analytical sciences research project

MSc Thesis Chemistry

Analytical Sciences

Research Project

Implementation of a Rapi-Fluor LC-FLR-MS method for the

relative quantification of released N-glycans from monoclonal

antibodies

By

Carlo Roberto de Bruin 12431737

March 2021

48 ECTS

August 2020 - March 2021

Supervisors: 1st Examiner: 2nd Examiner:

Sipke Sangers Rob Haselberg Andrea Gargano

Ewald van den Bremer

Rick Hibbert

Implementation of a Rapi-Fluor LC-FLR-MS method for the relative quantification of released N-glycans from monoclonal antibodies

2

MSc Thesis Carlo de Bruin

List of abbreviations

Abbreviation Definition

2-AA 2-aminoanthranilic acid

2-AB 2-aminobenzamide

ADCC Antibody-dependent cell-mediated cytotoxicity

AGC Automatic gain control

Arb Arbitrary units

AX Anion-exchange

BEH Bridged ethylene hybrid

CID Collision induced dissociation

DDA Data dependent acquisition

DMF Dimethylformamide

ETD Electron transfer dissociation

Fab Fragment antigen-binding

Fc Fragment crystallizable

FLR Fluorescence

HCD Higher energy collisional dissociation

HILIC Hydrophilic interaction liquid chromatography

ICH International Conference of Harmonisation

IgG Immunoglobulin

LOD Limit of Detection

LOQ Limit of Quantitation

m/z Mass to charge ratio

mAb Monoclonal antibody

MS Mass spectrometry

NHS N-hydroxysuccinimidyl

pmol picomol

PTMs Post translational modifications

PTS Performance Test Standard

RF Rapi-Fluor

RFMS Rapi-Fluor MS

RSD Relative standard deviation

RT Retention time

SPE Solid phase extraction

UHPLC Ultra-high performance liquid chromatography


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Table of Contents Abstract ......................................................................................................................................................... 4

1. Introduction ........................................................................................................................................... 5

2. Material & Methods .............................................................................................................................. 8

2.1 Materials ....................................................................................................................................... 8

2.2 Sample preparation ....................................................................................................................... 8

2.3 Analysis with UHPLC-FLR-MS ................................................................................................... 8

3. Method optimization ........................................................................................................................... 10

3.1 LC optimization .......................................................................................................................... 10

3.2 MS optimization .......................................................................................................................... 11

3.3 Recovery of the sample preparation procedure ........................................................................... 12

3.4 Data analysis ............................................................................................................................... 15

4. Method qualification ........................................................................................................................... 18

4.1 Linearity and Range .................................................................................................................... 18

4.2 Specificity ................................................................................................................................... 21

4.3 Accuracy ..................................................................................................................................... 24

4.4 Precision ...................................................................................................................................... 26

4.5 Robustness .................................................................................................................................. 28

5. Conclusions ......................................................................................................................................... 32

6. Future perspectives ............................................................................................................................. 33

7. Supplementary Figures & Tables ........................................................................................................ 34

8. References ........................................................................................................................................... 43

9. Additional resources and references ................................................................................................... 44

10. Acknowledgements ............................................................................................................................. 45


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Abstract

N-glycans are located at the Fc region of a monoclonal antibody (mAb). These compounds are known for

their possible impact on the effector function of a mAb. Therefore, the interest in the biopharmaceutical

industry for the characterization and quantitation of N-glycans is increasing. Released N-glycans are

challenging to analyze as they do not show UV nor fluorescence absorbance and have low ionization

efficacies. In the past years, an effective label was developed called RapiFluor-MS (RFMS), which can

accomplish the detection of released N-glycans with sufficient sensitivity. While this technique has great

potential, there is a lack of studies that qualified this protocol for multiple species of N-glycans. In this

study, a LC-FLR-MS method was optimized and successfully qualified for the characterization and relative

quantitation of 19 released N-glycans labeled with RFMS. Within these 19 released RFMS N-glycans, the

compounds contained properties such as fucosylation, galactosylation, sialylation and bisection. The

method was qualified by following the ICH (International Conference of Harmonization) guidelines

(CPMP/ICH/381/95) [30], along the parameters: linearity, range, quantitation limit (LOQ), detection limit

(LOD), specificity, accuracy, precision and robustness. In multiple parameters the performance of the MS

and FLR detector were compared. Eventually, the FLR data was qualified for the use of relative

quantification of the N-glycan profile and the MS data was qualified for the identification of RFMS N-

glycans. This method shows the potential to be expanded for more compounds and other future perspectives

for this released N-glycan analysis are indicated and discussed.


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1. Introduction

Monoclonal antibodies (mAbs) are biopharmaceuticals that are used increasingly for the treatment of a

wide range of diseases such as cancer and autoimmunity. These are Y-shaped proteins (see Figure 1) which

are mainly produced by plasma cells and are part of the immune system to neutralize pathogens. The most

common antibody selected for the production of therapeutic mAbs is immunoglobulin (IgG). Therapeutic

mAbs can be produced by injecting a mammal with an antigen that induces an immune response, which is

a common antibody production technique. The B cells in the blood produce antibodies which are harvested.

The isolated B cells are used to produce a hybrid cell line called a hybridoma, immortal cancer cells are

fused with the isolated B cells to form these hybridomas. The hybridomas can be grown in culture and

produces one type of mAb for each culture [1]. Currently, also in vitro techniques such as phage display

and modelling are used in therapeutic mAb production [2]. The effectiveness of therapeutic mAbs is

dependent upon multiple mechanisms, for example, their ability to link antigen recognition with an

appropriate effector function, to elicit a biological response in vivo that will treat the targeted disease [3].

The mAbs consist of an Fc region and two Fab domains. Within the Fab region there is the binding site

where specific antigens can bind and are neutralized. The Fc region is the lower part of the antibody which

provides the binding site for Fcγ receptors and hence is responsible for antibody effector mechanisms. In

addition, this is the region that contains N-glycans. In Figure 1, the characteristic structure of a mAb and

an example of glycosylation is shown.

Figure 1: An antibody comprises of two identical heavy chains in association with two identical light chains arranged in a

characteristic Y-shaped structure. The Fc region is glycosylated with variating N-glycans, the degree of fucosylation,

galactosylation, bisection and sialylation has impact on the effector function of the antibody [4].


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Studies over the last decade have determined that the effector function of mAbs is highly dependent upon

the structures of the N-linked glycans of the Fc domain of the mAb, because the three-dimensional shape

of the mAb can differ for variating glycosylation patterns [5-7]. For example, larger N-glycans, e.g. bi-

antennary complex type with terminal galactosylation, open up the Fc part in the CH2 region. Whereas

smaller attached N-glycans like the core structure favor a more “closed” Fc conformation. This open and

closed formation can greatly influence the effector functions induced by interactions of the Fc part with

receptor molecules [8]. Total removal of the N-glycans is highly detrimental to the effector function of

the mAb, but subtle differences in the glycan structure, can improve the bioactivity and function of the

mAbs significantly [9, 10]. To give some examples, a decrease of fucosylation increases the antibody-

dependent cell-mediated cytotoxicity (ADCC), which can enhance the efficacy of the mAb. In addition,

the presence of high-mannose glycan structures also increases the ADCC [11]. In summary, the degree

and distribution of fucosylation, galactosylation, mannosylation and sialylation of N-glycans is important

knowledge when producing therapeutic mAbs [12, 13].

Therefore, it is valuable to analyze and monitor these N-glycan profiles of therapeutic mAbs throughout

their developmental process. The host cellular production system including the bioreactor environment can

produce mAbs with different glycosylation profiles, hence cell culture conditions should be considered in

bioprocess development. Cell culture conditions such as dissolved oxygen, nutrient levels, pH and feed

strategies can all have considerable influence on the glycosylation profile of the mAb, which could affect

product quality and efficacy. Great improvements have been made in techniques for high resolution and

high throughput analysis of N-glycans such as hydrophilic interaction liquid chromatography (HILIC) and

mass spectrometry (MS) [14]. N-glycan analysis is challenging, because they are largely invisible to optical

detection methods which results in laborious sample preparation in order to release them enzymatically

from the protein and to label them to allow fluorescence detection. Multiple labels were used to fulfil this

purpose, such as 2-aminobenzamide (2-AB) and 2-aminoanthranilic acid (2-AA) [15] . These labels are

coupled to the glycan structure via a multi-hour reductive amidation to activate fluorescence detection.

While addressing the detectability of the N-glycans, it leaves the N-glycans hard to identify by mass

spectrometry due to the low ionization efficiency.

Waters developed a fast and efficient sample preparation for N-glycan analysis, Rapi-Fluor MS (RFMS)

[16]. This sample preparation method takes less than one hour in simple steps including deglycosylation,

efficient labelling and sample clean-up. For the deglycosylation an enzyme was used named peptide-N4-

(N-acetyl-β-D-glucosaminyl) asparagine amidase F (PNGaseF), this enzyme is the most frequently used for

N-glycan release and the efficiency has been well-established [17]. PNGaseF is an amidohydrolase that

removes intact asparagine-linked oligosaccharide chains from glycoproteins and glycopeptides [18]. In

addition, it is capable to work under high temperatures for fast N-glycan cleavage. The RapiGest surfactant

is an anionic surfactant that ensures the accessibility of the N-glycans on the mAb and it prevents the

precipitation of the mAb under heat denaturation conditions. The RFMS label consists of an N-

hydroxysuccinimidyl (NHS) carbamate, a quinolinyl core and a tertiary amine group (See Figure 2). These

components facilitate rapid labelling kinetics, highly sensitive fluorescence and high gas-phase proton

affinity for good ionization and sensitive mass spectrometry measurements in positive mode. This label

was compared to the other labels by multiple studies, which proved that RFMS labelling is more sensitive

in both fluorescence and mass spectrometry [16]. Nevertheless, this labeling technique is rarely used in


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literature compared to the other mentioned labels. Only a few studies were published that use RFMS

labeling for different mAbs, without a proper qualification of the used method [19, 20].

Figure 2: The structure of the RapiFluor-MS label, it consists of an N-hydroxysuccinimidyl (NHS) carbamate, a quinolinyl core

and a tertiary amine group.

The challenge regarding qualification of this released RFMS glycan assay is the amount of N-glycans

actually taken into account. For example, in the study of Lim et al. [21], the protocol was qualified for only

four major abundant RFMS N-glycans (G0F, G1F isomers and G2F), while more N-glycans are present in

their results. In addition, it was not mentioned how the recovery (range 90 % - 110 %) was calculated for

the qualified compounds. In their study, an IgG standard was used with ranging concentrations to obtain a

range in N-glycan concentration and to determine the linearity. This was done because there are no certified

RFMS released N-glycan standards for single compounds available on the market, which complicates a

validation protocol with a standard addition approach. Despite that this challenge is acknowledged, no

relative standard deviations or residual errors were shown within their linearity test. The main challenge of

performing a proper qualification is that the initial N-glycan concentration in an IgG cannot be known

beforehand. Additionally, the sample preparation procedure is optimized for an amount of 15 µg IgG using

a concentration of 2 mg/mL, therefore, it was decided to use a different approach in our study. A separate

Quantitative Standard (QS) standard is used to determine the linearity of the method to avoid the approach

of ranging IgG concentrations. In addition, a Performance Test Standard (PTS) is used to perform an

adequate accuracy and recovery test, which lacks in the qualification of Lim et al. The PTS represents a

certified standard with multiple compounds combined, it contains 19 released RFMS-N-glycans. In this

way, the protocol should be qualified for more N-glycans that have different properties. To obtain more

information on the N-glycan profile in a robust and reproducible manner, which is important information

during the production of mAbs.

In this study, a LC-FLR-MS method is implemented for the identification and relative quantitation of mAb

N-glycan profiles. In short, the N-glycans are released from mAbs labeled with RFMS, isolated by solid

phase extraction and finally analyzed by liquid chromatography coupled to fluorescence and mass

spectrometry detection. The capabilities of the method are checked and the method is optimized to increase

the performance within all steps (sample preparation, LC separation, MS detection). Afterwards, the method

is qualified for 19 released N-glycans (see Table 19) along the following parameters: linearity, range,

specificity, accuracy, precision, and robustness. Within these parameters the performance of the FLR and

MS detectors are compared to qualify if they are both capable of the identification and relative


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quantification of released N-glycans. Within these 19 released N-glycans multiple species are present such

as: fucosylated, galactosylated, sialylated and bisected N-glycans. Differences between the results of the

multiple species of N-glycans are indicated and discussed. Future perspectives are also given to further

improve the throughput and robustness of this released N-glycan analysis.

2. Material & Methods

2.1 Materials

The RapiFluor-MS kit was acquired from Waters Corporation, which was used for the sample preparation.

It consists of an intact mAb check standard (IgG control sample), PNGaseF, RFMS label-reagent, a HILIC

µ-elution plate and the necessary SPE reagents. Additional standards were also obtained from Waters such

as Performance Test Standard (PTS) and the RFMS Quantitative standard (QS). Two antibody batches were

used originating from Genmab BV (IgG-b12) and Sigma (I4506), solvents (ULC-MS quality) were

obtained from Biosolve and Merck.

2.2 Sample preparation

The sample preparation consists of three steps: deglycosylation, fluorescence labeling and glycan

enrichment by solid phase extraction (SPE). The experimental steps were based on the procedure given by

Waters. In Figure 3, a schematic overview of the complete method is displayed. In summary, 6 µL of

RapiGest SF solution was added to 7.5 µL of glycoprotein (2 mg/mL). The samples were heated for three

minutes at 90 ˚C and cooled afterwards. Then 1.2 µL PNGaseF was added to the samples and mixed, the

samples were incubated at 50 ˚C for five minutes, subsequently cooled for three minutes at room

temperature. For the fluorescence labeling step, a RapiFluor-MS solution was prepared and 12 µL was

added to the samples. The labeling reaction was completed in five minutes, where after the samples were

diluted by addition of 358 µL of acetonitrile in order to prepare the samples for the glycan enrichment step

with HILIC SPE. A HILIC µ-elution plate and vacuum manifold was used for the SPE step. First, the wells

were conditioned and equilibrated by water and 85 % acetonitrile. Then the samples were loaded, and

subsequently the wells were washed two times with 1/9/90 (v/v/v) formic acid/water/acetonitrile. Then the

N-glycans were eluted with three times 30 µL 200mM ammonium acetate in 5 % acetonitrile. Finally, the

samples were diluted with 310 µL of DMF/Acetonitrile solution and were ready for LC-FLR-MS analysis.

2.3 Analysis with UHPLC-FLR-MS

The labeled N-glycans were separated on a Vanquish UHPLC (Thermo Scientific) with a binary pump

system equipped with an ACQUITY Glycan BEH Amide column (2.1mm x 150mm, 1.7 µm particle

size)(Waters). A gradient was applied with 50mM ammonium formate buffer (pH 4.4) as mobile phase A

and 100 % acetonitrile as mobile phase B at a flow rate of 0.5 mL/min. The gradient was linear for 35.0

minutes from 80 % to 63 % of mobile phase B. Then, 100 % mobile phase A was employed for ~5.0

minutes. Afterwards, the column was re-equilibrated with 80 % of mobile phase B for 15 minutes. In Table


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2, the gradient details are shown. The injection volume was 16 µL (~8 picomol of the total amount of N-

glycans) and the column temperature was set at 45 ̊ C. The labeled N-glycans were detected by fluorescence

and mass spectrometry, which was performed by an Orbitrap Eclipse Tribrid MS from Thermo Fischer

Scientific. The Vanquish UHPLC (Thermo Scientific) was connected to both detectors by a split tubing

(1:1 ratio). The FLR detector was set at 264nm excitation and 425nm emission wavelengths. The MS

detection was carried out by data dependent acquisition (DDA) mode. Positive ionization was used at a

voltage of 3400V and temperatures of 300˚C and 250˚C were used in the ion transfer tube and as vaporizer

temperature. The sheath gas was set at 35 Arb and the auxiliary gas at 7 Arb. The MS1 scan employed a

range of 350-2000 m/z at a scan rate of 3 Hz, where a resolution of 120,000 was set. Precursor selection for

MS2 scans were restricted by an intensity threshold of 50,000 and dynamic exclusion was set at 6 seconds.

The precursor ions were isolated in the quadrupole and fragmented by higher energy collisional dissociation

(HCD). The MS2 resolution was set at 60,000 and the normalized HCD collision energy was 28 % with a

fixed energy mode.

Figure 3: Schematic overview of the complete method. Step 1: an intact glycoprotein is exposed to the RapiGest surfactant and

the enzyme PNGaseF to deglycosylate the glycoprotein. Step 2: All the released N-glycans were labeled with the RFMS label with

a reaction time of 5 min. Step 3: The N-glycans were purified with a sample clean-up step using a HILIC µ-elution plate and

vacuum manifold, afterwards the samples were diluted and ready for LC-FLR-MS analysis. The raw data was processed by

Genedata Expressionist software.


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3. Method optimization

An optimization was carried out in order to check the capabilities of the protocol in comparison to the

published results [16]. The optimization of the LC-FLR-MS analysis was performed by variating the

following parameters: LC gradient, MS ionization and MS/MS fragmentation. The sample preparation

procedure consists of three steps: deglycosylation, labelling and sample clean-up as described in Section

2.2. The procedure was tested through recovery testing by different experimental set-ups, which are

described below. Data analysis was performed with Genedata Expressionist software where multiple tools

were used to process the data and a final workflow was created for automated data analysis.

3.1 LC optimization

The LC-FLR-MS analysis was set up with the use of a certified standard named RFMS Glycan

Performance Test Standard. This standard contains 19 released N-glycans labeled with RFMS in a given

relative abundance ratio. Two linear gradients were evaluated, which were provided by Waters together

with the description of the procedure. The first gradient is intended for universal N-glycan profiling and

the second gradient for mAb N-glycan profiling (See Table 1 & Table 2 for the details).

Table 1: LC gradient universal N-glycan profiling and column temperature in ˚C.

Time (min.) Flow (mL/min.) % A % B T (˚C)

0.00 0.4 25 75 60

35.0 0.4 46 54

36.5 0.2 100 0

39.5 0.2 100 0

43.1 0.2 25 75

47.6 0.4 25 75

55.0 0.4 25 75

Table 2: LC gradient mAb N-glycan profiling and column temperature in ˚C.

Time (min.) Flow (mL/min.) % A % B T (˚C)

0.00 0.5 20 80 45

3.00 0.5 27 73

35.0 0.5 37 63

36.5 0.2 100 0

39.5 0.2 100 0

43.1 0.2 20 80

47.6 0.5 20 80

55.0 0.5 20 80

The universal profiling method was not capable to separate all the 19 N-glycans sufficiently. This was

due to the overlapping peaks of the N-glycans that contain sialic acids, which complicated annotation for

these compounds. However, the mAb N-glycan profiling gradient was capable of separating all 19 N-

glycans, also the present isomers (G1, G1F and G1F+GN) were sufficiently separated. The FLR

chromatograms of the gradients are shown below in Figure 4 & Figure 5, small differences were observed


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between both. It was decided to continue with the mAb N-glycan profiling method as the elution window

for the mAb related N-glycans was slightly larger, allowing for more time between the peaks. In the study

of Hilliard et al, the same protocol was used to analyze a NIST mAb, which contains 35 N-glycans [19].

In their chromatography, co-eluting peaks were observed with the use of the universal gradient.

Therefore, they also used the mAb N-glycan profiling gradient, which improved their results as well.

Figure 4: FLR chromatogram of released N-glycans originating from the PTS that was obtained by use of the universal gradient

(see Table 1).

Figure 5: FLR chromatogram of released N-glycans originating from the PTS that was obtained by use of the mAb profiling

gradient (see Table 2).

It has to be noted that these data show slight retention time differences with the data obtained for the

qualification experiments. This was due to an instrument maintenance event, where the piston seals of the

UHPLC pump were required to be replaced. The difference in retention times between injections of the

same batch and different batches of PTS were evaluated. The shift between compounds of the same batch

was negligible and the shift between compounds of different batches was also small (<0.1 min).

3.2 MS optimization

For the MS settings, a Thermo glycan template, containing MS and HESI source settings (See Section 2.3),

was used and further optimized where necessary. Minor adaptions were made to the original settings, for

example ETD fragmentation was tested, which could deliver additional fragment information for larger N-

glycans. As ETD fragmentation is known to produce cross ring fragments, which are c- and z- type ions

[22]. ETD fragmentation is most suitable for ions with high charge states (>2+), while most of the measured

N-glycans have a charge of 2+, only larger N-glycans such as A2F can produce a charge state of 3+. It was

notable that ETD is not capable of producing sufficient fragmentation for these N-glycans and is therefore

not suitable for the identification of released N-glycans (data not shown). Therefore, it was decided to not


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use scan time to acquire ETD fragmentation spectra. CID fragmentation was also tested, but HCD

fragmentation produced more N-glycan fragments with higher intensities.

HCD fragmentation is known to produce predominantly Y- and B-ions and depending on the collision

energy it may produce some cross ring fragmentation as well [23]. HCD fragmentation produces sufficient

fragments for the identification of released N-glycans and complements the specificity of the method. In

Figure 6, the fragmentation spectra of G0F is shown with annotated fragments.

Figure 6: The MS2 HCD (28% collision energy) fragmentation spectra of RFMS labeled G0F (m/z 1774,72), a characteristic N-

glycan fragment is observed at m/z 204 N-Acetylglucosamine (GlcNAc). The spectrum was dominated by Y- and B-ions and the

MS/MS score was 94.8, which confirms the identification of G0F.

The HCD collision energy was set at 28 % and delivered clear spectra, higher collision energies were also

tested but this led to loss of specific larger fragments. HCD fragmentation of N-glycans delivers mostly

Y- and B-ions, but there were also some low intensity C- and Z-ions present, which are cross ring

fragments. Dynamic exclusion was employed with an exclusion duration of 6 seconds, which resulted in

multiple spectra across the maximum of each peak.

3.3 Recovery of the sample preparation procedure

The sample preparation procedure was tested to evaluate its suitability. Therefore, the efficiency of the

sample clean-up procedure was determined. This was performed by collecting and analyzing all the SPE

steps: two wash steps, two load steps and three elution steps (See Table 3). After elution, the samples were

diluted in 310µL Dimethylformamide/Acetonitrile. For this experiment, a control IgG-b12 sample was used

from Genmab’s inventory. In total nine samples were used, three samples did not pass the SPE clean-up

procedure (SPE 0), another three samples passed the SPE-clean-up procedure once (SPE 1), while the last

three samples passed this procedure two times (SPE 2). During this optimization step, the experiment was


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performed multiple times due to some random errors. It was observed that the recovery was dependent on

a constant low flow rate. Otherwise, the compounds will flush through the column in the load step and the

recovery will be very low ~10 % (data not shown). Eventually, a suitable constant low flow rate was found

(2.5 Hg vacuum).

Table 3: Scheme clean-up, including the purpose of each step with the corresponding solution and amount in µL.

Step Purpose Solution Amount (µL)

1 Conditioning Water 200

2 Equilibration Water/Acetonitrile (15/85 %) 200

3 Load sample Samples 400

4 Washing Formic acid/Water/Acetonitrile (1/9/90 %) 600

5 Washing Formic acid/Water/Acetonitrile (1/9/90 %) 600

6 Elution 200mM Ammonium acetate in 5 % Acetonitrile 30



As expected, no signal was observed in the following fractions: load fraction (1), wash fractions and 2nd

elution fraction of the SPE 1 samples. The first three samples (SPE 0) that did not passed the SPE clean-

up procedure had no signal. This was because of an error in the sample composition (no elution buffer),

which caused elution of the compounds within the void time of the LC separation. Therefore, the relative

recovery of SPE 2 was calculated by comparing the samples of SPE1 and SPE2. Similar N-glycan

profiles were observed in SPE 1 and SPE 2 samples, however, the absolute intensities in SPE 1 were

higher than in SPE 2 (See Figure 8A & B).

It was speculated that this was due to the sample composition of the SPE 2 samples, which consisted of

90µL elution buffer and 310µL of diluent. They had this composition because they underwent SPE 1

already. Therefore, some N-glycans already eluted during the load step (2) of SPE 2. It was decided to

combine the peak areas of SPE 2 and the load step of SPE 2 to obtain the relative recovery results (See

Table 4). The control IgG-b12 has a simple glycosylation profile, the most abundant N-glycans consist of

G0F, G1F isomers and G2F.

Figure 7: N-glycan ratio of G0F, G1F isomers and G2F in SPE 1 & SPE 2 samples


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Figure 8: (A) Overlay FLR chromatograms of SPE 1 & SPE 2 samples. (B) Overlay FLR chromatograms of SPE 1 and the load

step fraction of SPE 2.

The recovery is in the 75-95 % range, the recoveries for each N-glycan differ slightly. However, it is

observed in Figure 7 that the ratio of N-glycans almost remains intact, independent of its losses in the

clean-up and that is the main result of this assay. Therefore, these results were sufficient to qualify the

method following the ICH guidelines.

Table 4: Relative recovery of the sample clean-up, obtained by combining the fractions of the load step and elution of the SPE 2

samples.

N-Glycan Recovery

G0F 95 %

G1F-6 80 %

G1F-3 75 %

G2F 84 %


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3.4 Data analysis

The data analysis was performed with the use of Genedata Expressionist software. This software comprises

of numerous activities that can be used to simplify and treat the data. The raw data was processed in a

created automated workflow, which was mainly optimized by trial and error of numerous settings within

each activity that was selected. The final workflow consists of the following processing steps: chemical

noise subtraction, RT alignment, FLR/MS peak detection, charge and adduct grouping, MS/MS

consolidation & peak detection and finally a glycan library search was done. These processing steps allow

automated analysis of the raw data and provides the N-glycan profile (in relative abundances) of each

analyzed sample. In Figure 10, an overview of the complete workflow with all activities is shown.

The MS data is visualized as an ion map, the y-axis depicts the retention time and the x-axis depicts the

m/z range. Signal Intensities were displayed as a color gradient, as in a heat map or tile plot. Acquired

MS/MS spectra are depicted as In Figure 9, an example of such an ion map is given of a typical raw N-

glycan data.

Figure 9: Example of ion map of a PTS sample, where the y-axis depicts the retention time and the x-axis depicts the m/z range.

The red dots represent acquired fragmentation spectra.

First, the raw data is preprocessed to reduce the noise and apply chromatographic smoothing. In this

activity multiple algorithms were used to preprocess the data and remove low intensity signals that were

indicated as noise. A moving average filter with a retention window of five scans was used to smooth the

data. In addition, the subtraction of noise was done by a clipping method, which sets all values below the

quantile to zero and this was combined with an intensity threshold. This tool also allows restriction of

certain retention times and m/z values. The FLR data was preprocessed with a baseline subtraction, where

a penalized least squares algorithm was used. After the preprocessing, all the retention times were aligned

between samples.


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Peak detection was performed with an ascent-based algorithm for both the MS and FLR data, which was

used to calculate the peak boundaries around the signals. Additionally, the algorithm searches for the

local maxima of each signal.

After the peak detection, all the peaks were clustered and grouped based on their isotope pattern, charge

states and adducts. In this way, all the information belonging to a compound was grouped together. For

these activities retention time and m/z boundaries were set at 0.1 minutes and 50ppm. In addition, all the

singletons were filtered out (peaks that could not be assigned to a cluster containing at least two isotope

peaks).

The MS/MS data was used to confirm the identification of the N-glycans. Therefore, the MS/MS data was

processed as well. All MS/MS data that was not located within the peak boundaries of grouped signals

was removed with a consolidation tool and the highest TIC was selected from each cluster. Again the

ascent-based algorithm was used to provide the MS/MS data that approaches the local maximum of the

peak. Afterwards, a deisotoping activity was used to recalculate the MS/MS fragments to singly charged

monoisotopic peaks. This simplified the data and improved the probabilistic scoring algorithm that was

used in the library activities.

Two library activities were used in this workflow, namely, the released glycan activity and the library MS

activity. Both of these activities were used for the annotation of N-glycans, however, the library MS tool is

intended for high throughput analysis. This library contains N-glycan names, masses and retention times,

the activity annotates peaks based on this features. The released glycan activity was only used when the

library MS activity was not able to annotate N-glycans. For example, if retention times are shifting, then

the released glycan activity is preferred. It provides manual review options, so it can be observed which

peaks to annotate and which not. In addition, the released glycan activity was used for the configuring of

MS/MS scores for each glycan. The MS/MS score is calculated from the Andromeda scoring algorithm,

which is based on the probability that the observed number of matches between the calculated and observed

fragment masses could have occurred by chance. This score indicates the certainty of successful

identification based on the detected fragments. After the annotation of all peaks, the data was transferred to

Excel where statistical analysis was performed. In Figure 11, the chromatogram of all the 19 released N-

glycans was shown with annotated features.

Figure 10: Overview of the N-glycan workflow for high-throughput analysis in Genedata Expressionist software.


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Figure 11: Chromatogram of all the 19 annotated glycans at their corresponding peak. See Section 7, Table 19, for the details of

each glycan.

For the quantification, different observables were used for the MS and FLR data. The FLR data is one-

dimensional data and therefore, the area of the peak was used (Figure 12), where the area under the curve

is subdivided into trapezoids and these values are summed. The MS data is two-dimensional data, because

peaks were recorded across time and mass, which were plotted in a two-dimensional planar where the axes

correspond to retention time and mass/charge ratio (m/z). For Orbitrap instruments the ion population

abundance is represented by the amplitude of the signal. In case of isotopically separated signals, the peak

width (in the m/z direction) of an isotope depends on the used resolving power used to acquire the transient.

Therefore the maximum intensities across the peak (in the RT direction) are integrated to obtain the

representative intensity read out for the mass spectrometry data (Figure 13).

Figure 12: Visual explanation of the integration of the peak area from FLR data. (The total value of the integral is obtained by

summing the volumes of all the trapezoids)

Figure 13: Visual explanation of the integrated maximum intensity (corresponds to the peak height integrated along its scans in

the RT direction) of a peak from MS data.


18


4. Method qualification

After method optimization, the method was qualified for batch profiling, following the ICH (International

Conference of Harmonization) guidelines (CPMP/ICH/381/95) [30]. The following parameters were tested:

linearity, range, quantitation limit (LOQ), detection limit (LOD), specificity, accuracy, precision and

robustness. The performance of each test is explained and the corresponding results are shown afterwards.

These experiments were performed using two mAbs: IgG (Sigma, I4506) and the PTS reference (Waters),

which consists of 19 RFMS labeled released N-glycans originating from the same IgG (Sigma, I4506). In

Table 19, the structures of all 19 N-glycans are shown without the RFMS label. Direct comparisons between

the PTS and IgG (Sigma, I4506) could be inaccurate, because of the PTS and the IgG originate from

different batches and could therefore show slightly deviating N-glycan profiles.

4.1 Linearity and Range

The linearity and range were assessed by a calibration curve from the Quantitative Standard (QS) from

Waters. This standard consist of a RFMS labeled peptide (RFMS -TPTTQSSVSSQTTR-OH), which is

derivatized by one mole equivalent of the RFMS label and enables quantitation based on fluorescence.

The fluorescence response is linearly proportional to the amount of RFMS label, therefore, fluorescence

peak areas can be used to quantify specific RFMS labeled N-glycans. The calibration curve was prepared

in a range of 0.04 pmol - 4 pmol (picomol). The acceptance criteria for the linearity and range was set

with a correlation coefficient (R2) of >0.99 over the specified range and RSD values below 10 % for each

measured amount of QS. In addition, the residual error was calculated for each amount of QS. The

calibration curve was also used to calculate the LOD and LOQ based on the standard deviation of the

response and the slope (See Equation 1). The QS was reconstituted in 90µL of 200mM ammonium

acetate in 5 % Acetonitrile and 310µL of diluent DMF/Acetonitrile. In this way, the conditions were

identical compared to real samples. Different injection volumes (0.1-10 µL) were used to obtain results

across the indicated range (0.04-4 pmol) and each amount of QS was analyzed in triplicate (n=3). There

was also a blank used with 90µL of 200mM ammonium acetate in 5 % Acetonitrile and 310µL of diluent

DMF/Acetonitrile. In Table 8 (see Section 7), an overview is shown of the samples and their features. The

linearity and range were evaluated for the FLR and the MS data separately

Equation 1: The LOD and LOQ were calculated based on the standard deviation of the response and the slope of the calibration

curve. In the depicted formulas, σ represents the standard deviation of the response and S represents the slope of the calibration

curve.

𝐷𝐿 =3.3𝜎

𝑆 & 𝑄𝐿 =

10𝜎

𝑆


19


Figure 14: Linearity of Quantitative Standard (RFMS -TPTTQSSVSSQTTR-OH peptide) within the range of 0.04 – 4 pmol for

the FLR (A) and MS (B) data, each amount of QS was measured in triplicate (n=3).

In Figure 14 A&B, the calibration curves are shown of the FLR and MS data. The precision of each data

point (amounts of QS in pmol) was evaluated to prove efficient detection across the range. This was

visualized in Table 6 and Figure 15, where the RSD and residual errors of both data are shown. The LOD

is 0.019 pmol and the LOQ is 0.057 pmol for the FLR data (amount of QS), which means that this method

can detect 0.019 pmol N-glycan from 4 pmol IgG. Or simplified, from the initial (15 µg) 100 pmol used

IgG, 0.475 pmol of N-glycan can still be detected and 1.425 pmol of N-glycan can be quantified. The LOD

and LOQ from the FLR data are well within the acceptance criteria of < 1 % (1 pmol) and < 3 % (3 pmol)

detected/quantified N-glycan from the total amount of IgG. From the MS data, the LOD is 0.095 pmol and

the LOQ is 0.29 pmol, which is 2.375 and 7.25 pmol of N-glycan per 100 pmol of IgG. In Table 5, a

summary of the results with the corresponding acceptance criteria was given.

Table 5: A summary of the results of the linearity and range experiment, with their corresponding acceptance criteria.

Parameter Results Acceptance criteria

Linearity &

Range

- R2 of 0.9999 (FLR), R2 of 0.9932 (MS)

- Range: 4 – 0.04 pmol, all points < 10 % RSD

- (FLR) LOD: 0.475 pmol N-glycan/100 pmol IgG (= < 1 %)

- (FLR) LOQ: 1.425 pmol N-glycan/100 pmol IgG (= < 3 %)

- (MS) LOD: 2.375 pmol N-glycan/100 pmol IgG (= > 1 %)

- (MS) LOQ: 7.25 pmol N-glycan/100 pmol IgG (= > 3 %)

- R2 > 0.99

- < 10 % RSD

- LOD < 1 %

N-glycan from total

amount IgG

- LOQ < 3 %

N-glycan from total

amount IgG

Table 6: %RSD values at each measured amount of the QS (n=3), calculated from the triplicate measurements of each amount.

Amount QS (pmol) % RSD FLR data % RSD MS data

0.04 3.3 % 3.2 %

0.1 0.1 % 4.4 %

0.2 3.6 % 4.2 %

0.4 0.5 % 1.3 %

1.0 1.3 % 3.4 %

2.0 0.3 % 8.7 %

4.0 1.3 % 7.7 %


20


From the results it can be stated that all FLR data was within the acceptance criteria. However, the MS

data had some results that were outside the acceptance criteria. The RSD values were within the

acceptance criteria, but it was already observed that lower amounts of QS comprised of higher RSD

values. In Figure 15, the residual error for the lowest amount of QS (0.04 pmol) showed a very high

value, which clarifies that the LOD and LOQ of the MS data were not within the acceptance criteria. For

the purpose of this method, it was not necessary to improve these results for the MS. Because, it was

decided to use the FLR data for the relative quantification and MS for identification of N-glycans.

Although, it is expected that a reduce of MS1 scan time may improve the LOD and LOQ of the MS.

Because, this will result in more data points on each peak, which could be useful for low abundant

compounds that comprise of small peaks. This can be done by lowering the orbitrap resolution for the

MS1 scan.

Figure 15: Plots of the residual errors in percentages for each amount of QS (0.04 – 4.0 pmol) from the FLR and MS data.


21


4.2 Specificity

The specificity was evaluated with the calculated mass accuracy, MS/MS score and the fragmentation

spectra for each N-glycan. The mass accuracy was calculated by comparing the theoretical mass and the

measured mass. This was performed with the reference PTS and treated IgG (Sigma, I4506) samples in

triplicate (n=3). Both of these samples contain the same N-glycans and their accuracies (in ppm) were

averaged. The acceptance criteria for the mass accuracy was set at < 5ppm. The acceptance criteria of the

MS/MS score was set a minimal score of 10 for successful identification. Fragmentation spectra of each N-

glycan with the highest score were visually checked if specific N-glycan fragments were present, for

example the N-Acetylglucosamine (GlcNAc) fragment at m/z 204.

Figure 16: Average mass accuracy in ppm and MS/MS score of each individual N-glycan measured in triplicate (n=3). The red

line indicates the acceptance criteria, for the mass accuracy all N-glycans needed to be <5 ppm, for the MS/MS score all N-glycans

needed a score above 10.

The average mass accuracy was < 1.2 ppm which is within the acceptance criteria of < 5 ppm. In addition,

all the detected N-glycans had a MS/MS score above the acceptance criteria of > 10 score. In Figure 16, all

the ppm values and consolidated MS/MS scores are shown for each N-glycan. In Figure 17, the HCD MS2

spectrum of G0 is shown to provide an overview of specific N-glycan fragments.

The N-glycan G0 is low abundant within the used IgG samples and PTS (See N-glycan profile in Figure

32). The relative abundance is around ~0.8 % and despite of this, the quality of the MS2 spectra was still

adequate to successfully identify G0 (MS/MS score of 99.8).


22


Figure 17: HCD MS2 fragmentation spectra of G0 (m/z 1628.66) . This spectrum shows characteristic fragments of N-glycan

fragmentation, for example the N-Acetylglucosamine (GlcNAc) fragment at m/z 204.

In the N-glycan profile three isomers were present, which were: G1, G1F and G1F+GN (see Figure 11).

The isomers are indicated with -3 and -6, because the difference between these isomers is the terminal

galactose residue at the α1.3 or α1.6 branch [24]. In Figure 18, the difference between these branches in the

G1F isomers is displayed. While chromatographic separation between isomeric compounds (e.g. G1F

isomers) was obtained, identifying the isomers still proposes a challenge. This is due to: the identical mass

of the isomers and that the typical MS fragmentation techniques, HCD, CID and ETD, are not able to

differentiate these isomers from each other. In Figure 19A & B, the MS2 spectra of the two G1F isomers

are shown.

Higel et al. also obtained chromatographic separation of G1F isomers and stated from quantitative data that

the higher intense α1.6 isomer elutes first followed by the lower intense α1.3 isomer [25]. In this study, this

pattern was the same for all isomers, the first eluting isomer was the most intense peak followed by the

other isomer which was less intense (see Figure 11). Because of this, the α1.6 isomer was indicated as first

eluting peak and the α1.3 isomer as second eluting peak for all three isomers (G1, G1F, G1F+GN) within

this research. However, it is suggested to reveal this structural differences via own research to confidentially

annotate these differences. Therefore, some possible solutions were indicated within the future perspectives

(see Section 6).


23


Figure 18: Schematic representation of the two G1F isomers and their specific α- and β-linkages.

Figure 19: HCD fragmentation spectra of two G1F isomers from an IgG sample, A elutes at 18.45 min. and B elutes at 19.04 min.

Minor differences in intensity of fragments are present, Genedata Expressionist annotates both isomers to each spectrum as the

fragmentation spectra are comparable.


24


4.3 Accuracy

To assess the accuracy of the method, two experimental set-ups were performed. The first experimental

set-up (Test A) evaluates the impact of the sample preparation on the already released N-glycans. While

the second experimental set-up (Test B) evaluates the impact of the SPE clean-up step on individual N-

glycans. In the first experimental set-up (Test A), spiked samples with and without sample preparation

steps were compared. The PTS was reconstituted in 25µL (200 pmol of total N-glycan) water and

aliquoted into two sets of triplicate (2 x n=3) samples. One set of samples (n=3) was directly injected

(1µL= 8 pmol) into the LC-MS-FLR instrument for analysis. While the second set of samples was first

processed using the sample preparation procedure (deglycosylation, RFMS-labeling and clean-up). These

samples were eventually diluted in 90µL 200mM ammonium acetate in 5 % acetonitrile and 310 µL of

DMF/Acetonitrile solution, and 16µL (8 pmol) was injected for LC-MS-FLR analysis. Afterwards the

two sets were compared with each other with respect of the obtained glycan profiles. The FLR data was

used for this tests and the criteria for acceptable accuracy boundaries were set at 80-120 %. The results of

the first test are shown below in Figure 20. The N-glycan profiles are shown in Section 7.

Figure 20: Accuracy of each N-glycan from the total N-glycan profile, the red dashed lines represent the boundaries of the

acceptance criteria (80-120 %).

In the results from the first experimental set-up (Test A), the accuracy of N-glycans (12 compounds) without

a sialic acid residue were within the acceptance criteria. Overall, it can be seen that the N-glycan profiles

of the two sample sets are comparable (see Section 7, Figure 32). However, the N-glycans which contain

sialic acid residues (Figure 20, A1F-Gal, A1, A1F, A1F+GN, A2, A2F and A2F+GN) are outside the

acceptance criteria. The results of N-glycans that contain sialic acids are corresponding to the results of the

precision and therefore will be discussed in Section 4.4.

In the second experimental set-up (Test B), two sets of triplicate (2 x n=3) IgG (Sigma, I4506) samples

were compared. The first set was processed by the sample preparation procedure while the second set was

excluded from the SPE clean-up step. Both sample sets were compared to each other based on the N-

glycan ratio, which resulted in the accuracy (see Figure 22). In addition, this experiment provides


25


information on the recovery of the SPE clean-up step. In Figure 21, an overlay of the chromatograms

from the samples “with SPE clean-up” and “without SPE clean-up” were shown.

Figure 21: Overlay of the chromatograms from the samples “including the SPE clean-up step” in blue and the samples “excluded

from the SPE clean-up step” in red, which represents a visualization of the 50 % recovery.

Figure 22: Accuracy of each N-glycan for the comparison between the samples “including SPE clean-up” and “excluded from

SPE clean-up”.

In the results from the second experimental set-up (Test B), the N-glycan profile of both set of samples

were very comparable. All expected N-glycans show accuracy within the 80-120 % acceptance criteria.

Although, the recovery of the clean-up step was low (45-55 %, range of all N-glycans), this does not have

an impact on the accuracy of the glycosylation pattern. This was due to the comparable recovery values of

all N-glycans. This result was also observed in the method optimization, see Section 3.3, and by Lauber et

al. [16]. Possible improvements in terms of recovery were indicated at the future perspectives (see Section

6).


26


4.4 Precision

The precision was assessed with nine homogeneous IgG (Sigma, I4506) samples, which were analyzed

divided over three days (3 x n=3). The repeatability was expressed in Relative Standard Deviation (RSD)

for each day (variance within one day, 3 x n=3) and the intermediate precision was also expressed in RSD,

which was derived from all samples together (variance between days, n=9). The RSD values were

calculated based on the percentage of each N-glycan. The precision was calculated for the FLR and the MS

data separately. The acceptance criteria for the repeatability and intermediate precision were at <10 % and

<20 % RSD. In Figure 23, the repeatability of each N-glycan for the FLR and MS data is shown.

Additionally, in Figure 24, the intermediate precision of the FLR and MS data is compared. The tables

including the RSD values for all individual N-glycans are shown in Section 7.

Figure 23: The repeatability (3 x n=3) of each N-glycan for the FLR and MS data, the red dashed line represents the acceptance

criteria.

Figure 24: The comparison of the intermediate precision (n=9) between the FLR and MS data for each N-glycan, the red dashed

line represents the acceptance criteria.

The repeatability of both FLR and MS data were within the acceptance criteria, as the overall RSD of all

N-glycans was below 10 % at each day (see Figure 23). It can be observed that N-glycans containing

sialic acid residues have higher RSD values than the N-glycans without a sialic acid residue. The

intermediate precision of the FLR data was also within the acceptance criteria as the overall RSD of all N-


27


glycans was below 20% (see Figure 24). In Figure 25, an overlay is shown of the nine precision samples.

The chromatograms are similar to each other, only some small variation in absolute intensity was

observed.

Figure 25: Overlay of the all the chromatograms from the nine IgG samples which were used for the precision tests distributed

over three days.

However, the intermediate precision of the MS data was higher than 20 % RSD and therefore not within

the acceptance criteria (see Figure 24). In the MS data, some low abundant compounds (G1-3 & G1F-

3+GN) and especially sialic acid containing compounds caused more variation between days. This result is

corresponding with the residual errors in the MS data from low amounts of QS (See Section 4.1).

Additionally, this could also be due to variating ionization efficiency of these compounds. This result

strengthened the decision of using FLR data for the relative quantification and MS the for identification of

N-glycans.

In the results of the accuracy, precision and robustness (upcoming results, see Section 4.5), it was found

that the seven N-glycans containing sialic acids (N-Acetylneuraminic acid) did not always meet the

acceptance criteria. In Section 7, the individual results (% RSD and % accuracy) of sialic acid containing

N-glycans for each test are given. These results could be caused by challenges in the chromatographic

separation, which results in shoulder/tailing peaks or broader peaks compared to the other N-glycans (see

Figure 11). An example of shoulder/tailing peaks from A2F and A2F+GN is shown in Figure 26. This can

cause more variation in the peak areas for these compounds, as the peak integration is more complicated

and less robust.

Figure 26: An example of the peaks of the sialic acid containing N- glycans. The shoulder/tailing peaks of A2F and A2F+GN

causes more variation in peak areas for these compounds in different samples.


28


The peak tailing of these compounds was caused by secondary interactions with the stationary phase, this

is probably due to the negative charge of sialic acids groups [26]. However, solutions and suggestions for

this challenge are made within the future perspectives, where possible follow up studies are indicated (see

Section 6).

4.5 Robustness

For the robustness, three different variations in the sample preparation procedure were evaluated, different

IgG concentrations, deglycosylation temperatures and three different sample formulations.

1. The first experiment to test the robustness was the influence of the amount of IgG. Next to the

standard amount of 15µg, two other amounts were tested: 7.5µg and 30µg in triplicates (n=3).

2. In the second experiment, the influence of different deglycosylation temperatures on the IgG

samples was evaluated. The standard deglycosylation temperature is 90˚C and the two tested

temperatures were 80˚C and 100˚C, all samples were tested in triplicates (n=3).

3. In the third experiment the influence of different drug formulations was tested. The tested drug

formulations were: Phosphate Buffered Saline (PBS) pH 7.4, 20 mM histidine, 250 mM sorbitol,

0.04 % polysorbate 80, pH 6.0 (buffer A) and 30 mM sodium acetate, 150 mM D-sorbitol, 0.04 %

polysorbate 80, pH 5.5 (buffer B). All samples were tested in triplicates (n=3).

For all these experiments the results were compared with original IgG (Sigma, I4506) samples, which

followed the standard sample preparation procedure. The FLR data was used for this tests and the

acceptance criteria for the robustness was set at <10 % RSD and 80-120 % accuracy for each N-glycan.

The tables including the RSD values for all individual N-glycans and the N-glycan profiles of all variations

within each robustness test are shown in Section 7.

In the first robustness test, different IgG amounts (7.5 & 30µg) were tested next to the standard amount

(15µg). Although, the variance of the tested amounts (7.5 & 30µg) was higher compared to the variance of

the standard amount (15µg), most of the compounds were within the acceptance criteria (<10 % RSD).

Also the accuracy was within the acceptance criteria (80-120 %). In Figure 27, an overlay of the

chromatograms from the different amounts is shown. In Figure 28, the RSD% and accuracy values of each

N-glycan are shown.

Figure 27: Overlay of the average chromatograms from the different IgG amounts (7.5, 15, 30µg = Red, Blue, and Green)


29


Figure 28: The RSD% (n=3) and accuracy values for each individual N-glycan of robustness test one, the red dashed lines

represent the boundaries of the acceptance criteria.

In Figure 27 & Figure 28, it is observed that the obtained N-glycan profiles are comparable to each other

for each of the tested IgG amounts. In addition, the intensity profile of the three amounts indicated a possible

linear relationship. Therefore, the total concentration of the detected N-glycans of each IgG amount was

compared with each other. This was done by summing the area and integrated maximum intensity of all N-

glycans for the FLR and MS data. This was displayed in Figure 29A&B.

Figure 29: A: The summed area of all the detected N-glycans from the FLR data for each tested amount of IgG. B: The summed

integrated maximum intensity of all the detected N-glycans from the MS data for each tested amount of IgG.

In this data, it is observed that the amount of N-glycans in 15µg IgG (100 %) is approximately twice as

much as the amount of N-glycans in 7.5µg IgG (52 & 46 %). This was not the case for the 30µg of IgG

(152 & 143 %) relative to the 15µg of IgG (100 %). This was probably because, the sample preparation

procedure was optimized for an amount of 15 µg IgG, and therefore, some steps of the sample preparation

procedure may not be fully effective for an amount of 30 µg IgG. Nevertheless, the accuracy of the N-

glycan profile of 30µg IgG was still within the acceptance criteria. This comparison between 7.5 & 15 µg

of IgG, strengthens the assumption that all N-glycans were successfully deglycosylated from the IgG, as

the absolute intensity (FLR & MS) increased by a factor two.


30


To evaluate the labeling efficiency, unlabeled N-glycan standards (6.4 pmol per standard) were directly

injected into the LC-FLR-MS setup. The integrated maximum intensity of these peaks were compared

with the observed signals of unlabeled N-glycans in the original samples. Only four minor signals of

unlabeled N-glycans (G0F, G1F isomers and G2F) were detected in the original samples, which were the

most abundant N-glycans in the total N-glycan profile. The signal of unlabeled G0F (Integrated max.

intensity= 1.0x104) in the original sample was compared with the signal of unlabeled G0F (Integrated

max. intensity= 1.8x106) in the standard based on their integrated maximum intensity. From this ratio it

was calculated that, the observed signal of unlabeled G0F in the original sample was 0.036 pmol, which

corresponds to 2% of the total injected amount of labeled G0F (1.8 pmol) of the original sample. Thus the

labeling efficiency of the method is 98% for G0F and it is assumed that this labeling efficiency is

comparable for the other N-glycans. These results confirm the statements of Lauber et al., in their study

the deglycosylation and labeling efficiency was optimized and stated to be accurate for complete

deglycosylation and labeling of N-glycans [16].

In the second experiment the influence of different deglycosylation temperatures on the IgG samples were

evaluated. The standard deglycosylation temperature is 90˚C and the two tested temperatures were 80˚C

and 100˚C. In Figure 30, the RSD% and accuracy values of each N-glycan are shown. The tables including

the RSD values for all individual N-glycans and the N-glycan profiles of all variations within each

robustness test are shown in Section 7.

Figure 30: The RSD% (n=3) and accuracy values for each individual N-glycan of robustness test two, the red dashed lines


Also in this test, the variance within the results of the tested deglycosylation temperatures was compared

to the variance of the standard deglycosylation temperature. All the variances within the tested

temperatures were within the acceptance criteria (<10 % RSD), except for some N-glycans containing

sialic acids. Also the accuracy was within the acceptance criteria (80-120 %), which means the method is

robust for minor deviations in deglycosylation temperatures.


31


In the third experiment the influence of different drug formulations was tested. The tested drug formulations

were: Phosphate Buffered Saline (PBS) pH 7.4, buffer A (20 mM histidine, 250 mM sorbitol, 0.04 %

polysorbate 80, pH 6.0) and buffer B (30 mM sodium acetate, 150 mM D-sorbitol, 0.04 % polysorbate 80,

pH 5.5). In Figure 31, the RSD% and accuracy values of each N-glycan are shown. The tables including

the RSD values for all individual N-glycans and the N-glycan profiles of all variations within each

robustness test are shown in Section 7.

Figure 31: The RSD% (n=3) and accuracy values for each individual N-glycan of robustness test three, the red dashed lines


From Figure 31 it is clearly that these drug formulations do not influence the N-glycan profile, as the

variance is comparable or even lower compared to the standard drug formulation (water). The variance

and accuracy of the tested drug formulations were within the acceptance criteria (<10 % RSD, 80-120 %

accuracy). Although, there were some exceptions for N-glycans that contained sialic acids within the PBS

buffer. In the second and third test of the robustness, it was observed that the results are robust against

deviations in deglycosylation temperatures and drug formulations. This an important feature of this

method, especially for the drug formulations. Because the method is intended to profile Genmab batches,

which are mostly formulated in the tested drug formulations. It is assumed that the clean-up step of the

sample preparation makes this possible and is therefore a critical step despite of its losses in recovery.


32


5. Conclusions

In this study, a LC-FLR-MS method for released RFMS N-glycans was implemented through a method

optimization and qualification. During the optimization it was found that HCD fragmentation provides

sufficient information for successful identification of released N-glycans and that ETD fragmentation was

not useful for this purpose. It was also found that the recovery of the sample clean-up was sensitive for

random errors, but has no significant impact on the accuracy of the N-glycan profile. The method was

successfully qualified for the relative quantification of the RFMS labeled N-glycan profile from mAbs.

Following the ICH guidelines, the following parameters were tested: linearity & range, specificity,

accuracy, precision and robustness. During the qualification, the performance of the FLR and MS detectors

were compared in multiple parameters. From these results, it was decided to use the FLR data for the relative

quantification and the MS data for the identification of N-glycans. A summary of the results is given in

Table 7. Compared to the studies in literature, this method was qualified for multiple species of N-glycans.

For example, the study of Lim et al. [19], to the best of our knowledge, the only study which described

validation of the RFMS method in literature, validated the RFMS method for four glycans.

From the total 19 N-glycans, 12 N-glycans were all within the acceptance criteria and therefore they are

suitable for the identification and relative quantification. Within these 12 N-glycans, compounds contained

properties such as fucosylation, galactosylation and bisection. On the other hand, the remaining 7 N-glycans

were not within all acceptance criteria. These were N-glycans that all were sialylated and are suitable for

identification purposes, but not for relative quantification. The N-glycans that contain sialic acid residues

comprised of difficulties in separation because of secondary interactions with the stationary phase (see

Section 4.4). Nevertheless, this method shows potential to be expanded for more N-glycans. Therefore,

other mAbs with different glycosylation profiles should be tested.

Table 7: Summary of the qualification results.

Parameter Results Acceptance criteria

Linearity &

Range

- R2 of 0.9999 (FLR), R2 of 0.9932 (MS)

- Range: 4 – 0.04 pmol, all points < 10 % RSD

- (FLR) LOD: 0.475 pmol N-glycan/100 pmol IgG (= < 1 %)

- (FLR) LOQ: 1.425 pmol N-glycan/100 pmol IgG (= < 3 %)

- (MS) LOD: 2.375 pmol N-glycan/100 pmol IgG (= > 1 %)

- (MS) LOQ: 7.25 pmol N-glycan/100 pmol IgG (= > 3 %)

- R2 > 0.99

- < 10 % RSD

- LOD < 1 %

N-glycan from total

amount IgG

- LOQ < 3 %

N-glycan from total

amount IgG

Specificity - Average mass accuracy < 1.2 ppm

- MS/MS score > 25

- < 5 ppm

- > 10 score

Accuracy - 12 N-glycans without sialic acids: ~108 % average accuracy

- 7 Sialic Acid containing N-glycans: ~70 % average accuracy

- 80-120 % Accuracy

Precision - Repeatability: (FLR) ~3.0 % average RSD,

(MS) ~3.6 % average RSD

- Intermediate precision: (FLR) 4.6 % average RSD,

(MS) 22.6 % average RSD

- < 10 % RSD

- < 20 % RSD

Robustness - Test 1: variating IgG amount

- Test 2: variating deglycosylation temperatures

- Test 3: variating drug formulations

- All below < 10 % RSD and ~99.9 % average accuracy

- < 10 % RSD

- 80-120 % Accuracy


33


Finally, for mAb characterization studies the RapiFluor (Waters) released N-glycan method was

successfully implemented and optimized to be used within the Genmab environment and sample types.

Additionally, standardized sample preparation procedure and Genedata Expressionist data analysis

workflows were established. Within this method, future improvements and perspectives were indicated (see

Section 6). Therefore, this study is a step forward in released N-glycan analysis.

6. Future perspectives

For future research perspectives, possible solutions were proposed for the N-glycans that contain sialic

acids. For the used IgG (19 N-glycans), the gradient could be made less steep to optimize the separation of

the sialic acid containing compounds. In literature, other separation methods have been found for N-glycans

that contain sialic acids. For example the study of Torii et al., in this study major sialylated N-glycans were

analyzed where a fractionation was performed with anion exchange and subsequently the fractions were

separated on a reversed phase column [26, 27]. Alternatively, another column could be used which is

specifically intended for sialic acid containing N-glycans. For example, the Acquity Premier Glycan BEH

C18 AX Column with the IonHance Glycan C18 AX Ammonium Formate Concentrate as mobile phase

[32]. This is a mixed-mode (anion-exchange/reversed-phase) column that provides a charge based

separation and extra resolution for acidic glycans. This might also simplify to distinguish between the

different types of sialic acids (N-Acetylneuraminic acid and N-glycolylneuraminic acid). It would be

interesting and valuable additional research to investigate these options. Such a complementary addition

could help significantly in expanding the range of N-glycans that could be measured.

Some other additional improvements for this method have been identified during method optimization. For

instance, while the sample preparation is fast and efficient, its recovery was found just to be 50 %. It was

observed that this low recovery was the result of the SPE-clean up step which requires careful execution,

to ensure a low flowrate through the SPE-wells. Otherwise, the compounds will flush through the column

in the load step and the recovery will be very low ~10 % (data not shown). Nowadays, there are multiple

options available to improve the recovery and handling of the clean-up step. The first option is the use of a

different SPE elution mechanism, which applies positive pressure with nitrogen instead of negative pressure

with a vacuum manifold. This will improve the repeatability and avoids human errors in vacuum control,

but an additional nitrogen connection would be needed [33]. Another option is the implication of the

Andrew Alliance robot, which is capable to execute the complete clean-up step [28]. This automation will

also enhance the precision and discards human errors completely. These improvements could make the

method more automated and robust, this could justify the additional implementation costs.

Another interesting research topic within glycan analysis is the distinguishment between isomeric

compounds. The chromatographic separation of isomers was achieved, but it was not possible to distinguish

them from the MS2 spectra. For this challenge, some possibilities were indicated and shortly described. In

the study of Zhao et al., they made use of MSn experiments in negative ion mode to distinguish isomeric

N-glycans based on signature D-ions, which are unique for specific isomers [29]. These ions are generated

by cleaving the core N-Acetylglucosamine (GlcNAc, blue boxes) residues and the 3-antenna. Such an in-

depth MSn study might be able to reveal the isomeric identity of these N-glycans.


34


Additionally, an enzymatic approach is suggested. Enzymes like β-N-Acetylhexosaminidase and α-

Mannosidase could aid in the identification of these G1F isomeric variants. The β-N-Acetylhexosaminidase

would cleave the terminal N-Acetylglucosamine (GlcNAc) monosaccharides followed by an α-

Mannosidase digestion which will cleave a single α1-3 linked mannose from the core ß-mannose, leaving

the single α1-6 linked mannose from the core ß-mannose intact. Subsequent N-glycan analysis would reveal

the isomeric difference due to change in retention time of the peaks.

7. Supplementary Figures & Tables

In this section, additional Figures and Tables are displayed. The caption of the Figures and Tables

explains the data and where it belongs referring to the results. In the tables, the results that are outside the

acceptance criteria of >10 % RSD and 80-120 % accuracy were underlined.

Table 8: Samples (Lin_0 – Lin_7) with corresponding amounts in pmol and their peak areas.

Sample Amount of QS

(pmol)

Average Peak Area (FLR) Integrated Max. Intensity

(MS)

Injection volume

(µL)

Lin_0 0 0 0 1

Lin_0 0 0 0 1

Lin_0 0 0 0 1

Lin_1 0.04 2.30E+04 3.55E+05 0.1

Lin_1 0.04 2.45E+04 3.79E+05 0.1

Lin_1 0.04 2.43E+04 3.67E+05 0.1

Lin_2 0.1 8.17E+04 1.29E+06 0.25

Lin_2 0.1 8.17E+04 1.39E+06 0.25

Lin_2 0.1 8.16E+04 1.30E+06 0.25

Lin_3 0.2 1.75E+05 3.20E+06 0.5

Lin_3 0.2 1.65E+05 2.96E+06 0.5

Lin_3 0.2 1.76E+05 3.16E+06 0.5

Lin_4 0.4 3.64E+05 7.00E+06 1

Lin_4 0.4 3.62E+05 7.18E+06 1

Lin_4 0.4 3.65E+05 7.03E+06 1

Lin_5 1.0 9.51E+05 2.13E+07 2.5

Lin_5 1.0 9.46E+05 1.99E+07 2.5

Lin_5 1.0 9.69E+05 2.10E+07 2.5

Lin_6 2.0 1.96E+06 4.85E+07 5

Lin_6 2.0 1.95E+06 4.12E+07 5

Lin_6 2.0 1.94E+06 4.29E+07 5

Lin_7 4.0 3.87E+06 9.24E+07 10

Lin_7 4.0 3.90E+06 8.63E+07 10

Lin_7 4.0 3.80E+06 1.01E+08 10


35


Table 9: Precision data of day 1 from the qualification, the relative standard deviations of all N-glycans for both the FLR and MS

data.

Precision Day 1

N-glycan FLR RSD Average RSD N-glycan MS RSD Average RSD

G0 0.8 % 2.4 % G0 0.4 % 3.2 %

G0F 0.9 % G0F 1.0 %

G0F+GN 1.2 % G0F+GN 1.3 %

G1-6 0.9 % G1-6 2.4 %

G1-3 0.0 % G1-3 2.1 %

G1F-6 1.1 % G1F-6 1.5 %

G1F-3 1.0 % G1F-3 2.7 %

G1F-6+GN 2.3 % G1F-6+GN 3.6 %

G1F-3+GN 2.0 % G1F-3+GN 6.2 %

G2 0.7 % G2 1.8 %

G2F 1.6 % G2F 2.8 %

G2F+GN 0.9 % G2F+GN 3.6 %

A1F-Gal 0.5 % A1F-Gal 0.9 %

A1 2.1 % A1 6.4 %

A1F 0.7 % A1F 1.5 %

A1F+GN 4.3 % A1F+GN 6.3 %

A2 6.8 % A2 0.5 %

A2F 6.9 % A2F 5.2 %

A2F+GN 11.1 % A2F+GN 9.6 %


data.

Precision Day 2


G0 1.2 % 3.6 %

G0 1.7 % 3.9 %

G0F 1.5 % G0F 3.1 %

G0F+GN 2.1 % G0F+GN 2.1 %

G1-6 0.5 % G1-6 3.3 %

G1-3 0.0 % G1-3 4.3 %

G1F-6 1.1 % G1F-6 3.7 %

G1F-3 1.2 % G1F-3 2.0 %

G1F-6+GN 1.1 % G1F-6+GN 0.3 %

G1F-3+GN 3.1 % G1F-3+GN 2.1 %

G2 1.2 % G2 2.1 %

G2F 1.0 % G2F 1.9 %

G2F+GN 1.9 % G2F+GN 1.0 %

A1F-Gal 5.2 % A1F-Gal 0.9 %

A1 9.4 % A1 9.1 %

A1F 3.6 % A1F 5.9 %

A1F+GN 5.09 % A1F+GN 7.7 %

A2 10.3 % A2 6.3 %

A2F 6.8 % A2F 5.0 %

A2F+GN 11.7 % A2F+GN 10.9 %


36



data.

Precision Day 3


G0 1.5 % 3.1 %

G0 3.2 % 3.7 %

G0F 2.5 % G0F 2.1 %

G0F+GN 1.3 % G0F+GN 4.5 %

G1-6 1.0 % G1-6 4.0 %

G1-3 0.0 % G1-3 4.3 %

G1F-6 4.8 % G1F-6 2.5 %

G1F-3 0.9 % G1F-3 5.0 %

G1F-6+GN 0.9 % G1F-6+GN 1.8 %

G1F-3+GN 1.3 % G1F-3+GN 6.8 %

G2 1.8 % G2 3.2 %

G2F 3.0 % G2F 1.1 %

G2F+GN 2.6 % G2F+GN 2.2 %

A1F-Gal 3.1 % A1F-Gal 3.9 %

A1 6.3 % A1 4.5 %

A1F 6.1 % A1F 4.8 %

A1F+GN 4.6 % A1F+GN 4.1 %

A2 6.1 % A2 6.0 %

A2F 6.4 % A2F 3.2 %

A2F+GN 5.2 % A2F+GN 2.9 %

Table 12: Intermediate precision from all combined days, the relative standard deviations of all N-glycans for both the FLR and

MS data.

Intermediate precision


G0 3,7% 4,6% G0 10,8% 22,6%

G0F 2,0% G0F 11,6%

G0F+GN 1,7% G0F+GN 5,6%

G1-6 2,2% G1-6 13,3%

G1-3 3,1% G1-3 34,5%

G1F-6 2,8% G1F-6 5,3%

G1F-3 1,3% G1F-3 12,4%

G1F-6+GN 2,4% G1F-6+GN 5,1%

G1F-3+GN 8,1% G1F-3+GN 91,6%

G2 2,1% G2 6,3%

G2F 1,8% G2F 2,1%

G2F+GN 9,3% G2F+GN 10,0%

A1F-Gal 3,0% A1F-Gal 64,5%

A1 6,5% A1 20,4%

A1F 4,0% A1F 15,6%

A1F+GN 6,7% A1F+GN 21,1%

A2 7,2% A2 28,8%

A2F 7,1% A2F 34,8%

A2F+GN 12,3% A2F+GN 36,3%


37


Table 13: Robustness data of day 1 from the qualification, the relative standard deviations and accuracy of all N-glycans for each

IgG amount.

Robustness day 1 RSD & Accuracy

N-glycan 7.5µg IgG RSD 7.5µg IgG

Accuracy

15µg IgG RSD

(standard)

30µg IgG RSD 30µg IgG

Accuracy

G0 2.5 % 110 % 0.8 % 10.6 % 112 %

G0F 2.0 % 104 % 0.9 % 1.9 % 102 %

G0F+GN 2.2 % 100 % 1.2 % 1.5 % 101 %

G1-6 2.9 % 101 % 0.9 % 3.5 % 99 %

G1-3 5.0 % 102 % 0.0 % 3.8 % 99 %

G1F-6 1.3 % 101 % 1.1 % 1.3 % 103 %

G1F-3 1.3 % 101 % 1.0 % 2.7 % 103 %

G1F-6+GN 2.3 % 101 % 2.3 % 0.6 % 99 %

G1F-3+GN 2.1 % 106 % 2.0 % 3.1 % 97 %

G2 2.6 % 102 % 0.7 % 6.5 % 97 %

G2F 2.7 % 93 % 1.6 % 3.2 % 97 %

G2F+GN 1.5 % 105 % 0.9 % 0.7 % 98 %

A1F-Gal 2.7 % 99 % 0.5 % 8.8 % 99 %

A1 8.0 % 93 % 2.1 % 17.3 % 84 %

A1F 6.5 % 96 % 0.7 % 1.7 % 95 %

A1F+GN 4.8 % 99 % 4.3 % 5.3 % 97 %

A2 7.3 % 95 % 6.8 % 2.4 % 95 %

A2F 6.2 % 94 % 6.9 % 3.5 % 98 %

A2F+GN 9.9 % 97 % 11.1 % 6.9 % 95 %


deglycosylation temperature.


N-glycan 80˚C RSD 80˚C Accuracy 90˚C RSD (standard) 100˚C RSD 100˚C Accuracy

G0 4.3 % 100 % 1.2 % 7.0 % 96 %

G0F 3.9 % 99 % 1.5 % 8.3 % 97 %

G0F+GN 4.3 % 98 % 2.1 % 6.3 % 96 %

G1-6 2.2 % 101 % 0.5 % 7.1 % 101 %

G1-3 1.4 % 102 % 0.0 % 5.7 % 101 %

G1F-6 2.1 % 100 % 1.1 % 3.7 % 100 %

G1F-3 1.4 % 102 % 1.2 % 2.3 % 102 %

G1F-6+GN 1.9 % 96 % 1.1 % 3.3 % 96 %

G1F-3+GN 1.1 % 107 % 3.1 % 5.1 % 106 %

G2 0.7 % 102 % 1.2 % 0.7 % 104 %

G2F 2.2 % 98 % 1.0 % 4.9 % 99 %

G2F+GN 2.5 % 109 % 1.9 % 4.8 % 108 %

A1F-Gal 4.2 % 99 % 5.2 % 4.3 % 103 %

A1 7.2 % 99 % 9.4 % 8.8 % 104 %

A1F 8.5 % 100 % 3.6 % 14.3 % 104 %

A1F+GN 12.2 % 104 % 5.1 % 20.9 % 105 %

A2 14.0 % 98 % 10.3 % 23.5 % 104 %

A2F 12.8 % 103 % 6.8 % 25.3 % 108 %

A2F+GN 13.6 % 106 % 11.7 % 29.3 % 111 %


38



drug formulation.


N-glycan Water RSD

(standard)

PBS RSD PBS

Accuracy

Buffer A

RSD

Buffer A

Accuracy

Buffer B

RSD

Buffer B

Accuracy

G0 1.5 % 1.9 % 101 % 3.5 % 97 % 7.0 % 101 %

G0F 2.5 % 3.2 % 100 % 1.6 % 97 % 2.2 % 101 %

G0F+GN 1.3 % 1.3 % 98 % 1.8 % 96 % 4.7 % 98 %

G1-6 1.0 % 1.9 % 96 % 2.8 % 95 % 4.3 % 98 %

G1-3 0.0 % 3.0 % 101 % 4.2 % 96 % 5.1 % 103 %

G1F-6 4.8 % 2.4 % 105 % 1.3 % 102 % 6.2 % 97 %

G1F-3 0.9 % 0.8 % 100 % 0.5 % 97 % 1.8 % 99 %

G1F-6+GN 0.9 % 0.9 % 100 % 1.9 % 99 % 3.7 % 102 %

G1F-3+GN 1.3 % 2.4 % 90 % 3.1 % 90 % 3.8 % 92 %

G2 1.8 % 1.8 % 102 % 3.1 % 98 % 3.6 % 102 %

G2F 3.0 % 0.5 % 102 % 0.5 % 103 % 1.9 % 99 %

G2F+GN 2.6 % 2.9 % 94 % 1.5 % 95 % 3.0 % 97 %

A1F-Gal 3.1 % 4.0 % 103 % 1.1 % 104 % 2.4 % 105 %

A1 6.3 % 4.5 % 101 % 2.9 % 103 % 1.1 % 106 %

A1F 6.1 % 5.0 % 99 % 1.2 % 104 % 1.3 % 105 %

A1F+GN 4.6 % 9.4 % 90 % 2.0 % 104 % 1.5 % 104 %

A2 6.1 % 12.3 % 91 % 2.0 % 108 % 1.1 % 109 %

A2F 6.4 % 12.5 % 82 % 1.6 % 107 % 1.0 % 108 %

A2F+GN 5.2 % 15.6 % 68 % 0.8 % 105 % 2.9 % 105 %

Table 16: Robustness data of day 1 from the qualification, the N-glycan profile of IgG for each tested IgG amount.

Robustness day 1 N-glycan profile

N-glycan 15µg IgG 7.5µg IgG 30µg IgG

G0 0.8 % 0.8 % 0.9 %

G0F 22.3 % 23.2 % 22.7 %

G0F+GN 4.2 % 4.2 % 4.3 %

G1-6 1.2 % 1.2 % 1.2 %

G1-3 0.4 % 0.4 % 0.4 %

G1F-6 21.1 % 21.2 % 21.6 %

G1F-3 9.6 % 9.7 % 9.9 %

G1F-6+GN 5.5 % 5.6 % 5.5 %

G1F-3+GN 1.0 % 1.1 % 1.0 %

G2 0.9 % 0.9 % 0.8 %

G2F 14.0 % 13.1 % 13.6 %

G2F+GN 2.4 % 2.5 % 2.3 %

A1F-Gal 2.3 % 2.3 % 2.3 %

A1 0.6 % 0.5 % 0.5 %

A1F 7.6 % 7.3 % 7.2 %

A1F+GN 2.2 % 2.2 % 2.1 %

A2 0.4 % 0.4 % 0.4 %

A2F 1.5 % 1.4 % 1.4 %

A2F+GN 2.1 % 2.1 % 2.0 %


39


Table 17: Robustness data of day 2 from the qualification, the N-glycan profile of IgG for each deglycosylation temperature.


N-glycan 90˚C 80˚C 100˚C

G0 0.8 % 0.8 % 0.8 %

G0F 23.0 % 22.9 % 22.3 %

G0F+GN 4.3 % 4.2 % 4.2 %

G1-6 1.1 % 1.1 % 1.2 %

G1-3 0.4 % 0.4 % 0.4 %

G1F-6 21.8 % 21.8 % 21.8 %

G1F-3 9.6 % 9.9 % 9.9 %

G1F-6+GN 5.4 % 5.2 % 5.2 %

G1F-3+GN 0.9 % 0.9 % 0.9 %

G2 0.9 % 0.9 % 0.9 %

G2F 14.2 % 13.9 % 14.0 %

G2F+GN 1.9 % 2.1 % 2.0 %

A1F-Gal 2.3 % 2.3 % 2.4 %

A1 0.5 % 0.5 % 0.5 %

A1F 7.6 % 7.6 % 7.9 %

A1F+GN 1.9 % 2.0 % 2.0 %

A2 0.5 % 0.4 % 0.5 %

A2F 1.3 % 1.4 % 1.5 %

A2F+GN 1.7 % 1.8 % 1.9 %

Table 18: Robustness data of day 3 from the qualification, the N-glycan profile of IgG for each drug formulation.


N-glycan Water PBS 7.4 Buffer A Buffer B

G0 0.8 % 0.8 % 0.8 % 0.8 %

G0F 22.4 % 22.4 % 21.7 % 22.7 %

G0F+GN 4.3 % 4.2 % 4.1 % 4.2 %

G1-6 1.1 % 1.1 % 1.1 % 1.1 %

G1-3 0.4 % 0.4 % 0.4 % 0.4 %

G1F-6 21.3 % 22.5 % 21.8 % 20.6 %

G1F-3 9.8 % 9.8 % 9.5 % 9.7 %

G1F-6+GN 5.3 % 5.3 % 5.2 % 5.4 %

G1F-3+GN 0.9 % 0.8 % 0.8 % 0.8 %

G2 0.8 % 0.9 % 0.8 % 0.9 %

G2F 14.0 % 14.3 % 14.4 % 13.8 %

G2F+GN 2.1 % 2.0 % 2.0 % 2.0 %

A1F-Gal 2.3 % 2.3 % 2.4 % 2.4 %

A1 0.5 % 0.5 % 0.5 % 0.5 %

A1F 7.9 % 7.9 % 8.2 % 8.3 %

A1F+GN 2.1 % 1.9 % 2.2 % 2.2 %

A2 0.5 % 0.4 % 0.5 % 0.5 %

A2F 1.5 % 1.2 % 1.6 % 1.6 %

A2F+GN 2.0 % 1.4 % 2.1 % 2.1 %


40


Figure 32: Comparison of PTS samples with and without sample treatment to provide an assessment of the accuracy. The sample

preparation procedure comprised of the following steps: deglycosylation, RFMS labeling and clean-up. The samples without

sample preparation were directly injected into the LC. The error bars were added for each N-glycan and visualizes the standard

deviation of the triplicate measurements.

Figure 33: Comparison of the N-glycan profiles from different IgG amounts (7.5, 15, 30µg). The error bars were added for each

N-glycan and visualizes the standard deviation of the triplicate measurements.


41


Figure 34: Comparison of the N-glycan profiles from the different deglycosylation temperatures (80, 90 and 100˚C). The error

bars represent the standard deviation of the triplicate measurements.

Figure 35: Comparison of the N-glycan profiles for the different drug formulations: water, PBS, buffer A and buffer B. The error

bars were added for each N-glycan and visualizes the standard deviation of the triplicate measurements.


42


Table 19: The N-glycan nomenclature, structure and theoretical mass of each N-glycan (without RFMS label) within the library

Genmab

name

N-Glycan composition Oxford

annotation

Structure Monoisotopic

Mass (g/mol)

G0 GlcNAc4Man3 A2

1316.487

G0F Fuc1GlcNAc4Man3 FA2

1462.544

G1-3 Gal1GlcNAc4Man3 A2G1

1478.539

G1-6 Gal1GlcNAc4Man3 A2G1

1478.539

G1F-3 Fuc1Gal1GlcNAc4Man3 FA2G1

1624.597

G1F-6 Fuc1Gal1GlcNAc4Man3 FA2G1

1624.597

G2 Gal2GlcNAc4Man3 A2G2

1640.592

G0F+GN Fuc1GlcNAc5Man3 FA2B

1665.624

G2F Fuc1Gal2GlcNAc4Man3 FA2G2

1786.650

G1F-3+GN Fuc1Gal1GlcNAc5Man3 FA2BG1

1827.677

G1F-6+GN Fuc1Gal1GlcNAc5Man3 FA2BG1

1827.677

A1F-Gal Fuc1Gal1GlcNAc4Man3NeuAc1 FA2G1S1

1915.693

A1 Gal2GlcNAc4Man3NeuAc1 A2G2S1

1931.688

G2F+GN Fuc1Gal2GlcNAc5Man3 FA2BG2

1989.729

A1F Fuc1Gal2GlcNAc4Man3NeuAc1 FA2G2S1

2077.745

A2 Gal2GlcNAc4Man3NeuAc2 A2G2S2

2222.783

A1F+GN Fuc1Gal2GlcNAc5Man3NeuAc1 FA2BG2S1

2280.825

A2F Fuc1Gal2GlcNAc4Man3NeuAc2 FA2G2S2

2368.841

A2F+GN Fuc1Gal2GlcNAc5Man3NeuAc2 FA2BG2S2

2571.920


43


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cerebral cortex. Glycoconj J, 2014. 31(9): p. 671-83.

27. Zamze, S., et al., Sialylated N-glycans in adult rat brain tissue--a widespread distribution of

disialylated antennae in complex and hybrid structures. Eur J Biochem, 1998. 258(1): p. 243-70.

28. Reed, C.E., et al., Automated Preparation of MS-Sensitive Fluorescently Labeled N-Glycans with

a Commercial Pipetting Robot. SLAS Technol, 2018. 23(6): p. 550-559.

29. Zhao, J., et al., Identification of Low Abundant Isomeric N-Glycan Structures in Biological

Therapeutics by LC/MS. Anal Chem, 2016. 88(14): p. 7049-59.

9. Additional resources and references

Table 20: The glycan application notebook contains all application notes and care/user manuals from Waters that are used for

information, with respect to the released glycan section.

Ref. Number Additional references Literature code

[30] ICH guidelines CPMP/ICH/381/95

[31] Glycan application notebook 720005532EN

[32] Acquity Premier Glycan BEH C18 AX 720007065EN

[33] Positive pressure processor 720003453EN

Table 21: Experimental codes that belongs to the used experiments for the qualification and optimization of the protocol. For each

experiment number the corresponding section is indicated.

Experiment code Section

201201_AS_0042 4.1

201228_AS_0010 4.2-4.5

201228_AS_0011 4.4+4.5

201228_AS_0012 4.4+4.5

201109_AS_0017 7.1


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10. Acknowledgements

After this period of research and studying the chapter of being a student will be closed. To look back on

this amazing period I would like to thank my family for the support during this time. Of course, I am also

very thankful to the people from both Universities of Amsterdam and Genmab, especially the supervisors

and examiners which were involved in my projects. The next chapter will be a PhD at the University of

Wageningen with many challenges ahead and may this end be a great start of what is coming next in life.

analytical sciences research project

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