the Production of Amorphous Solid Dispersions

by Cryomilling. Mol. Pharm. 17, 2987–2999 (2020).

120. Monteiro, A., Afolabi, A. & Bilgili, E. Continuous production of drug nanoparticle

suspensions via wet stirred media milling: A fresh look at the Rehbinder effect.

Drug Dev. Ind. Pharm. 39, 266–283 (2013).

121. Erik Kissa. 7.I.C. Effect of dispersant on communition. in Dispersions:

Characterization, Testing, and Measurement - (ed. Kissa, E.) (1999).

122. Higgins, J. D. & Leung, D. A New and Versatile Method for Preparing Drug

Nanoparticle Formulations for Enhancing Bioperformance. TechConnect Briefs

3. Biotech, 68–70 (2015).

123. Chen, L. et al. Fine grade engineered microcrystalline cellulose excipients for

direct compaction: Assessing suitability of different dry coating processes. Eur.

J. Pharm. Sci. 151, 105408 (2020).

124. Am Ende, D. J., Anderson, S. R. & Salan, J. S. Development and Scale-Up of

Cocrystals Using Resonant Acoustic Mixing. Org. Process Res. Dev. 18, 331–

341 (2014).

125. Hennart, S. L. A. et al. Particle Size Characterization of an Extra Fine Milled

Product. Part. Part. Syst. Charact. 29, 285–303 (2012).

197

126. Chen, S., Sheikh, A. Y. & Ho, R. Evaluation of effects of pharmaceutical

processing on structural disorders of active pharmaceutical ingredient crystals

using nanoindentation and high-resolution total scattering pair distribution

function analysis. J. Pharm. Sci. 103, 3879–3890 (2014).

127. Maughan, M. R., Carvajal, M. T. & Bahr, D. F. Nanomechanical testing

technique for millimeter-sized and smaller molecular crystals. Int. J. Pharm.

486, 324–330 (2015).

128. Aleandri, S., Schönenberger, M., Niederquell, A. & Kuentz, M. Temperature-

Induced Surface Effects on Drug Nanosuspensions. Pharm. Res. 35, (2018).

129. Cares-Pacheco, M. G., Calvet, R., Vaca-Medina, G., Rouilly, A. & Espitalier, F.

Inverse gas chromatography a tool to follow physicochemical modifications of

pharmaceutical solids: Crystal habit and particles size surface effects. Int. J.

Pharm. 494, 113–126 (2015).

130. Bond, L. et al. Differential scanning calorimetry and scanning thermal

microscopy analysis of pharmaceutical materials. Int. J. Pharm. 243, 71–82

(2002).

131. Harding, L., King, W. P., Dai, X., Craig, D. Q. M. & Reading, M. Nanoscale

characterisation and imaging of partially amorphous materials using local

thermomechanical analysis and heated tip AFM. Pharm. Res. 24, 2048–2054

(2007).

132. Barnes, T. J., Kempson, I. M. & Prestidge, C. A. Surface analysis for

compositional, chemical and structural imaging in pharmaceutics with mass

spectrometry: A ToF-SIMS perspective. Int. J. Pharm. 417, 61–69 (2011).

Contributions

Scientific acknowledgements

Within the own KU Leuven department, Bernard Appeltans is acknowledged for the

technical support in the laboratory. Elene De Cleyn acknowledges Janssen

Pharmaceutica for the usage of their facilities; mainly within the laboratory “Parenterals

and Liquids”. Within Janssen Pharmaceutica, the following persona are acknowledged

for the provided trainings and follow-up guidance: Jasmine Bogaerts (Technical

support in the laboratory), Maxim Verstraeten (Proofreading), Christopher De

Dobbelaere (mDSC), Jasper Jammaer and Linda Lauwerysen (LD), Alain Collas,

Brecht De Fre and Steve De Cort (SEM) and Tatsiana Khamiakova (DoE and

statistics).

Personal contributions

Elene De Cleyn conceptualised, scheduled, executed and written all experiments, data

and texts included in this thesis, with following exceptions:

• For Chapter 5, Tatsiana Khamiakova gave advice on the set-up of the Design

of Experiments, the statistical analysis and data interpretation. She applied

Bayesian statistics and provided the figures concerning Bayesian statistics in

chapter 5. She reviewed the related article, prior to publication.

• For Chapter 6, LD measurements with the Mastersizer 3000™ were performed

by Divya Bahadur and particle size measurements with differential centrifugal

sedimentation by Linda Lauwerysen.

Guy Van den Mooter and René Holm were the promotors of this PhD project and

helped to develop a research strategy and design the experiments. They reviewed and

edited the text of the given dissertation and the articles generated during this PhD

project.

Conflicts of interest statement

The authors would like to acknowledge Janssen Pharmaceutica for their financial

support. Apart from this acknowledgement, the authors declare no conflict of interest.

Curriculum Vitae

Education

2017 – present: Doctoral Training in Pharmaceutical Sciences (KU Leuven, BE,

Janssen Pharmaceutica, BE)

2016 - 2017: Advanced master’s in industrial pharmacy (KU Leuven, U Antwerpen, U

Gent, VU Brussel, BE)

Research topic (TCD Dublin, IE): “Establishing and using USP apparatus 4,

dissolution simulation and shadowgraph imaging to interpret dissolution profiles

of ibuprofen particles with varying sizes and morphologies”

2015 - 2016: Management: Preparatory programme (VU Brussel, BE)

2013 - 2015: Master of Science in Drug Development, specialisation in pharmacy (KU

Leuven, BE)

Research topic (U Liège, BE): “Development of an analytical method for the

determination of human insulin in medicinal products by micellar electrokinetic

chromatography”

2010 - 2013: Bachelor of Pharmaceutical Sciences (KU Leuven, BE)

Scientific Publications

• De Cleyn, E., Holm, R., Khamiakova, T. & Van den Mooter, G. Picking up good

vibrations: Exploration of the intensified vibratory mill via a modern Design of

Experiments. Int. J. Pharm. 120367 (2021). doi: 10.1016/j.ijpharm.2021.120367

• De Cleyn, E., Holm, R. & Van den Mooter, G. Exploration of the heat generation

within the intensified vibratory mill. Int. J. Pharm. 119644 (2020).

doi:10.1016/j.ijpharm.2020.119644

• De Cleyn, E., Holm, R. & Van den Mooter, G. Size Analysis of Small Particles

in Wet Dispersions by Laser Diffractometry: A Guidance to Quality Data. J.

Pharm. Sci. (2019). doi: 10.1016/j.xphs.2018.12.010

Presentations at (inter)national meetings

Poster presentations

• “Size analysis of small particles in wet dispersion by laser diffractometry: A

guidance to quality data”, ULLA Summer School, Helsinki, 04 Jul 2019


guidance to quality data”, 4th Spring Symposium, Leuven, 03 May 2019

Oral presentations

• “Picking up good vibrations: The intensified vibratory mill via a modern design

of experiments approach”, PSSRC Annual Meeting Online, 23 June 2020


guidance to quality data”, 20th forum of BSPS (Belgian Society of

Pharmaceutical Sciences), 20 May 2019

exploration of the intensified vibratory mill as viable particle ...

Documents

Transcript of exploration of the intensified vibratory mill as viable particle ...