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Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1648

Deciphering Binding Patterns of Therapeutic with Immune Cells

From Method Development to Application

SINA BONDZA

ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6206 ISBN 978-91-513-0902-6 UPPSALA urn:nbn:se:uu:diva-406875 2020 Dissertation presented at Uppsala University to be publicly examined in Rudbecksalen, Rudbecklaboratoriet, Dag Hammarskjölds Väg 20, Uppsala, Thursday, 7 May 2020 at 09:00 for the degree of Doctor of Philosophy (Faculty of Medicine). The examination will be conducted in English. Faculty examiner: Professor Mark Cragg (Academic Unit of Cancer Sciences, University of Southampton).

Abstract Bondza, S. 2020. Deciphering Binding Patterns of Therapeutic Antibodies with Immune Cells. From Method Development to Application. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1648. 68 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0902-6.

Reversible binding, for example between signaling molecules and receptors on the surface, is one of the main means to communicate information in cellular systems. Knowledge about how molecules interact is crucial for both understanding biological function and for therapeutic intervention. The cellular environment often makes -receptor interactions complex with the membrane providing structural support and containing other components that interfere with the interaction. One of the fastest growing drug classes for targeting cellular receptors are monoclonal antibodies (mAb), in particular within oncology. Therapeutic mAbs can have direct effects on target cells mediated via the Fab-domain and immune-related effects that are mediated via the Fc-domain. An example of the latter is activation of the by binding of its first component C1q to Fc-domains. Furthermore, immune cells can recognize Fc-domains via Fc-receptors and cause target cell death by a process called -dependent cellular cytotoxicity (ADCC). Increased understanding about structure-binding-function relationships facilitates rational drug design, as has been demonstrated with the development of next-generation mAbs that harbor a structural modification on their Fc-domain that strengthens the interaction with immune cells thereby increasing ADCC efficacy. In this thesis, assays for characterizing mAb binding and mAb mediated interactions on live cells were developed and applied to illustrate how detailed knowledge about binding processes helps to understand the relation between binding and biological function. Paper I describes a protocol for real-time interaction analysis of antibodies with live immune cells enabling binding measurements in a relevant cellular context with the data resolution needed to study complex binding processes. Paper II presents a novel real-time proximity assay that allows to study binding kinetics in connection with receptor dimerization and clustering thereby aiding in decipher complex interactions. In paper III, binding patterns of the CD20 mAbs rituximab, ofatumumab and obinituzumab were established on cells revealing that the fraction of bivalently bound mAbs differed resulting in dose-dependent affinities for rituximab and obinituzumab. In paper IV, a C1q binding assay to mAb opsonized cells was developed and it was shown that a higher degree of bivalent binding correlated with stronger C1q binding for the CD20 mAbs evaluated in paper III. In paper V, an assay to study mAb mediated cell-cell interactions was set-up and it was found that neutrophil engagement with target cells was similar for antibodies of IgG and IgA isotype.

Keywords: affinity, binding kinetics, CD20, cell-based assay, immunology, receptor-ligand interaction, therapeutic antibody

Sina Bondza, Department of Immunology, Genetics and Pathology, Medical Radiation Science, Rudbecklaboratoriet, Uppsala University, SE-751 85 Uppsala, Sweden.

© Sina Bondza 2020

ISSN 1651-6206 ISBN 978-91-513-0902-6 urn:nbn:se:uu:diva-406875 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-406875)

To all who supported me

List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Bondza, S., Foy, E., Brooks, J., Andersson, K., Robinson, J., Richalet, P., Buijs, J. (2017) Real-time Characterization of An- tibody Binding to Receptors on Living Immune Cells. Frontiers in Immunology, 8:455

II Bondza, S., Björkelund, H., Nestor, M., Andersson, K., Buijs, J. (2017) Novel Real-Time Proximity Assay for Characterizing Multiple Receptor Interactions on Living Cells. Analytical Chemistry, 89(24):13212–13218

III Bondza S., ten Broeke T., Leusen J.H.W., Buijs J. Bivalent Binding on Cells varies between CD20 Antibodies and is Dose- dependent. submitted Manuscript

IV Bondza S., Buijs J. Degree of Bivalent Binding correlates with C1q Binding Strength for CD20 Antibodies, Rituximab, Ofatu- mumab and Obinituzumab. Manuscript

V Brandsma, A.M., Bondza, S., Evers, M., Koutstaal, R., Ne- derend, M., Jansen, J.H.M., Rösner, T., Valerius, T., Leusen, J.H.W., ten Broeke, T. (2019) Potent Fc Receptor Signaling by IgA Leads to Superior Killing of Cancer Cells by Neutrophils Compared to IgG. Frontiers in Immunology, 10:704

Reprints were made with permission from the respective publishers.

Papers not included in this Thesis

Mansouri, L., Sutton, L.-A., Ljungström, V., Bondza, S., Arngården, L., Bhoi, S., Larsson, J., Cortese, D., Kalushkova, A., Plevova, K., Young, E., Gunnars- son, R., Falk-Sörqvist, E., Lönn, P., Muggen, A.F., Yan, X.-J., Sander, B., Enblad, G., Smedby, K.E., Juliusson, G., Belessi, C., Rung, J., Chiorazzi, N., Strefford, J.C., Langerak, A.W., Pospisilova, S., Davi, F., Hellström, M., Jern- berg-Wiklund, H., Ghia, P., Söderberg, O., Stamatopoulos, K., Nilsson, M., Rosenquist, R., (2014) Functional loss of IκBε leads to NF-κB deregulation in aggressive chronic lymphocytic leukemia. Journal of Experimental Medicine, 212(6):833–843

Bondza, S., Stenberg, J., Nestor, M., Andersson, K., Björkelund, H. (2014) Conjugation effects on antibody-drug conjugates: evaluation of interaction ki- netics in real time on living cells. Molecular Pharmaceutics, 11(11): 4154– 4163

Contents

Introduction ...... 11 Cellular communication and signaling ...... 11 Kinetics of pharmaceutical drugs ...... 13 Real-time Interaction analysis and homogenous interactions ...... 14 Heterogenous interactions ...... 18 1:2 interaction model ...... 18 1:1, 2-state interaction model ...... 20 Bivalent interaction model and ...... 20 Interaction Map ...... 21 Techniques for biomolecular real-time interaction analysis ...... 23 Biophysical measurement techniques ...... 23 Cell-based techniques ...... 24 LigandTracer ...... 25 Cancer and therapy options ...... 26 Monoclonal antibodies for cancer therapy ...... 28 Mechanisms of action of mAb therapy – with a focus on CD20 mAbs ... 30 Fab mediated effects ...... 30 Antibody-drug conjugates ...... 31 Fc-mediated effects ...... 32 Antibody dependent complement activation ...... 32 Antibody dependent cellular cytotoxicity and phagocytosis ...... 33 Clinical relevance of Fc-mediated effects ...... 35 Improving therapy with next-generation mAbs and alternative formats .. 36 IgA isotype for antibody therapy ...... 36 Bispecific antibodies ...... 37 Scope of Thesis ...... 39 Summary of Findings ...... 41 Paper I ...... 41 Real-time Characterization of Antibody Binding to Receptors on Living Immune Cells ...... 41 Aim and Background ...... 41 Results ...... 41 Discussion ...... 42 Paper II ...... 43

Novel Real-Time Proximity Assay for Characterizing Multiple Receptor Interactions on Living Cells ...... 43 Aim and Background ...... 43 Results ...... 43 Discussion ...... 45 Paper III ...... 46 Bivalent Binding on Cells varies between CD20 Antibodies and is Dose-dependent ...... 46 Aim and Background ...... 46 Results ...... 46 Discussion ...... 48 Paper IV ...... 49 Degree of Bivalent Binding correlates with C1q Binding Strength for CD20 Antibodies, Rituximab, Ofatumumab and Obinituzumab ... 4 9 Aim and Background ...... 49 Results ...... 49 Discussion ...... 51 Paper V ...... 52 Potent Fc Receptor Signaling by IgA leads to Superior Killing of Cancer Cells by Neutrophils Compared to IgG ...... 52 Aim and Background ...... 52 Results ...... 52 Discussion ...... 53 Conclusion and Future Perspectives ...... 55 Acknowledgements ...... 57 References ...... 59

Abbreviations

3-OST 3-O-Sulfotransferase ADC antibody-drug conjugate ADCC antibody-dependent cellular cytotoxicity ADCP antibody-dependentcellular phagocytosis B binding signal BCR B-cell receptor BLI biolayer interferometry Bmax maximum binding signal bsAb bi-specific antibody DNA deoxyribonucleic acid CD cluster of differentiation CDC complement-dependent cytotoxicity CDR complementary-determining regions CTLA-4 cytotoxic T lymphocyte-associated 4 C1q DPI dual polarization interferometry EGF epidermal growth factor EGFR epidermal growth factor receptor EMA European Medicines Agency ERK extracellular signal-regulated kinase Fab fragment antigen-binding F(ab)’2 dimeric fragment antigen-binding Fc fragment crystallizable FcγR Fc gamma receptor FcnR neonatal Fc receptor FcR fragment crystallizable receptor FDA Food and Drug Administration of the United States FRET förster resonance energy transfer HER2 human epidermal growth factor receptor 2 Ig immunoglobulin ITAM immunoreceptor tyrosine-based activation motif ITIM immunoreceptor tyrosine-based inhibitory motif ka association rate constant kd dissociation rate constant KD equilibrium or affinity

L ligand LT ligand-target complex LT* stabilized ligand-target complex mAb monoclonal antibody MAPK mitogen-activated protein kinase MHC major histocompatibility complex NK-cell natural killer cell OBI obinituzumab OFA ofatumumab PCD programmed cell death PD-1 programmed cell death protein 1 QCM quartz crystal microbalance RTX rituximab SIRPα signal regulatory protein α SPR surface plasmon resonance T target TRAIL tumor necrosis factor-related apoptosis-inducing ligand VEGF vascular endothelial growth factor VEGFR vascular endothelial growth factor receptor

Introduction

Cells are considered the basic unit of life. An organism can consist of only a single cell as is the case for bacteria or it can be multi-cellular and be made up of trillions of cells, as is the case for humans. Cells are enclosed by a lipid membrane studded with a plethora of different proteins that can serve as mark- ers to identify the cell as part of the organism or be involved in cellular com- munication. Cellular communication is essential for all multicellular organisms in order to coordinate single cells to work together in more complex assemblies such as tissues and organs. In some cases, the coordination of cells takes place via direct cell-cell contacts, in other instances, cells from different areas of the body need to share information. Communication between non-neighboring cells can occur via signaling molecules that are produced in one cell and then send to another cell where they dock onto receiver-proteins embedded in the plasma membrane, usually referred to as cell-surface receptors. Upon interac- tion with a signaling molecule cell surface receptors can become activated and transfer the signal from the extracellular into the intracellular space; a process that often involves a conformational change of the receptor. The signal is typ- ically passed on via the intracellular part of the receptor to a cascade of pro- teins interacting with each other, ultimately leading to a change in cellular behavior in response to the received information. Coordination of cells via molecular interactions enables an organism to perform more complex and ad- vanced tasks than what a single cell is capable of, thereby allowing a multi- cellular organism to become more than the sum of its parts1.

Cellular communication and signaling Signal transmission from the extracellular to the intracellular space is tightly regulated to allow response to an actual signal but prevent reaction to spurious stimuli caused by noise2. Molecules that bind reversibly to a specific site of a receptor are referred to as ligands and can be of varying chemical composition from small molecules to big proteins and also include pharmaceutical drugs. Ligands can have both activating and inhibiting impact on receptor signal- ing and binding of more than one ligand to one or several receptors can be necessary to mediate a signal. Besides ligand-receptor interactions, also mo- lecular interactions with other molecules on the cell surface contribute to and

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regulate signaling. Examples of the latter are receptor interactions with co- receptors, the formation of receptor homo- and heterodimers, as well as higher oligomeric forms and clusters3,4. Not only the proteins embedded in the cell membrane, but also its lipid composition and organization regulate signal propagation2. The notion that the cell membrane is not a random assembly of floating lipids and proteins but instead is an ordered structure with spatial organization became apparent dur- ing the last two decades. For instance, an emerging concept is that receptors are distributed non-randomly on the cell membrane and that organization of the membrane in micro-compartments limits diffusion and thereby increases the likelihood of interaction between receptors5. It has also been suggested that transient microdomains can be formed that facilitate clustering of certain receptors and thereby act as signaling platforms. These so-called lipid rafts can be understood as nanoscale assemblies of proteins and lipids in liquid- ordered states which are enriched in cholesterol, sphingolipids and glyco- sylphosphatidylinositol-anchored proteins6. The spatial organization of recep- tors in the membrane can influence the interaction between ligand and recep- tor, but ligand binding itself can also can influence the spatial organization of receptors, by for example cross-linking. This bi-directional interplay between ligand-receptor interactions and lateral membrane interactions highlights the importance of the cellular context for ligand-receptor interactions2. The recep- tor itself can also be influenced by the cellular environment through posttrans- lational modifications and tissue specific splice-variants which can have an impact on the interaction between ligand and receptor and thereby the infor- mation that is communicated into the cell7,8. In addition to structural and spatial organization, signaling is also con- trolled by time which adds additional complexity and specificity. Differences in temporal activation of signaling proteins can lead to distinct gene expres- sion profiles and thus cellular responses2,9,10. For example, blood insulin has a distinct temporal pattern that is mimicked by signaling molecules in the stim- ulus-receiving cells in the liver. Depending on whether the hormone leads to transient or sustained activation of the signaling pathway, different metabolic processes become active9. Another example for temporal regulation of signal- ing is the activation dynamics of ERK (extracellular signal-regulated kinase), an intracellular protein kinase involved in the MAPK (mitogen-activated pro- tein kinase) signaling pathway. Transient activation leads to proliferation, whereas a sustained activation of ERK results in differentiation of cells. The temporal activation profile of ERK is dependent on the number of activated receptors which is closely related to the number of ligand-receptor complexes formed on the cell surface. In this example, the number of ligand-receptor complexes was modified by regulating receptor expression10 but also the lig- and concentration surrounding the cell and the dynamic characteristics of the ligand-receptor interaction itself have an impact. The molecular recognition together with the local ligand and receptor concentrations define how fast

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complexes are formed whereas the stability of the interaction determines the lifetime of the ligand-receptor complex. The ratio between complex formation and decay describes how many complexes are present at a given time, which can be subjected to cellular control mechanisms, such as receptor internaliza- tion and degradation. For temporal control of signaling the reversible nature of molecular interactions is crucial11.

Kinetics of pharmaceutical drugs For ligands that are pharmaceutical drugs, binding to its target connects phar- macokinetics to pharmacodynamics. Pharmacokinetics describes the drug’s distribution in the body, which is influenced by adsorption, distribution, met- abolic processes that might alter or degrade the drug and excretion. The phar- macokinetic profile of a drug defines its local concentration at a particular site of the body, which influences the likelihood of a ligand being spatially close enough to its intended target for an interaction to occur. Binding kinetics rep- resents the dynamic characteristics of the interaction itself by defining its on- and off-rates and thereby impacts the fraction of the drug that will actually bind the target and duration of the bound state. Once being bound to its target a drug can trigger biochemical and physiological responses in the body, that are referred to as pharmacodynamics. For pharmaceutical drugs that are designed for a sustained inhibition of a biological process, reversibility of binding is often not desirable, as the aim is to maximize the drug residence time, i.e. the time a drug is bound to its tar- get12. A direct relationship between stability of the drug-target interaction and the efficacy of biological inhibition has been found for some inhibitors13,14. For these examples, the interaction stability determined the drug residence time, which has been predicted to only hold true when the off-rate is slower than the pharmacokinetic elimination15. Mathematical simulations, that take into account the possibilities of drug rebinding and drug depletion by target binding dependent on the relation between binding kinetics and pharmacoki- netics, indicate that both the on- and off-rate can influence drug residence time16,17. Considering depletion of free drug by target binding as a fraction that cannot be excreted is of particular relevance when targets are highly expressed and the drug binds with high affinity, as it is the case for many therapeutic antibodies within oncology. This phenomenon is also referred to as target me- diated drug disposition18. Another scenario where depletion of free drug should be taken into account is if extensive binding to secondary targets oc- curs. This applies again to therapeutic antibodies that can bind to the neonatal Fc-receptor and thereby evade lysosomal degradation and prolong their reten- tion in blood circulation19. Another factor to consider for antibodies is their large molecular size that impairs penetration into solid tumors. For high-affin- ity antibodies a “binding site barrier” effect has been described that refers to

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antibodies binding to the outer cell layers of a solid tumor mass and, because of their high affinity, remaining bound there which impedes tumor penetra- tion18. Due to the complex interplay between pharmacokinetics and binding kinetics that is affected by many factors specific for a given drug-target pair, general inferences from binding behavior to biological function are challeng- ing. Partially owed to this complexity, the binding parameter that is tradition- ally optimized during drug development is the binding strength or affinity that represents the ratio between off- and on-rate and there are several examples demonstrating that increased affinity can lead to improved drug efficacy20. However, since affinity is a constant that is based on the investigated system being at equilibrium, a condition not met in an in vivo situation, the binding kinetics of an interaction is generally a more precise measure when attempting to relate in vitro binding to in vivo drug residence time12,21.

Real-time Interaction analysis and homogenous interactions Theoretical models that assume a certain underlying binding mechanism are the basis on which the binding kinetics of a molecular interaction are quanti- fied. The simplest mechanism for a ligand (L) to interact with its target (T) is described by the Langmuir binding model, also referred to as 1:1 model that predicts the formation of one type of ligand-target (LT) complex22. This model assumes a one-step association and dissociation process that is fully reversible and the interaction is entirely described by two reaction rates: the association rate constant ka, that reflects the molecular recognition and the dissociation rate constant kd, that reflects the stability.

[] + [] [] [1]

The ratio of dissociation and association rate constants defines the equilibrium dissociation constant KD, also referred to as affinity and often used as a meas- urement for the binding strength of an interaction. The association and disso- ciation rate constants are also referred to as on- and off rates.

= [2]

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Figure 1: Interactions with the same affinity can have different binding kinetics: (1) fast on, fast off dynamics (2) intermediate on, intermediate off dynamics (3) slow on, slow off dynamics.

Molecular interactions with the same affinity can have distinct kinetic profiles, for instance a 10 nM affinity value can correspond to a “fast-on, fast-off” in- 5 -1 -1 -3 -1 teraction behavior with, for example, ka= 10 s M and kd = 10 s or a “slow- 3 -1 -1 -5 -1 on, slow-off” behavior with ka= 10 s M and kd= 10 s (Figure 1). For direct extraction of the rate-constants, the formation and dissociation of ligand-target complex is monitored over time. Real-time techniques for in- teraction analysis have various underlying measurement principles but have in common that the measured signal (B) is proportional to the number of lig- and-target complexes formed. In a typical real-time experiment one of the in- teraction partners is immobilized on a surface (referred to as target here) and the number is kept constant during the measurement. The first experimental phase is recorded in the absence of the second partner (referred to as ligand here) to establish a baseline signal (Figure 2). For the association phase, a defined ligand concentration is added in solution and ligand-target complexes

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begin to form which causes an initial linear signal increase. The speed of com- plex formation is proportional to the ligand concentration and ka. As ligand- target complexes are formed, some will also dissociate, causing the linear in- creasing signal at the very beginning of the association phase to slow down as the measurement continues. The resulting curvature during the association contains information about ka, kd and the ligand concentration. The dissocia- tion process will become more and more noticeable as the number of ligand- target complexes present increases, causing the signal increase per time unit to decrease until the formation and decay of complexes become equal. This state is called binding equilibrium, where the total number of ligand-target complexes stays constant as association and dissociation processes are equal and the signal does not change. The affinity defines the fraction of bound tar- gets at binding equilibrium dependent on ligand concentration. The ligand concentration needed for 50% target occupancy at equilibrium corresponds to the affinity value and this definition is used for affinity estimation with tradi- tional end-point binding assays that lack time-resolution.

[]∗[] = under equilibrium conditions [3] [] [] [] = ∗ under equilibrium conditions [4] []

For the dissociation phase, unbound ligand is usually removed from solution and the decay of ligand-target complexes is measured, resulting in a signal decrease. During the association phase, both association and dissociation of ligand to the target take place, however, if the interaction has an extremely slow kd, the dissociation will barely influence the signal change over time. In this case, curvature can only be achieved by approaching target saturation. The maximum binding signal in a particular assay is given by Bmax and represents the signal at full target occupancy. Information about the kinetic rate constant, Bmax and thereby the target saturation level (=B/Bmax) is derived from the non-linearity of the binding signal as a function of time and ligand concentration. When the number of targets is constant during the measurement and the ligand depletion can assumed to be negligible, the signal change over time for a 1:1 interaction is described by:

= ∗ [] ∗ − − ∗ [5]

For a 1:1 interaction, ligand binding needs to be monitored at least at two known concentrations that each need to result in pronounced curvature in or- der to unambiguously determine Bmax and the rate constants. This can either be done in two separate experiments or by increasing the ligand concentration during the assay.23-27

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Figure 2: Typical real-time binding trace: (1) Baseline: no free ligand is present, no LT-complexes are formed (2) Beginning of association phase: free ligand is intro- duced resulting in formation of LT-complexes and a linear signal increase (3) Curva- ture during association phase: LT-complexes are formed as ligand continues to bind, simultaneously ligand dissociates from existing LT-complexes causing a decrease in the signal change over time. The slope of the previously linear signal is slowly de- creasing resulting in visible curvature. This part of a real-time binding trace contains the most information about the binding properties and curvature is essential for accu- rate determination of the kinetic parameters and affinity. (4) Equilibrium: the rate of LT-complex formation is equal to the rate with which LT-complexes decay; thus, the overall number of LT-complexes is constant, resulting in a signal plateau. (5) Disso- ciation: free ligand is removed and bound ligand dissociates resulting in signal de- crease

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Heterogenous interactions Molecular interactions cannot always be adequately represented by a 1:1 bind- ing model. In a cellular environment, flexibility of ligand and target structures allow for conformational changes and ligands can have affinities to more than one type of target. These types of interactions cannot be described with single ka and kd values, as different variations of ligand-target complexes are formed making the interaction non-homogenous.

1:2 interaction model An example for a heterogenous interaction is the epidermal growth factor (EGF) binding to its receptor EGFR for which two receptor populations exist that have different affinities for EGF binding28. Binding of EGF promotes re- ceptor dimerization by inducing a conformational change that exposes the di- merization domain of EGFR thereby promoting the formation of homodimers or heterodimers with other receptor family members such as the human epi- dermal growth factor receptor 2 (HER2)3. In addition, EGFR can also form spontaneous dimers in the absence of EGF, although these dimers are less sta- ble29,30. The different conformational states of monomeric and dimeric EGFR have been suggested to represent the low and high affinity receptor population, respectively. Although this seems to be a simplified explanation of a more complex mechanism of signal transduction, the 1:2 binding model describes real-time binding data on live cells in an adequate manner31,32. The 1:2 model assumes that the ligand binds to two different target populations with distinct kinetic rate constants. The two interaction components are presumed to each follow a 1:1 interaction and to be independent from each other:

[] + [] [] and [] + [] [] [6]

Typically, one of the interaction components displays a “fast-on, fast-off” binding pattern whereas the other component displays more of a “slow-on, slow-off” pattern (Figure 3); as it is the case for EGF binding to EGFR on A431 cells. The EGF-EGFR interaction is cell-line dependent, which under- lines the importance of the cellular environment for an interaction but seems to follow a 1:2 pattern on most cell-lines32. The biphasic shape of the dissoci- ation phase reflects that two types of ligand-target complexes are present with distinct binding stabilities that are described by different kd values. A biphasic dissociation is characteristic for heterogenous interactions. For a 1:2 interac- tion, the two interaction components generally have different affinities and thus the fraction of bound target will vary between the two target populations. Since the affinity defines the fraction of bound targets in relation to ligand concentration, at low ligand concentration mostly high-affinity targets will be

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Figure 3: Example of a typical binding trace for a 1:2 interaction: simulated data in black, 1:2 fit in red (A) the 1:2 fit is depicted as the sum of the individual interaction components (B) the two interaction components that make up the 1:2 fit are depicted separately

bound. With increasing ligand concentration, the number of low-affinity lig- and-target complexes will increase and thus with increasing ligand concentra- tion the ratio between low- and high-affinity ligand-target complexes changes. As the two types of ligand-target complexes typically have different stabil- ities, a characteristic of a 1:2 interaction is a concentration-dependent dissoci- ation pattern. In contrast, homogenous interactions display a concentration in- dependent dissociation pattern as only one type of ligand-target complex is formed.

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1:1, 2-state interaction model Binding of a ligand to a receptor often involves a conformational change that stabilizes the bound state of ligand-target complex. A model that describes this scenario is the 1:1, 2-state model, also called induced fit or two state re- action model. This model describes three possible states for ligand and target: unbound state, an intermediate ligand-target complex (LT) and a stabilized ligand-target complex (LT*):

∗ [] + [] [] [ ] [7]

The 1:1, 2-state model describes the binding process with one set of rate con- stants (ka1 and kd1) and the conformational change that transforms the ligand- target complex into a stabilized state with a second set of rate constants (ka2 and kd2). It is assumed that the ligand can only dissociate directly from the intermediate complex. The overall affinity for a 1:1, 2-state interaction is given by multiplying the ratio of the rate constants for the binding and confor- mational dynamics.

= ∗ [8]

An example for 1:1, 2-state interaction is the binding of the 3-O-Sul- fotransferase (3-OST) to its substrate heparan sulfate, which is an essential part of protein-carbohydrate structures that are present in the extracellular ma- trix of many mammalian cells. After initial biosynthesis, heparan sulfate is structurally modified by various , one of which is 3-OST. The inter- action of 3-OST with heparan sulfate displays a biphasic dissociation phase with an incubation time-dependent pattern that is characteristic for a 1:1, 2 state interaction. Longer ligand incubation times allow a greater number of LT-complexes to transition into a stabilized state which results in slower over- all dissociation of the ligand.33

Bivalent interaction model and avidity Similarly, to the 1:1, 2-state model, the bivalent model describes a scenario in which ligand binding can be stabilized by interacting with a second target molecule. The bivalent model assumes that the ligand has two identical bind- ing sites and that upon formation of an LT-complex the ligand’s second bind- ing site can interact with a second target of the same type:

[] + [] [] + [] [] [9]

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Bivalent binding can occur, for instance, for antibodies if target molecules are sufficiently close to each other. Compared to a 1:1 interaction, the number of free targets decreases faster when bivalent binding occurs which can be visible in the association phase of the binding trace as a fast initial signal increase followed by a less steep signal increase as association slows down due to less available targets. The slow binding phase typically levels off slower than what would be expected for the shape of a 1:1 binding trace. If antibodies are bound bivalently, the measured dissociation is slower than for monovalent binding, as both arms need to dissociate from their targets in order for the antibodies to return to their unbound states. The dissociation-pattern is assumed to be de- pendent on the incubation time, as longer incubation times increase the likeli- hood of the ligand binding to a second target, thereby increasing the number of bivalent, more stable LT-complexes.34 If not all antibodies bind bivalently, the presence of both monovalent and bivalent complexes can cause the disso- ciation phase to display a biphasic shape; as it is predicted for interactions following the other two heterogenous binding models described here. Bivalent binding is an example for an interaction that is influenced by avid- ity. Avidity describes the accumulated strength of multiple individual, non- covalent and thus reversible, interactions that can be formed by ligands with multiple binding sites. The number of individual interactions that can be formed are referred to as valency. Avidity is not simply the sum of the indi- vidual affinities but an individual binding event can increase the local concen- tration for the other interactions and thereby increase their likelihood for bind- ing. Rebinding due to the ligand being held in proximity as long as one of its binding sites is attached to the target can increase the overall binding stability and strength considerably. Multivalent ligands do not automatically display avidity binding effects as these also depend on the geometric arrangement of ligand and target binding sites35. For example, a bivalent antibody might in- teract in a monovalent manner due to steric constraints that hinder simultane- ous binding of two expressed on the surface of a cell.

Interaction Map Heterogenous interactions can consist of more than two independent individ- ual interaction components, especially in cellular systems ligand-target inter- actions can be highly complex36. A mathematical method that can analyze the binding of a ligand to a heterogenous surface and is not limited to two inter- action components has been developed by Shuck and co-workers37,38. This mathematical approach assumes that each interaction component follows a 1:1 model, but does not assume a defined number of potential interaction compo- nents that make up the heterogenous interaction37. A modified version of this tool, called Interaction Map, has been developed and made commercially

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available. Interaction Map searches a defined two-dimensional space of dis- crete ka- and kd-values for independent 1:1 interactions whose kinetic infor- mation is contained in the shape of the measured real-time binding trace. In addition, weighing factors are assigned to the individual interaction compo- nents in accordance to their fraction of bound targets that contribute to the overall signal. The weighted sum of all interaction components represents the experimental binding curve. Visually, Interaction Map depicts all resulting in- teraction components in an on/off-plot with heat-map coloration indicating the contribution of an individual component to the overall observed heterogenous interaction (Figure 4). The kd-value or binding stability of each component is plotted against the x-axis, while the ka-value or molecular recognition is plot- ted against the y-axis, which results in interactions with the same affinity, but different dynamics to end up on the same diagonal.39 Apart from being able to define the kinetic rate constants for more than two interaction components, Interaction Map is especially useful when the underlying binding pattern of a heterogenous interaction is unclear36.

Figure 4: Interaction Map calculated from the binding trace depicted in Figure 3, re- sulting in two interaction peaks, which implies that the interaction consists of two defined interaction components. The position on the Map is given by the stability (kd- value) in x-axis direction and the molecular recognition (ka) in y-axis direction. Pixels lying on a diagonal from the lower left to the upper right have the same affinity value (KD), but different binding dynamics. More stable interaction components with slow off-rates are positioned towards the left of the Map, whereas interactions components reflecting good molecular recognition with a fast on-rate are positioned high up on the Map. The color of each pixel indicates the contribution to the overall binding process.

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Techniques for biomolecular real-time interaction analysis There are many techniques available to study different aspects of biomolecu- lar interactions40. The binding strength of an interaction can be evaluated with so-called end-point assays that typically measure the number of LT-com- plexes formed at equilibrium for various ligand concentrations, applying equation [4]. To not only extract the affinity, but also the rate constants of the binding dynamics, time-resolved measurement techniques are employed that generate a signal proportional to the number of LT-complexes present over time. In addition, real-time measurements give indications about the binding mechanism as heterogeneity can be visible in the shape of the binding trace.

Biophysical measurement techniques Several biophysical measurement principles have been applied for real-time interaction analysis and many of these have the advantage of using label-free ligands. Techniques that are based on optical systems for signal generation include surface plasmon resonance (SPR)41,42, biolayer interferometry (BLI)27 and dual polarization interferometry (DPI)43. The detection principles are all based on measuring changes in the refractive index close to a surface that is coated with targets: upon binding water molecules are replaced by ligand, which changes the mean refractive index in proximity of the surface. Most popular during drug development is SPR which is often used for high-through- put set-ups and several modified SPR techniques that allow for the collection of additional information, higher sensitivity and more robustness have been developed44-46. Besides optics-based detection, also methods relying on the reverse piezo- electric effect exist, such as quartz crystal microbalances (QCM). The reverse piezoelectric effect causes a crystal, the center piece of the QCM, to vibrate at a certain frequency when electricity is applied. The vibration frequency is de- pendent on the mass of the crystal, which changes when ligand binds to a crystal coated with target molecules26. Furthermore, the use of cantilevers for interaction analysis has been explored, primarily focused on DNA-DNA in- teractions. Ligand binding to cantilevers with targets immobilized on one side causes mechanical bending, which can be read out either via optical or piezo- electric systems47,48. Most of the aforementioned biophysical methods have been applied to characterize biomolecular interactions on cells, but mainly on fixated material in order to minimize the density fluctuations originating from cells49,50. Applications with live cells have shown that concentration depend- ent correlations between ligand binding and induced cellular responses can be detected. However, the registered signal does not measure ligand binding events but represents the overall cellular response, which is not necessarily

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proportional to the number of ligand-target complexes; a prerequisite for ex- traction of kinetic binding rates51-54.

Cell-based techniques For evaluating ligand binding to live cells, flow cytometry is a well-estab- lished method, especially within the field of immunology. In flow cytometry, cells or other particles are hydrodynamically focused along a narrow flow cell, so that on average one cell at a time passes a laser beam that can excite any cell-associated fluorescence. Flow cytometry can be multiplexed by using sev- eral fluorophores with distinct excitation and emission spectra and addition- ally how cells are scattering light is used to evaluated their size and granular- ity. Routinely used for end-point binding assays, flow cytometry has also been applied to measure interactions in real-time with fluorescently labeled lig- ands55,56. Achieving sufficient time resolution while discriminating the signal specific for binding evens over background fluorescence from free ligands and cellular autofluorescence is needed for data with signal/noise ratios that allow investigation and quantification of (heterogenous) binding patterns57,58. Time-resolved binding assays based on Förster resonance energy transfer (FRET) circumvent this issue as unbound ligand does not give any signal. In FRET the emission spectrum on one fluorophore (donor) overlaps with the excitation spectrum of a second fluorophore (acceptor) and only if the two are in close proximity, the acceptor can become excited by the energy emitted from the donor59. This allows for homogenous assays formats without the need to separate bound from unbound ligand. For FRET-based binding assays, la- beling of both ligand and target is necessary which usually requires transfec- tions with recombinant receptor variants for cell-based assays60. Fluorescent read outs can be done with multi-well plate readers, which are suitable for high throughput set-ups. For high through-put screening purposes, labeling of ligands is not feasible and methods using a competitor probe with known ki- netic parameters that is labeled with the acceptor dye have been developed. In such a set-up, ligands need to have overlapping binding sites with the probe and the relative decrease of fluorescent signal in presence of ligand compared to only the probe is detected. For these time-resolved competition experi- ments, the shape of the resulting binding trace is dependent on the kinetic pa- rameters of both competitor probe and ligand and, since the kinetics of the probe are known, the binding rate constants for the ligand can be calculated. Extraction of the kinetic parameters is more complex compared to methods that monitor ligand binding directly thus it is often assumed that the ligand binds according to a 1:1 model as heterogenous interactions are more difficult to recognize and evaluate61.

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LigandTracer A technique designed to study ligand-receptor interactions in real-time on live cells is LigandTracer. The instrument consists of an inclined, rotating support with a detector mounted above the upper part that tracks the ligand that is labeled with either fluorescence or radioactivity (Figure 5). Cells expressing the target receptor are grown in a defined area of a standard cell culture dish which is placed onto the inclined support. During the measurement, the dish is slowly rotating and depending on instrument type either fluorescence or radioactivity is registered from at least two areas of the dish: the target area containing the cells of interest and a reference area that can be cell-free or contain cells negative for the target receptor. The inclination of the system ensures that the bulk liquid containing unbound ligand is outside the detection area62.63. A typical assay includes a baseline measurement which is used to correct for autofluorescence of cells (if applicable), an association phase with at least two increasing ligand concentrations and a dissociation phase. Changes in lig- and concentration and incubation solution are performed manually by pausing the measurement and typical assay times range from several minutes to hours. Currently, the technology is limited to a maximum of two independent exper- iments that can be run simultaneously, when using a dedicated MultiDish, and the detection of one type of label.

Figure 5: Basic principle of the LigandTracer technology: Cells are grown in a local area of a cell culture dish. The dish is placed on an inclined and rotating support with a detector mounted above the upper part. Labeled ligand is added and during each rotation the fluorescent or radioactive signal from the cell area and a reference area is recorded, resulting in a background subtracted real-time binding curve.

As the formation of ligand-target complexes can be directly monitored in a relevant cellular environment, LigandTracer is suitable for studying binding mechanisms and exploring relationships between binding patterns and func- tion. For example, LigandTracer was used to study complement activation by

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C-reactive protein in response to tissue injury. Braig et al. demonstrated that C-reactive protein could only interact with complement component 1q (C1q) when bound to the lipid membrane of microvesicles where it underwent a con- formational change needed for C1q binding64. LigandTracer has also been used to explore how EGF interacts with EGFR on different cell-lines and how the interaction changes when exposed to a tyrosine kinase inhibitor, presuma- bly due to its effect on EGFR dimerization32,36. Originally designed to analyze interaction of molecules with live cells, LigandTracer has been adapted to study interactions between a variety of different target and ligand structures. Examples include the time-resolved characterization of how nanoparticles are loaded with protein over time and subsequent protein release65, as well as mo- lecular interaction analysis on fixated tissue66 and glucose uptake studies with fresh mouse tissue and organs67,68. The interaction between bacteria and vi- rus69 with host cells has been investigated e.g. by Bugaytsova et al. who could demonstrate that the that the adherence of helicobacter pylori to prokaryotic cells is pH-dependent with the bacterium releasing below pH 4 and being able to rebind above pH 4. This has been proposed by the authors as a mechanism employed by helicobacter pylori to escape from gastric cells that are shed into the acidic lumen and instead re-adhere on cells remaining in the gastric mu- cosa, leading to a persistent infection70.

Cancer and therapy options Receptor signaling is crucial for maintaining cellular function and homeosta- sis which is highlighted by the fact that deregulation in receptor signaling is involved in most of the hallmarks of cancer71. The term cancer is used for a group of diseases that are characterized by abnormal cell growth caused by genetic aberrations. Cancer is divided in different types depending on the tis- sue and cell type the malignant cells originate from. The hallmarks of cancer that directly contribute to fast proliferation of cells by de-coupling the cell from surrounding signals are the evasion of growth suppressors, self-sufficient growth signaling and the resistance to apoptotic signals. Especially abnormal- ities affecting proliferation have been shown to occur preferable on the recep- tor level compared to further down-stream in the signaling cascades72. Well- known examples are up-regulation of growth receptors such as EGFR and HER2, resulting in uncontrolled cellular proliferation73, downregulation or mutations in growth-limiting receptors such as transforming growth factor beta receptor74, as well as receptors mediating pro-apoptotic signals such as the death receptor CD95 (CD = cluster of differentiation)75. Apart from ignor- ing intercellular signaling that regulates cell growth and survival, malignant cells also overcome senescence, the cell’s intrinsic replication limit. Sustained blood vessel growth, called angiogenesis, ensures the nutrient supply of the growing tumor.

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One of the emerging hallmarks of cancer, that has become broadly accepted over the last decade, is the evasion of the immune system by tumors71. For example, malignant cells down-regulation MHC (major histocompatibility complex) class I molecules form their cell surface in order to escape from immune surveillance through cytotoxic T-cells76. If not detected and elimi- nated by the immune system, malignant cells will eventually invade other parts of the body to form metastases, thereby spreading the cancer. Due to the plethora of underlying molecular mechanism that can promote carcinogenesis, treatment approaches vary between different cancer types and patient groups and often several different therapies are combined in the effort to eradicate both primary tumor and metastases. The most wide-spread treat- ment option for cancer is surgery which is commonly used for the removal of primary tumors from solid cancers. Ionizing radiation as treatment can be used externally in form of focused beam therapy or internally in form of e.g. brachytherapy and is in both cases often focused on the primary tumor. Sys- temic treatment that also reaches metastases can be achieved by chemother- apy, that is the administration of cytotoxic drugs that typically act against fast dividing cells. The challenge with chemotherapy often lies in the lack of suf- ficient specificity to discriminate between normal and malignant cells and side effects can limit the dosage required for efficient eradication of cancer cells. Targeted therapy has improved specificity of treatment and generally re- duces side effects by employing molecules that recognize proteins either over- expressed or exclusively expressed in malignant cells or the cell type of origin77. Due to their involvement in many cancer-promoting processes and their accessibility, cell surface receptors are popular drug targets for targeted therapy approaches78. A treatment approach that frequently combines specific targeting with activation of the immune system is monoclonal antibody ther- apy that exploits the natural ability of antibodies to direct the immune system against cells expressing a specific target that is non-normal79. In recent years, cancer immunotherapy has rapidly gained attention and a variety of treatment approaches that activate or enhance the natural ability of the immune system to detect and eradicate malignant cells are being explored. One way of enhancing the immune system’s natural ability to recognize aber- rant cells is by blocking immunosuppressive checkpoints that are important for self-tolerance but can be exploited by malignant cells to escape immune surveillance80. Examples of negative immune regulators are two inhibitory co- receptors on T-cells, namely cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1). James Allison and Tasuku Honjo discovered the function of these receptors and demonstrated that their inhibition results in efficient anti-cancer response for which they received the Nobel Prize in Physiology or Medicine in 2018.81-84 There are also cell-based immunotherapies, for example adaptive cell therapy, that in- volves the isolation of T-cells from patients, which are then expanded ex vivo and then re-infused into the patient to enhance the natural anti-tumor response.

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It is also possible to genetically engineer the isolated T-cells to recognize a specific tumor antigen prior to re-infusion which circumvents the need for ex- isting reactive T-cells against the tumor85.

Monoclonal antibodies for cancer therapy One of the fastest growing drug-classes within oncology are monoclonal anti- bodies (mAbs) with more than 30 mAbs currently approved by the US Food and Drug Administration (FDA) and European Medicines Agencies (EMA) for therapeutic use in various cancer types86. The idea of using targeted “magic bullets” as therapeutics was already introduced over a century ago by Paul Ehrlich, who pointed out that successful therapy with limited toxicity requires a specific binding of the drug to a receptor associated with the disease; an ability that antibodies possess by nature87. Antibodies, also referred to as immunoglobulins (Ig) are Y-shaped glyco- proteins employed by the immune system to neutralize pathogens. Each anti- body is composed of four polypeptide chains: two identical heavy chains and two identical light chains that are held together by disulfide bonds (Figure 6). The resulting Y-shaped structure is divided in functional domains: two iden- tical Fab (fragment antigen-binding) domains with variable regions on their N-terminus and a Fc (fragment crystallizable) domain that is constant for all antibodies of the same isotype. The Fab domains contain so-called comple- mentary-determining regions (CDR) which are highly variable and determine target binding specificity. The Fc domain mediates the effector function of the antibody and can be recognized by a variety of immune cells via their Fc- receptors (FcR), as well as by complement proteins that are part of the humoral immune system. Antibodies exist in surface-bound and soluble form: expressed on the sur- face of B-cells they function as antigen receptor, called B-cell receptor (BCR) that facilitates the activation of the B-cell which can lead to secretion of solu- ble antibodies. In humans and other placental mammals five antibody isotypes are known: IgA, IgD, IgE, IgG and IgM and corresponding Fc receptors for isotype specific recognition exist for all. As secreted forms IgD, IgE, as well as IgG are monomers, whereas soluble IgA is a dimer and IgM is secreted as a pentamer. The most common isotype in blood is IgG which is crucial for controlling infection by marking pathogens or infected cells for phagocytosis and lysis by certain immune cells and can also activate the complement sys- tem. The IgA isotype is predominantly found in mucous membranes and is crucial for their immune barrier function, whereas IgE can trigger allergic re- actions, IgM is important for the early adaptive immune response and IgD plays a role in B-cell activation and participates in the respiratory immune response88.

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Figure 6: General antibody structure: The Y-shaped protein is made up by two light and two heavy chains that each consist of a constant and variable region. The Fab- domains mediate binding to a specific target epitope, whereas the Fc-domain mediates interactions with immune cells and other molecules involved in the immune response.

Therapeutic mAbs within oncology are currently exclusively of the IgG iso- type and first to reach market approval by the FDA in 1997 was rituximab for treatment of B-cell malignancies89. The use of antibodies as therapeutics was mainly made possible by a methodology break-through in 1975 with the in- vention of the hybridoma technology by Köhler and Milstein that enabled ef- ficient production of monoclonal antibodies90. MAbs produced by the hybrid- oma technology are mostly of mouse origin and have a higher chance of elic- iting an unwanted immune reaction than their human counterparts. The issue of immunogenicity with mouse mAbs was addressed with the invention of antibody chimerization, which was first described in 1984 by Morrsion et al., who created fusion proteins for both heavy and light chain from murine vari- able regions and human constant regions91. Further progress in biotechnology led to more sophisticated antibody engineering that allowed for a step-wise reduction of murine sequence content. Humanized antibodies were first intro- duced by genetically crafting the CDRs from a mouse mAb into the variable antibody domain of a human heavy-chain92 and later advances in phase display technology paved the road towards fully human mAbs93. The possibility to produce low immunogenic, high affinity drugs against specific antigen to- gether with good serum stability and long circulatory half-life has made mAbs increasingly popular over the last two decades, both for cancer and treatment of other diseases94.

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Mechanisms of action of mAb therapy – with a focus on CD20 mAbs Therapeutic antibodies elicit anti-tumor responses through different mecha- nisms of action that can either be mediated via the Fab domain or Fc domain of the antibody (Figure 7). In addition, mAbs can also be used as targeted agents to deliver toxic payloads specifically to malignant cells.

Figure 7: Summary of common mechanisms of action for therapeutic mAbs targeting tumor cells: Fab-mediated effects include ligand blockade, inhibition of receptor di- merization and cell death induction. Fc-mediated effects are immune-related and in- clude antibody-dependent cellular cytotoxicity or phagocytosis, as well as activation of the complement system that can lead to deposition of and subsequent for- mation of lytic pores, so called membrane attack-complexes. Moreover, mAbs can be used for targeted delivery of small molecule drugs in the form of antibody-drug con- jugates.

Fab mediated effects MAbs that employ Fab mediated effects often target signaling pathways that are directly or indirectly involved in tumorigenesis, such as cell proliferation or mechanisms that contribute to a favorable tumor microenvironment79. Ex- amples for mAbs that directly target growth receptors are cetuximab that binds EGFR and thereby blocks binding of its natural ligand EGF95 and pertuzumab that binds to the dimerization interface of HER2, thereby sterically preventing

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the formation of receptor dimers that are necessary for signal transmission96. Direct targeting of receptors involved in cell death such as agonistic mAbs against TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) re- ceptors that promote apoptosis, have been explored and also tested in clinical trials but did not show the desired efficacy97. One of the most explored thera- peutic targets for mAbs is CD20, a cell surface receptor that is expressed on normal and malignant B-cells from an early developmental stage on, but is lost upon differentiation into antibody secreting plasma cells98. Anti-CD20 mAbs are divided into type I and type II depending on whether they induce clustering of CD20 and this classification also separates mAbs according to their functional characteristics. Type II mAbs, such as obinitzumab and tosi- tumumab, have been show to induce a non-apoptotic form of programmed cell death (PCD)99,100. This type of cell death involves actin reorganization that is triggered by mAb binding, the dispersion of lysosomal content and finally the intracellular release of reactive oxygen species101. Induction of non-apoptotic PCD has also been demonstrated for antibodies targeting other than CD20100,102,103. Not only the malignant cells themselves drive tumor progression, but also normal cells are part of the tumor mass and interact with the malignant cells which can indirectly promote tumor cell growth by creating a growth support- ive and often immuno-suppressive microenvironment79. Therefore, mAbs tar- geting not the malignant cells directly have been developed, such as bevaci- zumab an anti-angiogenic mAb that binds vascular endothelial growth factor (VEGF, also called VEGF-A) and thereby prevents its interaction with its re- ceptor VEGFR. Bevacizumab is used as single-agent and in combination ther- apy where the normalization of vasculature, and thereby better penetration of chemotherapeutics, is thought to be the dominant mechanism of action104. Tar- geting of tumor stroma cells has been investigated for colorectal cancer, but a phase II clinical trial failed to show sufficient therapeutic efficacy105. In recent years, targeting immuno-suppressive checkpoints has gained immense atten- tion and several mAbs targeting the CTLA-4 and PD-1/PD-L1 T-cell check- points are approved and in clinical development. Currently approved mAbs include the anti-CTLA-4 ipilimumab that was first approved in 2011 for treat- ment of metastatic melanoma and three mAbs against PD-1 and another three mAbs targeting its ligand PD-L1 with indications for a variety of malignan- cies86,106. Targeting of immuno-suppressive checkpoints, especially of the PD- 1/PD-L1 checkpoint, is also investigated as combination therapy with other mAbs in a number of clinical trials.

Antibody-drug conjugates As mentioned above, mAbs can also be used as delivery vehicles for toxic payloads thereby combining the specificity and long circulatory half-life of

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mAbs with the efficacy of either small cytotoxic molecules or radionu- clides107. Some antibody-drug conjugates (ADC) or radioimmunoconjugates are developed for antigens that have already been successfully targeted in the clinic by so-called naked antibodies without payloads. Such an example is the naked mAb Trastuzumab targeting HER2 that has been developed into an ADC by conjugation with emtansine, a cytotoxic agent binding tubulin and thereby disturbing mircotubuli function108. Two radioimmunoconjugates have been approved by the FDA, namely ibritumomab tiuxetan-90Y and tosi- tumomab-131I, both targeting CD20, with the latter being voluntarily with- drawn from the market in 2014109. Radioimmunoconjugates have been fo- cused on hematological malignancies, as limited penetration together with a lower radio-sensitivity poses a challenge for solid malignancies107,110.

Fc-mediated effects The therapeutic efficacy of mAbs is also mediated via their Fc-terminus through involvement of the immune system. The Fc-terminus of IgG contains recognition motifs for the first complement component C1, for the gamma subtype of FcRs (FcγRs) expressed on a variety of immune effector cells and for the neonatal Fc receptor (FcnR). Binding to the latter does not result in any direct therapeutic effect but is responsible for the long circulatory half-life of IgG mAbs by preventing lysosomal degradation after endocytosis and thereby contributes to their efficacy19. The interaction between IgG and FcnR is pH- dependent with higher affinity binding at pH 6 in the acidic endosome and lower affinity binding at pH 7.4 which is responsible for release and thus re- cycling of IgG after being trafficked back to the cell surface111.

Antibody dependent complement activation Most of the currently approved mAbs, and all approved anti-CD20 mAbs, are of the IgG1 subtype that has the potential to efficiently activate the comple- ment system via the classical pathway. The first component of the classical complement pathway C1 consists of three subunits, of which C1q is responsi- ble for IgG binding and is comprised of six globular target recognition do- mains attached to a -like structure. The Fc-domain of cell-surface bound IgGs can be recognized by C1q and multiple IgG Fcs are needed for C1q binding that is most efficient to a hexameric arrangement of Fc-do- mains112. Binding of C1q to IgG hexamers leads to conformational changes that activate the proteolytic activity of the other two subunits of C1 and thereby triggers the complement cascade113. This protease cascade eventually leads to cleavage of the central complement component C3 into and C3b. The latter can bind to the cell surface and acts as an , promoting target cell phagocytosis. The other cleavage product C3a, as well as the further down in the cascade produced C5a are soluble anaphylatoxins that recruit leukocytes

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and can promote an inflammatory environment. Furthermore, C3b also acti- vates the C5 convertase that then produces the cleavage products C5a and C5b, thereby initiating the terminal phase of the complement cascade that eventually leads to the formation of the membrane attack complex that creates pores in the cell membrane resulting in cell lysis114. Complement-dependent cytotoxicity (CDC) as a mechanism of action has been studied in particular with anti-CD20 mAbs. As mentioned above, type I CD20 mAbs are defined by their ability to promote clustering of CD20 into lipid rafts, which has been shown to correlate with their ability to efficiently induce CDC115. This is presumably due to that mAbs bound in clusters facili- tates hexameric arrangements of Fc-domains resulting in C1q binding and triggering of the complement cascade. The ability to efficiently mediate CDC is the central functional characteristic of type I antibodies that separates them from type II CD20 mAbs, that instead induce direct cell death more effi- ciently116. Of the three naked CD20 mAbs currently approved for clinical use, obinituzumab is of type II whereas rituximab and ofatumumab are of type I, with the latter being most efficient in mediating CDC117. CD20 is thought to be involved in calcium signaling and is expressed as a homotetramer on the cell surface with two extracellular domains. Rituximab and obinituzumab bind to partially overlapping epitopes on the large extracellular loop, whereas ofa- tumumab recognizes amino acids on both the large and small extracellular loops118. The epitope bound by ofatumumab is more membrane-proximal119, which has been suggested to contribute to its efficacy in activating comple- ment120,121 and differences in binding orientation between obinituzumab and rituximab have been suggested to be responsible for their differing ability to initiate CDC122. Besides target epitope and binding orientation, also binding stability117 as well as the elbow hinge angle of the mAb122,123 have been dis- cussed as factors that can influence the functional properties of CD20 mAbs. Currently, it is not fully understood which of these factors are most important and the underlying characteristics that determine the type I/II classification of CD20 mAbs and thereby CDC efficacy are still being investigated123. The dogma that the Fc-domain of IgG is crucial for induction of CDC was recently challenged by the observation that anti-CD20 F(ab´)2 retain the ability to ac- tivate complement via the classical pathway, although to a lesser degree than their full-length IgG counterparts. In this study, the BCR was suggested as an alternative docking site for C1q and the phenomenon was termed as accessory CDC124.

Antibody dependent cellular cytotoxicity and phagocytosis A variety of immune cells express FcγRs that can mediate and enhance cell- cell interactions with IgG opsonized target cells. The human FcγR family is comprised of four activating receptors FcγRI, FcγRIIa, FcγRIIc and FcγRIIa that transmit signals via an immunoreceptor tyrosine-based activation motif (ITAM) and one inhibitory receptor FcγRIIb that employs an immunoreceptor

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tyrosine-based inhibitory motif (ITIM) for signaling125. In addition, FcγRIIIb also belongs to the FcγR family but does not have any known intrinsic signal- -9 -8 ing capacity. The only receptor with high affinity (KD= 10 -10 M) for mon- omeric IgG is FcγRI and it is assumed to be saturated in vivo by serum IgG. The remaining FcγRs display lower affinities towards monomeric IgG, which prevents immune activation by circulating antibodies. Immune complexes and antibody opsonized target cells have enhanced binding capacity towards FcγRs due to avidity effects and cross-linking of FcγRs results in phosphory- lation of their ITAMs or ITMIs that trigger down-stream signaling126. The ra- tio between cross-linked activating and inhibitory FcγRs determines if im- mune effector functions are triggered. Since the IgG subtypes IgG1-IgG4 dis- play distinct affinities towards the different FcγRs, they vary in their capabil- ity to activate immune effector cells127,128. Engagement of FcγRs with mAbs bound to target cells can lead to multiple effects. Most myeloid cells express FcγRs, including phagocytic cells, such as monocytes, macrophages and neutrophils. Signaling via the ITAMs of their FcγRs leads eventually to actin remodeling and the formation of membrane protrusions that engulf the target cell in a process termed antibody-dependent cellular phagocytosis (ADCP)129. Many cancers over-express CD47, a ubiqui- tously expressed “don’t eat me” protein that servers as marker of self, to avoid phagocytosis by macrophages130,131. CD47 interacts with the inhibitory recep- tor SIRPα (signal regulatory protein α) on macrophages and blockage of this myeloid immune checkpoint is currently investigated in clinical trials with an anti-CD47 mAb; both as single agent and in combination with other mAbs132. Another effector mechanism mediated by FcRs is antibody-dependent cel- lular cytotoxicity (ADCC), which can be mediated by several immune cells that all express FcRs and possess granules, such as natural killer cells (NK- cells), neutrophils and macrophages. ADCC is most widely studied in the con- text of NK-cells that primarily engage IgG coated target cells via FcγRIIIa. Upon activation, NK-cells release perforin and granzymes from their cyto- toxic granules into the immunological synapse formed with the target cell, ultimately leading to cell lysis and apoptosis via the perforin/granzyme cell death pathway133. Neutrophils mediate ADCC via a recently described necrotic process that involves mechanic destruction of the target cell membrane and was termed as trogoptosis134, in relation to the previously discovered process of trogocytosis. Trogoctyosis is named after the Greek word “trogo” which means to gnaw or nibble and refers to the uptake of small plasma membrane fragments by im- mune cells from target cells135. In the context of CD20, it has been shown that neutrophils engage in trogocytosis, leading to removal of mAb-CD20 com- plexes from the cell surface and this has been proposed as a mechanism by which malignant B-cells can escape anti-CD20 therapy. Interestingly, tro- gocytosis was observed for rituximab, but not obinituzumab with CLL cells

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isolated from patients136. Trogoptosis is mechanistically closely related to tro- goctytosis, but a larger portion of the target cell membrane is taken up and so far, trogoptosis seems to be a neutrophil specific feature, whereas trogocytosis has also been described for other immune cells. Neutrophils also express SIRPα and it has been demonstrated that inhibition of the SIRPα/CD47 check- point enhances cell killing via trogoptosis134. Besides via trogoctyosis, antigenic modulation of CD20 can also occur via FcγRIIb, the only FcγR expressed on B-cells. Cell surface bound CD20 mAbs can interact with FcγRIIb expressed on the same cell, which leads to internal- ization of the CD20-mAb-FcγRIIb complex and subsequent lysosomal degra- dation of the mAb137. Internalization occurs to a higher degree for type I mAbs compared to type II mAbs and could explain the increased therapeutic efficacy of obinituzumab compared to rituximab138. Antagonistic antibodies against FcγRIIb have been shown to prevent internalization of rituximab and have shown to enhance the efficacy of rituximab in vivo139. The antagonistic FcγRIIb mAb BI‐1206 is currently in clinical trials with the aim to enhance anti-CD20 therapy by preventing antigenic modulation and blocking inhibi- tory signals on immune effector cells140,141.

Clinical relevance of Fc-mediated effects The relative contribution of the different mechanisms of action to the thera- peutic efficacy is not completely resolved and likely to differ between mAbs. It is generally accepted that ADCC/ADCP plays an important role in vivo and for mouse models impaired in activating FcγRs loss of therapeutic activity was reported for rituximab and trastuzumab142. In cancer patients, a genetic polymorphism in FcγRIIIA that leads to lower affinity binding of IgG was re- ported to correlated with worse clinical outcome and seemed to support a role for NK-cells as the primary effector cells143. However, further studies evalu- ating the association between FcγR polymorphisms and clinical outcome yielded both confirmatory and contradictory results144-147. In vivo studies showed that mice impaired in NK-cell function retain the ability for B-cell depletion upon anti-CD20 therapy whereas depletion of macrophages signifi- cantly reduces the therapeutic activity of anti-CD20 mAbs and these findings thus support a role for macrophages as the main effector cells in mice148,149. Even though the notion that the complement system interacts with antibod- ies on cancer cells was already made a few decades ago, it is still debated to which extent complement activation contributes to the efficacy of mAb ther- apy. Malignant cells can express various negative regulatory proteins of com- plement and their expression levels have been correlated with negative clinical outcome in some studies whereas no correlation was found in others150-152. In patients treated with rituximab it was observed that complement proteins are rapidly depleted upon mAb injection implying efficient complement activa- tion153. However, a polymorphism in the C1qA gene that lowers C1q expres- sion levels was correlated with better clinical outcome in patients receiving

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rituximab treatment, implying an unfavorable role for complement154. Studies in mice also report varying results whether or not complement contributes to therapeutic efficacy in vivo148,149,155,156.

Improving therapy with next-generation mAbs and alternative formats Understanding how mAbs interact with target and immune cells to trigger dif- ferent mechanisms of actions has led to the design of so-called next-generation mAbs with improved effector functions. Especially, CD20 mAbs have demon- strated that how a mAb binds its target epitope can have a profound impact on its functional properties. As discussed above the ability to redistribute CD20 into lipid rafts is linked to CDC efficacy and internalization via FcγRIIb whereas antibodies that do not induce CD20 clustering are more efficient at inducing non-apoptotic PCD. The finding that C1q requires binding to hex- americ arrangements of IgG Fc for efficient complement activation resulted in the development of mAbs with engineered Fc-domains that promote non- covalent Fc-Fc interactions and thereby hexamer formation112. These mAbs have been shown to mediate CDC more efficiently and a type II CD20 mAb with barely any CDC activity did mediated CDC efficiently after the corre- sponding Fc mutations were introduced157. This exemplifies that knowledge about the molecular interactions involved in triggering a particular function allows for rational antibody design with the desired effector functions. A fur- ther example is the notion that fucose-residues normally found on the Fc-ter- minus of IgG sterically hinder optimal interaction with FcγRIII158. Afucosyl- ated mAbs have an increased affinity for FcγRIII expressed on effector cells and thus mediate ADCC/ADCP more efficiently. Glycoengineered next-gen- eration mAbs targeting CD20 that have reached the clinic stage are, for exam- ple, the approved mAb OBI that has an afucosylated Fc-terminus leading to enhanced ADCC compared to RTX and ublituximab and ocaratuzumab that have entered clinical trials159. The latter harbors two mutations in the Fc-re- gion that improve the binding strength to FcγRIIIa on NK-cells, irrespective of a known genetic polymorphism that can influence the affinity, and thereby ADCC activity for native IgG160,161. Ublituximab has low-fucose content and is currently evaluated in phase III clinical trials for use in multiple sclerosis and phase II/III trials for B-cell malignancies162.

IgA isotype for antibody therapy Besides improving existing mAbs, also the use of new and alternative anti- body formats is explored. An example for the latter is antibodies of IgA iso- type that are investigated for their clinical potential163. The main interest lies

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in the ability of IgA to engage FcαR expressed on neutrophils and macro- phages164. Neutrophils constitute the majority of circulating leukocytes and it has been demonstrated that IgA engages neutrophils more efficiently for target cell killing than IgG and FcαR-dependent anti-tumor activity of IgA has been show in mouse models165. The potential to more efficiently recruit and activate the most abundant leukocyte population makes IgA antibodies attractive for cancer therapy. There are, however, also major draw-backs compared to IgG such as the lack of a binding site for FcnR. This results in a much shorter half- life of IgA with an estimated 3-6 days in humans compared to 21 days for IgG and 15 hours versus 4 days in mice165,166. Efforts have been made to improve the half-life of IgA by introducing mutations and glycoengineering and also mixed antibody isotypes that combine the Fc domains of IgA and IgG have been explored167,168. Combining the two isotypes can not only overcome the half-life limitation of IgA, but also has the potential to efficiently engage ef- fector cells via both FcγR and FcαR, thereby attracting a broader cell popula- tion168. Apart from a FcnR binding site, IgA also lacks a binding site for C1q, but interestingly anti-CD20 IgAs have been shown to have CDC activity in vitro, which could potentially be linked to accessory CDC169.

Bispecific antibodies Improving the engagement of effector cells is currently one of the main appli- cations for bi-specific antibody formats. Bi-specific antibody (bsAb) is used as an umbrella term for a variety of molecular formats that have two antigen recognition sites that each target a distinct antigen. They can either bind two antigens on the same cell (cis) or two antigens on different cells (trans). The biggest bsAb group acting in trans are currently T-cell engagers that mediate cell-cell interactions with target cells and can promote killing via T-cells, typ- ically bypassing the MHC restriction170. This can result in very efficient tumor killing, as has been demonstrated with blinatumomab, a CD19xCD3 bsAb, that showed extremely good response rates in clinical trials which lead to its FDA approvement in 2014 for treatment relapsed or refractory acute lympho- blastic leukemia171. In earlier clinical trials with other mAbs and bsAbs that target CD3, related off-target effects were sometimes rather severe. These were thought to be mediated by binding of the Fc-terminus to other immune cells, resulting in e.g. cytokine release172-175. Therefore, today most T-cell en- gagers have either an inactive Fc-region or lack the Fc-region completely, as it is the case for blinatumomab. Engagement of other immune cells via bsAbs has also been explored and a CD20xFcαR with the aim to recruit neutrophils showed promising results in pre-clinical trials but did not show sufficient ef- ficacy in patients176,177. More efficient recruitment and ADCC via NK-cells is currently being investigated in phase II clinical trials with a bsAb targeting CD30 and FcγRIIIa for haematological malignancies178,179.

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Besides mediating cell-cell interactions between target and effector cells, bsAb formats are explored for a variety of other applications, including tar- geting receptors for transport into certain body compartments. This is of par- ticular interest for transport over the blood brain barrier with constructs that target transport receptors in the blood brain barrier and a disease specific an- tigen thereby enabling antibody therapy in the brain170. Another interesting application for bsAb is the possibility to target ubiquitously expressed anti- gens on a specific cell type: a bsAb with high affinity for CD19 and low af- finity for CD47, was shown to block the CD47/SIRPα myeloid checkpoint specifically on CD19 positive cells180. Conferring specificity via affinity mod- ulation of the two antigen recognition domains is a concept that has the poten- tial to explore antigens that were traditionally not thought to be suitable for targeted therapy.

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Scope of Thesis

The research presented in this thesis is focused on developing methods to in- vestigate the binding pattern between therapeutic antibodies and live immune cells and how these can be applied; in particular to mAbs targeting CD20.

In paper I, LigandTracer a method designed to characterize protein interac- tions with adherent cells, is adapted for real-time interaction analysis of anti- bodies with live immune cells.

In paper II, interaction analysis on live cells is combined with fluorescent quenching to obtain information about both binding kinetics and relative prox- imity of two different types of antibodies or other ligands bound to cell surface receptors.

In paper III, the methods developed in paper I and II are applied to investigate the binding pattern of the three therapeutic CD20 mAbs rituximab, ofatu- mumab and obinituzumab on live cells.

In paper IV, the binding pattern of rituximab, ofatumumab and obinituzumab is investigated on various B-cell lines and a C1q binding assays with mAb opsonized cells is developed and applied to study if and how C1q binding relates to the binding pattern of CD20 mAbs.

In paper V, an assay to study mAb mediated cell-cell interactions is set-up and used to study if IgG and IgA antibodies differ in their efficacy to mediate neu- trophil binding to CD20 expressing target cells.

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Summary of Findings

Paper I

Real-time Characterization of Antibody Binding to Receptors on Living Immune Cells

Aim and Background Detailed quantification of molecular interactions aids in understanding their mechanism and can give insights into biological signaling and drug function. As the regulation of ligand-receptor interactions is complex and can be influ- enced by the cellular environment, it is of advantage to characterize the inter- action on live cells of the relevant cell type. A method designed for interaction analysis on live cells is LigandTracer, which requires cells to stay adhered in a defined area of a Petri dish for several hours. In this paper, LigandTracer was adapted for cells growing in suspension cultures and applied to extract binding properties between antibodies and cell surface receptors relevant for immuno-oncology.

Results Suspension cells were tethered to petri-dishes using a previously described biomolecular anchor molecule (BAM). Viability and adherence of BAM teth- ered cells was confirmed and the vast majority of cells tolerated the experi- mental conditions (e.g. rotation, room temperature) they were exposed to dur- ing LigandTracer measurements. Comparing antibody binding traces for ad- herently growing cells seeded on tissue culture plates with traces for BAM adhered cells, did not show any indications for possible interference of BAM on interaction measurements. As one of the presented model systems, the in- teraction between the therapeutic mAb rituximab and Daudi cells expressing high levels of CD20 was characterized. As a control cell line in the reference area, CD20 negative K562 cells were used. Highly reproducible binding traces were obtained within an 8-hour time frame. To extract the binding properties, a 1:1 kinetic binding model was applied to the data. However, the dissociation phase was not well described by a single exponential decay assumed by the 1:1 model, indicating a more heterogenous interaction (Figure 8). Therefore, more complex models were used in the evaluation and of the applied models,

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the 1:1, 2-state model resulted in the most robust fitting for the binding traces presented in this paper. It was noted, however, that an extended binding study would be needed to conclude which kinetic model reflects the binding mech- anism of Rituximab most appropriately.

Figure 8: Binding trace for FITC-Rituximab interaction on live Daudi cells: meas- ured data (red, n=4) with a fit according to the 1:1 binding model (black). The end of the association phase as well as the shape of the dissociation phase deviate from the fitted line.

Further examples of antibody-cell interactions with CD3 and FcγRIIa as tar- gets, as well as other CD20 expressing cell lines, were analyzed in different laboratories, to demonstrate general applicability of the developed protocol.

Discussion The method presented in this paper enables the quantification of ligand bind- ing to live immune cells in a time-resolved manner. The ability to record time- resolved during several hours is especially valuable when analyzing modern binders with slow-off rates, as it is the case for therapeutic mAbs. As one ex- ample the rituximab-CD20 interaction was characterized, which seems to be of heterogenous nature. The are several factors that can make an interaction deviate from a homogenous 1:1 binding, such as the bivalent nature of a lig- and, internalization, different receptor target populations and other factors re- lated to the underlying biology of the target. For further understanding about the most appropriate model for evaluating rituximab binding to cells and the related underlying factor(s) for the heterogeneity a more extensive study would be needed.

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Paper II

Novel Real-Time Proximity Assay for Characterizing Multiple Receptor Interactions on Living Cells

Aim and Background Signal transmission by receptors is a highly regulated mechanism that can in- volve more than one signaling molecule as well as receptor dimerization or clustering. To date, real-time interaction analysis on live cells can track the binding of one labeled ligand, while the influence of other ligands or cell sur- face proteins on the interaction can only be inferred indirectly from the shape of the binding trace. If a second protein is in proximity, but does not interfere with the binding of the labeled ligand, it will not be noticed. In this paper, interaction analysis on living cells was combined with fluorescent quenching to simultaneously study binding kinetics and proximity in a time-resolved manner. This enables simultaneous quantification of two ligand-receptor in- teractions, if they occur in proximity.

Results As a general assay setup, first the binding of a fluorescently labeled ligand to cells is traced. The recorded fluorescent signal is proportional to the num- ber of ligand-receptor complexes formed on the cell surface. As a second step, a ligand labeled with a quencher compatible to the fluorophore of the first ligand is added. If the second ligand binds in close proximity to the first ligand, the fluorescent binding signal, generated by the first ligand, will de- crease (Figure 9).

Figure 9: Principle of the real-time proximity assay: The binding curve for the fluo- rescent ligand (yellow) and the theoretical curve for the quenching ligand (dotted grey) are shown. If both ligands bind in proximity on the cell surface, addition of the quenching ligand will reduce the fluorescent signal of the first ligand (yellow-gray).

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The signal reduction is proportional to the amount of quenching ligand bound in proximity of fluorescent ligand. From the relative signal change over time, information about the kinetic parameters can be extracted for both the fluores- cent and quencher labeled ligands. As a model system, the binding of fluorescently labeled trastuzumab (F- Tmab) and pertuzumab labeled with a quencher (Q-Pmab) to HER2 express- ing cells was studied. It is known that the two mAbs bind to different epitopes on HER2 and do not influence each other in their binding behavior. As ex- pected, F-Tmab bound to the cells resulting in a fluorescent signal that was reduced upon addition of Q-Pmab confirming that the mAbs bound in close proximity on the cell surface. Switching the labels of the antibodies resulted in the same observation and in both cases the rate constants and affinity could be extracted for the quencher labeled mAb. The observed quenching effect could be blocked with an excess of unlabeled mAb implying that quenching is not due to the presence of quencher in the incubation solution, but rather a specific signal caused by mAbs bound in proximity on the cell surface. Dif- ferent model systems were used to verify broader applicability, including co- localization of an anti-IgM antibody targeting the BCR and rituximab. Apart from the affinity and rate constants, also the Bmax value for an inter- action can be extracted from a binding trace. The Bmax value for the first inter- action represents the signal value for a particular assay when receptors are saturated by a fluorescent ligand. For the second interaction, only quenching ligands that bind in proximity to an already bound fluorescent ligand will con- tribute to a signal decrease. Thus, the Bmax value derived from the quenching phase of the curve depends on the maximum number of binding sites for the second ligand that are in proximity for binding sites of the first ligand. Be- sides, the second Bmax value also depends on the quenching efficiency, which is determined by the labeling degree of the ligands, intrinsic physical proper- ties of the chosen labels, as well as their physical proximity. Thus, when lig- ands from the same labeling batch are used, differences in Bmax ratios only reflect the number of ligands in proximity and their physical distance or prox- imity. The Bmax ratio can then be used to quantify the proximity of two mole- cules on the cell surface in relative terms and can be understood as a measure- ment for the quenched fraction of the first ligand. This was exemplified by performing the real-time proximity assay with Tmab targeting HER2 and Cetuximab, which targets EGFR on cells express- ing both receptors. The receptors are known to form heterodimers and treat- ment with Gefitinib has shown to increase the number of heterodimers. A quenching effect was observed, confirming that a fraction of the mAbs bound in proximity to each other on the cells (Figure 10). Kinetic parameters for both interactions could be extracted, demonstrating that presented method not only works for two ligands targeting the same receptor, but also for receptor dimers. Cells that were treated with Gefitinib displayed a 50% increase in the

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Bmax(Q)/Bmax(F) ratio, compared to untreated cells. As no significant differ- ences in the kinetic interaction properties was observed, the increase in the Bmax ratio is likely due to a higher level of co-localization caused by an in- crease in heterodimers on the cells.

Figure 10: (A) Binding of FITC-cetuximab to untreated (blue) or Gefitinb treated (red) SKOV3 cells. Addition of Q-trastuzumab (Tmab) resulted in a decrease of the binding signal (two lower traces), implying the presence of EGFR-HER2 heterodi- mers. The signal decrease was more pronounced for cells treated with 1µM Gefitinib (red lower trace). Kinetic fits with the 1:1 model are depicted in black. (B) Quantifi- cation of the Bmax(Q)/Bmax(F) ratios revealed a decrease in proximity signal of 50%.

Discussion The method presented in this paper enables to quantify two ligand-receptor interactions simultaneously that occur in proximity. This can be of interest when investigating if two compounds can be bound simultaneously to the same target structure, while also obtaining information about their kinetic properties. The assay can be used to monitor the binding of two proteins while assessing their relative co-localization on cells which allows for more detailed characterization of complex binding processes that involve receptor dimeriza- tion or clustering. This is valuable when trying to target such a process phar- macologically and might also aid in relating binding to biological function as a more complete picture of the interaction mechanism can be provided.

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Paper III

Bivalent Binding on Cells varies between CD20 Antibodies and is Dose-dependent

Aim and Background Therapeutic mAbs targeting CD20 are divided into two types: type I that me- diates clustering of CD20 into lipid rafts and efficiently activate complement and type II that does not induce clustering of CD20 and has low CDC activity, but instead mediates PCD more efficiently. The first component of the classi- cal complement pathway C1q binds to hexameric arrangements of IgG Fc- domains and the formation of Fc hexamers is thought to be facilitated by clus- tering. The underlying molecular properties of the mAbs that are driving the type I vs type II behavior are not yet fully understood and properties that have been discussed include binding orientation and stability. In this paper the bind- ing of the therapeutic anti-CD20 mAbs rituximab (RTX) and ofatumumab (OFA) that belong to type I, as well as of obinituzumab (OBI) which classifies as type II, with live cells was studied by real-time interaction analysis. The aim was to provide a more detailed understanding of the binding patterns and a potential basis for relating binding characteristics to biological function.

Results Real-time interaction analysis of RTX binding to the B-cell line Daudi re- vealed two distinct interaction components with a similar association rate con- stant (ka), but distinct dissociation rate constant (kd), implying that a portion of RTX molecules is stabilized by secondary binding events (Figure 11). Clus- tering of RTX molecules on the cell surface was confirmed with the real-time proximity assay described in paper II. Especially at high target occupancy, these experiments resulted in a higher quenching effect than anticipated, indi- cating that the off-rate in presence and absence of free antibodies in solution differed. To confirm this, different concentrations of fluorescent RTX were incubated with Daudi cells and replaced with equimolar amounts of unlabeled RTX to record dissociation in the presence of free ligand in solution. The pres- ence of free RTX in solution increased the dissociation of bound RTX and this effect became more pronounced with increasing concentrations. As kd directly relates to the affinity this resulted in the apparent affinity for RTX to become weaker at higher antibody concentrations, with a shift from 4.5-20 nM for the tested concentration range (7-60 nM). A concentration dependent affinity and replacement effect in the presence of free ligand further imply the presence of secondary binding events that

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Figure 11: Interaction Map analysis of ofatumumab (upper panel), rituximab (middle panel) and obinituzumab (lower panel). Left panel: Binding data was generated with FITC-labeled mAb at two increasing concentrations during the association phase and in the absence of free antibody in solution during the dissociation phase. Right panel: Data was generated with 60 nM FITC labeled mAb in the association phase that was exchanged for 60 nM unlabeled mAb during the dissociation phase to record the in- teraction behavior in the presence of free ligand in solution.

might be mediated by the bivalent nature or the Fc-part of the antibody. There- fore, the binding of RTX-Fab and RTX-F(ab’)2 to Daudi cells was evaluated. RTX-Fab displayed a clearly faster off-rate than RTX-F(ab’)2 indicating that bivalency is the main factor for stabilization of RTX on the cell surface and that Fc-Fc interactions might contribute as a secondary stabilizing factor. The stability of bound RTX-Fab was not influenced by free ligand in solution, whereas this was the case for RTX-F(ab’)2. In addition, the off-rate of RTX- Fab was concentration independent, as is expected for a 1:1 interaction. Thus, the concentration dependent binding stability of RTX seems to be mainly due to that at higher concentrations the presence of free epitopes available for bi- valent binding decreases. Additional experiments suggested that Fc-Fc inter- actions between bound and free RTX might increase the likelihood of binding close to already bound RTX resulting in a locally more crowded cell surface

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with less CD20 epitopes available for bivalent stabilization, thereby enhanc- ing the replacement effect and concentration dependent stability. Real-time interaction analysis with the type II mAb OBI and OBI-Fab re- vealed a similar binding pattern as for RTX: the portion of antibodies stabi- lized by bivalency is decreasing with increasing concentrations. However, binding of OBI was overall less stabilized by bivalency compared to RTX as indicated by a lower contribution of the weaker interaction component. Nev- ertheless, apparent affinities in the tested concentration range (5-60 nM) were 5-26 nM and thus similar to those for RTX, as the lower stability (kd) was compensated for by an increased molecular recognition (ka) resulting in a more dynamic interaction compared to RTX. Analyzing the binding pattern of OFA with Daudi cells resulted in one main interaction component that clearly dominated the binding process and only at concentrations above 30 nM a second, weaker interaction component became noticeable that represented less than 10% of the binding events. This translates to that over 90% of antibodies bind bivalently, even at the highest tested con- centration of 80 nM, resulting in very stable overall binding of OFA. The off- rate for OFA-Fab was almost 10-fold slower than for the RTX-Fab and OFA- Fab which displayed similar off-rates. Higher binding stability with one arm gives more time to find a second epitope in proximity for bivalent stabilization and can thus contribute to the high degree of bivalent binding observed for OFA. A replacement effect in the presence of unlabeled ligand and concentra- tion dependent stability was barely noticeable for OFA in the tested concen- tration range (10-80 nM).

Figure 12: Summary of binding pattern characteristics for ofatumumab, rituximab and obinituzumab

Discussion The binding pattern of RTX, OBI and OFA was established on live Daudi cells and it was found that the degree of bivalent binding differs for these mAbs: OFA has the highest fraction of antibodies stabilized by bivalency, fol- lowed by RTX and then OBI with the smallest fraction of antibodies being bound bivalently. The degree of bivalent stabilization correlates inversely with

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how dynamic the binding pattern for the mAb is, with OBI displaying the fast- est on- and off-rates. Binding stability correlates with the known CDC efficacy of these mAbs, that is OFA > RTX > OBI. As efficient complement activation by anti-CD20 mAbs is linked to their ability to induce clustering of CD20, this raises the question if CD20 mAbs with a high degree of bivalent binding have a higher capacity to cross-link CD20 tetramers on the cell surface, which might facilitate the formation of hexameric IgG-Fc platforms suitable for C1q binding. A deeper understanding if and how antibody-antigen interactions, such as bivalent target engagement, are related to functional properties has the potential to aid in rational drug design of next-generation mAbs.

Paper IV

Degree of Bivalent Binding correlates with C1q Binding Strength for CD20 Antibodies, Rituximab, Ofatumumab and Obinituzumab

Aim and Background Complement dependent cytotoxicity is on the mechanisms of action that ther- apeutic antibodies can mediate via their Fc-terminus. Binding of the first com- plement component C1 with its subunit C1q to multimeric IgG-Fc domains, preferably hexamers, triggers a protease cascade that eventually results in the formation of pores in the target cell membrane, leading to cell lysis. Contra- dicting results have been published on whether monovalent or bivalent target engagement is preferred for C1q binding and CDC efficacy. Studies with func- tionally monovalent anti-EGFR mAbs have shown that these induce CDC more potently than their functionally bivalent counterparts. We have investi- gated the binding pattern of the CD20 mAbs RTX, OFA, and OBI on a B-cell line in paper III and found that the degree of bivalent stabilization differed for these mAbs. Bivalent stabilization correlated with the known CDC activity of these mAbs, with higher bivalency corresponding to better CDC efficacy. The aim of this study was to confirm the binding pattern of the CD20 mAbs on additional cell lines and compare it to subsequent C1q binding to cells opso- nized with these mAbs.

Results The binding patterns observed on Daudi cells in paper III were confirmed on two additional cell lines for RTX and OFA and on one additional cell line for OBI. On all tested cell lines OFA displayed most stable binding and the least dynamic binding pattern, followed by RTX, whereas OBI displayed the least stable but most dynamic binding pattern. Previous data presented in paper III,

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implies that binding stability for these mAbs relates to the fraction of mole- cules engaged in bivalent binding. Binding of all three mAbs was more stable on Ramos cells than on Daudi cells. Binding of RTX and OFA to P493.6 cells seemed to be less stable than on Ramos cells, although differences were less pronounced than for Daudi cells. The stability of bound RTX-Fab did not no- ticeably differ between cell lines, suggesting that differences are due to sec- ondary stabilizing effects.

Figure 13: C1q binding to three different B-cell lines opsonized with either rituximab (upper figure) or ofatumumab (lower figure): Daudi cells (blue), Ramos cells (black) and P493.6 cells (orange)

A real-time binding assay, to establish C1q binding kinetics to live cells op- sonized with mAbs, was developed and generated unique information on the complex binding behavior of C1q to opsonized live cells. The interaction of C1q with RTX and OFA opsonized cells displayed a biphasic dissociation and was thus clearly heterogenous (Figure 13). Binding of C1q to cells opsonized

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with OFA resulted in an overall stronger and more stable interaction compared to RTX opsonized cells, whereas C1q binding to OBI opsonized cells could not be detected. Interaction Map analysis revealed two distinct interaction components for OFA and RTX opsonized cells. For both mAbs, the main dif- ference between the two interaction components was the binding stability, which resulted in half-life differences of more than 100-fold. The stable C1q interaction component had an affinity in the range 0.07-2.3 nM and the affinity for the weaker interaction component was in the range of 5-70 nM, depending on cell line-mAb combination. Binding of C1q was stronger on Ramos cells than Daudi cells for both RTX and OFA, which correlates with the differences in mAb binding stability on these cell lines. For P493.6 cells, C1q binding was more similar to Ramos cells for RTX, whereas for OFA the C1q interaction was more similar to Daudi cells. Generally, differences in C1q binding strength were more pronounced be- tween the mAbs than the differences among the cell lines for the same mAb. Even though the C1q interaction components differed mainly in their binding stability, it was also noted that the weaker C1q interaction component dis- played an overall faster on-rate for all cell line-mAb combinations. The inter- action peaks belonging to both the strong and weak component were elongated in y-axis direction on the Interaction Maps, implying heterogeneity in how C1q recognizes its targets.

Discussion The affinities reported here for the two interaction components of C1q binding to mAb opsonized cells are stronger than what has been reported for the inter- action of C1q with single IgGs in solution. Moreover, the elongated shape of the Interaction Map peaks is an implication for avidity effects and thus the two peaks very likely represent multivalent binding of C1q. The stable interaction component with affinity values in the low and sub nanomolar range presuma- bly represents binding of C1q to IgG-Fc hexamers. The less stable interaction component displayed an overall higher on-rate which could be explained by differences in valency between the components and likely represents binding of C1q to IgG-Fc multimers that are smaller than hexamers. A higher degree of bivalent binding for the CD20 mAbs investigated cor- related with an overall stronger C1q interaction on cells opsonized with the respective mAbs. For anti-EGFR mAbs it has been show that functionally monovalent mAbs induce CDC more efficiently, potentially since monovalent target engagement can result in a higher number of IgGs bound. If monovalent or bivalent antigen binding is preferable in regards to CDC activity might vary between receptors and could depend on e.g. lateral receptor mobility in the membrane. In particular, type I CD20 mAbs are known to induce clustering of mAb bound CD20 into lipid rafts, which may facilitate the formation of suitable C1q binding platforms and could thus explain why bivalent target en- gagement seems beneficial for CD20 mAbs in regards to interaction with C1q.

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Paper V

Potent Fc Receptor Signaling by IgA leads to Superior Killing of Cancer Cells by Neutrophils Compared to IgG

Aim and Background All therapeutic mAbs currently approved for clinical use are of IgG isotype. However, also the IgA isotype can mediate anti-tumor activity in vivo, which is dependent on FcαR expressed on neutrophils and macrophages. Neutrophils constitute the majority of circulating leukocytes and it has been demonstrated that IgA engages neutrophils more efficiently for target cell killing than IgG. The aim of this study was to investigate which factors might be responsible for superior target cell killing by IgA. For this purpose, the binding of IgA and IgG to their respective FcRs and their ability to mediate neutrophil-target cell interactions, as well as FcαR and FcγR expression and signaling was investi- gated.

Results On unstimulated primary neutrophils the expression of FcαR, the activating FcR for IgA, was found to be 2-fold lower compared to FcγRIIa, the activating FcR for IgG, and 10-fold lower compared to FcγRIIIb, which can bind IgG but does not have any intrinsic signaling capacity. The binding of primary neutrophils, isolated from healthy donors and la- beled fluorescently with calcein, was investigated to antibody coated surfaces and antibody opsonized target cells. Evaluating binding stability by letting neutrophils bind IgA or IgG coated surfaces and then subjected them to a se- ries of washes, showed better binding stability for IgA with an anti-Her2 mAb, but no statistically significant difference for mAbs against CD20 or EGFR. Exposing neutrophils to IgA or IgG coated beads, resulted in a trend, but no significant difference, for more neutrophils binding to IgA with an anti-CD20 mAb and no clear difference with EGFR as a target. The ability of IgA and IgG to mediate cell-cell interactions was investigated with Daudi cells ex- pressing CD20 as target cells. Binding of calcein labeled neutrophils was fol- lowed for 1 h and did not reveal any differences in how efficiently cell-cell contact was established. Removal of neutrophils to study the stability of the formed cell-cell interactions resulted in a trend for increased stability in the presence of IgA compared to IgG, although not statistically significant. In summary, even though FcαR has a lower expression than FcγR on neutrophils, IgA mediates neutrophil binding to antibody coated surfaces or cells at least as good as IgG.

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Figure 14: mAb mediated cell-cell interaction: (A) Example binding trace for inter- action between Daudi cells (=target cells) and neutrophils mediated either by anti- CD20 IgA (red) or IgG (black). (B) Average binding association and extrapolated half-life of Daudi-PMN (=peripheral mononuclear cell, referring to neutrophils) com- plex

Signal transduction induced by IgA or IgG binding to their respective FcRs was investigated in primary neutrophils and revealed that IgA induced higher levels of ERK phosphorylation, a kinase involved in the down-stream signal- ing pathway of activating FcRs. This suggests that engagement of FcαR re- sults in a stronger signaling response in neutrophils compared to FcγRIIa.

Discussion A possible explanation for why IgA mediates superior target cell killing by neutrophils is the stronger signaling response elicited upon FcαR engagement compared to FcγRIIa. It has been shown previously that IgA can be bound by two FcαRs simultaneously which could explain why IgA mediated neutrophil binding is at least equally stable as with IgG, even though FcαR expression is lower on neutrophils. As each FcαR can recruit an accessory subunit with two signaling motifs, so-called ITAMs, this would lead to signal transduction via four ITAMs whereas FcγRIIa harbors only one ITAM in its cytoplasmic do- main which could explain the weaker signaling response to IgG in neutrophils despite higher expression of activating FcRs. Experiments with IgAs, that have one of their binding sites for FcαR mutated, could confirm whether dif- ferences in binding stoichiometry are responsible for IgA eliciting a stronger signaling response in neutrophils.

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Conclusion and Future Perspectives

The aim of this thesis was to extend the toolbox for characterizing mAb bind- ing and mAb mediated interactions on live cells and to illustrate how this can be used to gain more detailed knowledge about these binding processes and thus help to relate binding to biological function. Paper I describes a protocol for real-time interaction analysis of antibodies with cells growing in suspension, which allows to measure ligand interactions with immune receptors in a relevant cellular context. The developed protocol was the basis for the studies conducted in papers III, IV and V and is also used by other laboratories to study mAb-immune cell interactions (personal com- munications). It is planned to explore the protocol for immobilization of pri- mary immune cells, which might result in the need for some technical adjust- ments in regards to e.g. incubation times, depending on cell type. In paper II, a novel real-time proximity assay is presented that allows to extract information about lateral receptor interactions and can thereby aid to decipher more complex interactions that involve multiple ligands, receptor di- merization and clustering. Signal transduction is often regulated by receptor oligomerization and obtaining information about this in connection to binding data can be valuable when trying to understand how ligand binding in such a system influences functional activity. In paper III, the real-time proximity as- say helped to decipher the heterogenous binding pattern of RTX that was ob- served in paper I. Moreover, ongoing projects explore the use of the proximity principle for measuring biomolecular interactions inside cells. In paper III and IV, the binding pattern of the CD20 mAbs RTX, OFA and OBI was established on B-cell lines and it was found that the fraction of mol- ecules engaging in bivalent binding differed between the mAbs. If mAbs tar- geting the same receptor differ in their degree of bivalent binding on cells, bivalency very likely differs for mAbs targeting different receptors. Moreover, depending on mAb properties and receptor distribution on the cell surface, there might be so-called “functionally bivalent mAbs” that mostly engage in monovalent binding on cells. This underlines the importance to analyze bio- logical interactions in a relevant environment, especially when trying to con- nect binding to function and implies that generalization between different bi- ological systems should be made with caution. In paper IV, a C1q binding assay to mAb opsonized cells was set-up that captured multivalent C1q binding, which, in contrast to monovalent C1q bind-

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ing, can trigger complement activation and thus represents functionally rele- vant interactions. For RTX, OFA and OBI, a higher degree of bivalent binding correlated with stronger C1q binding. To establish whether bivalent target en- gagement is generally beneficial for CD20 mAbs, additional antibodies should be tested and results confirmed with functionally monovalent counterparts. For both paper III and IV the results should also be confirmed on primary B- cells to ensure that the observations are the same on clinically relevant mate- rial. Moreover, it would be interesting to study the influence of Fc-Fc interac- tions on mAb binding patter and subsequent C1q interaction in more detail. As it is known that type I mAbs can be internalized via the FcγRIIb studying the mAb interactions at physiological temperature to estimate the influence of internalization on the binding pattern would also be of interest. In paper IV and V, assays for studying mAb mediated interactions of target cells with C1q (paper IV) and immune cells (paper V) are presented. These assays can be useful when trying to relate how mAb structure and binding relates to CDC and ADCC efficacy. Quantification of cell-cell interactions can also be applied in the development of bispecific antibodies that bind target epitopes on different cells such as T-cell engagers. Assays that allow to cap- ture binding processes related to the mechanisms of actions for mAbs can aid to understand differences in Fc-mediated mAb functions and explore if these are related to how a mAb interacts with its antigen on the target cell. As reversible binding is one of the main means to communicate infor- mation in cellular systems, understanding binding patterns helps to understand how biological responses can be triggered. If a molecule binds to and com- municates with another molecule is largely determined by its structure. The cellular environment can influence the structure of molecules, especially if these are embedded in a lipid membrane. Moreover, other factors, such as the geometrical arrangement, can impact the interaction between molecules and thus how and if information is communicated that may lead to the activation of a certain biological process. In this context, binding can be understood as a process that connects structure to biological function, especially when binding is explored in a cellular context. Increased understanding about structure-bind- ing-function relationships facilitates rational drug design, as has been demon- strated with the development of next-generation mAbs that harbor a slight structural modification on their Fc-domain that leads to improved binding strength to FcγRs on immune cells and thereby more efficient induction of ADCC. In drug development, binding is currently often measured in terms of affinity in purified model systems only as these allow for higher throughput. However, exploring binding in detail with a smaller set of candidate molecules in a cellular context and thereby understanding how binding properties relate to functional properties, has potential to increase our knowledge to make more informed decisions on drug design and screening parameters. Investing time to understand binding patterns might lead us to improved drugs faster.

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Acknowledgements

This thesis work would not have been possible without the support from the people around me. Thank you to fantastic colleagues, collaborators, friends and family!

Special thanks:

To Jos, for being an amazing supervisor and main support during this PhD! You made sure this PhD was not just done, but that I actually learned. Thank you for making it possible for me to do a research stay abroad and go to great conferences. I truly appreciate that you made sure that there was time for me to work on my PhD and all the time you took to discuss with me. Thank you for supporting me but also making sure that I became more independent with my research. You have not only given me scientific advice but also helped me grow as a person, for which I am very grateful. I could not have asked for a better mentor. Thank you!!

To Marika, for always having an open door and being such a wonderful, wel- coming person! I am very lucky to be part of your group and being able to count on your support means a lot. Thank you for the nice summer gatherings at your place and the writing day and after-works we had together.

To Karl, for being a really great boss and mentor! You lead by example and always make time to listen, no matter how busy (usually crazy busy) you are, people always come before business. I so appreciate the workspace you have created at Ridgeview. Thank you for believing in me and throwing me into that water to teach me how to swim!

To Peter, for teaching me in the lab before you were officially supervising me and also for making me aware that important aspects of research are happening outside the lab.

To Jeanette Leusen, for having me in your lab in Utrecht. I did learn so much by being exposed to the research environment in your group and I am im- mensely grateful for this experience. Thank you to all the people who taught me new techniques and supported me during my time, especially Toine, Mitchell, Rosanne and Susanne.

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To John, for checking in on me and making sure I was ok when things didn’t go so well. Knowing you care means a lot to me! Thank you also for all your patience when explaining software to me and for making those extra features that I wanted for all the weird data evaluations. To Anna, for being the most kind and helpful person. You made sure the lab was running and were baby-sitting cells for me more times than I can count. It was so helpful to know that I can always count on you – without you this PhD would have taken a lot longer. Thank you also for the fun climbing ses- sions, we should go again soon! To Hanna, for introducing me to Ridgeview and the world of binding kinetics. Thank you for supervising me as a master student and teaching me all the basics. I don’t think I had ever heard of binding kinetics before that one lecture you gave… To Jonas, for the good times we had in the office and when traveling together. Mumbai was certainly an experience. To Fredrik, for good talks and all the after-works and climbing sessions. Thank you for trying to explain active learning and programming to me. To João, for all the help trouble-shooting experiments and the good talks in the evenings when the office gets empty. To Hadis and Guillermo, for bringing good mood to the office. To Magnus, for always being interested in my research and available for dis- cussions.

To Diana, for your knowledge about lab experiments and life. Thank you for good talks and advice on both. To Anja, for all the encouragement and thoughtful talks. I hope you realize what a great person you are. To Sara, for helping me through the administrative jungle. Thank you for your positivity and all the nice fikas. To Vladimir Tolmachev, for your chemistry expertise. Thank you for all the scientific advice and kindness. To Bo Stenerlöw, for help with administrative issues and nice fikas, after- works and defense parties. To Andris, Anzhelika, Javad, Maryam, Mehran and Tabassom and all the oth- ers that have been at BMS throughout the years, for helping me out in the lab and encouraging words when needed.

To Axel, for all your support and understanding. Thank you for believing in me.

An meine Eltern, vielen Dank für all eure Unterstützung. Ihr habt immer an mich geglaubt und mich in meinen Entscheidungen bestärkt. Ohne euch, hätte ich diese Doktorarbeit nie angefangen.

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Acta Universitatis Upsaliensis Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1648 Editor: The Dean of the Faculty of Medicine

A doctoral dissertation from the Faculty of Medicine, Uppsala University, is usually a summary of a number of papers. A few copies of the complete dissertation are kept at major Swedish research libraries, while the summary alone is distributed internationally through the series Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine. (Prior to January, 2005, the series was published under the title “Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine”.)

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