1

EXAMENSARBETE INOM TEKNIK OCH LÄRANDE, AVANCERAD NIVÅ, 30 HP STOCKHOLM, SVERIGE 2017

Screening of ligand binding behavior

using a high-throughput method and

development of guidelines for a

learning material.

Oscar Kornher

KTH SKOLAN FÖR INDUSTRIELL TEKNIK OCH MANAGEMENT

2

Kartläggning av liganders

bindningsbeteende genom en

screeningmetod och utvecklandet av

riktlinjer för ett utbildningsmaterial.

Oscar Kornher

EXAMENSARBETE INOM TEKNIK OCH LÄRANDE PÅ

PROGRAMMET CIVILINGENJÖR OCH LÄRARE

Titel på svenska: Kartläggning av liganders bindningsbeteende genom en

screeningmetod och utvecklandet av riktlinjer för ett utbildningsmaterial.

Titel på engelska: Screening of ligand binding behavior using a high-

throughput method and development of guidelines for a learning material.

Teknikhandledare: Åsa Emmer, KTH.

Lärandehandledare: Helena Lennholm, KTH.

Ev. Uppdragsgivare: Cytiva (formerly GE Healthcare).

Examinator: Annica Gullberg, KTH.

3

Abstract

This study is comprised of two distinct parts, a chromatographic study and a didactic study.

Chromatographic study

The need for higher selectivity in chromatography purification has increased in recent years.

Multimodal resins, offering novel selectivity, are a possible solution to this demand. The purpose of

this study is to develop new multimodal resins with novel selectivity, using an iterative workflow.

Therefore, eleven novel multimodal ligands were screened according to binding behavior using a

high-throughput (HT) method. The mapping of binding behaviors was comprised of six proteins and

32 different binding buffers, with various salt concentrations and pH-levels, to allow for a wide, but

efficient mapping. The data generated from the screenings were presented using binding capacity and

partition coefficient and were evaluated against each other using principal component analysis (PCA).

The PCA created a ligand diversity map, which separated ligands in respect to binding behavior. The

information supplied by the diversity map can be used for selecting ligands for further research. The

results from presenting the data in the form of partition coefficient and binding capacity were

comparable, which indicated that the effects of initial protein concentrations were low. However, this

study found that there are advantages and disadvantages to using both quantities. It is possible that

some of the ligands screened in this study will be used in further research and it is likely that the

diversity map will facilitate the development of ligands with novel selectivity

Didactic study

With a rising demand for HT screening as a method to evaluate the binding behaviors of novel

ligands, in combination with the UN’s goal for quality education for all, the incentives for developing

quality educational materials for HT screenings have increased. A first step towards educational

materials is to outline common difficulties, thereby creating guidelines for developing educational

materials. In this study, guidelines for the development of learning materials for HT screenings have

been produced. The guidelines were based on identification of critical steps for the experimental

procedure and utilizing employer experience of learning materials in general. Two methods for data

collection was used in the didactic study: observations and interviews. The observations were

performed during the training of the screening method. For the interviews, three individual and one

focus group interview were held. Thereafter, a thematic analysis was performed on the data to

generate themes. The results from the observations and interviews indicated that the critical steps of

the learning process were the practical parts. The result of the thematic analysis showed the

identification of three themes: Cohesion, Facilitation and Interaction. These themes generated four

aspirational guidelines:

1. Identify crucial steps of the practical procedure.

2. Adapt to people with different backgrounds.

3. Maintain coherence throughout the learning process.

4. Utilize strategies for interaction, both human interaction and material interaction.

Keywords: Multimodal chromatography, screening experiments, high-throughput, PCA, learning

material, focus group interviews, thematic analysis, crucial steps, coherence and interaction.

4

Acknowledgements

First, I would like to thank my supervisors at Cytiva and KTH. I would like to thank Helena

Lennholm for the continued support with developing and refining the ideas for the learning material,

as well as always being invested in the process. I would also like to thank Åsa Emmer, for the

thoughtful comments and feedback throughout the project.

On Cytiva, I would like to thank Eva Heldin for her help with all things practical and for help with the

Kp. Furthermore, your warmth and kindness helped me feel welcome and remembered at the

company. I would also like to thank Gunnar Malmquist for the support with chromatographic

terminology and with the principal component analysis in SIMCA. I will also remember the

enthusiasm that you had for the project in all its aspect and how you managed to transfer that

enthusiasm to me. I will miss the Friday meetings with both of you, which have helped me

tremendously in this project.

I would like to extend a warm thank you to the people of my section for the enthusiasm with which

they have helped me with the experimental procedure, as well as for all the tips and tricks they

bestowed upon me.

Lastly, I would like to thank the participants of the interviews, without you this paper would not have

been possible.

5

List of content

Abbreviations .......................................................................................................................................... 7

1. Introduction ....................................................................................................................................... 8

1.1. Purpose ......................................................................................................................................... 9

2. Theoretical background ................................................................................................................... 9

2.1. Screening Experiments ................................................................................................................ 9

2.1.1. Basic Principles of Chromatography .................................................................................... 9

2.1.2. Multimodal chromatography ............................................................................................... 10

2.1.3. Capto MMC ImpRes resin .................................................................................................. 11

2.1.4. Protein purification strategies and High-throughput Process Development ....................... 12

2.1.5. HTPD and sustainable development ................................................................................... 12

2.1.6. PreDictor plates ................................................................................................................... 13

2.1.7. Calculations of binding capacity ......................................................................................... 14

2.1.8. Partition coefficient ............................................................................................................. 16

2.1.9. Principal component analysis .............................................................................................. 16

2.2. Didactic study ............................................................................................................................ 17

2.2.1. Theories about learning ....................................................................................................... 17

2.2.2. Three dimensions of learning .............................................................................................. 17

2.2.3. Active learning .................................................................................................................... 18

2.2.4. Focus group interviews ....................................................................................................... 18

2.2.5. Thematic analysis ................................................................................................................ 19

3. Methods and materials ................................................................................................................... 21

3.0.1. Materials ............................................................................. Error! Bookmark not defined.

3.1. Screening experiments ............................................................................................................... 21

3.1.1. Protein selection .................................................................................................................. 21

3.1.2. Gel preparation and plate filling ......................................................................................... 21

3.1.3. Buffer preparation ............................................................................................................... 22

3.1.4. Preparation of protein solutions .......................................................................................... 22

3.1.5. Screening experiments ........................................................................................................ 23

3.1.6. Data management ................................................................................................................ 24

3.1.7. Principal component analysis .............................................................................................. 26

3.2. Method for the didactic study .................................................................................................... 27

3.2.1. Data collection .................................................................................................................... 27

3.2.2. Observations ....................................................................................................................... 27

3.2.3. Interviews ............................................................................................................................ 27

3.2.3.1. Focus Group Interview ................................................................................................. 28

3.2.3.2. Individual interviews.................................................................................................... 28

6

3.2.4. Thematizing data ................................................................................................................. 28

3.2.5. Ethical considerations ......................................................................................................... 28

4. Results and Discussion .................................................................................................................... 29

4.1. Screening experiments ............................................................................................................... 29

4.1.1. Binding behaviors ............................................................................................................... 29

4.1.2. Principal component analysis .............................................................................................. 30

4.1.2.1. Model influence ........................................................................................................... 30

4.1.2.2. Confirmation of model separation ............................................................................... 33

4.1.2.3. Trends in ligand position .............................................................................................. 35

4.1.3. Discussion and outlook ....................................................................................................... 40

4.2. Results from the didactic study .................................................................................................. 41

4.2.1. Results from observations ................................................................................................... 41

4.2.2. Results from interviews ...................................................................................................... 42

4.2.2.1. Cohesion ...................................................................................................................... 42

4.2.2.2. Facilitation ................................................................................................................... 43

4.2.2.3. Interaction .................................................................................................................... 44

4.2.3. Discussion of reliability ...................................................................................................... 46

References ............................................................................................................................................ 48

Appendix ............................................................................................................................................... 51

7

Abbreviations AC – Affinity chromatography

CYT – Cytochrome C

DMODX – Distance to model

IEX – Ion exchange chromatography

GEHC – General electric healthcare

GF – Gel filtration

HIC – Hydrophobic interaction chromatography

HSA – Human serum albumin

HT – High throughput

HTPD – High-throughput process development

Kp – Partition coefficient

LAC – α-lactalbumin

LYS - Lysozyme

mAb – Monoclonal antibody

MMC – Multimodal weak cation exchanger

OVA - Ovalbumin

PC1 – Principal component 1

PC2 – Principal component 2

PCA – Principal component analysis

PLBL-2 – Phospholipase B-like 2 protein

SOP – Standard operating procedure

qc – Binding capacity

XM5 – x monoclonal antibody 5

8

1. Introduction The pharmaceutical industry is a gigantic, ever-growing industry with a turnover of over one trillion

US dollars worldwide in 2018 (Statista, 2020). Producing medicine for all types of diseases, the

pharmaceutical industry has become multifaceted, with most companies choosing to specialize in one

field of medicine. A significant category of drugs, forecast to become the most important category in

the foreseeable future, is the biopharmaceuticals (GE Healthcare, 2013). The name,

biopharmaceuticals or biologics, signifies that the origin of the pharmaceutical is biological and have

been manufactured or extracted therefrom (Rader, 2007). Common variants of biopharmaceuticals

include vaccines, allergenics, antibodies and hormones. With rising importance put on

biopharmaceuticals, the need for improved methods for preparation of these drugs have increased.

A large part of the preparation of biopharmaceutical drugs is the purification process, in which

chromatography is the common method of choice. For the last couple of years, a steady growth of

monoclonal antibody products for therapeutic use have been seen (Ecker et al., 2015). Also, new

multi-specific antibodies with increased molecular diversity, as well as difficult product related

impurities pose new purification challenges. In a particular group of process related impurities, known

as host cell proteins (HCPs), specific species have been identified as ‘difficult to remove’ due to the

similarities in characteristics and coelution between impurity and product (Singh et al., 2019). With

greater diversity, as well as increased similarities between product and impurity, the need for more

powerful polishing steps to attain required product quality have increased. This has led the

biopharmaceutical industry to continuously try to modify existing chromatographic resins to address

the need for higher polishing resolution. Common approaches to increasing polishing resolutions are

increasing efficiency or selectivity of the polishing process. One way to increase the selectivity of the

chromatographic resin can be to use multimodal (or mixed-mode) ligands, offering new selectivity

through the multifunctionality of the ligand. However, new ligands will have to pass through vigorous

testing before being possible to use. High-throughput (HT) methods for screening binding behaviors

of novel ligands can save time and resources in the research phase (GE Healtcare, 2009).

Likewise, with a rising demand for HT screening for researching novel ligands, the need for HT

screening training will increase. Consequently, the need for learning materials focused on HT

screening experiments will increase. This, in combination with the UN’s goal of quality education for

all (UN, 2020), incentivizes developing quality educational materials for HT screening experiments.

Furthermore, Cytiva (formerly GE Healthcare) has a long history of developing educational material

for biopharma. Therefore, to ensure high quality, the rich employee knowledge and experience of

educational materials on Cytiva (formerly GE Healthcare) should be taken advantage of. A first step

to developing educational materials could be to establish guidelines for creating materials, thereby

effectively streamlining the process of development.

1.1. Background This study consists of two parts: a chromatographic part and a didactic part. For the first part of the

paper, some additional information about the workflow created for Theel (2019) at Cytiva (formerly

GE Healthcare) needs to be supplied to understand the background.

The first part of this study continues the work started by Theel (2019) and is a part of a larger project

focused on workflows for the identification and development of novel ligands with unique selectivity.

The first step of the workflow is the characterization and synthesis of ligands. Through a virtual

ligand library, ligands were selected to cover an as broad region of ligand functionality as possible

and thereafter synthesized. In the second step of the workflow, the binding behaviors of the ligands

were mapped using a method for HT screening in combination with a PCA, to create a ligand

diversity map. In the third and final step, ligands from the diversity map were selected and analyzed in

several column studies to generate mechanistic models and draw conclusions about the binding and

9

separation of the ligands. The information generated in the screening and column experiments were

continuously used for an iterative approach to the synthesis of the ligands, generating a circular

workflow. The basis for this study was the second step of the workflow. The 13 ligands screened by

Theel (2019) was expanded by 11 new ligands in this study, creating a diversity map of 24 ligands in

total.

Since the results from the mapping are from two different studies, the question of reliability and

comparability becomes important. To explore the comparability of the results from the two studies,

two different quantities for presenting data, binding capacity (qc) and partition coefficient (Kp), were

introduced with the purpose of exploring how experimental factors, like protein concentrations, would

influence the results.

1.2. Purpose In the light of the two parts, this study has been designed to fulfill two purposes. The first purpose is

to develop new and powerful multimodal ligands with high selectivity. For this purpose, new and

powerful methods for developing ligands need to be established. In this study, the iterative HT

method combined with PCA, created in the paper by Theel (2019), is evaluated. Specifically, this

study will focus on exploring and refining the last step of the workflow, the mapping of ligand

binding behavior using PCA. This was done in part to ensure credibility of the method and in part to

minimize qualitative differences between studies to allow for comparability of results. This can be

condensed into two research questions:

- How effective is the HT method and PCA for mapping ligand binding behavior?

- What is the qualitative difference between using binding capacity and partition coefficient for

the mapping method?

As a part of this, the binding behavior of eleven novel multimodal ligands are mapped in this study.

The second purpose is to develop quality educational materials for screening experiments. A first step

in this direction, is to outline common difficulties in screening experiments to establish a template for

creating educational materials. In this study, four guidelines for the creation of educational materials

pertaining screening experiments are established. These guidelines are based on observations by the

author of the learning process for screening experiments as well as qualitative interviews focused on

exploring employee experience of learning materials on Cytiva (formerly GE Healthcare). The

research questions for the second part is:

- What are some common problems or critical steps in the workflow of screening experiments,

based on user experience?

- What are some guidelines for preventing common problems or critical steps when developing

a learning material based on employee experience?

2. Theoretical background

2.1. Screening experiments This section serves as an introduction to mapping of ligand binding behaviors via a HT method. The

method consists of chromatography experiments coupled with PCA. To better understand screening

experiments some background in chromatography, protein purification and binding behaviors is

needed. This section also explains the PCA and data management used in this study.

2.1.1. Basic principles of chromatography Chromatography is the most common preparative separation method for purification of proteins (GE

Healthcare, 2010). The general principle of chromatography is separation of substances by their

10

interaction with two phases: the mobile phase and the stationary phase. By varying the mobile phase,

different types of chromatography can be introduced. The most commons types of chromatography

are liquid and gas chromatography, and for this study liquid chromatography was used. In liquid

chromatography, the solute is usually dissolved in the mobile phase and then passed through a column

containing the stationary phase. Depending on the properties of the two phases, mobile and stationary

phase, some solutes may be prone to interact with the stationary phase. If interacting with the

stationary phase, the time to pass through the column is prolonged, also known as the retention time.

If two molecules exhibit a difference in interaction they may be separated in the system. (Harris,

2007)

The variability of the properties of the stationary phase, also known as chromatography resin or

medium, has prompted the development of several different liquid chromatography methods. Some of

the most common types are affinity chromatography (AC), gel filtration (GF), ion exchange

chromatography (IEX) and hydrophobic interaction chromatography (HIC) (Harris, 2007). The

varying characteristics of the stationary phases brings that these methods will separate solutes after

different properties. This means that separation of proteins by molecular interactions, size, charge or

hydrophobicity is possible. Ultimately, the method of choice strongly depends on the properties of the

target protein (or solute), and the other substances in the sample (GE Healthcare, 2010). Lately,

methods using more than one mode of action to separate target molecules, known as multimodal

chromatography, have gained interest due to an increasing need of selective monoclonal antibody

purification.

2.1.2. Multimodal chromatography Multimodal or mixed-mode chromatography utilizes resins that give more than one type of interaction

between the resin ligand and the sample components, thereby increasing the specificity of the

interaction (Yang & Geng, 2011). Although the use of common chromatographic techniques—such

AC, GF, IEX and HIC—is sufficient to achieve high purity of most targets, there are difficult cases,

where an optimal process has not yet been established. Typically, this happens if the impurities have

very similar characteristics as the product, and the chromatography method needs to give separation

by small differences between solutes (GE Healthcare, 2013).

Commonly, the ligands used in multimodal chromatography combines two well-known techniques,

like IEX and HIC. The modes of interaction can work independently or in concert, depending on the

situation. Figure 1 showcases the difference between a regular and a multimodal resin. For this study,

several analogues of Capto MMC, a multimodal ligand utilizing IEX and HIC interactions, were used.

Figure 1. Traditional chromatographic media (A) with one interaction site compared to multimodal chromatography media

(B) with two interaction sites offering novel selectivity. Courtesy of Cytiva (formerly GE Healthcare)

When analyzing the binding behaviors of monoclonal antibodies on multimodal ligands for varying

salt concentrations, a characteristic U-shape curve is normally obtained. This pattern of interaction is

strongly believed to be connected to the multifunctionality of multimodal ligands (Melander et al,

1986). The high binding at low salt concentrations is attributed the electrostatic interactions of the

ligands. However, as the salt concentration increases, the sites of interaction are masked by salt ions,

11

lowering the binding capacity. The high binding at high salt concentrations is attributed an increasing

importance of the hydrophobic interaction of the ligands (Melander et al., 1986). For an overview of

the interactions influencing the binding behavior of multimodal ligands, see figure 2.

Figure 2. A schematic of the different interactions contributing to the binding behavior of multimodal ligands. At low salt

concentration the ionic interaction is most prominent, whereas, at high salt concentration the hydrophobic effect is most

prominent. Courtesy of Cytiva (Formerly GE Healthcare).

2.1.3. Capto MMC ImpRes resin Capto MMC ImpRes is a resin designed and produced by Cytiva (formerly GE Healthcare), see figure

3. MMC stands for Multi-modal (weak-)Cation(-exchanger) and its structure allows for two main

types of interactions, ion exchange and hydrophobic interaction. This is due to the ligand’s two main

functional groups: the carboxyl group and the phenyl ring. The carboxyl group, which has a relatively

high pKa-value, causes electrostatic interactions, and can be classified as a weak cation exchanger.

The phenyl ring on the other hand causes a hydrophobic interaction. Although these two interactions

are most prominent, Capto MMC ImpRes exhibits other modes of action like hydrogen bonding and

thiophilic interactions. ImpRes stands for ‘improved resolution’ and signifies smaller beads in the gel,

which results in improved resolution. In this study, eleven analogues to Capto MMC ImpRes were

used with varying properties and modes of interaction. This allowed for a wider mapping of resin

performance to better be able to predict outcomes in downstream processes. (GE Healthcare, 2013).

Figure 3. An overview of the potential interaction sites for the Capto MMC ligand, showcasing thiophilic, ionic, H-bonding

and hydrophobic interaction sites. Courtesy of Cytiva (formerly GE Healthcare)

For this study, a total of 11 new ligands were screened. Combined with the previous screenings of 13

ligands (Theel, 2019), 24 ligands were screened in total. The ligands were named with a number after

the order of creation, L00 thereby being the reference ligand Capto MMC ImpRes and L01 the first

analogue created. A list of the ligands screened can be found in table 1. In this paper L14-L24 and

12

L00 were screened and the results were then evaluated in comparison to the previously screened L00-

L12. There was no ligand named L13.

Table 1. Ligands screened in Theel’s study and in this study.

Theel

(2019)

L00 L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12

This

study

L00 L14 L15 L16 L17 L18 L19 L20 L21 L22 L23 L24

2.1.4. Protein purification strategies and high-throughput process development The objective of any purification method is to obtain a high purity of the target protein as efficiently

and economically as possible (GE Healthcare, 2010). The purification, also known as the downstream

process, usually consists of three stages: capture, intermediate purification and polishing (CIPP) (GE

Healthcare, 2010). In the first step of the process, capture, the objectives are to isolate, concentrate

and stabilize the target product (GE Healthcare, 2010, 2013). For protein purification AC is preferred

for the capture-stage as it can achieve high purity levels in a single step.

In the second step, intermediate purification, most of the bulk impurities are removed. This includes

host cell proteins (HCP) and product related impurities. If the product is to be used on humans, further

precaution needs to be taken to remove viruses, endotoxins and nucleic acid. Sometimes, due to an

efficient capture-stage, the intermediate purification step is omitted in favor of one or several

polishing steps (GE Healthcare, 2010).

In the third and final step, polishing, the product reaches its final purity through the removal of closely

related molecules as well as fragments and aggregates of the target protein (GE Healthcare, 2010).

Polishing is often the most difficult step since achieving sufficiently high purification between similar

species in a solution can be challenging. Therefore, biopharmaceutical companies greatly invest in the

development and optimization of polishing steps.

For biopharmaceutical companies wanting to develop a new purification method, this usually means a

time-consuming and often costly series of experiments to determine the optimal process conditions

(GE Healthcare, 2010). However, by utilizing high-throughput process development (HTPD), many

types of experiments can be performed in parallel and in a smaller scale, saving both time and money

(McDonald et al., 2016). HTPD can be used to identify optimal binding or elution conditions, which

in turn can be used to predict column chromatography performance (McDonald et al., 2016; Giese et

al., 2017). In this study, HTPD was used in the form of 96-well plates allowing for several

replications of small-scale experiments with varying conditions.

2.1.5. HTPD and sustainable development Screening experiments were chosen as the method due to its low amount of chemicals used and waste

produced. Furthermore, screening experiments enable many conditions to be screened at the same

time to produce a wide mapping of the ligands, but at the same time saving both time and money.

In 2015, the UN adopted 17 goals to meet the 2030 Agenda for sustainable development (UN, 2020).

These goals try to tackle social, economic and environmental aspects of sustainable development. In

this project, three goals in particular are of interest: Good Health and Well-being, Quality Education

and Responsible Consumption and Production. The results of this project may aid in the development

of better chromatographic media, which in turn will be used for the purification of various drugs.

Therefore, this project may help the development of better health and well-being. Also, the low

amount of chemicals used, and waste produced, is indicative of responsible production and

13

consumption. Lastly, with the development of guidelines for a learning material, better educational

materials can be produced, further improving the quality of education.

Figure 4. The goals for sustainable development applicable to this study.

2.1.6. PreDictor plates

Figure 5. Scematic figure of a PreDictor plate. The hatching illustrates the bottom filter and the red dots represents the resin

particles. Above the resin is the protein solution. Courtesy of Cytiva (formerly GE Healthcare).

The experiments in this study made use of 96-well PreDictor microtiter plates filled with resin, see

figure 5. Each well has a small filter at the bottom, retaining the resin while simultaneously letting

solutes pass through. This enables added solutes to be separated by their interaction with the

chromatography resin. At equilibration, there will be a concentration in the solid phase and a

concentration in the liquid phase. By draining the liquid phase out of the plate and into a collection

plate, the two phases can be separated. Solutes with strong interactions would stay attached to the

resin, while weakly interacting solutes would be removed from the plate. Therefore, each well

functions like a miniature chromatography column, with the difference that the interaction is static. A

schematic illustration of the experiments in one well of a PreDictor plate can be seen in figure 6. The

PreDictor plates enables this to be done for many conditions simultaneously, and the binding behavior

of the resin prototype can therefore be mapped.

Figure 6. Schematic illustration of the experiment in a single well of a PreDictor plate. At the start all protein is in the liquid

phase (1). When equilibration is reached there is protein in the solid phase and liquid phase (2). After centrifugation the

phases have been separated (3).

14

To the describe the binding behavior of the ligands, the binding capacity and partition coefficient

were used. The binding capacity describes the ratio between the amount of bound protein and the

volume of the resin, which is how effective the resin is at binding the protein.

2.1.7. Calculations of binding capacity To calculate the binding capacity, several parameters are needed. Firstly, the mass of the bound

protein, mbound is needed. However, measuring the bound proteins can be very complicated and

instead, mass balance can be used. mbound must therefore be the difference between what was added to

the plate, mload, and the amount remaining in the liquid phase, munbound.

To find out the amount of loaded protein, the concentration of the protein solution filled into the well,

cload, is needed. cload can be determined using Lambert-Beers law, which states that:

Where A is the absorbance, c the concentration, d the path length and ε the molar extinction

coefficient. The absorbance of the protein solutions can be measured using a spectrophotometer and

will function as a reference for future calculations. However, to be able to calculate cload, the

extinction coefficient, ε, for each protein is needed. The extinction coefficient can be difficult to

predict or measure due to the varying conditions of the solutions and therefore a calibration curve

needs to be used. From the calibration curve, cload could be determined using ε = k = slope and d = 1

cm.

From cload the mass of the protein loaded into the plate, mload, could be calculated using the Vload.

For the next step, munbound needs to be determined, which consists of two parts mFT and mretained.

The liquid exiting the filter plate into the collection plate after the phase separation is called the flow-

through. This concentration, cFT, is equal to cunbound, and indicates how much of the protein that did not

bind to the resin. The mFT can be calculated by measuring the absorbance of the liquid exiting the well

and using the values for Vload and the slope of the calibration curve, k:

15

Furthermore, to ensure a correct calculation of munbound, a small amount of liquid retained in the filter,

Vretained, needs to be considered. This volume is proportionate to the volume of the flow-through.

Studies at Cytiva (formerly GE Healthcare) research and development department have shown that

the volume should be approximated to 6 μl plus 60 % of the resin volume. The mass contained in the

retained liquid volume, therefore becomes:

Likewise, the concentration of the unbound protein can be calculated with the data obtained from the

flow-through:

Combining equations (2) and (6) produces a new expression for the mass of the unbound proteins.

Thereby combining (1) and (10), the binding capacity can be described using known quantities:

However, due to the sensitivity of initial protein concentration on the binding capacity, other

quantities are sometimes preferred for describing the binding behavior. The thermodynamics of

protein adsorption is described by the adsorption isotherm, which describes the relation between

concentration of protein in the liquid and solid phases at equilibrium, in this case between the qc and

cunbound. One of the most frequently used isotherms to describe protein adsorption is the Langmuir

isotherm, which states that:

Where qmax is the maximum binding capacity, Kd is the equilibrium dissociation constant and cunbound

is the concentration in the liquid phase at equilibrium. The isotherm thus describes how q changes

with cunbound, which can be seen in figure 7.

16

Figure 7. A Langmuir isotherm where the maximum binding capacity, qmax, and dissociation constant, Kd, are indicated with

red lines. The plateau region indicates where the capacity is relatively independent of equilibrium concentration. Courtesy of

Cytiva (formerly GE Healthcare).

The binding capacity is sensitive to initial concentrations, cload, unless large excess of protein is

loaded, and the plateau of the binding isotherm is reached, as seen in figure 7. For this study, an initial

protein concentration of 0.3 g/L was used, which was outside the range of the plateau region. This

meant that small changes in the initial concentration of the protein solution impacted the binding

capacity. However, since the ratio between bound protein and unbound protein is described by the

Langmuir isotherm in figure 7, the ratio can be described by the slope of the isotherm. Furthermore,

since the slope is a straight line for low protein concentrations, this means the ratio is less impacted by

changes in initial protein concentration. It is difficult to maintain similar protein concentrations in the

low protein range through various experiments; and thus, the partition coefficient was introduced.

2.1.8. Partition coefficient The partition coefficient, Kp, describes the ratio of separation at equilibration of a solute between two

phases (Harris, 2007). In the case of plate experiments, the partition coefficient describes the ratio

between the concentration of proteins bound to the resin and the concentration of unbound proteins in

the solution. The bound proteins can be expressed by the binding capacity and Kp can therefore be

defined as:

Combining this with the expression for binding capacity in equation (11), Kp can be described as:

2.1.9. Principal component analysis Principal component analysis (PCA) is a method used to capture variance in a large set of data. It is

possible to handle data sets with multiple, potentially correlated, variables and observations for each

variable. PCA is a dimension reduction method, meaning that the aim of the method is to identify a

reduced set of features that represents the original data in a lower-dimensional subspace with a

minimal loss of information. The original correlated variables are transformed into uncorrelated and

orthogonal variables called principal components. These principal components can then be used to

visualize the data in 2D or 3D plots. The coordinate system consists of the principal components with

the first axis, corresponding to the direction along which the data vary the most. This means that the

17

first principal component (PC1) is the projection of the original data to the first principal axis and

captures the highest amount of variance in the data. Likewise, the second principal component (PC2)

is the projection to the second principal axis, orthogonal to the first, and captures the highest amount

of variance remaining in the data after the first component. The maximum number of principal

components corresponds to the number of dimensions in the original data set. However, due to fact

that the first principal component captures the highest variance in the data set, the last ones can

usually be omitted. Normally, only two or three components are needed to capture the majority of the

variance in the data set (Kherif & Latypova, 2020).

After applying PCA, each of the primary observations will have a value for every principal

component. These values are called scores and describes the projection of each primary observation

along the respective principal component. A score plot is a common tool to analyze the scores of all

primary observations. The score plot places the observations in an orthogonal 2D space with the two

first principal components on the axis.

Another pair of useful tools are the loadings and contributions plots. The loadings plot illustrates how

much every variable influences each principal component. This can be useful when trying to

understand which variables gave rise to the variance found in the data, or likewise, which did not. In

contrast, the contribution plot describes which variables gives rise to the difference found between

two groups of one or several observations. This may be important in order to identify what variables

influenced the position of certain observations.

2.2. Didactic study

2.2.1. Theories about learning There are many theories about learning. Some, like the one proposed by Piaget (1968), look inward at

the inner processes of acquisition, whereas other, like the one proposed by Vygotsky (1934), look

outward at the social process of learning through interactions between people (Illeris, 2001). For this

study, the focus will be on the theories of learning as an interaction between student and surroundings,

also known as the sociocultural theory, which was introduced by Vygotsky.

In the sociocultural theory all learning is situated, that is, all learning is placed in a surrounding,

which not only influences the learning, but also is a part of it (Illeris, 2001). Therefore, it is necessary

to include the relationship between learning and the surrounding in the sociocultural theory.

Within chemistry, and especially chemistry didactics, the act of imitation is a well-known concept.

Imitation alludes to the act of trying to imitate, mimic or otherwise copy what another person is doing

(Illeris, 2001). This form of learning is particularly common among children but is also used for goal-

oriented practices, where an instructor leads other people. Even in chemical industries, where the

method of following instructions through standard operating procedures (SOP), imitation is a common

practice. The SOPs are essentially the same way of learning as any cookbook-like recipe used in the

laboratory work in schools are, or for that matter, cookbook recipes.

2.2.2. Three dimensions of learning Illeris (2001) introduced three dimesons of learning: content, incentive and interaction.

The content dimension pertains to what is being learned. It can be knowledge, understanding, skills,

attitudes or patterns of behavior (Illeris, 2001). Through the content dimension the learner’s insight,

understanding and capacity—what the learner knows, understands and can do—is developed. This

dimension is usually intended when speaking about learning in an informal context.

The second dimension of learning is the incentive dimension. This dimension contains the motivation,

feelings and will to learn. To be able to learn effectively there needs to be some motivation for the

18

learning to occur. Our inner attitude towards what is being learned controls how much mental energy

is required to acquire new knowledge.

Lastly, the third learning dimension is the interaction dimension, which is influenced by the

sociocultural perspective on learning. The interaction dimension involves the interaction between

individual and surroundings. The interaction dimension helps learners to develop sociability, or their

ability to engage and function in various forms of social interaction between people.

Since the content of the learning material is predetermined, the focus of this study will be to explore

the incentive and interaction dimensions of the learning material.

Figure 8. The dimensions of any learning process. Adapted from Illeris (2001).

2.2.3. Active learning Active learning is learning where the students have to take an active part in their learning, rather than

sitting passively and listening (Johnson & Johnson, 2008). The active part can be described very

broadly as anything the teacher may ask a student to do—answer questions, solve problems or carry

out laboratory experiments (Felder & Brent, 2009). However, this does not imply simply moving or

talking as a part of being active. Instead, it is being involved and active in the learning process that

matters. There are several benefits to active learning compared to passive learning. Bonwell and Eison

(1989) mentions increased pupil thinking and writing skills, better adaption to student learning styles,

and higher student satisfaction as some of the positive aspects of active learning. Many universities

have therefore incorporated other forms of teaching than lectures into their methods of teaching, like

seminars, workshops and laboratory activities.

2.2.4. Focus group interviews Focus group interviews is a method used for obtaining qualitative data. A focus group interview

involves several people discussing a specific topic, hence the name focused (Wibeck, 2000). The

discussion is led by a moderator, whose main role is to initiate discussion and introduce new aspects

to the topic of discussion. Historically, it has mostly been used to explore marketing and marketing

research, however, it can be applied to other sociological fields as well (Wibeck, 2000).

19

Focus groups is a method for exploring width, rather than depth, of ideas and opinions (Wibeck,

2000). Although open questions are used in both individual interviews and focus group interviews, the

result will not be the same if one person or a group of people discuss a topic. Usually, a group of

people present a wider scope of ideas than one person would, although, at the cost of less in-depth

answers (Wibeck, 2000). For this reason, focus group interviews are a good way to gain an

understanding of a group of people’s attitudes and opinions on a topic. On the other hand, focus group

interviews can sometimes be more difficult to transcribe and analyze. Furthermore, focus groups

usually requires more practical planning for them to work effectively (Wibeck, 2000).

According to Morgan (1996a), focus group interviews can be divided into two subgroups, structured

and unstructured, depending on the level of involvement of the moderator. The more a moderator

guides the interaction in the group, the more structured the interview is. On the other hand, if the

group is left alone and the group members mostly speak to each other, the interview is unstructured

(Wibeck, 2000). Commonly, structured approaches are used when the subject of the interview is

sensitive, and the participants are vulnerable, or if the objective is to do some type of market research.

Unstructured approaches are used to study interaction and argumentation within a group, to better

understand what the participants think are important aspects of a topic and to identify spontaneously

generated ideas (Wibeck, 2000). There are advantages and disadvantages of both approaches. There is

always a risk that the moderator, when trying to maintain the focus of the interview, will influence the

group interaction (Morgan, 1996b). On the other hand, a structured approach ensures that the

important aspects of the topic will be covered during the interview. An unstructured approach cannot

ensure this, however, the interests of the participants are more naturally produced. Also, using an

unstructured approach, controversial topics can be brought to the surface by the participants

themselves, rather than forced upon them by the moderator. Lastly, unstructured approaches can be

difficult to analyze due to the unorganized and sometimes messy nature of free discussion.

Krueger (1997) lists five types of questions that should be prepared for a structured focus group

interview:

1. Opening questions

2. Introductory questions

3. Transition questions

4. Key questions

5. Ending questions

(Krueger, 1997)

The purpose of the opening questions is to make the participants more comfortable with each other

and loosen any tension present, thereby increasing group cohesion. Introductory questions are used to

introduce the topic of discussion. Commonly, participants are asked to reflect over past experiences in

connection to the topic (Wibeck, 2000). To guide the discussion to the important key questions,

transition questions can be used by the moderator. The most important questions of the discussion are

the key questions, and they are usually connected to what the researcher wants to know. Lastly,

ending questions are used to give the participants a final chance to express their attitudes towards a

topic, and reflect over the discussion. Often, the moderator ends by asking if there is something

someone would like to add or if something has been missed.

2.2.5. Thematic analysis A common way to process qualitative data is to use thematic analysis. It is a method for identifying,

analyzing and reporting patterns, or themes, within data. (Braun & Clarke, 2006). Using thematic

analysis, the objective is to create a bank of central themes or sub-themes in data. A benefit using

thematic analysis is that the method is not tied to any theoretical framework, and can therefore be

used more flexible within many frameworks.

20

Themes within data can be identified using two primary approaches in thematic analysis: in an

inductive or ‘bottom up’ way or in a theoretical or ‘top down’ way (Braun & Clarke, 2006). An

inductive approach means that the themes or patterns identified are strongly linked to the data itself.

The inductive analysis is a way of coding data without trying to fit it into a pre-existing framework, or

the researcher’s preconceptions. Instead, the developed ideas or themes are data-driven (Braun &

Clarke, 2006). In contrast, the theoretical approach tends to be driven by the researcher’s theoretical

or analytical interest in an area. Theoretical analysis usually provides a less detailed analysis overall,

but a more focused analysis on some aspect or theme of the data.

Braun and Clarke (2006) describes six phases for thematic analysis.

1. Familiarize yourself with the data.

2. Generate initial codes.

3. Search for themes.

4. Review themes.

5. Define and name themes.

6. Produce the report.

(Braun & Clarke, 2006)

(1) involves the transcribing and re-reading of data, noting down initial ideas. Thereafter follows

coding of interesting features of the data in a systematic way (2). These codes are then collected into

themes (3) and reviewed (4) in relation to the coded extracts and the data set as a whole. The themes

are then defined and named (5) and then used to tell the overall story of the analysis in the report (6).

(Braun & Clark, 2006)

21

3. Methods and materials In this section the methods used for the two studies will be presented. A list of materials and

equipment used in the first part can be found in appendix 1.

3.1. Method for screening experiments

3.1.1. Protein selection Table 2. Proteins used in the screening experiments with their source, code, isoelectric point (pI) and extinction coefficient

(ε) described.

Protein Source Code pI ε

Cytochrome C Equine heart CYT 10.3 1.574

Albumin Human serum HSA 4.7 0.523

Lysozyme Chicken egg white LYS 10.8 1.748

α - Lactalbumin Bovine milk LAC 4.2 2.284

Ovalbumin Chicken egg white OVA 5.5 0.603

X mAb 5 Chinese Hamster Ovary (CHO) XM5 8.6 1.580

The proteins used in the plate experiments were selected to cover a spectrum as broad as possible, and

thereby better the characterization of the multimodal prototypes. They were also selected with the

previously performed experiments in mind, making sure that comparability between the two studies

was possible. An exhausting list of the 6 proteins used in the plate experiments and their

characteristics can be found in table 4.

3.1.2. Gel preparation and plate filling

Figure 9. A 50 % slurry of gel and 20 % ethanol (1) was filled into the cube (2), which was used to create a defined volume

of resin. The gel was then transferred into a conical 175 mL falcon tube and filled with 55.3 g of 20 % ethanol, producing 5

% slurry (3). The slurry was filled into special vials (4) and fitted to a liquid handling robot that transferred the slurry to the

PreDictor plates.

To be able to explore the properties of the novel resins, they first had to be filled into the plates. The

plate filling process consisted of two steps: preparing the gel and filling the plate.

The gel was prepared by first adjusting its concentration to approximately 50 % using 20 % ethanol.

To be able to precisely measure the volume of gel needed, a cube that consisted of three parts that

could manually be removed or put together, was used. The top block of the cube consisted of a

conical bore, where the gel could be poured. The middle block contained a cylindrical bore with a

precisely defined volume of 3.004 ml. The bottom part had a frit on the top covering a tube leading to

22

a connector on the side. All three parts could be assembled, creating a closed channel, see figure 9.

Before assembling the cube, a microfilter was applied to the frit of the bottom block. The gel was then

resuspended and filled into the cube to the top block. A vacuum was then attached to the connector on

the bottom block of the cube, maintaining a negative pressure of 500 mbar, thereby removing the

liquid ethanol. To maintain comparability between plate fillings, the cube was attached to the vacuum

for an additional minute after the top surface of the slurry had dried. Afterwards, the top and bottom

blocks were carefully removed, and the gel plug transferred into a 175 ml Falcon tube. To achieve the

final concentration of a 5 % slurry, 55.3 mg of 20 % ethanol (equivalent to 57 ml at ρ = 0.97 kg/m3)

was weighed directly into the falcon tube.

For the second part of the plate filling process, the 5 % slurry was resuspended using a vortex-mixer

(Pennsylvania, USA). The solution was thereafter quickly pipetted into 8 ml glass vials, making sure

to resuspend the slurry between each loading of the pipette. The glass vials were then placed in a

liquid handling robot from Gilson. To ensure an evenly mixed slurry, the robot was fitted with a

special stirring device, providing each vial with an individual stirrer controlled by a magnetic field.

Empty filter plates were placed in the result zone of the robot and filled using a worklist in the

software Trilution LH. The plates were filled with 120 μl of 5 % slurry, resulting in 6 μl of resin in

each well. Lastly, the plates were sealed using a plastic foil and stored in fridge until use.

3.1.3. Buffer preparation The binding behavior between proteins and resins was investigated using 32 different conditions, with

varied salt concentration and pH. Eight salt concentrations were used, spanning between 0 and 1750

mM sodium chloride. The pH-values of the buffers spanned between 4.5 and 7.5, with a 25 mM

acetate stock solution being used for pH 4.5 and 5.5, and a 50 mM phosphate stock solution being

used for pH 6.5 and 7.5. To calculate the required volume of stock solution a program called

BufferWand (proprietary software of Cytiva (formerly GE Healthcare)) was used. BufferWand also

created a worklist used for a Tecan robotic workstation produced by Tecan Group Ltd (Männendorf,

Switzerland). The stock solutions were prepared according to the instructions of BufferWand and then

placed in the assigned vial compartments of the liquid handling robotic workstation. After applying

the script, the robot pipetted the buffers into a 48-well plate.

For the experiment, two types of buffer were used, one for diluting the protein solutions and one for

washing and equilibrating the plates. These had to be created separately using different Tecan robot

scripts. The washing buffer was created with the assigned pH-values and salt concentrations. The

buffer used for protein dilution, however, had to be generated at 1.5 times the concentration in order

to achieve the correct concentration after mixing it with the protein solution at a ratio of 2:3. Both

buffers were created using the same stock solutions.

3.1.4. Preparation of protein solutions For the preparation of protein solutions, 180 mg of protein was weighed in a weighing pan and then

transferred to a 200 ml volumetric flask. The protein was then diluted to the mark of the flask with a

storage buffer, producing a 0.9 g/L protein solution. To ensure that all the protein was transferred to

the volumetric flask the weighing pan was flushed with storage buffer several times. The storage

buffer consisted of a 5 mM phosphate buffer with a pH of 7 and had the purpose of conserving the

proteins without impacting the salt concentration or pH-value of the screening experiment. This

process was performed for five of the six proteins: cytochrome C, HSA, a-lactalbumin, lysozyme and

ovalbumin

To prepare the x mAb 5 stock solution, the antibody solution was first buffer exchanged, from an

acetate buffer (pH 4.8) used to conserve the stock solution to the 5mM phosphate buffer used for the

23

other protein solutions. This was done using an ÄKTA Pure system and connecting a HiPrep 26/10

desalting column, both from Cytiva (formerly GE Healthcare (Uppsala, Sweden)), to it. After the

buffer exchange, the concentration of the newly created solution had to be measured, using a

spectrophotometer at wavelength 280 nm. To do this, the desalted solution was diluted 10, 15 and 30

times respectively using an Eppendorf pipette, creating three solutions with lower concentrations. The

three dilutions were then measured using a photometer, and the measured absorbances were pasted

into Excel. Using the known extinction coefficient of x mAb 5 (1.58), the concentration of the three

dilutions could be calculated. With the dilution factor, the concentration of the buffer exchanged

xmAb5 solution could then be averaged. When calculating the average, the concentration of the 10

times diluted solution was ignored since it exceeded the absorbance threshold of 1.5, where the

absorption is linearly proportional to concentration. Lastly, 18.67 ml of the desalted 9.64 g/L xmAb5

solution was diluted to 200 ml, creating a 0.9 g/L xmAb5 solution.

To ensure high stability of the solutions, each solution was filtered and placed in 15 mL Falcon tubes.

The solutions were transferred into beakers and drained into a 20 mL Plastipak syringe and dispensed

through a 0.22 μm Sterivex filter. Until the day of the experiments the solutions were stored in a

refrigerator at 8 °C.

When performing the screening experiments, the final protein solutions had to be prepared. For this,

600 μl of the 1.5 buffer was mixed with 300 μl of 0.9 g/L protein solution in every loading well,

resulting in 0.3 g/L protein solutions. For the pipetting in this step, as well as the screening

experiments, an 8-channel Eppendorf pipette was used to speed up the process.

3.1.5. Screening experiments

Figure 10. General arrangement and assignment of the six proteins on the PreDictor plates.

The experiments were performed in 96-well plates, with each plate divided into three equal areas

consisting of 32 wells, or four columns and eight rows, see figure 10. Each area was used to screen

one protein, meaning that three proteins could be screened in the same plate. To screen all six

proteins, two plates were needed for each resin prototype. Usually, two resin prototypes were

screened simultaneously, meaning that 4 plates were screened during the same day. The plates were

screened according to the protein solutions used, meaning that two plates with different resin

prototypes, but the same protein solutions, were screened at the same time.

First, the aluminum foil on the bottom of the plates was removed and the plates were placed on 96-

well collection plate to prevent damage to the exposed filters. The 20 % ethanol used in the plate

filling process was then sucked out by mounting the plates on a vacuum manifold and applying a

vacuum. The vacuum was maintained until no liquid could be observed on the filter. Before applying

the proteins, the wells had to be equilibrated using the equilibration buffer. For this, 200 μl of

24

equilibration buffer was pipetted into each well and incubated for 1 min at 1100 rpm on an orbital-

shaker. Lastly, the liquid was removed using the vacuum manifold. This process was repeated three

times, but in order to keep the retention volume in the filter and resin constant, the liquid removal

process was changed from vacuum to centrifugation for the last repetition. The centrifugation was

performed at 500 g for 1 min. The plates were thereafter blotted against a tissue to prevent liquid to

escape from the bottom of the plates as a result of capillary forces.

In the next step, 200 μl of the 0.3 g/L protein solution was added to each well. The plate was then

mounted on the orbital shaker and incubated for 1 h at 1100 rpm. While the plates were incubated,

200 μl of the 0.3 g/L protein solution was also transferred to a 96-well UV-microtiter plate to serve as

a reference. After the incubation, the PreDictor plates were placed on UV-microtiter plates and

centrifuged for 1 min at 500 g. The two UV-microtiter plates containing flow-through and the

reference UV-microtiter plate were then measured using a spectrophotometer.

The spectrophotometer mixed the wells in the UV-plates using a built-in shaker for 5 s before

measuring the UV. This was to reduce the effect of air bubbles, and to ensure an even meniscus. The

photometer calculated the pathlength by measuring the absorbance at 900 nm, and the software could

then normalize the pathlength to 1 cm. This effectively improved the precision of the concentration

measurements by compensating for any difference in the amounts of liquid in the wells. The UV-

microtiter plates of the reference and flow-through were measured at 280 nm. The raw data generated

from the photometer was then transferred to Excel documents and stored until evaluation.

The general experimental procedure can be seen in figure 11 below. For these experiments the

analysis of wash and elution steps was omitted. Instead, the focus was on the flow-through and the

data of the binding capacity it generated.

Figure 11. General procedure for screening experiments in 96-well plates. Wash and elution step after the sample addition

were excluded for this study. Courtesy of Cytiva (formerly GE Healthcare).

3.1.6. Data management The raw UV-data from screening experiments were exported from the spectrophotometer program

and saved as Excel documents. The raw data was then transferred to a calculation template with

formulas for calculating mFT, mretained, mload, qc and Kp, see appendix 2. From this a separate graph of

the binding capacity and partition coefficient in relation to the salt concentration was created for every

protein. By keeping the salt concentration on the x-axis and binding capacity or partition coefficient

on the y-axis, while sorting the data after pH-values, the graph produced gives an overview of the

binding behavior. This was done for every ligand. Early in the project, the data was also transferred to

Assist, a program used to calculate and visualize binding behaviors. From Assist, 3D-graphs like the

25

one seen in figure 12 could be produced. However, due to the programs inefficient data handling,

leading to a lot of manual copying and pasting, Assist was later abandoned for Excel sheets.

For obvious outliers, where the measured absorbance in a well resulted in a negative binding capacity,

the binding capacity was set to 0. This was done to prevent impossible outliers from influencing the

outcome of the principal component analysis. Moreover, due to the way SIMCA (Sartorius Stedim,

Göttingen Germany), the program used for the PCA, is handling missing values by replacing them

with an average value, introducing a binding capacity of zero was more likely closer to the true value

of the binding capacity.

Figure 12. Two ways of visualizing the binding behaviors of the ligands. (A) Response curves of binding capacity created in

Excel. (B) Response surface of the binding capacity created in Assist.

0

2

4

6

8

10

12

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Bin

din

g ca

pac

ity,

q

[NaCl]

Binding capacity of Lysozyme on L18

pH 4,5

pH 5,5

pH 6,5

pH 7,5

A

B

26

3.1.7. Principal component analysis Table 3. General arrangement of observation and variables in SIMCA

Primary Observation

ID

Primary Variable

ID

Result zone

24 Ligands

Secondary Variable:

Proteins Measured Binding Capacities:

q(protein, pH, salt) Secondary Variable:

pH-values Measured Partition Coefficients:

Kp(protein, pH, salt) Secondary Variable:

Salt Concentrations

PCA was introduced to handle all the data produced and to be able to better compare different binding

behaviors among ligands. The data from the ligand screenings was compiled and structured in two

documents, one containing the qc data and one containing the Kp data. Each individual value for

protein, pH-value and salt concentration was set as one variable, resulting in 192 variables for every

resin. The data sheets were then separately imported to SIMCA, a program used for PCA. The ligand

names were selected as primary observation IDs and proteins, pH-values and salt concentrations were

set to secondary observations, see table 8. The measured binding capacities or partition coefficients

were placed in the result zone.

When applying the PCA, two methods for model construction was used. Initially, the first thirteen

previously screened ligands were selected. This was done to create a model based on the older

ligands, in part to ensure that the same pattern as previously obtained could be recreated, but also in

part to explore how the newly screened ligands would behave in the old model. After obtaining a

model for the older ligands, the newer ligands were projected onto the existing model by calculating

the scores and residuals from the model loadings by the SIMCA tool predict. This was the first type of

model construction. Secondly, one model was created based on all 24 ligands. This was done for both

binding capacity and partition coefficient data, in total generating four models, see table 9.

Before calculating the scores, the data was centered by subtracting the mean value of the binding

capacities or Kp-values. This was done to focus on variations rather than absolute values. UV-scaling

to unit variance was not necessary for the data sets as all the measured data had the same order of

magnitude. The tool autofit was used to fit the model.

Table 4. Table of models produced in SIMCA. Model 1 and 3 were created from the 13 original ligands, whereas model 2

and 4 were created using all 24 ligands. There was no L13.

Model number Raw data type Ligands of model Projected ligands

1 Binding capacity 13 (L00-L12) 11 (L14-L24)

2 Binding capacity 24 (L00-L24, -L13) -

3 Partition coefficient 13 (L00-L12) 11 (L14-L24)

4 Partition coefficient 24 (L00-L24, -L13) -

27

3.2. Method for the didactic study

3.2.1. Data collection The didactic part of this study aimed to develop guidelines for how a learning material can be

constructed using theories on didactics and employee experience. Therefore, two methods for

collecting data was used in the didactic study. Firstly, when learning how to perform screening

experiments in 96-well plates, difficulties were noted down from observations and own experiences.

Secondly, four interviews, one focus group interview and three individual interviews, were conducted

to gain a better understanding of learning difficulties when performing plate experiments as well as

learning difficulties in general. The addition of interviews was done to widen the collection of

experiences, and to generate themes for the guidelines, thereby answering the research question.

3.2.2. Observations The observations were performed by the author when being trained to perform screening experiments.

This included learning about theoretical and practical aspects of screening experiments. The practical

steps were taught by a person in the lab, whereas the theoretical parts were supplied by literature.

Different practical steps were taught by different people. In total four people were involved in the

teaching process, excluding the author. The aim of the observations was to identify crucial steps of the

learning progress and to gain insight about advantages and disadvantages of the chosen learning

approach. For something to be considered a critical step it had to be identified as experimentally

difficult or vital, either from the experience of the author or from the experience of the instructors.

The observations were then used as a foundation when developing questions for the interviews.

The observations were conducted for approximately three weeks when being taught how to perform

the experiments. The practical parts were taught by an instructor with a few days in between each

session and the time in between was used to study the theory behind the experiments. The sessions of

practical work consisted of showing and performing the experiments in the lab, first by the instructor

and then by the author. Furthermore, the instructor was a person with experience of the procedure,

which meant the person could answer questions about the experimental procedure or theoretical

background.

The observations performed were of the second order as according to Björndahl’s classifications

(Björndahl, 2002). This means that the observer partakes in the activities observed and later notes

down what has been observed (Björndahl, 2002). This was done for mostly practical reasons. When

being trained on performing plate experiments the approach was interactive and left little time to take

notes. Also, for some crucial parts of the experiments, there was a need to be efficient and focused as

to not make mistakes. Therefore, noting down what had been difficult or what had gone wrong after

the experiments were done, was the chosen approach.

3.2.3. Interviews To better gain an understanding of the purpose and scope of the educational material, as well as

people’s experience with learning materials in the past, several interviews were conducted. First, two

individual interviews pertaining the purpose and scope of the learning material were held. Thereafter,

a focus group interview was conducted with the objective of exploring thoughts and attitudes towards

learning materials in general at the company. These ideas were then used in depth when conducting

one additional, individual interview with one person who had worked with screening experiments in

the past.

Three out of the four interviews were held in Swedish and one individual interview was held in

English. The interviews were then transcribed in the language they were held in. Some of the quotes

used in this report have therefore been translated to English while trying to maintain the meaning of

the original statement as close as possible. The interviews were recorded using the researcher’s

mobile phone. The audio files were later used for the transcription and as an aid for the analysis.

28

The selection of people for the interviews were based mostly on convenience. Therefore, only people

from the research and development department of Cytiva (formerly GE Healthcare) were chosen.

Also, experience with performing plate experiments were valued as an important competence for the

individual interviews. However, very few people performed plate experiments on a regular basis,

which made the sample size relatively small. Therefore, only one person with previous experience in

plate screening was interviewed.

3.2.3.1. Focus Group Interview

The focus group interview was performed using a structured approach. This included preparation of

the five types of questions as introduced by Krueger (1996), see page 16. However, due to a lack of

time in the interview, the lines between some of the categories were blurred. There was however, a

clear introductory, as well as finishing part, with the key questions in the middle. The interview took

place on a Friday morning with five people present, two women and three men, as well as the

moderator. Due to the attendees’ busy schedule, the interview lasted approximately 40 minutes.

The interview started with the members having to reflect on their past interactions with quality

education, and what quality in the sense of education means. The discussion then continued to

learning experiences within the company, and the members’ experience of courses and learning

materials within the company. This was followed by a discussion of indications of a learning

material’s quality and which form the material should have. Lastly, the interview was concluded by

asking the participants if there was something additional to think of when developing a learning

material and if they had something else to add.

3.2.3.2. Individual interviews

To further develop attitudes and explore themes, three individual interviews were used in conjunction

to the focus group interviews. These interviews lasted approximately 30 minutes. To gain a better

understanding of the scope and purpose of the educational material, two of the individual interviews

were conducted with the supervisors of the project.

The individual interviews were conducted using a predetermined structure and set of questions.

However, follow-up questions were added depending on the responses of the participants. Therefore,

the individual interviews can be considered standardized interviews with open question and answers

(Björndahl, 2002).

3.2.4. Thematizing data The steps for thematizing data used in this study followed the six steps introduced by Braun and

Clarke (2006). After conducting the interviews, they were transcribed and divided into sections for

easier management. The interviews were then re-read and important parts or citations in respect to the

research question were color coded. These phrases and words were then compiled and categorized

into different themes or subthemes. In total, three themes and four subthemes were found. Certain

quotes were then used to emphasize these themes in the analysis.

3.2.5. Ethical considerations The ethical considerations for the interviews were based on the guidelines established by the Swedish

Research Council (2002). These guidelines consist of four aspects: the right be informed, the right to

give consent, the right to confidentiality and the right to utilization.

When invited to participate in the interviews, the participants were informed of the scope of the

project and their part in the interviews. They were also informed that participating was strictly

optional and that they would remain anonymous throughout the process. In addition, they were given

the contact information of the researcher if they had any further questions. Before the interviews, the

participants gave their oral consent to being recorded as well as the right to use their responses for

29

research purposes. The transcripts of the interviews were strictly handled by the researcher. The

participants names were replaced with letters and any mention of other people or companies were

coded. This was done to avoid any identification of the participants or any other parties, to ensure

honest responses. Therefore, the four requirements of the guidelines can be considered met.

4. Results and discussion In this section the results from the two parts will be presented and discussed. The results for the

chromatographic study will mostly focus on the PCA and the results from the didactic study will

focus on the observations and interviews.

4.1. Results from screening experiments The results from the screening experiments generated binding behaviors of the eleven novel

multimodal ligands. This study increased the number of ligands screened from 13 to 24 ligands. In

combination with the previously screened ligands, these new ligands were used to construct several

chromatographic diversity maps which separate ligands according to binding behavior. The diversity

maps can be used to generate directional hypotheses for what type of new ligands should be

investigated, or for picking possible candidates for further studies. In this section, the method for

producing diversity maps is explored and the validity of the diversity maps are examined. The

methods examined are two types of model constructs as well as presenting the data using qc or Kp. For

the validity of the maps, two approaches were used: confirmation of ligand separation according to

binding behavior by the PCA and variable influence on ligand position. In doing so, the reliability and

applicability of the diversity map are also discussed.

The absorbance data sometimes generated negative binding capacities for conditions with low or no

binding. This happened when the measured absorbance was higher in the collection plate than in the

in the loading plate and was the result of natural dispersion. Since the binding capacity cannot be

negative, this data was adjusted to a value of zero, indicating no binding of the protein to the ligand.

However, when transforming these data points into Kp values this, in turn, generated Kp values of

zero. Since Kp was used on a logarithmic scale, values of zero had to be replaced in order to be used.

However, the choice of the replacement could greatly influence the variance of the data, with numbers

close to zero generating large negative numbers, which the logarithmic scale was introduced to

reduce. Therefore, all Kp values of ≤ 1 were set to 1, which in turn generated logarithmic values of

zero. The impact of these adjustments will be further discussed under section 4.1.2.4 Kp vs qc.

4.1.1. Binding behaviors After screening the replicate of the reference ligand L00 and ligands L14 to L24, a binding behavior

plot, was created for each protein on each ligand. Overall, varying binding curves were observed for

the same protein on different ligands, showcasing that different ligands had different binding

behaviors. Furthermore, ligands showed varying binding curves depending on the protein used. This

indicated that the proteins selected enabled mapping of varying binding behaviors. Most proteins

exhibited a lower binding affinity at higher salt concentrations, regardless of ligand. The exception to

this would be the xmAb5, which exhibits higher binding capacities at high salt concentrations on

multimodal ligands due to the possible hydrophobic interaction of the ligand. For most ligands, the

characteristic U-shape, which describes the behavior of antibodies on multimodal ligands, was

observed for the XmAb5 response curves.

30

Figure 13. 3D plot of the binding behavior of L16 for the

protein HSA. The plot was created using the program

Assist.

Figure 14. Plot of binding behavior for L17 with xmAb5

for various salt and pH conditions created in Excel. The

binding behavior exhibits the characteristic U-shape.

4.1.2. Principal component analysis In this section the PCA is evaluated from the four aspects: model construct, ligand separation, variable

influence and Kp versus qc. Apart from the last section, the results presented here are from Kp data.

4.1.2.1. Model influence

This section explores the impact of model construct on ligand position by comparing the two types of

models created from the partition coefficient data.

Figure 15. Model 3 created using log Kp data from L00-L12.

When creating a model from the partition coefficient data for the first thirteen ligands, the scatter plot

in figure 15 was obtained. This data was recovered from Theel’s original screenings and the pattern

observed in figure 15 is similar to the model obtained in Theel’s paper (2019) based on binding

capacities in that both models are indicating an arrow-shape pattern. In total three principal

components were considered significant by the program. The first principal component explained 75.8

% of the variance in the data, the second principal component explained 10.5 % of the variance in the

data and the third principal component explained 6.3 % of the variance in the data. Together they

explained 92.6 % of the total variance in the data. However, for the analysis, the first two principal

components were focused on in order to easier visualize the results from the scores in a 2D-plot.

0

2

4

6

8

10

12

14

0 500 1000 1500 2000

Bin

din

g ca

pac

ity,

q

[NaCl]

Binding capacity of L17 for XM5

pH 4,5

pH 5,5

pH 6,5

pH 7,5

31

Thereafter, the newly screened ligands L14-L24 were projected onto the model created from the old

ligands. The result can be seen in figure 16. When examining the distance-to-model plot (DModX) for

all ligands in the model created for the old ligands, most of the new ligands were marked as outliers,

see figure 17. The DModX corresponds to the residual standard deviation of every observation in the

X-block and can be interpreted as the orthogonal distance to the model hyperplane. This means that

the majority of the new ligands did not fit into the old model created from the first thirteen ligands.

The exceptions were L00, L18, L22 and L23. The reference ligand replicate, L00, was expected to fit

the old model and the fact that it did, in combination with the proximity of the two replicates in the

score plot, lends credibility to the replicability of the experiments. The hope was that all of the new

ligands would fit into the model of the old ligands. However, this indicates that there might be

something in the binding behaviors of the new ligands that the model did not capture. This also shows

the complexity of binding behaviors and that the inclusion of new ligands increases the diversity of

the combined ligand library.

Figure 16. (Model 3) Scores for the 24 ligands based on log Kp values. The ligands screened in this paper (blue) projected

onto the model of binding capacity created from the earlier screened ligands (red).

32

Figure 17. Distance to model plot of the new ligands (blue) in the model created by the old ligands (red). The blue L00

represents the replicate screening of the reference ligand done in this paper. The plot indicates that most of the new ligands