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Quantitative Analysis of Monoclonal Antibody

Formulations Using Image and Fluorescence

Correlation Spectroscopies

A thesis submitted to the University of Manchester for the degree of

Doctor of Philosophy in the Faculty of Biology, Medicine and Health

2018

Maryam Shah

School of Health Sciences

1

List of Contents

LIST OF TABLES 5

LIST OF FIGURES 6

LIST OF ABBREVIATIONS 8

GENERAL ABSTRACT 10

DECLARATION 11

COPYRIGHT STATEMENT 12

CONTRIBUTIONS TO THESIS CHAPTERS 13

ACKNOWLEDGEMENTS 14

CHAPTER 1:

1 : INTRODUCTION................................................................................................. 15

Introduction .......................................................................................................................... 16

1.1 Monoclonal Antibodies as Therapeutics ................................................................... 16

1.2 Protein Aggregation .................................................................................................. 19

1.2.1 Protein Aggregation Consequences ................................................................... 19

1.2.2 Mechanisms of Protein Aggregation ................................................................. 19

1.2.3 Industrial Production of Therapeutic mAbs ....................................................... 20

1.2.4 Accelerated stability testing ............................................................................... 22

1.2.5 Factors contributing to protein aggregation ....................................................... 23

1.3 Analytical Approaches Utilised in the Detection and Characterisation of Protein

Aggregation .......................................................................................................................... 38

1.3.1 Light Scattering Methods ................................................................................... 39

1.3.2 Resonance Mass Measurement (Archimedes) ................................................... 43

1.3.3 Microscopic Methods......................................................................................... 45

1.4 Fluorescence-based Approaches ............................................................................... 47

1.4.1 Concept of Fluorescence .................................................................................... 47

1.4.2 Fluorescent Probes ............................................................................................. 49

1.4.3 Fluorescence Correlation Spectroscopy (FCS) .................................................. 56

1.4.4 Confocal Laser Scanning Microscopy and Imaging .......................................... 60

1.5 References ................................................................................................................. 68

2

CHAPTER 2

2 : AIMS AND OBJECTIVES .................................................................................. 82

2.1 Aims and Objectives ................................................................................................. 83

CHAPTER 3:

3 : EVALUATION OF AGGREGATE AND SILICONE OIL COUNTS IN PRE-

FILLED SILICONIZED SYRINGES: AN ORTHOGONAL STUDY

CHARACTERISING THE ENTIRE SUBVISIBLE SIZE RANGE ................................ 85

3.1 Abstract ..................................................................................................................... 87

3.2 Introduction ............................................................................................................... 88

3.3 Materials and Methods .............................................................................................. 90

3.3.1 Materials ............................................................................................................ 90

3.3.2 Methods.............................................................................................................. 91

3.4 Results ....................................................................................................................... 93

3.4.1 Fluorescent dye selection for proteinaceous aggregates and silicone oil droplets

for RICS analysis .............................................................................................................. 93

3.4.2 Assessment of mAb Aggregation in Siliconized PFS........................................ 94

3.4.3 Characterisation of Dispersed Silicone oil in PFS ............................................. 97

3.5 Discussion ............................................................................................................... 102

3.5.1 Considerations Regarding the Different Techniques ....................................... 102

3.5.2 Agitation in Siliconized PFS Increases Aggregation Formation ..................... 104

3.5.3 PS-20 limits the Formation of Aggregates in Siliconized PFS following

Agitation ......................................................................................................................... 105

3.6 Conclusions ............................................................................................................. 106

3.7 Acknowledgements ................................................................................................. 107

3.8 References ............................................................................................................... 108

CHAPTER 4:

4 : SELF-DIFFUSION IN HIGHLY CONCENTRATED PROTEIN

SOLUTIONS MEASURED BY FLUORESCENCE CORRELATION

SPECTROSCOPY ............................................................................................................... 112

4.1 Abstract ................................................................................................................... 114

4.2 Introduction ............................................................................................................. 115

4.2.1 Effects of molecular crowding ......................................................................... 115

4.2.2 Measuring solution viscosity - diffusion of tracer particles............................. 115

4.3 Materials and Methods ............................................................................................ 118

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4.3.1 Materials .......................................................................................................... 118

4.3.2 Methods............................................................................................................ 118

4.4 Results ..................................................................................................................... 121

4.4.1 Candidate dye for solution viscosity ................................................................ 121

4.4.2 Self-diffusion in highly concentrated protein solutions ................................... 125

4.5 Discussion ............................................................................................................... 132

4.5.1 Applicability of the GSE relation .................................................................... 132

4.5.2 Van Blaaderen’s model and exponential model .............................................. 133

4.5.3 Size and charge of the tracer ............................................................................ 133

4.6 Conclusion ............................................................................................................... 135

4.7 References ............................................................................................................... 136

CHAPTER 5:

5 : INVESTIGATING POLYSORBATE MICELLE FORMATION BY

FLUORESCENCE CORRELATION SPECTROSCOPY .............................................. 139

5.1 Abstract ................................................................................................................... 141

5.2 Introduction ............................................................................................................. 142

5.2.1 Polysorbates prevent surface-induced protein aggregation ............................. 142

5.2.2 Importance of surfactant concentration and the cmc ....................................... 142

5.2.3 Experimental detection of micelles .................................................................. 143

5.3 Materials and Methods ............................................................................................ 144

5.3.1 Materials .......................................................................................................... 144

5.3.2 Methods............................................................................................................ 144

5.4 Results and Discussion ............................................................................................ 146

5.4.1 Validation of SYPRO® Orange to determine the cmc .................................... 146

5.4.2 Determining the cmc by FCS with SYPRO® Orange ..................................... 147

5.4.3 FCS / SYPRO® Orange micelle detection in mAb solutions .......................... 152

5.5 Conclusion ............................................................................................................... 157

5.6 References ............................................................................................................... 158

CHAPTER 6:

6 : VISCOSITY CHANGE FOLLOWING AGGREGATION DEVELOPMENT:

CONFOCAL MICROSCOPY SYSTEM FOR ASSESSING AGGREGATION

DEVELOPMENT AND VISCOSITY SEQUENTIALLY ............................................... 161

6.1 Abstract ................................................................................................................... 163

6.2 Introduction ............................................................................................................. 164

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6.2.1 Protein aggregation and viscosity issues of biopharmaceutical products ........ 164

6.2.2 Relation between aggregation development and change in solution viscosity 164

6.2.3 Analytical Techniques to assess viscosity change following aggregation....... 165

6.3 Materials and Methods ............................................................................................ 167

6.3.1 Materials .......................................................................................................... 167

6.3.2 Methods............................................................................................................ 167

6.4 Results ..................................................................................................................... 171

6.4.1 Viscosity change with aggregation development following agitation of low

mAb concentrated solutions (1mg/ml) – effect of probe size on assessing changes in

viscosity 171

6.4.2 Viscosity change with aggregation development following agitation - high mAb

concentrated solutions (100mg/ml) ................................................................................ 178

6.5 Discussion ............................................................................................................... 180

6.5.1 Measuring microrheology using probes ........................................................... 180

6.5.2 How much agitation is needed to impact protein stability? ............................. 181

6.5.3 Protein aggregation at the air-water interface .................................................. 181

6.6 Conclusion ............................................................................................................... 183

6.7 References ............................................................................................................... 184

CHAPTER 7:

7 : FINAL CONCLUSIONS .................................................................................... 189

7.1 Final Conclusions .................................................................................................... 190

7.2 References ............................................................................................................... 193

LIST OF APPENDICES

APPENDIX 1: SUPPLEMENTARY INFORMATION FOR CHAPTER 1 ....................... 194

APPENDIX 2: SUPPLEMENTARY INFORMATION FOR CHAPTER 3 ....................... 196

APPENDIX 3: SUPPLEMENTARY INFORMATION FOR CHAPTER 4 ....................... 206

APPENDIX 4: SUPPLEMENTARY INFORMATION FOR CHAPETR 6 ....................... 215

APPENDIX 5: COMMENTARY ......................................................................................... 218

Word Count: 67,343

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LIST OF TABLES

CHAPTER 1:

Table 1.1: Comparison of techniques reported for protein characterisation (aggregation) ..... 39

Table 1.2: Guidelines for nano- and micro- sensor use (information provided by Malvern). . 44

Table 1.3: Comparison of fluorescence dyes in their applicability to study protein aggregation

with confocal microscopy. ....................................................................................................... 53

CHAPTER 3:

Table 3.1: Common terminology used for various protein aggregate size ranges ................... 89

Table 3.2 Measured protein concentrations in buffer-filled PFS solutions determined by MFI.

................................................................................................................................................ 100

CHAPTER 4:

Table 4.1: Available information of the three candidate dyes. .............................................. 122

Table 4.2: Log P values of Azure-B, ATTO-465 and ATTO-Rho6G. .................................. 123

Table 4.3: Diffusion times of Azure-B, ATTO-465 and ATTO-Rho6G. .............................. 123

Table 4.4: Calculated 𝑘1 and 𝑘2 for BSA samples measured by FCS and the RheoChip. ... 129

Table 4.5: Calculated 𝑘1 and 𝑘2 measured by FCS and the RheoChip. ............................... 130

CHAPTER 5:

Table 5.1: FCS parameters and PS: protein ratio of mAb solutions in presence of SYPRO®

Orange. ................................................................................................................................... 153

CHAPTER 6:

Table 6.1: FCS determined diffusion times and number of particles of 10mg/ml COE-08

solutions shaken at 500rpm for 4hrs, in presence of IgG-AF. ............................................... 175

Table 6.2: FCS determined diffusion times and number of particles of 10mg/ml COE-08

solutions agitated via rotation for 24hrs, in presence of IgG-AF. ......................................... 176

6

LIST OF FIGURES

CHAPTER 1:

Figure 1.1: Structure of a Monoclonal Antibody. .................................................................... 17

Figure 1.2: Size ranges and types of protein aggregates. ......................................................... 20

Figure 1.3: Downstream process for mAbs production. .......................................................... 21

Figure 1.4: Potential energy diagram illustrating the interaction energy over the distance

between two molecules. ........................................................................................................... 24

Figure 1.5: Analytical capabilities of protein aggregation detection methods. ....................... 43

Figure 1.6: Jablonski diagram illustrating the energy states of a molecule. ............................ 48

Figure 1.7: Chemical structures of commonly used fluorescent dyes. .................................... 54

Figure 1.8: FCS typical fluorescence signal and autocorrelation curve. ................................. 58

Figure 1.9: Basic set-up of a confocal microscope. ................................................................. 61

Figure 1.10: Systematic diagram of Raster Image Correlation Spectroscopy (RICS). ........... 64

CHAPTER 3:

Figure 3.1: Schematic diagram of RICS and labelling of dyes SYPRO® Red and SYPRO®

Orange. ..................................................................................................................................... 94

Figure 3.2: Protein particle counts in mAb PFS solutions measured by RICS, RMM and MFI.

.................................................................................................................................................. 96

Figure 3.3: Silicone oil droplet counts in buffer-filled PFS solutions measured by RICS,

RMM and MFI. ........................................................................................................................ 98

Figure 3.4: Silicone oil droplet counts in mAb PFS solution measured by RMM and MFI. 101

CHAPTER 4:

Figure 4.1: Inverse relative viscosity comparison between Vilastic-3 and FCS (with ATTO-

Rho6G and IgG-AF) of control samples. ............................................................................... 124

Figure 4.2: Normalised FCS autocorrelation function for IgG-AF solutions. ....................... 125

Figure 4.3: Variations of the ratio of diffusion times as a function of mAb concentration,

measured by FCS, of tracers IgG-AF and ATTO-Rho6G over different buffer conditions. . 126

Figure 4.4: Variation of ratio of diffusitives of different-sized fluorescent tracers compared

with the relative macro-viscosity as a function of protein concentration. ............................. 127

Figure 4.5: Relation between interaction parameter (kD) and the exponential coefficient (k).

................................................................................................................................................ 131

CHAPTER 5:

Figure 5.1: INT determined by spectrofluorometry of PS-20 solutions in water, in the

presence of SYPRO® Orange, in order to determine the cmc. ............................................. 146

Figure 5.2: INT determined by spectrofluorometry of PS-20 solutions in histidine/sucrose

buffer, in the presence of SYPRO® Orange, in order to determine the cmc......................... 147

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Figure 5.3: FCS-determined parameters of TX100 solutions in the presence of R123, in order

to determine the cmc. ............................................................................................................. 149

Figure 5.4: FCS-determined parameters of TX100 solutions in the presence of SYPRO®

Orange in order to determine the cmc.................................................................................... 149

Figure 5.5: FCS-determined parameters of PS-80 solutions in the presence of SYPRO®

Orange in water, in order to determine the cmc. .................................................................... 150

Figure 5.6: FCS-determined parameters of PS-20 solutions in the presence of SYPRO®

Orange in water in order to determine the cmc. ..................................................................... 151

Figure 5.7: FCS-determined parameters of PS-20 solutions in the presence of SYPRO®

Orange in histidine/sucrose buffer, in order to determine the cmc. ....................................... 151

Figure 5.8: Viscosity (cone plate method) plotted against the FCS diffusion times of

concentrated mAb solutions. .................................................................................................. 154

Figure 5.9: Isothermal titration calorimetry thermogram recorded for injection of 0.1% w/v

PS-20 into PBS. ..................................................................................................................... 155

Figure 5.10: Isothermal calorimetry results for injection of 0.1% w/v PS-20 into 10mg/ml

COE-03. ................................................................................................................................. 155

CHAPTER 6:

Figure 6.1: RICS (with SYPRO Red) determined protein particle counts for agitated 1mg/ml

COE-08 solutions. .................................................................................................................. 171

Figure 6.2: FCS relative diffusion time of ATTO-Rho6G 1mg/ml COE-08 agitated solutions.

................................................................................................................................................ 172

Figure 6.3: RICS (with SYPRO Red) determined protein particle counts for 4 hour shaken (at

500rpm) 10mg/ml COE-08 solutions..................................................................................... 174

Figure 6.4: RICS (with SYPRO Red) determined protein particle counts for 24 hour rotation

10mg/ml COE-08 solutions. .................................................................................................. 175

Figure 6.5: RICS (with SYPRO Red) determined protein particle counts for agitated 1mg/ml

COE-08 solutions. .................................................................................................................. 177

Figure 6.6: FCS relative diffusion of IgG-AF 1mg/ml COE-08 agitated solutions. ............. 178

Figure 6.7: RICS (with SYPRO Red) determined protein particle counts for agitated

100mg/ml COE-08 solutions. ................................................................................................ 179

Figure 6.8: FCS relative diffusion of IgG-AF 100mg/ml COE-08 agitated solutions........... 180

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LIST OF ABBREVIATIONS

ACF Autocorrelation Function

ANOVA Analysis of Variance

AUC Analytical Ultracentrifugation

APD Avalanche Photodiode

BSA Bovine Serum Albumin

CLSM Confocal Laser Scanning Microscopy

cmc Critical micelle concentration

Da Daltons

DLS Dynamic light scattering

DMSO Dimethyl Sulfoxide

DNA Deoxyribonucleic Acid

DVLO Derjaguin-Landau-Verwey-Overbeek

FCS Fluorescence Correlation Spectroscopy

FDA Food and Drug Administration

FRAP Fluorescence Recovery After Photobleaching

FT Fourier Transform

GFP Green Fluorescent Protein

HIAC High Accuracy liquid particle counter

HR Hard-sphere

HSA Human Serum Albumin

ICS Image Correlation Spectroscopy

IFABP Intestinal Fatty Acid Binding Protein

IgA Immunoglobulin A

IgG Immunoglobulin G

IgM Immunoglobulin M

IL

INT

Interleukin

Intensity

KCl Potassium Chloride

kICS k-space Image Correlation Spectroscopy

LOD Limit of Detection

MALLS Multi Angle Laser Light Scattering

MFI Micro-Flow Imaging

9

MS Mass Spectrometry

MSD Mean Square Displacement

NaCl Sodium Chloride

NMR Nuclear Magnetic Resonance

NTA Nanoparticle Tracking Analysis

PBS Phosphate-Buffered Saline

PGSL Probabilistic Global Search Lausanne

PMT Photomultiplier Tube

PPI Protein-Protein Interactions

PSF Point Spread Function

PS-20 Polysorbate-20

PS-80 Polysorbate-80

RA Rheumatoid Arthritis

RICS Raster Image Correlation Spectroscopy

RMM Resonance Mass Measurement

ROI Region of Interest

SI Supplementary Information

St. dev. Standard Deviation

SEC Size Exclusion Chromatography

SLS Static Light Scattering

SNR Signal to Noise Ratio

SPT Single Particle Tracking

STICS Spatiotemporal Image Correlation Spectroscopy

TEM Transmission Electron Microscopy

TICS Temporal Image Correlation Spectroscopy

TNF Tumour Necrosis Factor

Trp Tryptophan

UV Ultra Violet

2D Two Dimensional

3D Three-Dimensional

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GENERAL ABSTRACT

Biopharmaceuticals (e.g. monoclonal antibodies (mAbs)) must comply with regulations (i.e.

United States Pharmacopeia (USP) <788>) regarding their characterisation and stability.

mAbs contain hydrophilic and hydrophobic areas (the latter normally buried inside the bio-

macromolecule). Stress conditions (such as high temperature, freezing, shaking etc.) or high

concentrations may lead to the exposure of hydrophobic surfaces (i.e. following unfolding)

and subsequently lead to the formation of protein aggregates. Furthermore, high concentrated

mAb products (i.e. >100mg/ml) have the increased risk of intermolecular interactions which

is correlated with high viscosity. These issues pose significant challenges to the economic

manufacture of safe and effective protein therapeutics. Current technologies in characterising

protein formulations possess limitations in regards to size ranges, specificity, interacting with

solution components and concentration; thus there is a current drive in the development of

novel applications. This thesis studies the application of two fluorescence-based techniques in

characterising mAb solutions, in the context of downstream processing and formulation:

Raster image correlation spectroscopy (RICS) analyses to assess aggregation propensity; and

fluorescence correlation spectroscopy (FCS) in retrieving viscosity information. An important

step for both methods is the identification of appropriate (non-covalent) fluorescent probes.

To validate the application of RICS in charactering mAb aggregates in industrially relevant

formulations, particle formation (size and counts) in pre-filled syringes was evaluated as a

function of polysorbate-20 (PS-20) concentration, following agitation stress. PS-20 limited

agitation-induced aggregation whilst increasing the amount of silicone oil sloughing. Thus no

correlation between silicone oil and aggregation was observed. Following extrinsic labelling

of aggregates by hydrophobic dye SYPRO Red, RICS demonstrated its high specificity to

aggregates in mAb solutions containing surfactant and silicone oil. An improved selectivity

was observed in comparison to resonance mass measurement (RMM) and micro-flow

imaging (MFI), covering a broader size range and using small sample volumes.

Although nonionic surfactants such as PS-20 are widely used, their mechanisms in mAb

solutions are poorly understood. This is partly due to analytical limitations of current

technologies. The application of FCS with utilising SYPRO Orange is validated in accurately

determining the critical micelle concentration of (three) nonionic surfactants, along with the

micelle size. Moreover, the FCS/SYPRO Orange application is used to detect polysorbate

micelles in the presence of high concentration mAb and thus provides scope to assess micelle

behaviour in highly concentrated mAb formulations.

As an additional method to measure the microviscosity of mAb solutions, FCS was utilised in

measuring the self-diffusion of tracers in a wide range of mAb formulations, over a broad

concentration range. The diffusion of different sized tracers was investigated and compared

against bulk rheometry measurements. It was established a probe of size equal or larger than

the mAb gave relatable information to bulk rheometry.

RICS and FCS were (sequentially) applied to mAb solutions subjected to various forms of

agitation stress. A correlation was established between aggregation development (size and

counts) and changes in solution viscosity. Additionally an inverse relationship of agitation-

induced aggregation and protein concentration was observed. The potential of measuring

aggregation propensity and solution viscosity using the same system set-up is of great interest

to the industry due to small sample material and minimal operating time. Thus the combined

application of RICS and FCS has the potential to stand as a tool in the characterisation of

mAb (aggregate) solutions, particularly in relation to early formulation development.

11

DECLARATION

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other institute

of learning.

12

COPYRIGHT STATEMENT

The author of this thesis (including any appendices and/or schedules to this thesis) owns

certain copyright or related rights in it (the “Copyright”) and s/he has given The University of

Manchester certain rights to use such Copyright, including for administrative purposes.

Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may

be made only in accordance with the Copyright, Designs and Patents Act 1988 and

regulations issued under it or, where appropriate, in accordance with licensing agreements

which the University has from time to time. This page must form part of any such copies

made.

The ownership of certain Copyright, patents, designs, trademarks and other intellectual

property (the “Intellectual Property”) and any reproductions of copyright works in the thesis,

for example graphs and tables (“Reproductions”), which may be described in this thesis, may

not be owned by the author and may be owned by third parties. Such Intellectual Property

and Reproductions cannot and must not be made available for use without the prior written

permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property University IP

Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=24420), in any relevant

Thesis restriction declarations deposited in the University Library, The University Library’s

regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The

University’s policy on Presentation of Theses.

13

CONTRIBUTIONS TO THESIS CHAPTERS

Primary supervisor Alain Pluen, provided guidance and feedback for the Introduction

(Chapter 1), Aims and Objectives (Chapter 2) and Final Conclusions (Chapter 7).

Chapter 3

Maryam Shah, Zahra Rattray and Alain Pluen planned the study.

Maryam Shah performed the experiments with assistance from Katie Day.

Maryam Shah analysed the data.

Maryam Shah and Zahra Rattray prepared the manuscript, with comments from Alain Pluen,

Chris van der Walle, Robin Curtis, Katie Day and Shahid Uddin.

Chapter 4

Maryam Shah, Alain Pluen and Robin Curtis planned the study.

Maryam Shah, Aisling Roche and Peter Davis prepared the protein samples with guidance

from Katie Day.

Maryam Shah performed the FCS measurements.

Aisling Roche and Peter Davis performed the Rheochip measurements.

Maryam Shah and Alain Pluen analysed the data with modelling input from Daniel Corbett.

Maryam Shah and Alain Pluen prepared the manuscript, with comments from Robin Curtis,

Chris Van der Walle and Shahid Uddin.

Chapter 5

Maryam Shah and Alain Pluen planned the study.

Maryam Shah performed all the experiments and analysis of data except ITC; which was

conducted and analysed by Tom Jowitt.

Maryam Shah prepared the manuscript with comments from Alain Pluen, Chris Van der

Walle, Robin Curtis, Katie Day and Shahid Uddin.

Chapter 6

Maryam Shah and Alain Pluen planned the study.

Maryam Shah performed the experiments and completed all the data analysis.

Maryam Shah prepared the manuscript with comments from Alain Pluen, Chris Van der

Walle, Robin Curtis, Katie Day and Shahid Uddin.

14

ACKNOWLEDGEMENTS

I would like to express my gratitude to The University of Manchester for the PhD

opportunity, along with MedImmune and the BBSRC for sponsoring this project.

Additionally, there is a list of people to which I would like to extend my appreciation to:

Firstly I would like to thank my main supervisor, Alain Pluen, for his invaluable continuous

support over the last four years – not only with my PhD work but with everything else I took

on (i.e. conferences, workshops, placements). For allowing me to walk through the door and

let off steam, for giving me sometimes strong words to get the best out of me and providing

me with advice that I will take with me for the rest of my career.

My secondary supervisor and industrial supervisors: Robin Curtis, Chris Van der Walle,

Katie Day and Shahid Uddin - for their continuous encouragement throughout the years, for

supporting me and believing in me.

The additional opportunities given to me by my supervisory team have enhanced my skills,

personality and motivation. I would not have achieved my accomplishments without you and

I will forever be indebted to you all.

Academics in the School of Pharmacy, particularly Elena and Doug, for their words of

wisdom and comforting chats (in the office and in corridors) - I hope you realise the impact

of our little conversations in keeping me going.

My colleagues/friends at the University – Amal, Halah, Kathryn, Farrah, Shaun, Cat, James,

Alfredo, Rose – thank you for your support and I wish you all the very best in your journeys.

I would like to thank my family - my mother, my sisters, my brother, my grandparents, my

uncles, my mother-in-law, my father-in-law, sister-in-law’s and brother-in-law’s, my outlaw,

my niece and my nephews. You are my entire world and my underlying motivation, for my

career and my life. We have seen some tough times but I truly believe that together we can

accomplish anything.

My son, Riyad Shah, who came into this world in the final year of my PhD - you gave me

love and a reason to smile through the darkest of nights - even if it did add to the

sleeplessness! Having a baby and completing a thesis was definitely challenging, but I would

not have had it any other way.

Finally, last but certainly not the least, to a very special person who has been there for me

through the highs and the lows, who gave me the confidence to reach heights I thought I

would only dream of, who gave me the self-assurance be to true to who I am, my best friend,

my partner in crime, my soulmate, my husband,

Hanif Shah to you I dedicate this thesis.

15

1 : INTRODUCTION

16

Introduction

Over the recent decades mAbs products have taken a leading role in the bio-pharmaceutics

division. Since the 1980s to (May) 2017, the Food and Drug Administration (FDA) have

approved 66 mAbs for clinical use against a variety of illnesses including cancer and

autoimmune diseases. Their high demand and continuous research is due to their extremely

high specificity alongside minimal side effects.1-3

Nevertheless, these drugs may be unstable

and prone to aggregation. The presence of aggregates needs to be controlled as they may lead

to an immune response in the patient. Thus biopharmaceuticals must comply with regulations

(e.g. United States Pharmacopeia (USP) <788>) regarding their characterisation and

stability. However, detecting aggregates, evaluate the role of excipients in their formation or

the influence of protein-protein interactions especially at high mAb concentrations remains

an issue.

1.1 Monoclonal Antibodies as Therapeutics

Currently there are over 250 clinical trials for certifying already established mAbs for other

therapies and for the validation of new candidates. A few examples are: a humanised IgG3

(named 3F8) is in phase II trials for locating and killing tumour cells in patients with high-

risk neuroblastoma; an IgG1 (named MEDI-551), is in phase I trials directed against CD19 in

adult subjects with relapsed or refractory advanced B-cell malignancies; another IgG1,

Trastuzumab, is in phase III trials as a short duration preoperative therapy in patients with

HER2-neu positive operable breast cancer. These are just a representation of the large list of

clinical trials, and their success is reflected through passing the clinical trials stages leading to

their potential use in treatments. However, this is not always the case. For example,

alemtuzumab was being assessed on serum IL-6, IL-10, and IL-13 levels in patients with

relapsed and resistant classical NHL, but the phase II stage was terminated due to slow

accrual.4

The high specificity of mAbs is owing to their structure (See Figure 1.1). Antibodies are Y-

shaped quaternary structures with a molecular weight of around 150kDa. The structure can be

split into four polypeptide chains which are covalently linked (i.e. disulfide bonds); two

heavy chains and two light chains, where each heavy chain (around 50kDa) has about twice

the number of amino acids as each light chain (around 25kDa). The structure can also be split

into two halves as two fragment antigen binding (Fab) regions and the fragment crystalline

(Fc) region. The Fab regions comprise constant and variable domains where the variable

domains are situated at the amino terminal ends. These variable domains contain the

17

complementary determining regions (CDRs), which vary within different antibodies, and

determine the high diversity of mAbs and their high specificity to the antigen. Monoclonal

antibodies are mono-specific antibodies possessing affinity for the same antigen (i.e. have

identical CDRs) as they are made by identical immune cells that are all clones of a unique

parent cell. In contrast, polyclonal antibodies are made from several different immune cells.5-

7

Figure 1.1: Structure of a Monoclonal Antibody.

Structure of a monoclonal antibody split into four polypeptide chains, consisting of two heavy chains

and two light chains. Structure can also be split into the Fab region (comprising the variable domains

which contain the CDRs) and the Fc region. Adapted from Shukla et al.8

The Fc region serves as the binding site for complement components and leukocytes. The

structure contains only constant domains and determines the type of immunoglobulin (Ig)

isotype; IgA, IgD, IgE, IgG or IgM. Each Ig class has its own structure and function with

IgA consisting alpha heavy chains, IgD with delta heavy chains, IgE having epsilon heavy

chains, IgG having gamma heavy chains and IgM with Mu heavy chains.5 Furthermore, the

classes can be split into subclasses based on slight differences in the amino acid sequence in

the constant region of the heavy chains i.e. IgA contains two subclasses (IgA1 and IgA2) and

IgG contains four subclasses (IgG1, IgG2, IgG3 and IgG4). IgG is the most common isotype

used for therapeutic drugs. It is the only class that can promote antibody-dependent cellular

cytotoxicity (ADCC), the only class that can cross the placenta in humans and is the most

18

stable. It is important to note, however, that the different subclasses differ in their

effectiveness, for example, IgG4 does not fix complement.9,10

Immunoglobulins ultimately work in three different ways; by triggering an immune response

to attack, by blocking signals, or by acting as a transporter carrying drugs to cells. IgGs used

in cancer treatment can be split into two types; naked IgGs and conjugated IgGs. Naked IgGs

work independently whereas conjugated IgGs act as homing devices through being joined to

a chemotherapy drug, a radioactive particle or a toxin thereby transporting these substances to

the target cancer cells. Antigens that are expressed by human cancers have revealed a broad

collection of targets for therapy that are over-expressed, mutated, or selectively expressed

compared with normal tissues. One of the first malignant diseases with successful IgG

treatment was non-Hodgkin lymphoma (NHL), using rituximab. Rituximab is an IgG1 that

targets the CD20 antigen on B-lymphocytes and has shown to cause rapid depletion of

malignant CD20+ B-lymphocytes from the blood, bone marrow and lymph nodes in patients.

Rituximab treatment has been enhanced with combined cytokine treatment, for example, with

IFN--2 which increases the CD20-antigen expression on lymphoma cells thereby

enhancing the immune response.11

Zevalin and Bexxar are examples of conjugated

(radiolabelled) IgGs which are also used for NHL treatment. They both target the CD20

antigen on B-cells carrying different radiolabeled particles. Zevalin consists of rituximab

followed by ibritumomab tiuxetan and Bexxar consists of tositumomab.12

Another example

of an established IgG1 is trastuzumab, approved by the FDA in 1998. Trastuzumab is used in

breast cancer therapy targeting the over-expressed proto-oncogene, human epidermal growth

factor receptor 2 (HER2), thereby blocking the protein’s activity.5 Since 1997, 12 IgGs have

been approved by the FDA just for the treatment of solid tumours and haematological

malignancies.11

As well as cancer, IgG therapy is also used for other chronic diseases/illnesses. Rheumatoid

arthritis (RA) is the most common systemic autoimmune condition worldwide and is

characterized by remittent systemic autoimmune inflammation and painful progressive joint

deterioration.13

Numerous IgGs have been used to target tumour necrosis factor (TNF), a key

inflammatory cytokine in RA, which promotes inflammation by stimulating the release of

other cytokines such as IL-1, IL-6 and granulocyte macrophage-colony stimulating factor

(GM-CSF). Inhibition of TNF activity has shown to significantly reduce inflammation and

joint damage in RA patients. IgGs targeting TNF for RA therapy include infliximab,

19

adalimumab and golimumab. Rituximab is also used in RA therapy, through targeting B-cells

and blocking TNF activity.14-16

1.2 Protein Aggregation

1.2.1 Protein Aggregation Consequences

Protein aggregation can occur in vivo as pathological aggregation (in disease) and in vitro in

the industrial production of proteins. In vivo (in cells) uncontrolled aggregation has been

associated with a number of diseases, grouped as amyloidosis including Alzheimer’s disease,

Parkinson’s disease and Huntington’s disease. During biopharmaceutical production, protein

aggregates are typically net irreversible under the conditions they form and their levels must

be tightly controlled and limited for regulatory and safety reasons.17,18

Structural changes in

proteins resulting from aggregation reduce the efficiency of the product and could also

increase its immunogenicity i.e. increasing the risk of unwanted autoimmune responses

leading to inactivation of the drug and possibly adverse effects. Therefore, aggregates

detected during product production have to be removed, which increases costs due to reduced

product yield, and thereby reduces the efficiency of the bio-processing stages.19

1.2.2 Mechanisms of Protein Aggregation

Protein aggregation is essentially a major side reaction of protein folding. Functional proteins

typically fold into their 3-dimentional structure where - simplistically - their shell is

hydrophilic and core is hydrophobic. Synthesised proteins may not fold correctly or folded

proteins may unfold exposing the hydrophobic core. These exposed hydrophobic regions

becoming attracted to each other and consequently form intermolecular structures i.e. protein

aggregates.20

The aggregation process can occur through a variety of mechanisms. Five main mechanisms

have been postulated, but it should be noted that these mechanisms are not mutually

exclusive and more than one can occur at different stages21

: (1) reversible self-

complementary native monomer-monomer association forming small oligomers, these can

develop into larger oligomers leading to aggregates becoming irreversible; (2) changes in

conformation or partial folding promoted by stress mechanisms, leading to altered monomers

to associate; (3) changes in conformation initiated by a difference in covalent structure,

usually caused by chemical degradation, resulting in the new complementary structures to

aggregate; (4) nucleotide-controlled aggregation which is common for visible particles; when

an aggregate of sufficient size develops, the growth is called ‘critical nucleus’, where

20

addition of monomers and formation is rapid; (5) surface induced aggregation where

aggregation starts via native monomer binding to a surface resulting in conformational

change (to increase contact area with surface), this can then be followed by mechanisms (2)-

(4). Mechanism 1 aggregates are native-like; hence, if large aggregates induce immune

responses then it is more likely that these proteins will cross-react the native monomer.

Mechanisms 2 to 5 are primarily made from non-native monomers thus it is more likely that

they will have altered potency as well as altered immunogenicity (as altered monomers

present different epitopes). Examples of conditions where these aforementioned mechanisms

occur will be given in the subsequent sections. From these mechanisms, various types of

aggregates can develop. Although there is no uniform terminology for aggregate types,

classification systems have been introduced based on their size and their structure, illustrated

in Figure 1.2: (1) reversible noncovalent small aggregates (e.g. dimers, trimers; nanometre

particles); (2) irreversible noncovalent aggregates (tend to be in the submicron size range);

(3) large aggregates which can be reversible if noncovalent (with a diameter up to around

3m) and (4) large irreversible aggregates (which can become visible >100 m).

Figure 1.2: Size ranges and types of protein aggregates.

Size ranges and types of protein aggregate structures. The different structures are not limited to

certain size ranges and can overlap. Figure not to scale. Adapted from Philo et al.22

and Weinbuch et

al.23

1.2.3 Industrial Production of Therapeutic mAbs

MAbs are susceptible to a variety of degradation routes which they are exposed to at various

points in their life-time. The overall stages of protein biopharmaceutical development consist

of cell culture, manufacture, storage, and administration. The manufacturing process, or the

downstream process, is a vital part of biopharmaceutical production as it is essentially the

purification process, and protein aggregation has been reported at levels of 30% in the cell

21

culture.1 The downstream process is outlined in a schema in Figure 1.3 and consists of a

capture stage, polishing chromatography steps for the removal of impurities, steps for viral

clearance, and ends with ultrafiltration/diafiltration to formulate and concentrate the product.

The steps are monitored to keep the protein in optimal conditions involving pH, temperature,

ionic strength and salts, resulting in a stable product that is able to carry out the functions

intended.8

Figure 1.3: Downstream process for mAbs production.

Downstream process for mAb production consists of various stages initiating from cell culture and

ending with formulating the product into the desired concentration. Adapted from Shukla et al. 24

The downstream purification process usually initiates with ‘Protein A affinity

chromatography’ as the capture step. It is based on the specific binding affinity between the

Fc region of mAbs and the immobilized ‘protein A ligand’. The step involves the cell culture

supernatant to be loaded on the column followed by product elution from the column at low

pHs, thereby removing contaminants;24

glycine-hydrochloric acid is an example of an elution

buffer with pH 2.8.25

At the end of this step the purity of samples needs to reach 98%.

Unfortunately this step has several limitations; one is the high cost and the second, and most

importantly, is the low pH used. The low pH can result in the formation of soluble aggregates

and precipitates. This then adds burden to the polishing steps as insoluble aggregates can be

ultimately formed from the aforementioned species. Another consequence of aggregation at

this stage is the increased risk of reducing the column’s lifetime due to clogging of the

chromatography column.24

Strategies have been developed to address the aggregation

problem at this stage. For example, pre-treatment of the solution buffer to remove

precipitating impurities through using stabilizing additives; optimising the temperature; and

working on the slope of transition from wash to elution8 – these are discussed later.

The polishing steps aim to reduce the product-process impurities such as high molecular

weight aggregates. There are at least two polishing steps during the downstream process and

various methods are used such as cation-exchange chromatography (CEX), anion-exchange

chromatography (AEX), hydrophobic interaction chromatography (HIC) and hydroxyapatite.

One of the two steps usually uses AEX and HIC which are flow-through modes i.e. where the

22

product does not bind to the column. To assist in viral clearance, there are also at least two

dedicated steps of viral inactivation and size-based viral removal by viral filtration. The last

step in the downstream process is ultrafiltration/diafiltration (UF/DF) which reduces the

storage volume and exchanges the product into the formulation buffer to produce the drug

substance.8,24

1.2.4 Accelerated stability testing

The FDA and ICH guidelines state the requirements of stability testing data to assess how the

product changes over time.26

Therapeutic proteins need to retain their efficacy during long

periods of storage over multiple years. However, stability issues have shown to arise; a

common degradation route is aggregation development over time. Stability tests are carried

out to confirm that a drug remains active and does not create degradation products. The data

required from stability testing is an important requirement for regulatory approval on the drug

and is utilised to create recommendations on shelf life and storage conditions – including

temperature and the storage device.27

Real-time stability studies are time-consuming, with a minimum study time of 12 months.

The product is stored at room temperature, under natural light and expected levels of

humidity where the drug will be kept/sold.28,29

As an alternative measure, rapid testing is

carried out in the form of accelerated stability testing with a testing time of around 6 months,

with suggested sampling times of 0, 3, and 6 months.30

Accelerated stability testing is used by

formulation scientists early on in formulation development to assess the optimal formulation

conditions for the product. Hence, it is classed a predictive tool of protein stability.

Accelerated stability testing involves situating formulations under several high temperatures

to force degradation. At least four different stress temperatures are recommended in order to

retrieve a statistically viable result. Guidelines state the accelerated storage condition must be

> 15 C above the ambient storage condition; usually around the 40

C mark. Stress

conditions are applied that may influence the products stability post-production such as

moisture, light, pH, agitation and packaging, to test the robustness of the protein

formulation.31

Here, the stages are mimicked, for example, agitation stress can be mimicked

using shaking experiments. The rates of degradation pathways under these accelerated

conditions, such as aggregation, are used to predict what the rates would be under real-time

storage conditions i.e. how long it would take to have an unacceptable amount of aggregates

23

in the product. Following stress, the samples are refrigerated and then assayed

simultaneously.32

1.2.5 Factors contributing to protein aggregation

The environmental stressors leading to, or accelerating, protein aggregation, through the

mechanisms outlined above (section 1.2.2), include temperature, pH, ionic strength, salts and

material interfaces. These potential stressors need to be controlled during production and they

also need to be kept stable after production, where the risk of protein aggregation is

heightened during transport and storage via mechanical and/or agitation stress. Thus, these

are not only classed as ‘stressors’, but also ‘controls’ as they can be manipulated to help

reduce unwanted protein-protein interactions (PPIs); which have been associated with an

increased risk of protein aggregation. Additionally, these factors are used in accelerated

stability studies, to assess aggregation development and develop aggregation models. This

section introduces protein-protein interactions and protein aggregation stressors.

1.2.5.1 Protein-protein interactions (PPIs)

In relation to the development of protein aggregates, there has been a vast amount of studies

dedicated to understanding and controlling non-specific protein-protein interactions (PPIs).

PPIs are linked to problems in product development such as protein aggregation and

viscosity, due to the close proximity of protein molecules particularly at high protein

concentrations (discussed later – section 1.2.5.6).33

Therefore, dedicated areas of study

include (i) mechanisms of PPIs; (ii) the driven forces leading to/changing the nature

(attraction or repulsion) of PPIs and (iii) methods/techniques to measure PPIs.

Interactions between protein molecules (in solution) can be explained by a basic model

involving electrostatics (contributing to repulsion) and Van der Waal forces (contributing

towards attraction). The model is with respect to the distance between the molecules (Figure

1.4).34,35

Common formulation parameters i.e. pH, ionic strength, and nature and

concentration of excipients (discussed further in subsequent sections) have been known to

affect conformational stability of proteins and aggregation mechanism rates (aggregation

mechanism one – section 1.2.2).36-38

For example, pH and NaCl concentration has been

shown to mediate protein-protein electrostatic repulsions.34

Van der Waals interactions are

weak interactions that occur between molecules in close proximity to each other. As the

molecules move further apart, the potential energy due to repulsion decreases. In the potential

energy diagram (Figure 1.4), the minimum potential energy point correlates with the Van der

24

Waals distance whereby the interaction energy greatly increases when the distance between

two atoms is smaller than 3.8A.

Figure 1.4: Potential energy diagram illustrating the interaction energy over the distance between

two molecules.

Potential energy diagram illustrating the interaction energy over the distance between two molecules,

whereby electrostatics contribute towards repulsion and van der Waals forces contribute towards

attraction. Adapted from Barnett et al. and Lund et al.34,35

Measuring PPIs has shown strong utilisation in aiding in the development of optimal

formations. The osmotic second viral coefficient (B22) is a well-established measure of weak

protein-protein interactions. The measure provides a direct link to the pair protein potential or

the mean force of interaction: the free energy of the system when two protein molecules are

held at some specified separation, relative to when they are infinitely far apart, averages with

respect to all possible configurations of the solvent molecules and usually with respect to the

orientation of the protein molecules: for PPIs, two protein molecules must be within the range

of spatial attraction and must be correctly orientated so the hydrophobic patches are face-to-

face. A positive B22 value indicates net repulsion between protein molecules such that

protein-solvent interactions are superior to PPIs, whereas, a negative B22 indicates net

attraction between protein molecules and the solvent is classed as a ‘poor solvent’. B22 has

been utilised in many applications; for example, it has shown to be utilised in tailoring

experimental conditions to result in protein-protein attractions leading to protein

crystallisation.39-41

Furthermore, a strong correlation between the B22 and protein solubility

25

has been demonstrated with the level and nature of PPI been proposed to predict the change

in protein solubility.42

With the development of analytical technologies, a more efficient way

of measuring PPIs has been developed through the interaction parameter (kD). The use of kD

is heightening in studies as it is amendable to high throughput. As a linear relationship

between the two parameters has been established this, there is a robust case of using kD

instead of B22.43,44

Nevertheless, a strong correlation measuring PPI (i.e. through the B22) at dilute

concentrations and protein aggregation at high concentrations is proving difficult to

establish45,46

and so alternative measures / models to predict aggregation are still an important

area of focus.

1.2.5.2 Temperature

High temperatures are known to induce protein denaturation through breakage of hydrogen

bonds in the proteins secondary structure. Subsequently, proteins unfold which exposes the

hydrophobic regions that are normally protected. Consequently this may lead to aggregation

in order to decrease the amount of exposed hydrophobic surfaces. Irreversible aggregation

development due to high temperatures has been demonstrated through spectroscopy methods.

For example, the amide I band (which is the backbone of polypeptides) becomes well defined

at high temperatures and the band has shown to remain intense after cooling; thereby

representing the development of irreversible aggregates at the high temperatures.47

Another

major process in the early stages of aggregation, caused by thermal stress, is the unfolding of

the -helix. This unfolding of the native structures (of the protein) was shown to occur at

temperatures of 40-45C, and the unfolding became clearer after 55C (which is regarded as

the melting temperature, Tm, at which half of the protein is in an unfolded state). Here, the

highly solvent exposed regions unfolded first and was the origin of aggregates, and the buried

region still maintained the regular structure in aggregates, and underwent a further unfolding

at the later aggregation stage.48

Cold denaturation has been reported to result in a loss of functional activity. Therapeutic

drugs are often stored frozen at temperatures ranging from -20C to -80C to maintain their

ability and reduce the chances of microbial growth. However, aggregation has shown to

develop through destabilization from the cryo-concentration (the concentration of chemical

constituents in a liquid due to freezing) of the protein and co-solutes leading to denaturation

at the water interface (i.e. mechanism (2) and mechanism (5) – section 1.2.2). Similarly,

26

aggregation can arise during freeze-thaw procedures.49

Freezing an aqueous solution slowly

results in pure crystals of water forming (because the freezing temperature of water is

higher). This slow rate of cooling has a large impact on protein stability as it increases the

time the protein is exposed to the high salt concentrations and the destabilizing pH

conditions, prior to the temperature becoming sufficiently low so that protein degradation is

limited. In addition, the large surface area of the small crystals can increase aggregation

through surface denaturation of the protein (i.e. mechanism (5) - section 1.2.2). As an

alternative, flash freezing is too fast for the water to form pure crystals resulting in no

precipitation. However, a rapid cooling rate does not ensure optimal protein stability.

Henceforth, it is a widespread rule not to freeze the same batch of protein twice.50

Studies have debated whether the individual domains of mAbs (CH3, CH2, CH1, VH, CL and

VL) (Figure 1.1) have intrinsic thermodynamic stabilities, or whether the thermodynamic

stability of the individual domains does not render to the thermodynamic stability of the full

protein. In the latter case, there may be additional stability components due to inter-domain

interactions. Properties of the individual domains of mAbs have been investigated. For

example, Lazar et al. reported that the CH3 domain on IgG1s have a higher Tm compared to

the other domains thereby making CH3 less resistant to cold denaturation.49

Recent work on a

study into a human-like single chain antibody fragment (scFv) has highlighted the limitations

with using conformational stability, following thermal stress acceleration studies, as

predictors of aggregation propensity.51

Further work is necessary on the kinetics of denaturation for long-term storage at low

temperatures, especially since the thermodynamic ability varies with different mAbs and

different proteins. Subsequently, the thermodynamic stability of each mAb needs to be

considered independently.

1.2.5.3 pH

mAbs are exposed to low (acidic) pHs at several stages during production i.e. during affinity

chromatography and viral clearance in the downstream process, and post-downstream with

freeze-thaw steps, exposure to UV radiation and mechanical stress. Low pH values have been

shown to convert IgG1 antibodies to soluble reversible aggregates due to destabilizing both

the Fc and Fab regions thereby decreasing the conformational stability. Additionally,

increasing the temperature resulted in the transition to the irreversible aggregate state.52

27

Meanwhile, during IgG4 development the transition in pH has shown to induce protein

aggregation rather than exposure to low pH - implying that there should be no concern over

prolonged incubation at low pH for viral clearance during downstream processing. The

aggregation problem arises during changes from low to high or from high to low pH. During

the transition in pH, aggregates became irreversible at neutral pH; indicating that they

became kinetically trapped in an altered conformation and would require a substantial energy

barrier to resume to the normal conformation. Increasing the temperature gave enough energy

to revert the sample to its native conformation; nevertheless, this was only the case with

hIgG4-A antibody - thereby, demonstrating the different properties between the subclasses.

In addition, the ionic strength also affected the stability of the IgG4s in this study, but ionic

strength alone did not induce significant changes in conformation.53

1.2.5.4 Excipients

1.2.5.4.1 Buffers and salts

The buffer and added salts are classed as the main excipients in protein formulations. The pH

and ionic strength of solutions is mainly modified by the addition of organic salts which has

an effect on protein stability, PPIs and protein solubility.54,55

Each buffer composition has its own advantages and disadvantages and their compatibility

varies with different analytes and experimental applications. For example, Tris buffer is

known to be sensitive to changes in temperatures (in terms of pH changes) whereas

phosphate’s pH is not dependent on temperature but has shown to inhibit kinases.56

As well

as deciding on the buffer composition prior to purification, the ionic strength of the buffer

may be changed during the purification steps i.e. to prevent nonspecific interactions between

proteins and the (chromatography) column.54

For instance, NaCl is an ionic stabiliser used to

enhance protein solubility. In ion exchange chromatography a low concentration of NaCl is

used for loading the protein onto the column (5-25mM) and a high concentration (or change

in pH) is used for elution. In other types of chromatographic separations, such as gel filtration

and affinity columns, the NaCl concentration is greatly increased; cases have gone up to 500

mM NaCl to help prevent the nonspecific interactions between proteins and the column.56

Salts have a strong effect on protein solubility thus there is the need to understand salt-protein

interactions as well as salts’ effect on protein-protein interactions (PPIs). Changes in ionic

strength have shown to affect the rate of aggregation significantly: proteins with high

hydrophobic content on their surfaces, from unfolding, tend to have low solubility in aqueous

28

solutions. Charged amino acid residues can interact with the ionic groups in the solvent (i.e.

salts) and increase/decrease the solubility of the protein.52,57

At low salt concentrations (<

0.1M) the proteins behave as charged colloids interacting with the surrounding salt ions

resulting in an electrical double-layer force between the proteins. The net ion concentration in

the double layer surrounding the protein is greater than in the bulk solution and when the

protein double layer overlaps, a repulsive force is created. With increasing the ion

concentration, the PPIs become attractive (less repulsive) due to the range of the double layer

decreasing (explained by the DLVO theory). At higher salt concentrations (> 1M) the salt

induced attraction cannot be explained by the electrical double layer because the range of the

attraction is very small in concentrated solutions. Here, solubility is reduced due to attractive

PPIs originating from salting-out effects, which are related to the position of the ion in the

Hofmeister series.40

Proteins are most stable when they are hydrated. Once a certain ionic

strength is reached, the water molecules are no longer able to form a protective hydration

barrier around the protein surface leading to a decrease in solubility, thus, precipitation and/or

aggregation could follow. Hence, the greatest salting-out effect is from the most strongly

hydrated anions. Before protein chromatography, salting out was the major method used to

purify proteins.58,59

1.2.5.4.2 Other excipients

Other types of excipients include sugars (such as sucrose and trehalose) which prevent

unfolding during lyophilisation (dehydration), hydrophilic polymers (such as hydroxyl ethyl

starch), non-ionic surfactants that protect from surface-induced agitation stress (such as

polysorbate 80 (PS-80) and polysorbate 20 (PS-20)), amino acids (such as histidine, arginine

and glycine), and preservatives to prevent microbial growth (such as benzyl alcohol and

phenol).60,61

Around 80% of commercially available mAbs contain either PS-20 or PS-80 in the

formulation. They are also known through their trade names of Tween 20® and Tween 80

®,

respectively.62

Polysorbates are amphiphilic surfactants composed of fatty acid esters of

polyoxyethylene sorbitan.63

The hydrophobic nature is provided by the hydrocarbon chain

and the hydrophilic part is provided by the ethylene oxide units. PS-20 and PS-80 have a

common backbone and only differ in the structures of the fatty acid chains. Both PS-20 and

PS-80 have proven very efficient at preventing aggregation at the air/water interface,

particularly following agitation. Two main mechanisms of polysorbates have been postulated.

The first is preventing aggregation through polysorbate directly interacting with hydrophobic

29

regions of the proteins. However, this mechanism requires further understanding, particularly

in relation to polysorbate interaction with mAbs and more so in high concentrated mAb

solutions; as the current literature is quite conflicting.64

The second mechanism for protection

by polysorbates is competitive binding to surfaces (such as glass, air,); this mechanism is

proposed to be the main mechanism in mAb solutions. Due to their amphipathic nature,

polysorbate monomers accumulate at interfaces where they orient themselves to minimise

exposure of the hydrophobic parts to the aqueous solution (known as the hydrophobic effect:

adsorption of polysorbate at solid interfaces results in removal of the hydrophobic surface

from the water molecules as water prefers to hydrogen bond with itself).64,65

The surfactant concentration is an important parameter affecting surfactant behaviour in

solution. A sufficient amount is required in order to carry out their protective properties. The

critical micelle concentration (cmc), defined as the concentration at which micelles start to

form, is related to surface saturation by surfactant monomers. As the surfactant concentration

increases and the surfaces become saturated, the surfactant monomers form micelles in the

solution to evade the hydrophilic environment. Initially micelles are spherical aggregates with

the hydrophilic tails pointing outwards into the solution. The surfactant concentration chosen

in formulations tends to be just above the cmc. The formulation scientist cannot just simply

pick a high surfactant concentration (above the cmc) as Agarkhed et al. demonstrated

destabilising effects with PS-80 at high concentrations.66

Additionally, too high concentration

can be detrimental as surfactant-protein complexes can form. These complexes may be

immunogenic to patients.67,68

The cmc is a thermodynamic equilibrium and affected by

factors such as temperature and solutes, thus the cmc needs to be determined for the specific

formulation.65,69,70

Therefore determining the cmc and the optimal surfactant concentration

per protein formulation is strongly recommended. Fluorescence-based techniques and surface

tension for determining the cmc have been used for many years.71-74

However, one reason for

the lack of understanding and the scarce data in the literature on polysorbate-protein

interaction is the lack of technologies which can effectively characterise surfactant in the

presence of protein. Thus the development of techniques is required in order to assess micelle

behaviour in presence of other solution component, including protein.

Polysorbates do need to be used with caution, as cases have been found where degraded

polysorbate can lead to chemically modifying proteins. Additionally, opposite effects of

polysorbate have been observed where presence of PS-20 enhanced aggregation in thermal

stress experiments.63,69,75

30

1.2.5.5 Mechanical / Agitation stress

After product formulation, the final step is product filling i.e. into vials or syringes. There are

no further purification steps after the fill; hence, it is essential that compatibility of the protein

formulation with the filling equipment is assessed before this stage. Mechanical stress can

ensue at this stage due to the use of pumps.1 Post-production, agitation stress can impact

protein stability during transport/shipping. The effect of agitation on protein aggregation has

been assessed extensively and is well documented.76-78

Mechanical/agitation stress can lead

to/or accelerate aggregation through the increased occurrence of interactions at air-liquid

interfaces and material surfaces due to shear forces and local thermal effects - leading to

spontaneous adsorption of the protein molecules to the interfaces leading to protein unfolding

and aggregation. During the engagement of proteins to the air-liquid interface, the exposure

of protein to air increases with the increased development of irreversible aggregates (i.e.

aggregation mechanisms (5) and (3) – section 1.2.2).77

79

In addition, the materials used may have an impact on protein aggregation such as glass from

vials, rubber from stoppers and silicone oil from syringes. The level of adsorption between

different interfaces e.g. syringe surfaces such as glass, silicone oil80-82

is a growing area of

research, along with the combined effect of agitation stress; the mechanisms still need to be

fully explored. Additionally, the use of excipients i.e. polysorbates at preventing surface

induce aggregation is a focus area. Such studies are based on methods which mimic real-life

mechanical/agitation stress that the protein solution experiences during manufacture or

shipping; such as pumps, stirrers and horizontal or vertical shakers.83,84

1.2.5.5.1 Silicone oil in Syringes

Pharmaceutical companies are increasingly using pre-filled syringes (PFS) as an alternative

to vials for subcutaneous injection. A developing challenge that has come under the attention

of the biopharmaceutical industry is silicone oil particle formation.85

The majority of PFS are

made from glass and silicone oil is needed as a lubricant for their functionality i.e. to

facilitate smooth and easy movements of plungers with barrels and also in hypodermic

needles to reduce frictional drag and pain. Silicone oil also prevents adsorption of solution

components (e.g. protein) on the glass surface.86

Silicone oil has been used for over fifty

years in industrial applications. It is similar to traditional hydrocarbon oil except that its

molecular chain replaces carbon units with siloxane units. For siliconization, trimethylsiloxy

end-blocked polydimethylsiloxane (PDMS) in various viscosities is generally used. For

primary packaging components e.g. PFS the most commonly used silicone oil is DOW

31

CORNING® 360 Medical Fluid.87

Characteristics making PDMS suitable for pharmaceutical

applications include high thermal stability (at very hot and cold temperatures), low sensitivity

to UV radiation or oxidation agents and it is non-toxic.88

The PDMS molecule is spiral

shaped therefore easily compressible and is surrounded by methyl groups which are

responsible for the chemical properties of PDMS i.e. low interaction and low viscosity. These

chemical properties aid the effective distribution of PDMS on surfaces (homogenous

siliconization) thus making it an effective lubricant. The hydrophobicity of the methyl groups

make PDMS insoluble in water, and preferential binding to surfaces.87,88

A sufficient quantity of silicone oil is needed to create a homogeneous coating. However,

increasing the amount of silicone oil has been associated with silicone oil particles in the

solution. Particle formation during transport has been suggested to accelerate protein

aggregation. Silicone oil contamination was first reported in 1985 where elevated blood

glucose levels were reported in patients who were administered cloudy insulin from syringes;

later analysis revealed protein-particle formation from silicone oil.89

Another case where

aggregation became a problem during transport and administration came through the reports

of sustained elevation of intraocular pressure (IOP) and inflammation; after the intravitreal

use of bevacizumab and ranibizumab. Characterization of the particles, from bevacizumab

repackaged in the same types of plastic syringes as those used by external compounding

companies, showed that indeed nearly all the particles were silicone oil micro-droplets.

Additionally, from controlled lab experiments inflicting mechanical shock, there was an

increase in silicone oil droplets in the bevacizumab formulation, indicating that silicone oil

contaminants could indeed be responsible for the IOP elevations. Reports on IOP spikes from

ranibizumab are significantly fewer, and this could be due to the fact that ranibizumab are

stored in glass vials and are only exposed to the syringe for a brief time. Thus, the risk of

protein aggregation with silicone oil droplets is relatively lower compared to bevacizumab

repackaged in plastic syringes.27

It has been strongly suggested that silicone oil released into

the IgG solution can provide a nucleation site for aggregation of the proteins after

destabilizing the protein formulation.82

Based on such studies, is questioned whether silicone

oil itself has impact on aggregation, or whether the solution needs to be disturbed first.

There is a lack of knowledge on the effects of silicone oil on mAb products, and with the

growing number of mAbs in development and the increasing use of PFS, the area requires

further consideration. It is not currently possible to predict the sensitivity of different proteins

to silicone-oil induced aggregation. The study by Thirumangalathu et al. is one of the first

32

reported investigations in the literature on silicone oil induced aggregation of mAbs,

assessing anti-streptavidin IgG1. Silicone oil greatly enhanced the loss of soluble monomeric

protein during agitation compared to agitation alone. Moreover, agitations at higher speeds

lead to accelerated monomer loss. An interesting and very useful finding was that PS-20

completely inhibited silicone oil-induced loss of IgGs during agitation. The predominant

mechanism is assumed to be competition with the protein for adsorption to the air-water

interface (mechanism (5)).85

In another study, it was demonstrated that increasing the ionic

strength through the addition of salts (KCl and NaCl) reduces IgG1 protein aggregation at the

silicone oil water interface.90

Due to the strong correlation between particle counts and mechanical/agitation stress in

silicone-lubricated syringes, further studies are necessary to create the ideal shipping

guidelines for therapeutic products - in terms of storage and handling. It is also substantial to

find an analytical technology which is able to differentiate between protein, silicone oil and

protein-silicone particles; this is a current area of interest. Raman spectroscopy has shown the

capability to do this but requires further validation.91

Flow cytometry or FACS (fluorescence-

activated cell sorting) has also been proposed to differentiate between the groups. However,

data analysis did underline the lack of sensitivity / inconsistency of FACS to smaller

particles.92

Syringes are siliconized by either (i) spraying pure liquid silicone oil on the interior of the

surfaces of the syringe barrel or by (ii) baking on a layer that is sprayed as an emulsion of

silicone oil and water. The latter results in a layer that adheres to the surface and is far less

prone to silicone oil droplets breaking into the protein solution.87

Published literature has

ultimately focused on silicone oil particles (using spiking studies) and particle formation,

leaving research on the impact of the layer limited. A recent study focused on this method

through evaluating IgG1 solutions with the immobilized silicone oil water interface;

represented through covalently siliconized borosilicate glass beads. During agitation, through

end-over-end rotation, the glass beads remained stationary and the IgG1 solution passed over

them resulting in protein aggregation. It was suggested that if there was no air gap in the

syringes then aggregation could be minimal due to the minimised transport of the protein

solution over the silicone oil water interface. Nevertheless, this method of analysis allowed

the measurement of protein particle formation or aggregation without the interference of

silicone oil droplet particles while still assessing the effects of silicone oil induced

aggregation. Hence, standard aggregation detection techniques could be used to analyse the

33

aggregates (size exclusion chromatography and micro flow imaging), without the concern of

identifying silicone oil particles.93

To overcome the silicone oil-aggregation problem it has been suggested to substitute silicone

oil with another lubricant. However, the suggested replacements so far, such as Teflon, may

also cause problems - hydrophobic surfaces on Teflon have also been associated with protein

aggregation due to adsorption of its water interface.85

Further understanding on the

mechanisms and development of protein aggregation in the presence of silicone oil and

agitation effects is required in order to develop ways to minimise the unwanted

developments. By this means, facilitating a long term stabilized formulation which can

withstand the agitation and mechanical stress during handling and transportation of the

therapeutic drugs. To do this, as mentioned, technologies are required which can characterise

and differentiate between particles, i.e. protein and silicone oil, in the solution.

1.2.5.6 High Protein Concentrations and Viscosity Issues

1.2.5.6.1 High mAb dose requirements

Problems associated with protein stability are often amplified at high protein concentrations;

the close proximity between protein molecules promotes protein-protein interactions, leading

to elevated aggregation tendency.94

The development of high protein concentration

formulations are often needed to meet dose requirements of therapeutic drugs. Doses of 100-

300mg are often necessary to achieve significant clinical effects. This is not much of an issue

via intravenous routes; however, intravenous routes require the aid of health care

professionals, which can be an inconvenience for patients requiring regular administration

and also costs the healthcare sector substantial expenses. Thus, subcutaneous routes are

increasingly being used to enable patient self-administration. However, due to the doses of

mAb required, along-side the volume restrictions of subcutaneous injections (less than

1.5mL), highly concentrated drugs formulations are required (i.e. hundreds of mg/mL).95

As well as stability issues, using high protein concentrations, there are also delivery and

analytical challenges. Analytical challenges are in relation to the concentration limits of many

analytical techniques i.e. many techniques cannot characterise protein aggregates at high

mAb concentrations (see section 1.3). Delivery challenges are related to viscosity issues

when administering the drug through injection – discussed in the next section (1.2.5.6.2).

34

1.2.5.6.2 Issues with high viscosity in therapeutics

Regulations give guidance on the evaluation of syringe performance.96

This is often

addressed assessing the ‘syringeability’ and the ‘injectability’ of the therapeutic solution.

Syringeability refers to the ability of a solution to pass easily through a hypodermic needle

and includes factors such as ease of withdrawal and accuracy of dose measurements. It is

often assessed as the force required for the injection at a given injection rate. Injectability

refers to the performance of the formulation during injection and includes factors such as

force required for injection and evenness of flow.97

Both aspects should be characterised and

understood during product development. High viscosity leads to poor syringeability due to

high injection force required.98

The viscosity of solutions increases as a function of protein

concentration. At high concentrations (i.e. > 100mg/ml) viscosities often reach above the ~20

cP limit for subcutaneous injection, which would require a high injection force of more than

30 N over a suitable injection time of around 1 minute. High injection force is undesirable as

linked with high injection pain in patients.99,100

Many aspects have been assessed in order to address problems with syringeability such as the

needle geometry i.e. length, diameter. Although wider needles will aid syringeability, fine

needles reduce pain of injection; typical configurations for prefilled syringes are 25 G and 27

G.101

Thus, based on patient’s comfort and compliance and necessities of the syringe system,

the needle geometry is quite restricted. Once the syringe system and mAb concentration has

been chosen, the formulation scientist can only influence the viscosity by formulation

parameters such as pH, ionic strength, salts and types of excipients. It is known that the

material’s viscosity is a physical property that is sensitive to material’s characteristics i.e. the

protein itself and to the properties of the surrounding environment i.e. solution

components102,103

Thus, these aforementioned parameters are investigated to ensue optimised

delivery.98

The link between viscosity and PPI is a developing research area, with studies showing high

solution viscosity can result from protein self-association / aggregation. However, methods

(or models) for predicting the viscosity behaviour of concentrated solutions from dilute

solutions still require understanding and development. Such studies often assess the

properties of different formulations (i.e. effect of ionic strength and pH) which can reduce

solution viscosity and can also act as models for relating viscosity behaviour between dilute

and concentrated mAb solutions.43,104-106

35

1.2.5.6.3 Measuring and predicting viscosity – the study of flow

It is essential to measure solution viscosity during early stages of formulation, due to the

potential issues with viscosity which can develop (as discussed in the previous chapter). To

understand viscosity and the fundamentals principles of rheology techniques it is important to

understand the difference between Newtonian and non-Newtonian fluids. A simple linear

relation is described between shear stress and shear rate, known as Newton’s Law of

viscosity (this is now the fundamental principal in rheology techniques as described above):

the viscosity (η) is linearly proportional to the shear stress over the shear rate, for Newtonian

fluids. Examples of Newtonian fluids include water, organic solvents and honey. Low protein

concentration solutions have also shown to displace Newtonian behaviour.107

Non-Newtonian

fluids have a viscosity which is not fixed. It shows a ‘shear thinning’ or ‘shear thickening’

behaviour where viscosity decreases or increases, respectively, with applied shear rate. Shear

thinning behaviour has been observed with high mAb concentration solutions and following

aggregation. Studies have indicated aggregation causing the shift from Newtonian to non-

Newtonian behaviour.100,108

Other studies have also observed similar affects, relating

attractive PPIs to shear thinning behaviour.107,109

Rheometry is the study of flow. There are two forms of flow measured on rheometers: the

shear flow and extensional flow; the shear flow is the most commonly and easily measured.

For shear flow, the fluid is described as layers of fluid sliding over one another with each

layer moving faster than the one underneath it, such that the bottom layer is stationary. An

external force in the form of a ‘shear stress’ acts on the fluid over a unit area. This causes the

layers to move with the top layer moving the furthest at a distance x. A displacement gradient

is created across the layers and is termed as the ‘shear strain’. For fluids, the shear strain will

increase with the period of applied stress. The velocity gradient created is termed as the

‘shear rate’ – the change of strain over time. The ‘shear viscosity’, also known as the

‘dynamic viscosity’, is calculated as the coefficient between the shear stress and shear rate.

This is a quantitate measure of the internal fluid friction.110,111

Thus, the shear viscosity (),

measured by conventional rheometers, is defined as the shearing stress per unit rate of shear

strain via Newton’s formula.112

This is also known as macro-rheology. There are numerous

methods to measure solution viscosity and each come with their own set of benefits and

limitations. At a macroscopic level, the viscosity is the rate of transfer of momentum in a

liquid.113

A well-known conventional bulk rheometer utilised in many studies is the cone and

plate rheometer.105,114-116

Sample is loaded into a measurement plate and a solvent trap is used

to prevent solvent evaporation. One issue identified is the surface adsorption of the solution

36

components which can influence the measurement of bulk shear rheology of surfactant-free

protein solutions. Many proteins are known to adsorb at solution/material and solution/air

interfaces.117

Other commonly used techniques measuring bulk rheometry include glass

capillary and falling ball viscometers. The capillary viscometer measures the time required

for level of liquid to fall from one mark to another.118

There are techniques where there is no

solution/air interface i.e. microfluidic platforms. Microfluidic platforms are increasingly

being developed and used as they allow measurements at high flow rates (high shear stress)

thus allowing to explore viscosities in mAb solutions at similar high flow rates achieved

during injection.117

A common limitation for bulk rheometry is the large volume requirements which are in the

millilitre range. Due to limited material at early stages, viscosity is normally postponed until

later studies when more material is produced. Another limitation is the failure to detect local

variation of properties. Micro-rheology was developed to overcome these limitations of bulk

rheology. At a microscopic level, the solution viscosity is the resistance to solute mobility.113

Micro-rheology is the rheology in the micron size domain, thus having the advantage of

requiring small sample volume thereby making it suitable for early formulation development.

The second advantage is having the ability to examine local heterogeneity of the material and

thus changes between formulations. The third advantage is the non-invasiveness to samples,

as no external force is required.119

Agreements between different rheology technique are rare,

nevertheless, it has been shown by Del Giudice et al. where seven different rheological

techniques were compared, consisting of macro-rheology, micro-rheology and microfluidic

platforms and showed agreements across the techniques in hydroxyethyl cellulose aqueous

solutions.120

Micro-rheology techniques are based on the measurement of the motion of tracer particles in

the fluid and can determine the zero-shear viscosity (ZSV).120

The ZSV, as the name

suggests, is the viscosity measured at a shear rate approaching zero. It is difficult to measure

very low shear rates using standard rheometers – thus is it often extrapolated through

accessing obtainable shear rates. The ZSV is an important property as it is a sensitive

indicator of changes in a products formulation, thus useful in formulation development. The

effect of protein clusters on solution viscosity is a developing area, as already stressed. It has

been shown that only the low-shear viscosity is sensitive to the presence of such clusters,

particularly in unstable protein solutions;121

thus conventional rheometers cannot detect this

37

change in viscosity driven by particle clusters at low shear rates. However, the artefacts with

the current micro-rheology methods need to be assessed.122

Commonly used micro-rheology techniques include particle tracking, diffusing wave

spectroscopy (DWS), and DLS-based micro-rheology techniques.119

They all exploit the

Brownian motion of particles to obtain local rheological properties. The main problems

associated with the techniques are finding the correct tracer particle to probe the rheological

response (e.g. size of the probe), potential interaction between the probes and the protein, and

the resolution of the apparatus used.119

DWS requires large concentrations of tracer particles

such that they are high enough to dominate the scattering which leads to an increased

potential of the tracer particles interacting with proteins.123

He et al. reported the use of DLS

for measuring viscosity of mAb solutions. Polystyrene beads of known size were utilised

such that the size of the beads is large enough to allow easy separation of the DLS signals

between the beads and the protein; a size of 150nm radius was used. Initial data was

promising with glycerol solutions, showing good agreement with literature and the cone-plate

method. However, further studies124

measuring highly concentrated mAb solutions showed

potential issues suggesting bead-protein interaction which affected the viscosity value. This

was more apparent in complex buffers i.e. in the presence of salts. Thus, due to the pitfall

with the use of beads, this system is not applicable for mAb solutions in complex buffers

which would be industrially relevant.125

Fluorescence correlation spectroscopy (FCS) measures the diffusion of a molecule through

fluctuations in the fluorescent intensity caused by particles moving in and out of the solution.

The principles of FCS can be seen in section 1.4.3.1 and in the literature.126,127

With the

development of confocal microscopy and photo-detection techniques, FCS has become quite

advanced and accurate for analysis of dynamic information; thus has been used extensively in

the characterisation of proteins.128-131

The application of FCS to measure viscosity from the

diffusion of a dye is not recent. The microscopic dynamical properties, such as viscosity,

were derived from microscopic fluctuations i.e. diffusion of the probe, many decades ago.129

The first application of FCS using probes to measure micro-viscosity was in cells: Yoshida et

al, presented the application of FCS to investigate the microenvironment of the internal space

of organelles.132

Several groups have applied the system to characterise viscosity in aqueous

solutions: Holyst et al. utilised FCS to measure the diffusion of nanoprobes in crowded

environments; mimicking the crowded environment in cells and thus assessing different

rheological behaviours.133

A Korean group utilised FCS in assessing the micro-viscosity of

38

sucrose solutions. The accuracy and reliability of the system was demonstrated and it was

proposed as a promising tool for study of micro-viscosity of liquids.134

Similar FCS

applications have been used with glycerol135

and surfactant solutions.133

It is yet to be utilised

in mAb solutions.

To summarise this section, three areas of research require development in order to support the

development of highly concentrated mAb solutions suitable for subcutaneous delivery: (i)

understand the behaviour of the mAb at high concentrations, (ii) high throughput analytical

tools to estimate and predict rheology behaviour at high mAb concentrations, and (iii)

understand the role of the formulation in controlling solution viscosity.

1.3 Analytical Approaches Utilised in the Detection and Characterisation of Protein

Aggregation

Detection of protein aggregates in therapeutic drugs is proscribed by the national legislation

as laid out by US, European and Japanese Pharmacopoeia standards; recent revision of

USP23 <788>.136

The size ranges of protein aggregates are diverse (see Figure 1.2) and the

United stated Pharmacopeia and the European Pharmacopoeia currently define concentration

limits in parental solutions as > 10µm. Nevertheless, regulatory authorities expect

quantitative characterisation of micron particles 1-10 µm and qualitative characterisation of

submicron particles 0.1-1 µm in the early stages of development.23

In industry and academia, there are numerous techniques that have been developed over the

years to detect and characterise protein aggregates and each come with their own set of

benefits and limitations. Many techniques do cover the size range required by regulatory

authorities – but such techniques possess limitations in terms of accuracy and data

interpretation. Additionally, most analytical techniques have limitations in relation to their

ability to measure high protein concentrations (Table 1.1); such techniques require sample

pre-treatment (i.e. dilution) prior to measurement.

39

Table 1.1: Comparison of techniques reported for protein characterisation (aggregation)

Comparison of techniques reported for protein characterisation (aggregation).23,137,138

Technique Advantages Limitations

SEC

(SEC-MALLS)

• Good separation of large

molecules from small molecules

• Accurate determination of

molecular weight for large

regular-shaped proteins

• Low sample volume requirement

• Preserves biological activity

• Only a limited number of bands

developed

• > 10% difference in molar mass

required to have good resolution

• Poor molecular weight

determination for irregular shaped

proteins

• Aggregates can be lost by non-

specific binding to column

• Time consuming

DLS • Good for determining size

distribution of particles

• Good repeatability

• Low sample volume requirement

• No sample loss

• Non-invasive

• Less accurate for distinguishing

small oligomers

• Inaccurate for poly-disperse

samples

• Found to over-estimate mean size

of clusters

• Concentration limit of 25 mg/ml

• Sensitive to contaminants i.e. dust

NTA • Sizes and counts particles

individually

• Accurate data

• Quick data acquisition

• Concentration limits thus sample

dilution required

• Small aggregates missed due to

poor resolution of small aggregates

• Adsorption and sheer reported to

result in aggregates

MFI • Quick sample set-up

• Can distinguish matter (e.g.

silicone oil) from protein

• Direct visualisation of particles

• Size, count and shape of each

particle provided

• Large sample volume requirement

• Use of glass increases risk of

aggregation

• Can over-estimate size

• Poor particle differentiation for

particles < 4m

RMM

Archimedes

• Good for molar mass

• Can distinguish matter (e.g.

silicone oil) from protein

• Accurate data for particles < 2

m

• Sample loss due to clogging

• No differentiation between

different density particles (no

refractive index detector)

• Possible misclassification of

particle size

• Concentration limit thus sample

dilution often required

1.3.1 Light Scattering Methods

Light scattering methods are used extensively in protein aggregation analysis. Light

scattering can be described by Tyndall scattering or Rayleigh scattering, and light scattering

techniques are generally based on one or the other. Rayleigh scattering requires the particles

to be much smaller the wavelength of the light, whereas Tyndall scattering occurs when the

size of the particles in question are about the same size or larger than the wavelength being

40

described. The intensity of the scattered light depends on the wavelength of the incident light,

the angle of observation, and the size and shape of the particles (generally, spherical

particles). All light scattering instruments consist of a light source (i.e. a laser), a

spectrometer (for the optical set-up for defining the scatter angle volume), a detector, a signal

analyser and a computer software for the analysis.139

Common light scattering techniques include static light scattering (SLS), dynamic light

scattering (DLS), and size-exclusion chromatography (SEC) with multi angle laser light

scattering (MALLS), each providing different information on the protein sample based on the

instrument’s capabilities.

1.3.1.1 Static Light Scattering (SLS)

SLS, also known as ‘total intensity light scattering’ and ‘differential light scattering’, is the

classical light scattering method, measuring the intensity of the scattered light to obtain the

molecular weight. The measures quantify the ‘excess Rayleigh ratio’, which is directly

proportional to the intensity of the scattered light in excess of the light scattered by the pure

solvent. Subsequently, knowledge of the ratio verses the scattering angle and concentration is

utilised to determine the molar mass, size, and self-interactions of the sample.140

SLS determines the weight-averaged molar mass of the macromolecules in the solution, thus

the data is more representable if the solution is mono-disperse. There are many limitations

with SLS which limit its use in relation to protein aggregation. SLS analysis is limited to

macromolecules in the range of 50 x 103 to 50 x 10

6 g/mol (or 1 m) (see Figure 1.5), as the

SLS theory (Rayleigh-Gans-Debye approximation) becomes inapplicable for very large

macromolecules.

1.3.1.2 Dynamic Light Scattering (DLS)

DLS, also known as photon correlation spectroscopy or quasi-elastic light scattering, has

been a well-established method for over 40 years. SLS and DLS differ in that SLS measures

time average intensities while DLS measures real-time intensities and thus dynamic

properties. DLS is based on the Brownian motion of particles where particles or molecules in

solution cause (laser) light to be scattered at different intensities - thereby imprinting

information on their motion – and time-dependent fluctuations in the scattered light are

recorded by a fast photon counter. The fluctuations are directly related to the rate of diffusion

of the molecule through the solvent (i.e. the diffusion coefficient, a common parameter used

in analysing protein aggregates), which is calculated via fitting a correlation curve to an

41

exponential function. The diffusion coefficient is the main quantity measured by DLS and is

subsequently used to calculate the hydrodynamic radius (the radius of a hard sphere with the

same diffusion coefficient) using Stokes-Einstein relationship (see Equation 1.1).137

𝑅ℎ =𝑘𝑇

6𝜋𝜂𝐷⁄ Equation 1.1

Where 𝑅ℎ represents the hydrodynamic radius, k is the Boltzmann constant, T is the

temperature, 𝜂 represents the viscosity and D is the diffusion coefficient.

DLS is used mainly for aggregates in the nanometre range, and benefits include that it is not

destructive to the sample and requires little preparation time (as much as SLS in fact). A

practical advantage over SLS is the level of immunity to stray light, which permits

measurements in small volumes with free surfaces. On the other hand, DLS is not as sensitive

as SLS hence would require higher concentrations.140

A noticeable issue for highly polydisperse samples is that the scattering of a few large

particles can over-power the small particles, such as monomers. As DLS is an ensemble

technique, it tries to recover a particle size distribution from the combined signal of all

particles present in the sample, thereby resulting in the overestimation of mean size clusters.

Hence, with the intensity-based size measurements, the data does not necessarily reflect the

different sizes present in the samples. In such cases it maybe more reliable to use volume or

weight based distributions; but these are also bias to larger particles to some extent.141,142

Consequently, an inaccurate distribution will result in unreliable data on the derived sizes and

other parameters analysed. To summarize, DLS appears to be less suitable for particles in the

micrometre range, limited to a particular protein concentration range (i.e. < 25mg/ml) and

based on many assumptions that may not represent real samples.137

1.3.1.3 SEC-MALLS

Size-exclusion chromatography (SEC) is a commonly used chromatographic method where

molecules in solution are separated by their size. The SEC column consists of porous

polymer beads of different sizes so that when the solution passes through the column the

particles with the larger hydrodynamic volumes have a shorter path and smaller particles

travel further. The size-based separation allows a calibration curve to be derived from a set of

known analytes to be used to estimate the molecular weight of the unknown analyte.143

The

technique is typically applied to large molecules or macromolecular complexes such as

proteins. However, as proteins vary in their overall shape, their hydrodynamic radius does not

42

correlate accurately with their molecular weight.144,145

Hence, the accuracy of the determined

molecular weight can be questioned. Furthermore, the SEC column itself has many

drawbacks and potential issues that are still under investigation. For example, the presence of

high salt systems has shown to increase the potential of particulates forming in the mobile

phases, thereby affecting the system and column performance. In addition, protein aggregates

have been known to interact and bind to the column (clogging), resulting in the loss of some

aggregates.146,147

SEC is applied very frequently in aggregation detection despite not meeting

the ideal requirements of an aggregation detection technique.148

For example, most analytical

methods (including SEC) change the local environment of the protein, either by dilution or by

contact with other substances such as eluents. An ideal method would have minimal changes

in local environment.

SEC is generally coupled with other techniques to permit characterisation of the separated

fragments, such as MALLS. MALLS is a process of light passing through the solvent causing

the light to scatter off the axis where intensity is measured at different angles to the beam. If

the solute is of a different refractive index, then there is excess light scattering dependant on

the concentration of the solute or on the molar mass on the scattering angle. Angular

dependence maybe used for size of particles > 200 kDa; thus with SEC-MALLS, monomer

concentration as well as size can be determined.149

A common problem with light scattering techniques is the impact of small amounts of dust or

other small particulates in the sample solution. All solutions have to be thoroughly clear of

dust and supra-molecular particles (especially for smaller molecules); otherwise, this may

lead to non-representable data. The total light scattered is based on the sum of the intensities

scattered by each species – if the mass of a dust particle is a million times that of the protein,

then only one-millionth of the concentration of dust particles is required to produce the same

scattering intensity as the protein.140,150

Furthermore, light scattering techniques often require

sample dilution which introduces uncertainty in the analysis as the particle population is no

longer representative of the concentrated solution. Also, dilution can stress the protein

samples. As a result, samples that are more likely to change their properties after dilution are

fundamentally excluded from investigation. In addition, they require a separate measurement

of the refractive index for determining other characteristics such as concentration.

43

Figure 1.5: Analytical capabilities of protein aggregation detection methods.

Analytical capabilities, i.e. detectable size ranges, of protein aggregation detection methods used in

industry and academia. The grey area specifies the size ranges required to be detected / characterised

by regulations. Adapted from Zolls et al.137

1.3.2 Resonance Mass Measurement (Archimedes)

The recently developed Archimedes is a non-optical technique which harnesses the technique

of resonance mass measurement (RMM), whereby the frequency of a mechanical resonator

changes when a mass is added. Archimedes detects particle sizes ranging from 0.05 µm to 5

µm in diameter and a very attractive feature is the ability to distinguish between protein and

foreign matter: The sample solution is flushed through a micro-channel inside a resonating

cantilever. Particles pass through the sensor one-by-one and their mass is measured. The net

change in frequency is proportional to the buoyant mass of the particles passing the

channel;151

where light-weight positively buoyant particles (i.e. silicone oil droplets) and

dense negatively buoyant particles (i.e. protein particles) can be clearly discriminated as they

increase and decrease the frequency of the cantilever respectively. The ParticleLab software

separates the data for the two populations without the need for additional analysis.

Archimedes also provides information on concentration, fluid density and fluid viscosity.

44

The RMM Archimedes utilises two sensors, namely the nano-sensor and micro-sensor, and

their selection is based on the expected particle size range. Malvern provided some

information (see Table 1.2) which lists the polystyrene latex beads sizes, the theoretical value

of the buoyant mass and an estimate of protein aggregates’ sizes based on a density of 1.32

g/mL.

Table 1.2: Guidelines for nano- and micro- sensor use (information provided by Malvern).

Polystyrene latex (density 1.05) Proteins (density 1.32)

Nano-sensor Micro-sensor Buoyant mass

0.216 m 1.78E-16 0.118 nm

0.296 m 7.16E-16 0.162 nm

0.400 m 0.400 m 1.77E-15 0.219 nm

0.498 m 0.498 m 3.41E-15 0.272 nm

0.707 m 0.707 m 9.75E-15 0.387 nm

0.994 m 0.994 m 2.71E-14 0.543 nm

1.019 m 2.92E-14 0.557 nm

1.587 m 1.10E-13 0.868 nm

1.999 m 2.20E-13 1.093 nm

3.002 m 7.47E-13 1.641 nm

4.998 m 3.45E-12 2.733 nm

In Table 1.2, the light grey stands for conditions for which the users need to set the limit of

detection (LOD) manually; the automatic measurement capability of the instrument

corresponds to the dark grey cells, while where caution should be exercised with the particles

concentration (upper size range of sensors) corresponds to the red cells.

Weinbuch et al. compared data obtained between Archimedes and micro-flow imaging (MFI)

(MFI overview given in section 1.3.3.2), where Archimedes detected a higher fraction of

silicone oil for sizes above 1 µm while MFI detected more protein particles. Overall, from the

samples covered in the overlapping size ranges between both techniques, Archimedes data

was considered to be more accurate. However, a major limitation of the Archimedes is the

potential clogging of larger particles (> 0.5µm) in the column, thus making its actual upper

limit lower than the proposed upper limit in Table 1.2. In addition, there is ambiguous

differentiation between particles of a similar mass because of the physical detection principle.

For example, there is no differentiation possible if there is more than one particle type with

higher density than the buffer (e.g. protein and rubber).23

Here, there would be two particle

types both illustrating negative buoyancy. Furthermore, Archimedes faces a challenge in the

presence of complexes consisting both protein and silicone oil: protein-silicone oil complexes

45

have been observed in protein solutions (e.g. lysozyme and mAbs) spiked with silicone oil

emulsions.152,153

The Archimedes RMM system could miscalculate the size due to the

simultaneous influence of both material densities on the density of the complex. These

complexes could also be completely missed due to the higher density compensating for the

lower density.23

1.3.3 Microscopic Methods

1.3.3.1 Atomic Force Microscopy (AFM) and Transmission Electron Microscopy

(TEM)

AFM and TEM both have wide analytical capabilities (see Figure 1.5). TEM is the main

electron microscopy method, and involves the isolated samples to be illuminated by an

electron beam thereby offering images of the particles in high resolution. However, sample

preparation can be potentially damaged by the electron beam.137,154

AFM involves the sample

to be scanned mechanically using a cantilever; again, offering high resolution images and

seems suitable for the study of protein aggregates.155

However, a disadvantage of all

microscopic techniques is that they only analyse a small fraction of the sample – thus the data

may not be representable of the whole sample. In addition, both AFM and TEM are

expensive and acquisition of the data is time consuming.137

1.3.3.2 Micro-Flow Imaging (MFI)

Micro-flow imagine (MFI) is an emerging flow imaging technique covering the sub-visible

protein size range of 1-400 µm. MFI provides a database containing the count, size, and

morphology of particles and has become very quickly established in the biopharmaceutical

industry. MFI has been applied in many protein formulations, characterising large protein

particles156,157

due to its size range matching regulations and its ability to differentiate

between proteins and foreign matter, such as silicone oil.23,158

In a study on IgG1 aggregation

following freeze-thaw cycles, protein particles (aggregates) were seen to be highly

heterogeneous in shape ranging from small dense fibres to large ribbon-like aggregates.159

The main advantage of image techniques is the direct visualisation provided to obtain

qualitative information. Direct imaging means the system does not rely on a correlation

between particle size and the scattering/optical signal; no calibration is required. The

technique involves particles to be illuminated by light and bright-field images are captured in

successive frames by a CCD camera. The system software (i.e. MFI View Analysis Suite -

MVAS) extracts particle images by using a sensitive threshold to identify pixel groups which

define each particle. A continuous sample stream passes through a glass flow cell of 80-400

46

m in diameter as an imaging field. The flow cell is centered in the field-of-view of a custom

magnification system having a well characterised depth-of field. The combination of system

magnification and flow-cell depth determined the accuracy of the measured sample

concentration. The successive frames are analysed in real time. Typical volumes range from

< 0.25 to tens of millilitres, which is a large volume in comparison to other techniques;

however this allows the detection of large particles which are present in low

concentrations.160,161

Particle differentiation is done though applying custom-designed morphology filters, with

parameters such as intensity, circularity and aspect ratio. Subpopulations can then be isolated

and independently analysed. Parameters are set based on available information or using

information obtained from using control samples. For example, MFI has been used to select

silicone oil particles in protein solutions using a simple aspect ratio as a filter, for particles ≥

5 m – an accuracy of 96% has been achieved. However, due to diffraction and pixilation

effects, the minimum limit for particle size is around 4 m, where useful morphological

information can be obtained and thus used for particle differentiation.156,159

It should be noted that the development of a customised filter does require extra work, in

order to obtain information to set the range of several morphology parameters such that a

certain sub-population can be selected. This would need to be carried out on case-by-case

basis depending on the materials.158

Furthermore, the presence of glass adds the risk of proteins binding to the glass surfaces

which can increase the risk of surface-induced aggregation (see mechanism (5) – section

1.2.2). Another disadvantage is the requirement sample dilution and since it is a light-based

technique, data on particle number/size can be overestimated with high concentrated

samples.137,156

As mentioned, Archimedes (RMM) and MFI have been used together in studies. Archimedes

has been suggested for particles < 2 m and MFI for particles > 2 m (Figure 1.5), hence the

two methods could be used together to cover a broader size range – in relation to assessing

the effects of silicone oil on protein aggregation.23

As they are such new techniques

(especially the Archimedes), further validation is required to determine their accuracy and

reliability. Studies have already shown problems with Archimedes approaching its upper

limit and MFI in its lower limit i.e. < 4m in particle differentiation.

47

1.3.3.3 Nanoparticle Tracking Analysis

Nanoparticle tracking analysis (NTA) is a microscopic method developed by NanoSight Ltd

in 2006, for characterising analytes in the nanometre size range. It combines laser scattering

microscopy with a charge-couples device (CCD) camera which enables visualization and the

tracking of particles individually. The technique is defined as a counting upgrade of

DLS.155,162,163

Through the comparison of DLS and NTA, NTA was shown to accurately

analyse polydisperse samples which is a benefit over DLS. On the other hand, NTA has a

lower limit of detection (LOD) of around 30nm in diameter, and for very low refractive index

materials such as protein aggregates, detection of particles < 80nm becomes increasingly

difficult. This particular issue is heightened in high background sample types containing

higher concentration of monomeric non-aggregated protein. DLS is capable of detecting

particles < 5nm, (see Figure 1.5) but as mentioned earlier, the results from DLS can be

subjected to sample bias. Further limitations of NTA include the need for prior sample

dilution; the lengthy time taken for parameter adjustments, unlike DLS which is very user-

friendly; and low reproducibility (see Table 1.1 for a comparison of the aforementioned

techniques). Thereby there is a current effort at combining the two techniques together where

they can compensate for each other’s limitations.163-165

1.4 Fluorescence-based Approaches

Fluorescence based approaches are not used commercially for mAb aggregation

characterisation; however their use is increasing as their applications and advantages become

apparent. This section aims to cover the applicability of fluoresce in protein aggregation

characterisation and the application and recent advances of confocal laser scanning

microscopy.

1.4.1 Concept of Fluorescence

Fluorescence is the result of a series of electron-stage processes, involving the emission of

light following excitation, and fluorophores can be simply described as molecules that

possess fluorescence characteristic. Excitation states can be created by physical (i.e.

adsorption of light), mechanical (i.e. friction) or a chemical mechanism – by a source of

higher energy.166

It is comprised of a three-stage process: (i) excitation: a photon supplied by

an external source is adsorbed by the molecule in the ground state generating an excited

electronic state which has a higher energy level; (ii) excited state-lifetime: the higher energy

level is rapidly converted to the relaxed excited state (also known as vibrational relaxation);

(iii) emission: the molecule emits a photon from the relaxed state and returns to the ground

48

state. A Jablonski diagram is typically used to schematically illustrate fluorescence

activity.167

In Figure 1.6 the ground state (S0) and the higher energy levels of the first (S1) and

second (S2) excited singlet states are illustrated by stack of horizontal lines; with the thicker

line representing the electronic energy levels and the thinner lines representing the various

vibrational energy states. In the figure, the first absorption transition occurs from the lowest

ground state energy level to a higher vibrational energy level of S2. The second transition

occurs from the second vibrational level of the ground state to the highest vibrational energy

of S1. Relaxation from this long lived state is accompanied by emission (fluorescence). There

are other relaxation pathways which can compete with the fluorescence emission process. For

example, the excited state energy can be dispersed non-radiatively as heat (as illustrated in

Figure 1.6 by the dashed purple arrow) or the excited molecule can collide with another

molecule and transfer energy as quenching (dashed red arrow in Figure 1.6) thus resulting in

decreased fluorescence intensity.

Figure 1.6: Jablonski diagram illustrating the energy states of a molecule.

Jablonski diagram illustrating the energy states of a fluorescent molecule. S0 representing the ground

state where the fluorophore is non-excited and S1 and S2 are electronic excited states following

absorption. Adapted from Olympus Corporation.168

It has been stated that a single fluorophore has the ability to generate thousands of detectable

photons. Thus, fluorescence is based on the property of molecules to adsorb light at a

49

particular wavelength and to subsequently emit the light at a longer wavelength. The shift in

wavelength is termed as the Stokes shift.167,169

Many fluorescence based techniques have been developed such as fluorescence microscopes,

fluorescence scanners, micro-plate readers, flow cytometers and spectrofluorometers. Over

the last 10 years, fluorescence has been associated with two Nobel prizes in chemistry: In

2008170

for the discovery and development of the green fluorescent protein, GFP; and in

2014171

for the development of super-resolution fluorescence microscopy.

1.4.2 Fluorescent Probes

1.4.2.1 Fluorescent probes – important properties

The important characteristics which need to be considered with fluorophore selection are

discussed in this section. The first is fluorescence intensity (FI) which is dependent on several

factors: the quantum yield, intensity of the excitation source, the fluorophore concentration,

path length, the molecular extinction coefficient of the fluorophore, the adsorption

coefficient, and the photo-stability of the dye.172

For fluorescence microscopy, high quantum

yield is required. The quantum yield, also known as the quantum efficiency, represents the

efficiency of a fluorophore in absorption and emission of photons; defined as the ratio of

number of photons emitted over number of photons adsorbed. Fluctuations in stability

resulting in reduced efficiency can occur with dyes due to environmental factors such as pH,

proximity to quenchers or laser light.173

Another encountered issue is photobleaching.

Photobleaching is a destruction of a fluorophore due to high intensity illumination and/or

prolonged illumination, in its excited state resulting in the loss of fluorescence.129,172

Also,

background fluorescence can occur as a result of autofluorescence and compromises

fluorescence measurements thus must be subtracted from the analysis. Autofluorescence is a

naturally-occurring phenomenon that is observed with biological structures (intrinsic

fluorescence).129

1.4.2.2 Fluorophores Utilised in Protein Characterisation

Fluorophores can be divided into two classes, namely intrinsic and extrinsic. Intrinsic

protein fluorescence (or auto fluorescence) is derived from naturally fluorescent amino acid

residues: phenylalanine, tryptophan and tyrosine. Only the latter two are used experimentally

as their quantum yields are high enough to give a fluorescence signal. In the protein’s native

state they are located within the core of the protein, whereas, in the unfolded/partially folded

state they become exposed, hence they are used to follow protein folding.174

Gaudet et al.

50

demonstrated the potential use of a capillary-based intrinsic fluorescence technique to obtain

dissociation constants determined from a change in protein stability. Through this, they

determined the parameters of the thermodynamics of protein stability, focusing on the

interaction between rapamycan and the 12kDa FK506 binding protein, a target used in

prostate cancer treatment. As this technique did not require labelling, it is in principle

applicable to any protein with intrinsic fluorescence, particularly tryptophan residues.175

In

another approach, a high throughput formulation based on a fluorescence micro-plate reader

was used to select protein stabilizing excipients for the formulation of salmon calcitonin;

used in osteoporosis treatment, approved by the FDA in 1975. In this study, intrinsic

tryptophan fluorescence was compared with extrinsic fluorescence, and in the case of salmon

calcitonin, the fluorescence intensity of tryptophan only showed a change in the strongly

aggregated formulations; whereas the fluorescence intensity from extrinsic fluorescence was

more sensitive in detecting aggregation.176

Intrinsic fluorescence has been used for a number of papers attaining valuable input into

characterising protein unfolding.177-179

The range of commercially available fluorophores is

very diverse and can be split into many categories based on their origin and/or properties.

Natural dyes are derived from natural resources such as plants, animals, fruits and minerals.

Synthetic dyes have two main advantages over natural dyes: cost and consistency. Over the

years, the development of new synthetic dyes came with greater variation and versatility

(with a wide colour range) and increase in photo-stability.180-182

For fluorescence microscopy

synthesised (extrinsic) compounds are typically used that have some degree of conjugated

double bonds. Such compounds have ring structures (aromatic molecules) with π bonds that

easily distribute outer orbital electrons over a wide area. The more conjugated bonds the

molecule has, the lower the excited energy requirement and the longer the wavelength the

exciting light can be. The emitted light is shifted in the same direction. Moreover, the

efficiency increases with the number of π bonds, as measured by fluorescent quantum

yield.167

Extrinsic dyes can be covalently attached to proteins (via the ɛ-amino group of lysine, the α-

amino group of the N-terminus, or the thiol group of cysteine) or non-covalently attached (via

hydrophobic or electrostatic interactions). Here, for biopharmaceutical formulations, the

interest is in non-covalent extrinsic dyes, specifically dyes which bind specifically to protein

hydrophobic surfaces. Such dyes, in a hydrophobic environment, exhibit strong fluorescence

(high quantum yield) whereas in an aqueous environment they are insoluble and fluorescence

51

is strongly quenched.183,184

Hawe et al. carried out in-depth research into the currently most

common dyes used in protein characterisation: 1-anilinonaphthalene-8-sulfonate (ANS),

4,4’-bis-1-anilinonaphthalene-8-sulfonate (Bis-ANS), 9-diethylalamino-5-benzophenozazine-

5-one (Nile Red), dicyanovinyl-julolidine (DCVJ), Congo Red and Thioflavin T (ThT) -

focusing on their capabilities and limitations in protein characterisation. Different dyes are

suited to the many different aspects to protein characterisation such as assessment of protein

denaturation, folding and molten globular intermediates, detection of amyloid fibrils,

assessment of surface hydrophobicity and protein aggregation; thus the choice of dye should

be suited to the application. The various fluorescent dyes have shown to exhibit different

specificity and selectivity for different aggregate structures and sizes. Table 1.3 summarises

the dyes used and the relevant properties, whilst Figure 1.7 illustrates their chemical

structures. ANS and Bis-ANS have demonstrated to be very suitable for early stage protein

aggregation i.e. small reversible aggregates. It is known that at these early stages, protein

aggregation species are present in low concentrations and may exhibit a short life span.183

Fink et al. characterised IL-1, an important mediator of the inflammatory response,185

at

early stages of folding comparing ANS and tryptophan fluorescence. With tryptophan

fluorescence, the results indicated that aggregation events during folding can be potentially

mistaken as folding events, hence are missed. Additionally, previous light scattering

techniques, such as ‘stopped-flow light scattering’, were not able to detect the early small

aggregates, illustrating the high risk of missing aggregates with such techniques. It was

shown that the detected early aggregates with ANS fluorescence were indeed reversible as

there was no soluble protein loss at this stage.20,186,187

Bis-ANS has been used to assess

aggregation in thermally-stressed recombinant human factor VIII; using differential scanning

calorimetry. When comparing the data against intrinsic fluorescence data, again, data

indicated that some small aggregates were missed at the lower temperature analysed (37C)

with intrinsic fluorescence.188

In a study by Lindgren et al. of the aggregation states of the

protein transthyretin (TTR), ANS fluorescence intensity increased with increasing

aggregation size and Bis-ANS was shown to interact strongest with the monomeric A-state

and unfolded monomers.189

A major pitfall with the use of extrinsic dyes in measuring

aggregation is any interference of the extrinsic dye with the protein molecules. ANS-based

probes have shown to bind to some proteins (BSA190

and cytochrome c191

) through

electrostatic interactions, and in some cases ANS has shown to interfere with the protein

structures – shown to inhibit heat-induced aggregation of carbonic anhydrase.192

Bis-ANS has

52

also shown similar activity with insulin B-chain and alcohol dehydrogenase, with a proposed

use of protein stabilizer.193

The main limitation with ANS and Bis-ANS in protein

characterisation is in relation to their wavelengths i.e. requiring ultraviolet (UV) light. The

confocal system would need to be modified before their use (see Table 1.3).

Another pitfall of using extrinsic dyes is changing the solution environment in terms of PPIs.

Quinn et al. demonstrated how the addition of small fluorescent molecules can increase the

net attraction between proteins (human gamma-D crystalline, HGD) in solution with an

increase in the liquid-liquid phase separation temperature; although this increase did not

affect protein aggregation. These results indicate the need to compare between labelled and

unlabelled conditions to ensure correct data interpretation; with the increasing use of

fluorescence in protein solution characterisation, this aspect is very important.194

53

Table 1.3: Comparison of fluorescence dyes in their applicability to study protein aggregation

with confocal microscopy.

Comparison of fluorescence dyes in their applicability to study protein aggregation with confocal

microscopy (raster image correlation spectroscopy).183

195

184,196

Dye / Probe Molecular

formula

Excitation

Maxima

(nm)

Emission

Maxima

(nm)

Application Limitations

ANS

C16H13NO3S 350-380 505 Surface

hydrophobicity,

unfolding,

aggregation

Found to interfere

with protein

structures

Requires UV

Bis-ANS

C32H22K2N2O6S2

385-400 515 Surface

hydrophobicity,

unfolding,

aggregation

Found to interfere

with protein

structures

Requires UV

Nile Red

C20H18N2O2 540-580 660 Surface

hydrophobicity,

unfolding,

aggregation

Stable between pH

4.5 to 8.5

Recommended to

potentially

investigate

DCVJ

C16H15N3 450 480-505 Micro-viscosity of

protein

environment

Has low

sensitivity at low

concentrations

Sensitive to

solution viscosity

CCVJ

C16H16N2O2 435-440 490 Micro-viscosity of

protein

environment

Sensitive to

solution viscosity

SYPRO

Orange

Proprietary

information

(not available)

300-472 570 Surface

hydrophobicity,

unfolding,

aggregation

Labels surfactants

and silicone oil

SYPRO Red Proprietary

information

(not available)

300-550 630 Surface

hydrophobicity,

unfolding,

aggregation

Recommended to

potentially

investigate

ThT C17H19ClN2S 450 480-490 Fibrillation High

concentration

required.184

Congo Red C32H22N6Na2O6S2 497 614 Fibrillation Sensitive to pH

Poor binding

specificity to

aggregates

observed

54

Figure 1.7: Chemical structures of commonly used fluorescent dyes.

Chemical structure of commonly used fluorescent dyes: (A) ANS, (B) Bis-ANS, (C) DCVJ, (D) Nile

Red, (E) CCVJ, (F) ThT and (G) Congo Red. Adapted from Hawe et al.183

55

As well as detecting small aggregates, fluorescent dyes have shown the ability to detect large-

sized aggregates present at low concentrations. Demeule et al. proposed the technique of Nile

Red staining with fluorescence microscopy, to detect protein aggregates. Nile Red

fluorescence was able to detect micron-sized aggregates present at low concentrations. They

suggested that the ability to detect aggregates with Nile Red at early time points could reduce

the number of samples in long-term stability studies. It was also proposed that the high

sensitivity of extrinsic dyes could help gain size information about aggregates when

employing extrinsic dyes in fluorescent microscopy.184

However, dyes are known to be

sensitive to the solution, and Nile Red has shown to possess high sensitivity to pH and salts,

along with Congo Red;197

and thus limits their use in biopharmaceutical formulations.

SYPRO Orange is a fluorescence probe relatively new in the area of protein aggregation, in

comparison to the other aforementioned dyes. SYPRO Orange and SYPRO Red were

developed in 1996 by Molecular Probes Inc. for protein gel staining.198

SYPRO Red has not

been applied in labelling protein aggregates in solution, whereas SYPRO Orange been used

extensively to monitor protein folding under thermal stress195,199

and has demonstrated to be a

sensitive dye in detecting protein aggregates at low and high concentrations.183,195

It is one of

the main dyes used in differential scanning fluorometry (DSF), a screening method ultimately

used for defining the protein Tm in the development of IgG formulations. In a study by

Ablinger et al. a major limitation of SYPRO Orange has been conveyed, where only

formulations without surfactants can be analysed due to the dye’s interaction with the highly

hydrophobic parts of the surfactants. When the surfactant is above the critical micelle

concentration (cmc), SYPRO Orange is transferred into the hydrophobic core of the micelles

resulting in bright fluorescence of the dye and consequently high background. The

comparatively small increase of the fluorescence intensity, due to the unfolding of proteins, is

consequently masked.200

DCVJ is a dye which belongs to a group of fluorescent molecular

rotors that are mainly sensitive to the viscosity of the environment. DCVJ has been shown to

be suitable for detecting early oligomers ranging in size from 300 to 500 kDa, whereas

Thioflavin T was most appropriate for the detection of mature fibrils, but it did bind to early

oligomers of TTR as well.189

DCVJ has been proposed as an alternative to SYPRO Orange in

the determination of the Tm in the presence of surfactants. However, it showed to possess

limited sensitivity at low protein concentrations. Hence, the use of DCVJ with DSF would

only be recommended with high solution concentrations and SYPRO Orange would be

recommended for all solution concentrations but only in the absence of surfactants.200,201

However, no recommendations for IgG solutions at low concentrations in the presence of

56

surfactants have been suggested. Ablinger et al. proposed the use of another molecular rotor,

9-(2-carboxy-2-cyanovinyl)julolidine (CCVJ), which demonstrated to be suitable to detect

protein aggregation in the presence of surfactants with DSF for the first time. However this

study only looked at granulocyte-colony stimulating factor (G-CSF) characterisation;200

application is required with mAbs.

The applicability of each dye also varies between different techniques requiring fluorescence.

Fluorescence-based analyses will be discussed in sections 1.4.3 onwards.

1.4.3 Fluorescence Correlation Spectroscopy (FCS)

1.4.3.1 Principles of FCS

Fluorescence correlation spectroscopy (FCS) was originally developed by Magde et al. in the

early 1970s to assess the chemical dynamics of DNA-drug intercalation,202

but its interest and

use did not arise until the 1990s when Rigler et al. pushed the sensitivity of the technique to

single-molecule level with the advances of lasers; prior to this, the technique suffered from

poor signal to noise ratios mainly due to low detection efficiency and insufficient background

suppression.203,204

It is now an established technique in the field in biochemistry and

biophysics due to its extremely high sensitivity.205

FCS is a single molecule technique, analysing fluctuations in the fluorescence intensity (FI)

in the small measurement volume as fluorophores diffuse in and out of the volume. FCS is

based on the assumption that a Gaussian distribution of diffusing events will occur. The

fluorescence intensity fluctuations are detected in photon counts through a laser beam

focused by a microscope objective, as a time series. It is important to note that FCS studies

are amendable to artefacts associated with the use of fluorophores. The requirement imposed

on a fluorophore, utilised in FCS, include high quantum efficiency, large absorption cross

section and strong photo-stability (see section 1.4.2.1). Fluctuations in FI occur from

chemical (reactions, quenching etc.), biological (anomalous diffusion can occur, association

of molecules and other aggregation phenomena) and physical (molecular motion, photo-

physical interactions and changes in conformation) effects of the fluorophore of interest, as

well as from random affects (noise).206

In the FCS process, the excitation laser light is directed by a dichroic mirror into a water

immersion objective that focuses the light in a calibrated volume inside the sample –

calibration is carried out with a well-known dye with established properties e.g. rhodamine

green.129

Changes in the diffusion behaviour of fluorescent molecules entering and leaving

57

the detection / confocal volume (typically on the order of femtolitres) are monitored.

Subsequently, each fluorescence signal is collected and focused onto a pinhole, so that the

laser beam waist inside the sample is imaged onto the pinhole space. The conjugation of the

objective and the pinhole creates a spatial filter, which cuts the sampling volume to a

diffraction limited size. The fluorescence signal is then collected directly by an avalanche

photodiode and processed into an autocorrelation function (See Equation 1.2).126,127

𝐺(𝜏) =⟨𝐹(𝜏)𝐹(𝑡 + 𝜏)⟩

⟨𝐹(𝑡)⟩2⁄ Equation 1.2

where 𝐺(𝜏)is the correlation function, 𝑭(𝒕) describes the fluctuations of a signal, 𝐹(𝜏) is

the difference in fluorescence intensity between time (t) and the mean value, and 𝑡 + 𝜏

represents a later time.

The autocorrelation function for translational diffusion can be calculated from Equation 1.3:

𝐺(𝜏) = 1 +1

𝑁(

1

1+𝜏 𝜏𝐷⁄) (

1

1+(1 𝑠⁄ )2(𝜏 𝜏𝐷⁄)1/2

Equation 1.3

where 𝜏𝐷is the translational diffusion time, N is the number of particles and s is the structure

parameter existing at the same time in the focal volume.207

A typical fluorescence signal and correlation curve is shown in Figure 1.8. Through statistical

analysis information can be determined on protein dynamics, such as the number of particles

(inversely proportional to the amplitude of the ACF), the decay shape (demonstrates the

nature of the transport process) and the diffusion time.

58

Figure 1.8: FCS typical fluorescence signal and autocorrelation curve.

(left) A typical FCS count rate example showing fluorescence fluctuations and (right) autocorrelation

curve which lead to the generation of parameters following analysis. The measured correlation

function reflects the kinetics of molecules diffusing in and out of the detection volume (number of

particles and diffusion time).

1.4.3.2 Experimental use of FCS

The advances in optics and instrument design have allowed FCS to be applied to increasingly

complex problems, where other methods have encountered difficulty. The key desirable

properties of FCS include that it is non-invasive to the sample and requires small quantities of

fluorophores as it is a single molecule technique. Additionally, FCS is classed as high

throughput due to small measurement times.128,205

This section will cover some examples to

reveal the wide application of FCS.

FCS has been validated to provide reliable and reproducible measurements through its high

sensitivity for protein characterisation.128,129

For example, FCS was used to assess the

mechanisms of arginine in protein stabilisation. The amino acid has shown to improve the

refolding yield of proteins during formulation and suppress aggregation, but its mechanisms

are not understood. FCS provided an insight into the mechanisms, through demonstrating

that arginine inhibits the formation of partially folded intermediates, in the unfolding

transition of BSA.130

An Indian group have used FCS extensively for assessing the effect of a

variety of conditions on protein stabilization. In one particular study, Alexa Fluor 488 was

utilised to determine changes in hydrodynamic radius of lysozyme in the presence of

additives i.e. morpholinium salts and arginine.131

In another study, the hydrodynamic radius

of the protein, IFABP, labelled with fluorescein, was determined in different folding states.

59

The data was demonstrated to be consistent with independent light scattering

measurements.208

FCS has been extensively used in biological systems and is classed as a highly sensitive tool

studying molecular interactions on live cells.209

The technique has been used to improve

selection strategies for molecular libraries; with precise selection at the single molecule level.

The detection of specific interactions between phage with displayed antibody fragments and

fluorescently labelled antigen is one example of FCS demonstrating high throughput.205

FCS has also become an important tool in polymer science in dilute210

and concentrated

solutions.211

Cherdhirankorn et al. utilised FCS to measure the diffusion of tracers of different

sizes in polystyrene solutions over a broad range of concentrations and demonstrated its use

of investigating simultaneously local and global dynamics.211

Fluorescence spectroscopy

methods have proven to be very useful in assessing the diffusion in crowded environments.

Typically a trace protein and molecular crowders are chosen and the diffusion of the tracer

protein is measured by FCS.212

This system has been applied to many studies. Banks et al.

investigated the diffusion in crowded globular protein and random-coil polymer solutions as a

model system for diffusion in the intracellular environment. This was the first report of strong

observations of anomalous diffusion of proteins in crowded solutions.213

Random-coil

polymers were typically used as macromolecular crowding agents because of their

availability and ease of experimental manipulation. Globular proteins (e.g. ribonuclease,

human serum albumin, immunoglobulins) are now preferred to be utilised to create a more

realistic portrayal of the physiological environment.214-216

It is envisioned that this

aforementioned approach with the application of computational tools can be developed to

unravel the biophysical contributions to motion of protein and interaction in cellular

environments. This is through assessing properties such as molecular weight, size and shape

of the protein and solution environment to assess electrostatic interactions.214,216

FCS has been applied to elucidate molecular dynamics in the various regimes. The diffusion

of single molecules and nanoparticle can provide information about the viscoelastic

properties i.e. nano-rheology.215

FCS utilises a small amount fluorescent molecules to

measure the flow properties. This application of FCS has overcome many barriers of previous

measures such as bead-surface interaction (e.g. in DLS based systems) and insufficient

resolution.134,206,217

60

There are many intrinsic limitations which need to be considered for FCS measurements,

particularity with polymer solutions. Slight changes in the refractive index, coverslip

thickness, laser beam, pinhole adjustments or optical saturation can cause major distortions in

the confocal volume – this then affects the measured parameters such as the diffusion

time.215,218

The critical properties of the choice of fluorophore have already been stated.

Decays in the autocorrelation curves due to photophysical and photochemical processes have

been observed by many groups. The choice of fluorophore is a critical process to retrieve

meaningful results. Refractive index mismatch has been described for many systems. In

relation to polymer systems, depending on the polymer and the solvent, severe problems can

occur when the refractive index is very different to the value of water. In this situation, the

calibration with typical reference dyes (i.e. rhodamine-6G) in water carries errors. Solutions

to this problem have been proposed and demonstrated by means of alternative calibration

methods; where the reference probe is of known molecular weight or mass and the diffusion

of the probe in dilute solutions is also known.215,219,220

Zettl et al. used the known molecular

weight of rhodamine-B labelled polymer chains of varied lengths in dilute solutions to

determine the size of the confocal volume; such that polymers can be used for FCS

calibration in the organic solvent toluene. The molecular weight of the polymers ranged from

10 to 1000 kDa and a clear dependence of molecular weight on diffusion time is observed.

These times were correlated to the waist radius of the observation volume and the diffusion

coefficient. It was found that the determined waist beam size was very different to the values

determined with rhoadmine-6G in water. Since the molecular weight dependence of the

diffusion of polymer solutions is well established for most organic solvents, the same

procedure can be applied to calibrate the set-up for other solvents. Thus a calibration

procedure is established for FCS measurements in organic solvents.221

In the recent years, FCS has been applied in the detection of surfactant micelles, more so, in

determining the cmc in buffer solutions. The method involves the use of a hydrophobic dye

which incorporates into the surfactant micelle complex and the change in the measured

parameter as a function of surfactant concentration is determined as the cmc.222-224

1.4.4 Confocal Laser Scanning Microscopy and Imaging

1.4.4.1 Confocal microscope set-up

The first confocal fluorescence microscopes were developed in the 1990s and their use in the

field of protein characterisation is becoming an increasing interest. The refinement of

mainstream confocal laser scanning microscopes (CLSM) techniques using fluorescent

61

probes has provided significant advances in optical microscopy. Confocal microscopes image

mainly by (i) reflecting light off a specimen or (ii) by stimulating fluorescence from dyes

applied to the sample of interest.

The basic set-up of a fluorescence confocal microscope is illustrated in Figure 1.9. The dye is

illuminated with the laser at a specific wavelength and an image is formed. The mirrors used

are termed as ‘dichroic mirrors’ which reflects the shorter wavelength light and transmits the

longer wavelength light. The shorter (main) light is reflected and passed to the sample

through the objective and the longer-wavelength light from the fluorescent sample passes

through the objective and the dichroic mirror.225

The emitted light is subsequently passed

through a pinhole which excludes any out of plane light resulting in optical sectioning of the

sample – producing a background free image.226

Emitted photons are most commonly

detected by a photomultiplier tube (PMT); modern equipment have hybrid detectors or

avalanche photodiode detectors. A point-by-point image construction is made by focusing a

point of light sequentially across the sample i.e. measuring one pixel at a time (the pixel

being defined as the shift of the confocal volume).227

The images are subsequently stored

using computer media and analysed using computer software(s) to extract meaningful

information.

Figure 1.9: Basic set-up of a confocal microscope.

Basic set-up of a confocal microscope with a laser excitation source which illuminates the fluorescent

dye molecule. Light from the laser is scanned across the sample with rotating mirrors and focused

through a pinhole onto a detector. The excitation light is blue and the emitted light is green. Adapted

from Semwogerere et al.225

1.4.4.2 Image Analysis

Confocal microscope generates pictograms which contains lots of useful information. With

the collaboration between microscope users and bioimage informatics, a diverse range of

image analysis tools have been developed and applied to extract the information obtained in

62

the images. These vary from routine fluorescence intensity measurements to particle tracking

to image time series. One software tool which has become a modern image processing

platform is ImageJ, which is used for diverse applications including material sciences,

astronomy and medical imaging.228

Fiji is a distribution of ImageJ and facilitates the

transformation of new algorithms into ImageJ plugins. The tools are collaboration platforms

between computer science and biological research.229

An example of a particle tracking

technique is single particle tracking (SPT). SPT is used to follow isolated molecules as they

move in the cell. New methods on confocal microscopy have evolved to reveal spatial and

temporal information; allowing the execution of the location and time of when molecules

interact. Applying image correlation spectroscopy, assessing time series, allowed the

quantification of maps of complex molecular interactions for the first time.230-232

1.4.4.3 Image Correlation Spectroscopies

Image correlation spectroscopy (ICS) was first developed by Peterson et al. in the late 1980s

as an image analogue of FCS to observe protein cell movements.233

The spatial

autocorrelation function is determined from images or image series through scanning

adjacent pixels and lines. The ICS approach can be applied to images acquired by any

microscope and all image types. Unlike single-pointed FCS, ICS does not require the fast

diffusion of fluorophores, allowing the system to study slow dynamics or even chemically

fixed cells. ICS has been continuously developed to measure additional parameters, where

each method is an extension on the previous one.227

Kolin and Wiseman reviewed ICS developments. With the original spatial image correlation

spectroscopy (SICS), a spatial autocorrelation function is calculated from the intensities

recoded in the pixels of individual images.227

SICS can measure the number density and

aggregation state of fluorescently labelled macromolecules, however, the technique cannot

extract dynamics because it only analyses the spatial fluctuations in one image. Brock et al.

demonstrated the use of SICS to characterise spatial distributions of IgE-coated

membranes.234

Temporal ICS (TICS), also known as ‘image cross-correlation spectroscopy’

or ‘dynamic image correlation spectroscopy’, was introduced as an alternative to FCS for

slow moving fluorescent proteins. Temporal correlations between images are collected in a

time series which allows the determination of the diffusion coefficient and the flow speed.

However, the limitation of TICS is although it is able to measure the magnitude, it cannot

determine the direction (velocity); as only the time between frames is considered. This

limitation was overcome through combining TICS with SICS to calculate a full

63

spatiotemporal function (STICS) thereby determining the velocity of the protein

molecules.227

Another variant, k-space ICS (kICS) has the ability of photo-bleaching

correction, an important point as photo-beaching of fluorophores has shown to significantly

affect the diffusion coefficient and the flow speed obtained from TICS and STICS. At the

time of the review by Kolin et al. kICS could not be applied to small regions (< 32 x 32

pixels) of cells thereby limiting the analysis.227,235

The Wiseman group, in 2010, found that

the bias was absent when images series were simulated with a periodic boundary condition,

whereby particles that exit one side of the image appear on the opposite side of the image. A

number of additional findings are also presented which would benefit a kICS user.236

The aforementioned image spectroscopy analyses allow the measurement of the magnitude

and velocity of proteins. However, the analyses are for slow-moving (large) particles. With

temporal ICS the fastest diffusion coefficient measurable is around 10-9

cm2/s, however, small

molecules (proteins in solution) diffuse faster than the limits of TICS because the

fluorophores enter and leave the small focal volume long before a subsequent image is

acquired. Hence, intensity fluctuations in adjacent images in the image series are completely

uncorrelated.227

FCS could be used in such cases (i.e. for fast-moving particles) but as stated

earlier, FCS is not compatible with all CLSMs and image analysis provides additional

information.227

1.4.4.4 Raster Image Correlation Spectroscopy (RICS)

RICS was developed by Digman et al. to exploit the spatial correlation of diffusion so that

diffusion could be measured in every region of a cell. In the initial study raster scans were

used to measure the diffusion of fluorescent beads, enhanced green fluorescent protein

(EGFP), in solution and inside the cytoplasm of living cells. RICS was presented a powerful

analysis tool for measuring dynamic processes (fast and slow) using a standard confocal

microscope.127,237

From a mathematical perspective, RICS is an extension of ICS which produces raster

(graphical) images and can measure fast dynamics. RICS combines the high temporal

resolution of single-pointed FCS (microseconds) and the low temporal resolution of ICS

(seconds) with the added capability of spatial correlation; where points are measured as

different positions and at different times simultaneously. Time structures are introduced into

the image since different parts of the image are acquired at different times. The microscope

generates an image by using galvanometer mirrors to raster a laser beam across a sample,

64

recording one pixel at a time. Hence, RICS uses spatial correlation to measure the diffusion

coefficient of a sample imaged on a CLSM (Figure 1.10). Through comparison studies,

similar information has been retrieved between RICS and FCS.227,237

Figure 1.10: Systematic diagram of Raster Image Correlation Spectroscopy (RICS).

Systematic diagram of RICS: RICS is based on the use of acquired confocal microscope images where

particles have been fluorescently labelled. The laser beam scans across each pixel and each pixel

represents a variation in fluctuation. Through the auto-correlation of these images, the size and

kinetics of the labelled-species (i.e. protein) over time can be determined.

1.4.4.4.1 The mathematics of RICS

As mentioned earlier, images contain hidden temporal and spatial information that need to be

extracted to determine dynamics. The diffusion of a particle in uniform medium can be

described by the relationship in Equation 1.4. The equation has two distinct parts – a

temporal part and a spatial exponential Gaussian term.

𝐶(𝑟, 𝑡) = 1

(4𝜋𝐷𝑡)3/2exp (−

𝑟2

4𝐷𝑡 ), Equation 1.4

where D is the diffusion coefficient and 𝐶(𝑟, 𝑡)is proportional to the probability of finding the

particle at position r, at time t, when the particle was at the origin, r = 0 at time t = 0. A

particle at the origin (at t = 0) has a distance r from the origin with a Gaussian distribution

where the variance is dependent on the time and diffusion coefficient of the particle. When

the concentration is sampled at different spatial locations, the spatial autocorrelation function

decays with a characteristic length that is dependent on the diffusion coefficient and the size

65

of the illumination volume. Expressions for the spatial autocorrelation function assume that

the intensity fluctuation is due to the diffusion of a particle that is small compared to the point

spread function (PSF). In the temporal domain, a random motion is performed by the particle

where the (end-to-end) distance is dependent on the on the square root of time. In the spatial

domain, the particle has a probability to be at a distance from the original position as

described by the Gaussian relation in Equation 1.4. The scanning part of the correlation

function 𝑆(𝑥. 𝑦) where 𝑥 is the abscissa and 𝑦 is the ordinate is expressed in Equation 1.5 in

terms of the pixel size, 𝛿𝑟 (in the range of 0.1-0.2 m), the pixel residence time 𝜏 (in the

range of 5-100 s) the pixel sequence number 𝑛. For RICS, the theory developed for spatial

raster-scan is expanded in regards to the spatial part of the correlation (Equation 1.4) in terms

of pixel size (expanded range of 0.05-0.2 m), pixel resident time (expanded range of 2-100

s) and the line repetition time (typically in the 3-100 s range); thus, making RICS

accessible to most researchers using standard confocal microscopes.

The overall correlation function is given by 𝐺𝑠 (𝑥, 𝑦) = 𝑆(𝑥. 𝑦) 𝖷 𝐺(𝑥, 𝑦), where 𝐺(𝑥, 𝑦) is

the autocorrelation function without scanning.

S(x, y) = exp(−

1

2[(2xδr

w0)2+(2yδr

w0)2]

(1+4D(x+ny)τ

w02 )

)

Equation 1.5

𝐺(𝑥, 𝑦) = 𝛾

𝑁(1 +

4𝐷(𝑥+𝑛𝑦)𝜏

𝑤02 )

−1

(4𝐷(𝑥+𝑛𝑦)𝜏

𝑤𝑧2 )

−1/2

Equation 1.6

Spatial correlations depend on the spatial overlap and the time interval between adjacent

pixels. Longer time intervals between data points decrease correlation at shorter spatial scales

but increase correlation at distant pixels. The correlation of an image series for raster scan

pattern appears on three different timescales: Adjacent pixels on the horizontal axis are

separated by microseconds, the pixels along the vertical axis are milliseconds apart and pixels

from successive image frames are seconds apart. Thus, the difference in sampling time is

exploited to measure a range of diffusion coefficients – from very fast to slow diffusion.127,237

The resolution of a microscope is limited by diffraction of the excitation source. Acquired

images are a convolution of the PSF with the point source emission spreading over a number

of adjacent pixels arising from diffraction.227

With TICS, the spatial resolution is equal to that

of the image and a temporal resolution relating with the frame rate of the acquired image.235

66

Thus, TICS is limited with the imaging rate with maximum diffusion coefficient of 10-9

cm2/sec for a typical confocal setup. Small fluorophores with rapid diffusion rates would be

undetected.227

Using RICS on a confocal laser scanning microscope it is possible to measure

dynamics of rapidly diffusing species. Spatial resolution at pixel level for raster images and

dynamic processes can be obtained only for millisecond dynamics.127,237

With RICS it is

possible to apply a mean contribution filter from either mobile or immobile species present in

the sample.227

Following the development of RICS, as described in the aforementioned sections (in 2005),

the Gratton group (in 2008)238

provided guidelines for performing RICS analysis on a CLSM

in terms of instrument settings and image acquisition settings. It is recommended that for a

high spatial resolution, 50 to 100 images are required in order to reach a good signal-to-noise

ratio; low concentration samples require more frames. The correlation function,

simplistically, is characterised by two parameters: the amplitude of the function and the

characteristic decay (correlation time). It is clearly shown in the images the shape of the

spatial ACF is a reflection of the particle motion. The paper pointed out the importance of the

scan speed such that if the molecules are moving slower than the beam is scanning, the

spatial ACF will be insensitive to the dynamics. Also, when optimising the image acquisition

settings and the ACF fitting parameters, it is important to ensure there are enough data points

and a good fit to the ACF.238

1.4.4.4.2 The scope of RICS in measuring protein dynamics in

formulations

The work outlined with RICS has been mainly in cells, demonstrating RICS is a powerful

tool for measuring dynamics of proteins and lipids using commercial confocal

microscopes.127,232,238,239

Then, in 2012, Hamrang et al. reported a novel application for RICS

in the characterisation of protein diffusion of fluorescently labelled bovine serum albumin

(BSA) samples. The suitability of RICS as a tool for the quantitative assessment of protein

diffusion in solution was evaluated through comparing the data with DLS and FCS. The

diffusion of BSA solutions as a function of formulation (pH and ionic strength) and

denaturing conditions (thermal stress) was assessed. The diffusion coefficients obtained were

consistent across the three techniques. A decrease in the hydrodynamic radii was observed

with increasing ionic strength and an increase in hydrodynamic radii as a function of protein

concentration. The study proposed RICS as an orthogonal technique in the deduction of

protein aggregation. RICS was validated as a respectable application for assessing protein

67

behaviour and as a meaningful tool in the spatiotemporal interpretation of macromolecular

dynamics.138

In 2015, RICS was applied to characterise mAb aggregate solutions (Appendix

1).240

Aggregates were extrinsically labelled with SYPRO Red and visualised with confocal

microscopy prior to RICS analysis. Low concentrated mAb solutions (1 and 10mg/ml) were

subjected to thermal and freeze-thaw stress. Complementarity was demonstrated between

aggregate size ranges measured with RICS, MFI and DLS. SYPRO Red was successfully

utilised as an extrinsic labelling probe, without interfering with the protein itself. Thus, this

study provided scope for the assessment of RICS (with SYPRO Red) on assessment of

aggregation development in complex formulations i.e. in the presence of solution components

e.g. surfactants, silicone oil.

68

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223. Luschtinetz F, Dosche C 2009. Determination of micelle diffusion coefficients with

fluorescence correlation spectroscopy (FCS). Journal of Colloid and Interface Science

338(1):312-315.

224. Zettl H, Portnoy Y, Gottlieb M, Krausch G 2005. Investigation of Micelle Formation

by Fluorescence Correlation Spectroscopy. The Journal of Physical Chemistry B

109(27):13397-13401.

225. Semwogerere D, Weeks ER 2005. Confocol Microscopy. London: Taylor & Francis.

226. Paddock S 2000. Principles and practices of laser scanning confocal microscopy.

Molecular Biotechnology 16(2):127-149.

227. Kolin D, Wiseman P 2007. Advances in Image Correlation Spectroscopy: Measuring

Number Densities, Aggregation States, and Dynamics of Fluorescently labeled

Macromolecules in Cells. Cell Biochemistry and Biophysics 49(3):141-164.

228. Schneider CA, Rasband WS, Eliceiri KW 2012. NIH Image to ImageJ: 25 years of

image analysis. Nature Methods 9(7):671-675.

229. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T,

Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri

K, Tomancak P, Cardona A 2012. Fiji: an open-source platform for biological-image

analysis. Nature Methods 9(1):676-682.

230. Digman MA, Gratton E 2012. Scanning Image Correlation Spectroscopy. BioEssays:

news and reviews in molecular, cellular and developmental biology 34(5):377-385.

231. Digman MA, Wiseman PW, Choi C, Horwitz AR, Gratton E 2009. Stoichiometry of

molecular complexes at adhesions in living cells. Proceedings of the National Academy of

Sciences of the United States of America 106(7):2170-2175.

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232. Digman MA, Wiseman PW, Horwitz AR, Gratton E 2009. Detecting Protein

Complexes in Living Cells from Laser Scanning Confocal Image Sequences by the Cross

Correlation Raster Image Spectroscopy Method. Biophysical Journal 96(2):707-716.

233. Peterson NO, Hoddelius PL, Wiseman PW, Segar O, Magnusson KE 1993.

Quantitation of membrane receptor distributions by image correlation spectroscopy: concept

and application. Biophysical Journal 65(3):1135-1146.

234. Broek W, Huang Z, Thompson N 1999. High-Order Autocorrelation with Imaging

Fluorescence Correlation Spectroscopy: Application to IgE on Supported Planar Membranes.

Journal of Fluorescence 9(4):313-324.

235. Kolin DL, Costantino S, Wiseman PW 2006. Sampling Effects, Noise, and

Photobleaching in Temporal Image Correlation Spectroscopy. Biophysical journal

90(2):628-639.

236. Schwartzentruber JA 2010. k-Space Image Correlation Spectroscopy (kICS):

Accuracy and Precision, Capabilities and Limitations. PhD Thesis. McGill University.

Quebec, Canada.

237. Digman MA, Sengupta P, Wiseman PW, Brown CM, Horwitz AR, Gratton E 2005.

Fluctuation Correlation Spectroscopy with a Laser-Scanning Microscope: Exploiting the

Hidden Time Structure. Biophysical Journal 88(5):33-36.

238. Brown CM, Dalal RB, Hebert B, Digman MA, Horwitz AR, Gratton E 2008. Raster

image correlation spectroscopy (RICS) for measuring fast protein dynamics and

concentrations with a commercial laser scanning confocal microscope. Journal of Microscopy

229(1):78-91.

239. Rossow MJ, Sasaki JM, Digman MA, Gratton E 2010. Raster image correlation

spectroscopy in live cells. Nature Protocols 5(11):1761-1774.

240. Hamrang Z, Hussain M, Tingey K, Tracka M, Casas-Finet JR, Uddin S, van der Walle

CF, Pluen A 2015. Characterisation of Stress-Induced Aggregate Size Distributions and

Morphological Changes of a Bi-Specific Antibody Using Orthogonal Techniques. Journal of

Pharmaceutical Sciences 104(8):2473-2481.

82

2 : AIMS AND OBJECTIVES

83

2.1 Aims and Objectives

As highlighted in the Introduction Chapter, the ability to determine aggregation propensity

with minimum modification of the solutions or as early as possible in the process is essential

to improve bioprocess design and formulation of monoclonal antibodies (mAbs). A related

aspect/consequence of protein intermolecular interactions, particularly at high concentrations,

is the (increased) solution viscosity. It is hypothesized in this thesis that the use of

quantitative fluorescence techniques, namely raster image correlation spectroscopy (RICS)

and fluorescence correlation spectroscopy (FCS) (sequentially) can provide information on

proteins aggregation propensity and protein-protein interactions in highly concentrated

solutions and in real formulations - with high specificity, without interfering with sample

components and using small sample volumes.

Thus the aims of this thesis are:

To attempt to detect the whole range of sub-visible particles (specifically protein

aggregates) in real formulations,

To screen and select existing fluorescent dyes/probes to allow for viscosity

measurements such that changes in local rheological properties can be measured,

To demonstrate the potential use of fluorescence techniques in studying monoclonal

antibody solutions,

To relate rheology to aggregation to improve aggregation models.

To meet the aforementioned aims, the objectives of this thesis are to:

Validate and apply RICS (following extrinsic labelling by SYPRO Red) in mAb

aggregate solutions in the presence of other solution components - This involves assessing

the concentration of aggregate particles in industrially relevant formulations. The other

techniques used need to (together) cover a broad size range, measure particle counts, and

have the ability of particle differentiation or the specific selection of protein aggregates.

In Chapter 3 the effect of silicone oil and surfactants (polysorbate-20) are assessed on mAb

aggregation following agitation in pre-filled syringes. RICS measured size-distributions (and

concentrations) are compared against MFI and Archimedes (as orthogonal techniques).

Additionally, all three techniques are assessed on their analytical capabilities in terms of size

ranges and particle differentiation. To allowing comparison of RICS data with MFI and

Archimedes (which have concentration limits), a low mAb concentration of 10mg/ml is used.

84

Later in the thesis (Chapter 6), RICS is applied in the characterisation of high concentrated

i.e. 100mg/ml mAb solutions, subjected to various forms of agitation stress.

Assess the use of fluorescence probes (with FCS) in retrieving viscosity information of

mAb solutions – Fluorescent probes have been utilised in determining rheological information

through measuring their self-diffusion. To assess this application in mAb solutions, the

diffusion of different sized probes are measured by FCS using different proteins, (model

protein BSA and two mAbs with different aggregation propensities), a range of mAb

formulations (as a function of pH, salt and ionic strength) and over low to high mAb

concentrations. The data is compared with bulk rheology measurements and applied to

existing models.

To contribute towards aggregation models using fluorescence based techniques - An

additional method for assessing surfactant micelle behaviour is evaluated. Surfactants such

as polysorbate-20 are commonly used in protein formulations to protect against surface-

induced aggregation. However, their mechanisms are poorly understood – especially in mAb

solutions. The applicability of FCS with SYPRO Orange is assessed on labelling surfactant

micelles, in Chapter 5. The method is used to determine the critical micelle concentration of

nonionic surfactants (including polysorbate-20) and subsequently in detecting micelles in

high concentration mAb solutions.

To assess the relation between aggregation development and solution viscosity – To

do this a range of samples need to be generated with various aggregate size and

concentrations. For this purpose, in Chapter 6, various agitation conditions are manipulated

(based on agitation type, speed and duration) to generate a range of aggregate size

distributions and aggregate counts. Subsequently, FCS is applied in measuring changes in

solution viscosity and RICS in characterising aggregation development (sequentially).

85

3 : EVALUATION OF AGGREGATE

AND SILICONE OIL COUNTS IN

PRE-FILLED SILICONIZED

SYRINGES: AN ORTHOGONAL

STUDY CHARACTERISING THE

ENTIRE SUBVISIBLE SIZE RANGE

86

Int. J. Pharm., 519 (2017) pp 58-66.

Research Article

Evaluation of Aggregate and Silicone oil Counts in Pre-filled Siliconized Syringes: An

orthogonal study characterising the entire subvisible size range.

Maryam Shaha, #

, Zahra Rattraya, b, #

, Katie Dayc, Shahid Uddin

c, Robin Curtis

d, Christopher

F. van der Wallec and Alain Pluen

a*

a, School of Health Sciences, University of Manchester, Manchester, UK

b, Present address: Department of Therapeutic Oncology, Yale School of Medicine, New

Haven, USA

c, MedImmune Ltd, Formulation Sciences, Granta Park, Cambridge, UK

d, School of Chemical Engineering and Analytical Sciences, University of Manchester,

Manchester, UK

#, authors contributed equally

*, corresponding author: Alain.Pluen@manchester.ac.uk

87

3.1 Abstract

Characterisation of particulates in therapeutic monoclonal antibody (mAb) formulations is

routinely extended to the sub-visible size-range (0.1-10 m). Additionally, with the increased

use of pre-filled syringes (PFS), particle differentiation is required between proteinaceous and

non-proteinaceous particles such as silicone oil droplets. Here, three orthogonal techniques:

Raster Image Correlation Spectroscopy (RICS), Resonance Mass Measurements (RMM) and

Micro-Flow Imaging (MFI), were evaluated with respect to their sub-visible particle

measurement and characterisation capabilities. Particle formation in mAb PFS solutions was

evaluated with increasing polysorbate-20 (PS-20) concentrations. All three techniques

provided complementary but distinct information on protein aggregate and silicone oil

droplet presence. PS-20 limited the generation of mAb aggregates during agitation, while

increasing the number of silicone oil droplets (PS-20 concentration dependant). MFI and

RMM revealed PS-20 lead to the formation of larger micron-sized droplets, with RICS

revealing an increase in smaller sub-micron droplets. Subtle differences in data sets

complicate the apparent correlation between silicone oil sloughing and mAb aggregates’

generation. RICS (though the use of a specific dye) demonstrates an improved selectivity for

mAb aggregates, a broader measurement size-range and smaller sample volume requirement.

Thus, RICS is proposed to add value to the currently available particle measurement

techniques and enable informed decisions during mAb formulation development.

Key words: particle, monoclonal antibody, protein aggregation, silicone oil, primary

packaging, raster image correlation spectroscopy,

88

3.2 Introduction

There is an estimated production of 3.5 billion pre-filled syringe (PFS) units per year for

therapeutic biopharmaceutical drug (e.g. monoclonal antibody (mAb)) administration, with a

potential to grow to 6.7 billion units by 2020.1,2

The increase in PFS use is driven by factors

such as the ease of use, advantages in safety, reductions in drug overfill and patient self-

administration; all of which reduce the incidence of hospitalisation and associated costs.3

One of the challenges for the formulation scientist is to ensure the stability of the formulated

mAb throughout the products lifetime, in the preferred presentation. Protein aggregation has

been found to arise during and after fill-finish steps; which may develop from mechanical

and/or agitation stress or from interaction with primary packaging components.4 Silicone oil

is a widely-utilised lubricant in PFS, facilitating ease of plunger movement in syringes and

injection with hypodermic needles;5 however, exposure to sloughed silicone oil droplets has

been suggested to adversely impact formulation stability.6,7

Initial indication of adverse

effects from silicone oil was found in the 1980s following correlation of insulin particle

formation with elevated blood glucose levels, in diabetics administered with the product.4

Later studies on agitation stress have shown the loss of soluble protein in PFS to be a

particular problem during transportation.7 Furthermore, agitation at higher speeds was

correlated with an increase in monomer loss in reported shaking studies.5 Subsequently, a

number of silicone oil related mechanisms underlying particulate formation have been

proposed, exemplified by dispersed droplets acting as nucleation sites for protein

aggregation;8 adsorption-destabilization of protein onto the silicone oil/water interface;

5 and

silicone oil droplet surface charge neutralisation by adsorbed proteins resulting in

agglomeration.9,10

The size range of protein and silicone oil particulates is generally wide (Table 3.1 presents

the various size ranges and common terminologies used).11-14

The United States

Pharmacopeia (USP) chapter ‘Particulate Matter in Injections’ <788> defines concentration

limits for particles in parental solutions that are ≥ 10 and 25 m.15

USP chapter ‘Subvisible

Particulate Matter in Therapeutic Protein Injections’ <787> makes the recommendation to

monitor particles < 10 µm, with a supporting chapter <1787> giving guidance on the

expanded techniques that can be used and size ranges.16

Based on the USP

recommendations, the commercially available Micro-Flow Imaging (MFI) system, detecting

particles from approximately 1 m to 400 m,17,18

is commonly used in the industry to assess

89

sub-visible particulates alongside more established USP methods such as light

obscuration.15,16

The potential immunogenic risk of smaller sub-visible aggregates (0.1-10

m) has been discussed by Carpenter et al19

and Singh et al20,21

and regulatory submissions

therefore may include quantitative characterisation of micron-sized aggregates (1-10 μm) and

qualitative characterisation of sub-micron aggregates (0.1-1 μm) in the early stages of

development.13,22

With the current particle detection technologies, an ‘analytical gap’ around

1 m still remains; consequently there is a drive for the development of new particle

metrology tools.23

Furthermore, there is a high interest in developing technologies which are

also capable of particle differentiation i.e. between protein and foreign matter, such as

silicone oil. In response to this predicament, in the last decade several new analytical

technologies have been introduced in order to detect and characterise aggregates; offering the

capability to extend the detectable size range of particles from 30 nm to 10 m, through

combining orthogonal technologies.24

For example, the recently developed Resonance Mass

Measurement (RMM) system (Archimedes) has been utilised alongside MFI, as a particle

metrology tool to bridge the analytical size ‘gap’ for particulates in the 0.5-5 µm size range,

and similar to MFI, discriminate between silicone oil droplets and protein aggregates.

However, the focus of the study was on large sub-micron and micron-sized particles through

the utilisation of the RMM ‘micro sensor’, with a lower detection limit of 0.5 m13,22

Table 3.1: Common terminology used for various protein aggregate size ranges

Common terminology used in the literature for various aggregate size ranges.17,19,25,26

Common terms Size in Diameter

Nano-metre aggregate, oligomer < 100 nm

Sub-micron aggregates 0.1-1 m

Smaller sub-visible aggregates 0.1-10 m

Sub-visible particles, micron aggregates 1-100 m

Visible particles >100 m

Analytical size gap 0.5-5 µm

Raster Image Correlation spectroscopy (RICS) is an image analysis tool, originally developed

by Digman et al.27

We recently reported a comparison of particle size distributions in the gap

region with the novel application of RICS, by extrinsic aggregate labelling, against Dynamic

Light Scattering (DLS) and MFI, in simple mAb formulations (e.g. in the absence of silicone

oil and surfactant). RICS was demonstrated to measure a broad particle size range (i.e. 10 nm

- ~100 m) for stressed mAb samples (i.e. thermal and freeze-thaw stress);28

thereby

providing scope for the application of RICS in more complex formulations.

90

This manuscript reports the quantitative evaluation of protein and silicone oil particulates

formed in PFS solutions, both within and outside the analytical size gap range. We compare

the complementary of RICS, detecting particles from 30 nm-10 m, against RMM and MFI

which are capable of particle sizing over the sub-micron (~ 0.1- ~ 5 µm, through the use of

the nano and micro sensor) and micron (> 1 µm) sizes ranges, respectively. The PFS

solutions, in the presence and absence of polysorbate-20 (PS-20), were subjected to agitation

stress via end-over-end rotation, used to model stress during transportation.7,29

There are

numerous studies assessing the mechanisms of mAb aggregation.30,31

and the effects of

silicone oil7,10,13,32

or polysorbate surfactants33,34

in influencing the aggregation process; such

studies include novel methods to reduce in situ mAb aggregation in PFS.35

However, the

focus has been the larger sub-visible size range of particulates i.e. > 0.5 m;36-38

due to the

current lack of available technologies that are sensitive to the detection of smaller particles,

whilst capable of differentiating between proteinaceous and foreign particulates (e.g. silicone

oil). Herein, the ability of RICS to characterise aggregates in solutions containing silicone oil

droplets via extrinsic fluorescent dyes is also evaluated: the selectivity of RICS (through the

use of a specific dye) is compared with the efficiency of RMM and MFI (based on particle

buoyancy and optical parameters for RMM and MFI, respectively) in particle differentiation.

The assessment of size and concentration of particulates generated in siliconized PFS

containing formulated mAb is reported utilising all three techniques.

3.3 Materials and Methods

3.3.1 Materials

A bi-specific monoclonal antibody (IgG1, MW 204kDa, pI 8.9-9.2), herein termed ‘COE-08’,

was kindly provided by Medimmune (Cambridge, UK). 1 mL, long, sterile, ready to fill BD

HypakTM

glass siliconized syringes were purchased from Becton Dickinson and Company

(New Jersey, US).

All buffer components including sucrose, L-histidine and PS-20 were of analytical grade or

higher, purchased from Sigma Aldrich (Dorset, UK) and used without further purification. To

minimise potential degradation of the polysorbate, 10% w/v PS-20 stocks are made in batches

at Medimmune (to ensure minimal use of the original product bottle) and used within a fixed

expiry date. All stock solutions are subsequently tightly sealed and stored at 4C.

SYPRO® Red and SYPRO® Orange dyes were obtained from Thermo Scientific

(Leicestershire, UK) at a concentration of 5000× (in DMSO). All buffers and solutions were

91

prepared with Millipore de-ionised water (18 MΩ.cm) and pre-filtered prior to stress

experiments.

3.3.2 Methods

3.3.2.1 Sample Preparation

All solutions were prepared in a pH 6 buffer composed of 25 mM histidine and 235 mM

sucrose. COE-08 solutions were prepared at a final concentration of 10 mg/mL in the

presence of 0, 0.02 and 0.05% w/v PS-20 and placed in syringes. Control syringes were filled

with buffer containing the same PS-20 concentrations i.e. in the absence of mAb. Solutions

were placed in the syringes ensuring a consistently sized air bubble, of a height of

approximately 1mm. Multiple syringes were used per condition to ensure sufficient sample

volume for all three instruments. Syringes were placed on an end-over-end rotator at ambient

temperature (21C) in a thermostatically-controlled environment for a 24 hour agitation

period at 20 rpm. In parallel, non-agitated samples (of the same described solutions) were

stored in an open rack on the benchtop.

3.3.2.2 Analysis of Particulates with Confocal Microscopy (RICS)

SYPRO® Red and SYPRO® Orange (Thermo Scientific, Leicestershire, UK), used to label

protein aggregates and silicone oil droplets, respectively, were added to samples (post-

experiment) 15 minutes prior to visualisation with confocal microscopy at a final working

concentration of 2.5×.

A Zeiss 510 Confocor 2 (Zeiss, Jena, Germany) confocal microscope equipped with a c-

Apochromat 40×/1.2NA water-immersion objective was utilised for image acquisition. For

SYPRO® Red solutions, imaging was carried out by exciting the dye with a Helium-Neon

laser at 543 nm and the emitted fluorescence collected above 585 nm (LP585 filter set).

Excitation of SYPRO Orange® was carried out at 488 nm (Argon laser) and the emitted

fluorescence collected with a 560-615 nm bandpass filter. Confocal image time series of

1,024 × 1,024 pixel resolution were captured over 100 frames with a corresponding pixel

dwell time of 6.4 microseconds. In-house RICS software (ManICS) was applied to analysis

of images acquired using confocal microscopy. A full description of the RICS algorithm has

been described elsewhere.27,39

The aforementioned image time series were sub-divided into

32x32 pixels region of interest (ROI) and the diffusion coefficients (D) within each ROI was

generated (Figure 3.1a). All fits possessing a R2 below 0.7 were discarded from the fit data

prior to generation of particle size distributions (explanation can be seen in Appendix 2,

A2.1).

92

RICS-derived diffusion coefficients were subsequently converted to particle diameter using

the Stoke-Einstein equation (following determination of solvent viscosity):

𝐷 = 𝑘𝑇 3𝜋𝜂𝑎⁄ Equation 3.1

Where D refers to the diffusion coefficient, k refers to the Boltzmann constant, T the

temperature at which the measurements were performed, η solvent viscosity and a the

hydrodynamic diameter.

3.3.2.3 Resonant Mass Measurement (RMM)

Particle size analysis using RMM is based on frequency shifts that are proportional to particle

buoyant mass, and depend on the sensitivity of a resonator 40

. An Archimedes system

(Malvern, UK) was utilised for RMM of positively- (silicone oil droplets) and negatively-

buoyant (protein aggregates) particles. Both the nano and micro sensor were utilised for all

solutions. The limit of detection (LOD) was set at 0.01 Hz (corresponding to 0.07 µm for

protein particles and 0.17 m for silicone oil particles) and 0.03 Hz (corresponding to 0.33

µm for protein particles and 0.68 m for silicone oil particles) for the nano and micro

sensors, respectively. System set-up and cleaning procedures are described by the

manufacturer and elsewhere.13,35

3.3.2.4 Micro-flow Imaging (MFI) Analysis

MFI analysis was performed using a Protein Simple MFI 5000 series (Protein Simple,

California, USA). Millipore filtered pure water and particle-free buffer (5 mL) was purged

through the system to remove residual particles prior to measurements and reduce the

baseline prior to data acquisition for each sample. Subsequently, the sample was introduced

at a flow rate of 0.5 mL/min, the illumination optimized and 0.5 mL of sample analysed at a

corresponding flow rate of 0.1 mL/min. Bright-field images, morphometric (i.e. equivalent

circle diameter (ECD) and aspect ratio) and particle data obtained from the analysis of

agitated and non-agitated samples were subjected to analysis of particle counts, morphology

and size distribution.

A customised filter was adapted from previous studies40,41

and applied to differentiate

between silicone oil and proteinaceous particles using Origin 2016 (OriginLab Corporation,

Northampton, MA, USA). MFI data obtained from solutions containing silicone oil only or

COE-08 aggregates only were utilised to create a customised discriminant analysis filter

based on four MFI parameters: aspect ratio, intensity mean, intensity minimum and intensity

standard deviation (cf. Sharma et al.42

). Discriminant analysis uses known sample to build a

model that can aid data stratification through establishing particle identity (i.e. COE-08 or

93

silicone oil). The analysis was applied to each MFI-generated dataset. To set the mAb

standard during development of the customised filter, an aliquot of COE-08 in buffer was

subjected to agitation via end-over-end rotation (for 24 hours) in de-siliconized syringes.

3.3.2.5 Statistical Analysis

Unless otherwise stated, a non-parametric one-way ANOVA was performed to assess the

influence of stress type on resultant size distribution/particle counts. A calculated probability

(i.e. p-value) equal or less than 0.05 was considered to be statistically significant.

3.4 Results

The formation of protein aggregates and silicone oil droplets in PFS, in the presence and

absence of agitation stress, was evaluated as a function of PS-20 concentration (0, 0.02 and

0.05% w/v). MFI, RMM and RICS were utilized, as described in the methods, to selectively

evaluate protein aggregate formation and silicone oil sloughing, over the broadest size range.

For each technique, the particle counts were separated, where applicable, into size ranges of

(i) < 0.07 m (RICS only), (ii) 0.07-0.5 m (RICS and RMM), (iii) 0.5-5 m (RICS, RMM

and MFI; with MFI only detecting particles > 1 m), and (iv) > 5 m (RICS, RMM and

MFI). The size ranges were chosen with respect to the RMM nano sensor analytical range of

0.07-0.5 m.

3.4.1 Fluorescent dye selection for proteinaceous aggregates and silicone oil

droplets for RICS analysis

Since confocal microscopy and RICS analysis rely on fluorophores, RICS may distinguish

between particles originating from different materials (in this case, protein vs silicone oil)

provided a dye with relevant physicochemical properties is selected. SYPRO® Red was

previously used to label protein aggregates in simple formulations.28

In this study, SYPRO®

Red was used to label protein aggregates in more complex formulations (i.e. in the presence

of silicone oil and / or surfactant micelles).

Micrographs (Figure 3.1b) suggested no apparent labelling of silicone oil and / or PS-20

micelles by SYPRO® Red that would interfere with the data obtained from labelled protein

aggregates. Moreover, following RICS analysis, the images acquired of buffer-only PFS

solutions (i.e. in absence of mAb), did not generate any conclusive data as an insufficient

correlation (with R2 < 0.7) was obtained. This result indicated the significantly higher affinity

of SYPRO® Red for proteinaceous aggregates compared to silicone oil droplets and/or PS-20

micelles.

94

A second dye was required for labelling silicone oil droplets. As an initial search did not

reveal a fluorophore capable of selectively labelling silicone oil droplets (but not

proteinaceous aggregates), SYPRO® Orange was assessed in labelling silicone oil droplets in

buffer-only PFS solutions i.e. in the absence of mAb (COE-08).43,44

Micrographs (Figure

3.1b) illustrated labelling of silicone oil droplets by SYPRO® Orange. Following RICS

analysis of the acquired buffer-only PFS solution images (with SYPRO® Orange), sufficient

correlations (with R2 > 0.7) were obtained. Thus, RICS analysis (with SYPRO® Orange) of

silicone oil droplets was assessed in non-mAb solutions only (see Appendix 2, A2.2 for

further explanation).

To clarify, for RICS analysis, SYPRO® Red was utilised to label protein aggregates in mAb

PFS solutions, and SYPRO® Orange for labelling silicone oil droplets in mAb-free PFS

solutions.

Figure 3.1: Schematic diagram of RICS and labelling of dyes SYPRO® Red and SYPRO® Orange.

a. Schematic diagram of RICS. RICS is based on the use of acquired confocal images where particles

have been fluorescently labelled. Through the autocorrelation of the images, the diffusion time is

determined. Depending on the timescale of the process, pixel (micro-seconds), line (milliseconds) or

frame (seconds) correlation methods can be used. Adapted from Digman et al.27

b. Confocal

micrographs representing labelling by SYPRO® Red (Ex: 543 nm, LP585 nm) (top) and SYPRO®

Orange (Ex: 488 nm, BP560–615 nm) (bottom) to PS-20 and sloughed silicone-oil droplets from

agitated syringes. Micrographs indicate no labelling to silicone-oil by SYPRO® Red and labelling of

silicone-oil by SYPRO® Orange.

3.4.2 Assessment of mAb Aggregation in Siliconized PFS

Protein particle counts (particle counts per mL) by each technique (RICS, RMM and MFI)

are presented in Figure 2. A broad range of COE-08 aggregate sizes is illustrated in the non-

agitated (Figure 3.2, left) and agitated (Figure 3.2, right) PFS solutions consistent with our

95

previous work.28

The size distributions of the protein particles, clearly showing the outliers,

can be seen in the SI (Appendix 2, A2.3).

When assessing the particle profiles of the three techniques (Figure 3.2), complementary is

observed in relation to (i) higher aggregate counts in the absence of PS-20 following agitation

and (ii) the greater presence of smaller particles: significantly higher (p < 0.05) absolute

aggregate counts were measured in agitated PFS solutions in the absence of PS-20, by all

three techniques. An overall assessment of the separated particle size ranges indicates that the

larger the particle size, the smaller the observed particle count will be (a typical trend already

found in aggregate solutions).20,45

In relation to this assessment, the two main observations

were, (i) significantly lower absolute particle concentrations (particles per mL) detected by

MFI in comparison to RMM, for all PFS solutions (approximately three orders of magnitude)

and (ii) the higher absolute aggregate counts in the 0% w/v PS-20 agitated samples were due

to the significantly higher particle counts (p < 0.05) in the 0.5–5 m size range by MFI and

significantly higher particle counts (p < 0.01) in the 0.07–0.5 m size range by RMM and

RICS.

Additionally, when assessing the particle size ranges across the three techniques, a pattern is

observed when considering each technique’s analytical capability, the sampled volume, and

the trend of low incidence of larger particles. MFI utilises the largest sampled volume and

detected particles > 5 m in all PFS solutions. By RICS and RMM, only the 0% w/v PS-20

agitated PFS solutions contained particles larger than 5 m in diameter. The same sample

when analysed by MFI contained the highest particle concentration in the > 5 m size range.

Similarly, by RICS only the agitated solutions contained particles in the 0.5-5 m size range;

and the same samples contained higher particle counts by RMM and MFI, in comparison to

the non-agitated solutions (Figure 3.2).

96

Fig

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.

97

3.4.3 Characterisation of Dispersed Silicone oil in PFS

3.4.3.1 Silicone oil Droplets in PFS Containing Buffer Only (no mAb)

Dispersed silicone oil droplets in buffer-filled non-agitated and agitated PFS solutions, in the

presence of 0, 0.02 and 0.05% w/v PS-20, were characterized by RMM, RICS and MFI as

described in the methods. Silicone oil droplet counts are presented in Figure 3.3 and the size

distributions can be seen in the SI (Appendix 2, A2.3).

It is observed that the presence of PS-20 resulted in significantly higher total droplet counts

(p < 0.05), for non-agitated and agitated PFS solutions (Figure 3.3). This is an interesting

outcome as the PS-20 solutions had the lowest protein aggregate counts, as seen in Figure

3.2. The results illustrate that PS-20 had a more dominant effect than agitation in the

sloughing of silicone oil in PFS.

Similarly to the mAb profiles, higher concentrations of smaller sized oil droplets are detected

by all three techniques (Figure 3.3), for all solutions. The smaller size ranges for RMM (i.e.

0.07-0.5 m) and RICS (< 0.07 and 0.07-0.5 m) detected significantly higher oil droplet

counts (p < 0.05) in the presence of PS-20 (non-agitated and agitated solutions). Larger

silicone oil droplets i.e. > 5 m, detected by MFI, were also greater in presence in PS-20

agitated samples.

As with the mAb data in Figure 3.2, the differences in sampled volumes across the techniques

and the low incidence of larger particles may explain the results in the overlapping size

ranges. For example, RMM only measured particles larger than 5m in the 0.05% w/v PS-20

PFS solutions (non-agitated and agitated). RICS detected silicone oil particles in the 0.5–5

m size range in the presence of PS-20 (0.02 and 0.05% w/v) or following agitation, and only

detected silicone oil particles larger than 5 m in the 0.05% w/v PS-20 agitated solutions

(Figure 3.3).

98

Fig

ure 3

.3: S

ilicon

e oil d

rop

let cou

nts in

bu

ffer-filled P

FS

solu

tion

s mea

sure

d b

y RIC

S, R

MM

an

d M

FI.

Silicon

e oil d

rop

let cou

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bu

ffer-filled P

FS solu

tion

s, in th

e presen

ce an

d a

bsen

ce of a

gita

tion

, as a

fun

ction

of P

S-20

con

centra

tion

(0%

, 0.0

2% a

nd

0

.05

% w

/v). Ho

rizon

tal a

nd

vertical a

xis represen

ts pa

rticle cou

nts (p

article co

un

ts per m

L) determ

ined

by R

ICS, R

MM

an

d M

FI for size ra

ng

es (i) <0.07

m

, (ii) 0.07

–0.5

m

, (iii) 0.5–

5

m a

nd

(iv) >5

m. A

xis scale varies p

er techn

ique to

ease visu

alisa

tion

of d

ata

. Va

lues rep

resent a

verag

e cou

nts a

nd

erro

r ba

rs represen

t the std

. dev. fo

r n = 3

.

99

3.4.3.2 RMM and MFI Characterisation of Silicone oil Droplets in mAb PFS

Samples

Silicone oil droplet counts in the mAb PFS solutions were determined by RMM and MFI

(distinguishing between proteinaceous particles and silicone oil droplets as described in the

methods) and are presented in Figure 3.4. The silicone oil size distributions can be seen in

the SI (Appendix 2, A2.3).

Both RMM and MFI showed a greater concentration of larger silicone oil droplets in the

presence of PS-20 following agitation (PS-20 concentration dependant): similar to the buffer-

filled PFS data (Figure 3.3), the only solutions containing particles larger than 5 m by RMM

were in the presence of PS-20. Following agitation, MFI detected a higher concentration of

particles larger than 5 m in the PS-20 samples. Conversely, unlike RMM, MFI data showed

differences in silicone oil droplet counts between the mAb PFS (Figure 3.4) and buffer-only

PFS solutions (Figure 3.3): significantly higher total droplet counts were observed in the 0%

w/v PS-20 agitated mAb PFS solutions (Figure 3.4). Particle size separation showed this was

due to the significantly higher particle counts in the 0.5-5 m size range (p < 0.01).

It is important to note that differentiation of protein and silicone oil particles with MFI

proved problematic; even with the use of the discriminant analysis described in the methods.

This is a problem observed in previous papers due to optical similarities between protein and

silicone oil particles with a diameter less than 4m.17,46

Table 3.2 presents the (apparent)

protein concentrations in buffer-only (i.e. mAb-free) PFS solutions determined by MFI,

following discriminant analysis. The apparent protein concentrations determined for the non-

agitated and agitated PFS solutions were above the background count limit of the MFI system

i.e. the threshold for a clean run of water (determined as 200 particles per mL). Thus, particle

differentiation issues between protein and silicone oil following discriminant-analysis are

indicated. This result is exacerbated for particle numbers in the lower size range, i.e. 0.5- 5

m; as the concentration for particles > 5 m is less than the background limit, unlike the

0.5-5 m size range. Thus it is possible that the (apparent) higher silicone oil particles per mL

in the 0% (w/v) PS-20 (mAb PFS) agitated solutions (Figure 3.4) could be a result of particle

differentiation issues between protein and silicone oil in the lower MFI size-range in this

study (i.e. 0.5-5 m).

100

Table 3.2: Measured protein concentrations in buffer-filled PFS solutions determined by MFI.

Measured protein concentrations in buffer-filled PFS solutions determined by MFI (following

discriminant analysis). Values represent averages with std. dev. for n=3.

Solution Total (1-10 m) 1-5 m > 5 m

Buffer in PFS Non-Agitated 1796 ± 393 1711 ± 380 85 ± 27

Buffer in PFS Agitated 4133 ± 313 4004 ± 370 129 ± 57

101

Fig

ure 3

.4: S

ilicon

e oil d

rop

let cou

nts in

mA

b P

FS

solu

tion

mea

sure

d b

y RM

M a

nd M

FI.

Silicon

e oil d

rop

let cou

nts in

mA

b P

FS solu

tion

s, in th

e presen

ce an

d a

bsen

ce of a

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tion

, as a

fun

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of P

S-20

con

centra

tion

(0%, 0

.02%

an

d 0

.05%

w/v). H

orizo

nta

l an

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l axis rep

resents p

article co

un

ts (pa

rticle cou

nts p

er mL) d

etermin

ed b

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M an

d M

FI for size ra

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es (i) < 0.0

7

m, (ii)

0.0

7–

0.5

m

, (iii) 0.5–

5

m a

nd

(iv) >5

m. A

xis scale va

ries per tech

niq

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ease visu

alisa

tion

of d

ata

. Va

lues rep

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verag

e coun

ts an

d erro

r b

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e std. d

ev. for n

= 3.

102

3.5 Discussion

3.5.1 Considerations Regarding the Different Techniques

The information gained from the various commercially-available particle metrology

technologies has recently been subjected to considerable discussion, as highlighted by Ripple

and Dimitrova47

and Quiroz et al.26

Common to all techniques extrapolating data obtained

from small sample volumes or in dilute samples, is the uncertainty in particle-size data

approaching the technique detection limit. The substantial differences in sampled volumes

across the three techniques in the present study: RICS (~2 x 10-9

mL) < RMM (~4 x 10-6

mL

and ~1 x 10-4

mL by the nano and micro sensors, respectively) < MFI (3 x 10-1

mL), may

explain the differences observed in the data in the overlapping size ranges between the

techniques. Low sampled volumes reduce the likelihood of detecting larger particles present

in low concentrations.24

On the other hand, a significant sample volume is required to

generate statistically significant particle counts per dose unit. Consequently, the argument for

poor precision of new techniques in favour of light obscuration is debatable since all

techniques are known to suffer from caveats. For example, in the case of light obscuration

method HIAC (high accuracy liquid particle counter) and MFI, both of which are optical-

based particle counting techniques, the techniques are influenced by the refractive index

difference between protein particles and the formulation. As highlighted by Ripple and Hu48

and Zölls et al,46

the change in refractive index at higher concentrations has led to the

underestimation of particle concentrations. Hence, although novel and emerging techniques

may not be appropriate for quality control in their current state, they are capable of

monitoring the early stages of aggregation, require minimal sample volume and are therefore

directly relevant to early stages of formulation development. Thus the main focus of the

present work was to assess the presence of particulates that may not be easily detected by

light obscuration or MFI i.e. in the submicron size range and smaller. This manuscript

compares the particle trends / concentrations across MFI, RMM (micro and nano sensor) and

RICS.

MFI has received much attention in the analysis of large protein particles (i.e. >1 m)42,49,50

as the volume and the size-range matches regulations; in regards to their morphology, and

recently in differentiating between protein and silicone oil particles using customised

filters.13,41

The discriminant analysis used in this study is based on certain particle parameters

relating to apparent optical properties and circularity (aspect ratio, intensity mean, intensity

minimum and intensity standard deviation), devised by Weinbuch et al.13

However, due to

103

some optical similarities between mAb and silicone oil, the reliability of this analysis has

previously been questioned for particles < 4m.13,17,46

Supporting previous literature, this

study observed a significant apparent presence of COE-08 particles above the background

count limit in mAb-free PFS solutions (Table 3.2). The misclassification error with MFI may

offer some explanations for the possible effects observed in the 0.5-5 m size range in this

study i.e. accounting for the higher oil droplet concentration in the 0% w/v PS-20 agitated

solutions in Figure 3.4, which is inconsistent with other data-sets. Thereby, for mixed

solutions (i.e. protein and silicone oil), it is recommended to utilise MFI alongside another

method possessing an overlapping size range (covering 1-5 m) and also capable of particle

differentiation i.e. RMM.13,17

Overall, results from the current study are consistent with previous reports showing

complementarity between RMM and MFI13

(Figure 3.2 for protein aggregates, and Figures

3.3-3.4 for silicone oil droplets). This is true when accounting for the strong dependence

between number of particles and their sizes,26,45

wherin a significantly greater aggregate count

in the lower sub-visible size range was measured following and prior to agitation in PFS.

Through the use of the RMM nano sensor and RICS, the smaller-sized aggregate population

was analysed, detecting particles < 0.5 m in diameter. There are limited published reports on

the use of RMM,13,46,51

and to our knowledge, this is the first study reporting the use of both

RMM sensors on the same solutions. A careful observation of size ranges detected by RMM

micro and nano sensors (shown in Appendix 2, A2.3) revealed uncertainties regarding the use

of both detectors. While the size ranges of the nano and micro sensors are intended to

overlap, an overlap of particle sizes is not always observed, raising questions about the

likelihood of detecting poorly-populated larger particles in the small sampled volume. The

same observation was found in the silicone oil data acquired by RMM (Appendix 2, A2.3).

It is noteworthy that RMM exploits the differences in density to distinguish between particles

but requires the use of both the nano and micro sensors to cover a broad size range, which

increases measurement time and sample consumption. Furthermore, RMM has particle

concentration limits, accruing errors for samples with low particle counts,13

but also for

samples with particle counts > 2×106 particles/mL where there is a risk of high coincidence

and dilution is required.51,52

Concentration limits appear to be a downfall for many

technologies characterising in the lower size range. For example, Nano-Particle Tracking

Analysis (NTA), which also has the ability of particle differentiation through using

fluorescence, has similar concentration limits to RMM. The combined effect of adsorption

104

(from contact with glass and stainless steel during measurement) and sheer (during injection)

has also been reported to create aggregates.53,54

With regard to RICS, the main challenge was the selection of an appropriate dye. In this

study, the selectivity of SYPRO® Red for protein aggregates was demonstrated, with

SYPRO® Orange labelling both protein aggregates43,55,56

and silicone oil droplets (Figure

3.1). When using RICS, fluorescent dye selection should be considered on a case-by-case

basis. However, following the selection of the fluorophore, no change of detector was

required and the trends for particle size and concentration observed with RMM were

consistent with those observed by RICS.

It should be mentioned that comparison of particle concentrations from MFI, RMM or RICS

data needs consideration due to fundamental differences in measurement between these

techniques. MFI and RMM both use flow that can potentially bias the movement of

particulates such that particles are counted within a certain sampled volume. With the present

settings, RICS detects a number of particles in the focal volume of the objective, but particles

moving by Brownian motion may cross this focal volume more than once. Indeed, Figures

3.2 and 3.3 suggest higher particle counts than those determined by RMM and MFI. Thus, in

the current setting, direct comparison of particle counts between RMM, MFI vs RICS cannot

be made. It is foreseen that microfluidics may be of use with RICS; Rossow et al. have

demonstrated the use of RICS in the presence of flow.57

It is important to consider the uncertainties carried by different technologies, especially when

comparing the acquired data-sets across multiple techniques. Nevertheless, considering all of

the above points, the trends across the three techniques i.e. the effect of agitation and the

presence of PS-20 on aggregation formation vs the dispersion of silicone oil droplets in the

PFS solutions, are the same.

3.5.2 Agitation in Siliconized PFS Increases Aggregation Formation

All samples containing 0% w/v PS-20 contained a significantly larger aggregate count

following agitation, which is consistent with the widely known effect that agitation has on

protein solutions sheared at the air-water interface.34,58

Due to the increased use of PFS in fill-

finish manufacturing, it is important to understand the impact of siliconized syringes on the

stability of formulated mAb during storage and transport in the presence or absence of

agitation. Previous literature indicate that the presence of silicone oil can result in aggregation

increase following agitation; the effect being silicone oil concentration dependant.32

Other

105

studies have indicated that silicone oil itself i.e. in the absence of an additional stress such as

agitation, does not impact aggregation formation.5,7

In this study, silicone oil droplets and aggregated protein were detected in all mAb-filled

(COE-08) PFS solutions (Figure 3.2 for aggregates and Figure 3.4 for silicone oil). As the

PS-20 solutions contained the lowest aggregate counts (Figure 3.2) whilst containing the

highest silicone oil droplet counts (Figures 3.3-3.4), no correlation between protein

aggregation and silicone oil droplet presence was observed. Based on this, and previous

literature, it may be that the effect of silicone oil on mAb stability is a case by case basis.

Considering the surface-active properties of PS-20, the observed increase of silicone oil

droplets generated in PS-20 samples (Figure 3.3) was consistent with previous reports.36

This

observation needs to be tempered against the imaging method used for RICS which relies on

inverted microscopy of a small sample volume within a narrowly-defined plane of focus

across which droplets move by Brownian motion and by virtue of their density relative to the

bulk. Here, we have assumed that PS-20 stabilised silicone oil droplets in solution,9 and

silicone oil droplets in the absence of PS-20 have the same density and are therefore

positively buoyant in aqueous solution. Nevertheless, silicone oil extracted from the surface

by PS-20 appears to increase the number of droplets in the micrometre region (‘outliers’

observed in Appendix 2, A2.3).

There is another explanation which requires consideration in regards to the higher

concentration of droplets measured in PS-20 samples. As discussed, solutions with PS-20 had

a greater presence of small-sized silicone-oil droplets. PS-20 has been shown in the literature

to affect droplet stability; slowing droplet association. Based on this, in absence of PS-20 a

higher concentration of larger droplets would be expected. As a consequence, in the absence

of PS-20, the sloughed droplets may be too large to be detected by the techniques (i.e.

droplets in the micro range)5 – specifically RMM and RICS. However, MFI has the capability

of detecting very large droplets (> 100 m59

) and a higher concentration of large droplets (i.e.

< 5m) is not observed in the absence of PS-20 (Figure 3.3).

3.5.3 PS-20 limits the Formation of Aggregates in Siliconized PFS following

Agitation

Agitated PFS solutions in the absence of PS-20 generated the highest protein aggregate

counts following agitation (Figure 3.2). More so, the presence of PS-20 reduced the protein

particle counts to their respective baselines, i.e. the counts in non-agitated solutions for the

same PS-20 concentration. RICS and RMM showed that PS-20 significantly reduced small

106

sub-visible aggregates, while MFI showed that PS-20 limited the development of larger

aggregates (Figure 3.2). A number of studies have attempted to explain the protective

mechanisms of polysorbates in preventing aggregation (see Khan et al34

). The predominant

mechanism is assumed to be adsorption competition between the surfactant and the protein at

the air/liquid (or glass/liquid) interface. As a result, adsorption-denaturation of the protein at

these interfaces is attenuated.5,34,60

A further suggestion is that surfactant molecules may form

micelles (at concentrations above the critical micelle concentration) that shield exposed

protein hydrophobic surfaces, assuming partial or complete denaturation, and attenuate

protein-protein interactions.61,62

Protein destabilising effects in increasing polysorbate concentrations have been observed in

other studies.33,63

In this study, no significant differences in aggregate formation were

observed between the two PS-20 concentrations (Figure 3.2), although a higher concentration

of silicone oil droplets is observed in the 0.05% w/v concentration (Figures 3.3-3.4).

Nevertheless, determining the optimal polysorbate concentration for a specific mAb

formulation must be accounted for during formulation development.

It should be noted that, alongside the use and assessment of the technologies, the novelty of

this study is the assessment of agitation-induced protein aggregation development with the

effect of (i) silicone oil sloughing and (ii) PS-20. Previous literature either assesses one factor

only (i.e. PS-20 inhibition of agitation-induced aggregation) or applies the method of spiking

studies; and not siliconized PFS. Thus the approach of this study is more representative of a

pharmaceutical formulation subjected to transport stress, and also allowed direct assessment

of silicone oil sloughed droplets and agitation-induced aggregation – which in this case,

showed no relation between the two.

3.6 Conclusions

All three techniques demonstrated that the presence of PS-20 in mAb solutions contributes to

a significant reduction in proteinaceous aggregates following agitation, consistent with the

surfactant activity of PS-20. Comparison of the data sets imply that there is no interplay

between the sloughing of silicone oil droplets in PFS and the exacerbation of protein

aggregate formation in the sub-visible size range. While advanced particle characterisation

technologies are available to the formulation scientist, it is still the case that this is a

challenging area and emerging methods, while welcomed, may not have yet achieved the

expected capability of bridging the current sizing ‘gap’ for sub-visible particles.

107

Nevertheless, our data show that they provide complementary information and support

methods such as MFI. This study demonstrates that RICS analysis may expand the scope for

sub-visible particle sizing/characterisation and is an orthogonal technique to RMM. Since

confocal microscopy is well established, RICS offers the potential for widespread application

in laboratories where specialist equipment may not be available.

3.7 Acknowledgements

MS was supported by a Biotechnology and Biological Sciences Research Council (BBSRC)

‘BRIC’ studentship with MedImmune Ltd.

108

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and Silicone Oil Lubrication on the Stability of Pharmaceutical Proteins. Journal of

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39. Hamrang Z, Pluen A, Zindy E, Clarke D 2012. Raster image correlation spectroscopy

as a novel tool for the quantitative assessment of protein diffusional behaviour in solution.

Journal of Pharmaceutical Sciences 101(6):2082-2093.

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2013. Micro-flow imaging and resonant mass measurement (archimedes) - complementary

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41. Strehl R, Rombach-Riegraf V, Diez M, Egodage K, Bluemel M, Jeschke M, Koulov

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Microflow Imaging Analysis. Pharmaceutical Research 29(2):594-602.

42. Sharma DK, Oma P, Pollo MJ, Sukumar M 2010. Quantification and characterization

of subvisible proteinaceous particles in opalescent mAb Formulations Using Micro-Flow

Imaging. Journal of Pharmaceutical Sciences 99(6):2628-2642.

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Throughput Thermal Scanning Method for Rank Ordering Protein Formulations. American

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45. Ripple DC, Narhi LO 2015. Protein Particles (0.1 µm to 100 µm). State-of-the-Art

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112

4 : SELF-DIFFUSION IN HIGHLY

CONCENTRATED PROTEIN

SOLUTIONS MEASURED BY

FLUORESCENCE CORRELATION

SPECTROSCOPY

113

Research article

Self-diffusion in Highly Concentrated Protein Solutions measured by Fluorescence

Correlation Spectroscopy

Maryam Shaha, Daniel Corbett

b, Aisling Roche

b, Peter Davis

b#, Katie Day

c, Shahid Uddin

c,

Christopher F. van der Wallec Robin Curtis

b, and Alain Pluen

a*

a, School of Health Sciences, University of Manchester, Manchester, UK

b, School of Chemical Engineering and Analytical Sciences, University of Manchester,

Manchester, UK

c, MedImmune Ltd, Formulation Sciences, Granta Park, Cambridge, UK

#, Present address: Department of Molecular Biology and Biotechnology, University of

Sheffield, Sheffield S10 2TN

114

4.1 Abstract

Biopharmaceuticals (e.g. monoclonal antibodies (mAbs)) are formulated at high

concentrations to meet patient dose requirements. However at these high concentrations i.e. >

100mg/ml, intermolecular interactions are heightened leading to stability and viscosity issues.

Fluorescence correlation spectroscopy (FCS) has shown strong scope for measuring micro-

rheology of aqueous solutions using fluorescence probes. In this study, the method’s use in

mAb solutions, through measuring the diffusion of tracers of different size, is demonstrated.

Additionally, solutions of model protein bovine serum albumin are measured and the data

compared with bulk rheometry (i.e. macroviscosity), and the applicability of existing models.

It is observed that the applicability of the generalised Stokes Equation relation is dependent

on the tracer size (in comparison to crowder size) and any crowder-tracer interactions. Using

a tracer of similar size to the crowder (which does not interact with solution components) can

extract valuable information on the effect of solution properties (i.e. pH, salts, ionic strength)

on changes in local rheological behaviour.

115

4.2 Introduction

4.2.1 Effects of molecular crowding

Molecular crowding in cells and biopharmaceutical solutions has recently attracted attention

of both experimentalists and theorists.1,2

One of the direct repercussions of molecular

crowding is the change in viscosity observed in biopharmaceutical injections. Indeed, the

current move to limit the number of injections and improve the patient’s life by increasing the

mAbs concentration (well above 100 mg/ml) results in higher viscosity that may exceed the

limit of ‘syringeability’ (about 50 mPaS for most cases) to subcutaneous routes; as well as

bringing manufacturing difficulties to industries.3-7

Viscosity is governed by weak protein-

protein interactions (attractive and repulsive forces) and possibly by conformational changes

likely to happen at high protein concentrations.8,9

In previous studies10-12

mAbs demonstrated

diverse viscosity-concentration profiles unveiling a sharp exponential increase in solution

viscosity with increasing mAb concentration. However, even if these may be mitigated by

parameter such as pH, ionic strength, ions,13-15

a better monitoring of viscosity change

especially at highly concentrated protein solutions is necessary to provide guidance for

protein formulation developments.16

Typically, for a solution component to be termed as a ‘crowder’, it comprises more than 80%

of the volume. For the purpose of this study, the term ‘crowder’ refers to the fundamental

solution component i.e. in a protein formulation it would be the protein (regardless of the

protein concentration).

4.2.2 Measuring solution viscosity - diffusion of tracer particles

A number of approaches have been used and often rely on the generalised Stokes Einstein

relation. Methods include pulsed-field-gradient (PFG) NMR,17

forced Rayleigh scattering,

Taylor dispersion, fluorescence recovery after photobleaching (FRAP),18

dynamic light

scattering (DLS),19

and X-ray photon correlation (XPCS). Each of these techniques is

typically tailored for a certain range of concentrations or diffusion coefficient values.

Muramatsu and Minton studied the diffusion of tracer proteins in solutions crowded with

proteins via measurements of boundary spreading.20,21

In this study21

, the fractional reduction

of the diffusion of the tracer increased with increasing size of tracer species, and with

decreasing size of background species. Importantly, tracer diffusion in non-dilute solution has

been extensively utilised for polymer solutions studies.22-24

Indeed recent studies showed

molecules diffusion in polymer solutions can sense the microscopic friction which for

116

sufficiently large molecular probes is proportional to the friction coefficient extracted from

viscosity measurements. A concentration dependence of the self-diffusion had been observed

which may be represented by a semi empirical exponential law depending on the diffusant

size.22,25

Fluorescence correlation spectroscopy (FCS) is an ideal tool to study diffusion of tracers in

mAbs solutions as it measures the fluorescence fluctuations in the focal volume (less than one

fL). FCS is validated as a highly sensitive method to measure diffusion of fluorescently

labelled molecules in solution as well as their interactions,26-28

and has been utilized to

determine the long-time self-diffusion and large scale motion in a number of protein solutions

studies.1,2,24,28

Importantly, in these experiments, the friction experienced by tracer particles

can be expected to scale with the macroscopic solution viscosity (c), such that the long-time

diffusion coefficient DL may be considered to behave as predicted by the generalized Stokes–

Einstein (GSE) relation.29

𝐷𝐿 =𝑘𝐵𝑇

6𝜋𝜂(𝑐)𝑅𝐻

Equation 4.1

Where 𝑘𝐵𝑇 denotes the thermal energy and RH is the hydrodynamic radius of the

fluorescently labelled tracer. Provided that only the viscosity depends on the concentration c

of the dispersed particles but not the particle’s (apparent) hydrodynamic size, long-time

translational diffusion coefficients may thus be expected to follow the same concentration

dependence as the inverse viscosity of the same solution. Interestingly using proteins as

molecular crowders has resulted in different and sometimes conflictual results; indeed

depending whether the molecular crowders were proteins or polymers resulted in either

confirming or invalidating the GSE relation. An important point to remember is translational

diffusion measurements solely report on the tracer species, whereas viscosity measurements

are strongly dominated by the specific interactions among the crowder molecules due to their

much higher volume fraction. For example, Balbo et al.1 measured the self-diffusion tracer

proteins, fluorescently labelled BSA and IgG, in non-labelled BSA and IgG solutions

(respectively). After plotting the normalized translational diffusion coefficients 𝐷𝐿/𝐷𝐿,0, as a

function of protein volume fraction and computer modelling, the authors claimed the

dependence on crowder type appeared to be more important than the dependence on the

tracer type.1 This notion is supported by the findings of Zorrilla et al. They measured the

translational diffusion of apomyoglobin (apoMb) in concentrated ribonuclease-A (RNase -A)

and human serum albumin (HSA) solutions, using FCS. The translational diffusion of

117

labelled apoMb-m (tracer at nM concentration) in HSA and RNase-A solutions was

significantly slowed down by increasing concentrations of the crowder molecules compared

to its diffusion in dilute buffer.24

Overall, their results agreed with Muramatsu and Minton

results, and they proposed empirical relationships for estimation of local translational

viscosity from the determined bulk viscosity.24,30

In three recent papers, Roos et al. observed self-diffusion of proteins in concentrated protein

solutions using NMR and FCS. In the first one, Roos et al by PGNMR31

found that upon

increasing the protein concentration, the translational diffusion ofcrystallin nicely

followed the trend measured for the inverse solution viscosity. In a following studies, Roos et

al.2 found that long-time translational diffusion scales with macroscopic viscosity and small

solvent molecules diffuse faster than predicted from the SE relation, at least with regards to

the (macroscopic) solution viscosity.29

Contemplations on whether the diffusion of protein in crowding agents was anomalous have

received some attention. Interestingly when diffusion was found to be anomalous, the

crowding agent was a polymer: for example Banks and Fradin (2005) demonstrated this using

streptavidin in dextran solutions with anomality clearly appearing at high concentrations;

anomalous diffusion exponent.32

The effect of tracer size in crowded solutions has received considerable attention when these

crowders are polymers. Michelmann-Ribeiro et al. noticed that the diffusion of these particles

was affected differently in poly(vinyl alcohol) (PVA) solutions.33

Holyst et al. explored this

further with poly(ethylene glycol) (PEG) solutions. For probes smaller than the size of PEG,

the nanoviscosity is measured which is several orders of magnitude smaller than the

macroviscosity. For probes equal to or larger than PEG, the macroscopic value of viscosity

was measured. The study established a clear relation between the nano and macroviscosity as

based on the crossover length scale; determined from the crowder radius (of gyration), the

correlation length (distance between entanglement points of polymer chains; which is

affected by the polymer concentration) and the probe size.34

Here, two monoclonal antibodies with different behaviours and bovine serum albumin (BSA)

(as a model protein) are used as crowding agents; particular attention was taken to the charge

of the tracer molecule in order to determine the effect of tracer sizes on the self-diffusion.

The translation diffusion is compared with bulk rheometry (macroviscosity) and the

application of the GSE relation is assessed.

118

4.3 Materials and Methods

4.3.1 Materials

The monoclonal antibodies, termed as COE-03 (IgG1, MW 145kDa, pI 8.44) and COE-19

(IgG1, MW 145kDa, pI 7.4-7.9) were kindly provided by MedImmune Ltd (Cambridge, UK).

Azure-B, ATTO-465, ATTO-Rho6G, polysorbate-20, polysorbate-80, IgG-AF and IgM-AF

probes (mouse IgG1 isotype control, Alexa Fluor 488 conjugates) and buffer components

(histidine and sucrose) were acquired from Sigma Aldrich (Dorset, UK).

Rhodamine Green™ was acquired from Invitrogen (Paisley, UK). Lab-Tek Nunc® eight-well

chamber slides, scintillation vials, toluene and sucrose were obtained from Fisher Scientific

Ltd (Leicestershire, UK).

All buffers and solutions were prepared with Millipore de-ionised water (18 MV.cm) and pre-

filtered prior to stress experiments.

To reach high mAb concentrations (above 50mg/ml) mAbs were concentrated with 50 kDa

MWCO amicon ultra-centrifugal filter units (Sigma Aldrich, Dorset, UK).

4.3.2 Methods

4.3.2.1 Log P using spectrophotometry

4.3.2.1.1 Sample Preparation

To determine the Log P values (to confirm the hydrophilicity) of Azure-B, ATTO-465 and

ATTO-Rho6G, the partition coefficient (P) was determined using spectrophotometry.

Two concentrations for each dye were chosen (2M and 4M in buffer) and a 1:1 ratio of the

dye in buffer and toluene were prepared in scintillation vials. The scintillation vials were

placed on an A600 Rocker (Denley Instruments Ltd, West Sussex, UK) for each

concentration and a sample of from the buffer phase was removed after 1 hour and after 48

hours. Each experiment was repeated in triplicate.

4.3.2.1.2 Fluorescence Spectrophotometry and Data Analysis

A Cary Eclipse Fluorospectrophotometer (Varian Ltd., Australia) was utilised. The

concentration of the dye in buffer and subsequently toluene (concentration of dye in toluene =

original concentration – concentration of dye in buffer) was determined.

To determine the partition coefficient𝑃𝑡/𝑏, the following equation was used:

119

𝑃𝑡/𝑏 =𝐶𝑡𝑜𝑙𝑢𝑒𝑛𝑒

𝐶𝑏𝑢𝑓𝑓𝑒𝑟⁄

Equation 4.2

Where, 𝐶𝑡𝑜𝑙𝑢𝑒𝑛𝑒 is the concentration of dye in toluene and 𝐶𝑏𝑢𝑓𝑓𝑒𝑟 is the concentration of dye

in the buffer.

The partition coefficient (P) is thereby the quotient of the two concentrations; hence the log P

is simply the logarithm to base 10 of the quotient.

To test for significance, statistical tests, one-way ANOVA followed by post-hoc comparison

were applied.

4.3.2.2 Fluorescence Correlation Spectroscopy (FCS)

The experimental approach has been described elsewhere.35

Briefly, the Argon laser and the

Helium-Neon laser of a Zeiss Confocor2 LSM 510 META (Zeiss, Jena, Germany) and a

40×/1.2NA water-immersion objective were utilised. For the different probes, different

Argon laser lines were utilised: 488nm for IgG-AF and IgM-AF, 458nm for ATTO-465 and

514nm for ATTO-Rho6G. The Helium-Neon laser line of 633 nm was utilised for Azure-B.

The laser beam waist for the Argon lasers was determined by measuring Rhodamine Green™

(published diffusion coefficient of 2.8 × 10−6

cm2/s

36) and the laser beam for Helium-Neon

laser was determined by Alexa Fluor 647 (published diffusion coefficient of 3.0 × 10-6

cm2/s

37,38), using Equation 4.3:

𝜏𝐷 =𝜔02

4𝐷𝐿⁄

Equation 4.3

Where 𝜏𝐷 corresponds to the diffusion time and 𝟂0 is the laser waist beam.

Samples were loaded in duplicate into the corner of Lab-Tek Nunc eight-well chamber slides

(Fisher Scientific, Leicestershire, UK) and duplicate measurements collected. A constant

volume of the dyes was added to each solution in order to have approximately the same

number of particles per focal volume. The 40x 1.2W NA collar was adjusted in order to

maintain the number of particles per focal volume. Diffusion measurements were performed

at least 40 times and the acquisition time was varied between 5 s and 60 s per run depending

on the concentration. For the single-exponential model the following equation was applied to

fit the autocorrelation curve (G(τ)):

2/1

2

1

111

1)(

D

D SNG

Equation 4.4

120

Where N is the number of particles and S is the structure parameter.

4.3.2.3 Vilastic-3 for viscosity measurement

The Vilastic-3 Viscoelasticity Analyser (Vilastic Scientific, Inc., Austin, TX) was operated.

The oscillatory flow is generated at a selected frequency (i.e. 1 Hz) in a precision

measurement tube. The resolution of the magnitude and the phase of the pressure and volume

flow allows the calculation of the viscous and elastic components of the shear strain, shear

rate and shear stress at the tube wall. The stress (directly proportional to the pressure) is

proportional to the shear force which produces the shear strain and the shear rate

(proportional to the volume flow) within the fluid. The viscosity, 𝞰, is the ratio of the shear

stress to shear rate.

The dynamic viscosity of the mAb solutions was measured at a frequency of 1 Hz over a

range of shear rates (1-100 s-1

) at 25C. The Vilastic-3 standard sample cups were used

requiring approximately a volume of 2ml per sample.

Measurements were performed 100 times and the output was averaged and repeated in

triplicate for each sample.

4.3.2.4 RheoChip in measuring shear viscosity

Shear viscosity measurements were performed using the in-house RheoChip platform1 .The

RheoChip is a microfluidic rheometry device made of polymethyl methacrylate (PMMA)

using soft lithographic methods to create three internal rectangular channels of widths 800

m, 200 m and 100 m respectively and nominal height 50 m. Channels are oxygen

plasma treated to make hydrophilic and protein adhesion resistant. All experiments are

performed using the 200 m channel unless otherwise specified. The channel contains a

pressure tap, at a distance from the inlet sufficient for one-dimensional, Poiseuille-like,

laminar flow to be achieved. A second pressure tap is located 30mm further along the

channel. These pressure taps are each fitted with pressure detection unit consisting of two

identical linear strain gauge pressure transducers, enabling the differential pressure drop

across the length of channel between the taps to be measured. The RheoChip is calibrated

using deionised water with known viscosity corresponding to the measured temperature

during calibration. A calibration constant is established for each chip, 𝑓, where

𝑓 =1

(Δ𝑃

𝑄)𝐴𝑣𝑔.

Equation 4.5

121

where Δ𝑃 is the differential pressure, 𝑄 is the imposed flowrate and the Avg subscript reflects

average over multiple shear rates. The RheoChip is filled with the solvent of the sample and

allowed to reach steady state prior to measurement. A 1 mL glass syringe filled with protein

sample is secured in a Nexus 6000 syringe pump (Chemyx, TX, USA) and connected to the

RheoChip apparatus, ensuring there are no air bubbles in the system. The sample is pumped

into the chip in a cycle of flowrates from 10 mL/hr to 2 mL/hr in increments of 2 mL/hr, each

time allowing sufficient time for steady state flow to be achieved. The pressure drop across

the channel at each flow rate is detected and relayed to a data acquisition platform consisting

of National Instruments CompacDAQ 9172 chassis, a National Instruments 9237 data

acquisition modulus and LabView software (National Instruments). Shear viscosity is

calculated relative to the aforementioned calibration by the following formula,

𝜂𝑠𝑎𝑚𝑝𝑙𝑒 = 𝑓. 𝜂𝐻20 (Δ𝑃

𝑄)𝑠𝑎𝑚𝑝𝑙𝑒

Equation 4.6

where 𝑓 is the chip calibration constant, 𝜂𝐻20 is the viscosity of deionised water at calibration

temperature.

4.4 Results

4.4.1 Candidate dye for solution viscosity

While there are a huge variety of dyes (commercially) available for fluorescence microscopy,

not all dyes will perform well with FCS. Dyes for FCS need to be bright i.e. high extinction

coefficient and high quantum yield, low singlet-to-triple state quantum yield, and low photo-

bleaching.27

Additionally, for the measurement of viscosity (of mAb solutions), through the

translational diffusion time, the dye needs to be hydrophilic and not bind to solution

components (including protein).

A literature search was carried out to select candidate dyes to measure the viscosity of mAb

solutions using FCS. Criteria for dye selection were (i) hydrophilic, (ii) carry the same charge

to the mAbs used (buffers would be at a pH below the PI and so mAbs would be positively

charged in the experimental solutions) and have (iii) fluorescence stability over the pH range

studied (around pH 5 – pH 9).

Three candidate dyes, namely Azure-B, ATTO-465 and ATTO-Rho6G, were selected based

on available information in the literature and from manufacturers – summarised in Table 4.1.

Experiments were carried out to test the aforementioned properties / criteria. Firstly, the log P

(hydrophilicity) was determined for all three dyes.

122

Table 4.1: Available information of the three candidate dyes.

Available information of the three candidate dyes (Azure-B, ATTO-465 and ATTO-Rho6G) chosen

to investigate.40-43

Dye Excitation

maxima

Emission

maxima

Hydrophilic Log P Charge Available

Comments

Azure-B 648 662 Yes Mixed

values

Cationic pH stable

ATTO-

465

420-465 508 Yes Not

known

Cationic High thermal and

photo-stability; pH

stable; strong

absorption and good

fluorescence, large

Stokes-shift, good

water solubility

ATTO-

Rho6G

535 560 Yes Not

known

Cationic A new Rhodamine

dye; shows strong

absorption; high

fluorescence

quantum yield, high

thermal and photo-

stability; pH stable;

highly suitable for

single-molecule

detection applications

and high-resolution

microscopy

4.4.1.1 Hydrophilicity of candidate dyes

Table 4.2 summarizes the calculated log P values (at pH 6.0), which are all negative, thereby

signifying that all three dyes are indeed hydrophilic. Calibration curved can be seen in the SI

(Appendix 3, A3.1). Two different shaking times were used: Generally, times of around 24-

48 hours are recommended for such experiments (to ensure saturation has been reached). A

shorter time of 1 hour was also chosen (as the dye concentrations chosen were well below the

stated solubility limit). Statistical tests revealed Azure-B was significantly less hydrophilic (p

< 0.05) than the ATTO dyes. No significant differences were observed between

concentrations or between shaking times (at p = 0.05) for the same dye.

123

Table 4.2: Log P values of Azure-B, ATTO-465 and ATTO-Rho6G.

Log P values of Azure-B, ATTO-465 and ATTO-Rho6G using two shaking times of 1 hour and 48

hours. Values represent averages with std. dev. for n=3.

Dye Concentration (μM) Log P (1 hour) Log P (48 hours)

Azure B 2 -0.11 ± 0.07 -0.07 ± 0.03

4 -0.24 ± 0.14 -0.07 ± 0.05

ATTO-465

2 -1.38 ± 0.17 -1.49 ± 0.18

4 -1.45 ± 0.39 -1.33 ± 0.31

ATTO-Rho6G

2 -0.72 ± 0.21 -1.56 ± 0.27

4 -1.21 ± 0.64 -1.10 ± 0.49

4.4.1.2 Fluorescence Stability

One of the most crucial properties of the candidate dye is the fluorescence stability (necessary

for FCS) which is reflected through the count rate over the measurement time. The ATTO

dyes displayed constant average fluorescence intensities over time during the measurements,

thereby representing stable fluorescence i.e. indicating the fluorophores were not photo-

bleached during the measurement. However, Azure-B displayed some significant larger peaks

in the count rate (in comparison to the constant average pattern) which may be due to the

presence of some insoluble components of the dye (example count rates can be seen in the SI

Appendix 3, A3.2). This is supported by the log P values of Azure-B (Table 4.2).

The FCS diffusion times of Azure-B, ATTO-465 and ATTO-Rho6G in his/suc buffer (pH6.0)

were determined at dye concentrations of 1nM, 10nM and 100nM (Table 4.3). The diffusion

times showed large variability within the same dye concentration (i.e. the repeats) and

between the different concentrations, measured with Azure-B and ATTO-465. ATTO-

Rho6G, on the other hand, gave similar diffusion times across the three concentrations. Based

on these results, no further experiments were carried out with Azure-B and ATTO-465.

Table 4.3: Diffusion times of Azure-B, ATTO-465 and ATTO-Rho6G.

Diffusion times of Azure-B, ATTO-465 and ATTO-Rho6G at 1nM, 10nM and 100nM (in buffer).

Values represent mean with std. dev for n=5.

Dye Concentration (nM) Diffusion time (s)

AZURE-B 1 60 ± 3

10 32 ± 5

100 35 ± 8

ATTO-465 1 44 ± 20

10 35 ± 10

100 59 ± 9

ATTO-RHO6G 1 35 ± 1

10 36 ± 1

100 36 ± 1

124

4.4.1.3 Control Viscosity Experiment of Sucrose solutions

As a control experiment, the viscosity of a range of sucrose concentrations were assessed

with FCS/ATTO-Rho6G and compared with vilatsic-3 rheometer viscosity. Sucrose is used

commonly in formulations as a stabiliser and is known as a cryoprotectant. The typical

concentrations used in to provide stability to proteins in buffers range between 0.5% and 10%

(w/v).44,45

Viscosity values have been determined up to 70% (w/w) in the literature.46,47

Concentrations chosen for the control experiment ranged from 0.5% to 50% (w/w). A linear

relation is established between the diffusion of ATTO-Rho6G and the vilastic-3 viscosity

(Figure 4.1, left). Additionally, the viscosity profiles of vilastic-3 and ATTO-Rho6G were

compared with the viscosity profile determined from the translation diffusion of IgG-AF;

whereby the same behaviour was observed for all three methods (Figure 4.1, right).

Figure 4.1: Inverse relative viscosity comparison between Vilastic-3 and FCS (with ATTO-Rho6G

and IgG-AF) of control samples.

Inverse relative viscosity (η0/η) of a range of sucrose concentrations (from 0% to 50% (w/w))

measured by (left) Vilastic-3 against FCS ATTO-Rho6G diffusion (right) Vilastic-3 (black squares),

ATTO-Rho6G (red circles) and IgG-AF (blue triangles). Plots represent mean ± std. dev for n=3.

The viscosity data was in line with published viscosity data of sucrose solutions, for all

methods.46

Thus, these results show scope in the measurement of solution viscosity through

the self-diffusion of small dye ATTO-Rho6G and IgG-AF, using FCS – as the results were

comparable between the diffusion of the probes, bulk rheometry values and the literature.

To assess any interaction with ATTO-Rho6G and solution components, a broad range of

mAb concentrations and excipients (e.g. polysorbate) were investigated. Results (including

equilibrium dialysis) indicated insufficient interaction with the dye and the solution

125

components tested. Additionally a linear relation between ATTO-Rho6G diffusion time and

mAb solution viscosity was modulated. Results can be seen in the SI (Appendix 3, A3.3).

4.4.2 Self-diffusion in highly concentrated protein solutions

In this section the self-diffusion of probes are assessed over a range of protein concentrations

as a function of pH, salt and ionic strength. Through comparing data between the probes (of

different size), and the macroviscosity using the RhoeChip, the effect of probe type and size

in determining rheological behaviour of protein solutions is analysed.

Figure 4.2 presents a typical example of the variation of the normalised autocorrelation curve

of IgG-AF with increasing concentration of mAbs used in this study (here COE-03). The

ACF appears to reflect the diffusion of one species only and shifts to the right with increasing

concentrations. The insert in Figure 4.2 presents the mean square displacement (MSD), based

on the model by Rathgeber et al.48

⟨∆𝑟2(𝑡)⟩ = 3

2𝑤𝑥𝑦2 (

1

�̅�𝐺(𝑡)−1) Equation 4.7

The dash lines (Figure 4.2 insert) indicate a linear dependence between the MSD and time; as

the MSD corresponding to the different concentrations do all show dependence close to 1

with time, the diffusion of IgG-AF appears to follow GSE relation, consequently is not

anomalous.

Figure 4.2: Normalised FCS autocorrelation function for IgG-AF solutions.

Normalised FCS autocorrelation function (ACF) for IgG-AF in a range of COE-3 concentrations,

ranging from 0 to 220mg/ml. The average time the fluorescent probe spends in the focal volume is

given by the characteristic decay time of the ACF. Insert: the mean square displacement of the IgG-

AF COE-03 solutions, showing dependence to 1 over time.

126

4.4.2.1 Effect of buffer and ionic strength on ratio of diffusion times

Figure 4.3 presents the effect of the change of the ratio of diffusion times as a function of

protein concentration, for different solution conditions and for ATTO-Rho6G and IgG-AF.

For all conditions, the change of the ratio of diffusion times is significantly less in presence

of ATTO-Rho6G than of IgG-AF. Experimental conditions do not affect the ratios for

ATTO-Rho6G but the mAb concentration; therefore indicating that the fluorescent dye is not

sensitive to differences related to the buffer conditions and is only affected by the mAb

concentration. On the other hand, IgG-AF appears to be sensitive to the influence of the

buffer and ionic strength on the microenvironment.

Figure 4.3: Variations of the ratio of diffusion times as a function of mAb concentration, measured

by FCS, of tracers IgG-AF and ATTO-Rho6G over different buffer conditions.

Vitiations of the ratio of diffusion times as a function of mAb concentration, measured by FCS, of

tracers IgG-AF (top row) and ATTO-Rho6G (bottom row) in COE-3 (left) and COE-19 (right)

solutions in different buffer conditions, at T =20C. Plots represent mean ± std. dev for n=3

4.4.2.2 Influence of tracer size

Many studies (particularly for polymers as crowding agents) suggest an influence of the

tracers’ size on the relation between ratios of diffusion times to nano- or micro-rheology;

however, this has not been specifically determined in protein solutions. To this extent, the

behaviour in mAb solutions and in solutions of a model protein, BSA, different sizes of

tracers were used and the ratio of the diffusion coefficients compare against the relative

127

viscosity determined using the RheoChip. For mAb solutions, ATTO-Rho6G, IgG-AF and

IgM-AF were chosen, whilst for BSA solutions, BSA-AF and TMR-peptide were used.

Figure 4.4 presents the influence of tracers’ size on the change of the ratio of diffusion times.

Figure 4.4: Variation of ratio of diffusitives of different-sized fluorescent tracers compared with the

relative macro-viscosity as a function of protein concentration.

Variation of the ratio of diffusivities of different-sized fluorescent tracers measured by FCS - Atto-

Rho6G, IgG-AF and IgM-AF in mAb (COE-19) solutions (left) and BSA-AFA and TMR-peptide in

BSA solutions (right) - are compared with the relative macro- viscosity (measured by RheoChip), as a

function of the protein concentration. Plots represent mean ± std. dev for n=3

Figure 4.4 clearly demonstrates that an effect of tracer size is observed in mAb and BSA

solutions. The fluorescent dye, ATTO-Rho6G, is barely sensitive to the presence of proteins

even at high concentrations (ratio increased 2-3𝗑 at 160 g/L) whilst IgG-AF and IgM-AF

show greatly increased retardation (Figure 4.4, left). The variation of the IgM-AF ratio of

diffusion times matches the evolution of the relative (RheoChip) macroviscosity but not IgG-

AF; thus, in this example, an equivalent hydrodynamic radius to the one of the crowder

molecule is not enough. On the other hand in the case of the model protein solutions, a tracer

size effect is observed as the retardation observed for TMR-peptide is less than the one

observed in presence of BSA-AF; however, a noticeable difference with the aforementioned

solutions, the variation of the ratio of the diffusion times matches the variation of the relative

viscosity indicating that in this case the hydrodynamic radius of the tracer molecule is

sufficient to determine the macroviscosity (Figure 4.4, right).

The observed slowdown of tracers has been addressed using the hard-sphere (HS) model

(termed model 1 herein) as suggested by Roos et al.2:

128

𝐷𝐿(∅𝐻𝑆)

𝐷𝐿,0≅

(1−∅𝐻𝑆)3

1+(3 2⁄ )∅𝐻𝑆+2∅𝐻𝑆2+3∅𝐻𝑆

3 Equation 4.8

Where ∅𝐻𝑆 = 𝑘∅, with 𝑘>1, ∅ is the effective volume fraction and 𝐷𝐿 denotes the long-time

translational diffusion coefficient.

However a simpler model (model 2) can also be used to describe the retardation and the

change of viscosity as shown by Conolly et al:16

𝜂 = 𝜂0𝑒𝑘𝑐 Equation 4.9

Where 𝜂 is the solution viscosity at any given mAb concentration (𝑐), 𝜂0 is the solution

viscosity at infinite dilution and 𝑘 is exponential coefficient.

Table 4.4 and 4.5 summarizes the different values of 𝑘 obtained for both models for BSA and

mAb solutions, respectively. For the purpose of this study, 𝑘 for model 1 is termed as 𝑘1 and

𝑘 for model 2 is termed as 𝑘2. Simplistically, 𝑘1=1 proves translational diffusion follows HS

behaviour such that higher 𝑘1 values (i.e. away from 1) represent higher levels of interaction.

Similarly for the exponential model the higher the 𝑘2 value, the higher the level of

interaction. Both models can be utilised to compare levels of interaction between different

solution conditions.

The data following application of model 1 show BSA-AF in BSA solutions (Table 4.4)

behaviour is close to the HS model; where values are in close agreement with Roos et al.2 As

expected for mAb solutions (Table 4.5), the values of 𝑘1 diverge clearly from 1 towards 2

and over: according to Roos et al., such behaviour would suggest that the behaviour of IgG in

mAbs solutions does not only involve weak inter-protein interactions. The effect is more

prominent for COE-19.

From applying model 2, an effect of the size of the tracer is observed in BSA and mAbs

solutions (i.e. kTMRpeptide < kBSA-AF ≈kRheochip and kAtto < kIgG ≤ kRheoChip ≤ kIgM, respectively);

more precisely in mAbs solutions, kIgM is at least equal to kRheochip (the only time it is similar

is for COE-19 pH6.5 and COE-03 pH6.5).

A correlation between the interaction parameter, 𝑘𝐷, and 𝑘2 has been demonstrated in the

literature.16

Herein, 𝑘𝐷 values of COE-03 solutions were determined and compared with

determined 𝑘 values with IgG-AF; for both model 1 and model 2. Initial observation showed

no correlation. However, separating the data as a function of pH and salts, an effect of pH is

observed (Figure 4.5). Comparing between the two models, the more complex model (model

129

1) had a better correlation of k with the 𝑘𝐷 (R2

of 0.99 and 0.86 for 𝑘1 and 𝑘2 values,

respectively). However, no relation was observed between 𝑘𝐷 and 𝑘 for ionic strength.

For both models, the presence of excipients does not affect the change of the relative

viscosity or the change of the diffusion times for BSA solutions.

Table 4.4: Calculated 𝐤𝟏 and 𝐤𝟐 for BSA samples measured by FCS and the RheoChip.

Calculated 𝑘1 and 𝑘2 for BSA samples from the diffusion of tracers measured by FCS (BSA-AF and

TMR-peptide) and the RheoChip (𝑘2 only). Values represent means with std. dev for n=3.

Tracer Condition 𝒌𝟏 𝒌𝟐

RheoChip

no excipients N/A 0.0082 ± 0.0001

ArgHCl N/A 0.0086 ± 0.0001

ArgGlu N/A 0.0085 ± 0.0001

Sucrose N/A 0.0088 ± 0.0001

BSA-AF

BSA no excipients 1.38 ± 0.04 0.0088 ± 0.0006

BSA ArgHCl 1.28 ± 0.06 0.0078 ± 0.0006

BSA ArgGlu 1.33 ± 0.04 0.0083 ± 0.0005

BSA Sucrose 1.19 ± 0.04 0.0072 ± 0.0004

TMR peptide

BSA no excipients 0.98 ± 0.02 0.0059 ± 0.0000

BSA ArgHCl 0.97 ± 0.04 0.0057 ± 0.0003

BSA ArgGlu 0.99 ± 0.04 0.0058 ± 0.0003

BSA Sucrose 1.07 ± 0.04 0.0065 ± 0.0003

130

Table 4.5: Calculated 𝐤𝟏 and 𝐤𝟐 measured by FCS and the RheoChip.

Calculated 𝑘1 and 𝑘2 for mAb samples from the diffusion of tracers measured by FCS (ATTO-

Rho6G, IgG-AF, IgM-AF) the RheoChip (𝑘2 only). Values represent means with std. dev for n=3.

Tracer mAb

Condition 𝒌 from

model 1

𝒌 from

model 2

RheoChip

COE-03

pH 9 Tris N/A 0.0228 ± 0.0011

pH 6.5 (His Suc) N/A 0.0127 ± 0.0002

pH 5.5 acetate N/A 0.0124 ± 0.0007

pH5 no salt N/A 0.0115 ± 0.0002

pH5 250mM NaCl N/A 0.0103 ± 0.0007

COE-19

pH5 no salt N/A 0.0251 ± 0.0003

pH5 250mM NaCl N/A 0.0185 ± 0.0008

pH6.5 250mM NaCl N/A 0.0182 ± 0.0001

pH8 250mM NaCl N/A 0.0167 ± 0.0003

ATTO-Rho6G

COE-03

pH 9 Tris 1.16 ± 0.07 0.0074 ± 0.0003

pH 6.5 (His Suc) 1.13 ± 0.07 0.0069 ± 0.0004

pH 5.5 acetate 1.34 ± 0.07 0.0077 ± 0.0006

pH5 no salt 1.21 ± 0.04 0.0070 ± 0.0001

COE-19

pH5 no salt 1.05 ± 0.03 0.0059 ± 0.0001

pH5 250mM NaCl 1.03 ± 0.04 0.0061 ± 0.0002

pH6.5 250mM NaCl 1.05 ± 0.04 0.0060 ± 0.0001

pH8 250mM NaCl 1.05 ± 0.03 0.0058 ± 0.0002

IgG-AF

COE-03

pH 9 Tris 2.50 ± 0.09 0.0188 ± 0.0005

pH 6.5 (His Suc) 2.17 ± 0.02 0.0140 ± 0.0002

pH 5.5 acetate 2.35 ± 0.02 0.0161 ± 0.0003

pH5 no salt 1.86 ± 0.11 0.0118 ± 0.0002

pH5 250mM NaCl 1.69 ± 0.06 0.0108 ± 0.0005

pH 5 250mM NaSCN 2.10 ± 0.05 0.0137 ± 0.0005

COE-19

pH5 no salt 3.18 ± 0.17 0.0208 ± 0.0003

pH5 250mM NaCl 2.27 ± 0.09 0.0147 ± 0.0005

pH6.5 250mM NaCl 2.14 ± 0.05 0.0138 ± 0.0002

pH8 250mM NaCl 2.51 ± 0.12 0.0150 ± 0.0002

IgM-AF COE-19

pH8 250mM NaCl 2.74 ± 0.05 0.0196 ± 0.0001

pH6.5 250mM NaCl 2.50 ± 0.08 0.0186 ± 0.0006

pH 5 no salt 5.62 ± 0.28 0.0408 ± 0.0002

COE-03 pH 6.5 (His Suc) 2.76 ± 0.10 0.0155 ± 0.0007

131

Figure 4.5: Relation between interaction parameter (kD) and the exponential coefficient (k).

Relation between kD and the k determined from model 1 (left) and model 2 (right) as a function of pH

and (inserts) as function of salt for COE-03 solutions. 𝒌 calculated from the FCS diffusion of IgG-AF.

Plots represent average with std. errors.

As mentioned above, the retardation of IgG-AF is usually less than the change of relative

viscosity at high concentrations (at concentrations ≥ 100mg/mL). This can be seen on Figure

4.4 by a direct comparison which clearly shows that as soon as the ratio of diffusivities

increases to 5 and above, a difference is observed. However, the influence of the

experimental conditions i.e. pH, ionic strength, ions, on the IgG-AF retardation parallels the

one observed for the change of the relative viscosity. Consequently IgG-AF can be used to

inform of the solution’s environment. On the other hand, the observed retardation for IgM-AF

varies considerably depending on the experimental conditions used for these experiments: it

may follow the change of the relative viscosity (e.g. COE-03 pH6.5 and COE-19 pH6.5

250mM) or deviate and have a stronger effect (COE-19 pH 5 and pH 8 250mM). Data from

Table 4.5, illustrate how much these deviate. Figure 4.3 can be similarly explained. At high

concentrations, shear thinning causes divergence in the ratio of diffusion times plots

indicating possible protein-protein interaction leading to aggregation. The deviation of 𝑘1 and

𝑘2 values coincide with these diffusion plots (Table 4.5). For example, the pH5 (no salt)

condition showed the most divergence for COE-03 at high concentrations (with IgG-AF) and

also had the highest 𝑘 values (𝑘1=3.18±0.17, 𝑘2=0.020.8±0.0003) for that group of

conditions.

Interestingly COE-19 has interesting physical behaviours i.e. highly prone to aggregation

(personal communication) whilst COE-03 behaviour is more what would be expected. This

suggests that although a size effect is observed for IgM-AF, it may be an unreliable tool to

132

use to evaluate the viscosity of a solution due to other aspects; such as interaction with the

mAb (COE-19).

4.5 Discussion

4.5.1 Applicability of the GSE relation

The concentration dependence of viscosity and translational diffusion was compared through

plotting the ratio of diffusion.

For the translational diffusion of ATTO-Rho6G and IgG-AF in sucrose solutions, the GSE

relation seems applicable (Figure 4.1); the self-diffusion of both tracers is correlated with

solution macroviscosity, such that the translational diffusion is dependent on the crowder

concentration and not the crowder size.

For protein solutions, the crowder size became important, coinciding with Balbo et al.1 and

also Holyst et al.34

Comparing the data with model protein BSA and the two mAbs, it is the

difference in size between the tracer and the crowder which significantly affects the observed

retardation behaviour of ratio of diffusion over concentration. The ratio of diffusitives for

sucrose solutions was similar for small dye ATTO-Rho6G and IgG-AF, although the two

tracers are magnitudes apart in terms of size; however, both ATTO-Rho6G and IgG-AF are

larger than sucrose molecules. For BSA solutions, a tracer the same size of the crowder was

sufficient enough to follow the GSE relation (and not a tracer smaller than the crowder)

(Figure 4.3 - 4.4). However, for mAb solutions, the tracer needed to be larger than the

crowder in order to relate to the (RheoChip) macroviscosity and thus follow the GSE-

relation.

Herein we study the effects of the crowders on the self-diffusion of tracers using FCS. For

mAb solutions, IgG-AF is sensitive to the difference in data as a function of pH and ionic

strength whereas ATTO-Rho6G was insensitive. For example, as the pH of the solutions

approach the pI of COE-03 (pI of around 9), an increased change in IgG-AF ratio of

diffusitives is observed; likely to be due to intermolecular interactions close to the pI, where

the molecular charge is zero.10

For COE-19, the addition on NaCl reduces the friction

experienced by IgG-AF. It is known that COE-19 is susceptible to aggregation (personal

communication) and here it is indicated that the addition of NaCl has a significant effect on

limiting intermolecular interactions (Figure 4.3). The stabilising effects of NaCl are well-

known in the literature such that NaCl is commonly added in formulations to enhance protein

solubility.49-51

133

4.5.2 Van Blaaderen’s model and exponential model

Van Blaaderen’s model considers proteins as hard spheres (HS) subject to intermolecular

interactions. However their size can be readjusted using an effective radius, corresponding to

the effective volume fraction, thus 𝑘1 represents the deviation of this radius and 𝑘1 = 1

indicates the presence of only rather weak inter-protein interactions. Results from Table 4.5

and 4.6 indicate this is mainly observed for BSA-AF in BSA solutions (𝑘1 ranging from 1.19

to 1.38), whilst the HS model does not appear to be valid for both mAbs tested (COE-03 and

COE-19) using IgG-AF (𝑘1 ranging from 1.69 to 3.18). These results coincide with the

evidence-based discussion by Roos et al., on the coupling between translation diffusion of a

tracer and protein-tracer interaction; stating the applicability of models is protein-specific.

The model proposed by Conolly et al.16

seemed to indicate a good correlation between 𝑘2 and

kD for different mAbs measured at a certain experimental condition; however this does not

apply herein when comparing different conditions for the same mAb (here COE-03) (Figure

4.5) limiting the application of this model.

4.5.3 Size and charge of the tracer

In this study by Zorrilla et al., the diffusion-retarding effect was found to be distinctive for

each crowder solution i.e. for the same crowder concentration (250 mg/ml) an 8-fold increase

was obtained for RNase-A solutions, and a 3.5-fold increase for HSA solutions. This

difference can be attributed to the crowder obstacle distributions in the solution which in turn

affects the effective accessible volume to the tracer molecules. The RNase is more

heterogeneous with all the cavities between adjacent structures (tetramers) are occupied with

RNase-A monomers; whilst the HSA cavities are free for the tracer molecules. Weak protein

interactions may also need to be considered. Non-specific crowder-crowder, crowder-tracer,

and tracer-tracer interactions are dependent on a number of characteristics such as the size,

shape, rigidity and charge of both the crowder and the tracer. In the study, the tracer (poMb-

m) self-associated in RNase-A solutions whilst remained as monomers in HSA-solutions (i.e.

an effect of tracer size on diffusion).24

The charge of the different proteins are likely to have

affected these results (of self-association); with the PI of apoMb-m 8.8, RNase PI of 9.3 and

HSA PI of 4.5.

Recently, Roos et al.2 studied the crowding effects for understanding in vivo behaviour of

proteins using FCS and NMR. The authors found that long-time translational diffusion scales

with macroscopic viscosity. With regard to the same concentration dependence of long-time

134

translational diffusion, effective-sphere behaviour cannot even qualitatively describe the hen

egg white lysozyme experimental data. Similarly Roos et al. by PG-NMR found that upon

increasing the protein concentration, the translational diffusion of aB-crystallin nicely

followed the trend measured for the inverse solution viscosity. Despite its large size and

oligomeric structure, -crystallin in dilute solution behaves like a normal rigid globular

protein, showing no specificity in Brownian dynamics compared to other, even much smaller,

proteins.31

Rothe et al. observed the small solvent molecules diffuse faster than predicted

from the SE relation, at least with regards to the (macroscopic) solution viscosity. Under

crowding conditions these small probe molecules diffuse in an environment of much larger

surrounding particles, which renders the validity of the effective-medium approach

questionable with regards to estimating the macroscopic dispersion viscosity.29

Assessing the current literature, despite the high interest in crowding effects for

understanding in vivo behaviour of proteins, the effect on Brownian dynamics remains little

studied and controversial. Sherman et al. investigated intramolecular diffusion of denatured

molecules, studying the dependence of the intrachain dynamics on the concentration of the

denaturant, guanidinium chloride (GdmCl). The increase of the GdmCl concentration

expands the protein chain and significantly altered the viscosity. The intrachain diffusion

coefficient (𝐷) should depend on the internal friction generated by the probed loop itself and

its interaction with other parts of the chain, and on solution viscosity. However, 𝐷 values did

not change over the range of GdmCl concentrations in which solvent viscosity increases. One

explanation suggested was the changes in intrachain diffusion which may cancel the viscosity

effect. Another suggested explanation was that the intramolecular diffusion maybe dominated

by a profoundly constant internal friction. In order to investigate further, microscopic

investigations of the internal friction is required.52

Dauty et al. studied the influence of molecule crowding on the translational diffusion of a

range of macromolecules of different size and properties (including proteins, double stranded

DNAs and dextrans). The diffusion (𝐷) of all particles were reduced exponentially with

increasing Ficoll-70 concentration. The difference in change of diffusion was not influenced

by the tracer size as a function of Ficoll-70 concentration thus the study demonstrated a

previously unrecognised insensitivity of crowding. Our results with sucrose solutions

(discussed earlier) complement these findings. The HS model of diffusion in crowded

solutions (Equation 4.8) does not apply to these data.

135

4.6 Conclusion

Measurements of translation diffusion as a function of concentration can provide useful

information on the effects of molecular crowding on solution viscosity. FCS is chosen to

measure the translational diffusion (of tracers) due to the many orders of magnitude of time it

can accurately measure, using very dilute tracer concentrations. The data presented in this

study show a correlation with translation diffusion of the tracers and concentration of

crowder. However, whether the GSE relation is applicable and if the tracer is sensitive to

changes in solution properties (i.e. pH, salts, ionic strength), this is dependent on the tracer

size and tracer properties (which is dependent on the crowder properties); such as tracer-

crowder interaction (as indicated by data with IgM-AF). Utilising a tracer of size equal (or

larger) to the crowder size can detect changes in micro-rheology (providing it is the true self-

diffusion of the tracer which is measured). This study, along with related literature, shows the

scope of the FCS application to provide insight into intermolecular interactions governing

high viscosity of highly concentrated mAb solutions. With the development of computational

models, this application could reduce the number of studies in early formulation

development.

136

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139

5 : INVESTIGATING

POLYSORBATE MICELLE

FORMATION BY FLUORESCENCE

CORRELATION SPECTROSCOPY

140

Research Article

Investigating Polysorbate Micelle Formation by Fluorescence Correlation Spectroscopy

in monoclonal antibody solutions

Maryam Shaha, #

, Katie Dayb, Shahid Uddin

b, Tom Jowitt

a, Robin Curtis

c, Christopher F. van

der Walleb and Alain Pluen

a*

a, School of Health Sciences, University of Manchester, Manchester, UK

b, MedImmune Ltd, Formulation Sciences, Granta Park, Cambridge, UK

c, School of Chemical Engineering and Analytical Sciences, University of Manchester,

Manchester, UK

#, first author

*, corresponding author: Alain.Pluen@manchester.ac.uk

141

5.1 Abstract

Polysorbates are commonly used in biopharmaceutical (e.g. monoclonal antibody (mAb))

formulations to prevent surface-induced protein aggregation. The critical micelle

concentration (cmc) is an important parameter for surfactant functionality, thus it is taken into

account when deciding on the surfactant concentration. However, the mechanisms of

polysorbate micelle behaviour in high concentrated mAb solutions are still unclear, partly due

to the limitations of analytical techniques. This study demonstrates the use of fluorescence

correlation spectroscopy (FCS), with hydrophobic dye SYPRO® Orange, in labelling

surfactant micelles. As the dye only fluoresces though hydrophobic interaction, i.e.

incorporating into the micelle core, the cmc of three nonionic surfactants (Triton X-100,

polysorbate-20 and polysorbate-80) was accurately determined along with the micelle radius

(in water and in buffer). Additionally, micelles are detected in the presence of mAb, thus

demonstrating the scope of the method in assessing polysorbate behaviour in highly

concentrated mAb formulations.

142

5.2 Introduction

5.2.1 Polysorbates prevent surface-induced protein aggregation

Monoclonal antibodies (mAbs) are prone to interaction with surfaces which can lead to

partial unfolding and (subsequently) protein aggregation. In order to prevent surface-induced

aggregation, which is accelerated by agitation stress, stabilising agents are added in

formulations. Non-ionic surfactants have shown to be very efficient at preventing, or at least

considerably reducing, aggregation at interfaces.1,2

The surfactant is usually added after

ultrafiltration-diafiltration and prior to the bulk freezing step during manufacturing.3

Polysorbate-20 (PS-20) and polysorbate-80 (PS-80) are the most commonly used non-ionic

surfactants, with around 80% of commercially available mAbs containing either PS-20 or PS-

80 in the formulation.4 The two main mechanisms proposed for protein stabilisation are (i)

through competing with proteins at adsorption sites on interfaces and (ii) through interaction

with proteins by binding to hydrophobic regions of protein surfaces. Both mechanisms

prevent protein aggregation by reducing protein-protein interactions. From these, the first

mechanism is classed as the most dominant mechanism for mAb solutions. Studies have

shown surfactants are thermodynamically favoured over proteins for adsorption at the

interface.3,5,6

Several studies have observed interaction with polysorbates for proteins with

hydrophobic regions on surfaces such as human growth hormone and bovine serum

albumin.3,7-10

Whereas, studies on mAb-polysorbate interaction have reported interaction

between mAb and polysorbates to be very weak / negligible and likely play no significant

role for stabilisation.3,11

5.2.2 Importance of surfactant concentration and the cmc

The functionality of surfactants is dependent on the surfactant concentration; as the protective

effects of surfactants is related to the coverage of the surface by surfactant monomers. Thus,

the functionality of surfactants is affected by the critical micelle concentration (cmc), defined

as the concentration above which micelles start to form spontaneously. The cmc is the most

commonly preferred functionality-related parameter as it reflects complete surface coverage

to prevent protein-interface interaction. Below the cmc polysorbates are monomeric, whilst

above the cmc micelles form as the surfaces are saturated with polysorbate and additional

surfactant form micelles due to their amphipathic nature. Micelles are self-assembled

surfactant complexes formed to limit the interaction of the nonpolar groups (tails) to the

aqueous solution.4,12

The typical concentration range used for nonionic surfactants is 0.001-

0.1% (w/v). The concentration chosen is usually based on the lowest effective concentration

143

which stabilised the protein upon interfacial stress; this is usually just above the cmc. A

concentration of surfactant too high may result in the formation of protein-micelle complexes

which may cause immune responses.13-15

The cmc for almost all surfactants are listed in the literature for pure water. However, the cmc

is affected by parameters such as temperature; whereby a reduction in the cmc has been

correlated with increasing temperature for nonionic surfactants.16-18

The cmc of ionic

surfactants is also affected by changes in pH and ionic strength, but this has not been

observed for nonionic surfactants.19,20

The cmc measured in a specific formulation (rather

than the pure PS in water) is termed as the ‘apparent cmc’.

5.2.3 Experimental detection of micelles

Typically, to determine the cmc a measured parameter is correlated against surfactant

concentration and the onset of micelle formation is experimentally observed by a significant

change in the measured parameter.21

There are a large number of techniques that exist to

measure the cmc of surfactants. Common methods include surface tension,3 conductivity,

spectrophotometry22,23

and light scattering (e.g. dynamic light scattering, DLS).24,25

Advantages of such techniques include ease of use and that the equipment is largely

available. Disadvantages include costly equipment, time-consuming and interference from

solution components. Measuring the cmc in presence of proteins is not straightforward. This

may partly be the reason for the limited literature on the effect of mAb concentration on cmc

and micelle behaviour, especially in high concentrated mAb solutions. For example, DLS is

affected by solution components (e.g. protein monomers, protein aggregates, surfactants,

silicone oil, dust etc.), thus can only be applied to determine the cmc in pure surfactant

solutions. Protein molecules tend to be a similar size to micelles and the system cannot

differentiate between the two populations.13

Surface tension is used to determine cmc in

protein solutions, however it only deducts presence of micelles indirectly from the data of

surface saturation and so cannot provide direct information on presence of micelles.13

Patist

et al. have shown while assessing nine nonionic surfactants, that dye micellization methods

were more accurate at determining the cmc in comparison to surface tension measurements

which were sensitive to the presence of surface active impurities.26

In the last few years,

Horiuchi et al. proposed the use of ultrasonic resonance technology (URT) to determine cmc

in protein solutions. URT catches signal from changes in protein structure and hydration

condition around the protein. The presence of protein amplifies the signal of ultrasound

velocity so far that micellisation was detectable. However, URT is sensitive to foreign matter

144

(particularity large particles) and also unable to determine the cmc in protein-free

formulations.13

Fluorescence correlation spectroscopy (FCS) is a single molecule technique, measuring

fluctuations in the fluorescence intensity in the confocal volume. It yields the translational

diffusion coefficient of a fluorescent molecule in a solution. The method has been applied to

determine the cmc of various surfactants, including PS-20, in buffered solutions (in absence

of mAb) using commercially available dyes.27-30

Herein, this work demonstrates the scope of

FCS, using a moderately hydrophobic dye SYPRO® Orange, to detect micelles in high

concentration mAb solutions.

5.3 Materials and Methods

5.3.1 Materials

SYPRO® Orange and Rhodamine-123 (R123) dyes were obtained from Thermo Scientific

(Leicestershire, UK).

Surfactants (PS-20, PS-80, and Triton X-100 (TX100)) and buffer components (L-histidine)

were of analytical grade or higher, purchased from Sigma Aldrich (Dorset, UK) and used

without further purification. All buffers and solutions were prepared with Millipore de-

ionised water (18 MΩ.cm).

The monoclonal antibody, COE-03 (IgG1, MW 145kDa, pI 8.44), was supplied by

MedImmune at stock concentration of 40.2 mg/ml.

5.3.2 Methods

5.3.2.1 Sample Preparation

The buffer formulation used for all solutions (pure polysorbate, pure mAb or a mixture of

both) was 25 mM histidine, 240 mM sucrose at pH 6.0.

All solutions with SYPRO® Orange were prepared to a final concentration of 0.2× and 1× for

FCS and spectrophotometry measurements, respectively. All solutions with R123 were

prepared to a final concentration of 10 nM for FCS measurements.

The range of concentrations measured for each surfactant varied in order to obtain enough

points before and after the change of slope in the concentration plots. Dilutions were prepared

from stock concentration of 5% w/v surfactant.

145

The mAb (COE-03) solution was concentrated via ultracentrifugation using an amicon ultra-4

centrifugal filter unit with a membrane of 50kDa (Sigma, Dorset), to a concentration of

approximately 180 mg/ml.

5.3.2.2 Spectrofluorometry

Fluorescence intensity (INT) was measured with a Tecan Safire plate reader (Tecan Trading,

Switzerland) which utilises the Magellan V7.1 software for system set-up. SYPRO® Orange

samples were excited at 460 nm and emitted at 560-615 nm. For each measurement, 200 l of

sample was measured.

5.3.2.3 Fluorescence correlation spectroscopy (FCS)

A Zeiss LSM 510 ConfoCor 2 setup (Zeiss, Jena, Germany) equipped with a 40x/1.2NA

water immersion objective was used. For each measurement, 100 l of sample was measured.

The Argon laser was operated at 488nm for SYPRO Orange and R123 and fluorescence

collected at 560-615nm and 505-550nm, respectively. System calibration was performed with

Rhodamine GreenTM

(diffusion coefficient of 2.8 × 10-6

cm2/sec; Life technologies). System

calibration determines the laser beam waist size and optimises the optical setup for further

experiments. The laser beam waist size is estimated using Equation 5.1:

𝜏𝐷 =𝜔𝑜2

4𝐷⁄

Equation 5.1

where, 𝜏𝐷 corresponds to the diffusion time, ωo is the laser waist beam and D is the diffusion

coefficient.

After calibration, the determined laser waist beam size can be applied to the subsequent

experimental data to determine the diffusion coefficient through applying Equation 1. The

radius is then determined using the Stokes Einstein equation (Equation 5.2):

𝐷 = 𝑘𝑇 6𝜋𝜂𝑅⁄ Equation 5.2

where, 𝐷 is the diffusion coefficient, 𝑅 is the hydrodynamic radius, 𝑘 is Boltzmann’s constant

and 𝜂 represents the solution viscosity.

5.3.2.4 Rheometer measured viscosity of mAb solutions

Viscosity (mPa.s) was measured using an Anton Paar MCR-301 rheometer (Anton Paar, UK)

with CP40-0.3 cone/plate attachment. 150µl of sample was measured with a shear rate of

1000 s-1

.

146

5.3.2.5 Isothermal titration calorimetry (ITC) of mAb binding to PS-20

ITC was used to identify any mAb (COE-03) binding interaction with PS-20. A Malvern

PEAQ ITC (Malvern, UK) was used to quantitate molecular interactions by measuring the

heat transfer during a binding event. Binding affinity, stoichiometry and entropy of a reaction

is detected by titrating in a ligand (i.e. PS-20), from an injector into a well containing the

protein. A series of small aliquots are injected until the binding detected reaches equilibrium.

Twenty injections of 0.1% w/v PS-20 at 25C (10 l per injection) were titrated into the well

containing 10mg/ml mAb, with a three minute spacing. A blank experiment in which PS-20

was titrated into buffer was also used to subtract the heat of dilution of PS-20. The molar

binding enthalpy (∆𝐻) were analysed using the manufacturer software and the kD determined

from the fit of ∆𝐻 to molar ratio.

5.4 Results and Discussion

5.4.1 Validation of SYPRO® Orange to determine the cmc

In order to validate FCS-SYPRO® Orange as an appropriate method, the cmc of PS-20 in

water (Figure 5.1) and in buffer (Figure 5.2) was determined initially using

spectrofluorometry in presence of SYPRO® Orange (as described in the methods). The cmc

in water was determined as 0.0072 (±0.0046) % w/v PS-20 which is consistent with

literature.31

The cmc of PS-20 in buffer was determined at a lower concentration of 0.0031

(±0.0015) % w/v. However, these results were not statistically different (at p = 0.05).

Figure 5.1: INT determined by spectrofluorometry of PS-20 solutions in water, in the presence of

SYPRO® Orange, in order to determine the cmc.

INT determined by spectrofluorometry of PS-20 solutions in water, in the presence of 1𝗑 SYPRO®

Orange. Plots represent averages with std. dev for n=3. Cmc determined from point of intersection at

0.0072% w/v (linear fits applied using Origin Software; error of 0.0046 % determined).

147

Figure 5.2: INT determined by spectrofluorometry of PS-20 solutions in histidine/sucrose buffer, in

the presence of SYPRO® Orange, in order to determine the cmc.

INT determined by spectrofluorometry of PS-20 solutions in histidine/sucrose buffer (pH6), in the

presence of 1𝗑 SYPRO® Orange. Plots represent averages with std. dev for n=3. Cmc determined

from point of intersection at 0.0031% w/v (linear fits applied using Origin Software; error of 0.0015

% determined).

5.4.2 Determining the cmc by FCS with SYPRO® Orange

The cmc of nonionic surfactant Triton X-100 (TX100) has been previously determined using

FCS by Pineiro et al. using R123 dye.30

In this section, we repeat their method and

subsequently present the use of SYPRO® Orange to determine the cmc using FCS.

With R123 solutions, the diffusion coefficient is used to determine the cmc and we

extrapolate a value of 0.017(±0.012) % w/v (Figure 5.3) which coincides with literature.30

Below the cmc, the diffusion time will be that of the dye alone in solution, whereas above the

cmc the dye will incorporate into the micelle. However, not all of the dye will be bound –

there will also be some free dye in solution and the proportion of free and bound dye will

vary depending on the surfactant concentration. The dye molecule is constantly exchanging

between the aqueous and micelle environment, and due to the fast exchange equilibrium two

species are not observed. As the surfactant concentration (and so the micelle concentration)

increases, less of the dye will be free and thus the ‘mean diffusion time’ (combined diffusion

time of free and bound dye) will increase. Thus, the cmc can be determined through plotting

the (mean) diffusion time against the surfactant concentration.30

With SYPRO® Orange solutions, the cmc is determined using the FCS determined number

of particles. SYPRO® Orange will only fluoresce when incorporated into the micelle and so

below the cmc, the number of fluorescent particles is fundamentally nil and the diffusion time

is not representable due to poor correlation (illustrated by chi2 values). Above the cmc, the

148

FCS number of particles is correlated with the surfactant concentration and the diffusion time

represents the (apparent) micelle diffusion time, such that the micelle radius can be directly

determined without the need of complex data analysis. Using this approach, the cmc was also

determined as 0.017 (±0.011) % w/v (Figure 5.4). Above the cmc, a radius of around 5nm is

determined from the diffusion time of SYPRO® Orange solutions (as described in the

methods), which is also consistent with previous literature of a TX100 micelle (values 4.84

and 5.3nm have been determined by DLS).32

Thus, it can be established that SYPRO®

Orange is incorporated into micelles. Below the cmc, the chi2

(correlation fit) is very poor and

thus the diffusion time carries no worth for these measurements.

One point which requires discussion is the non-existent plateau in the figure of TX100 and

R123 (Figure 5.3). The diffusion coefficient for micelles should (eventually) plateau out

above the cmc - as seen in the INT measurements (Figures 5.1 and 5.2) and TX100 with

SYPRO® Orange (Figure 5.4). The difference between the two probes (R123 and SYPRO®

Orange) may explain this difference of data. Freire et al.33

assessed the behaviour of cationic

fluorophore R123, with various nonionic and ionic surfactants. R123 presented affinity to

surfactant micelles with the behaviour explained by the partition equilibrium model with the

existence of free dye and bound dye. At the surfactant concentrations used, a certain

proportion of the free dye was always present – thus we may need to reach much higher

surfactant concentrations in order to observe the plateau with R123. Pineiro et al. also stated

that it is incorrect to assume the fraction of free dye and bound dye are two static species;

empirical models are required for quantifications. The figures in the Pineiro paper show the

start of a plateau to be around 0.6 – 1.2 % w/v TX100.30

This concentration range is much

higher than the concentration range assessed herein (highest concentration of 0.3% w/v

TX100) (Figure 5.3). The lack of plateau for any following graphs (i.e. Figures 5-5-5.7) may

also be explained by the need to assess higher surfactant concentrations.

149

Figure 5.3: FCS-determined parameters of TX100 solutions in the presence of R123, in order to

determine the cmc.

FCS-determined diffusion coefficient (D) (filled black triangles), counts per molecule (filled blue

squares), and chi2 (open grey diamonds) of TX100 solutions in the presence of 10nM R123. Symbols

represent averages plus std. dev for n=5. Cmc determined from point of intersection of 0.017% w/v

(linear fits – bold black lines - applied using Origin Software; error of 0.012 % determined).

Figure 5.4: FCS-determined parameters of TX100 solutions in the presence of SYPRO® Orange in

order to determine the cmc.

FCS-determined number of particles (filled black squares) diffusion time (filled red circles), and chi2

(open grey diamonds) of TX100 solutions (in water) in the presence of 0.2𝗑 SYPRO® Orange.

Symbols represent averages plus std. dev (n=5).Cmc determined from point of intersection as 0.017%

w/v (linear fits – bold black lines - applied using Origin Software; error of 0.011 % determined).

150

This initial data with TX100 validate the FCS/SYPRO® Orange method to determine the

cmc on the nonionic surfactant. The method was then applied to polysorbates, PS-80 and PS-

20. Figures 5.5 and 5.6 present the FCS data (using SYPRO® Orange) for PS-80 and PS-20

samples in water with determined cmc values of 0.0016 (±0.0012) % and 0.007 (±0.003) %

w/v, respectively, which match the literature. PS-20 solutions in histidine/sucrose buffer were

also measured and a slightly lower cmc of 0.0036 (±0.002) % w/v was determined (Figure

5.7) – similar to the cmc value determined by spectrofluorometry. Again, statistical

differences (at p = 0.05) were not observed between the cmc of PS-20 in water and in buffer.

The radius was determined from the diffusion time of SYPRO® Orange solutions at PS

concentrations above the cmc: For water and buffer solutions, a radius of around 3nm was

determined which coincides with the literature (values between 2.7 – 3.5 nm have been

determined for PS-20 and PS-80 micelles).34-36

For all solutions, deterioration in the chi2 is

observed as a function of surfactant concentration, particularly below the apparent cmc.

Figure 5.5: FCS-determined parameters of PS-80 solutions in the presence of SYPRO® Orange in

water, in order to determine the cmc.

FCS-determined number of particles (filled black squares) diffusion time (filled red circles), and chi2

(open pink diamonds) of PS-80 solutions in the presence of 0.2x SYPRO® Orange in water. Symbols

represent averages plus std. dev (n=5). Cmc determined as 0.0016% w/v (linear fits – bold black lines

- applied using Origin Software; error of 0.0012 % determined).

151

Figure 5.6: FCS-determined parameters of PS-20 solutions in the presence of SYPRO® Orange in

water in order to determine the cmc.

FCS-determined number of particles (filled black squares) radius (filled red circles), and chi2 (open

pink diamonds) of PS-20 solutions in the presence of 0.2x SYPRO® Orange in water. Symbols

represent averages plus std. dev (n=5). Cmc determined as 0.007% w/v (linear fits – bold black lines -

applied using Origin Software; error of 0.003 % determined).

Figure 5.7: FCS-determined parameters of PS-20 solutions in the presence of SYPRO® Orange in

histidine/sucrose buffer, in order to determine the cmc.

FCS-determined number of particles (filled black squares) diffusion time (filled red circles), and chi2

(open pink diamonds) of PS-20 solutions in the presence of 0.2𝗑 SYPRO® Orange in histidine/sucrose

buffer pH6. Symbols represent averages plus std. dev (n=5). Cmc determined as 0.0036% w/v (linear

fits – bold black lines - applied using Origin Software; error of 0.002 % determined).

152

Based on all of the above results, it can be established that FCS with the utilisation of

SYPRO® Orange, is a sensitive measure of detecting micelles and can accurately determine

the cmc of solutions along with the micelle size. The cmc values of the three surfactants

(TX100, PS-20 and PS-80) are largely different e.g. by a magnitude of around 4.4𝗑 between

PS-20 and PS-80 (in water). As aforementioned, substantial differences in the cmc of

nonionic surfactants, due to solution properties, are typically not observed. Both techniques

in this study, i.e. spectrofluorometry and FCS, detected apparent differences in cmc for

different solvents (i.e. between water and buffer) however these were not statistically

different (there was a smaller differences in magnitude between the (apparent) cmc values i.e.

around 2.2𝗑 between PS-20 in water and PS-20 in buffer). Thus current methods are not

sensitive to measuring changes in cmc of a nonionic surfactant in different buffers (in

absence of mAb).

5.4.3 FCS / SYPRO® Orange micelle detection in mAb solutions

Concentrated mAb solutions of 75, 100 and 150 mg/ml were assessed with a number of PS-

20 concentrations above the apparent PS-20 cmc in buffer (determined previously). SYPRO®

Orange with mAb alone (i.e. in absence of surfactant) generated an insufficient correlation to

generate any data, as did the dye alone in buffer, indicating insufficient interaction between

the hydrophobic dye and native protein (data not shown).

For each PS-20 concentration assessed (0.007%, 0.01%, 0.02% and 0.05% w/v) the diffusion

time, number of particles and chi2

were compared between the mAb solutions and the

respective buffer solution. The PS: protein ratio was determined for all mAb solutions

(through calculating the mol/L for COE-08 and PS-20 for each solution and determining the

ratio of PS-20 over protein) and compared with the data. The only mAb solution which did

not show significant differences (p < 0.05) across the parameters (i.e. diffusion time and

number of particles) was the 75mg/ml mAb 0.05% w/v PS-20; which had the highest PS:

protein ratio (Table 5.1).

153

Table 5.1: FCS parameters and PS: protein ratio of mAb solutions in presence of SYPRO®

Orange.

FCS parameters (diffusion time, number of particles and chi2) of mAb solutions in presence of

SYPRO® Orange, along with the PS: protein ratio. Values represent averages with their std. dev for

n=5.

PS-20

Concentration

(% w/v)

mAb

Concentration

Number of

Particles

Diffusion

Chi2 PS: protein

ratio

0.007 Buffer only 26 ± 3 242 ± 20 2E-6 ± 1E-6

75mgml* 42 ± 6* 391 ± 111* 2E-5 ± 1E-5* 0.11

100mgml* 63 ± 15* 474 ± 118* 3E-5 ± 2E-5* 0.08

0.01 Buffer only 40 ± 3 224 ± 7 2E-6 ± 9E-7

75mgml* 49 ± 9* 302 ± 58* 8E-6 ± 6E-6* 0.16

100mgml* 63 ± 20* 421 ± 148* 4E-5 ± 2E-5* 0.12

0.02 Buffer only 57 ± 3 223 ± 12 6E-7 ± 3E-7

75mgml 69 ± 6* 248 ± 26 3E-6 ± 2E-6* 0.31

100mgml* 97 ± 31* 411 ± 123* 8E-6 ± 7E-6* 0.24

150mgml* 115 ± 10* 708 ± 162* 4E-5 ± 3E-5* 0.16

0.05 Buffer only 82 ± 6 232 ± 11 2E-7 ± 2E-7

75mgml 77 ± 7 258 ± 35 2E-6 ± 2E-6 0.79

100mgml* 183 ± 53* 588 ± 114* 3E-6 ± 6E-7* 0.59

* indicates significant difference with the respective buffer only solution (i.e. at same PS-20

concentration).

Diffusion Time

Using the Stokes Einstein relation (Equation 5.2) and Equation 5.1, a relation between the

diffusion time and solution’s viscosity is obtained. Studies have demonstrated the correlation

between solution viscosity with protein concentration and the solution viscosity has been

reported to affect both the FCS diffusion time and number of particles.37-39

To assess if the

relation applies to this data, the viscosity of the mAb solutions was measured, as described in

the methods, and comparisons were made with both FCS parameters. A poor correlation was

determined (R2 of 0.32) between the solution viscosity and the FCS number of particles (data

not shown). The relation between the FCS diffusion time and the viscosity of the mAb

solutions is shown in Figure 5.8 and a correlation is observed; although not very strong (R2

value of 0.64). As the data shows that the diffusion time of SYPRO® Orange surfactant

solutions in the presence of mAb does not statistically change to suggest the presence of large

structures, the diffusion time change is likely a result of change in viscosity combined with

the change in number of particles.

154

Figure 5.8: Viscosity (cone plate method) plotted against the FCS diffusion times of concentrated

mAb solutions.

Macro-viscosity (cone plate method) plotted against the FCS diffusion times (with 0.2𝗑 SYPRO®

Orange) of concentrated mAb solutions (75, 100, 150 mg/ml COE-03).

mAb-PS Interaction

Due to the possible mAb-polysorbate interaction (resulting in mAb-PS complexes) at high

mAb concentrations, the binding of PS-20 to mAb (COE-03) was investigated using

Isothermal titration calorimetry (ITC).

To test the levels at which PS-20 is usable as a ligand, several different concentrations were

titrated ranging from 1% w/v to 0.05% w/v PS-20. At concentrations greater than 0.1% w/v

large interaction heats observed were most likely due to dissolution of the micelles. Based on

this, higher concentrations than 0.1% w/v were not used due to extremely large heats of

dilution (Figure 5.9). PS-20 solution (0.1% w/v) was titrated into 10mg/ml COE-03.

Subtraction of PS-20 alone gave the results shown in Figure 5.10.

155

Figure 5.9: Isothermal titration calorimetry thermogram recorded for injection of 0.1% w/v PS-20

into PBS.

Isothermal titration calorimetry thermogram recorded for injection of 0.1% w/v PS-20 solution into

PBS showing quite large heats.

Figure 5.10: Isothermal calorimetry results for injection of 0.1% w/v PS-20 into 10mg/ml COE-03.

(top) Isothermal titration calorimetry thermogram recorded for injection of 0.1% PS-20 into 10mg/ml

COE-03 and (bottom) the theoretical curve fitted to the integrated data of heat released to the molar

ratio.

156

Based on this analysis, there seems to be a very slight interaction with a total negative

enthalpy change of 1.29 kcal/mol. The fit seems to suggest that the interaction has a kD of

approximately 24 M, with approximately 4 binding sites (n-value of 0.28). This is indicative

of a very slight endothermic process. This heat change may be attributed by surfactant

binding to protein and/or micelle dissociation.40-42

Nevertheless, this interaction is very weak

and barely detectable, thus the experiment failed to detect any mAb-PS interaction for the

conditions assessed.

Number of particles

In the water or buffer only PS-20 solutions (i.e. in absence of mAb) the FCS number of

particles increased with PS-20 concentration, for concentrations above the cmc (Figure 5.5

and Figure 5.7). As the dye is incorporated into the micelle, this suggests the FCS number of

particles correlates with the micelle population. The question is, can this relation be applied

to solutions containing high concentration mAb: FCS is prone to artefacts with high

concentration solutions, thus the parameters and other solution factors need to be considered

with mAb solutions. An increase in the diffusion time with the number of particles could be a

possible effect of refractive index issues which have led to a larger detection volume and thus

longer diffusion time; rather than a result of a change in the fluorescent population i.e.

micelle numbers.38,43

In this study, both the diffusion time and the number of particles

increase with increasing mAb concentration, although a linear correlation is not established

due to the aforementioned influence of solution viscosity. As ITC failed to support an

increase in number of particles, the apparent increase of particles is likely to be related to the

increase in measured volume and not on the creation of mixed micelles.

PS: Protein ratio

The only mAb solution which did not show a change in diffusion time or number of particles

was the solution with the highest PS: protein ratio of 0.79 (Table 5.1). This is an interesting

finding suggesting the micelle behaviour is affected when the polysorbate ratio is below a

certain level (or when the protein ratio is above a certain level). However, the explanation for

the change in data here, and what is represents, is beyond the scope of this study. Moreover,

this result needs confirmation. It would be constructive to add more ratios to the data set in

order to assess if PS: protein ratio is influencing the measured data - solutions with PS:

protein ratios larger than 0.79 and in between 0.79 and 0.59 (the next highest PS: protein ratio

which did show a change in the measured data). The surfactant: protein ratio has been classed

157

as an important parameter on the protective effects of surfactants i.e. at preventing agitation-

induced aggregation.8,10,44

Another factor which should be considered is the properties of the

protein itself; as the surface activity varies between proteins thus the competition between the

protein and the surfactant molecules (at interfaces) will be affected.9

5.5 Conclusion

The behaviour of proteins and polysorbates are complex, and in protein formulations the

properties of the protein (hydrophobicity, surface activity) and concentration (or ratios) need

to be considered. To enable further understanding of their behaviours, developments of new

analytical systems are required. In this study, it is shown that fluorescence correlation

spectroscopy (FCS), utilising SYPRO® Orange dye, can be used to determine the cmc, as

well as the micelle size of nonionic surfactants. The potential application of detecting

micelles in the presence of mAb, using the FCS-SYPRO® Orange application, may provide

valuable input in gaining information on micelle behaviour in mAb formulations and thus

contribute towards formulation development.

158

5.6 References

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A Review of Protein-Surfactant Interactions and Novel Analytical Methodologies.

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Chemical Society.

2. Shah M, Rattray Z, Day K, Uddin S, Curtis R, van der Walle CF, Pluen A 2017.

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orthogonal study characterising the entire subvisible size range. International Journal of

Pharmaceutics 519(1):58-66.

3. Khan TA, Mahler H-C, Kishore RSK 2015. Key interactions of surfactants in

therapeutic protein formulations: A review. European Journal of Pharmaceutics and

Biopharmaceutics 97(1):60-67.

4. Martos A, Koch W, Jiskoot W, Wuchner K, Winter G, Friess W, Hawe A 2017.

Trends on Analytical Characterization of Polysorbates and Their Degradation Products in

Biopharmaceutical Formulations. Journal of Pharmaceutical Sciences 106(7):1722-1735.

5. Mahler H-C, Senner F, Maeder K, Mueller R 2009. Surface activity of a monoclonal

antibody. Journal of Pharmaceutical Sciences 98(12):4525-4533.

6. Hillgren A, Lindgren J, Aldén M 2002. Protection mechanism of Tween 80 during

freeze–thawing of a model protein, LDH. International Journal of Pharmaceutics 237(1):57-

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7. Ruiz-Peña M, Oropesa-Nuñez R, Pons T, Louro SRW, Pérez-Gramatges A 2010.

Physico-chemical studies of molecular interactions between non-ionic surfactants and bovine

serum albumin. Colloids and Surfaces B: Biointerfaces 75(1):282-289.

8. Bam NB, Cleland JL, Yang J, Manning MC, Carpenter JF, Kelley RF, Randolph║

TW 1998. Tween protects recombinant human growth hormone against agitation-induced

damage via hydrophobic interactions. Journal of Pharmaceutical Sciences 87(12):1554-1559.

9. Deechongkit S, Wen J, Narhi LO, Jiang Y, Park SS, Kim J, Kerwin BA 2009.

Physical and biophysical effects of polysorbate 20 and 80 on darbepoetin alfa. Journal of

Pharmaceutical Sciences 98(9):3200-3217.

10. Chou DK, Krishnamurthy R, Randolph TW, Carpenter JF, Manning MC 2005.

Effects of Tween 20® and Tween 80® on the Stability of Albutropin During Agitation.

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11. Garidel P, Hoffmann C, Blume A 2009. A thermodynamic analysis of the binding

interaction between polysorbate 20 and 80 with human serum albumins and

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Chemistry 143(1):70-78.

12. Cui X, Mao S, Liu M, Yuan H, Du Y 2008. Mechanism of Surfactant Micelle

Formation. Langmuir 24(19):10771-10775.

13. Horiuchi S, Winter G 2015. CMC determination of nonionic surfactants in protein

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Biopharmaceutics 92(1):8-14.

14. Hermeling S, Schellekens H, Crommelin DJA, Jiskoot W 2003. Micelle-Associated

Protein in Epoetin Formulations: A Risk Factor for Immunogenicity? Pharmaceutical

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15. Singh SK 2011. Impact of Product-Related Factors on Immunogenicity of

Biotherapeutics. Journal of Pharmaceutical Sciences 100(2):354-387.

16. Mohajeri E, Noudeh GD 2012. Effect of Temperature on the Critical Micelle

Concentration and Micellization Thermodynamic of Nonionic Surfactants: Polyoxyethylene

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17. Chen L-J, Lin S-Y, Huang C-C, Chen E-M 1998. Temperature dependence of critical

micelle concentration of polyoxyethylenated non-ionic surfactants. Colloids and Surfaces A:

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18. Hait SK, Moulik SP 2001. Determination of critical micelle concentration (CMC) of

nonionic surfactants by donor-acceptor interaction with lodine and correlation of CMC with

hydrophile-lipophile balance and other parameters of the surfactants. Journal of Surfactants

and Detergents 4(3):303-309.

19. Fuguet E, Ràfols C, Rosés M, Bosch E 2005. Critical micelle concentration of

surfactants in aqueous buffered and unbuffered systems. Analytica Chimica Acta 548(1–

2):95-100.

20. Thongngam M, McClements DJ 2005. Influence of pH, Ionic Strength, and

Temperature on Self-Association and Interactions of Sodium Dodecyl Sulfate in the Absence

and Presence of Chitosan. Langmuir 21(1):79-86.

21. Tadros T 2013. Critical Micelle Concentration. Encyclopedia of Colloid and Interface

Science. Berlin, Heidelberg: Springer.

22. Kapoor RC, Chand P, Aggarwala VP 1972. Spectrophotometric determination of

critical micelle concentration of nonionic surfactants. Analytical Chemistry 44(12):2107-

2109.

23. Dominguez A, Fernandez A, Gonzalez N, Iglesias E, Montenegro L 1997.

Determination of Critical Micelle Concentration of Some Surfactants by Three Techniques.

Journal of Chemical Education 74(10):1227.

24. Movchan TG, Soboleva IV, Plotnikova EV, Shchekin AK, Rusanov AI 2012.

Dynamic light scattering study of cetyltrimethylammonium bromide aqueous solutions.

Colloid Journal 74(2):239-247.

25. Sutherland E, Mercer SM, Everist M, Leaist DG 2009.

Diffusion in Solutions of Micelles. What Does Dynamic Light Scattering Measure? Journal of

Chemical & Engineering Data 54(2):272-278.

26. Patist A, Bhagwat SS, Penfield KW, Aikens P, Shah DO 2000. On the measurement

of critical micelle concentrations of pure and technical-grade nonionic surfactants. Journal of

Surfactants and Detergents 3(1):53-58.

27. Zettl H, Portnoy Y, Gottlieb M, Krausch G 2005. Investigation of Micelle Formation

by Fluorescence Correlation Spectroscopy. The Journal of Physical Chemistry B

109(27):13397-13401.

28. Luschtinetz F, Dosche C 2009. Determination of micelle diffusion coefficients with

fluorescence correlation spectroscopy (FCS). Journal of Colloid and Interface Science

338(1):312-315.

29. Yu L, Tan M, Ho B, Ding JL, Wohland T 2006. Determination of critical micelle

concentrations and aggregation numbers by fluorescence correlation spectroscopy:

Aggregation of a lipopolysaccharide. Analytica Chimica Acta 556(1):216-225.

30. Pineiro L, Freire S, Bordello J, Novo M, Al-Soufi W 2013. Dye exchange in micellar

solutions. Quantitative analysis of bulk and single molecule fluorescence titrations. Soft

Matter 9(45):10779-10790.

31. Wan LSC, Lee PFS 1974. CMC of Polysorbates. Journal of Pharmaceutical Sciences

63(1):136-137.

32. Aivaliotis M, Samolis P, Neofotistou E, Remigy H, Rizos AK, Tsiotis G 2003.

Molecular size determination of a membrane protein in surfactants by light scattering.

Biochimica et Biophysica Acta (BBA) - Biomembranes 1615(1):69-76.

33. Freire S, Bordello J, Granadero D, Al-Soufi W, Novo M 2010. Role of electrostatic

and hydrophobic forces in the interaction of ionic dyes with charged micelles. Photochemical

& Photobiological Sciences 9(5):687-696.

160

34. Tang X, Huston KJ, Larson RG 2014. Molecular Dynamics Simulations of Structure–

Property Relationships of Tween 80 Surfactants in Water and at Interfaces. The Journal of

Physical Chemistry B 118(45):12907-12918.

35. Karjiban RA, Basri M, Rahman MBA, Salleh AB 2012. Structural Properties of

Nonionic Tween80 Micelle in Water Elucidated by Molecular Dynamics Simulation.

APCBEE Procedia 3(1):287-297.

36. Kumari H, Kline SR, Atwood JL 2014. Aqueous solubilization of hydrophobic

supramolecular metal-organic nanocapsules. Chemical Science 5(6):2554-2559.

37. Jung C, Lee J, Kang M, Kim SW 2014. Viscosity-Dependent Diffusion of Fluorescent

Particles Using Fluorescence Correlation Spectroscopy. Journal of Fluorescence 24(6):1785-

1790.

38. Sherman E, Itkin A, Kuttner YY, Rhoades E, Amir D, Haas E, Haran G 2008. Using

Fluorescence Correlation Spectroscopy to Study Conformational Changes in Denatured

Proteins. Biophysical Journal 94(12):4819-4827.

39. Tian Y, Martinez MM, Pappas D 2011. Fluorescence Correlation Spectroscopy: A

Review of Biochemical and Microfluidic Applications. Applied spectroscopy 65(4):115-124.

40. McClements DJ 2000. Isothermal Titration Calorimetry Study of Pectin−Ionic

Surfactant Interactions. Journal of Agricultural and Food Chemistry 48(11):5604-5611.

41. Bordbar A-K, Taheri-Kafrani A, Mousavi SH-A, Haertlé T 2008. Energetics of the

interactions of human serum albumin with cationic surfactant. Archives of Biochemistry and

Biophysics 470(2):103-110.

42. Kresheck GC, Hargraves WA 1981. Enthalpy titration studies of the binding of

surfactants to polyvinylpyrrolidonel. Journal of Colloid and Interface Science 83(1):1-10.

43. Jorg E, Ingo G, Digambara P, Jorg F 2004. Art and Artefacts of Fluorescence

Correlation Spectroscopy. Current Pharmaceutical Biotechnology 5(2):155-161.

44. Serno T, Carpenter JF, Randolph TW, Winter G 2010. Inhibition of agitation-induced

aggregation of an IgG-antibody by hydroxypropyl-β-cyclodextrin. Journal of Pharmaceutical

Sciences 99(3):1193-1206.

161

6 : VISCOSITY CHANGE

FOLLOWING AGGREGATION

DEVELOPMENT: CONFOCAL

MICROSCOPY SYSTEM FOR

ASSESSING AGGREGATION

DEVELOPMENT AND VISCOSITY

SEQUENTIALLY

162

Research Article

Viscosity Change Following Aggregation Development: Confocal microscopy system for

assessing aggregation development and viscosity sequentially

Maryam Shaha, #

, Katie Dayb, Shahid Uddin

b, Robin Curtis

c, Christopher F. van der Walle

b

and Alain Pluena*

a, School of Health Sciences, University of Manchester, Manchester, UK

b, MedImmune Ltd, Formulation Sciences, Granta Park, Cambridge, UK

c, School of Chemical Engineering and Analytical Sciences, University of Manchester, Manchester,

UK

#, first author

*, corresponding author: Alain.Pluen@manchester.ac.uk

163

6.1 Abstract

Two aspects are assessed in this study: (i) the effect of aggregate development (size and

counts) on change in solution viscosity and (ii) the effect of monoclonal antibody (mAb)

concentration on agitation-induced aggregation. Two different forms of agitation (rotation

and shaking), various agitation times (from 2hrs to 24hrs) and different shaking speeds

(500rpm and 2000rpm) were chosen to generate different particle sizes. Raster image

correlation spectroscopy (RICS) is employed to characterise aggregation solutions. This is the

first reported application of RICS characterising high concentrated mAb solutions. The

micro-rheology of the mAb solutions is determined by the diffusion of fluorescence probes;

measured by fluorescence correlation spectroscopy (FCS). It is observed that the change in

viscosity is correlated with aggregate development in terms of size and counts, for both the

low (1mg/ml) and high (100mg/ml) mAb concentrations assessed. The complexity of

aggregation mechanisms are highlighted through the measured inversely proportional

relationship between agitation-induced aggregation and protein concentration. The practical

advantages of utilisation of RICS and FCS on the same system are also portrayed

demonstrating their scope as a potential tool/package in biopharmaceutical formulation

development.

164

6.2 Introduction

6.2.1 Protein aggregation and viscosity issues of biopharmaceutical products

Regulatory authorities expect the manufacture to ensure product quality of

biopharmaceuticals (e.g. monoclonal antibody (mAb) products) during storage, transport and

until it patient administration, stating ‘The storage conditions and the length of studies chosen

should be sufficient to cover storage, shipment and subsequent use’ (ICH Q1A 2.1.7).1,2

These guidelines set out the principal requirements for the safe storage and distribution of

time- and temperature-sensitive pharmaceutical products.3

Non-native protein aggregation affects product quality, potency and safety4-6

and is a major

degradation route during manufacture, formulation, and storage of biopharmaceuticals. This

issue is often heightened at high concentrations i.e. > 80 mg/ml,7,8

which are needed to meet

dose requirements (e.g. for subcutaneous injection).9-11

At these high protein concentrations

the tendency of attractive protein-protein interactions (PPIs) increases due to the close

proximity of protein molecules.12

An additional related problem with high mAb

concentrations is solution viscosity. High viscosity accounts for issues with syringeability and

injectability at high protein concentrations; leading to increased risk of unsuccessful

administration and adverse effects in patients.10,13,14

Studies have shown mAbs exhibit a wide

range of viscosity profiles showing exponential increases in viscosity with protein

concentration. As intermolecular interactions (e.g. PPI) have shown to influence the viscosity

profiles, studies have attempted to relate the nature and magnitude of PPIs at dilute

concentrations to predict the behaviour of proteins at high protein concentrations. However,

due the complexity of aggregation mechanisms, which vary with protein concentration and

solution properties, a different approach is needed.15-20

Solution properties influencing protein

behaviour include pH and ionic strength as well as the molecular weight and morphology of

protein aggregates.21-24

6.2.2 Relation between aggregation development and change in solution

viscosity

A number of studies have observed a correlation between aggregate size and solution

viscosity. For example, Barnett et al. assessed the relation between changes in aggregate

structures and solution viscosity in conditions where PPIs were highly repulsive. -

chymotrypsinogen (aCgn) solutions were evaluated at concentrations 0.5-1 mg/ml and a

direct link between aggregation growth (aggregate molecular weight) and increase in solution

viscosity (zero-shear viscosity) is illustrated, shown to be consistent with polymer solution

165

theory (which states solution viscosity is influenced by concentration, molecular weight and

morphology of protein aggregates).25

Nicoud et al. studied the heat-induced aggregation of a

mAb at a higher concentration range of 20-60 mg/ml. Thermal stress caused an increase in

average hydrodynamic radius which increased the solution viscosity (zero-shear viscosity)

and correlated with protein concentration.26

Similarly, Lilyestrom et al. observed a linear

dependence of viscosity on reversible self-association (RSA) in terms of cluster size for two

mAbs, at a high concentration of 175mg/ml.27

Other studies have also obtained similar results

demonstrating a correlation between aggregate size in the sub-visible size range and low-

shear viscosity.28-30

6.2.3 Analytical Techniques to assess viscosity change following aggregation

6.2.3.1 Aggregation characterisation – limitations and future direction

The limitations of aggregate characterisation techniques are partly to blame for the difficulty

to predict how size and concentration of aggregates will affect solution viscosity. The two

main limitations are related to (i) characterising size ranges and (ii) concentration limits of

the technique. These are discussed briefly: (i) Protein particles formed as a result of

aggregation can span many orders of magnitude form nanometres to micrometres. The actual

distribution of particle sizes and concentrations is difficult to obtain due to the limitations of

many techniques that are commonly utilised e.g. light scattering techniques. Most techniques

provide ‘weighted averages’ for aggregates but these are biased towards larger particles in the

solution.25,31

An assessment of the influence of the aggregate size distribution on the viscosity

may be more useful. Furthermore, multiple techniques are required to cover a broad size

range, using commercially available techniques; as not one technique covers the whole size

range of mAb aggregates. This increases sample volume and analysis time.31

(ii) The

concentration limits of many aggregation characterisation techniques (e.g. analytical ultra-

centrifugation and light scattering techniques) make it difficult to assess aggregation at high

concentrations.11,31

Diluting the sample is not a simple answer as diluted samples will no

longer represent the solution; as reversible aggregates may be lost following dilution. This is

one of the reasons reversible self-association (RSA) is poorly studied, thus this is a major

limitation when characterising high concentration solutions.32

Aggregation resulting from the

formation of RSA or PPI are major precursors of high viscosity, particularly at high

concentrations.33

Another common issue with technologies is the requirement of large sample

volumes31

thus, as mentioned earlier, making it difficult to assess aggregation and viscosity

during early formulation development when material is limited.

166

The novel application of raster image correlation spectroscopy (RICS) in the characterisation

of biopharmaceutical stability was demonstrated in 2012 with BSA samples.34

In 2015, the

applicability of RICS in characterising aggregate size distributions, with the use of extrinsic

labelling, was shown for low concentrated mAb solutions35

and subsequently the specificity

of the method was demonstrated in the presence of solution components (e.g. polysorbate and

silicone oil) – where trends for particle size and concentration was consistent with other

methods.36

These aforementioned studies have demonstrated the scope of RICS for sub-

visible particle sizing for early formulation development – with its high specificity, broad size

range, and small sample volume requirements. In this paper, the applicability of RICS in

characterising high concentrated mAb solutions (i.e. 100mg/ml) is demonstrated.

6.2.3.2 Micro-rheology to assess viscosity change

All of the aforementioned studies measuring the viscosity of protein solutions (section 6.2.2)

were using low-shear or zero-shear viscosity techniques. Such microrheology techniques (e.g.

dynamic light scattering, diffusive wave spectroscopy) exploit the Brownian motion of

particles to obtain local rheological properties; and have demonstrated their sensitivity as

indicators of particle formation and growth. The techniques have been used to assess changes

in rheological properties of a wide range of solutions including dilute polymer solutions,37-39

polymer gel40

and live cells.41,42

Additional advantages include that they are the non-invasive

to samples and require small sample volumes.39,43,44

Several groups have utilised fluorescence correlation spectroscopy (FCS) to measure the

microviscosity of aqueous solutions from the diffusion of fluorescent probes (e.g. dyes).45,46

The first application was by Yoshida et al. in cells.47

We previously demonstrated the use of

two probes, ATTO-Rho6G dye and IgG-AF conjugate, with FCS in retrieving viscosity

information of (unstressed) mAb solutions.48

Herein, we utilise the aforementioned probes

(ATTO-Rho6G and IgG-AF) to determine mAb solution viscosity in the presence of protein

aggregates.

In this study the sequential use of RICS and FCS on the same confocal system

permits/allows to retrieve ample information on mAb solutions; in terms of aggregate growth

and solution viscosity, respectively. Expanding the scope of the techniques, we demonstrate

the use of RICS (with extrinsic labelling) on characterising high protein concentration

solutions and FCS (using fluorescent probes) in solutions containing aggregates. The effect

167

of aggregate size and concentration on solution micro-rheology is assessed through imposing

agitation conditions on mAb solutions to generate various aggregate size ranges.

6.3 Materials and Methods

6.3.1 Materials

The mAb, COE-08 (IgG1, MW 204kDa, pI 8.9-9.2), was kindly provided by Medimmune

(Cambridge, UK).

SYPRO® Red dye was obtained from Thermo Scientific (Leicestershire, UK) at a

concentration of 5000 𝗑 (in DMSO). ATTO-Rho6G dye, IgG-AF probe (mouse IgG1 isotype

control, Alexa Fluor 488 conjugate) and buffer components, sucrose and L-histidine, were

purchased from Sigma Aldrich (Dorset, UK). All buffers and solutions were prepared with

Millipore de-ionised water (18 MV.cm) and pre-filtered prior to stress experiments.

6.3.2 Methods

6.3.2.1 Sample preparation

All solutions were prepared in a pH 6 buffer composed of 25mM histidine and 235mM

sucrose. COE-08 solutions were prepared at a final concentration of 1mg/ml or 100mg/ml.

The supplied stock of COE-08 is around 50mg/ml. To reach the concentration of 100mg/ml,

the mAb was concentrated with a 50 kDa MWCO amicon ultra-centrifugal filter unit (Sigma

Aldrich, Dorset, UK).

For agitation, mAb samples were placed in vials at 21C in a thermostatically-controlled

environment. Vials were agitated via rotation (at 20rpm) or shaking at various speeds: (i)

end-over-end rotation for 16 and 24 hours; (ii) shaking at 500rpm for 4, 8, and 16 hours; and

(iii) shaking at 2000rpm for 2 hours and 8 hours.

6.3.2.2 Analysis of particulates and viscosity with confocal microscopy

A Zeiss 510 Confocor 2 (Zeiss, Jena, Germany) confocal microscope equipped with a c-

Apochromat 40×/1.2NA water-immersion objective was utilised for RICS and FCS optical

paths.

6.3.2.2.1 RICS for characterising aggregate particles

SYPRO® Red (Thermo Scientific, Leicestershire, UK) used to label protein aggregates was

added to samples (post-experiment) 15 min prior to visualisation with confocal microscopy at

a final working concentration of 2.5 𝗑.

168

Imaging was carried out by exciting the dye with a Helium-Neon laser at 543 nm and the

emitted fluorescence collected above 585 nm (LP585 filter set). Confocal image time series

of 1,024 × 1,024 pixel resolution were captured over 100 frames with a corresponding pixel

dwell time of 6.4 microseconds. In-house RICS software (ManICS) was applied to analysis

of images acquired using confocal microscopy. A full description of the RICS algorithm has

been described in the literature.34,49

The aforementioned image time series were sub-divided

into 32x32 pixels region of interest (ROI) and the diffusion coefficients (D) within each ROI

was generated (as shown in36

).

RICS-derived diffusion coefficients were subsequently converted to particle diameter using

the Stoke-Einstein equation (following determination of solvent viscosity):

𝑅ℎ =𝑘𝑇

6𝜋𝜂𝐷⁄ Equation 6.1

Where 𝑅ℎ represents the hydrodynamic radius, k is the Boltzmann constant, T is the

temperature, 𝜂 represents the viscosity and D is the diffusion coefficient.

For each solution, similar to the analysis method in the previous RICS study,36

particle counts

were separated into size ranges of (i) <0.05 m, (ii) 0.05–0.5 m, (iii) 0.5–5 m, and (iv) >5

m.

6.3.2.2.2 FCS as a tool for viscosity

The working concentration of ATTO-Rho6G was 100nM and the working concentration of

IgG-AF was 0.001mg/ml, for all solutions. Again, the fluorescent probes were added to

samples post-experimental stress.

The Argon laser was operated at 514nm for ATTO-Rho6G (ex/em, 535nm/560nm) and at

488nm for IgG-AF (ex/em, 488nm/525nm). System calibration was performed with

Rhodamine GreenTM

(ex/em, 503nm/528nm, diffusion coefficient of 2.8 × 10-6

cm2/sec; Life

technologies50

). System calibration determines the laser beam waist size and optimises the

optical setup for further experiments. The laser beam waist size is estimated using the

following equation:

𝜏𝐷 =𝜔02

4𝐷⁄

Equation 6.2

where 𝜏𝐷 corresponds to the diffusion time, 𝟂0 is the laser waist beam and D is the diffusion

coefficient.

169

100l of sample was added into each well of the glass bottomed eight-well chamber slide,

and measurements were performed at 10-20 runs, each over a range of measurement times

from 10 seconds to 60 seconds depending on the sample i.e. protein concentration

(measurement time chosen is the minimum time required for a correlation curve to yield

reliable values for the diffusion time). The output was averaged for each sample and repeated

in pentuplicate.

Single component and two-component fits were applied to the acquired FCS data. The two

component model allows the detection (and characterisation) of the presence of two different

populations of fluorescent molecules i.e. the unbound dye and the bound dye (for example, to

the mAb).

For the single component fits, the following equation was applied:

𝐺(𝜏) = 1 + 1 𝑁⁄ (1 + 𝜏 𝜏𝐷⁄ )−1(1 + 𝑆2 𝜏 𝜏𝐷⁄ )

−0.5 Equation 6.3

where 𝐺(𝜏) is the correlation function, N is the number of particles, 𝜏𝐷is the diffusion time,

and S is the structural parameter.

For the two-component fits, the correlation function is determined using the following

equation:

𝐺(𝜏) = 1 + 1 𝑁𝑝⁄

(

(

𝑓1

(1+𝑡 𝜏𝐷𝑖,1⁄ )(1+𝑡

𝑆2𝜏𝐷𝑖,1⁄ )

12⁄)+(

𝑓2

(1+𝑡 𝜏𝐷𝑖,2⁄ )(1+𝑡

𝑆2𝜏𝐷𝑖,2⁄ )

12⁄)

)

Equation 6.4

where 𝜏𝐷,1corresponds to the diffusion time of species one, Np is the number of particles, 𝑓1 is

the proportion of species one contribution, S is the structural parameter, 𝑓2 is the proportion

of species two contribution and 𝜏𝐷,2 corresponds to the diffusion time of species two.

For all FCS measurements, the F-test is applied between the one-component and two-

component models to assess whether the addition of parameters statically improved the fit (at

p = 0.05). If results indicated that the two-component model did not statistically improve the

fit in comparison to the one-component model, only results from the one-component model

are exploited and it is assumed that only one population of fluorescent particles are measured

e.g. the unbound dye in solution.

170

6.3.2.3 Statistical tests

Unless otherwise stated, a non-parametric one-way ANOVA was performed to assess the

influence of stress type on resultant size distribution/particle counts and solution viscosity. A

calculated probability (i.e. p-value) equal or less than 0.05 was considered to be statistically

significant.

171

6.4 Results

6.4.1 Viscosity change with aggregation development following agitation of low

mAb concentrated solutions (1mg/ml) – effect of probe size on assessing

changes in viscosity

An evaluation of the aggregate propensity following agitation was initially tested with

1mg/ml COE-08 solutions using RICS as shown in Figure 6.1. The shaking 500rpm 4hr

condition showed an insufficient correlation to generate any exploitable data, indicating a low

count of aggregates in the solution. Aggregate particles were detected in all other solutions.

Figure 6.1: RICS (with SYPRO Red) determined protein particle counts for agitated 1mg/ml COE-

08 solutions.

RICS (with SYPRO Red) determined protein particle counts (particles / fL) for agitated 1mg/ml COE-

08 solutions in vials. Data separated into size ranges of (i) < 0.05, (ii) 0.05-0.5, (iii) 0.5-5 and (iv) >

5 m. Values represent means with std. dev for n=3. Significant differences (p < 0.05) obtained

between rotation 24hrs and shaken 2000rpm with all other solutions in the 0.05-0.5 m size range;

indicated by*.

172

Figure 6.1 shows that particle size distributions changed with the agitation method: The

500rpm 8hr shaken solution did not seem to have any particles larger than 0.5 m, measured

by RICS. Significant differences in the 0.05-0.5 m size range were obtained where the 24hr

rotation solutions (p < 0.05) and the 2000rpm shaken solutions (p < 0.05) obtained

significantly higher concentrations than the other solutions; but not significant to each other

at p = 0.05.

Concomitantly the diffusion time of tracers in these solutions were measured by FCS. Figure

6.2 illustrates the FCS data (ratio of diffusitives) with ATTO-Rho6G of the agitated

solutions. Interestingly, the 24hr rotation solutions (p < 0.01) and 2000rpm shaken solutions

(p < 0.05) obtained significantly higher relative viscosities in comparison to all other

solutions; but not significant with each other at p = 0.05.

Figure 6.2: FCS relative diffusion time of ATTO-Rho6G 1mg/ml COE-08 agitated solutions.

FCS determined relative diffusion time of ATTO-Rho6G 1mg/ml COE-08 agitated solutions. Values

represent means with std. dev for n=3. Significant differences (p < 0.05) obtained between rotation

24hrs and 2000rpm 2hrs with all other solutions (p = 0.05); indicated by*.

From this initial data, the type of stress (speed and duration) had an effect on aggregation

development, in terms of size range and concentration (Figure 6.1). Overall the 2000rpm 2hr

condition generated similar data (aggregate and viscosity) as the 24hr rotation condition,

although the difference in duration time was large, suggesting the 2000rpm may be a harsher

stress than rotation. The 16hr rotation and 16hr 500pm shaking had similar outcomes,

indicating similar effect of 500rpm shaking and end-over-end rotation on aggregation.

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Comparing the RICS (Figure 6.1) and FCS data (Figure 6.2), an increase in viscosity is

observed in solutions with the greatest aggregate counts. No difference in ATTO-Rho6G

ratio of diffusitives is observed between the presence of no aggregates (shaken 500rpm 4hrs

solution), small aggregates (<0.5 m, shaken 500rpm 8hrs) and the development of large

aggregates (>5 m, shaken 500rpm 16hr and rotation 16hrs).

ATTO-Rho6G is a small molecule tracer dye. The sensitivity of larger particles to solution

viscosity has been shown in the literature.46,51

In our earlier study (Chapter 4)48

, the diffusion

IgG-AF has been shown to give relatable information on the micro-viscosity of mAb

solutions. The solutions assessed were unstressed solutions (i.e. containing no protein

aggregates) and so any labelling/interaction between IgG-AF and mAb aggregates needed to

be assessed before the use of IgG-AF in solutions containing aggregates (in this study).

Interaction between IgG-AF and mAb aggregates

Two experiments were carried out to assess any labelling/interaction between IgG-AF and

mAb (COE-08) aggregates. In these experiments 10mg/ml COE-08 (in histidine-sucrose

buffer at pH 6), samples were agitated to generate aggregate particles. RICS (with SYPRO®

Red) was carried out to assess aggregation development (size and concentration), followed by

FCS for a range of incubation times with IgG-AF. For RICS, SYPRO® Red was added 15

minutes prior to measurement, in the absence of IgG-AF. For FCS, IgG-AF solutions were in

the absence of SYPRO® Red. The first experimental condition was 10mg/ml COE-08

shaken at 500rpm for 4hrs. This condition did not generate any particles above 5m (Figure

6.3). It was assessed if IgG-AF labelled aggregates were visible with RICS. The amount of

detected aggregates was substantially lower by IgG-AF, in comparison to SYPRO Red (in

relation to aggregate size and counts) (SI, Appendix 4).

174

Figure 6.3: RICS (with SYPRO Red) determined protein particle counts for 4 hour shaken (at

500rpm) 10mg/ml COE-08 solutions.

RICS (with SYPRO Red) determined protein particle counts (particles / fL) for 4 hour shaken (at

500rpm) 10mg/ml COE-08 solutions in vials. Data separated into size ranges of (i) < 0.05, (ii) 0.05-

0.5, (iii) 0.5-5 and (iv) > 5 m. Values represent means with std. dev for n=3.

FCS was carried out at different time points following incubation with IgG-AF, from 30

minutes to 24hrs. Table 6.1 presents the determined FCS diffusion times of the COE-08

shaken samples. The data idicates insufficient interaction between the IgG-AF and mAb

aggregates, as the diffusion time is consistent with that of an IgG particle52,53

for all time

points. Labelling of aggregates would results in larger diffusion times. A very low level of

peaks in the FCS count rates was observed (≤ 4%) such that the average diffusion time

determined for each measurement (i.e. over 20 runs) was not affected by runs which

contained peaks (removal of runs with peaks did not change the data). Thus indicating the

lack of large fluorescent particles e.g. IgG-AF-mAb complexes. Also, the measured number

of particles (𝑛) did not change over the incubation times. Additionally, the F-test between the

one and two component FCS models showed no significant differences; thus implying the

presence of only one population of fluorescence particles i.e. free IgG-AF.

175

Table 6.1: FCS determined diffusion times and number of particles of 10mg/ml COE-08

solutions shaken at 500rpm for 4hrs, in presence of IgG-AF.

FCS determined diffusion times and number of particles of 10mg/ml COE-08 solutions shaken at

500rpm for 4hrs, in presence of IgG-AF for different incubation times. Samples were acquired for 10-

20 seconds acquisition time and 20 runs, for each measurement. Values represent means with std. dev

for n=5. Incubation time with IgG-AF Diffusion time (s) Number of particles

30 mins 269 ± 8 4 ± 1

1hr 265 ± 3 4 ± 1

2hr 249 ± 6 4 ± 1

4hr 249 ± 8 6 ± 2

8hr 247 ± 7 5 ± 2

16hr 247 ± 5 5 ± 1

24hr 253 ± 5 4 ± 1

As the first experiment did not generate large aggregates (i.e. > 5m) (Figure 6.3) the second

condition chosen was 10mg/ml COE-08 agitation via rotation for 24hrs to generate larger

aggregates and subsequently assess interaction with IgG-AF. Figure 6.4 illustrates the RICS

determined aggregate data.

Figure 6.4: RICS (with SYPRO Red) determined protein particle counts for 24 hour rotation

10mg/ml COE-08 solutions.

RICS (with SYPRO Red) determined protein particle counts (particles / fL) for 24 hour rotation

10mg/ml COE-08 solutions in vials. Data separated into size ranges of (i) < 0.05, (ii) 0.05-0.5, (iii)

0.5-5 and (iv) > 5 m. Values represent means with std. dev for n=3.

The FCS diffusion time of IgG-AF solutions up to and including 8hr incubation time were

consistent with that of free IgG-AF (i.e. not bound to mAb aggregates), whereas an increase

in the diffusion time was observed for the 16hr and 24hr incubation times. The number of

176

particles did not significantly change over the incubation times; thus, the increase in diffusion

time was not attributed to an increase in 𝑛 (which would suggest possible FCS artefacts).

After 8hrs, an increase in the peak level in the count rates is observed, representing the

detection of large particles i.e. aggregates, with a high peak level for 16hr and 24hr in the

count rates (based on the percentage of runs containing peaks for each measurement) (Table

6.2). Thus, the FCS data indicates some level of interaction between IgG-AF and mAb

aggregates after 8hrs of incubation (Table 6.2), in the presence of large aggregates (Figure

6.4); as interaction was not observed in the first experiment which did not have particles >

5m (Figure 6.3).

Table 6.2: FCS determined diffusion times and number of particles of 10mg/ml COE-08

solutions agitated via rotation for 24hrs, in presence of IgG-AF.

FCS determined diffusion times and number of particles of 10mg/ml COE-08 solutions agitated via

rotation for 24hrs, in presence of IgG-AF for different incubation times. Values represent means with

std. dev for n=5. Incubation time Diffusion time (s) Number of particles Peak level in count rate

30 minutes 288 ± 20 3 ± 1 < 10%

1hr 290 ± 10 3 ± 1 < 10%

2hr 288 ± 36 3 ± 1 < 10%

4hr 285 ± 38 2 ± 1 < 10%

8hr 298 ± 48 2 ± 1 20-30%

16hr 1464 ± 398 2 ± 1 40-70%

24hr 2131 ± 313 2 ± 1 > 70%

Therefore, it is recommend running FCS measurements with IgG-AF, for mAb aggregate

solutions, within a 4hr incubation time of IgG-AF as the experiments indicate negligible level

of interaction between IgG-AF and aggregates within this time frame.

We have already assessed labelling with ATTO-Rho6G and solution components, showing

insufficient interaction.48

Thus the FCS diffusion times of both probes can be determined in

protein formulations containing mAb aggregates.

Probe size on measuring solution viscosity

RICS analyses were repeated to ensure equivalent aggregation development (size and counts)

as with the previous experiments (and reproducibility of the experiment), with the additional

condition of 2000rpm shaken 8hrs (Figure 6.5). The same pattern was observed with

aggregation development for the agitation conditions and no significant differences obtained

at p = 0.05). Thus comparison can be made between the two FCS data sets: ATTO-Rho6G

177

(Figure 6.2) and IgG-AF (Figure 6.6). The additional condition of 2000rpm shaken 8hrs

produced similar data to the 2000rpm 2hrs condition.

Figure 6.5: RICS (with SYPRO Red) determined protein particle counts for agitated 1mg/ml COE-

08 solutions.

RICS (with SYPRO Red) determined protein particle counts (particles / fL) for agitated 1mg/ml COE-

08 solutions in vials. Data separated into size ranges of (i) < 0.05, (ii) 0.05-0.5, (iii) 0.5-5 and (iv) >

5 m. Values represent means with std. dev for n=3. Significant differences (p < 0.05) obtained

between rotation 24hrs and shaken 2000rpm with all other solutions in the 0.05-0.5 m size range;

indicated by*.

Similar to the ATTO-Rho6G data (Figure 6.2), significantly higher relative viscosities (p <

0.05) are obtained from the IgG-AF diffusion times (Figure 6.6) of solutions containing high

concentration of aggregates (i.e. rotation 24hr, 2000rpm 2hr and 2000rpm 8hr conditions)

(Figure 6.5). No difference of the ratio of diffusitives was observed between solutions

containing no aggregates (shaken 500rpm 4hrs) and solutions containing small aggregates

(shaken 500rpm 8hrs). Different to the ATTO-Rho6G data, significant differences (p < 0.05)

were observed between the IgG-AF determined ratio of diffusitives of the solution containing

178

no aggregates (shaken 500rpm 4hrs) and the solution containing a wide size range of

aggregates (rotation 16hrs and shaken 500rpm 16hrs) (Figure 6.6).

Figure 6.6: FCS relative diffusion of IgG-AF 1mg/ml COE-08 agitated solutions.

FCS relative diffusion time of IgG-AF 1mg/ml COE-08 agitated solutions. Values represent means

with std. dev for n=3. Significant differences between rotation 24hrs, 2000rpm 2hs and 2000rpm 8hrs

with all other solutions; indicate by *. Significant difference between 500rpm 4hrs with 16hr

conditions (500rpm and rotation); indicated by *b.

6.4.2 Viscosity change with aggregation development following agitation - high

mAb concentrated solutions (100mg/ml)

Building on these encouraging results, the same concept was applied to concentrated mAbs

solutions. The agitation conditions were split into three groups based on aggregation

development with 1mg/ml solutions, so that a smaller number of conditions can be selected to

cover a wide aggregation spectrum for 100mg/ml solutions: (i) high agitation stress

conditions generating high concentration of aggregates in the sub-visible size range i.e.

rotation 24hrs and shaking 2000rpm (2hr and 8hrs); (ii) middle agitation stress conditions

generating wide size range of aggregates i.e. shaken 500rpm 16hrs and rotation 16hrs; (iii)

low level stress conditions of shaken 500rpm 8hrs (low level of aggregation / small

aggregates) and shaken 500rpm 4hrs (no aggregation). Four conditions were chosen for the

100mg/ml experiments: (i) shaken 2000rpm 2hrs (high agitation stress), (ii) shaken 500rpm

16hrs (middle agitation stress) and (iii) shaken 500rpm 8hr and 4hr (low agitation stress).

The generated RICS data of 100mg/ml mAb solutions following agitation stress is shown in

Figure 6.7.

179

Figure 6.7: RICS (with SYPRO Red) determined protein particle counts for agitated 100mg/ml

COE-08 solutions.

RICS (with SYPRO Red) determined protein particle counts (particles / fL) for agitated 100mg/ml

COE-08 solutions in vials. Data separated into size ranges of (i) < 0.05, (ii) 0.05-0.5, (iii) 0.5-5 and

(iv) > 5 m. Values represent means with std. dev for n=3. Significant differences (p< 0.05) obtained

between shaken 2000rpm solution with all other solutions in the 0.05-0.5 and 0.5-5m size range;

indicated by*.

To simplify the study, when comparing the RICS 100mg/ml data to the 1mg/ml data (and in

future discussions), only Figure 6.5 will be utilised for 1mg/ml solutions.

As observed with 1mg/ml solutions (Figure 6.5), the 100mg/ml mAb shaking 500rpm 4hr

condition did not apparently lead to aggregates formation according to RICS analysis.

Additionally, after comparing across size ranges, significant differences (p < 0.05) were

observed between 2000rpm 2hr condition and all other solutions in the 0.05-0.5 and 0.5-5 m

size ranges (Figure 6.7). Comparing the 1mg/ml and 100mg/ml conditions, the total

aggregate concentration was significantly higher (p < 0.05) in the 1mg/ml conditions.

Again, in parallel, diffusion was measured using FCS. IgG-AF FCS data for 100mg/ml

agitated solutions is presented in Figure 6.8. The shaken 2000rpm 2hr solution (containing

high level of aggregation) had a significantly higher viscosity (p < 0.05) and the shaken

500rpm 4hr solution (containing no aggregates) had a significantly lower viscosity (p < 0.05)

than all other solutions. Comparing the IgG-AF FCS data between 1mg/ml and 100mg/ml

180

solutions, the 100mg/ml solutions generated much higher relative viscosities, approximately a

four-fold increase, for all conditions; thus indicating the mAb concentration is the major

parameter for solution viscosity.

Figure 6.8: FCS relative diffusion of IgG-AF 100mg/ml COE-08 agitated solutions.

FCS relative diffusion time of IgG-AF 100mg/ml COE-08 agitated solutions. Values represent means

with std. dev for n=3. Significant differences between 2000rpm 2hrs with all other solutions;

indicated by *. Significant differences between 500rpm 4hrs with all other solutions; indicated by *b.

6.5 Discussion

6.5.1 Measuring microrheology using probes

One of the major drawbacks of microrheology techniques (e.g. DLS based microrheology

techniques) is the potential interaction between the tracer and protein structures which in turn

can influence the data.39,54

Thus, it was vital to assess the potential interaction of IgG-AF and

mAb aggregates in this study (interaction of ATTO-Rho6G had previously been evaluated48

).

It was found that after prolonged incubation with IgG-AF, some interaction is observed with

mAb aggregates (as indicated by Tables 6.1-6.2 and SI, Appendix 4). With a short incubation

time (i.e. < 4hrs) the level of interaction is negligible and does not influence the resulting data

of IgG-AF self-diffusion. Thus, it is validated that the self-diffusion of IgG-AF can be

determined (by FCS) in the presence of mAb aggregates.

The sensitivity of larger particles to solvent viscosity has been demonstrated in the literature,

such that a crossover between the micro and macro scale viscosity has been demonstrated

based on the size of the probe.51,55

Our previous study using ATTO-Rho6G and IgG-AF

showed that IgG-AF measured the influence of the buffer and ionic strength on the

microenvironment; whereas ATTO-Rho6G did not i.e. was insensitive to the changes.48

Here,

181

Figures 6.2 and 6.6 again illustrate the role of the probe size to observe potential changes in

relative viscosity. The relative diffusion of IgG-AF established a difference between no

aggregate solutions and solutions with a wide range of aggregates, whereas ATTO-Rho6G

was insensitive to these changes in the local rheological behaviour of the solutions.

6.5.2 How much agitation is needed to impact protein stability?

Agitation experiments are used to mimic manufacture and transport stress to determine each

protein’s susceptibility to agitation-induced aggregation. To mimic these processes vortexing,

stirring, rotating or shaking experiments are often conducted.56,57

Rotation has been found to

represent transport stress very well,58,59

and so the 16hr rotation condition in this study should

represent 16hrs of real-time transport stress. Higher shaking speeds and high temperatures are

used for accelerating aggregation (accelerated stability studies). The transport time of

products varies - transporting a batch from one manufacturing site to a distribution site

abroad can take a couple of days. Stability studies assessing the effect of temperature /

agitation during transport are typically performed for durations of 8hrs (representing one

shift), 24hr (daily work) or 72hrs (temperature of standard local transport time).2 Thus,

herein, a wide range of agitation times are assessed.

In this study, rotation and 500rpm shaking showed similar impacts on aggregation and

viscosity change. As a significant change (p < 0.05) in viscosity was only observed following

the development of large aggregate particles, after 16hrs of agitation (500rpm shaking), it is

implied that a short transport time (≤ 8hrs) was not sufficient enough to effect protein

behaviour. However, this observation is only true for the mAb assessed in this study (COE-

08) at ambient temperature.

Agitation time is one parameter which has been assessed extensively in the literature, utilised

to assess aggregation kinetics.60-62

In this study, the 500rpm conditions for 1mg/ml mAb

samples illustrated aggregation development as a function of shaking duration: 4hr (no

apparent aggregates), 8hr (small aggregates) and 16hr (wide aggregate size-range) (Figure

6.5). As the time points are quite far apart, 500rpm shaking could be a very useful agitation

condition to assess aggregation kinetics.

6.5.3 Protein aggregation at the air-water interface

The effect of agitation stress on aggregation development is very well documented. The main

stress parameter proposed for agitation-induced aggregation is the air-water interface. As air

is more hydrophobic than water, proteins adsorb at the interface to minimise the exposure of

182

the hydrophobic residue to the aqueous solution. During agitation, there is an increase in the

amount of air-water interface interactions which may facilitate, or enhance, surface-mediated

unfolding / aggregation.36,58,59,63-65

Due to the various conditions assessed i.e. agitation type,

agitation speed and protein concentration, a number of factors are assessed in this study:

6.5.3.1 Agitation speed

Assessment of aggregation development between 500rpm and 2000rpm shaking conditions

shows aggregation development as a function of agitation speed. As shown in Figure 6.5,

shaking at 2000rpm for 2hrs generated higher aggregate concentrations than shaking at

500rpm for 16hrs. The difference is more prominent through comparing aggregation

development between the two conditions for the same agitation time (i.e. 8hrs), showing the

aggregation rate at 2000rpm to be much higher than 500rpm (an 8-fold increase). These

results complement the literature with aggregation development correlating with agitated

speed, although the literature is scarce, particularly for mAbs. Grigolato et al. assessed effect

of mechanical agitation on amyloid formation. The shaking speed increased both the

fragmentation rate and the primary nucleation rate of amyloid fibrils.62

Fleischman et al.

assessed shipping-induced aggregation on mAb solutions and similarly observed significant

increase in particle counts with increase in shaking speed.57

The reason for the limited

literature is because shaking experiments have not been found to yield a good fit in

comparison to real-time shipment; as mentioned earlier, rotation is a much better

representation.57

The speeds generally used in shaking conditions in the literature have been

less than 400rpm.57,62,64

6.5.3.2 Protein concentration

It is expected that at higher mAb concentrations, aggregation development increases; due to

the increased risk of protein self-association from molecular overcrowding.66,67

This

behaviour has been observed in the literature where increased aggregation is observed at high

protein concentrations following thermal stress.26,68

In relation to agitation stress, the rate of

aggregation is expected to be proportional to the combination of protein concentration and

the surface area of the air/water interface. However, in this study an inverse relation between

aggregation and protein concentration is seen; between 1mg/ml (Figure 6.5) and 100mg/ml

solutions (Figure 6.7). This is not the first instance such a phenomenon is observed, although

this may be the first reported study assessing a high mAb concentration i.e. 100mg/ml (rather

than a dilute concentration range). In a study by Teuheit et al.69

aggregation decreased with

higher protein concentrations when induced by agitation. Pegylated megakaryocyte growth

183

factor and pegylated granulocyte colony stimulating factor were subjected to shaking,

vortexing or stimulated shipping (using specially designed vibrational table) and the

aggregation percentage was analysed (using SEC-HPLC). A dilute concentration range of

proteins were assessed of 1, 10, and 20 mg/ml. The unexpected result was explained by the

rate-limiting effect on aggregation at the air/water interface and the air/water interface to

protein ratio that is greatest with decreased protein concentration. Accelerated aggregation is

due to the high ratio of air/water interface to proteins such that if the aggregation rate is kept

constant, there will be higher aggregation development with increased ratio of air/water

interface. Whereas at increasing temperature, the rate limiting step is the collision rate

between protein molecules in the solution; rather than contact with air/water interface. A

similar finding was also observed in an earlier study by Krielgaard et al. on recombinant

human factor XIII solutions. The percentage of soluble aggregates increased with decreasing

protein concentration following agitation and freeze-thaw. Again, a dilute concentration

range was assessed; of 1, 5 and 10 mg/ml.70

Other studies have also found higher

concentrated protein solutions to be more resistant against freeze-thaw induced protein

denaturation.71-73

With a similar explanation to agitation, the effect could be attributed to the

smaller percentage of denaturation at the boundaries of the interfaces. The surface-area of the

interfaces e.g. ice-liquid interface, is finite thereby limiting the amount of protein which can

accumulate (and subsequently denature) at the interface.74

6.6 Conclusion

The impact of aggregation development on solution viscosity was enabled through assessing

subtle changes in aggregation development (size and counts) following various agitation

conditions. Both protein concentrations assessed (1mg/ml and 100mg/ml) show an influence

of the presence of large aggregate particles on solution viscosity; although early stages of

aggregation did not affect local rheology. Additionally, comparison of data sets implies

agitation-induced aggregation is inversely affected by mAb concentration. The potential of

characterising aggregation and the solution viscosity from the same sample is of great interest

to the biopharmaceutical industry due to the low sample consumption and short operating

time. This study demonstrates the ample information which can be retrieved on aggregate

development and solution viscosity using RICS and FCS sequentially on the same confocal

set-up; where high concentrated protein solutions can be assessed and thus contribute towards

ensuring the stability of biopharmaceuticals.

184

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189

7 : FINAL CONCLUSIONS

190

7.1 Final Conclusions

Protein aggregation is a major degradation route during biopharmaceutical (e.g. monoclonal

antibodies) development. The mechanisms of aggregation still require further investigation;

particularly at high concentrations which are required to meet patient dose requirements. In

relation to this, the development of novel techniques is a major research area with a focus on

the detection of early aggregation i.e. small reversible aggregates, using small sample

material.

To characterise the broad size range of aggregates, and to overcome the limitations of a single

method, several approaches are used to determine sample aggregation. The two main

limitations with current methods are: (i) change of the local environment and (ii) high

concentration.1 The present study has shown RICS has a broad size range and specificity to

aggregates. Its principle using the spatiotemporal fluorescence intensity variations from

confocal images can be transferred to real proteins formulations to quantify the presence of

protein aggregates (sizes and concentrations). In Chapter 3, following a comparison between

RICS, RMM and MFI, RICS appeared to be an orthogonal technique to RMM with its own

set of advantages and disadvantages; for example sensors do not need to be changed (to cover

a broad size range) or the need for sample dilution but its sampling volume is too small and

relies on Brownian motion. Nevertheless, RICS bridged the gap (around 1m) between

RMM and MFI which have issues with particle differentiation towards their limits of

detection.2

Micro-rheology methods such as FCS are able to measure local variation of properties thus

may contribute towards models of predicting protein solution behaviour at high

concentrations. Here, in Chapter 4, FCS is applied in measuring the microviscosity of protein

solutions through measuring the self-diffusion of various sized tracers (e.g. ATTO-Rho6G

dye and IgG-AF for mAb solutions and TMR-peptide and BSA-AF for BSA solutions). It is

shown that using a tracer which is equal to or larger than the size of the crowder (i.e. mAb)

the ratio of diffusion is relatable to values of macroviscosity. For a tracer of the size equal to

the size of the crowder, (at least) changes in the solution environment can be informed –

although they may not be transferable to bulk rheometry values. Other micro-rheology

methods (such as DLS-based methods) measure self-diffusion of polystyrene-based tracers

that have a high risk of interaction with the protein particles and thus interfere with the data,

particularly at high concentrations.3,4

The probes used in FCS for mAb solutions e.g. ATTO-

Rho6G and IgG-AF were assessed for stability and potential interaction with solution

191

components (e.g. protein, surfactants etc.) before their application. As the quantity of

fluorophore required for FCS is small, the risk of interaction between probe and solution

components is minimal. Nevertheless, for each solution, interaction between the tracer and

the crowder need to be considered on a case by case basis. As seen in this study, although

IgM-AF seemed to give relatable information to bulk rheometry (of mAb solutions),

interaction between IgM-AF and mAb was indicated and thus IgM-AF cannot be utilised in

this application.

Another application of fluorescence proposed in this work is the use of SYPRO Orange

labelling and FCS to detect the presence of surfactant micelles and determine the cmc

accurately along with the micelle size (Chapter 5). Further investigations are required to

assess contribution of FCS artefacts (refractive index issues) on the measured diffusion time

and number of particles. Nevertheless, as the protein molecules are excluded from the data,

the approach has potential to assess polysorbate micelle behaviour in the presence of protein.

Hence, this application could provide insight into polysorbate mechanisms in high

concentrated protein formulations.

There are no established methods validated by regulatory authorities for the prediction of

long-term stability of mAb products. This is due to the complex nature of mAb solutions;

especially at high concentrations (see commentary – Appendix 5). To tackle this problem,

analytical tool boxes have been suggested.5 The use of RICS and FCS on the same system

can be classed as one of these analytical tool boxes, which may provide information on

solution properties in terms of aggregation and solution viscosity (respectively). Indeed

during early formulation development, limited material is available thus many techniques

requiring large sample volumes cannot be used to assess aggregation and viscosity. RICS and

FCS both require a couple of hundred of microlitres a useful point for high concentration

solutions. System set-up time is also low as both platforms can be setup on the same confocal

system. In the finale of the thesis, the sequential use of RICS and FCS is demonstrated (in

Chapter 6). The viscosity change following aggregation development is characterised as a

function of agitation type, agitation speed, and protein concentration. A correlation is

established between aggregation development (size and counts) and changes in solution

viscosity. Illustrating the complexity of mAb behaviour, an inverse relation is observed with

agitation-induced aggregation and mAb concentration.

192

As a final conclusion, in this thesis fluorescence based correlation spectroscopy analyses

have been utilised to assess mAb aggregation propensity in the context of downstream

bioprocessing and formulation. The use of RICS and FCS on the same confocal system offers

a high throughput method for accelerated stability assessments. The ability to determine

aggregation propensity with minimum modification of solutions or as early as possible in

formulation development is a major contributor to improve bioprocess design and

formulation of mAbs. It is envisaged that through the expansion of the hardware capability

e.g. heated stages for multi-well plates and automated measurements and analysis, the

RICS/FCS confocal system could be utilised to select appropriate buffer systems for mAb

products and thus act as a potential tool for formulation scientists in biopharmaceutical

product development.

193

7.2 References

1. Demeule B, Gurny R, Arvinte T 2007. Detection and characterization of protein

aggregates by fluorescence microscopy. International Journal of Pharmaceutics 329(1–2):37-

45.

2. Shah M, Rattray Z, Day K, Uddin S, Curtis R, van der Walle CF, Pluen A 2017.

Evaluation of aggregate and silicone-oil counts in pre-filled siliconized syringes: An

orthogonal study characterising the entire subvisible size range. International Journal of

Pharmaceutics 519(1):58-66.

3. Kang H, Ahn KH, Lee SJ 2010. Rheological properties of dilute polymer solutions

determined by particle tracking microrheology and bulk rheometry. Korea-Australia

Rheology Journal 22(1):11-10.

4. He F, Becker GW, Litowski JR, Narhi LO, Brems DN, Razinkov VI 2010. High-

throughput dynamic light scattering method for measuring viscosity of concentrated protein

solutions. Analytical Biochemistry 399(1):141-143.

5. Schermeyer M-T, Wöll AK, Kokke B, Eppink M, Hubbuch J 2017. Characterization

of highly concentrated antibody solution - A toolbox for the description of protein long-term

solution stability. mAbs 9(7):1169-1185.

194

APPENDIX 1

195

SUPPLEMENTARY INFORMATION FOR CHAPTER 1

A1.1 : Characterisation of Stress-Induced Aggregate Size Distributions and

Morphological Changes of a Bi-Specific Antibody Using Orthogonal Techniques

196

APPENDIX 2

197

APPENDIX 2: SUPPLEMENTARY INFORMATION FOR CHAPTER 3

A2.1 Discarding fits with R2 < 0.7

The reason to keep data with a R2 larger than 0.7 lies in the difficulty to relate R

2 to the fit of

the surface by a single diffusion fitting model (compared to fit a FCS/DLS curve with a

similar model). To assert our confidence in the diffusion coefficient determined by RICS, a

solution of 100nm diameter fluorescent beads (FluoSpheres, ThermoFisher) was used (stock

solution was dilute 1000x without further treatment), imaged using the Zeiss LSM510 and

then analysed with our software ManICS. Using ROIs, more than 1000 pairs (density, N and

diffusion coefficient, D) were obtained. These data were plotted as N (number of particles) vs

D (diffusion coefficient) and clusters in the data were sought: there should be only one cluster

with outliers (Figure A2.1).

A Gaussian Mixture Model with 2 or more components can be used (Please note that the

analysis was carried out using sklearning from http://scikit-

learn.org/stable/modules/mixture.html#variational-bayesian-gaussian-mixture in which the

BayesianGaussianMixture object implements a variant of the Gaussian mixture model with

variational inference algorithms). The number of components must be large enough to

exclude the outliers from the main distribution, but low enough to ensure that the main

distribution is not split in the process.

The Mixture with the highest weight is the distribution of interest (the centroid of which is

the mean N,D for our beads population). The 95% confidence ellipse for this population can

then be calculated (X2 = 5.991). The assumption (for a single species population) is that R2 is

higher towards the centroid of the cluster and lower for (N, D) pairs further away.

Knowing the 95% confidence ellipse, the mean R2

outside and inside the 95% confidence

ellipse can be calculated. The mean R2 value outside the confidence ellipse is 0.7, which we

use as a criterion (R2

> 0.7) for rejecting outliers (Figure A2.1).

198

Figure A2.1: Diffusion coefficient determined by RICS of a solution of 100nm diameter fluorescent

beads (FluoSpheres, ThermoFisher). Graph plots represent N (number of particles) vs D (diffusion

coefficient). The mean R2 value outside the confidence ellipse is determined as 0.7.

199

A2.2 Labelling of Particles by SYPRO® Red and SYPRO® Orange

(i) Efficiency of SYPRO® Red and SYPRO® Orange in labelling mAb

aggregates and silicone-oil

Two experiments were carried out in order to assess the labelling abilities of the two dyes

(SYPRO® Red and SYPRO® Orange), using 10mg/ml COE-08 in 0% (w/v) PS-20 histidine-

sucrose buffer (as described in the main manuscript) (Figures S2-S3). First, the mAb solution

was agitated in siliconized PFS for a 24 hour agitation period (as described in the main

manuscript). Solutions were subsequently analysed by RICS following incubation with

SYPRO® Red or SYPRO® Orange. The purpose of the experiment was to assess the size

ranges and the concentrations measured in the presence of the two dyes; in order to determine

if silicone-oil data can be selectively obtained in the presence of mAb, through data

subtraction i.e. SYPRO® Orange labelling minus SYPRO® Red labelling. Initial observation

of the particle size distributions (Figure S2) showed a larger particle size distribution

measured in the presence of SYPRO® Red, in comparison to SYPRO® Orange. When

comparing with the particle size distributions from the manuscript, the particle size

distribution in the presence of SYPRO® Red (Figure S2, left) was similar to the COE-08 data

(described later, Figure S4) and the SYPRO® Orange distribution (described later, Figure S2,

right) was similar to the silicone-oil data (Figure S5). This observation alongside the lack of

labelling by SYPRO® Orange for particles above 1 m indicated potential issues with mAb

(COE-08) aggregate labelling by SYPRO® Orange.

Figure A2.2: Particle size distribution from agitated mAb-filled PFS solutions, detected by RICS,

following incubation with SYPRO® Red and SYPRO® Orange. Sample means are represented by ■,

medians by a horizontal line, and minima/maxima by ×.

A second experiment with 10mg/ml COE-08 agitated (for 24 hours) in de-siliconized PFS

was undertaken for comparison. Figure A2.3 presents the particle concentrations determined

200

by the two dyes, separated into the same size ranges used in the manuscript i.e. (i) < 0.07, (ii)

0.07-0.5, (iii) 0.5-5 and (iv) > 5 m.

Figure A2.3: Particle counts in agitated mAb-filled siliconized (top) and de-siliconized (bottom) PFS

solutions. Horizontal and vertical axis represents particle counts determined by RICS (particles/mL)

following incubation with SYPRO® Red (left) and SYPRO® Orange (right) for size ranges (i) <

0.07m, (ii) 0.07-0.5m, (iii) 0.5-5m and (iv) >5m. Values represent average counts and error

bars represent the std. dev. for n=3.

Statistical tests (one-way ANOVA) showed significantly higher particle concentrations were

measured in the presence of SYPRO® Red in comparison to SYPRO® Orange (p < 0.05) for

the agitated PFS solutions, for all size ranges. For the de-siliconized PFS solutions,

significantly higher particle concentrations were observed (p < 0.05) in the 0.07-0.5 m and

0.5-5 m size ranges. As the maxima for particles measured in the presence of SYPRO®

Orange in the siliconized and de-siliconized PFS samples was 1.09 m and 1.17 m,

respectively, the particle concentration in the > 5 m size range was nil.

If SYPRO® Orange sufficiently labelled both aggregates and silicone-oil, a higher particle

concentration would be expected in comparison to the concentration measured in the

presence SYPRO® Red (labelling only aggregates), for the same sample. As this is not what

is observed, it can be concluded that SYPRO® Orange cannot be utilised to label both mAb

aggregates and silicone-oil droplets.

201

Thus, with the use of the current dyes SYPRO® Red and SYPRO® Orange, the

characterisation of silicone-oil droplets in the presence of mAb cannot be performed. Instead,

it is proposed to use SYPRO® Red to measure mAb aggregates (in the presence of other

solution components such as silicone-oil) and SYPRO® Orange to measure silicone-oil in

mAb-free solutions only; as we have done in the paper.

(ii) SYPRO Orange in labelling PS-20 micelles

Following incubation with SYPRO® Orange, although insufficient labelling of PS-20

micelles is observed, a brighter background is present (Figure 1). The brighter background

may suggest interaction of the fluorophore with PS-20 micelles (as the PS-20 concentrations

used here are above the known CMC1,2

). Fluorescence correlation spectroscopy (FCS) was

utilised to detect PS-20 micelles in the presence of SYPRO® Orange. Table A2.1 presents

the determined PS-20 micelle radius (for both PS-20 concentrations used), with values that

are supported by previous literature.3-5

To ensure micelles were not selected in the RICS

analysis (for mAb or silicone-oil data), a cut-off was set at 30 nm for RICS analysis (for all

solutions).

Table A2.1: Radius of PS-20 micelles determined through FCS analysis following incubation with x

2.5 SYPRO® Orange (Ex: 488nm, BP 560-615nm). Zeiss LSM 510 ConforCor 2 setup (Zeiss, Jena,

Germany) equipped with a c-Apochromat 40×/1.2NA water-immersion objective was utilised. Values

represent averages with std. dev. for n=5.

PS-20 Concentration (w/v) Radius (nm)

0.02% 3.25 ± 0.39

0.05% 3.09 ± 0.16

202

A2.3 Size distribution of COE-08 and silicone-oil particles detected by RICS, RMM and

MFI

Size distributions showing mean diameters and upper/lower ranges of protein aggregates and

silicone-oil particles, measured by RICS, RMM and MFI are presented in Figures A2.4-A2.6.

Figure S4 illustrates aggregate particles detected in mAb PFS solutions and Figures S5 and

S6 represent silicone-oil particles measured in buffer-only PFS solutions and mAb PFS

solutions, respectively.

Figure A2.4: Size distribution of COE-08 particles in PFS, detected by RICS (following incubation

with SYPRO Red), RMM (nano and micro sensor) and MFI (refined by discriminant analysis) in the

presence and absence of agitation stress, as a function of PS-20 concentration. Sample means are

represented by ■, medians by a horizontal line, and minima/maxima by ×.

203

Figure A2.5: Size distribution of silicone-oil particles in buffer-filled PFS, detected by RICS (following

incubation with SYPRO Orange), RMM (nano and micro sensor) and MFI (refined by discriminant analysis), in

the presence and absence of agitation stress, as a function of PS-20 concentration. Sample means are

represented by ■, medians by a horizontal line, and minima/maxima by ×.

204

Figure A2.6: Size distribution of silicone-oil particles in mAb PFS solutions, detected by RMM (nano and micro

sensor) and MFI (refined by discriminant analysis), in the presence and absence of agitation stress, as a

function of PS-20 concentration. Sample means are represented by ■, medians by a horizontal line, and

minima/maxima by ×.

205

A2.4 References

1. Fuguet E, Ràfols C, Rosés M, Bosch E 2005. Critical micelle concentration of

surfactants in aqueous buffered and unbuffered systems. Analytica Chimica Acta 548(1–

2):95-100.

2. Mittal KL 1972. Determination of CMC of polysorbate 20 in aqueous solution by

surface tension method. Journal of Pharmaceutical Sciences 61(8):1334-1335.

3. Luschtinetz F, Dosche C 2009. Determination of micelle diffusion coefficients with

fluorescence correlation spectroscopy (FCS). Journal of Colloid and Interface Science

338(1):312-315.

4. Carnero RC, Molina-Bolívar J, Aguiar J, MacIsaac G, Moroze S, Palepu R 2003.

Effect of ethylene glycol on the thermodynamic and micellar properties of Tween 20. Colloid

and Polymer Science 281(6):531-541.

5. Khlebtsov BN, Chumakov EM, Semyonov SV, Chumakov MI, Khlebtsov NG 2004. Study of complex micellar systems by static and dynamic light scattering. Saratov Fall

Meeting 2003: Coherent Optics of Ordered and Random Media IV. Saratov, Russia: Russian

Federation.

206

APPENDIX 3

207

APPENDIX 3: SUPPLEMENTARY INFORMATION FOR CHAPTER 4

A3.1 Spectrophotometry calibration curves of candidate dyes

To determine the concentration of dye following rocking (for the Log P), calibration curves

were produced for the candidate dyes i.e. Azure-B, ATTO-465 and ATTO-Rho6G (Figure

A3.1).

Figure A3.1: Calibration curve of (A) Azure-B (B) ATTO-465 and (C) ATTO-Rho6G determined

using spectrophotometry. Symbols and error bars show mean ± standard deviations respectively for

n=3. R2 value of calibration curves are 0.941, 0.997 and 0.993 respectively.

208

A3.2 Count rates of candidate dyes

Example (FCS) count rates of the candidate dyes (Azure-B, ATTO-465 and ATTO-Rho6G)

are illustrated in Figure A3.2.

Figure A3.2: Typical FCS count rates of (TOP) Azure-B 100nM - large peak outliers are indicated

with arrows, (MIDDLE) ATTO-465 100nM and (BOTTOM) ATTO-Rho6G 100nM.

209

A3.3 ATTO-Rho6G labelling of mAb and other solution components

To investigate any labelling of mAb (COE-08) by ATTO-Rho6G, a broad range of mAb

(COE-08) concentrations (1mg/ml - 75mg/ml) were assessed with two concentrations of

ATTO-Rho6G (1nM and 100nM). The FCS diffusion times are presented in Figure A3.3.

Figure A3.3: Diffusion times of (LEFT) 1nM and (RIGHT) 100nM ATTO-Rho6G in the presence of

COE-08 (concentrations from 1mg/ml to 75mg/ml). Symbol illustrates the mean (± st. dev) (n=5).

An increase in the diffusion times with increasing COE-08 concentrations with ATTO-

Rho6G was established and statistical analysis (one-way ANOVA with post hoc

comparisons) showed that the difference in diffusion times between the varied mAb

concentrations were significant, for all comparisons (p < 0.05). No significant differences (at

p = 0.05) were observed between the one-component and two-component models for all

measurements which indicates the presence of only one population of fluorescent parties i.e.

the unbound dye.

The viscosity of the mAb solutions was determined (independently) using a Vilastic-3

rheometer (as described in the main methods). Results illustrated a strong correlation with the

rheometer determined viscosity and the diffusion time for 1nM and 100nM ATTO-Rho6G

determined by FCS (Figure A3.4). Thus, a linear relation between ATTO-Rho6G and

solution viscosity is established.

210

Figure A3.4: Viscosity (from vilatic-3) of COE-08 concentrations from 1mg/ml to 75mg/ml against

FCS-determined diffusion times with (LEFT) 1nM ATTO-Rho6G and (RIGHT) 100nM ATTO-Rho6G.

Graph illustrates the mean (± st. dev). A linear relationship is established (linear regression fit

applied using Origin Software), indicating that η τD. R2 values of the slopes are 0.971 and 0.997,

respectively.

Equilibrium dialysis (using a 96-well Equilibrium Dialysis Block and dialysis membrane

with a 12-14 kDa MWCO) was carried out to rule out weak binding to the mAb (COE-08),

which with rapid on/off rates would produce an 'effective" diffusion time that increases with

COE-08 concentration and would not necessarily produce a two-component fit (which would

be seen if on/off rates are slow). Equilibrium dialysis offers the ability to study low affinity

interactions that are undetectable using other methods. Figure A3.5 illustrates the calibration

curve of ATTO-Rho6G determined by spectrophotometry. The determined ‘fraction bound’

values are presented in Figure A3.6, which had no significant differences (at p = 0.05) when

comparing the mAb samples with their respective control samples (i.e. no mAb), for each dye

concentration. Thus, indicating insufficient interaction between ATTO-Rho6G and COE-08

over a wide range of concentrations.

211

Figure A3.5: Calibration curve of ATTO-Rho6G determined using spectrophotometry. Symbols and

error bars show mean ± standard deviations respectively for n=3. R2 value of 0.999 determined.

Figure A3.6: Fraction bound values following equilibrium dialysis. Data measured by

spectrophotometry. Symbols represent means ± st. dev at n = 3. Dashed blue lines represent the

control sample data (st.dev around the mean) per dye concentration, showing no significant

differences between the control and mAb samples.

The diffusion times of the solutions following equilibrium dialysis using lower dye

concentrations i.e. < 200nM were determined by FCS. With FCS, both sides of the well were

analysed (i.e. the mAb side too). Figure A3.7 presents the diffusion times determined in each

well chamber following equilibrium dialysis.

212

Figure A3.7: FCS determined diffusion times of dye and COE-08 solutions following equilibrium

dialysis. Data represents means ± st. dev at n = 3.

Solutions from ‘dye only’ chambers gave a diffusion time of around 35s (consistent with

previous data). Solutions from ‘mAb chambers’ show an increase in diffusion time as mAb

concentrations increases, for each dye concentration (again, consistent with previous data).

Stats (F-test between chi2

values) between the one and two component models showed no

significant differences (at p = 0.05) between the fits of the models. Thus, insufficient

labelling is indicated of COE-08 with the hydrophilic dye, ATTO-Rho6G.

Next step was to assess binding of ATTO-Rho6G with protein aggregates and other solution

components. Table A3.1 presents the FCS diffusion times of stressed COE-08 solutions

(conditions chosen are known to generate aggregates1) and polysorbate solutions. No

significant differences (at p = 0.05) of the FCS fits were observed between the one and two

component models, for all measurements, indicating the presence of only one population of

fluorescent particles i.e. dye in solution. The labelling of mAb or PS (micelles would be

present at the concentrations assessed) should generate a diffusion time that is larger than

250s; this would equate to a radius of around 3nm. Thus the observed slight increase in

diffusion time represents an increase in the solution viscosity. Significant differences were

observed (p < 0.05) with the protein solutions (more so with the aggregate solutions), and the

higher PS concentrated solutions in comparison to buffer only solution.

213

Table A3.4: FCS diffusion times of 100nM ATTO-Rho6G solutions containing mAb, mAb

aggregates and a range of polysorbate solutions. Values represent mean ± st. dev for n=5.

Solution Diffusion Time (s)

Buffer only 36 ± 2

10mg/ml COE-03 unstressed 41 ± 1

10mg/ml COE-03 58C overnight 45 ± 3

10mg/ml COE-03 80C overnight 62 ± 11

PS-20 solutions:

0.005%

0.02%

0.05%

1%

0.02% + 10mg/ml COE-03

36 ± 2

36 ± 2

36 ± 2

45 ± 4

50 ± 6

PS-80 solutions:

0.001%

0.02%

0.04%

1%

0.02% + 10mg/ml COE-03

36 ± 2

35 ± 1

37 ± 3

42 ± 4

49 ± 4

214

A3.4 References

1. Hamrang Z, Hussain M, Tingey K, Tracka M, Casas-Finet JR, Uddin S, van der Walle

CF, Pluen A 2015. Characterisation of Stress-Induced Aggregate Size Distributions and

Morphological Changes of a Bi-Specific Antibody Using Orthogonal Techniques. Journal of

Pharmaceutical Sciences 104(8):2473-2481.

215

APPENDIX 4

216

APPENDIX 4: SUPPLEMENTARY INFORMATION FOR CHAPTER 6

A4.1 Aggregate labelling comparison between IgG-AF and SYPRO Red

To assess if labelling of aggregates by IgG-AF was visible with RICS, 4hr 500rpm shaken

10mgml COE-08 solutions were compared with labelling by SYPRO Red. SYPRO Red has

been validated for successful labelling of aggregates through comparing measured size ranges

and concentrations with orthogonal techniques.1,2

Figure A4.1 shows the low level of

labelling by IgG-AF in comparison to extrinsic labelling by SYPRO Red: Following

overnight incubation with IgG-AF, only small aggregates were detected by IgG-AF (i.e. < 0.5

m) and the concentration of aggregates was substantially lower (p < 0.05) for each size

range, in comparison to data with SYPRO Red.

Figure A4.1: RICS (left) with SYPRO Red and (right) IgG-AF determined aggregate concentrations

(particles / fL) for 4 hour shaken (at 500rpm) 10mg/ml COE-08 solutions in vials. Data separated into

size ranges of (i) < 0.05, (ii) 0.05-0.5, (iii) 0.5-5 and (iv) > 5 m.

217

A4.2 References

1. Hamrang Z, Hussain M, Tingey K, Tracka M, Casas-Finet JR, Uddin S, van der Walle

CF, Pluen A 2015. Characterisation of Stress-Induced Aggregate Size Distributions and

Morphological Changes of a Bi-Specific Antibody Using Orthogonal Techniques. Journal of

Pharmaceutical Sciences 104(8):2473-2481.

2. Shah M, Rattray Z, Day K, Uddin S, Curtis R, van der Walle CF, Pluen A 2017.

Evaluation of aggregate and silicone-oil counts in pre-filled siliconized syringes: An

orthogonal study characterising the entire subvisible size range. International Journal of

Pharmaceutics 519(1):58-66.

218

APPENDIX 5

219

APPENDIX 5: COMMENTARY

A5.1 Commentary: New Perspectives on Protein Aggregation during

Biopharmaceutical Development

Commentary

New Perspectives on Protein Aggregation during Biopharmaceutical Development

Maryam Shah

Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health,

University of Manchester, Manchester, UK

Abstract

Protein aggregation during biopharmaceutical (e.g. monoclonal antibodies) development

impacts yields, production costs and must be controlled during formulation in order to ensure

the clinical efficacy of the drug product: understanding aggregation mechanisms and

developing mitigating strategies are imperative. This commentary reflects on recent progress

made in the field of protein aggregation with considerations on novel detection techniques,

novel excipients, interfacial phenomena and prediction of aggregation rates. Furthermore,

future directions of research are speculated based on opinions of several academics and

industrialists: an academic perspective with industrial focus.

Introduction to protein biopharmaceutical development

Biopharmaceutical proteins constitute a growing family of medicines for many therapeutic

areas including oncology, inflammation and autoimmunity, infectious disease, and

cardiovascular and metabolic disease. In the last decade more than 170 biologics have been

approved for clinical use and around a third of these are monoclonal antibodies (mAbs).1

There are, however, a number of associated challenges in their manufacturing and

formulation including controlling and predicting the reversible and irreversible formation of

protein aggregates. Aggregation can lead to loss of product recovery following production

and purification, and places constraints on how the protein is formulated for storage; as the

formulation constitutes (i.e. salts, excipients2,3

) and storage conditions (i.e. temperature4,5

)

impact protein stability. Moreover, research suggests aggregation can cause unwanted side

effects in patients.6,7

Thus, there are strict regulations on the level of aggregation in protein

biopharmaceuticals.8-10

220

Biopharmaceutical companies have in-house (proprietary) approaches to minimise

aggregation, based on experience. For companies with both Research and Development arms,

a wealth of bioprocess expertise has been accumulated, including the relationship between

mAb stability and domain architecture to the amino acid sequence.11

However, ushering in a

non-empirical, rational approach a priori would be of great benefit when translating results

for the aggregation behaviour of one protein family (e.g. mAbs) to another (e.g. bispecific

mAbs or peptide fusions). Such an approach would require commensurate improvement of

measuring and detecting aggregates, their kinetics, and accompanying predictive models. The

ability to predict aggregation requires an improved understanding of the underlying inter- and

intra-molecular mechanisms. To date, the strategy has been to bring together robust in silico

models with quantitative analytical measurement to provide insight to the aggregation

process; from nucleation to visible particulate, particularly across the so-called ‘gap region’

around 1 m.12,13

This strategy has been particularly profitable under collaborative ventures

between academic and industrial partners.

This commentary reflects on recent progress made in the field of (non-covalent) protein

aggregation (i.e. physical, non-covalent changes). Additionally, from an academic

perspective with industrial focus, speculations on the future direction of research are reported,

asking: what are scientists aiming to achieve when using a specific measurement technique or

predictive model; and how important is our knowledge of the knowns and unknowns as

related to protein aggregation?

Measuring protein aggregation: scope, limitations and future technologies

Much effort has been put into developing technologies able to detect and characterise protein

aggregates. With the increased interest in the characterisation of smaller sub-micron sized

aggregates (0.1 – 1 m),14-16

the need for technologies able to detect small aggregates in

highly concentrated mAb formulations is a future requirement that will be paramount to

predicting shelf life of liquid drug products.

A major advance was in the quantitative characterisation of aggregate size distributions,

particularly aggregates generated by the dynamic process of ‘reversible self-assembly’ (RSA)

which, for mAbs, would typically be in the order of tens of nanometres in diameter.

Analytical ultracentrifugation (AUC) is a useful technique for characterising such aggregates

(e.g. monomer, dimer, trimer) and in quantifying protein association as a function of protein

concentration; and thus calculating the respective equilibria that may exist.17

Current AUC

221

technology using optical detection systems (UV/vis absorption and Rayleigh interference) is

limited to protein concentrations less than 50 g/L. Above this limit samples require dilution

prior to measurement. Sample dilution introduces uncertainly in the predictive value of the

analytical method since the resultant aggregate population is no longer representative of that

present in the concentrated solution, a consequence of perturbation of the equilibria present in

a mAb sample which undergoes RSA. One possible solution arises from protein detection

technologies based on Schlieren optics, which do not require dilution and are compatible with

AUC instruments. Ironically, such optical systems were formally available in earlier AUC

instrumentation and therefore could be reintroduced in a relatively simple manner.

The requirement for sample dilution is not restricted to AUC: other technologies including

conventional methods such as dynamic light scattering (DLS) and emerging techniques

covering the sub-visible size-range such as resonant mass measurement (RMM)

(Archimedes®, Malvern Ltd., UK) and nanoparticle tracking analysis (NTA), are also poorly

suited to cope with characterising aggregates in highly concentrated mAb solutions (> 100

g/L) due to inherent ambiguity in the interpretation of data acquired.18-21

Measurement of

aggregates in highly concentrated mAb solutions is relevant because high doses may be

needed to meet a therapeutic response, and in the case of chronic administration via

subcutaneous injection, a volume limit of around 1 ml is common (at least for a pre-filled

syringe).22

Another requirement in the advancement of technologies is the ability to differentiate

between protein and foreign matter. For example, mAb solutions in pre-filled syringes often

contain sloughed silicone-oil particles following agitation / transport and commercially

available techniques detecting small aggregates do not have the ability to differentiate. The

recently developed (RMM) Archimedes® system has the ability to differentiate between

protein and foreign matter (based on particle buoyancy) although the approach has

concentration limits. Archimedes has been used alongside micro-flow imaging (MFI), to

cover a broad size range.16,23

MFI has received much attention due to the volume and size-

range matching regulations and providing useful information on the morphology of large

protein particles i.e. > 1 m. However, due to optical similarities between particles (e.g.

protein and silicone-oil), significant misclassification errors have been observed for particles

< 4 m. Thus, the ‘analytical gap’ around 1 m still remains.16

The adaption of existing

image correlation spectroscopy (ICS) analyses for the measurement of protein aggregates has

very recently been demonstrated: Raster Image Correlation Spectroscopy (RICS) has been

222

utilised to selectively characterise aggregates in the presence of other solution components,

though the use of fluorescence labelling, covering a broad size-range. Due to the specificity

of the technique and small sample volume requirement, there is strong scope for the

application of RICS in complex mAb solutions, especially in relation to early formulation

development.23

The application of RICS in high concentrated mAb solutions (i.e. > 100

mg/ml) is a worthwhile next step.

Can we predict protein aggregation rates?

As previously mentioned, to address this question we require an understanding of the

molecular pathways towards an aggregated state and their concomitant determinants. A key

stumbling block is the detection of aggregate prone state(s) or structural conformers, which

occur transiently at low concentrations relative to natively folded proteins. It is hypothesised

that structural intermediates may nucleate aggregation through transient exposure, for

example, of a relatively hydrophobic peptide loop on the domain body. Testing this

hypothesis in silico would immediately present the currently intractable problem of searching

across a vast number of structural intermediates for their propensity to self-assemble.

Experimental approaches inducing transient structural states may currently be more

productive, so long as there exists analytical techniques able to probe the dynamic nature of

protein unfolding at high concentrations. A simple but well established measure of protein

conformational stability used during antibody engineering and formulation development is

the melting temperatures (Tm), determined by differential scanning calorimetry (DSC) or

differential scanning fluorimetry (DSF). A thermogram from either technique can at least

establish that the first unfolding event begins at a temperature above that of the highest

storage temperature used to induce stress under accelerated stability studies (ICH

guidelines24

).

Industrial approaches to predicting aggregation rates on storage usually rely on

extrapolating kinetics measured under accelerated conditions such as higher temperatures.

Recent report describes the limited accuracy of accelerated stability methods in predicting

aggregation rates25

and their adoption in the biopharmaceutical industry for this purpose is

unclear. In many cases extrapolating shelf-life from accelerated conditions is inaccurate due

to competing aggregation pathways whose relative rates change with temperature, which is a

cause of non-Arrhenius behaviour in protein aggregate rates.25,26

As such, predictions require

a broad range of temperatures, involving the measurement of monomer loss over very long

timescales.

223

The second virial coefficient, B22, is often used as a surrogate parameter for correlating

storage stability, which relies on the assumption that native-state protein-protein interactions

are similar to those between partially folded intermediates in aggregation pathways. More

insight can be gained by developing coarse-grained models that reflect the structural

determinants of protein-protein interactions. These models could be used for predicting the

concentrated solution behaviour, which was characterized in terms of the protein-protein

Kirkwood-Buff integral, G22. The importance for understanding the effects of solution non-

ideality on aggregation at high protein concentration is highlighted.27,28

These approaches are

applicable for capturing the impact of electrostatic interactions on aggregation pathways, but

are not universally reliable as buried hot-spots, not exposed in the native state, have a strong

influence on aggregation.29,30

A major goal for the protein formulation community is the establishment of a

database relating protein structure and environment (solution, interface and excipient

conditions), to aggregation behaviour. This is largely due to the inevitable absence of

harmonisation in environment conditions across the numerous research papers describing

protein aggregation, coupled to very limited structural information on the protein itself (often

merely the relative mass and isoelectric point). However, aggregation studies on non-mAb

proteins which have such a goal in mind have demonstrated its feasibility. A recent study into

a human-like single chain antibody fragment (scFv) characterised the effects of multiple

mutational strategies (patch overcharging, charge substitution, salt bridge addition,

hydrophobic surface residue removal), on the conformational and colloidal stability of the

protein.31

Elsewhere, systematic mutant analyses show that single and double amino acid

mutations to γD-crystallin can have dramatic effects on protein phase separation (assessing

aggregation, liquid-liquid phase separation and crystallisation) and its dependence on

temperature; these measurements provide insight into the energetics of anisotropic protein-

protein interactions.32-34

Protein phase diagrams for chemically modified γD-crystallin also

demonstrated that relatively minor changes to the protein surface change the thermodynamics

of protein-protein interactions and therefore RSA: this may be important in the development

of antibody drug conjugates (ADCs). However, HT bulk solution phase measurements as a

function of temperature (i.e. through differential static light scattering; Stargazer-384™,

Harbinger) are not necessarily sensitive to the subtle changes in bulk solution properties35

and

identifying the key protein surface drivers is currently a research focus.

224

At the interface

Understanding how the fill finish process and associated primary packing impact upon mAb

aggregation is an important focus in development. Determining the nature of the adsorbed

mAb layer at the air/liquid and solid/liquid interface is not routinely performed in industry

but is an active area of investigation in academia, often through collaboration. Such studies

are directly relevant to understanding the origin of particulate formation upon vial shaking,

for instance. Similarly, aggregation is as much an issue during post-production (transport,

storage, etc.) as it is during production, and shaking studies are a routine assessment of

formulation robustness in regard of titrating an appropriate concentration of (typically)

polysorbate-20/-80. Work from the Friess group has shown that compression and

decompression of the protein film, which mAbs form at the liquid-air interface, results in

aggregate formation, depending on the extent of protein-protein interaction.36

The stabilizing

role of polysorbate surfactants was demonstrated to attenuate interface induced aggregation

of mAbs on account of their faster adsorption kinetics to the interface in comparison to

proteins. Nevertheless, polysorbate surfactants must be obtained at the highest possible purity

since they are well characterised to undergo hydrolysis leading to the generation of fatty

acids; this not only removes surfactant activity (and therefore increases mAb instability at the

interface) but may also promote mAb degradation in the bulk solution.36

While alternative,

synthetic surfactants, such as triblock copolymers of polypropylene oxide and polyethylene

oxide have application in therapeutic mAb formulations, the predominance of the

polysorbates remains. Similarly, novel surfactants based on trehalose fatty acid esters have

been synthesised, characterised and tested in relevant formulation scenarios such as shake-

induced stress, but have not been adopted further in mAb formulation.37

It is increasingly thought that the nature of the mAb layer adsorbed at the solid/liquid

interface has impact on the bulk stability, through adsorption-desorption events concomitant

with structural deformation, leading to non-native conformers nucleating aggregation in bulk

solution. This research effort is ongoing and high resolution analytical techniques probing

surface adsorption have been brought to bear on this challenge, specifically ellipsometry and

neutron reflection. It has been shown that such data can be interpreted through molecular

simulation of the adsorbed layer. At low concentrations there is minimal evidence for

unfolding at the silicon oxide (glass) interface and the most noticeable parameter affecting on

mAb adsorption is buffer pH, most likely on account of the electrostatic forces between the

mAb surface charge and surface. In the context of industrial formulation and fill-finish,

225

changing buffer pH may offer a route to modulating surface adsorption, assuming such a

requirement was identified as necessary during development.38

What use are novel excipients?

Excipients commonly used during the formulation of mAbs include amino acids, polyols,

salts, sugars, and surfactants. It seems unlikely that this status quo will remain given recent

reports and patents39

identifying new excipients that equally mitigate the physical and

chemical instability of biological drugs. A key driver for this field being the reduction of

viscosity of highly concentrated mAb solutions, but other more general drivers may be to

improve a medicinal product (e.g. through a change in its pharmacokinetics) and therefore

patient benefit, gain a product differentiation, or increase drug product quality and simplify a

manufacturing step or bioprocess. Leveraging research from the related field of protein

expression and refolding, arginine salts have become a strong focus in recent years and salt

forms other than the hydrochloride salt have provoked industrial applications, particularly

arginine glutamate,40,41

although pharmaceutical grades of the dry powder remain

outstanding. Non-arginine excipients are in earlier phases of investigation but include

hydrophobic anions42

and 2,6-pyridenicarboxylic acid.42

The latter suffers from low

solubility, limiting its application unless new, more aqueously soluble salt forms can be

identified. Nevertheless, these hydrophobic anions do not appear to perturb protein tertiary

structure and do reduce the viscosity of concentrated mAb solutions. Further work would be

required to better understand their performance during long-term stability studies, for

example, comparison of aggregation and fragmentation rates against arginine salts.

There is also a wider research effort to characterise the low-affinity binding domains/amino

acids to arginine and anions described above. It is possible that such binding sites represent

aggregation ‘hot spots’ on the protein surface. Their identification would in principal

facilitate the rational design of ‘custom excipients’. To this goal, molecular dynamics

simulations of local protein surface patches (or indeed, the whole mAb) are likely to become

an important research tool: excipients that bind to these hotspots could be identified through

molecular docking routines. A related experimental approach has been to determine the

binding energies of common excipients to surface regions of a Fab fragment, ranking the

binding energies for a hotspot against the Tm and aggregation rate.25

Bringing a novel excipient into clinical testing poses a challenge that would demand a clear

rationale for their inclusion into a mAb formulation. In regard of clinical trials, the European

Medicines Agency (EMA)43

states: “For novel excipients, details are to be given on their

226

manufacturing process, characterisation and control in relevance to product safety”.

Similarly, EMA guidelines for Marketing Authorisation Application require documentation

as would be expected for a new drug substance: “Full details of manufacture, characterisation

and controls with cross references to supporting safety data should be provided for novel

excipients, according to the drug substance format.” In the US, the Food and Drug

Administration does not outline specific guidance for novel excipients unless included in a

Biologics License Application (or New Drug Application). The industry would therefore

need to assess the risk of investment necessary for safety and efficacy documentation, and the

potential risk to delay approval of the drug product containing the novel excipient, against the

critical advantages gained, such as a unique capability or purpose.

Concluding remarks

From an industrial aspect, aggregation is statistically understood but not mechanically

unravelled: individual companies have standardised stress testing using an approach which is

mostly effective at getting rid of the very bad contenders. However, stress studies depend on

the mechanism of aggregation (pathway) thus the question for academics remains on what is

the rate limiting factor. It is questioned whether understanding pathways is necessary or

finding conditions limiting aggregation would be sufficient. In the literature, studies focusing

on predicting aggregation is quite low in comparison to measuring aggregation (e.g.

technologies, modelling) and controlling aggregation (e.g. use of excipients).

Characterising, modelling and predicting aggregation of biopharmaceuticals is a complex

task far from being answered. On the whole, this review highlights the need for further

research collaboration between industry and academia, as, as difficult as this problem may be

for mAbs, these molecules may be considered as ‘well behaved’ compared to novel entities

e.g. peptides, affimers, fragments but also mixtures of proteins.

Acknowledgements

This commentary includes discussions which took place during the meeting ‘Recent

Breakthroughs and New Perspectives in Protein Aggregation’, February 2017, sponsored by

BioProNET (grant code BB/L013770/1) and University of Manchester (Division of

Pharmacy and Optometry). The author would like to gratefully acknowledge the following

for their contribution to the commentary: Professor Christopher Roberts (University of

Delaware), Dr Jennifer McManus (Maynooth University), Professor Paul Dalby (University

College London), Professor Stephen Harding (University of Nottingham), Professor

227

Wolfgang Friess (LMU Munich) and Dr James Austerberry (University of Manchester).

Special thanks go to Dr Alain Pluen (University of Manchester), Dr Robin Curtis (University

of Manchester) and Dr Chris Van der Walle (Medimmune) for their contribution and

invaluable support.

228

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