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# Protein stability at interfaces

Marianne Weidenhaupt

► To cite this version:

Marianne Weidenhaupt. Protein stability at interfaces. Biochemistry, Molecular Biology. UGA, Ed Chimie et Sciences du Vivant, 2019. tel-04488024

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https://hal.science/tel-04488024

Submitted on 4 Mar 2024

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Communauté
UNIVERSITÉ Grenoble Alpes

**Manuscrit**

Pour obtenir le diplôme

**Habilitation à Diriger des Recherches**

**DE LA COMMUNAUTE UNIVERSITE GRENOBLE ALPES**

Spécialité : **Chimie et Sciences du Vivant**

Présenté par

**Marianne WEIDENHAUPT**

**Laboratoire des Matériaux et du Génie Physique**

**École Doctorale Chimie et Sciences du Vivant (CSV)**

**Protein stability at interfaces**

HDR soutenue publiquement le **22 janvier 2019**
devant le jury composé de :

**Mme Séverine SIGRIST**

CEO Defymed, Strasbourg (F), Rapporteure

**M Alain ASTIER**

Chef de Pôle Honoraire, PUI H. Mondor, Crétail (F), Rapporteur

**M Jean-Luc LENORMAND**

Professeur Université Grenoble Alpes, Grenoble (F), Rapporteur

**Mme Lene Jørgensen**

Associate Professor, University of Copenhagen (DK), Examinatrice

**M Nicolas MOUZ**

Chef Dépt. Antibody Discovery Sanofi, Vitry-sur-Seine (F), Examinateur

**M Hans GEISELMANN**

Professeur Université Grenoble Alpes, Grenoble (F), Président

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# Acknowledgements

I would like to express my deepest gratitude to all those who have contributed to the science that is presented in this manuscript: students, technicians, researchers and teaching colleagues and all those who do the jobs “behind the scene” that allow everyone to work in fine conditions. I am fortunate to have a workplace I enjoy going to every day.

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En groussen Merci gëllt mengem Papp a mengem Brudder, déi mech ëmmer ënnerstetzt hun and déi houfrech sin op all dât wât ech a mengem Beruf errécht hun. Meng Mamm a meng Tat'Anny bleiwen ëmmer bei mer a léiwer Errënnerung.

I also want to thank Grandma and Granddad for their unconditional support since we first met 27 years ago.

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My husband and my daughters are my best support. They are a continuous source of motivation, inspiration and dedication at work, at home and in our fantastic mountain adventures. Ta very much!
Table of contents

<table><tr><td><u>PART I</u></td><td>1</td></tr><tr><td><u>CURRICULUM VITAE</u></td><td>1</td></tr><tr><td>1 <u>A CARRIER OVERVIEW</u></td><td>2</td></tr><tr><td>2 <u>CURRENT POSITION</u></td><td>6</td></tr><tr><td>2.1 RESEARCH ACTIVITIES</td><td>6</td></tr><tr><td>2.2 SUPERVISIONS AND MENTORING</td><td>9</td></tr><tr><td>2.3 TEACHING AND RELATED RESPONSIBILITIES</td><td>11</td></tr><tr><td>2.4 OTHER PROFESSIONALLY RELEVANT RESPONSIBILITIES</td><td>11</td></tr><tr><td>2.5 PUBLICATIONS</td><td>12</td></tr><tr><td><u>PART II</u></td><td>15</td></tr><tr><td><u>PROTEIN STABILITY AT INTERFACES</u></td><td>15</td></tr><tr><td>1 <u>INTRODUCTORY WORDS</u></td><td>16</td></tr><tr><td>2 <u>PROTEINS AND THEIR STABILITY</u></td><td>17</td></tr><tr><td>2.1 PROTEIN STRUCTURE DESCRIPTION</td><td>19</td></tr><tr><td>2.2 FOLDING FOR FUNCTION, YES AND NO</td><td>20</td></tr><tr><td>2.3 NATIVE STATE, AMORPHOUS OR HIGHLY ORDERED AGGREGATE STATES</td><td>22</td></tr><tr><td>2.4 THE AMYLOID STATE</td><td>24</td></tr><tr><td>2.5 KINETICS OF AMYLOID FIBRE FORMATION</td><td>26</td></tr><tr><td>2.6 MISFOLDING AND AGGREGATION IN VIVO</td><td>27</td></tr><tr><td>2.7 AMYLOID AGGREGATION AND DISEASE</td><td>29</td></tr><tr><td>3 <u>PROTEINS AS THERAPEUTICS</u></td><td>30</td></tr><tr><td>3.1 UNIQUE ASSETS AND THE PRICE TO PAY</td><td>31</td></tr><tr><td>3.2 THE CHALLENGES OF PROTEIN PRODUCTION</td><td>33</td></tr><tr><td>3.3 PROCESSES INDUCING INTERFACIAL STRESS DURING PROTEIN PRODUCTION</td><td>36</td></tr><tr><td>3.4 PROCESSES INDUCING INTERFACIAL STRESS DURING PROTEIN DELIVERY</td><td>37</td></tr><tr><td>4 <u>PROTEINS AT INTERFACES</u></td><td>38</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>4.1</td>
      <td>PROTEIN ADSORPTION, UNFOLDING AND REARRANGEMENTS AT SOLID INTERFACES</td>
      <td>40</td>
    </tr>
    <tr>
      <td>4.1.1</td>
      <td>PARAMETERS INFLUENCING PROTEIN INTERFACIAL ACTIVITY</td>
      <td>40</td>
    </tr>
    <tr>
      <td>4.1.2</td>
      <td>ADSORBED MASS AND SURFACE COVERAGE</td>
      <td>42</td>
    </tr>
    <tr>
      <td>4.1.3</td>
      <td>UNFOLDING AND REARRANGEMENTS UPON ADSORPTION</td>
      <td>43</td>
    </tr>
    <tr>
      <td>4.1.4</td>
      <td>REVERSIBILITY OF ADSORPTION</td>
      <td>46</td>
    </tr>
    <tr>
      <td>4.2</td>
      <td>PROTEIN ADSORPTION AND UNFOLDING AT LIQUID INTERFACES</td>
      <td>46</td>
    </tr>
    <tr>
      <td>4.3</td>
      <td>PROTEIN ADSORPTION AND UNFOLDING AT AIR INTERFACES</td>
      <td>49</td>
    </tr>
    <tr>
      <td>4.4</td>
      <td>METHODS TO CHARACTERIZE PROTEIN INTERFACIAL ADSORPTION</td>
      <td>50</td>
    </tr>
    <tr>
      <td>4.5</td>
      <td>FORMULATION STRATEGIES</td>
      <td>54</td>
    </tr>
    <tr>
      <td>5</td>
      <td>INSULIN ADSORPTION AND AGGREGATION AT HYDROPHOBIC INTERFACES: A CASE STUDY</td>
      <td>57</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>A TRIBUTE TO PHD STUDENTS AND CO-WORKERS</td>
      <td>57</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>HUMAN INSULIN</td>
      <td>58</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>INSULIN AMYLOID AGGREGATION AT HYDROPHOBIC SURFACES</td>
      <td>60</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>AGGREGATIVE PEPTIDES AND INSULIN AGGREGATION AT HYDROPHOBIC SURFACES</td>
      <td>65</td>
    </tr>
    <tr>
      <td>5.5</td>
      <td>INSULIN AGGREGATION AT A DYNAMIC TRIPLE INTERFACE</td>
      <td>72</td>
    </tr>
    <tr>
      <td>6</td>
      <td>MONOCLONAL ANTIBODY ADSORPTION AT MATERIAL SURFACES</td>
      <td>79</td>
    </tr>
    <tr>
      <td>6.1</td>
      <td>THE NEED FOR EARLY STABILITY STUDIES IN FORMULATION OPTIMIZATION</td>
      <td>79</td>
    </tr>
    <tr>
      <td>6.2</td>
      <td>ADSORPTION KINETICS OF M<sub>AB</sub>A AT HYDROPHOBIC AND HYDROPHILIC SURFACES</td>
      <td>82</td>
    </tr>
    <tr>
      <td>6.3</td>
      <td>POLYSORBATES PREVENT ADSORPTION BUT DO NOT DISPLACE M<sub>AB</sub>A ON HYDROPHOBIC AND HYDROPHILIC SURFACES</td>
      <td>85</td>
    </tr>
    <tr>
      <td>6.4</td>
      <td>DETERMINATION OF APPARENT K<sub>ON</sub> RATES FOR M<sub>AB</sub>A AND POLYSORBATES AT A HYDROPHOBIC SURFACE</td>
      <td>89</td>
    </tr>
    <tr>
      <td>6.5</td>
      <td>PERSPECTIVES ON MOLECULAR SURFACE ADSORPTION OF M<sub>AB</sub>S AND SURFACTANTS</td>
      <td>93</td>
    </tr>
    <tr>
      <td>7</td>
      <td>REFERENCES</td>
      <td>96</td>
    </tr>
    <tr>
      <td>8</td>
      <td>ANNEXE 1</td>
      <td>106</td>
    </tr>
    <tr>
      <td>9</td>
      <td>ANNEXE 2</td>
      <td>108</td>
    </tr>
    <tr>
      <td>10</td>
      <td>REPRINTS OF ARTICLES</td>
      <td>111</td>
    </tr>
  </tbody>
</table>
Part I

Curriculum vitae
# 1 A carrier overview

My scientific career started with a PhD thesis in microbial genetics and biochemistry at the Department of Microbiology of the ETH in Zurich (1996). I then went to Grenoble for my postdoctoral research and worked on the structure-function relationship of different proteins, learning to do science at the interface between physics, chemistry and biology (Figure 1-1). During those years I also worked in different settings, be it with industrial partners or on service platforms, which was enriching and valuable. In 2006, I obtained a permanent position as "Maître de Conférence" (Associate Professor) at Grenoble-INP Phelma and researcher at the Laboratory of Materials and Physical Engineering (LMGP). Below is a brief description of the various positions I held and the scientific projects I worked on.

<figure><img src="image_8.png" /><figcaption>Figure 1-1: Timeline showing my positions and research projects since my PhD thesis</figcaption></figure>

- DBMS : Département de Biologie Moléculaire et Structurale

- BBSI : Laboratoire de Biochimie et Biophysique des Systèmes Intégrés

- IBS : Institut de Biologie Structurale

- LIM : Laboratoire d'Ingénierie des Macromolécules

- EMBL : European Molecular Biology Laboratory

- Phelma : Physique Electronique et Sciences des Matériaux

- LMGP : Laboratoire des Matériaux et du Génie Physique

## Endocytosis and membrane fusion in Dictyostelium discoideum

1996-1998

**Laboratory:** CEA-Grenoble, DBMS, Laboratoire de Biochimie et Biophysique des Systèmes Intégrés.

**Head:** Michel Satre

**Funding:** Postdoc grant of the Swiss National Research Foundation and "Contrat de Collaborateur Temporaire Etranger" (CTE) of the CEA-Grenoble

D. discoideum is a simple unicellular eucaryote, which is used as a model organism because of several distinct properties, among which high endocytic rates and a well-studied
intercompartmental traffic, based on vesicular fusion. The NSF-SNAP-SNARE machinery was discovered in the late 1980's as governing compartmental specificity of membrane fusion in eucaryots. Inter membrane docking complexes are mediated by the association of SNARE proteins, upon which cytosolic factors, like SNAP proteins and the ATPase NSF are recruited to form a fusion complex. Fusion is achieved by ATP hydrolysis mediated by NSF and the release of the complex. Small GTP-binding proteins of the Rab family govern the rate of the fusion events. Vesicular traffic and the molecular machinery involved have since been intensely studied in many organisms and diverse functional processes. The discoveries in these fields have led to numerous breakthroughs in cell biology and medicine and the Nobel Prize in Physiology or Medicine was awarded in 2013 to J Rothman, R Scheckman and TC Südhof for their ground breaking contributions.

In CEA Grenoble, we have identified, cloned, purified and characterized several proteins (NSF, SNAP isoforms) of the vesicular fusion machinery in *Dictyostelium*. These findings have been published in the following papers:

**Weidenhaupt M.**, Bruckert F. and Satre M. (1998) Identification of the *Dictyostelium discoideum* homolog of the N-ethylmaleimide-sensitive fusion protein. *Gene*, 207, 53-60

**Weidenhaupt M.**, Bruckert F., Louwagie M., Garin J. and Satre M. (2000) Functional and molecular identification of novel members of the ubiquitous membrane fusion protein α- and γ-SNAP (soluble NSF-attachment proteins) families in *Dictyostelium discoideum*. *Eur J Biochem*, 267, 2062-2070

## Kinetic parameter modulation in antibody-antigen interactions

1998-2000

**Laboratory:** CEA-Grenoble, IBS, Laboratoire d'Ingénierie des Macromolécules

**Head:** Thierry Vernet

**Funding:** Postdoc grant of the EU 4th PCRD (BIO4-CT98-0502)

The engineering of antibody-antigen interaction parameters allows understanding the structure-function relationship governing the recognition and binding of an immun complex. We were particularly interested in the contribution of framework amino acids, which do not belong to the paratope, but can nevertheless contribute to binding affinity. In a comprehensive directed mutagenesis approach we replaced framework residues from the VH-
VL interface and studied their effect on association and dissociation rate constants by surface
plasmon resonance (Biacore). Using several different antibody-antigen systems, we
demonstrated that framework residues (especially charged ones) can affect kinetic interaction
parameters, albeit only marginally and in an unpredictable manner. We identified position
VL34 as a key amino acid linked to fast dissociation rates. These findings have been published
in the following papers:

Ben Khalifa M., **Weidenhaupt M.**, Choulier L., Chatellier J., Rauffer-Bruyère N., Altschuh D. and Vernet T. (2000) Effects of mutations at the VH-VL interface of Fabs depends on the structural context. *J Mol Recogn* 13, 127-139

**Weidenhaupt M.**, Ben Khalifa M., Hugo N., Choullier L., Altschuh D. and Vernet T. (2002) Functional mapping of conserved, surface-exposed charges of antibody variable domains, *J Mol Recogn* 15, 94-103

Hugo N and **Weidenhaupt M**, Beukes M, Xu B, Janson JC, Vernet T and Altschuh D. (2003) Identification of a key position for rational engineering of dissociation rates in antibodies, *Protein Eng.* 16, 381-386

VE-cadherin, an adhesive receptor affecting leucocyte and tumor cell migration

2000-2002

Laboratory: CEA-Grenoble, IBS, Laboratoire d'Ingénierie des Macromolécules

Head: Danielle Gulino-Debrac

Funding: Postdoc grant "Formation recherche" of the Luxembourgish government (BFR 00/011)

Cadherins are adhesion molecules that govern cell-cell interactions in tissues. Among different tissue-specific cadherins, vascular endothelial (VE) cadherin plays a major role in remodeling and maturation of the vascular endothelium. The extracellular part of VE cadherin consists of five homologous domains that self-associate in a Ca²⁺-dependent hexamer in solution. Using recombinant proteins with different combinations of the extracellular domains of VE cadherin, we characterized the molecular assembly mechanism underlying hexamer formation. We showed that self-assembly as intermediate dimers is necessary to build homotypic interactions. We also investigated the functional role of VE cadherin in maintaining vascular endothelium integrity. This integrity is challenged by leucocyte transmigration and also during cancer metastasis. We have identified two metalloproteases, elastase and cathepsin G, which
cleave VE cadherin, thus allowing neutrophil transmigration from the blood towards underlying tissues. These findings have been published in the following papers:

Bibert S, Jaquinod M, Concord E, Ebel C, Hewatt E, Vanbelle C, Legrand P, **Weidenhaupt M**, Vernet T and Gulino-Debrac D. (2002) Synergy between extracellular modules of VE cadherin promotes homotypic hexameric interactions, *J Biol Chem*, 277, 12790-12801

Hermant B, Bibert S, Concord E, Dublet B, **Weidenhaupt M**, Vernet T, Gulino-Debrac D. (2003) Identification of proteases involved in the proteolysis of VE cadherin during neutrophil transmigration, *J Biol Chem*, 278, 14002-14012

**Escherichia coli response to deuterium**

2002-2006

**Laboratory:** EMBL-Grenoble, Deuteration laboratory

**Head:** Stephen Cusack

**Funding:** Postdoc grant of the EU (HPRI-CT-2001-50035 and NMI3, HII3-CT-2003-505925)

The deuteration isotope labeling lab at EMBL-ILL in Grenoble is a service facility to produce fully deuterated proteins for neutron protein crystallography studies. Indeed, key hydrogen atoms, that cannot be seen by X-ray analysis alone, can be located in molecular structures using the neutron Laue diffractometer LADI at ILL. The production of fully D-labelled macromolecules is done using bacterial expression systems. I was involved in the initial setup of this facility and worked particularly on the molecular response mechanism underlying bacterial adaptation to deuterated media. In order to understand and improve the adaptation process, we investigated the deuterium effect on *E. coli* in a comparative proteomic approach using 2D gel electrophoresis. Several interesting candidate proteins were identified. To my knowledge, this research project was not pursued after my leave. The following paper was published:

Dunne O, **Weidenhaupt M**, Callow P, Martel A, Moulin M, Perkins SJ, Haertlein M and Forsyth VT (2016) Matchout deuterium labelling of proteins for small-angle neutron scattering studies using prokaryotic and eukaryotic expression systems and high cell-density cultures. *Eur Biophys J* 46, 425-432
## 2 Current position

Since 2006, I work as a "Maître de Conférence" (Associate professor) at G-INP Phelma and researcher at the Laboratory of Materials and Physical Engineering (LMGP). I am sharing the direction (since 2018) of the team "Interface between Materials and Biological Matter" (IMBM) which is headed by Catherine Picart. Our team is composed of 3 Professors/Associate Professors, 1 CR2 CNRS researcher, 2 research engineers (CNRS) and 1 assistant engineer (fixed-term contract, 50% LMGP, 50% CIME Nanotech).

Our common research area is the study of complex physico-chemical phenomena taking place at the interface between materials, biomolecules or cells and air. We develop both fundamental aspects in this field and applied research, often in collaboration with public (e.g. university hospitals) or private, industrial partners of the biomedical sector (e.g. pharma industry). Our research projects fall into two thematic groups:

1. Biomolecules at the interface and biomimetism

2. Tissue Engineering and cancer therapy

My research activities focus mainly on thematic 1 "Biomolecules at interfaces" and I collaborate, to a minor extent, in other research projects. The following chapter describes this in more detail.

### 2.1 Research activities

Since 2007, my research focuses mainly on the stability of therapeutic proteins at the interface with materials and air (see also Part II). This work is done in close collaboration with industrial partners who work in the sectors of production, marketing and delivery of drugs (Table 2-1). Major results of the research projects that I have lead together with Franz Bruckert (LMGP) between 2007 and 2018 in the field of therapeutic protein stability at interfaces, are presented in Part II of this manuscript.
<table><caption>Table 2-1: Research contracts in the field of therapeutic protein stability at interfaces</caption><thead><tr><th>Year</th><th>Secured funding</th><th>Research project</th><th>Collaborative Partners</th></tr></thead><tbody><tr><td>2007-10</td><td>Cifre ANRT<br>PhD grant<br>+ 90 k€</td><td>Protein stability at the material interface</td><td>Becton Dickinson (Mangiagalli)</td></tr><tr><td>2009-10</td><td>ANR (09-BIOT-11)<br>200 k€</td><td>Stability of therapeutic proteins</td><td>LFB (Huille)<br>Becton Dickinson (Mangiagalli)<br>CEA LCBM (Forge)</td></tr><tr><td>2009-12</td><td>Macodev<br>PhD grant</td><td>Insulin amyloid fibre formation at material surfaces</td><td></td></tr><tr><td>2013-16</td><td>Cifre ANRT<br>PhD grant<br>+ 90 k€</td><td>Therapeutic protein aggregation at the triple interface solid-liquid-air</td><td>EVEON (Le Masne, Vomscheidt, Tempelaere)</td></tr><tr><td>2017-20</td><td>Cifre ANRT<br>PhD grant<br>+ 90 k€</td><td>Stability study of therapeutic antibodies and surfactants for device ability</td><td>Sanofi (Huille, El Kechai, Filipe)</td></tr><tr><td>2018-21</td><td>Innovation grant<br>ANR-15-IDEX-02<br>Technician 12 months<br>+ 35 k€</td><td>LabCom LMGP EVEON<br>Preservation of therapeutic proteins in automated drug reconstitution and delivery devices</td><td>EVEON (Authesserre, Tempelaere)</td></tr></tbody></table>

In the forthcoming years (funded up to 2021) we will take up the challenge of creating a "**Joint Laboratory**" (LabCom), in the framework of the French National Research Agency's efforts to foster collaborations between public research and small or intermediate-sized enterprises. This project will unite EVEON (http://eveon.eu/) and the LMGP to optimize the compatibility of microfluidic protocols and device components of EVEON's automated reconstitution systems with different therapeutics.

<figure><img src="image_9.png" /><figcaption>Figure 2-1: Eveon's Intuity® mix platform. Credits to EVEON</figcaption></figure>

The Intuity® mix platform (Figure 2-1) is an example of an automated preparation device developed by EVEON. It can solubilize, mix and distribute lyophilized therapeutic proteins. It consists of a disposable cartridge that holds 3 containers: a vial containing the lyophilized powder, a vial containing the solvent and an injection device (typically a syringe). First the solvent is pumped into the powder

vial to dissolve the drug, which is finally filled into the syringe at the desired dose. The liquid handling protocols and the component materials within the device generate interfaces
between the protein solution, solid materials and air. The impact of these, often dynamic, interfaces has to be controlled in order to avoid or minimize interfacial adsorption and aggregation of the drug. Special attention will be given to the choice of materials in contact with the therapeutic solution and to fluidic parameters such as the impact of pressure, flow, volume and the presence of air. Our common objective is to develop a library of optimized combinations of component materials and reconstitution protocols adapted to different therapeutics.

Besides therapeutic protein stability at interfaces, I also collaborate in other research projects within LMGP and beyond (Table 2-2). In these collaborative projects my contribution is based on my expertise in protein biochemistry, molecular biology and biomedical applications. The following chart represents the current partitioning of my research time in different projects.

<figure><img src="image_10.png" /><figcaption>Figure 2-2: Chart showing the partitioning of my current research time in different ongoing projects</figcaption></figure>

Over the years I have managed to collaborate in diverse research projects that are all centred on the common theme: Interactions between materials and biology. The materials part includes electrodes, micro or nanostructured materials, multi-layered polyelectrolyte thin films, liposomes and the biology part extended from proteins and nucleic acids to pro- and eukaryote cells. The following table gives an overview of these collaborative projects.

<table><thead><tr><td>Year</td><td>Research Project</td><td>Collaborative Partners</td></tr></thead><tbody><tr><td>2008-09</td><td>Microstructured materials for cell adhesion and directed migration</td><td>P Schiavone, T Tzvetkova (CEA LTM) A Stéfanou (TIMC-IMAG)</td></tr><tr><td>2007-10</td><td>Electrochemical synchronisation of cell spreading</td><td>D Delabouglise (SIMAP, LMGP IMBM team) and F Bruckert (LMGP IMBM team)</td></tr></tbody></table>
<table><caption>Table 2-2: List of research projects I was or am involved in</caption><tr><td>2009-12</td><td>Electrochemical analysis of anaerobic redox activity of <i>Escherichia coli</i></td><td>V Stambouli and D Delabouglise (LMGP IMBM team)</td></tr><tr><td>Since 2014</td><td>Rapid diagnostic immune assays using micromagnets MagIA diagnostics (startup company)</td><td>P Kauffmann (MagIA), O Cugat (G2Elab), N Dempsey (Néel Institute), P Marche (IAB), F Bruckert and D Delabouglise (LMGP IMBM team)</td></tr><tr><td>Since 2015</td><td>Tissue engineering: delivery of bioactive molecules through polyelectrolyte films applied to cancer</td><td>C Picart (LMGP, IMBM team)</td></tr><tr><td>Since 2016</td><td>Protein interactions with lipid membranes</td><td>C Picart (LMGP, IMBM team)</td></tr><tr><td>Since 2016</td><td>Nature inspired biological glues (ANR-15-CE08-0003)</td><td>C Vendrely (ERRMECe, Univ Cergy-Pontoise, LMGP visitor)</td></tr><tr><td>Since 2018</td><td>Electrical detection of PCR-amplified DNA on Si nanonets</td><td>C Ternon, V Stambouli (LMGP FunSurf team)</td></tr><tr><td>Year</td><td>Research &amp; Education Project</td><td>Collaborative Partners</td></tr><tr><td>2016-21</td><td>Research and Education in active coating technologies for the human habitat; ANR-PIRE-REACT, (https://react.seas.upenn.edu/)</td><td>CNRS DR11, ILL, CEA, CNRS Alsace, CNRS DR 16, GIANT, UPenn (US) Alabama Sate Univ (US)</td></tr></table>

## 2.2 Supervisions and Mentoring

During my postdoc studies, I mentored the PhD students Myriam Ben Khalifa-Cubizolles and Bastien Hermant at IBS Grenoble as well as several Master students. Since 2006, when I joined the LMGP, I yearly supervise 1-2 **Master I / II students** and have co-supervised the thesis of 6 **PhD students** (Table 2-3). I am currently the thesis Director of G Lefèbvre who does a Cifre PhD thesis in collaboration with Sanofi (Vitry, France) (see also Part II, 6 Monoclonal antibody adsorption at material surfaces). I was enrolled in 4 PhD thesis committees: L Altamura (PhD ED Phys, 2015), J Pansieri (PhD ED Phys, 2017), C Lu (PhD ED CSV, 2017), A Ho Shui Ling (PhD ED IMEP2, 2018).

Besides PhD students, I also supervise since 2010 an **assistant engineer** who works on research projects at LMGP and is in charge of the technical aspects of the Biotechnology facility which I head at CIME-Nanotech. My involvement in this facility is detailed hereafter (2.3 Teaching and related responsibilities). I have supervised **2 ATER fellows** (M Amari and M
Lourdin) for their teaching and research work and mentored the teaching activity (Monitorat) of 3 PhD students (A Arnaud, I Bouchnak and M Chieb).

A list of the Bachelor and Master students I supervised since 2010 is given in Annexe 1.

<table><caption>Table 2-3: Summary of the PhD students I have supervised</caption><thead><tr><td>Year</td><td>PhD thesis</td><td>Supervision</td><td>Publications (MW co-author)</td><td>Current position</td></tr></thead><tbody><tr><td>2006-09</td><td>Marius Socol<br>ED Phys UGA Grenoble</td><td>D Delabouglise 50%<br>F Bruckert 40%<br><b>M Weidenhaupt 10%</b></td><td>Microel. Engin 2009, 86, 1485-1487.<br>Bioelectrochemistry 2010, 79, 198-210</td><td>Postdoc LAAS<br>Toulouse</td></tr><tr><td>2009-12</td><td>Uyen Nguyen<br>ED Phys UGA Grenoble</td><td>D Delabouglise 40%,<br>V Stambouli 40%<br><b>M Weidenhaupt 10%</b></td><td>Electrochimica Acta 2014, 130, 200-205</td><td>Postdoc<br>PennState Univ.,<br>US</td></tr><tr><td>2007-10</td><td>Thomas Ballet<br>ED Phys UGA Grenoble<br>Cifre Becton Dickinson</td><td>F Bruckert 50%<br><b>M Weidenhaupt 50%</b></td><td>Bulletin of the Polish Academy of Sciences 2010, 58, 303-315.<br>Biochemistry 2012, 51, 2172-2180</td><td>Head of Chemical & Material Laboratories, Sanofi, Vitry</td></tr><tr><td>2009-12</td><td>Laurent Nault<br>ED Phys UGA Grenoble</td><td>F Bruckert 50%<br><b>M Weidenhaupt 50%</b></td><td>Acta Biomaterialia 2013, 9(2), 5070-5079.<br>FEBS Letters 2013, 587, 1281-1286.</td><td>Scala development<br>Engineer at Niji</td></tr><tr><td>2013-17</td><td>Karim Chouchane<br>ED Phys UGA Grenoble</td><td>F Bruckert 50%<br><b>M Weidenhaupt 50%</b></td><td>J Phys Chem B 2015, 119(30), 10543-10553<br>J Photochem Photobiol B: 181, 89-97</td><td>Looking for a postdoc position</td></tr><tr><td>2013-17</td><td>Thibaut Frachon<br>ED Phys UGA Grenoble</td><td>F Bruckert 50%<br><b>M Weidenhaupt 50%</b></td><td>Langmuir 2016, 32(49), 13009-13019</td><td>Formulation<br>Engineer at Sanofi (Vitry)</td></tr><tr><td>2017-20</td><td>Guillaume Lefèbvre<br>ED CSV UGA Grenoble<br>Cifre Sanofi (Vitry)</td><td><b>M Weidenhaupt 100%</b></td><td></td><td>Thesis ongoing</td></tr></tbody></table>
## 2.3 Teaching and related responsibilities

I hold a position as an Associate Professor (Maître de Conférence, HC échelon 5) at the Engineer school for Physics, Electronics and Material sciences (Phelma) of Grenoble INP and teach at the Master's level I and II in Biomedical Engineering, in the International Master Nanotech and the Master Nanomedicine. At University Grenoble Alpes (UGA) I teach in the Master Nanobiosciences. My courses cover fundamental aspects in molecular biology, biochemistry, cell biology and immunology as well as engineering applications in biomedicine. I lecture and supervise practical labworks and project work of cohorts of 20-60 students per track. My courses are all given in English, except for a few hours of orientation courses in Biomedical Engineering at the bachelor level, which are given in French. I am also a regular teacher in the highschool programs of the campus GIANT, Nano@school and Inn.OTech. as well as in the European programs EduNano, Eurodots and ESONN.

At Phelma, I am part of the **managing team of the Biomedical Engineering** track and have been in charge of **international relations** of this track since 2008. As an international coordinator, I yearly welcome 10-20 international students in our programs (Erasmus exchanges and double degree students) and mentor the same number of Phelma students for their exchanges abroad. I am also **responsible for the Biotechnology training facility** at CIME Nanotech since 2006 (https://cime.grenoble-inp.fr/?q=fr/biotechnologies). This facility hosts yearly approximately 6000 student hours and it is currently also used by 3 research labs (LMGP, G2Elab, LTM) and hosts the research activities of 2 startup companies (MagIA diagnostics and Nanobiose). On 300m² it offers state of the art equipment to express, purify and analyse nucleic acids and proteins. It is equipped for bacterial and eukaryote cell culture (security level I), hosts a microscopy room (fluorescence microscopy) and state of the art equipment for biochemical and biophysical characterizations.

## 2.4 Other professionally relevant responsibilities

My career has allowed me to take part in the organisation of numerous science events, to participate in funding boards and in reviewing commissions. Table 2-4 gives an overview of these activities.
<table><caption>Table 2-4: Participation in scientific organisation and reviewing committees</caption><thead><tr><td>Year</td><td>Program</td><td>Task</td></tr></thead><tbody><tr><td>2009</td><td>CNRS wokshop: Sciences de la Miniaturisation et Biologie (SMB), Grenoble</td><td>Organisation committee and scientific committee member</td></tr><tr><td>Since 2011</td><td>GIANT International Internship Program (GIIP)<br>http://www.giant-grenoble.org/en/tag/giip-en/</td><td>Organisation committee member</td></tr><tr><td>Since 2011</td><td>French-American Workshop (FAW) Campus Giant,<br>http://www.giant-grenoble.org/en/faw2018/</td><td>Organisation committee member</td></tr><tr><td>2010-15</td><td>Fonds National de la Recherche, Luxembourg<br>(FNR)</td><td>Member of the funding board in Life Sciences and Medicine</td></tr><tr><td>Since 2016</td><td>Université Franco-Allemande (UFA) Saarbrücken<br>Germany</td><td>Member of the funding board in Engineer Sciences, Natural Sciences, Informatics and Architecture</td></tr><tr><td>Since 2017</td><td>European school for Nanosciences and Nanotechnologies (ESONN)<br>https://www.esonn.fr/</td><td>Organisation committee member</td></tr><tr><td>Since 2017</td><td>UFR Chemistry and Biology University Grenoble Alpes</td><td>Invited board member</td></tr><tr><td>2018</td><td>Workshop: Semi-conducting Nanomaterials for Health, Environment and Security Applications, Grenoble</td><td>Scientific committee member</td></tr></tbody></table>

## 2.5 Publications

Hereafter is a list of all the peer-reviewed publications I co-authored (last update October 2018): 33 publications, 7 first author, 11 last and communicating author, 2 reviews.

### Publications:

1. **Weidenhaupt M.**, Fischer H-M., Acuña G., Sanjuan J. and Hennecke H. (1993) Use of a promoter-probe vector system in the cloning of a new NifA-dependent promoter (ndp) from *Bradyrhizobium japonicum*. *Gene* **129**, 33-40

2. **Weidenhaupt M.**, Schmid-Appert M., Thöny B., Hennecke H. and Fischer H-M. (1995) A new *Bradyrhizobium japonicum* gene required for free-living growth and bacteroid development is conserved in other bacteria and in plants. *Mol Plant Microb Inter* **8**, 454-464

3. **Weidenhaupt M.**, Rossi P., Beck C., Fischer H-M. and Hennecke H. (1996) *Bradyrhizobium japonicum* possesses two discrete sets of electron transfer flavoprotein genes: *fixA*, *fixB* and *etfS*, *etfL*. *Arch Microbiol* **165**, 169-178
4. **Weidenhaupt M.**, Bruckert F. and Satre M. (1998) Identification of the *Dictyostelium discoideum* homolog of the N-ethylmaleimide-sensitive fusion protein. *Gene*, **207**, 53-60

5. **Weidenhaupt M.**, Bruckert F., Louwagie M., Garin J. and Satre M. (2000) Functional and molecular identification of novel members of the ubiquitous membrane fusion protein α- and γ-SNAP (soluble NSF-attachment proteins) families in *Dictyostelium discoideum*. *Eur J Biochem*, **267**, 2062-2070

6. Ben Khalifa M., **Weidenhaupt M.**, Choulier L., Chatellier J., Rauffer-Bruyère N., Altschuh D. and Vernet T. (2000) Effects of mutations at the VH-VL interface of Fabs depends on the structural context. *J Mol Recogn* **13**, 127-139

7. **Weidenhaupt M.**, Ben Khalifa M., Hugo N., Choullier L., Altschuh D. and Vernet T. (2002) Functional mapping of conserved, surface-exposed charges of antibody variable domains, *J Mol Recogn* **15**, 94-103

8. Gu Z., **Weidenhaupt M.**, Ivanova N., Pavlov M., Xu B., Su ZG. and Janson J-C (2002) Chromatographic methods for the isolation and refolding of proteins from *E. coli* inclusion bodies, *Prot Expr and Purif*, **25**, 174-179

9. Bibert S, Jaquinod M, Concord E, Ebel C, Hewatt E, Vanbelle C, Legrand P, **Weidenhaupt M**, Vernet T and Gulino-Debrac D. (2002) Synergy between extracellular modules of VE cadherin promotes homotypic hexameric interactions, *J Biol Chem*, **277**, 12790-12801

10. Hugo N and **Weidenhaupt M**, Beukes M, Xu B, Janson JC, Vernet T and Altschuh D. (2003) Identification of a key position for rational engineering of dissociation rates in antibodies, *Protein Eng.* **16**, 381-386

11. Hermant B, Bibert S, Concord E, Dublet B, **Weidenhaupt M**, Vernet T, Gulino-Debrac D. (2003) Identification of proteases involved in the proteolysis of VE cadherin during neutrophil transmigration, *J Biol Chem*, **278**, 14002-14012

12. Delanoë-Ayari H., Lenz P., Brevier J., **Weidenhaupt M.**, Vallade M., Gulino D., Joanny J.F. and Riveline D. (2004) Periodic adhesive fingers between contacting cells. *Phys Rev Lett*, **93**, 108102.

13. Tzvetkova-Chevolleau T., Yoxall E., Fuard D., Bruckert F., Schiavon P. and **Weidenhaupt M.** (2009) Microscale adhesion patterns for the precise localization of amoeba. *Micro Eng*in **86**, 1485-1487.

14. Socol M., Lefrou C., Bruckert F., Delabouglise D. and **Weidenhaupt M.** (2010) Synchronization of Dictyostelium discoideum adhesion and spreading using electrostatic forces. *Bioelectrochemistry* **79**, 198-210.

15. Bruckert F. and **Weidenhaupt M.** (2010) Applications of micro- and nanotechnology to study cell adhesion to material surfaces (Review). *J Adhesion Science and Technology* **24**, 2127-2140.

16. Ballet T., Boulangé L., Bréchet Y., Bruckert F. and **Weidenhaupt M.** (2010) Protein conformational changes induced by adsorption onto material surfaces: an important issue for biomedical applications of material science. *Bulletin of the Polish Academy of Sciences – Technical Sciences* **58**, 303-315.

17. Ballet T., Bruckert F., Mangiagalli P., Bureau C, Boulangé L, Nault L., Perret T. and **Weidenhaupt M.** (2012) DnaK prevents human insulin amyloid fiber formation on hydrophobic surfaces. *Biochemistry*, **51**, 2172-2180.

18. J. M. Lackner, W Waldhauser, P Hartmann, F Bruckert, **M Weidenhaupt**, R Major, M Sanak, M Wiesinger and D Heim. (2012) Hemocompatibility of Inorganic Physical Vapor Deposition (PVD) Coatings on Thermoplastic Polyurethane Polymers. J. Funct. Biomater. **3**, 283-297.

19. Pandey LM, Le Denmat S, Delabouglise D, Bruckert F, Pattanayek SK and **Weidenhaupt M.** (2012) Surface chemistry at the nanometer scale influences insulin aggregation. *Colloids Surf B Biointerfaces*, **100**, 69-76.

20. Blaire G, Masse A, Zanini LF, Gaude V, Delshadi S, Honegger T, Peyrade D, **Weidenhaupt M**, Dumas-Bouchiat F, Bruckert F, Cugat O et Reyne G (2013) Hybrid Bio-Mag-MEMS combining magnetophoresis and dielectrophoresis Eur. Phys. J. B 86: 165

21. Nault L, Guo P, Jain B, Bréchet Y, Bruckert F and **Weidenhaupt M.** (2013) Human insulin adsorption kinetics, conformational changes and amyloidal aggregate formation on hydrophobic surfaces. *Acta Biomaterialia* **9**(2), 5070-5079.

22. Nault L, Vendrely C, Bréchet Y, Bruckert F and **Weidenhaupt M.** (2013) Peptides that form β-sheets on hydrophobic surfaces accelerate surface-induced insulin amyloidal aggregation. *FEBS Letters*, **587**, 1281-1286.

23. Nguyen U, Grekov D, Geiselmann J, Stambouli V, **Weidenhaupt M** and Delabouglise D (2014) Anodic deposit from respiration metabolic pathway of *Escherichia coli*. *Electrochimica Acta* **130**, 200-205.

24. Dalonneau F, Liu XQ, Sadir R, Almodovar J, Mertani HC, Bruckert F, Albiges-Rizo C, **Weidenhaupt M**, Lortat-Jacob H, Picart C (2014) The effect of delivering the chemokine SDF-1α in a matrix-bound manner on myogenesis. *Biomaterials* **35**(15), 4525-4535.
25. Serre P, Stambouli V, **Weidenhaupt M**, Baron T and Ternon C (2015) Silicon nanonets for biological sensing applications with enhanced optical detection ability. Biosens. Bioelectron. 68: 336-342.

26. Chouchane K, Vendrely C, Amari M, Moreaux K, Bruckert F and **Weidenhaupt M** (2015) Dual effect of (LK)nL peptides on the onset of insulin amyloid fiber formation at hydrophobic surfaces. J Phys Chem B 119(30), 10543-10553

27. Dunne O, **Weidenhaupt M**, Callow P, Martel A, Moulin M, Perkins SJ, Haertlein M and Forsyth VT (2016) Matchout deuterium labelling of proteins for small-angle neutron scattering studies using prokaryotic and eukaryotic expression systems and high cell-density cultures. Eur Biophys J 46(5), 425-432

28. Frachon T, Bruckert F, Le Masne Q, Monnin E and **Weidenhaupt M** (2016) Insulin aggregation at a dynamic solid-liquid-air interface. Langmuir 32(49), 13009-13019

29. Delshadi S, Blaire G, Kauffmann P, Fratzl M, Devillers, T, Delabouglise D, **Weidenhaupt M**, Dempsey N, Cugat O, Bruckert F and Marche P (2017) Rapide immunoassay exploiting nanoparticles and micromagnets: proof-of-concept using ovalbumin model. Bioanalysis 9(6), 517-526

30. Liu XQ, Fourel L, Dalonneau F, Sadir R, Leal S, Lortat-Jacob H, **Weidenhaupt M**, Albiges-Rizo C and Picart C (2017) Biomaterial-enabled delivery of SDF-1α at the ventral side of breast cancer cells reveals a crosstalk between cell receptors to promote the invasive phenotype. Biomaterials 127, 61-74

31. Lubart Q, Vitet H, Dalonneau F, Le Roy A, Kowlalski M, Lourdin M, Ebel C, **Weidenhaupt M** and Picart C (2018) Role of Phosphorylation in Moesin Interactions with PIP2-Containing Biomimetic Membranes. Biophys J 114(1), 98-112

32. Chouchane K, Pignot-Paintrand I, Bruckert F, **Weidenhaupt M** (2018) Visible light-induced insulin aggregation on surfaces via photoexcitation of bound thioflavin T. J Photochem Photobiol B: Biology, 181, 89-97

33. Migliorini E, **Weidenhaupt M** and Picart C (2018) A practical guide to characterize biomolecule adsorption on solid surfaces (Review) Biointerphases, 13 (6), in press.

## Patents:

I am a co-author on 2 patents:

1. FR 3002536 A1: Use of peptide having alpha helix conformation to inhibit protein aggregation in contact with material surface

2. WO 2014111187 A1: Method for capturing, method for detecting and kit for capturing a molecule in a sample

A full list of oral and poster communications at conferences since 2010 is given in Annexe 2.
Part II

**Protein stability at interfaces**
# 1 Introductory words

This manuscript is meant to set the general scientific background in which I have developed my work on **Therapeutic Protein Stability at Interfaces**. Both fundamental aspects of protein biochemistry and the use of these molecules as an industrial product in the context of healthcare are treated. It was a challenge to shine light on both fundamental and applied protein science and to develop a fair measure for both.

I chose to start with a presentation of proteins, reviewing their structural stability and analysing the different functional and non-functional folds they can adopt (chapter 2 Proteins and their stability). In this chapter, the amyloid aggregate is highlighted because it took a central part in our work on insulin. I then switch to the use of proteins as therapeutics in today's medicine, highlighting specific constraints accompanying their development in the healthcare industry (chapter 3 Proteins as therapeutics). After a description of current production processes, I focus on stability issues of therapeutic proteins. Interfacial stress that proteins undergo during production and delivery is highlighted in particular. Chapter 4 (Proteins at interfaces) addresses the state of the art knowledge on protein activity at interfaces. Their adsorption, unfolding and aggregation at solid-liquid, liquid-liquid and liquid-air interfaces is described with a particular insight on the parameters that affect these processes. A summary of the most used techniques that allow to study protein interfacial activity at the solid-liquid boundary is also presented.

This general context prepares the reader to appreciate the motivation and impact of our work on the stability of insulin at hydrophobic interfaces (solid and air). In Chapter 5, I chose to present three aspects of our research:

* the role of hydrophobic surfaces in insulin amyloid aggregation

* the role of aggregative peptides in insulin aggregation at hydrophobic surfaces

* the role of a dynamic triple interface in insulin amyloid aggregation

The vast majority of the results presented in this chapter were obtained by four PhD students (Thomas Ballet, Laurent Nault, Karim Chouchane and Thibaut Frachon) which I supervised together with Franz Bruckert. I acknowledge also the contribution of Charlotte Vendrely (Associate Professor Univ Cergy-Pontoise, Errmece lab, visiting researcher at LMGP) and Ugo Hevin (technician) as well as numerous students who contributed during internships and
project work. The final chapter presents my current research project (Chapter 6) on the adsorption behaviour of monoclonal antibodies and surfactants at solid interfaces in the context of formulation optimisation. The experimental results of this work have been obtained by Guillaume Lefèbvre who is currently doing a Cifre PhD in collaboration with Sanofi. I conclude this manuscript with an outlook on the development of new approaches to image protein adsorption at interfaces and propose my view on future work in this area.

## 2 Proteins and their stability

Proteins are large molecules that are essential for the survival of the cells of all known living organisms. They consist of a linear arrangement of amino acids linked together by peptide bonds between the carboxy terminal group of one amino acid and the amino terminal group of the next one (Figure 2-1).

<figure><img src="image_11.png" /><figcaption>Figure 2-1: The tetrahedral carbon of each amino acid is denoted Cα, the side chains are represented by a numbered R (residue) in green. Peptide bonds are planar and do not allow rotation. Phi and psi angles, defining rotational orientation around the Cα. Figure credits to Molecular Biology of the Cell 5th ed. (© Garland Science 2008)</figcaption></figure>

The sequence of amino acids in a protein is defined by the nucleotide sequence of its corresponding gene. The standard genetic code encrypts 20 amino acids that differ by their side chain and are used by most living species. A gene is transcribed on demand by RNA polymerase, which synthesizes an RNA molecule (transcription) that contains the information necessary to assemble the amino acids of the corresponding protein. This RNA transcript is used by the ribosome to manufacture the protein, catalysing the peptide bond formation between individual free amino acids (translation) and assembling them like beads on a string.
Proteins were identified and studied as early as the mid 19th century but the difficulty to purify them in large enough amounts hampered detailed scientific studies until the 20th century. Insulin became the first protein to be sequenced by Frederick Sanger in 1949 (Nobel prize 1958), thus establishing the linear arrangement of amino acids in proteins. This achievement was soon to be followed by the first structure determinations of hemoglobin and myoglobin in 1958 by Max Perutz and Sir John Kendrew (Nobel prize 1962). Today (Sept 2017) the protein data bank (PDB) has 133'277 entries of biological macromolecular structures including 41'843 distinct proteins and other biomolecules and the number of structural and functional data increases daily (Figure 2-2).

<figure><img src="image_12.png" /><figcaption>Figure 2-2: The reviewed protein sequence number in the Swiss-prot data base over the time. Note that in 2010, the data base was reorganized, which explains the lower entry number increase since 2010. Figure taken from http://www.uniprot.org/statistics/Swiss-Prot on Aug 30th 2017)</figcaption></figure>

Proteins have been used by mankind long before they were identified as such, for nutritious purposes of course (plant and animal protein) but also for diverse manufacturing processes (fermentation, paper and leather fabrication). Nowadays their use bridges from the food industry to medicine, and they are omnipresent in our daily life in their natural or in a modified (recombinant) form to wash our cloths efficiently, transform our food or cure diseases.
## 2.1 Protein structure description

The linear arrangement of amino acids in a protein is described in its primary sequence. Many proteins enter a spontaneous folding process starting at their exit from the ribosome. This folding process is driven by thermodynamically favourable interactions between the atoms composing the amino acids and the water molecules surrounding the protein.

Four levels of folding are described starting from the primary sequence cited above. The secondary structure level is defined by characteristic local interactions between atoms of neighbouring amino acids (Figure 2-3). The most common secondary structure elements are alpha helices, parallel and antiparallel beta sheets and turns. They are stabilized by hydrogen bonds between the atoms (mainly N and O) of the peptide main chain. The constraints imposed by the planar nature of the peptide bond (Figure 2-1) and the steric limitations due to the L-isomer selectivity in naturally occurring amino acids, guide folding, together with the maximization of stabilizing interactions. The tertiary structure describes the global 3D arrangement of the protein amino acid chain and results in the so-called native or folded conformation (Figure 2-3). It is stabilized by interactions between distant amino acid atoms, such as electrostatic forces, van der Waals forces, hydrogen bonds and the exclusion of water molecules (hydrophobic interactions). Disulfide bonds between cysteine amino acids can stabilize the structure in oxidizing conditions. The tertiary structure is of critical importance because it generally ensures the biological activity of the protein. A loss of tertiary structure almost always leads to a loss of the protein function. The final structural level is the quaternary structure that describes the spatial arrangement of proteins composed of several polypeptide subunits (Figure 2-3).

<figure><img src="image_13.png" /><figcaption>Figure 2-3: Levels of protein structure. Figure taken from Lehninger Principles of Biochemistry, 3rd ed. (© Worth Publishers Inc. 2000)</figcaption></figure>
Protein folding occurs within milliseconds and is mostly spontaneous but takes place in a highly crowded, complex, cellular environment. Molecular chaperone proteins, some of which are associated to ribosomes, help the folding process. Being present in all cellular compartments, they are the warrants of proteostasis, regulating protein folding, unfolding and turnover¹,².

## 2.2 Folding for function, yes and no

Protein folding is dictated by energetics and thermodynamics and they have the built-in capacity to fold spontaneously as evidenced by Christian Anfinsen in his groundbreaking work on folding/unfolding of ribonuclease³. Soluble proteins that fold in an aqueous environment therefore tend to form globular structures with non-polar, hydrophobic cores and hydrophilic shells, thus maximizing the interactions with the surrounding water molecules. Membrane proteins, like porins for example, adopt the reverse order, presenting hydrophilic centres and hydrophobic shells embedded within the lipid bilayer in the membrane. The main forces contributing to the interactions between protein and solvent atoms in the folded molecule are electrostatic, van der Waals and hydrophobic interactions as well as hydrogen bonding.

Proteins fold within milliseconds and this timescale does not allow a folding process based on a random exploration of all possible folds. It is believed today that folding efficiency is based on a limited ensemble of possible folding pathways that progressively narrows down like a funnel. The folding process starts at the top of the funnel from energetically disfavourable unfolded states and proceeds downwards via energetically more favourable intermediates towards a stable native fold at the end of the funnel (Figure 2-4).

The search for a free energy minimum depends on a multitude of weak interactions and thus, for every protein, there are several potential routes down the folding funnel involving different, partially folded intermediates with more or less native-like conformations. Stable key-residue interactions bias the population of all possible intermediate folding states, thereby paving the route to the final native state⁴. From the energetic point of view, stable folds are not limited to native (i.e. functionally active) states, but include different aggregated states (Figure 2-4), generally associated with a loss of function or even with disease (see 2.7
Amyloid aggregation and disease). In some cases, however, the aggregate can also be functional and this is especially true for the amyloid state⁵.

In the ordered structure of the native conformation of a protein the atoms are not rigid but are more or less flexible according to their position. This small amplitude dynamics is associated with functional activity, for example in the active site of enzymes or in the paratope of an antibody. Despite such local movement, the overall integrity of the native conformation remains conserved and numerous proteins are therefore able to crystallize, allowing the determination of their 3D structure by X-ray scattering. The field of protein structure determination was largely guided by this approach for the first 50 years of its existence.

<figure><img src="image_14.png" /><figcaption>Figure 2-4: Scheme representing the funnel-shaped energy landscape that proteins traverse during their folding process. The native state and stable oligomer or aggregate states are represented as local energy minima. Chaperones are involved in folding or unfolding partially folded states. Figure credits to¹</figcaption></figure>

Yet, there is another class of proteins, described since the late 1990's as intrinsically disordered proteins (IDPs), which have no stable 3D conformation but they function as an ensemble of highly dynamic interchangeable conformational states⁶. Thus the "sequence-structure-function" paradigm, established 100 years earlier by Emil Fischer⁷, has been largely reviewed in recent years. IDPs come in a variety of "formats" and can be completely unstructured or contain parts of varying lengths (loops, linkers, usually >50 residues) that do
not adopt a unique fold and are connected (or not) to folded domains. Disorder predicting
algorithms estimate that around 30% of all eukaryotic proteins are at least partially
disordered⁸ and IDPs are involved in a plethora of biological functions serving as scaffolding
hubs as well as in regulatory processes. A common feature of IDPs is that their binding to
partner molecules (proteins or nucleic acids) is often accompanied by a transient folding of
the corresponding binding region. The underlying mechanisms are still in debate but are likely
to be based on conformational selection (folding before binding) or induced fit (folding after
binding) or combinations/compromises of these two alternatives⁹. The lack of a unique stable
structure can provide functional advantages that take all their biological sense in the high
complexity environment of a eukaryotic cell. For example, coupling high specificity and low
affinity may allow faster switching between on- and off-states in regulation processes¹⁰.

Thus proteins are macromolecules that use the entire folding space from completely unfolded
to highly compact to execute their functions.

## 2.3 Native state, amorphous or highly ordered aggregate states

The folding process of a protein includes many different conformational states before
attaining its native (= functional) fold. The latter and those corresponding to intermediate
folds represent local free energy minima separated by energy barriers (Figure 2-4). Different
folding states coexist in the cell and the population of each of them is defined by the rates of
converting one into the other (Figure 2-5). On top of their individual thermodynamic stability,
chaperones, posttranslational modifications, as well as synthesis and degradation rates rule
the existence and lifetime of different protein conformational states. In the crowded
environment of the cell (average protein to protein distance < 100Å), protein folding/not
folding can be unproductive and lead to off-pathway aggregates, which are generally
unordered. These are taken care of by chaperones, which assist their refolding or by the
protein degradation machinery (Figure 2-5).

Highly ordered structures, like the amyloid fibre, arise when intermolecular interactions
become dominant. Because of its multimeric nature, the accumulation of the amyloid state is
thermodynamically dependent on the protein concentration, whereas the free energy
associated with the native state of a protein is concentration independent. Therefore, a critical
concentration can be defined beyond which the amyloid state is more stable than the native state of a protein. For Aβ42, involved in Alzheimer's disease, a critical amyloid concentration (cac) has been determined recently *in vitro* in physiological conditions¹¹. At a cac of around 90 nM (concentration interval 60-120 nM), the fraction of aggregated Aβ42 (early oligomers) rises sharply. These concentrations are compatible with the physiological concentrations of Aβ, which are in the pico- to nanomolar range, taking into account their strong tendency to adsorb at interfaces inducing local accumulation¹².

Proteins thus exist in a so-called kinetically metastable native fold as long as the kinetic barrier existing between the native and amyloid states keeps them there¹³. Chaperones for example, have evolved to be the guardians of these kinetic barriers (Figure 2-4). In a pathological situation this kinetic barrier is broken and amyloid proteins accumulate.

<figure><img src="image_15.png" /><figcaption>Figure 2-5: Different possible folding states of a protein are shown. Chaperones, posttranslational modifications, synthesis and degradation rates tune the inter-conversion rates between existing states. Figure adapted from²⁹.</figcaption></figure>
## 2.4 The amyloid state

The amyloid state can in principle be adopted by any protein because it emanates from the tendency of polypeptides to form hydrogen bonds between backbone atoms and the fact that these can be most efficiently packed in extended beta sheets¹⁴. However, topological constraints make the packing into a fibril structure more difficult for polypeptide chains longer than 150 amino acids¹³. The average protein length is between 300-500 amino acids, which reflects the fact that evolution has limited the access to the amyloid state by adapting protein size (the other limitation being concentration). In this context it is also worth noticing that most amyloid proteins associated with disease are rather small peptides or proteins (Table 2-1).

<figure><img src="image_16.png" /><figcaption>Figure 2-6: Structural model of the beta sheet spine of the 40-residue beta amyloid peptide (Abeta 1-40) fibril associated with Alzheimer's disease. A The red arrow indicates the fibre axis. The inter beta sheet distances in the fibre axis (4.7 Å) and in the perpendicular axis (variable) are indicated. B Lateral packing in the Abeta 1-40 fibre. Figure drawn with Protein Workshop from PDB entry 2LMN using 6 (A) and 12 (B) peptide chains.</figcaption></figure>

The amyloid structure is highly ordered and defined by a continuous, repetitive stack of beta sheets, separated by 4.7 Å, that runs along the fibril axis (Figure 2-6). This inter beta sheet distance is common to all amyloid fibrils and independent of the protein sequence. The resulting twisted beta sheet stack, also known as the cross beta spine, has a distinguished X
ray diffraction pattern, first described in 1968¹⁵. Several such beta sheet stacks are then
arranged side by side involving side chain interactions. The beta sheet spacing in the axis
perpendicular to the fibril is variable, depending on the packing constraints of the side chains
involved.

Beyond the cross beta diffraction pattern, amyloid fibrils share several common
characteristics: binding of the dyes Congo Red and Thioflavin T, a characteristic green-yellow
birefringence of Congo Red and unusual stability. Their formation follows a lag-dependent
cooperative kinetics and they show self-seeding properties (see 2.5 Kinetics of amyloid fibre
formation). Amyloid fibrils, also called protofibrils, appear as unbranched structures that are
generally a few nanometers wide but several micrometers long (Figure 2-7A). They present a
left-handed helical twist. Hydrogen bonding between backbone atoms of adjacent beta sheets
give the fibre extreme stability, a high Young's modulus and remarkable tensile strength¹⁶. The
mechanical properties of amyloid fibres are also a consequence of the association of several
protofibrils that wind around each other to form twisted ribbons, tubes, tapes or ropes. This
fibre diversity is mainly due to the sequence of the amino acid side chains which are involved
in the lateral association of protofibrils¹⁷. The twist in the protofilaments can accompany the
left-handed twist of the fiber such that the interactive sections between the protofilaments
are constant. Such a "correlated twist" has been documented for insulin amyloid fibres¹⁸ and
fibres of other proteins (e.g. Sup35¹⁹). It has also been shown that fibres formed by the same
protein (insulin, for example) contain different numbers of protofibrils (2, 4 or 6) and different
spatial arrangements (Figure 2-7B). This polymorphism takes its roots at least in part in the
assembly conditions that influence the lateral packing of protofilaments via protruding side
chains.
<figure><img src="image_17.png" /><figcaption>Figure 2-7: Representations of amyloid fibres. A Electron microscopy image of fibers of the islet amyloid polypeptide associated with type2 diabetes. B 3D reconstitution maps showing the polymorphism of insulin amyloid fibres with 2 (a), 4 (b) or 6 (c) protofibrils wound around each other and a twisted ribbon (d). Figure credits to<sup>17</sup> (A) and <sup>18</sup> (B).</figcaption></figure>

## 2.5 Kinetics of amyloid fibre formation

The transition from the native to the amyloid state involves a conformational change leading to the establishment of the cross beta sheets, observed in the fibril spine. This conversion can be achieved through two mechanisms: nucleation or seeding.

The kinetics of amyloid fibre formation follows a typical sigmoidal reaction time course, characteristic of nucleated polymerization (Figure 2-8). At the molecular level, the lag phase translates the formation of different oligomeric states in the creation of an aggregative nucleus (primary nucleation). Such oligomers result from intermolecular interactions of at least partially unfolded monomers. This process is inherently very difficult to study because it is based on dynamic multi-component equilibria. Elegant microfluidic approaches have been developed to follow the elusive early stages of amyloid aggregation. In confined microdroplets single primary nucleation events can be detected and their subsequent spatial as well as temporal evolution monitored<sup>20</sup>. When a critical size is reached, soluble monomers are readily incorporated into the primary nuclei. During the elongation phase, fibrils start to form from primary nuclei by successive incorporation of soluble monomers. Seeding with preformed fibrils can bypass nucleation and directly lead to fibril elongation. Secondary nucleation events
drive the growth of the fibrils (Figure 2-8). Examples of well described secondary nucleation mechanisms include fibril fragmentation and fibril- or material surface-triggered nucleation. These contribute considerably to the kinetics of amyloid aggregation²¹,²². Fibril growth is limited by the availability of soluble monomers and typical kinetic curves thus present a plateau phase, when no free monomers remain in solution.

<figure><img src="image_18.png" /><figcaption>Figure 2-8: Typical nucleation-dependent polymerization model of amyloid fibril formation. During the lag phase, monomeric forms of the amyloid-forming protein self-aggregate into soluble oligomers which further transform into nuclei (nucleation). The nuclei are extended rapidly during the elongation phase, and fibrils grow. Fibrillation can reach a plateau phase when the soluble monomers have all been integrated into fibers. Figure adapted from¹⁶⁷.</figcaption></figure>

## 2.6 Misfolding and Aggregation in vivo

A misfolded intermediate, occupying a local free energy minimum (Figure 2-4), can become a "trap" on the folding route of the protein. In general, misfolding exposes protein regions that are normally buried, to the solvent, where they can potentially interact with neighbouring molecules to which they normally would not bind. Such interactions give rise to the formation of aggregates, which can be unorganized (amorphous), or highly ordered, like the amyloid fibre (Figure 2-4).

The amino acid sequences of IDPs have evolved to avoid aggregation and stay soluble for function. Indeed they are intrinsically enriched in charged and polar amino acids, thus avoiding
intermolecular association²³. Nevertheless many of the most common misfolding diseases involve peptides or proteins that are intrinsically disordered in their soluble state (Table 2-1).

*In vivo*, four conditions that are associated with the formation and accumulation of protein aggregates have been identified: mutations, translational errors, environmental stress and ageing, all of them resulting in a higher propensity for the protein to misfold²⁴. The cell therefore deploys sophisticated strategies to maintain proteostasis and avoid an accumulation of protein aggregates: chaperones assist folding and keep misfolding under control and proteases together with the proteasome degrade incorrectly folded proteins²⁴. Beyond this first line of defence, the cell is able to sequester accumulated protein aggregates in specific compartments, thereby minimizing possible toxic outcomes mediated by their interactions. Inclusion bodies in bacteria and the mammalian aggresome illustrate this strategy²⁵. Autophagy has also been shown to participate in the clearance of accumulated protein aggregates²⁶.

The Panther geneontology software lists 172 hits in the chaperone class for *Homo sapiens* and 14 for *Escherichia coli* (http://pantherdb.org/ consulted on September 14th 2017). These findings illustrate that both pro- and eukaryotic cells deploy considerable efforts to overcome protein aggregation-related issues. It seems to be the price to pay for the intricately complex structure-function relationship that proteins have adopted to execute life. It also follows that when the cell protection is outrun and aggregates accumulate too much, the cell dies and disease follows for the affected organ.

In this context one should not forget that partial unfolding of a protein is not necessarily detrimental but sometimes necessary, for example during translocation across a membrane. Such programmed unfolding is often assisted by chaperones²⁷.
## 2.7 Amyloid aggregation and disease

The interest in amyloid aggregates has rapidly developed in recent years due to their association with a number of incurable diseases that challenge modern societies with ageing populations on a global basis²⁸. Alzheimers's disease and Type II diabetes are among the most prevalent. The World Health Organization (WHO) has issued an action plan to tackle dementia, of which Alzheimers's disease accounts for 60-70% of the cases (http://www.who.int/mental_health/neurology/dementia/action_plan_consultation/en/). Today 45 million people have dementia and this number will increase as the number of people aged >60 years is predicted to rise from 900 million to 2 billion worldwide between 2015 and 2050 (http://www.who.int/features/factfiles/ageing/en/, consulted in Sept 2017). Type II diabetes is another amyloid-related disease (>400 million patients worldwide) linked to age and lifestyle in modern societies.

<table><caption>Table 2-1: Selection of a number of human diseases associated with protein misfolding and amyloid aggregation. Credits to ²⁹.</caption><thead><tr><th>Disease</th><th>Aggregating protein or peptide</th><th>Polypeptide length (number of residues)</th><th>Structure of protein or peptide</th></tr></thead><tbody><tr><td colspan="4"><strong>Neurodegenerative diseases</strong></td></tr><tr><td>Alzheimer's disease</td><td>Amyloid-β peptide</td><td>37-43</td><td>Intrinsically disordered</td></tr><tr><td>Spongiform encephalopathies</td><td>Prion protein or its fragments</td><td>230</td><td>Intrinsically disordered and α-helical</td></tr><tr><td>Parkinson's disease</td><td>α-synuclein</td><td>140</td><td>Intrinsically disordered</td></tr><tr><td>Amyotrophic lateral sclerosis</td><td>Superoxide dismutase 1</td><td>153</td><td>β-sheet and Ig-like</td></tr><tr><td>Huntington's disease</td><td>Huntingtin fragments</td><td>Variable</td><td>Mostly intrinsically disordered</td></tr><tr><td>Familial amyloidotic polyneuropathy</td><td>Transthyretin mutants</td><td>127</td><td>β-sheet</td></tr><tr><td colspan="4"><strong>Non-neuropathic systemic amyloidosis</strong></td></tr><tr><td>Amyloid light chain (AL) amyloidosis</td><td>Immunoglobulin (Ig) light chains or its fragments</td><td>~90</td><td>β-sheet and Ig-like</td></tr><tr><td>Amyloid A (AA) amyloidosis</td><td>Serum amyloid A1 protein fragments</td><td>76-104</td><td>α-helical and unknown fold</td></tr><tr><td>Senile systemic amyloidosis</td><td>Wild-type transthyretin</td><td>127</td><td>β-sheet</td></tr><tr><td>Haemodialysis-related amyloidosis</td><td>β₂-microglobulin</td><td>99</td><td>β-sheet and Ig-like</td></tr><tr><td>Lysozyme amyloidosis</td><td>Lysozyme mutants</td><td>130</td><td>α-helical and β-sheet</td></tr><tr><td colspan="4"><strong>Non-neuropathic localized amyloidosis</strong></td></tr><tr><td>Apolipoprotein A1 (Apo A-1) amyloidosis</td><td>Apo A-1 fragments</td><td>80-93</td><td>Intrinsically disordered</td></tr><tr><td>Type II diabetes</td><td>Amylin</td><td>37</td><td>Intrinsically disordered</td></tr><tr><td>Injection-localized amyloidosis</td><td>Insulin</td><td>21 and 30</td><td>α-helical and insulin-like</td></tr></tbody></table>

Today the list of amyloid-associated diseases comprises around 50 disorders, some systemic, some localized, associated with neurodegeneration or not (Table 2-1). The proteins associated with these show no particular sequence similarity, nor are they related by their native structure or function. Remarkably they all share the same, highly ordered, cross beta sheet structure, indicative of the amyloid fibril in their aggregated state (see 2.4 The amyloid state).
Why the accumulation of amyloid aggregates causes cell death is not clear. In systemic amyloidoses the sheer quantity of accumulated aggregates in vital organs causes organ failure but in neurodegenerative diseases, there is no particular correlation between the quantity of fibre deposits and the pathological stage of the disease³⁰. Therefore it is likely that intermediate oligomers and not the final deposits interfere with vital functions in the cell. Many *in vitro* studies point to the toxicity of prefibrillar oligomeric intermediates, that lead to increased membrane permeability, collapsed ion homeostasis and oxidative stress³¹. It is likely that exposed hydrophobic stretches, mostly present in relatively disordered oligomers that precede fibril formation, lead to deleterious inappropriate interactions³². The term "protein metastasis" has been coined to describe the molecular mechanisms contributing to the conversion of a functional soluble proteome into self-propagating aggregates that trigger cytotoxicity²⁹. Therapeutical strategies tackling the inter conversion rates of the different conformational states of the proteome -soluble, oligomeric intermediates, amyloid fibrils- are the most promising to maintain cellular proteostasis and cure amyloid diseases. A breakthrough in this direction has already been made with tafamidis, a transthyretrin tetramer kinetic stabilizer that slows disease progression in Transthyretin Type Familial Amyloid Polyneuropathy patients³³.

### 3 Proteins as therapeutics

In the last 50 years molecular and cell biology have allowed tremendous progress in our molecular understanding of disease. Together with genetic engineering and the industrial development of biotechnology, a new class of drugs, called biopharmaceuticals, has emerged. Synonyms for "biopharmaceuticals" are also "biotherapeutics," "biologicals" or "biologics". The production of these molecules through genetic recombination and cell expression has immensely improved their purity and overall quality over previous methods of extraction and purification from animal or plant tissues. Diagnosis and therapy have thus entered the age of target molecules with precise biological function that can be tuned to purpose.

Human insulin was the first biopharmaceutical to be produced from bacteria and marketed by Eli Lilly in 1982 (Eli Lilly acquired the licence from Genentech). Since then numerous other biomolecules are industrially manufactured from living cells, with proteins being the most
important class. Among the 10 most sold pharmaceuticals in 2016, so-called blockbusters, five are proteins and four of those are monoclonal antibodies (Table 3-1).

<table><caption>Table 3-1: The 10 most sold biopharmaceuticals in 2016 as compiled by Quintiles IMS for "Les entreprises du médicament" (LEEM). Accessible at http://www.leem.org/article/marche-mondial-1; * note that Enbrel is a fusion protein containing only the Fc fragment of an IgG1.</caption><thead><tr><td>Product</td><td>Company</td><td>Therapeutic class</td><td>Protein/ mAb</td><td>Global market share in 2016 (%)</td></tr></thead><tbody><tr><td>Humira</td><td>Abbvie</td><td>Rheumatoid arthritis</td><td>✓/✓</td><td>2.0</td></tr><tr><td>Harvoni</td><td>Gilead Sciences</td><td>Hepatitis C</td><td></td><td>1.8</td></tr><tr><td>Lantus</td><td>Sanofi</td><td>Diabetes</td><td>✓/✓</td><td>1.4</td></tr><tr><td>Enbrel*</td><td>Pfizer</td><td>Rheumatoid arthritis</td><td>✓/✓</td><td>1.1</td></tr><tr><td>Remicade</td><td>MSD</td><td>Rheumatoid arthritis</td><td>✓/✓</td><td>0.9</td></tr><tr><td>Seretide</td><td>GSK</td><td>Asthma</td><td></td><td>0.8</td></tr><tr><td>Xarelto</td><td>Bayer</td><td>Thrombosis</td><td></td><td>0.8</td></tr><tr><td>Mabthera</td><td>Roche</td><td>Lymphoma</td><td>✓/✓</td><td>0.7</td></tr><tr><td>Crestor</td><td>Astra Zeneca</td><td>Cholesterol</td><td></td><td>0.7</td></tr><tr><td>Lyrica</td><td>Pfizer</td><td>Epilepsy</td><td></td><td>0.7</td></tr></tbody></table>

The total annual sales of all monoclonal antibody products was around 75 billion USD in 2013³⁴ and the 2016 sales of Humira was estimated to 16 billion USD alone. This reveals today's trend for the development of therapies based on proteins and in particular on antibodies.

## 3.1 Unique assets and the price to pay

The first reason why proteins are valuable therapeutics is because of their unparalleled interaction specificity. Indeed, this has allowed new and more effective therapeutic solutions for instance in autoimmune diseases such as arthritis, which for decades could only be treated symptomatically. The mAb-based therapeutic Humira targets specifically the tumor necrosis factor alpha (TNFα), thus providing a directed, molecularly relevant therapeutic impact.
This specificity property is tightly linked to their three-dimensional structure, which is inherently labile and sensitive to the physico-chemical conditions of their environment (see 2 Proteins and their stability).

A second reason that makes proteins and in particular antibodies very attractive for therapy is the fact that they can be engineered to combine particular and multiple functions. Indeed, genetic recombination and molecular biochemistry allow to fuse protein domains with different functional properties. Examples of such constructs are antibody-fusion proteins, antibody-drug conjugates, bispecific antibodies and glyco-engineered antibody variants. A recent review on the state of the art of engineered antibodies can be found in reference ³⁵.

Most traditional drugs are small organic compounds with structures based on covalent bonds between their atoms. Paracetamol for example has the general formula $C_8H_9NO_2$ and a molecular mass of 151g/Mole (Figure 3-1). They are therefore very stable and suitable for various production processes and administration formats. They are mostly produced by chemical synthesis from precursors and their production methods are based on decade-old robust protocols.

<figure><img src="image_19.png" /><figcaption>Figure 3-1: Comparison of an IgG antibody (150'000 g/mol) on the left and paracetamol (151 g/mol) on the right. Structures are not drawn to scale.</figcaption></figure>

Proteins are made generally of several hundred amino acids (average molecular weight of an amino acid: 110 g/Mole), illustrating their inherent complexity. Beyond the number of atoms, the three-dimensional conformations that proteins adopt is based on a particular combination of weak forces, like van der Waals interactions, electrostatic interactions, hydrogen-bonding or the hydrophobic effect. Their stability is optimized for their *in vivo* activity and tolerates only small changes of the environmental conditions (see chapter 2 Proteins and their stability).
## 3.2 The challenges of protein production

The manufacturing process for therapeutic proteins is far more complicated than the chemical synthesis of organic compounds. They are expressed in and purified from live cells, once their genetic information has been cloned and introduced to a host cell. Bacteria, yeast, insect and mammalian expression systems are used, the latter being indispensable for the production of posttranslationally-modified proteins (e.g. glycosylation) which cannot be achieved in bacteria. The majority of monoclonal antibodies for example is expressed in Chinese hamster ovary (CHO) cell systems. Transformed strains and cell lines are stored in frozen cell banks and transferred into nutrient medium for culturing. The following steps are necessary for the production of therapeutic proteins: sequential culture in increasing volumes (bioreactor fermentation), purification (low and high resolution), formulation and quality control (Figure 3-2).

<figure><img src="image_20.png" /><figcaption>Figure 3-2: Flowchart of protein production manufacturing processes. Figure credits to ³⁶</figcaption></figure>

In virtually every one of the above steps, efforts are made towards automated or even disposable platforms in order to optimize cost effectiveness and product quality. These two main goals also drive implementation efforts towards continuous culturing (as opposed to batch culturing) in the pharmaceutical industry. Because continuous processes are characterized by a high inherent reproducibility, they guarantee constant product quality³⁷.
Proteins being inherently not very stable, it is crucial to monitor the state of the molecules
throughout the manufacturing process and to guarantee a final product quality. Indeed,
during the production processes, proteins are exposed to many stress factors that can lead to
quality losses:

* environmental factors such as pH, temperature, ionic strength or high concentration

* chemical modifications such as deamidation, hydrolysis or oxidation

* mechanical factors such as shear stress

* interfacial stress at the liquid-solid, liquid-liquid and liquid-air boundaries

Figure 3-3 below shows a compilation of the different factors that can impact protein quality along the manufacturing and delivery pipeline. It appears that many of these factors involve interfaces between the protein solution and either a solid material, the air or a second distinct liquid phase.

The processing steps, leading to the final drug product, contain several critical operations that are particularly prone to impact protein stability at interfaces. Here I briefly discuss the most prominent ones: freeze-thawing, filtration, filling and lyophilisation.
<figure><img src="image_21.png" /><figcaption>Figure 3-3: Chart presenting different production and delivery processes for therapeutic proteins and the factors that can affect product quality. Figure credits to ⁴¹</figcaption></figure>
## 3.3 Processes inducing interfacial stress during protein production

### Protein freeze-thawing

Freezing a drug production bulk provides operational flexibility but can also lead to severe stability issues due to cryoconcentration. An uncontrolled movement of the freeze-front provokes exclusion of the solutes from the ice-liquid interface and thus leads to a concentration build-up in the liquid, yet to be frozen. This can affect protein stability directly or indirectly by buffer components that are also sensitive to this process³⁸. The extent of cryoconcentration is especially high when the freeze front velocity is slow. Besides cryoconcentration, so called "cold denaturation", resulting from the decrease of water density and therefore the solvent-excluded volume effect at low temperatures, can unfold proteins locally and lead to aggregation³⁹. Thawing of a bulk protein solution can further enhance protein stability issues initiated by cryoconcentration, and recrystallisation has to be minimized by appropriate mixing. Specific technologies have therefore been marketed (e.g. Celsius Pak) that allow controlling, and generally accelerating, the freeze-thawing rates in order to minimize protein stability issues⁴⁰.

### Protein filtration

Filtration of a bulk protein solution through a 0.22 µm filter ensures that all viable microorganisms are eliminated. Besides the pressure stress applied to the solution to pass the filter pores, selective binding of the protein (or formulation components) to the filter membrane is often observed⁴¹⁻⁴³. Typically, cellulose acetate or polyvinylidene difluoride (PVDF) materials are used for their low protein binding properties combined with strong mechanic properties and/or chemical resistance. The adsorption loss but also the increased propensity of unfolding and aggregation upon binding to the filter membrane has to be considered during protein production.

### Protein filling and storage in liquid state

The therapeutic protein formulations are filled into so called primary containers for storage, transportation and delivery. These containers are usually glass vials or syringes, with associated stoppers, plungers and closing devices. Leachables and extractables from container surfaces are known to impact protein stability⁴⁴. Siliconized material parts are particularly prone to adsorb proteins and to release silicon oil droplets into the solution, on which proteins
adsorb and aggregate⁴⁵. The filling protocols can introduce high shear rates, which affect the proteins particularly at the interfaces with the container materials, the silicon or the air⁴⁶.

## Protein lyophilization

Due to chemical or physical instability, protein therapeutics are often lyophilized to increase their shelf life. This procedure consists in the removal of most of the water, in the absence of which many covalent degradation phenomena are impossible and diffusion-based aggregation is seriously limited. A lyophilization cycle consists in freezing, annealing, primary and secondary drying phases. The destabilizing effects discussed for the freezing of a protein solution also apply here. The ice is removed by sublimation during the primary drying and residual water is then removed by carefully increasing the temperature. This residual water is often essential for the structural integrity of the protein as hydrogen bonds between water molecules and protein amino acids stabilize the native structure. Therefore excipients are added (e.g. sucrose) to substitute for the loss of stabilizing hydrogen bonds.

## 3.4 Processes inducing interfacial stress during protein delivery

The vast majority of protein-based therapeutics has to be delivered through the blood or lymph system. They cannot be taken orally because of their degradation and inconsistent adsorption throughout the digestive system. This is the reason why they are usually administered either intravenously or subcutaneously using an injection device. The subcutaneous injection is favoured for chronic disease treatment (e.g. diabetes) because the patient can do it autonomously and at home. However, the volume that can be injected under the skin is limited to about 2 mL and thus a specific subcutaneous dosage, that takes this constraint into account, has to be developed. In general, intravenously injected therapeutics are efficient only at very high doses (several tens of mg.mL⁻¹). Indeed, in cancer therapy the tumor penetration of therapeutic mAbs is rather poor (0.01% of the applied dose)⁴⁷. High therapeutic doses have two main drawbacks with immediate implications on their stability before and during delivery: an increased propensity for aggregation and high viscosity⁴⁸. Both are a direct consequence of the small intermolecular distance that exists in highly concentrated formulations.
Before administration, lyophilised products have to be reconstituted which can add a number of stress factors such as interactions with surfaces in pumping devices, cavitation and shear stress⁴⁹. Because of the elevated viscosity the glide force needed to inject the therapeutic solution subcutaneously is increased⁵⁰. Injection devices such as prefilled syringes are therefore lubricated with silicon oil which has been shown to affect protein stability⁵¹. Indeed, silicone oil droplets that shed into solution present a liquid-liquid interface to which therapeutic proteins can adsorb. This adsorption has been shown to lead to aggregation in numerous cases ⁵²⁻⁵⁴.

## 4 Proteins at interfaces

Interfaces are defined as the boundaries between different matters or matters in different states, such as the solid-liquid, liquid-liquid and liquid-air interfaces. Protein interactions with these interfaces have been studied widely and have numerous practical applications and implications in different economic sectors (medicine, food industry, environment etc). For example, surface-adsorbed protein layers are used by cells to adhere, a process which can be either desired (e.g. cell colonization on scaffolds in tissue engineering) or not (e.g. biofouling of ship hulls). Due to the diversity of their chemical groups and their amphiphilic character, proteins are able to interact with a wide range of surfaces and interfaces. Here I focus on protein interfacial activity in the context of therapeutic protein production, storage and delivery as active molecules.

For therapeutic proteins, which are active in solution, the interfaces of interest are those that delimit this solution. In the majority of the cases, the interface with solid materials and the interface with air as well as the triple interface where solid, liquid and air meet are critical. They are almost always unavoidable and are present for example in a glass vial as represented on Figure 4-1.
<figure><img src="image_22.png" /><figcaption>Figure 4-1: A glass vial containing a therapeutic protein solution and an air headspace is shown on the left. On the right, the liquid-solid interface is highlighted in blue, the liquid-air interface in red and the triple interface in green.</figcaption></figure>

The liquid-liquid interface, which occurs when two immiscible liquids are in contact with each other, also plays a major role in the context of therapeutic proteins. Indeed, pharmaceutical emulsions of protein droplets in organic solvents or encapsulation within liposomes represent examples of novel delivery strategies in an effort to optimize pharmacokinetics⁵⁵. On the other hand, droplets of silicon oil, which is widely used as a lubricant in delivery devices, are known to act as a liquid-liquid interface, affecting protein stability ⁵¹,⁵⁶.

The phenomena occurring at interfaces can be divided into 4 steps (Figure 4-2):

* Diffusion

* Adsorption/desorption

* Unfolding

* Rearrangements

<figure><img src="image_23.png" /><figcaption>Figure 4-2: Schematic representation of the different steps in protein adsorption at the liquid-air interface.</figcaption></figure>

Diffusion, adsorption/desorption and unfolding are observed for all interfaces, liquid-solid, liquid-liquid and liquid-air. Rearrangements (relaxation, reorientation) of proteins at solid interfaces can be triggered at high surface density⁵⁷. By contrast with solid interfaces, liquid
and air interfaces are flexible and can be compressed and dilated. Such interfacial surface variations can promote aggregation and affect the local density of adsorbed proteins by lateral rearrangements in the interfacial protein network. In the following chapters protein adsorption and unfolding processes are presented with particular emphasis on solid interfaces, which are the most studied. Rearrangements of adsorbed protein networks are presented with their effects on protein aggregation. Finally particularities at liquid and air interfaces are discussed.

## 4.1 Protein adsorption, unfolding and rearrangements at solid interfaces

### 4.1.1 Parameters influencing protein interfacial activity

Protein adsorption is most studied at the solid-liquid interface. It is a very common phenomenon, in that most proteins are likely to adsorb on a solid surface whenever they meet one. The most important driving forces for protein adsorption on a surface are hydrophobic interactions, electrostatic interactions and internal structural rearrangements of the protein⁵⁸. The following parameters, amongst others, are influencing interfacial protein adsorption and in particular surface adsorption (Table 4-1).

<table><caption>Table 4-1: Selected parameters influencing protein adsorption. Table adapted from ⁵⁹</caption><tr><td>Solution</td><td>pH, ionic strength</td></tr><tr><td>Protein</td><td>size, shape, charge, pl, "hardness"</td></tr><tr><td>Surface</td><td>charge, hydrophobicity</td></tr></table>

As the **pH** determines the overall charge of the protein, it can promote or inhibit their adsorption rate, on charged surfaces. Protein-protein interactions (repulsions or attractions) are also depending on their charges and can stabilize or destabilize adsorbed proteins, affect their packing density at the surface and facilitate or hamper the adsorption of new proteins in close proximity at the surface. At the pl, where the net charge of a protein is zero, the highest packing densities are generally observed⁶⁰.

The **ionic strength** of a solution defines the distance (Debye length) at which electrostatic interactions are effective in an electrolyte solution. It thus influences attractive or repulsive interactions between charged proteins and surfaces, affecting the adsorption kinetics. In the
same way as pH, the ionic concentration of the solution can also influence the way proteins interact with their neighbouring protein molecules during adsorption on a surface⁶¹.

<figure><img src="image_24.png" /><figcaption>Figure 4-3: Schematic representation of the adsorption of a globular (A) and an elongated protein (B) on a solid surface. The elongated protein can adsorb in a flat (left) or upright (right) position, thereby influencing the packing density on the surface. Figure adapted from¹⁶⁸.</figcaption></figure>

The **size and shape** of proteins influences their diffusion speed. Small proteins, which arrive quicker at the surface, usually dominate early adsorption steps. In protein mixtures, larger proteins can replace smaller proteins at the surface during later stages of adsorption because their interaction surface is typically bigger and they therefore bind stronger. This phenomenon, known as the Vroman effect⁶², illustrates that protein adsorption at a solid surface can be competitive and pass through different phases with characteristic profiles of adsorbed species. The overall shape of a protein can be symmetric (globular proteins, e.g. albumin, insulin) or asymmetric (elongated, Y-shaped proteins, e.g. fibronectin, IgG) and they can both adsorb with different orientations on a surface. For asymmetric proteins in particular, their packing density at an interface is highly influenced by the orientation of the adsorbed proteins (Figure 4-3). Lateral rearrangements increasing protein-protein interactions can also change surface adsorption density.

Given the structural complexity of proteins, it is very difficult to find simple ways of predicting their adsorption behaviour at interfaces. An attempt to classify proteins into “hard” and “soft” ones, describing their propensity for structural modification upon surface binding was made by Arai and Norde⁶³. “Hard” proteins tend to adsorb on surfaces undergoing none or only small conformational changes, whereas more extensive structural reorientations accompany
surface adsorption of “soft” proteins. Because of their low internal stability “soft” proteins tend to adsorb well on both hydrophobic and hydrophilic surfaces, compensating the loss of hydration of the protein and the surface and electrostatic interactions by a gain in folding entropy. “Hard” proteins, on the other hand, tend to adsorb preferentially on hydrophobic surfaces and their adsorption is largely dominated by hydrophobic and electrostatic interactions.

Proteins tend to adsorb more strongly on hydrophobic than hydrophilic surfaces, on charged than on uncharged surfaces. In experimental studies **surface hydrophobicity and charge** are often tuned to reflect a given material of interest. This is commonly achieved by functionalisation using self-assembled monolayer thin films such as in silanisation of hydroxyl groups on glass or metal oxides. Functionalisations with polymers by dip- or spin-coating are also used. Protein adsorption behaviour is also studied on thicker polyelectrolyte multilayers, such as polyethylene-oxide or -glycol (PEO, PEG) brushes, for example. Such functionalisations have been shown to minimize protein adsorption through steric effects and excluded volume effects⁶⁴,⁶⁵. Finally, model lipid membranes can also be used to mimic protein adsorption at surfaces present in cells and tissues.

### 4.1.2 Adsorbed mass and surface coverage

Several techniques (see 4.4 Methods to characterize protein interfacial adsorption) are routinely used to measure adsorbed protein mass as a function of time or surface area. The results vary with the experimental setups (variable surface area, for example), the timescales and the sensitivities of the respective techniques and it remains challenging to compare different studies. In most cases, the irreversibly adsorbed surface coverage corresponds to a protein monolayer although stably adsorbed protein multilayers have also been documented⁶⁶. Very often repulsion between adjacent adsorbed proteins entail monolayer formation. Multilayers are typically found in conditions that reduce protein-protein repulsion and promote aggregation. Different models have been used to describe the adsorption/desorption behaviour and the surface coverage of proteins at a solid interface⁶⁷. The Langmuir model⁶⁸ (see Eq 1), initially developed for the adsorption of gas molecules,
considers adsorption, desorption and a limited surface area. Two important conditions underlying the Langmuir model however clash with protein adsorption at surfaces:

* first, the equilibrium hypothesis does not apply as desorption experiments generally lead to a residual, irreversibly bound protein fraction and desorbed proteins can potentially re-adsorb at the surface.

* second, the Langmuir model does not account for interactions between adsorbed molecules. The characteristics of proteins however clearly affect their interfacial activity such as the enormous variability of proteins in terms of size, structure and charge distribution, and their sensitivity to environmental conditions.

$$ \theta (c) = c / \left( c + \frac{k_d}{k_a} \right) \quad \text{Eq.1} $$

Eq 1: Langmuir isotherm with $\theta(c)$, the protein surface coverage as a function of concentration, c, the protein concentration, $k_a$ and $k_d$ the first order adsorption and desorption rate constants, respectively.

Experimentally, at high protein concentrations ($c \gg k_d/k_a$), surface coverage values are observed to be significantly lower than 1, which is accounted for by assuming randomly distributed surface binding sites. Indeed, in the random sequential adsorption model (RSA) protein binding sites present an irregular distribution on the surface such that, at saturation, proteins do not overlap and gaps are left which are too small to fit another protein. The maximum surface coverage predicted with RSA depends on the “footprint” of the protein (area covered by the adsorbed protein) and is assumed to be between 54 and 56%⁶⁹. Again this model is limited in that it does not consider desorption or protein rearrangements on the surface (see 4.1.3 Unfolding and rearrangements upon adsorption) that lead to significantly higher observed surface coverages. The final surface coverage seems to be a fine balance between the time it takes for proteins to adsorb at the surface (hence depending on the bulk concentration) and the time and surface available for each protein to establish a stable footprint (relaxation) on the surface⁷⁰. At high concentration, for example, when the surface is covered quickly, individual proteins do not have enough surrounding surface to maximize their footprint and the saturation level will therefore increase⁵⁸.

### 4.1.3 Unfolding and rearrangements upon adsorption

In solution, a protein is surrounded by a shell of water molecules that are associated with it and contribute to its stable conformation. Upon surface adsorption, proteins undergo rapid
conformational changes to increase the surface contact area and stabilize the adsorption by a maximisation of interaction forces with the surface (footprint)⁵⁸. For example, a protein will re-orientate segments with a positive charge to a negatively charged surface, thus promoting attractive electrostatic forces. On a hydrophobic surface, protein adsorption is accompanied by an entropy gain resulting from the release of surface-adsorbed water molecules and ions and turning previously buried hydrophobic side chains towards the surface⁵⁸. Such surface-induced re-orientations result in partial unfolding of the adsorbed proteins so that a stabilisation at the interface can be achieved. The protein-surface interactions that are created upon adsorption as well as the entropy gain from conformational relaxation contribute to a new free energy minimum of the adsorbed state of the protein. In general, structural changes in adsorbed proteins are more prominent on hydrophobic surfaces than on hydrophilic ones and vary according to the inherent stability of the protein. The consequence of this relaxation (unfolding and/or reorientation) is the establishment of an often irreversibly bound protein fraction (at least within the timeframe of most experimental approaches).

Beyond protein-surface interactions, protein-protein interactions also influence the final state of the adsorbed species⁶⁷. Lateral interactions via electrostatic forces gain importance as adsorption proceeds and the distance between neighbouring adsorbed proteins decreases. The pH, which determines the net charge of proteins, thus influences the final packing density on the surface⁶⁰. Moreover, lateral repulsion has been described as being the main cause for "desorption by adsorbed neighbours", a process in which desorption increases with surface coverage⁷¹. Lateral protein-protein interactions influence the conformational state of the adsorbed protein in such a way that the balance between protein-protein and protein-surface interactions changes, thus inducing an abrupt increase in desorption rates at a threshold surface coverage⁷². This has been documented for example for bovine serum albumin (pI 4.5) at pH 8⁷¹.
The kinetics of surface adsorption is often sigmoidal pointing out that adsorption rates increase with surface coverage. Such positive cooperativity is based on protein self-assembly, either loosely, involving the electrostatic field around adsorbed proteins, or into inter-molecularly bound clusters (aggregates) at the surface (Figure 4-4)⁶¹,⁷³. In general, at low surface coverage, Langmuir-type adsorption takes place which is subsequently, at higher surface coverage, replaced by a cooperative adsorption⁶¹.

<figure><img src="image_25.png" /><figcaption>Figure 4-4: Langmuir-type adsorption: proteins adsorb at any available binding site. Cooperative adsorption: proteins adsorb in close proximity to pre-adsorbed proteins. Figure adapted from ⁶¹.</figcaption></figure>

Surface adsorption can induce the formation of intermolecular protein aggregates. In general, protein aggregates are defined as irreversible complexes between two or more protein monomers, which present regions of extended, strong, non-covalent interactions between the monomers. Such surface-bound clusters (aggregates) can be formed either by lateral diffusion or by direct binding onto pre-adsorbed proteins⁷⁴. The latter is the case for hydrophobic surface-induced amyloid fibrillation of insulin for example⁷⁵,⁷⁶. In this case, adsorbed proteins form clusters (nuclei) on the surface which, at a critical size, induce the growth of surface-bound amyloid fibres (see 5.3 Insulin amyloid aggregation at hydrophobic surfaces). Upon agitation, such protein aggregates can be released in solution which can trigger further surface aggregation and sometimes aggregate growth in solution. In the case of therapeutic proteins, surface-induced aggregate formation (and their release into solution) is considered as an important risk factor. The understanding of the underlying mechanisms, the design of more stable molecular variants and the control of the processes involved are all subject to intensive research efforts⁷⁷.
#### 4.1.4 Reversibility of adsorption

Protein surface adsorption can be considered reversible (or irreversible) according to three different conditions: reversible upon dilution, reversible upon changing the physico-chemical conditions (pH, ionic concentration etc) and reversible upon exchange with other proteins (see for example “Vroman effect” 4.1.1 Parameters influencing protein interfacial activity). Adsorption to hydrophobic surfaces is usually irreversible with respect to dilution whereas adsorption on hydrophilic surfaces, which is dominated by electrostatic interactions, is subject to reversibility upon pH or ionic strength changes in the solution<sup>78</sup>. Homomolecular exchange at the surface has been documented for several proteins and surfaces<sup>79,80</sup>, whereas other proteins do not undergo such exchanges<sup>81</sup> or change their behaviour depending on the surface<sup>82</sup>. More importantly, in the context of therapeutic proteins, is the question whether the protein released from the surface retains its conformational change or not. For bovine serum albumin it was found that the reversibility of the structural changes induced by adsorption was highly dependent on the surface properties<sup>79</sup> and the degree of relaxation during adsorption<sup>80</sup>. Identifying and quantifying the presence of surface-released protein aggregates and probing their conformational state represents a milestone in pharmaceutical quality control of protein therapeutics (see 4.4 Methods to characterize protein interfacial adsorption).

### 4.2 Protein adsorption and unfolding at liquid interfaces

The liquid-liquid interface is relevant for therapeutic proteins that are formulated as emulsions (water in oil) or those in which the proteins are present as (nano-) particles that are used as drug delivery systems. The latter are created using nanoprecipitation protocols based on desolvation from a water-organic solvent mixture<sup>83</sup>. While in emulsions the aqueous protein solution is in contact with a non-volatile oil, the organic solvents used in particle formation are usually volatile.

An undesired liquid-liquid interface in the form of silicon oil droplets in therapeutic solutions has been widely studied in the literature. Prefillable syringes, which have numerous advantages over vial packaging in terms of dosing accuracy, storage and disposal convenience, are classically made of glass and need lubrication for accurate gliding properties of the plunger.
stopper. This is achieved by spraying a silicon oil film onto the glass body of the syringe or applying a silicon oil emulsion that can be stabilized by thermal fixation<sup>84</sup>. Depending on the process, the silicon film is more or less resistant and, upon storage and handling, silicon oil droplets can leak into solution. These droplets are not only undesired for injection but moreover affect protein stability as they trigger adsorption and aggregation. Thus novel lubrication methods and materials for prefilled syringes that do not need lubrication are currently researched. Krayukhina and coworkers have compared therapeutic mAb stability with respect to syringe material and silicon oil lubrication and clearly demonstrate that lubricated glass syringes take the highest toll on protein stability<sup>85</sup>.

Similarly to solid interfaces, protein destabilization is generally increased with the hydrophobicity of the oil/solvent phase<sup>86</sup>, although cases are reported where the opposite is true<sup>87</sup>. Thus experimental protocols can hardly be translated from one system to another and have to be optimized for each protein formulation. Protein adsorption at the liquid-liquid (or liquid-air) interface affects surface tension and rheology. These properties can be experimentally assessed to measure protein adsorption kinetics at these interfaces: the pendant drop technique for example measures the dynamics of interfacial surface tension using an optical contact angle measurement and contour analysis of a pendant drop of a protein solution inside an oil solution (or in air) (Figure 4-5).

The interfacial surface tension of the droplet decreases as a function of time as proteins accumulate at the liquid-liquid interface. Three regimes are generally described for a dilute protein solution: an induction regime (I on Figure 4-5), during which protein adsorption at the interface is diffusion-limited and too low to noticeably change the tension; as soon as enough proteins have adsorbed, interfacial tension decreases rapidly, reflecting the accumulation of stably adsorbed and relaxed proteins at the water-oil interface (regime II); the interfacial tension decrease eases out in regime III, when the layer of adsorbed proteins changes slowly conformation to seek for energetically favourable interactions. More and more proteins accumulate on the water side and a viscoelastic, gel-like network establishes.
<figure><img src="image_26.png" /><figcaption>Figure 4-5: Schematic of the pendant drop technique (top left). Typical dynamic interfacial tension response of a dilute protein in an aqueous solution adsorbing to the oil-water interface (top right). The schematics below illustrate the adsorption of native proteins (circles) at the interface and their subsequent relaxation (ellipses) upon formation of an interfacial network. Figure adapted from<sup>89</sup>.</figcaption></figure>

It is interesting to mention that during the induction regime (I on Figure 4-5) proteins effectively accumulate at the interface but surface tension variations are measurable only beyond approximately 50% of surface coverage for proteins<sup>88</sup>. Indeed the surface pressure as a function of surface coverage is highly dependent on the molecular size of the adsorbed molecule, indicating that the number of surface contacts is prevailing in the surface pressure of a liquid phase. This is why diluted protein solutions usually show an extended induction regime<sup>89</sup>. Moreover the times of the induction regime observed for ovalbumin at the liquid-air and liquid-heptane interface vary by 2 orders of magnitude (1000 s vs. 10 s, respectively), although the entire surface pressure response is quite similar for both interfaces<sup>89</sup>. This illustrates that the characteristics of the hydrophobic phase affect the protein's interfacial activity and translates the difference in affinity and denaturation kinetics that the protein has for the two hydrophobic interfaces.

Although protein adsorption kinetics at the liquid-liquid interface can be studied in a relatively straightforward way, assessing the conformation of proteins at this interface is more challenging. Indeed, the light scattering properties of emulsions often prohibit techniques that allow assessing protein conformation in solution like for example circular dichroism (CD). Indirect measurements, like turbidity, are then usually used.
Due to the flexibility of liquid-liquid (and liquid-air) interfaces, compression, dilation or
mechanical forces interrupting the interface can affect the stability of the adsorbed proteins.
This has been studied in particular at the liquid-air interface (see 4.3 Protein adsorption and
unfolding at air interfaces).

## 4.3 Protein adsorption and unfolding at air interfaces

The air constitutes a major hydrophobic interface in the context of therapeutic protein
solutions because it is omnipresent and very often dynamic. Liquid-air interfaces exist as
entrapped bubbles and headspaces in containers throughout manufacturing, storage and
delivery processes of protein drugs. Although an overwhelming amount of studies addressing
liquid-air interfacial stress on proteins are reported in the literature, the exact mechanism of
how it affects protein stability is still not fully understood⁹⁰.

Similarly to the liquid-liquid interface, surface tension measurements are also routinely
employed to follow protein adsorption at the air interface (see also 4.2 Protein adsorption
and unfolding at liquid interfaces). A striking example of how lowering surface tension by
protein adsorption at a liquid-air interface contributes to a vital function, is found in our lungs.
Lung surfactant is a lipid-protein complex that forms a film delimiting the wet tissue from air
in the alveoli. This interfacial film lowers the surface tension such that the pressure needed to
maintain vital alveolar volume upon expiration is reduced, preventing their collapse. In the
absence of surfactant, typically insufficiently developed in premature babies, rupture of the
lung tissue occurs when their collapsed alveoli are forced open by breathing efforts⁹¹,⁹².

In the context of therapeutic protein solutions, adsorption of proteins from the bulk at the air
interface follows similar regimes than those described for the liquid-liquid interface. Some
adsorbed proteins have been shown to change conformation at the air-liquid interface, while
others are known to remain in their native state⁹³. Agitation and air bubble coalescence are
the main causes entailing variations in the surface area at the air-liquid interface. These
dynamic increases and decreases of the interfacial surface impact directly on the density of
adsorbed proteins and thereby exacerbate particle formation⁵³,⁹⁴⁻⁹⁶. It has been shown that
repeated compression and dilation cycles of the air-liquid interface causes the formation of
IgG aggregates⁹⁷. Bee and coworkers concluded that particles form during compression of the
interface, when the distance between interfacially adsorbed proteins is reduced, and are released into the bulk during dilation⁹⁷.

Synergistic effects on the stability of therapeutic proteins have been documented for different stresses at the air-liquid interface. Rudiuk and co-workers showed that, during agitation, a repetitive mechanical breaking of the air-liquid interface by a penetrating needle resulted in the formation of IgG aggregates⁹⁸. In a recent study, Torisu and coworkers have shown that dropping a mAb solution vial, which causes cavitation, combined with agitation significantly enhances the formation of mAb aggregates⁴⁹.

Furthermore the triple interface line, where solid, liquid and air meet (see Figure 4-1), can move along the material surface during agitation, potentially exposing surface-adsorbed proteins to the air. A partial dehydration can then contribute to the unfolding and aggregation of adsorbed proteins. This phenomenon, highlighted by T Frachon during his PhD work⁹⁹, (see 5.5 Insulin aggregation at a dynamic triple interface), pinpoints to the complexity of the effects that can arise at interfaces and impact on protein stability.

Altogether, the combination of stresses that can entail protein loss or degradation at interfaces call for careful attention in the design of manufacturing, shipping, storage and delivery protocols for therapeutic proteins.

## 4.4 Methods to characterize protein interfacial adsorption

The characterization of molecular adsorption on a solid surface requires the use of analytical techniques, which are able to detect very low quantities of molecules, generally in a liquid environment, without modifying the adsorption process during acquisition.

Here I focus on the most used techniques for adsorption/desorption measurements on solid surfaces. A broad distinction can be done based on real time vs. equilibrium measurements concerning the adsorbed amount (expressed as $\Gamma$: adsorbed mass per surface area or $d_A$: adsorbed layer thickness) and the state of the adsorbed species (hydration, conformation) (Table 4-2). Real time measurements allow following adsorption and desorption processes with a decent time resolution (at best in the range of seconds) which is pertinent for processes that show distinct phases with characteristic and evolving parameters in time. A full
qualitative and quantitative description of protein adsorption at surfaces also needs to
investigate adsorbed protein layers at “equilibrium”, or, more precisely, to do “endpoint
measurements” at surface saturation.

The following parameters are commonly addressed in adsorption studies:

* Adsorption and desorption kinetics

* Number of adsorbed proteins per surface area

* Adsorbed layer thickness and hydration

* Conformation and arrangement (orientation) of adsorbed proteins

In Table 4-2 techniques to analyse different properties of proteins adsorbed on solid materials are compared in terms of sensitivity, penetration depth, ease of use and ability to perform parallel measurements. Techniques based on fluorescence (intrinsic or with labels) are not considered here nor are techniques that need large-scale facilities like neutron or X-ray scattering. This comparative table is extracted from a recent review¹⁰⁰ that I have co-authored with my colleagues Elisa Migliorini and Catherine Picart. For a full description of each of these techniques and references, the reader is referred to the reprint of the publication.
<table><thead><tr><th>Method</th><th>Physical signal</th><th>Deduced parameters</th><th>Sensitivity Γ, d<sub>A</sub></th><th>Sensing depth (d<sub>P</sub>, d<sub>A</sub>)</th><th>Type of surface</th><th>Drawbacks</th><th>Advantages</th><th>Ease of use</th></tr></thead><tbody><tr><td>ELISA</td><td>Colorimetry, Fluorescence, Luminescence</td><td>Amount Γ<sub>eq</sub> (endpoint)</td><td>fM range</td><td></td><td>Multiwell plate (plastic); beads</td><td>Necessity for specific antibody<br>Diffusion limited<br>Long time (hours)</td><td>High specificity and sensitivity<br>Automatization<br>High throughput</td><td>++++</td></tr><tr><td>SPR</td><td>Evanescent wave<br>Variation of resonance angle</td><td>Γ (time)<br>Kinetic interaction parameters<br>Affinity<br>Concentration</td><td>1 pg/mm<sup>2</sup>, Few tens of nm</td><td>d<sub>P</sub> ~20 nm</td><td>Functionalizable Au-coated chips</td><td>Relatively small sensing depth<br>Cost of sensors<br>Limited parallelism</td><td>Very high sensitivity<br>Real time analysis</td><td>++</td></tr><tr><td>SPRi</td><td>Evanescent wave<br>Fixed angle; Δ of reflected light</td><td>Γ (time); Kinetic interaction parameters; Affinity</td><td>1 pg/mm<sup>2</sup></td><td>d<sub>P</sub> ~20 nm</td><td>functionalizable Au-coated prism</td><td>Relatively small sensing depth and high cost of sensors</td><td>Very high sensitivity<br>Real time analysis<br>Parallel analysis of several hundred spots</td><td>++</td></tr><tr><td>OWLS</td><td>Evanescent wave<br>Transverse electric and magnetic modes excited in the waveguide</td><td>n<sub>A</sub>, d<sub>A</sub>, Γ (time)</td><td>&lt; 1 ng/cm<sup>2</sup>, 0.1 nm</td><td>d<sub>P</sub> ~150 nm<br>d<sub>A</sub>max ~400 nm</td><td>SiO<sub>2</sub>-TiO<sub>2</sub> sol gel waveguide layer</td><td>One provider (MicroVacuum)<br>One measurement at a time<br>n<sub>A</sub> and d<sub>A</sub> intrinsically linked (Γ being the invariant)</td><td>Real time analysis<br>High sensitivity (&lt; 1 ng/cm<sup>2</sup>, 0.1 nm for d<sub>A</sub>)<br>Now available at high throughput in microplates</td><td>+</td></tr><tr><td>DPI</td><td>Evanescent wave<br>Phase shift of TE and TM waves<br>Analysis of interference pattern</td><td>n<sub>A</sub>, d<sub>A</sub>, density ρ<sub>A</sub><br>Γ (time)</td><td>&lt; 1 ng/cm<sup>2</sup>, 0.1 Å<br>n<sub>A</sub> ~10<sup>-4</sup></td><td>unknown</td><td>Si oxynitride</td><td>Only relative parameters can be obtained<br>Sensitive to wavelength instabilities<br>Sensitive to mechanical vibrations<br>n<sub>A</sub> and d<sub>A</sub> intrinsically linked (Γ being the invariant)</td><td>Very high sensitivity (0.001 for n<sub>A</sub>; 0.01 nm for d<sub>A</sub>)<br>Interaction with the sample over several mm or cm</td><td>+++</td></tr></tbody></table>
<table><caption>Table 4-2: Comparison of the different surface-sensitive techniques used to characterize the adsorption of biomolecules on a surface: their physical principle, deduced parameters, sensing depth (d<sub>P</sub>), type of surface, drawbacks, advantages and ease of use are compared. d<sub>A</sub>: film thickness (nm); d<sub>P</sub>: penetration depth; n<sub>A</sub>: refractive index; Γ: adsorbed mass in mass per surface area (ng/cm² or µg/cm²); SPR(i): surface plasmon resonance (imaging); OWLS: optical waveguide lightmode spectroscopy; DPI: dual polarization interferometry; ATR-FTIR: attenuated total reflection Fourier transform infrared spectroscopy; SE: spectroscopic ellipsometry; QCM-D: quartz crystal microbalance with dissipation monitoring; AFM: atomic force microscopy</caption><tr><td>ATR-FTIR</td><td>Evanescent wave IR light transmitted through a high n guiding crystal (Absorbance)</td><td>Secondary structure of proteins IR peaks of chemical groups</td><td>100 ng/cm², ~ 1 nm</td><td>d<sub>P</sub> ≈ 900 to 1500 nm</td><td>Crystal of: ZnSe Si Ge</td><td>Need to work in D<sub>2</sub>O Not highly sensitive Large volume required (mL) Peaks need to be deconvoluted</td><td>Sensing over long distance Specificity of IR bands Hydration state (H-bonds)</td><td>+</td></tr><tr><td>SE</td><td>Change in light phase or intensity: Ψ, Δ of reflected light</td><td>n<sub>A</sub>, d<sub>A</sub></td><td>&lt; 1 nm</td><td>From 1 nm (UV) to &gt; 1 µm (IR)</td><td>Au or SiO<sub>2</sub></td><td>Complicated modeling to estimate n<sub>A</sub> and d<sub>A</sub> based on Ψ and Δ. One measurement at a time.</td><td>Measure of optical masses in real time with high sensitivity. No limitation in refractive index and film density High penetration depth.</td><td>+</td></tr><tr><td>QCM-D</td><td>Change in frequency shift of quartz crystal (Δf) and of dissipation (ΔD)</td><td>Hydrated mass, Viscoelastic modulus</td><td>1 ng/cm², 0.1 nm</td><td>~ 1 µm</td><td>Au, SiO<sub>2</sub>, TiO<sub>2</sub>, Zn, Mb</td><td>Measurement of the molecular mass adsorption always coupled with the solvent. Energy dissipation impacts mass calculation</td><td>Real time analysis Viscoelastic properties 4 measurements in parallel</td><td>+++</td></tr><tr><td>AFM</td><td>Tip-sample forces</td><td>x-y-z positions, Young's modulus, chemical mapping</td><td>~0.1 nm</td><td>Up to 100 nm</td><td>All surfaces</td><td>Very difficult to image very soft films. The resolution depends on the tip curve-radius Highly sensitive to working environment No high-throughput</td><td>Imaging molecules in their physiological environment. x-y-z information on surface distribution</td><td>+</td></tr></table>
## 4.5 Formulation strategies

In order to guarantee product quality, a liquid formulation, based on an adequate mixture of various excipients, is generally developed to maintain the protein stable and active at the target concentration¹⁰¹,¹⁰². Liquid formulation science is anchored on three essential points: find the cause for aggregation, assess and quantify the types of aggregates and finally stabilize the active compound with the right combination of excipients. In order to design an efficacious formulation, a continuous interactive progression involving all three steps is necessary.

<table><caption>Table 4-3: Overview of excipients used in 37 commercial mAb formulations. Credits to ¹⁶⁹</caption><thead><tr><th rowspan="2">Excipients</th><th colspan="3">Frequency of Inclusion in MAb Formulations (%)</th></tr><tr><th>All</th><th>Lyophilized</th><th>Liquid</th></tr></thead><tbody><tr><td>Polysorbate 80</td><td>57</td><td>45</td><td>62</td></tr><tr><td>Polysorbate 20</td><td>19</td><td>36</td><td>12</td></tr><tr><td>Poloxamer 188</td><td>3</td><td>0</td><td>4</td></tr><tr><td>Mannitol</td><td>8</td><td>9</td><td>8</td></tr><tr><td>Sorbitol</td><td>3</td><td>9</td><td>0</td></tr><tr><td>Sucrose</td><td>35</td><td>82</td><td>15</td></tr><tr><td>Trehalose</td><td>14</td><td>18</td><td>12</td></tr><tr><td>Dextrose</td><td>3</td><td>9</td><td>0</td></tr><tr><td>Dextran 40</td><td>3</td><td>9</td><td>0</td></tr><tr><td>NaCl</td><td>49</td><td>18</td><td>62</td></tr><tr><td>Arginine</td><td>8</td><td>0</td><td>12</td></tr><tr><td>Glycine</td><td>8</td><td>0</td><td>12</td></tr><tr><td>Methionine</td><td>3</td><td>0</td><td>4</td></tr><tr><td>Acorbic acid</td><td>3</td><td>0</td><td>4</td></tr><tr><td>NaOAc</td><td>3</td><td>9</td><td>0</td></tr><tr><td>Phosphate</td><td>32</td><td>27</td><td>35</td></tr><tr><td>Citrate</td><td>11</td><td>9</td><td>12</td></tr><tr><td>Acetate</td><td>16</td><td>0</td><td>23</td></tr><tr><td>Tris</td><td>3</td><td>0</td><td>4</td></tr><tr><td>Succinate</td><td>3</td><td>9</td><td>0</td></tr><tr><td>Histidine</td><td>35</td><td>27</td><td>38</td></tr></tbody></table>

The first step in formulation development is to determine the optimal pH. In general, therapeutic protein solutions are formulated at a pH that differs from the pI of the protein, in order to favour the correct ionization underlying the native fold of the protein. A slightly acidic pH is usually chosen because chemical degradation processes (e.g. deamidation) are enhanced at neutral or basic pH. The most used buffer components for mAb formulations are sodium phosphate- and histidine-based, the latter being well adapted for slightly acidic formulation conditions (pKa His ~ 6) (Table 4-3). Surfactants are usually included in formulations with the aim to reduce interfacial tension and stabilize the protein in solution. Their amphiphilic nature (which they share with proteins) is at the base of their surface activity. Their preferential accumulation at interfaces is assumed to prevent the protein from doing so and they thus would act

as kinetic competitors in interfacial partitioning. Besides this kinetic stabilization mechanism, surfactants can also act via colloidal stabilization in solution, thus covering the protein and preventing it in this way from interfacial adsorption¹⁰³. The most used surfactants are polysorbates (PS 80 and PS20) as can be seen from the compilation in Table 4-3.

Formulations further contain stabilizers that are small molecules that can have anti-oxidant, chelating or cryoprotective properties. For the latter, sugars (e.g. sucrose and trehalose) and
polyols (e.g. mannitol and sorbitol) are widely used to stabilize the native conformation of proteins during freeze-thaw cycles and lyophilisation (Table 4-3).

To assess the efficacy of a liquid formulation, protein aggregative particles are quantified in solution. Such particles are classified according to their size into the following categories: submicron (<1µm), sub-visible (1-100µm) and visible (>100µm) particles. Different size-based detection techniques are used to assess the formation of protein aggregates in formulated solutions (see Figure 4-6)¹⁰⁴.

<figure><img src="image_27.png" /><figcaption>Figure 4-6: Techniques used to assess size and quantity of aggregates, plotted on a molecular size-line. Between 50-1000 nm, the availability of adequate characterization techniques is limited to laser diffraction, dynamic light scattering, and electron microscopy (submicron gap). NTA, nanoparticle tracking analysis; DLS, dynamic light scattering; SE-HPLC, size-exclusion high-performance liquid chromatography; AF4, asymmetric flow field flow fractionation; AUC, analytical ultracentrifugation; EM, electron microscopy, SDS-PAGE, sodium dodecyl sulphate polyacrylamide gel electrophoresis; FACS, fluorescence-activated cell sorting.

Figure credits to¹⁰⁴.</figcaption></figure>

US and European pharmacopeia regulations aim at assessing the quality of a therapeutic protein solution by establishing an upper limit to the number of particles of a given size in a liquid formulation. Particles that are smaller than 10 µm are considered innocuous for patient safety. Bigger particles are analysed by light obscuration and microscopy techniques and the regulatory limit of their number is correlated to the injectable volume of the corresponding drug (Table 4-4). Visible particles are not tolerated and are assessed by visual inspection in front of white and black backgrounds (Ph. Eur.9.0, 2.9.20).

It should be noted that the origin of particles found in liquid formulations can also be non-proteinaceous. They can emanate from materials in contact with the solution (e.g. glass, rust,
silicon oil) or from the environment (e.g. dust, fibres). Excipients used in formulations can also represent a source of impurities and should therefore be of the highest purity. As they usually represent a major part of the components of a liquid formulation, their impact on the pharmacokinetic properties of the active drug has to be taken into account¹⁰⁵.

<table><caption>Table 4-4: Limits of particle numbers for quality assessment of liquid formulations (European pharmacopea 9.0, 2.9.19 Particulate contamination: sub-visible particles). Credits to G Lefebvre PhD in Pharmacy UCBL, 2017 N°38)</caption><thead><tr><th></th><th>Light obscuration</th><th>Microscopy</th></tr></thead><tbody><tr><td>Number of replica</td><td>3</td><td>3</td></tr><tr><td colspan="3">Small injectable volume (≤ 100 mL)</td></tr><tr><td>Particles ≥ 10 µm</td><td>≤ 6000 per container</td><td>≤ 3000 per container</td></tr><tr><td>Particles ≥ 25 µm</td><td>≤ 600 per container</td><td>≤ 300 per container</td></tr><tr><td colspan="3">Large injectable volume (> 100 mL)</td></tr><tr><td>Particles ≥ 10 µm</td><td>≤ 25 per mL</td><td>≤ 12 per mL</td></tr><tr><td>Particles ≥ 25 µm</td><td>≤ 3 per mL</td><td>≤ 2 per mL</td></tr></tbody></table>

Beyond protective formulation additives, other strategies exist to stabilize therapeutic proteins in solution and optimize their pharmacokinetic properties. For example the protein can be chemically modified, either by mutation or recombination with other stabilizing protein domains (thus creating a protein analogue), or by acylation or PEGylation¹⁰⁶⁻¹⁰⁸. For example, rapid-acting insulin analogues have been obtained by mutating amino acids involved in self-association. These insulin analogues (Lispro (Eli Lilly); NovoRapid (Novo Nosrdisk)) are preferentially monomeric which accelerates their delivery into the bloodstream upon subcutaneous injection. The attachment of fatty acids (acylation) can increase circulation time by association with serum albumin, which has been successfully implemented with the insulin analogue Detemir (Novo Nordisk)¹⁰⁹.
# 5 Insulin adsorption and aggregation at hydrophobic interfaces: a case study

## 5.1 A tribute to PhD students and co-workers

In this chapter the research of 4 PhD students, whom I co-supervised with Franz Bruckert, and numerous other contributors is presented. Thomas Ballet (2010, Cifre PhD LMGP-Becton Dickinson) started the research project on the stability of insulin in contact with hydrophobic surfaces and developed invaluable tests based on 96-well microplates and functionalized beads to follow aggregation kinetics. He demonstrated that nucleation and fibre growth take place on the hydrophobic surface and not in solution<sup>76</sup>. Laurent Nault (2012) analysed the sequence of events at the surface and the conformational state of surface-adsorbed insulin using surface sensing techniques such as surface plasmon resonance and infrared spectroscopy<sup>110</sup>. He also developed aggregative peptides as a tool to study surface-induced aggregation<sup>111</sup>. Karim Chouchane (2017) completed the analysis of the effects of short peptides on surface-induced insulin aggregation and showed that these can either accelerate or inhibit the aggregation process depending on their concentration<sup>112</sup>. He demonstrated that they cooperatively accelerate nucleation at very low concentrations by binding to hydrophobic surfaces and inhibit aggregation in solution by binding to fibrillary insulin at higher concentrations. He also discovered that the energy of visible light can trigger the aggregation of surface-adsorbed insulin via bound ThT<sup>113</sup>. Thibaut Frachon (2017 Cifre PhD LMGP EVEON) shed new light on insulin stability at interfaces by analysing the effect of a dynamic triple line where hydrophobic surface, air and liquid meet. He combined a microfluidic setup with fluorescence and interference microscopy to document in real time the formation of insulin aggregates at a moving triple line where adsorbed proteins aggregate under the effect of local dehydration<sup>99</sup>.

In the following the focus is on the highlights of our research on insulin stability at interfaces. For more detailed information the reader is referred to the reprints of the publications associated with this work<sup>76,99,110-113</sup>, which are compiled at the end of this manuscript. The molecular tools, experimental protocols and the design of devices to produce these results are valuable achievements, at the same level as the actual data they have allowed to produce.
## 5.2 Human insulin

Insulin, the most produced therapeutic protein, is administered to diabetes patients by daily sub-cutaneous injections. It has been used for diabetes management for roughly 80 years and was the first therapeutic protein to be produced recombinantly in bacteria. Insulin is also used as a model protein in research including studies on protein stability at interfaces.

Human insulin is a small globular protein of 51 amino acids composed of two chains (A-chain: 21 aa and B-chain: 30 aa, Figure 5-1) linked by two interchain disulfide bridges (cysteines A20 and B19, A7 and B7 (Figure 5-1)). The A-chain is folded into two alpha helices (A1-A8 and A12-A20) and contains an internal disulfide bridge (between cysteines A6 and A11). The B-chain presents a central, hydrophobic alpha helix (B8-B19) surrounded by the two interchain disulfide bridges, and a turn (B20-B23) (PDB 3I3Z and 3I40 <sup>114</sup>).

<figure><img src="image_28.png" /><figcaption>Figure 5-1: Human insulin primary, secondary and tertiary structure. A, Primary and secondary structure of human insulin. Disulfide bridges are represented by a dotted line and alpha helices by sinusoids. B, Monomeric tertiary structure. The color code represents the hydrophobicity of the amino acids from blue for most hydrophilic to red for most hydrophobic. N<sub>A</sub> N<sub>B</sub> and C<sub>A</sub> C<sub>B</sub> indicate N and C-termini of the A- and B-chain, respectively. Figure credits to T Ballet, PhD thesis, 2010, UJF, ED Phys.</figcaption></figure>

In solution the quaternary structure of insulin is in equilibrium between the monomeric, dimeric and hexameric forms which coexist *in vivo* as well as *in vitro*. The monomer is the
biologically active form and the hexamer is the most stable one, used for insulin storage in granules. Insulin dimerization is stabilized by the formation of an antiparallel beta sheet between the C-termini of two B-chains and the packing of the B-chain alpha helices which increases the number of hydrophobic interactions in the dimer<sup>115</sup>. An insulin hexamer is composed of three dimers held together by two zinc ions that are conjugated by the imidazole ring of histidine B10 of each monomer<sup>116</sup>. Most of the hydrophobic side chains are located inside the hexameric structure, isolated from the solvent, resulting in an increased stability. The vast majority of our experiments are done at 0.5 g.L<sup>-1</sup> (86 µM) in a Zn-free buffer. Based on data obtained for bovine insulin <sup>117,118</sup>, the monomer/dimer/hexamer distribution for a 0.5 g.L<sup>-1</sup> insulin solution at pH 7, 25 °C (in a 0.1 M TRIS-HCl and 0.1 M NaCl buffer), is estimated to be 5 %, 10 % and 85 %, respectively.

Recombinant human insulin produced in yeast (I2643 Sigma-Aldrich) is purchased as a powder and has to be solubilized before use. The solubilisation protocol involves a pH cycling between acidic and neutral pH in which the precision of the pH and the time the solution is kept at each pH impacts on the quality of the prepared solution. Hence, a meticulous preparation of insulin stock solutions is one of the corner stones in the reproducibility of the adsorption and aggregation experiments using this protein. Stock solutions (86 µM in 25 mM TRIS-HCl pH 7.4, 125 mM NaCl) are filtered and stored at 4°C.

Insulin amyloid aggregation is observed under two conditions:

- Acidic pH (pH 2) and high temperature (60°C)

- Neutral pH (pH 7), hydrophobic surfaces and agitation

In the latter case a temperature of 37°C is routinely used to speed up experimental protocols. Our work has been done exclusively in the latter condition and our standard insulin aggregation test consists in agitating (1200 rpm) an insulin solution (86 µM, 0.5 mg/mL) in a hydrophobic 96-well plate at pH 7.4. Thioflavin T (20 µM) is used as a conformational marker and its fluorescence properties in the fibre-bound state (λex ThT 450 nm; λem ThT 480 nm) are used to monitor insulin amyloid aggregation kinetics in time. A typical insulin aggregation kinetics is shown in Figure 5-2. The lagtime, during which aggregation nuclei are formed on
the hydrophobic surface, delimits the phase during which no ThT-positive aggregates can be recorded. It is followed by a growth phase during which insulin amyloid aggregates accumulate rapidly until no more monomeric insulin is available in solution (plateau phase). The lagtime is highly variable due to the stochastic nature of nuclei formation and is typically around 2-4h in our protocol. It should be noted that the reproducibility of the lag time also depends on the quality of the insulin solution during its preparation and storage.

The following subchapters describe insulin stability in the presence of hydrophobic interfaces, starting by rather macroscopic observations and then progressively revealing a more detailed molecular mechanistic view of the phenomena that produce insulin amyloid fibres at these interfaces.

<figure><img src="image_29.png" /><figcaption>Figure 5-2: Insulin aggregation kinetics followed by ThT fluorescence in time. Coloured lines indicate kinetic data of individual replicates, the black line shows the mean curve with standard deviations. The lag phase is delimited by the lagtime (determined by the intercept of the slope (red dotted line) with the time axis). The growth phase and plateau phase are indicated. Figure credits to U Hevin.</figcaption></figure>

## 5.3 Insulin amyloid aggregation at hydrophobic surfaces

As early as the 1940's Waugh described the "Fibrous modification of Insulin"¹¹⁹ which he observed upon heating an acid insulin solution. At neutral pH, the aggregation of insulin into amyloid fibres was revealed when the first delivery systems involving pumps and syringes were introduced in the 1970s and 1980s¹²⁰. Indeed, insulin deposits obstructing pumps and
catheters were regularly observed, and, when disposable syringes were introduced, silicone oil was identified as a major source for insulin aggregation¹²¹,¹²². It became apparent that insulin aggregation at neutral pH is mediated by its adsorption on hydrophobic surfaces and agitation was an important factor¹²³. Sluzky analysed the effects of agitation rates, surface hydrophobicity, surface area and insulin concentration⁷⁵,¹²⁴. From these results, she proposed that insulin monomers could partially unfold upon reversible adsorption on hydrophobic surfaces. Such unfolded monomers could then either refold or interact in solution with other unfolded monomers to form aggregation nuclei, which eventually would lead to insulin amyloid aggregates in solution (Figure 5-3).

<figure><img src="image_30.png" /><figcaption>Figure 5-3: Model representing the mechanism of insulin aggregation at a hydrophobic interface as proposed by Sluzky¹⁷⁰.</figcaption></figure>

In Thomas Ballet's PhD thesis, he developed a 96-well based assay to measure human insulin amyloid aggregation based on Thioflavin T (ThT) fluorescence⁷⁶,¹²⁵. This assay format allowed for multiple replicates and different solution conditions to be assayed simultaneously, thus enabling statistically sound experimental data to be produced in a relatively short time. He used functionalized beads and 96-well microplates with different hydrophobicities as solid interfaces and demonstrated that a hydrophobic surface pre-incubated with insulin could accelerate the formation of aggregation nuclei of a fresh insulin solution. On the other hand, the insulin solution from the pre-incubation experiment was not able to aggregate faster than a fresh insulin solution when put in contact with new hydrophobic surfaces. This
demonstrated that the aggregation nuclei are formed on the hydrophobic surface and remain strongly adsorbed on this surface (Figure 5-4).

Moreover, it was shown that fibril growth is a mechanism that takes place on the hydrophobic surface, before the fibres are shed into solution by agitation. Ballet also documented that hydrophobic surface-induced insulin amyloid aggregates have identical characteristics to those described in the literature that had been obtained in acid conditions and at high temperature¹²⁵. Indeed cross-seeding experiments showed important lagtime accelerations and similar growth rates independently of the method used to generate insulin fibres¹²⁵.

<figure><img src="image_31.png" /><figcaption>Figure 5-4: Model representing the mechanism of insulin aggregation at a hydrophobic interface as proposed by Ballet ¹²⁵. Triangles represent native (mono-, di- and hexameric) and rectangles represent partially unfolded insulin monomers. The red and blue colours represent a schematic distribution of hydrophobic (red) and hydrophilic (blue) parts of the protein. Figure adapted from Ballet ¹²⁵.</figcaption></figure>

In order to analyse insulin adsorption at hydrophobic surfaces in situ and in real time, to quantify adsorbed amounts, and to follow secondary structure changes of insulin accompanying its adsorption, surface-sensing techniques were used. Surface plasmon resonance (SPR) is sensitive to refractive index changes in the vicinity of a functionalized gold surface and one can therefore measure adsorption/desorption kinetics with an adequate time resolution (seconds)(Table 4-2). Using a dually functionalized gold prism, half hydrophobic, half hydrophilic (Figure 5-5 A), Laurent Nault studied insulin adsorption using SPR in the imaging mode (SPRi). It was demonstrated that insulin adsorbed exclusively on the hydrophobic surface, the signal on the hydrophilic surface being due to refractive index changes of the protein solution compared to buffer (Figure 5-5 B and C).
<figure><img src="image_32.png" /><figcaption>Figure 5-5: A Schematic representation of a dually functionalised gold prism with respective water contact angles (WCA) for the hydrophobic and hydrophilic sides. B SPRi reflectivity signal on the hydrophobic (bold line) and hydrophilic (thin line) sides obtained for a 86 µM insulin (HI) solution followed by a buffer wash. C HI association (red) and dissociation (green) kinetics on the hydrophobic surface as revealed by the difference signal (hydrophobic signal minus hydrophilic signal). Figure adapted from<sup>110</sup>.</figcaption></figure>

The adsorption of insulin on the hydrophobic surface rises sharply in about 1 minute followed by a slower almost linear adsorption phase. Similarly for desorption, a rapid and a slow decrease in the SPRi signal is observed. There is thus a defined sequence of adsorption events which starts with a very rapid monolayer formation on the surface followed by further binding of soluble insulin on top of the surface-adsorbed monolayer. The amplitude of the fast adsorbing insulin signal saturates with increasing concentrations of insulin in solution and a total amount of 260 ng.cm⁻² is reached, when the entire hydrophobic surface is covered. Surface-adsorbed protein amounts reported in the literature are generally in the order of several hundred ng.cm⁻²<sup>126</sup>. The dissociation kinetics corroborates the existence of two distinct pools of insulin at the surface: a strongly adsorbed layer, which desorbs very slowly, is covered by a more loosely adsorbed layer which dissociates rapidly upon dilution with buffer. With contact time the strongly adsorbed insulin pool becomes more important indicating that adsorbed insulin stabilizes further insulin binding at the surface.
To analyse the conformational state of insulin during the adsorption process on hydrophobic surfaces, attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) was used. It was confirmed that soluble and surface-aggregated insulin differed in their respective amide I band signatures, with a characteristic 1650 cm⁻¹ band present in soluble insulin and two bands at 1660 and 1630 cm⁻¹ for aggregated insulin¹¹⁰,¹²⁷. Sequential acquisition of FTIR spectra in time showed that adsorbed insulin has a specific infrared signature, distinct from soluble or aggregated insulin. In the adsorbed protein, the amide A and amide II' bands are shifted to lower energies which goes in pair with an increase in hydrogen bonding. The amide I band of adsorbed insulin is compatible with the presence of intra and intermolecular beta-sheets (bands at 1635, 1687 and 1708 cm⁻¹). The evolution of the FTIR signatures with time corroborated the existence of different adsorbed insulin pools already characterized by their association and dissociation kinetics (SPRi). Other studies also point towards a decrease in alpha-helical structure upon adsorption to hydrophobic surfaces¹²⁸. It should be noted however that in acidic conditions and at elevated temperature and longer incubation times adsorbed insulin was shown to remain in a close to native conformation on hydrophobic surfaces¹²⁹.

<figure><img src="image_33.png" /><figcaption>Figure 5-6: Sequence of insulin adsorption on hydrophobic surfaces. Schematic representation of the different conformational states of insulin (soluble, adsorbed, amyloid and aggregated). Triangles represent native and rectangles represent unfolded insulin monomers. The red and blue colours represent a schematic distribution of hydrophobic and hydrophilic parts of the protein, respectively. The characteristics in terms of dissociation kinetics, infrared signature and ThT fluorescence are given below for each state. Figure adapted from ¹¹⁰</figcaption></figure>
Using fluorescence microscopy to observe ThT fluorescence of adsorbed insulin flowing over a hydrophobic glass surface in a flow chamber, the conformational state of the protein could be followed (Figure 5-6). (1) Monomeric insulin adsorbs rapidly on the hydrophobic surface, which is accompanied by a characteristic infrared signature (amide A and amide II' band shifts). At this stage aggregation nuclei are in formation on the surface but no ThT fluorescence can yet be measured. This adsorption is accompanied by alpha helix to beta sheet transitions in the protein and in this process the hydrophobic material surface (or air interface) are known to act as catalysts. Indeed they provide a platform for an accelerated formation of ThT-positive aggregation nuclei (2) ¹³⁰⁻¹³². Amyloid fibres then grow on the surface from these nuclei and are finally shed into solution (3).

Insulin adsorption and aggregation at hydrophobic surfaces is thus a complex, multistep phenomenon in which conformationally different protein populations exist and which results in the formation of amyloid fibres that are released from the surface upon agitation. Beyond surface hydrophobicity, surface roughness also influences insulin adsorption rates¹³³. Several other recent studies using functionalized nanoparticles or quantum dots also confirm the importance of hydrophobicity in surface-induced amyloid insulin aggregation¹³⁴,¹³⁵. Insulin is stable when agitated in a hydrophilic 96-well microplate, indicating that the presence of a hydrophobic air interface alone is insufficient to trigger amyloid fibrillation¹²⁵.

## 5.4 Aggregative peptides and insulin aggregation at hydrophobic surfaces

In an attempt to elucidate the fibrillar structure of amyloids, the minimal amino acid sequence capable of producing these structures was analysed for diverse proteins. For insulin, Ivanova and coworkers showed that two peptides, SLYQLENY and LVEALYL, present in the A- and B-chain of insulin, respectively, formed amyloid fibrils on their own¹³⁶. Analysing the structure of these fibrils they proposed a fibril model for insulin (Figure 5-7). The LVEALYL segments are stacked into tightly packed beta sheets, forming the dry steric zipper characteristic of the spine of amyloid fibres whereas the LYQLENY segments are on the periphery of the spine. The B-chain LVEALYL segment thus undergoes an alpha helix to beta sheet transition during insulin
amyloid aggregation. The disulfide bridge conservation between the A- and B-chain forces the A-chain segment to also adopt a beta sheet conformation, located at the edge of the fibril.

<figure><img src="image_34.png" /><figcaption>Figure 5-7: Fibril model of insulin. Left, Native structure of the insulin dimer (PDB 1GUJ), A chain in red, B chain in blue, LVEALYL segment in dark blue and SLYQLENY in dark red. Middle, View down the fibril axis of four beta-sheets in the crystal structure of B chain LVEALYL. The two sheets forming the dry steric zipper interface are in blue. Water molecules are shown as green spheres. Right, View of fibril model, looking down the fibril axis. Figure credits to <sup>136</sup></figcaption></figure>

While the SLYQLENY peptide had no significant effect on insulin aggregation kinetics, the LVEALYLV peptide was shown to accelerate the lagtime of insulin aggregation at sub stoichiometric concentrations (1:1/40) and to inhibit nucleation and fibril growth at equimolar concentrations in acidic conditions<sup>136</sup>. We showed that the acceleration of insulin amyloid aggregation in the presence of the LVEALYL peptide is also effective at neutral pH and is strictly dependent on the presence of hydrophobic surfaces<sup>76</sup> (Figure 5-8), which had not been examined by Ivanova.

<figure><img src="image_35.png" /><figcaption>Figure 5-8: Human insulin (HI 86 µM) aggregation kinetics monitored by ThT fluorescence in the absence and presence of LVEALYL peptide (26 µM) during agitation in a multiwell plate at 37°C. Figure adapted from <sup>76</sup></figcaption></figure>
We showed that minute amounts of LVEALYL peptide remained stably adsorbed at hydrophobic surfaces and were responsible for the dramatic acceleration of insulin aggregation. A series of mutants of this peptide were also used to probe their effect on hydrophobic surface-mediated insulin aggregation<sup>111</sup>. Repetitive peptides alternating L and K residues were selected, because it was known from the literature that these would adsorb in beta sheet conformation on hydrophobic surfaces<sup>137</sup>. From SPRi studies and ATR-FTIR measurements, it was confirmed that peptides adopting a beta sheet structure when adsorbed on hydrophobic surfaces are capable to accelerate the formation of aggregative nuclei and therefore decrease the lagtime of insulin aggregation kinetics<sup>110</sup>. Moreover two other important characteristics were highlighted in this study:

- the accelerating effect could be obtained with amino acid sequences that are not present in the insulin primary sequence, like repetitive (LK) or (IS) sequences of various lengths. This confirmed that the adsorbed conformation of the peptide, namely beta sheet, was the dominating feature and not the primary sequence. Moreover the effect was correlated to the surface binding energy of the peptide on a hydrophobic surface.

- the accelerating effect was obtained at largely substoechiometric concentrations (with respect to the insulin concentration) which do not allow full surface coverage, indicating that aggregation nuclei form as discrete "islands" on the surface and not from a continuous peptide/protein layer (Figure 5-9).

<figure><img src="image_36.png" /><figcaption>Figure 5-9: Observation of amyloid nuclei. AFM (tapping mode) images of silanized glass coverslips incubated at 37°C, 1200 rpm for 2h with peptide (LK)4L alone (A), for 1h with insulin solution (86 µM) without peptide (B) or with 0.2 µM of (LK)4L peptide (C). The scale bar represents 500 nm. Figure credits to <sup>112</sup></figcaption></figure>
The discovery of the properties of these aggregative (LK)nL peptides provided a tool to study the formation of surface-bound aggregation nuclei as the peptides could be employed simultaneously as aggregation inducers and tools to analyse the aggregates they triggered.

<figure><img src="image_37.png" /><figcaption>Figure 5-10: A Human insulin (86 µM) was incubated with (LK)₄L (open squares) and (LK)₅L (closed squares) at different concentrations in a hydrophobic 96 well plate at 37°C with agitation. The lagtime is represented as a function of concentration. B Pre-adsorption of (LK)₅L (=LK11) at different concentrations and their influence on the aggregation lagtime. Figure credits to <sup>112</sup></figcaption></figure>

Using (LK)nL peptides of various length (2<n<5) we characterized their effects on insulin aggregation at hydrophobic surfaces as a function of their concentration<sup>112</sup>. For (LK)4L and (LK)5L, the aggregation lagtime is considerably shortened when these peptides are used in solution at 0.1 µM or lower concentrations. Above 0.3µM, the peptides have the opposite effect, i.e. delaying the lagtime (Figure 5-10 A). We have shown that these peptides cooperatively accelerate the formation of surface-bound aggregative nuclei through stable surface adsorption. The pre-adsorption of the peptides on the hydrophobic surface is
sufficient to trigger this acceleration effect (Figure 5-10 B) and the lagtime decrease goes in pair with a strong reduction in the variability associated with insulin amyloidal aggregation.

From these results, we concluded that surface-bound peptide patches facilitate the alpha helix to beta sheet transition in surface-adsorbing insulin by contact with lateral inter-peptide beta sheets (Figure 5-11). This mechanism drastically accelerates the formation of aggregative nuclei at the surface and alleviates the stochastic nature characterizing primary nucleation in amyloid aggregation kinetics.

<figure><img src="image_38.png" /><figcaption>Figure 5-11: Scheme illustrating the accelerating effect of surface-adsorbed peptide patches on the formation of insulin aggregation nuclei. Surface-bound LK<sub>5</sub>L peptide complexes (represented with blue leucine and red lysine residues) cooperatively and stably adsorb on the hydrophobic surface (square) in beta sheet conformation. The hydrogen-bonding axis is represented by a grey transparent double-headed arrow. Upon adsorption of native insulin, these peptides accelerate the formation of aggregation nuclei. Figure adapted from <sup>112</sup>.</figcaption></figure>

For (LK)₄L and (LK)₅L the accelerating effect on insulin aggregation kinetics is gradually lost at concentrations > 0.5 µM when an inhibitory effect is observed (Figure 5-10 A). We have demonstrated that this inhibition emanates from excess peptide in solution which prevents insulin from aggregating into growing amyloid fibres by stably binding to them. Electrostatic interactions between positively charged Lys (K) residues and negatively charged insulin (pI = 5.3) at pH7.5 play a role in this inhibition as shown by experiments using varying salt concentrations¹¹². Co-precipitation experiments using a rhodamine labelled (LK)₅L peptide, TAMRA-(LK)₅L, demonstrated that this peptide binds strongly to insulin aggregates, depleting the solution of the peptide with growing amounts of fibres¹³⁸. This strong binding of the (LK)₅L peptide to the growing fibre is responsible for the inhibition of insulin aggregation kinetics. By adding increasing concentrations of (LK)₅L peptide in solution at different time points (60 and
120 min) during the insulin aggregation kinetics (Figure 5-12), we show that the growth rate is reduced as a function of concentration and tends towards a minimal growth rate. The aggregation can thus not be completely stopped, probably due to the generation of secondary nucleation sites. Such secondary nucleation sites are known to be induced by fibre breaking for example as a result of agitation¹³⁹. The growth rate decrease is more pronounced when the peptide is introduced at 60 min, a time point in the kinetics, when insulin nuclei and fibres are less abundant. The minimal growth rate obtained after introducing the peptide at 120 min is twice as big as the one, when the peptide is introduced at 60 min. This is likely due to the amount of peptide remaining available in solution for inhibition, which is smaller at 120 min when the amount that can be bound to fibres is larger.

<figure><img src="image_39.png" /><figcaption>Figure 5-12: Plot showing the decrease of the insulin aggregation growth rate as a function of (LK)₅L in solution. The wells of a hydrophobic multiwall plate are pre-incubated for 10 min with 5 µM (LK)₅L, they are washed, and insulin is added at 86 µM and incubated at 37°C with shaking. At timepoint 60 (closed circles) and 120 (open circles) minutes the indicated amount of (LK)₅L peptide is added in solution and the growth rate is measured. Figure adapted from ¹³⁸</figcaption></figure>

The inhibited growth rate recovers after several hours of incubation and the time needed to recover to the initial growth rate is shorter when larger amounts of fibrils are present at the addition of inhibiting concentrations of (LK)₅L peptide (i.e. at later stages in the aggregation kinetics)¹³⁸. Beyond binding to growing insulin amyloid fibres, excess (LK)₅L peptides in
solution also interfere with the formation of effective aggregation nuclei. We therefore
propose that the inhibiting effect of (LK)nL peptides in excess in solution is based on their
strong binding to growing amyloid precursors and fibres.

From the ratio of bound peptide to amyloid insulin (1 peptide for 4-10 insulin molecules)¹³⁸,
we predict that the peptide does not simply bind to the growing end of a fibre. The inhibition
is thus more complex than a simple capping mechanism, preventing fibre elongation. There
seems to be a distribution of peptide along the amyloid aggregate. This is also corroborated
by preliminary results of fluorescence transfer experiments between fibre-bound thioflavinT
and the TAMRA fluorophore of the peptide (Figure 5-13). Indeed the emission peak of fibre-
bound ThT (centred at 480 nm) overlaps with the excitation peak of TAMRA (centred at 557
nm) between 450-550 nm, thus allowing fluorescence resonance energy transfer (FRET)
between the two fluorophores if they are at close distance. When excited at 450nm (λex ThT),
ThT bound to insulin fibres transmits the adsorbed energy to nearby bound TAMRA-(LK)₅L
which in turn emits a fluorescence signal at 583 nm (λem TAMRA) (Figure 5-13, 0.5 µM
(orange) and 1 µM (grey) TAMRA). With increasing amounts of TAMRA-labelled peptide, the
fluorescence emission of TAMRA is self-quenched probably due to a local interference
between closely bound fluorophores (Figure 5-13, TAMRA > 1 µM).

<figure><img src="image_40.png" /><figcaption>Figure 5-13: Fluorescence emission spectra of 86 µM insulin amyloid fibres incubated with thioflavin T (ThT) alone, or ThT with increasing amounts of TAMRA-(LK)₅L peptide. The excitation wavelength is 450 nm (λex ThT). Figure credits to W Labriji, N Yezza, C Lilliebjörn and A Gansmandel.</figcaption></figure>
In conclusion, peptides, like (LK)nL peptides, adsorbing in beta-sheet conformation on hydrophobic surfaces accelerate the formation of aggregative nuclei that trigger insulin amyloid fibre growth on the surface. This effect is seen at low peptide concentrations (<< 1 µM), is strictly dependent on the presence of a hydrophobic surface and is based on a stable cooperative binding of the peptide at the surface. At higher concentrations (>3µM), when they stay in excess in solution, these peptides inhibit the insulin aggregation rate and bind to amyloid fibres and pre-aggregates. This binding is at least in part stabilized by electrostatic interactions and interferes with the effective incorporation of insulin monomers into growing fibrous aggregates.

Our study with peptides that interfere with insulin amyloid aggregation has allowed to shed light on the nucleation process at hydrophobic surfaces and on the growth mechanism of amyloid aggregates triggered by insulin surface adsorption. Beyond these fundamental results it has also allowed the development of fluorescent markers (TAMRA labelled peptides) that can be used to localize aggregates and to study their internal composition.

## 5.5 Insulin aggregation at a dynamic triple interface

Knowing that hydrophobic surfaces and agitation are both essential to trigger insulin amyloid aggregation at pH7, it is tempting to speculate that the movement of liquid over the hydrophobic surface is somehow involved in the trigger of the aggregation process. More precisely, the agitation of a solution in a container constantly moves the air-liquid interface up and down the container wall thus provoking a dynamic sweep of the triple line over the solid surface. The movement of the liquid generates a zone of intermittent wetting where liquid and air alternately recover the container wall (Figure 5-14).

Such dynamic triple interfaces are very frequently encountered in the handling of protein solutions. For therapeutic proteins, this is the case during production and storage, but also during reconstitution, when pumping of the solution can create bubbles that move and might cavitate or during administration protocols when the therapeutic solution is manipulated in perfusion bags with headspaces. Recent studies have documented the effect of a changing
liquid-air interface in the context of therapeutic protein aggregation without being able to
fully describe the fate of proteins at a moving triple line⁹⁷,¹⁴⁰⁻¹⁴³.

<figure><img src="image_41.png" /><figcaption>Figure 5-14: Moving triple interface upon agitation of a container with a liquid protein solution and an air headspace. The area swept by the triple interface is represented in green and its extension is indicated by a double-headed green arrow.</figcaption></figure>

Karim Chouchane designed an experiment to precisely locate the formation of insulin amyloid aggregates at a hydrophobic surface, under solution agitation¹³⁸. In a hydrophilic glass tube containing an insulin solution supplemented with ThT, a hydrophobic glass rod was introduced and rotated (1200 rpm for 2h) (Figure 5-15 A). ThT fluorescence was subsequently monitored along the glass rod (Figure 5-15 B) and it was shown that ThT-positive aggregates accumulate exclusively at the triple interface where the hydrophobic glass rod, the insulin solution and the air meet. The area covered by ThT-positive aggregates extends indeed over the surface that the inner meniscus of the solution sweeps when agitated by the hydrophobic glass rod.

No ThT-positive aggregates were observed on the hydrophilic glass tube and only a few fluorescent particles could be found on the permanently immersed surface of the hydrophobic glass rod. This shows that insulin preferentially unfolds and aggregates at the dynamic triple line due to the combination of two effects: a stable adsorption at the hydrophobic surface and a concomitant partial dehydration induced by intermittent wetting.
<figure><img src="image_42.png" /><figcaption>Figure 5-15: A Scheme of the experimental setup with a hydrophobic glass rod rotating in an insulin solution contained within a hydrophilic glass tube. B ThT fluorescence along the glass rod after 120 min incubation at 1200 rpm and 37°C. The red arrowheads indicate the triple interface.</figcaption></figure>

In an effort to observe in real time what happens to proteins at a dynamic triple line, Thibaut Frachon designed a microfluidic setup allowing the movement of an insulin solution back and forth over a hydrophobic surface, thereby displacing the triple line locally and continuously in a controlled way (Figure 5-16). Combining reflection interference contrast (RICM) and fluorescence microscopy, the morphology of the liquid film and the formation of insulin amyloid deposits on the material surface was monitored, in real time and simultaneously.
<figure><img src="image_43.png" /><figcaption>Figure 5-16: Microfluidic setup A, side view. B, top view. Blue colour represents the protein solution, white the air and grey the hydrophobic chip material. Dimensions are in mm. The x-axis represents the length of the channel and defines the position at which images are taken. Upper and lower levels correspond to the far left and far right meniscus positions during a hydration/dehydration cycle. ROI represents the microscope objective position and the field of view during image acquisition.</figcaption></figure>

Inside the microfluidic channel, made of a hydrophobic plastic, three distinct areas can be identified: a zone (blue) that is constantly wet extends from the inlet until the lower level position of the solution meniscus, a zone (dotted) that is intermittently wet is bordered by the lower and upper levels of the solution meniscus, and a zone (white) that remains always dry is located beyond the upper level of the solution meniscus (Figure 5-16). Pumping an insulin solution back and forth in this channel, results in insulin aggregation with a nucleation-dependent kinetic, characteristic for amyloid aggregation (Figure 5-17A). The ThT-positive aggregate distribution within the channel was imaged at the plateau phase (Figure 5-17B). Amyloid aggregates grow only in the intermittently wet zone, where the triple interface is constantly moved over the hydrophobic surface. These aggregates are not desorbed and deposited elsewhere as the constantly wet and dry zones present very low levels of ThT fluorescence. The difference of the fluorescence signal between the always wet and intermittently wet regions shows that the presence of a triple interface is critical to induce insulin surface aggregation and that shear stress alone combined with a hydrophobic surface is not enough to trigger protein aggregation.
<figure><img src="image_44.png" /><figcaption>Figure 5-17: Insulin aggregation triggered by a moving triple interface. A Aggregation kinetics as a function of time (insulin 86 µM, displaced volume 2µL, cycling time 15s, 37°C). B ThT-positive aggregate distribution along the microchannel. The positions of the upper and lower level of the solution meniscus during cycling is indicated on the X axis.</figcaption></figure>

RICM allows to observe the behaviour of the liquid film as it dewets the channel surface upon retraction of the insulin solution (Figure 5-18B, D and F). Liquid droplets are observed during dewetting and by analysing their position in time during wetting/dewetting cycles, it was shown that these droplets reform at identical positions on the dewetted surface during the lag and growth phase of the aggregation. At the plateau phase, a continuous liquid film covers the initially hydrophobic channel surface (Figure 5-18F). The identical repositioning of the droplets during the experiment is due to surface-adsorbed insulin that creates energetically favourable wetting zones, anchoring the liquid at the surface and thereby changing the water-retaining properties of the material. ThT fluorescence images (Figure 5-18A, C and E) indicate that insulin aggregates grow as circles that coincide with the border of the droplets (Figure 5-18C). These fluorescent rings grow eccentrically and expand on the surface until a continuous aggregate layer is formed (Figure 5-18E) that retains a thin water film during dewetting (Figure 5-18F). At each sweep of the triple line, a new layer of insulin aggregates is formed at the outer border of the already formed fluorescent aggregate ring (Figure 5-18G). No aggregates form at the centre of the droplet despite the fact that insulin is present in solution in this area.
<figure><img src="image_45.png" /><figcaption>Figure 5-18: The position of ThT-positive insulin aggregates correlates with the position of droplets on the dewetted channel surface. A, C and E show ThT fluorescence images taken during the lag, growth and plateau phases, respectively, in the intermittently wet zone when the channel was filled with the insulin solution. B, D and F show RICM images taken at the same time points when the intermittently wet zone was exposed to the air. G Insulin amyloid aggregates grow eccentrically. Superimposition of a two THT fluorescence images taken at a 20 cycle interval. THT-positive amyloid aggregates are represented in red for the first image and in green for the following one. Overlapping fluorescent pixels between both images appear in yellow.</figcaption></figure>

A thorough analysis of our results points towards the following detailed description of the parameters governing molecular aggregation at a dynamic triple interface (Figure 5-19):

Insulin stably adsorbs at hydrophobic surfaces and creates aggregation nuclei that act as surface anchoring points for the protein solution as the liquid sweeps over the surface. The droplets constitute the insulin supply and proteins adsorbed at the solid-liquid and air-liquid interfaces collide at the triple line inducing higher local concentrations and therefore intensifying intermolecular interactions. During dewetting, insulin from the air-liquid interface is incorporated into existing fibres. The partial dehydration induced by the receding liquid film favours intermolecular interactions between adsorbed proteins and protein fibrils, leading to amyloidal fibre growth. The driving force for the conformational change could in part be
provided by the release of solvation water around the insulin molecules concomitant with the
formation of intermolecular hydrogen bonds. Our results imply that amyloid aggregates grow
at the surface around the droplets rather than being formed elsewhere (e. g. at the liquid-air
interface) and being deposited there. This is especially true when protein adsorption is strong
enough to prevent their dissociation from the surface as the liquid is withdrawn.

<figure><img src="image_46.png" /></figure>

Figure 5-19: A Schematic representation of the parameters governing insulin aggregation at a dynamic triple interface. B: Schematic view of insulin aggregate growth at the triple interface. Step 1: liquid droplets are anchored at the surface by surface-adsorbed proteins. Step 2: ThT-positive insulin aggregates start to appear at the edge of the droplets. Step 3: the aggregates grow radially outwards of the droplets. Step 4: the microchannel surface is entirely covered by amyloid fibres retaining a continuous liquid film. Figure adapted from ⁹⁹.

These experiments establish a predominant role of dynamic triple interfaces over hydrophobic
solid or air interfaces alone for insulin aggregation. Indeed, it was shown earlier, that insulin
is stable when agitated in a hydrophilic microwell plate, where a mobile liquid-air interface is
present¹²⁵. The liquid-air interface is thus less efficient at promoting insulin aggregation. We
have shown that shear stress alone is not responsible for insulin aggregation as no ThT-
positive aggregates accumulate in the permanently wet zone, where the same fluid
movement is applied as in the intermittently wet zone. Aggregation is therefore mostly due
to dehydration of surface-absorbed proteins at the triple line.

Looking back at the experimental setups used in the literature and in our own work to assess
protein stability at interfaces, it appears that in the vast majority of the cases, a stable surface
adsorption combined with a moving liquid-air interface is present when solutions are agitated
in one way or another in the presence of air. A partial dehydration of adsorbed proteins by intermittent wetting similar to the processes observed in our microchannel setup is therefore likely to occur. Unfortunately, it is difficult and most studies do not aim at analysing the triple interface but rather follow protein aggregates in the bulk solution or at a particular interface where they accumulate, without tracing their origin. Intermittent wetting zones are likely to represent the primary location for preferential protein aggregation, rather than liquid-air or solid-liquid interfaces alone.

Pharmaceutical processes involving both a hydrophobic solid surface to which proteins can strongly adsorb and a moving air interface due to voluntary mixing (reconstitution or administration protocols) or fortuitous shaking (transport), should therefore be carefully monitored for the risk of potential protein aggregation. It is noteworthy to mention that surfactants, like polysorbate 80, protect insulin from aggregation at a dynamic triple interface in our microchannel system⁹⁹. This emphasizes the importance of the role that such formulation additives play and justifies a thorough inspection of the molecular mechanisms involved in this stabilization.

# 6 Monoclonal antibody adsorption at material surfaces

This chapter presents my current research activity in collaboration with the Department of Formulation in Sanofi (Vitry sur Seine, France). The results have been obtained by Guillaume Lefèbvre who is currently doing a Cifre PhD thesis (2017-2020) under the guidance of Vasco Filipe (Sanofi) and myself.

## 6.1 The need for early stability studies in formulation optimization

Our research on insulin stability at interfaces has highlighted the primary role that adsorption of proteins at hydrophobic interfaces plays in the formation of protein aggregates. Indeed, stable interfacial adsorption is the very trigger of stability issues. As long as it can be minimized or, even better, avoided, the protein is not submitted to interfacial stresses like, for example, dehydration in the case of intermittent wetting, and its stability will not be affected. Therefore it is important to investigate the interfacial activity of a protein with appropriate *in situ*
characterisation techniques and to propose protocols that can be implemented into the
development pipeline to address this topic.

Therapeutic monoclonal antibody formulations often include surfactants that stabilize the protein in solution (Table 4-3). The choice of the surfactant type and concentration has to be led by the molecular characteristics of the drug, the aggregation mechanism that it should prevent and the nature of the interfaces that it encounters¹⁴⁴. Nonionic polysorbates (PS), such as PS80 or PS20, are often used because of their pronounced surface activity and their low toxicity¹⁴⁵. In theory, surfactants protect proteins in solution by a thermodynamic or a colloidal stabilization mechanism¹⁰³,¹⁴⁵. In the first case, their preferential adsorption to interfaces shields the surface and prevents access for the protein at the interface. Adsorption-induced aggregation can thus efficiently reduced. Colloidal stabilisation can be achieved by direct molecular interaction with the protein, thus lowering its surface activity and preventing homomolecular aggregation in solution. While direct PS-mAb interactions have been shown to be too weak to impact its colloidal stability¹⁴⁶, numerous studies document that interfaces (solid, liquid and gaseous) pre-adsorbed with PS, prevent protein binding (for recent examples see ¹⁴⁷⁻¹⁴⁹). An orogenic displacement can also occur, whereby the surfactant adsorbs gradually, starting from nucleation sites until a critical surface pressure is attained at which previously adsorbed proteins desorb¹⁵⁰.

One could assume that the protective effect of surfactants is reflected by the
surfactant:protein molar ratio in therapeutic formulations, at least in the case of colloidal
stabilisation. Comparing more than 50 different commercial therapeutic mAb formulations for
their PS content, it appears that there is no obvious correlation between the mAb dosage and
the surfactant concentration (Figure 6-1). There are thus no easy-to-implement rational
guidelines that could direct the choice and concentration of surfactants in formulation
optimization.
<figure><img src="image_47.png" /><figcaption>Figure 6-1: Plot showing the mAb concentration as a function of surfactant concentration for 53 different therapeutic mAb formulations (36 formulations with PS80 (blue diamonds), 17 formulations with PS20 (red squares)). PS80, polysorbate 80; PS20, polysorbate 20. Figure credits to G Lefèbvre.</figcaption></figure>

The adsorption behaviour of a protein and surfactants at a given material surface can be used
to guide drug candidate selection and formulation optimization. It is therefore our aim to
develop biophysical characterization protocols that allow studying the adsorption and
desorption properties of mAbs and surfactants at the solid-liquid interface. Our first approach
is based on surface plasmon resonance imaging (SPRi) which allows to follow these events in
real time and in parallel on different relevant surface chemistries. Chapters 6.2-6.4 summarize
the kinetic SPRi studies that have been obtained so far for a given mAb and surfactant
formulation. In chapter 6.5 I propose to use surface-enhanced ellipsometric contrast
microscopy (SEEC) to image surface adsorption events with molecular resolution in order to
provide a full picture of the interface activity of molecules in complex formulations. We focus
in a first instance on the solid-liquid interface and will then analyse the liquid-air and triple
interface. An overview of the experimental approaches is given in Table 6-1. Our research
efforts aim at contributing to the development of tools and knowledge to understand
molecular interface adsorption and guide formulation optimization. These contributions will
help to anticipate device and container compatibility in early stability studies of drug
candidates which has become an important topic. Indeed, the healthcare shift towards patient
self-injection and homecare has initiated a rapid development of innovative drug storage,
reconstitution and injection devices raising new challenges for biotherapeutic stability¹⁵¹.
<table><caption>Table 6-1: Layout of the experimental approaches with parameters to be analysed and expected output.</caption><thead><tr><th>Experimental step</th><th>Technique used</th><th>Molecules</th><th>Parameters to be analysed</th><th>Output</th></tr></thead><tbody><tr><td rowspan="2">Individual, sequential and concomittent adsorption studies</td><td>SPRI</td><td>mAbs<br>Polysorbates<br>Poloxamer</td><td>Surface chemistry<br>Concentration<br>Competitional binding</td><td>adsorption/desorption<br>behaviour vs time<br>adsorbed/desorbed<br>amount</td></tr><tr><td>SEEC</td><td></td><td></td><td>surface coverage/density<br>vs time<br>layer thickness</td></tr><tr><td>Kinetic adsorption studies</td><td>SPRI</td><td>mAbs<br>Polysorbates<br>Poloxamer</td><td>Surface chemistry</td><td>apparent k<sub>on</sub> rates</td></tr><tr><td>Conformational adsorption studies</td><td>Fluorescence<br>FTIR</td><td>mAbs<br>Polysorbates<br>Poloxamers</td><td>Surface chemistry<br>Air interface</td><td>conformational signature</td></tr></tbody></table>

## 6.2 Adsorption kinetics of mAbA at hydrophobic and hydrophilic surfaces

We have started working on the adsorption properties of a monoclonal antibody (termed mAbA) and different surfactants, supplied by Sanofi. The immunoglobulin is a glycosylated monoclonal IgG antibody of isotype 1, formulated at 30 g.L⁻¹ in a 20mM histidine buffer at pH6.0 containing 10% sucrose and 0.02 %w/v polysorbate 80. mAbA has been prepared without polysorbate for the experimental analysis. The following surfactants have been tested so far:

<table><caption>Table 6-2: Characteristics of polysorbates; the critical micellar concentration (CMC) is indicated for pure water.</caption><thead><tr><th>Surfactant</th><th>Supplier</th><th>CMC<br>(%w/v)</th><th>N<sub>ag</sub><br>(micelle aggregation number)</th><th>Micellar size<br>(nm)</th></tr></thead><tbody><tr><td>Polysorbate 80 (PS80)</td><td>Seppic</td><td>0.00332</td><td>280</td><td>10.7</td></tr><tr><td>Polysorbate 20 (PS20)</td><td>Seppic</td><td>0.00299</td><td>150</td><td>8.5</td></tr></tbody></table>
We are performing the adsorption studies on a Horiba Scientific SPRi-Lab+™ instrument. A chemical functionalisation protocol was used to obtain a prism sensor surface that is covered with hydrophilic polyethyleneglycol-thiol (SH-PEG<sub>8</sub>, water contact angle: 36° ± 3°) on one half and hydrophobic hexadecane-thiol (SH-C16, water contact angle: 103° ± 2°) on the other half<sup>110</sup>. This dual gold functionalisation allows to record the adsorption behaviour of mAbA and PS in parallel on the hydrophobic and hydrophilic surfaces. For this several analysis spots are defined on the surface, where the SPR signal is recorded. A sensogram is then plotted (Figure 6-2) which shows the average reflectivity measured on several spots as a function of time. The adsorption of mAbA is much more prominent on the hydrophobic surface than on the hydrophilic one (Figure 6-2A), with reflectivity difference maxima after rinsing on the hydrophobic surface ΔRH = 6 RU (mean ΔRH = 7.4 ± 1 RU, n=3) and on the hydrophilic surface ΔRh = 0.2 RU (mean ΔRh = 1.2 ± 1 RU, n=3), respectively.

<figure><img src="image_48.png" /><figcaption>Figure 6-2: A: Sensograms representing the successive adsorption and desorption kinetics of two mAbA solutions (first injection 0.25 g.L<sup>-1</sup>, second injection 0.625 g.L<sup>-1</sup>) on the hydrophobic (blue) and hydrophilic (red) surfaces. Blue numbers indicate the ΔR measured on the hydrophobic surface at the indicated time points (double-headed arrow). Bf 1x represent buffer washes. B: ΔR values as a function of mAbA concentration measured on the hydrophobic (blue) and hydrophilic (red) surfaces. Sensograms display mean reflectivity signals obtained for at least three independent measurements at 4 different locations on the prism.</figcaption></figure>

This preferential adsorption of mAbA on hydrophobic surfaces increases with the antibody bulk concentration (Figure 6-2B) and the surface contact time and is observed for all concentrations tested (0.005-0.25 g.L<sup>-1</sup>) within the limit of the saturation of the SPR signal.

Successive injections of mAbA separated by a buffer wash indicate that the antibody can bind, albeit not stably, on previously adsorbed antibodies which therefore form a monolayer on the surface (Figure 6-2A). In our study, while mAbA has a net positive charge at pH6, the functionalized hydrophobic and hydrophilic surfaces are not charged. We therefore attribute
the surface activity of mAbA predominantly to hydrophobic interactions. Experimentally, we
measured a maximal stable mAbA adsorption at 0.25 g.L⁻¹ of 0.9 ± 0.1 ng.mm⁻² (5 min contact
time). Given the dimensions of the hydrophobic surface of the prism (71.25 mm²), this
corresponds to approximately 64 ± 7ng. This is slightly lower than the adsorbed amounts
published for other IgGs⁴³,¹⁴⁷, which are in the order of 10 ng.mm⁻². Differences on the
amounts of surface-adsorbed mAbs should however be compared with care as they have been
obtained on chemically different surfaces, with different protocols and techniques that have
different sensitivities (TIRF, QCM-d, see also 4.4 Methods to characterize protein interfacial
adsorption).

Bee and colleagues¹⁵² have calculated the theoretical adsorption footprint and loading values for a mAb (IgG) monolayer according to different packing efficiencies. For the densest, end-on packing order and the least dense, side-on random sequential adsorption order, they found 0.26 and 1.04 m².mg⁻¹, (3.85 and 0.96 ng.mm⁻²) respectively. Considering these surface packing orders, 68.5-274.0 ng mAbA could potentially bind on the hydrophobic contact surface of our SPRi prism (71.25 mm²). Likewise a monolayer mass of 2 ng.mm⁻² was predicted for antibodies adopting a "flat-on" orientation on the surface by Buijs and colleagues¹⁵³. We therefore conclude that the adsorbed mAbA amount, remaining after a buffer wash on the hydrophobic surface of the prism, is in accordance with a low density, side-on random packing order. Wiseman and Frank¹⁵⁴ analysed the orientation of an IgG1 antibody adsorbed on a hydrophobic surface as a function of its concentration by QCM-d. They conclude that up to a surface coverage of 2 ng.mm⁻², antibodies are adsorbed very rigidly in a "flat-on" orientation. Above this surface-coverage, antibodies approaching the hydrophobic surface bind in the remaining free interstices on the surface and adopt a more end-on orientation with a weaker coupling to the surface. Our sensogram, tracing the adsorption and desorption of mAbA on the C16-SH-coated surface (Figure 6-2A), shows an initial fast adsorption phase (first 30 s), followed by a slower adsorption phase (beyond 30 s). These distinct phases could potentially reflect the primary "flat-on" adsorption described by Wiseman and Frank¹⁵⁴, followed by a filling-up of free surface spaces between bound mAbA molecules. The subsequent flowing of buffer results in a partial desorption of bound mAbA, presumably dissociating weakly bound "end-on" antibodies. The remaining adsorbed mass (0.9 ± 0.1 ng.mm⁻² for mAbA at 0.25 g.L⁻¹
and 100µL.min⁻¹) is in accordance with the value of 2ng.mm⁻², determined by Wiseman and Frank¹⁵⁴. Furthermore, a second injection of mAbA did not result in an increased reflectivity signal (Figure 6-2A), documenting that irreversible mAbA adsorption relies on available surface area and cannot be mediated by adsorption on previously adsorbed antibodies. This is consistent with results obtained by Bee and colleagues¹⁵⁵ who demonstrated monolayer coverage for a mAb interaction with a variety of hydrophilic nanoparticle surfaces. It is noteworthy that conformational changes in the Fab region of an IgG adsorbed on Teflon have been documented¹⁵⁶ as well as aggregation upon IgG adsorption to stainless steel⁸¹. In the latter case, antibody aggregation was occurring by successive layers of IgGs adsorbing on top of an irreversibly bound surface monolayer.

## 6.3 Polysorbates prevent adsorption but do not displace mAbA on hydrophobic and hydrophilic surfaces

In order to test whether polysorbates were able to protect mAbA from adsorption at surfaces we followed the SPRi signal during a sequential experiment using the polysorbates at 0.02% w/v: first polysorbates were put in contact with the functionalized prism surface followed by a washing step and then mAbA was flushed over the surface at 0.01 g.L⁻¹. As shown on Figure 6-3A, PS80 at 0.02% w/v adsorbs readily on the hydrophobic surface (ΔRH = 6 RU) while its adsorption on the hydrophilic surface remains very low (ΔRh = 0.5 RU). A subsequent injection of mAbA increases the SPRi signal very slightly but a buffer wash demonstrates that mAbA was not able to stably adsorb on any of the two surfaces. In a similar way, a pre-incubation of the prism surface with PS20 at 0.02% w/v prevents mAbA adsorption. A second injection of PS80 or PS20 shows that additional polysorbate molecules are able to bind on the pre-adsorbed hydrophobic surface, where mAbA molecules could not stably bind.
<figure><img src="image_49.png" /><figcaption>Figure 6-3: A: Sensograms representing the successive adsorption and desorption kinetics of a PS80 solution at 0.02% w/v followed by a mAbA solution at 0.01 g.L⁻¹ and a second PS80 solution at 0.02% w/v on the hydrophobic (blue) and hydrophilic (red) surfaces. B Sensograms representing the successive adsorption and desorption kinetics of a mAbA solution at 0.01 g.L⁻¹, followed by a PS80 solution at 0.02% w/v and a second mAbA solution at 0.01 g.L⁻¹ on the hydrophobic (blue) and hydrophilic (red) surfaces. Blue numbers indicate the ΔR measured on the hydrophobic surface at the indicated time points (double-headed arrow). Bf 1x represent buffer washes. Sensograms display mean reflectivity signals obtained for at least three independent measurements at 4 different locations on the prism.</figcaption></figure>

We then performed the SPRi study in the reverse order and injected first the mAbA at 0.01 g.L⁻¹, followed by a buffer wash, before flushing the surface with the polysorbates at 0.02% w/v. As can be seen on Figure 6-3B, the mAbA binds preferentially to the hydrophobic surface and stays bound after rinsing with buffer (ΔRH = 7.5 RU). The increase of the SPRi signal after the subsequent injection of PS80 at 0.02% w/v (ΔRH = 10.5 RU) shows that an additional mass has been adsorbed at the hydrophobic surface, likely representing PS80 molecules that have been able to bind in between or on adsorbed mAbA molecules. This adsorbed mass desorbs extremely slowly upon buffer rinsing from the hydrophobic surface. An additional injection of mAbA at 0.01 g.L⁻¹ does not change the SPRi signal anymore proving that no further molecular adsorption is possible. These results indicate that PS80 does not desorb previously bound mAbA from the hydrophobic surface. On the hydrophilic surface, the SPRi reflecticity signals measured upon contact with mAbA or PS80 do not allow concluding whether one molecular species remains adsorbed or is replaced by the other at the surface as both mAbA and PS80 bind very poorly to the hydrophilic surface. Similar results were obtained for PS20. The masses of PS20 and PS80, bound to a hydrophobic surface that is pre-adsorbed with mAbA, are very similar, indicating that both polysorbates are equally capable to fill in exposed free surface spaces or bind to adsorbed mAbA.
When polysorbates are adsorbed before mAbA injection, they completely prevent mAbA adsorption on the hydrophobic surface (Figure 6-3A). This is in agreement with the results of Kapp and colleagues¹⁴⁷ who have documented the same surface protection by a pre-adsorption of PS80 for two different IgG1 and IgG2 antibodies on a hydrophobic surface by TIRF and QCM-d. Joshi and colleagues¹⁵⁷ have shown a similar protective effect for a pre-coating with PS80 on lysozyme adsorption on a hydrophobic surface but a PS80 pre-coating could not prevent recombinant factor VIII adsorption¹⁵⁸. A second injection of polysorbates shows that additional polysorbate can bind, where mAbA could not. This is likely due to the smaller size of polysorbates, which favours them for fitting into free surface spaces compared to the mAbA. The protective effect of a polysorbate precoating is likely to be dual: first, polysorbates occupy the hydrophobic interaction sites on the surface and mAbA cannot bind anymore and second, the hydrophilic heads of polysorbates sterically repulse mAbA molecules which cannot efficiently approach the surface.

When mAbA is adsorbed first to the hydrophobic surface, and polysorbates are injected subsequently after a buffer wash, the reflectivity signals show that additional polysorbate can bind in between or on surface-bound mAbA molecules (Figure 6-3B). There is no evidence for desorption and replacement of mAbA molecules by polysorbates on the hydrophobic surface. Indeed the reflectivity signal after the polysorbate injection (ΔRH=10.5 for PS80) is higher than the maximal ΔRH values obtained for polysorbate adsorption alone (not shown). We thus conclude that polysorbates are able to bind in addition to bound mAbA to the hydrophobic surface but do not displace mAbA while binding. A second injection of mAbA does not lead to an increase of the reflectivity signal (Figure 6-3B) that is observed for sequential injections of mAbA in Figure 6-2A. This indicates that the polysorbates bound in between or on adsorbed mAbs prevent further mAb adsorption efficiently. Kapp and colleagues show a drop in the intrinsic antibody fluorescence signal (TIRF) upon PS80 (2x CMC) rinsing of an IgG adsorbed on a hydrophobic surface¹⁴⁷. They attribute this to a partial desorption of the antibody although the QCM-d signal of the same experimental sequence does not show a decrease in the adsorbed mass after the PS80 wash. PS80 does not desorb recombinant factor VIII from a hydrophobic surface as shown by ellipsometry¹⁵⁸. Couston and colleagues, however, showed that polysorbates can desorb an adsorbed mAb from a hydrophobic silica surface with
efficiencies depending on the surface affinity of the polysorbate<sup>159</sup>. Similarly Serno and colleagues have shown efficient surface displacement of an IgG from the air-water interface by PS80<sup>148</sup>. In our case PS20 and PS80, at concentrations 10 times higher than their CMC, are incapable of displacing mAbA from the hydrophobic surface, despite their binding. It could be speculated that the surface orientation of the irreversibly adsorbed mAbA layer does not leave enough free space or is too strong for efficient polysorbate nucleation to establish a critical surface tension to displace mAbs. The extent of the orogenic effect depends strongly on the surfactant, the protein and the surface properties and can therefore not be generalized from one experimental system to another.

To conclude, these first experiments show that mAbA adsorbs preferentially as a monolayer on hydrophobic surfaces. Pre-adsorbed PS shields the surface from mAbA binding, so that the latter is no longer able to adsorb. However, once mAbA is bound, PS cannot displace it from the hydrophobic surface. These findings provide the first input into the molecular adsorption behaviour of the antibody and the surfactants, examined individually as a function of surface hydrophobicity.

In the therapeutic formulation mAbA is present at 30 g.L⁻¹ and PS80 at 0.02 %w/v. The competition for surface adsorption depends on the speed at which each molecule reaches the surface which is given by: V<sub>ads</sub> = k<sub>onapp</sub> [C], in which k<sub>onapp</sub> represents the apparent association rate constant of the molecule with the surface and [C] its respective concentration. Knowing the apparent association rate constants of formulation molecules for a given surface would thus allow predicting which molecule would adsorb first as a function of their respective concentrations.

It is known that the chosen formulation concentrations of mAbA and PS80 guarantee the stability of mAbA. This said, the molecular mechanism stabilising mAbA is not established and it is critical to understand protein-surfactant-surface interactions to guide formulation optimization. In order to investigate the competition between mAbA and PS80 for adsorption at a hydrophobic surface in the formulation conditions, we therefore set out to determine the apparent association rate constants of mAbA and PS for a bare hydrophobic surface.
## 6.4 Determination of apparent $k_{on}$ rates for mAbA and polysorbates at a hydrophobic surface

SPR is widely used as a biosensing technique for the determination of association and dissociation rate constants in molecular interactions (Table 4-2). In the vast majority of the cases, one interacting species (ligand) is immobilized on the sensor surface while the second one (analyte) is flown over the surface in a controlled manner. The ligand is immobilized in such a way that the number of binding sites is very low compared to the concentration of the analyte in solution in order to avoid mass transport limitations¹⁶⁰. In our case however, there is no immobilized ligand to which mAbA or PS bind, the surface-associated binding sites are unknown and they are potentially very abundant. It is therefore likely that rapid and massive binding of the analyte (mAbA or surfactant) in solution to the hydrophobic surface creates a depletion layer in the bulk analyte concentration near the surface, limiting its adsorption (Figure 6-4).

The formation of such a depletion layer implies that the diffusion of analyte from the bulk solution cannot match the removal of analyte by surface binding.

<figure><img src="image_50.png" /><figcaption>Figure 6-4: Schematic view of the prism surface in a common SPR binding study between a mAb and a limited number of surface-bound ligands (left) and our study with unknown and abundant surface binding sites (right). The blue shading represents the local concentration of mAb at the vicinity of the surface, [mAb]l, compared to the initial injected mAb concentration [mAb]inj.</figcaption></figure>

For the determination of analyte association rate constants, however, it is assumed that the bulk concentration of analyte remains constant throughout the flow chamber at its initial injected value. Association rate constants commonly measured by SPR are in the range of 10⁴-10⁵ M⁻¹·s⁻¹, which is orders of magnitude below that for diffusion-limited reactions for proteins (10⁹-10¹⁰ M⁻¹·s⁻¹)¹⁶¹ and therefore depletion of the bulk analyte is usually not observed¹⁶².
In our study, however, we demonstrate a striking dependence of the association of mAbA with the hydrophobic surface on the flow rate and the position on the surface inside the flow chamber with respect to the inlet (Figure 6-5).

<figure><img src="image_51.png" /><figcaption>Figure 6-5: Plot showing the kinetic association slopes as a function of position in the flow chamber (distance definition, see Figure 6-6) at a flow rate of 100 µL/min (left) and 400 µL/min (right) for the indicated mAbA concentrations.</figcaption></figure>

Therefore it is clear that a depletion layer forms, which places mAbA molecules too far from the surface for diffusion to allow them to approach the surface sufficiently in order to adsorb. This depletion layer increases, the further the solution flows over the hydrophobic surface (cf distance d in Figure 6-6) and hence the association rate becomes more and more underestimated with the distance along the flow chamber. This effect is especially pronounced at high mAbA concentrations and for high flow rates (Figure 6-5).

An efficient strategy to limit the creation of a depletion layer is to minimize the number of binding sites for mAbA on the surface. Knowing that mAbA does not bind to hydrophilic surfaces we chose to functionalize a gold prism such that the adsorption kinetics of mAbA could be studied at the smallest area possible when the antibody encounters the hydrophobic surface (Figure 6-6).
<figure><img src="image_52.png" /></figure>

Figure 6-6: Schematic representation of the surface of a prism that is uniformly functionalized with hydrophobic hexadecane-thiol (blue, left) and one that is partly functionalized with polyethyleneglycol-thiol (red) and hexadecane-thiol (blue) (right). The study window is indicated by a dotted rectangle. The SPR signal is recorded at a spot that is positioned at the very border between the two surface chemistries and that has a minimal diameter (Ø) (far right).

In this way it is possible to limit the depletion of the bulk mAbA concentration and to guarantee that the concentration of mAbA, in the vicinity of the surface position on which the binding kinetics is recorded, is approximately equal to the initial injected concentration.

We thus chose to record the mAbA binding kinetics for the hydrophobic surface right at the frontline between the hydrophilic and hydrophobic surface functionalisations and to limit the spot size to 33 µm (minimal spot size for our SPRi setup) (Figure 6-6). With these parameters we were able to estimate the apparent kon of mAbA for the hydrophobic surface to 1.4 ± 0.1 · 10⁶ M⁻¹·s⁻¹. Our preliminary results (Figure 6-7) show that above a flow rate of 800 µL/min, the apparent kon rate stabilizes. Unfortunately the spot size cannot be further decreased than 33 µm using the software associated with our SPRi apparatus but it is likely that even smaller spot sizes could provide a more accurate estimation. An extrapolation down to the minimal binding site area corresponding to the dimensions of a mAb size (3 x 10 nm), results in an estimated kon app of 1.8-2.7 · 10⁷ M⁻¹·s⁻¹.
So far no apparent molecular association rate constant for a bare surface without immobilized ligands has been described in the literature. This is certainly in part due to the experimental difficulty. Our SPRi setup allows to manually save images of the surface at a given spot position but this is only possible by stopping the experiment. If such images could be saved with a good time resolution, their analysis would allow to refine our data by minimizing the analysed surface area even further.

<figure><img src="image_53.png" /><figcaption>Figure 6-7: Apparent mAbA kon rates for a hydrophobic hexadecane-thiol surface as a function of spot diameter and flow rate. When data were obtained with multiple replicas (3 or more) the mean value is indicated and the error bars represent the standard deviation.</figcaption></figure>

Once the apparent association rate constants for mAbA and PS are determined, one can theoretically predict which molecular species adsorbs first as a function of their respective formulation concentrations. It will then be necessary to verify that, at the proposed PS80 concentration (0.02 % w/v) used in the formulation of mAbA (30 g/L), the latter is effectively protected from surface adsorption by kinetic competition with PS80. For an injection of a mixture of mAbA and PS80, if mAbA adsorbs first at the hydrophobic surface, PS80 will not be able to desorb it (see results Figure 6-3B). However if the opposite is the case, mAbA will not be able to bind at the hydrophobic surface (see results Figure 6-3A). The presence of adsorbed mAbA can be tested using the SPRi signal of protein G or a secondary anti-IgG1 antibody that can bind to adsorbed mAbA. It might also be possible that the surface adsorption properties of the surfactants change in the presence of protein. Evidence for accelerated adsorption of PS in the presence of protein has been reported in the literature¹⁶³. It is assumed that proteins can disrupt self-association of surfactants into micelles, thereby releasing monomers that will