Vrije Universiteit Amsterdam

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1 Vrije Universiteit Amsterdam MSc Chemistry Analytical Sciences Literature thesis Separation techniques for the quantification of protein aggregates by Debbie van der Burg October ECT Supervisor: Prof. Dr. G.W. Somsen 2 nd Examiner: Henk Lingeman

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3 Debbie van der Burg i

4 Abstract Biopharmaceuticals are an upcoming field in the pharmaceutical industry. Biopharmaceuticals consist of proteins, which have the tendency to aggregate. The presence of protein aggregates has shown to cause adverse effects and safety issues. Detection of protein aggregates is crucial in order to ensure safety, efficacy, and quality of the drug product. For the quantification of protein aggregates, several techniques could be applied. In some cases, for example for heterogenous samples, separation is required for accurate quantification. The main challenge during protein aggregate quantification is that factors related to separation techniques could disrupt the aggregate distribution, resulting in unreliable results. These factors include dilution, shear stress, change of solvent conditions, adsorption to surfaces, and physical filtration. This literature thesis focusses on separation techniques that could be applied for the quantification of protein aggregates without disrupting the higher order structure. These techniques include size exclusion chromatography (SEC), asymmetric flow field-flow fractionation (AF4), analytical ultracentrifugation (AUC), disk centrifugation or differential centrifugal sedimentation (DCS), capillary gel electrophoresis (CGE), and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The advantages, limitations, and applications of these techniques are discussed in this thesis. Debbie van der Burg ii

5 List of abbreviations Abbreviation AF4 AFM AUC CD CE CGE DCS DLS EM ESI FDA FT-IR LO MALS MALDI ME MS NMR OM SDS SDS-PAGE SE SEC SLS SV TP UV Meaning Asymmetric flow field-flow fractionation Atomic force microscopy Analytical ultracentrifugation Circular Dichroism Capillary electrophoresis Capillary gel electrophoresis Differential centrifugal sedimentation Dynamic light scattering Electron microscopy Electrospray ionization Food and Drug Administration Fourier transform infrared Light obscuration Multi angle light scattering Matrix assisted laser desorption ionization Maximum entropy Mass spectrometry Nuclear magnetic resonance Optical microscopy Sodium dodecyl sulfate Sodium dodecyl sulfate polyacrylamide gel electrophoresis Sedimentation equilibrium Size exclusion chromatography Static light scattering Sedimentation velocity Tikhonov-Phillips Ultraviolet Debbie van der Burg iii

6 CONTENTS 1 Introduction Protein aggregation Pathways Classification Influencing factors and analysis Non-separation techniques Turbidity Light scattering Light obscuration Particle counters Microscopic techniques Spectroscopic techniques Separation techniques Size exclusion chromatography Asymmetric flow field-flow fractionation Centrifugation Analytical ultracentrifugation Disk centrifugation Electrophoresis SDS-PAGE Capillary gel electrophoresis Hyphenation separation - detection Light scattering Multi-angle light scattering Dynamic light scattering...43 Debbie van der Burg iv

7 5.2 Electrospray Ionization Mass Spectrometry Technique evaluation Discussion and comparison Case studies involving separation techniques Case study 1: Antibody analysis with SEC, AF4 and SV-AUC Case study 2: AF4 vs SEC for the analysis of submicron aggregates Case study 3: SDS-PAGE vs CGE for the analysis of stressed IgG samples Conclusion References...56 Debbie van der Burg v

8 1 Introduction Protein-based pharmaceutical products, or biopharmaceuticals, have become an important factor in the pharmaceutical industry. The availability of different classes of biopharmaceuticals, such as antibodies, hormones, and enzymes, provides useful tools in a number of treatments for human diseases. Including treatments for several types of cancer, autoimmune and inflammatory diseases, and metabolic disorders [1], [2]. Non-human proteins in biopharmaceuticals could be recognized as foreign by the immune system, and provoke an immune response. This could be dangerous for the patient. In order to reduce immunogenicityrelated problems, recombinant human proteins, which are believed not to evoke an immune response, are used. Despite the high quality of biopharmaceuticals and the use of recombinant human proteins, immunogenicity remains an important concern [1] [3]. It is a key limitation for the clinical use of biopharmaceuticals since it causes adverse events such as neutralization of endogenous protein or reduced efficacy [2], [4]. Proteins can assemble into large aggregates which do not have the same properties as the native protein. Protein aggregation is a general term for the self-association of proteins into assemblies other than the native quaternary structure [2], [3]. It is believed that protein aggregates are more easily recognized by the immune system than the native protein. It is generally recognized that the presence of aggregates is one of the main risk factors for inducing immune responses [1] [3]. Biopharmaceuticals have proven to be effective in treating a wide range of diseases. Over the past years, the number of biopharmaceuticals has been growing. At present, about 100 different biopharmaceuticals have been approved for clinical use by the Food and Drug Administration (FDA) [5]. The presence of protein aggregates is a major drawback of the use of biopharmaceuticals and can directly be related to bioavailability, drug efficacy, shelf life, and potential negative side effects like adverse immune responses [6]. Immune responses caused by protein aggregation cause problems for both patient safety and product efficacy. Loss of efficacy is problematic for all biopharmaceuticals, especially if the product is lifesaving. Safety consequences of immunogenicity vary widely and are often unpredictable. A few safety concerns associated with immunogenicity are anaphylaxis, cytokine release syndrome, infusion reactions, non-acute reactions, and cross-reactivity to endogenous proteins [7]. In order to ensure safety, efficacy, and quality, the early detection and characterization of protein aggregates are critical [8]. The characterization of protein aggregates could also help Debbie van der Burg 1

9 for developing strategies for aggregation inhibition. Different techniques are utilized for the quantification and characterization of protein aggregates. Techniques such as visible or microscopic inspections, spectroscopic techniques, and light scattering techniques are often used for this purpose. These techniques do not separate the protein aggregates from the monomer species or from other analytes. Since these techniques measure an average value of all protein aggregates in solution, little information is provided about solutions containing both small and large aggregates. For these solutions, separation is required for the quantification of protein aggregates. Several separation techniques are utilized for the analysis of protein aggregates. Mobile phase and column interactions, dilution and the sample preparation required, however, could alter the protein aggregate conformation and distribution. Separation techniques could, therefore, provide unreliable results. This literature thesis will focus on the possibilities to use separation techniques for the quantification and characterization of intact protein aggregates, without disrupting the higher order structure during analysis. 2 Protein aggregation 2.1 Pathways Proteins consist of polypeptide chains which are folded into a three-dimensional structure. Many different types of forces are involved in protein folding. These include hydrophobic and electrostatic interactions, covalent bonding, hydrogen bonding, and van der Waals forces. The hydrophobic residues are folded to the inside of the protein, whereas the hydrophilic residues are directed to the outside of the protein. Stress conditions that upset the balance of the forces folding the protein lead to conformational change. When hydrophobic residues are directed to the outside of the protein after a conformational change, the solubility decreases and proteins tend to aggregate [9]. Various modification reactions such as deamidation, oxidation, isomerization, and peptide bond cleavage negatively affect the conformational stability. Stress conditions such as temperature fluctuations, light, shaking, and ph adjustments can induce protein aggregation during each of these stages also affect conformation stability and induce aggregation [3]. Protein aggregates can form throughout the life cycle of a drug product, including production, storage, handling and delivery of the drug product [3]. Protein aggregates are formed via a diversity of pathways depending on the environmental conditions and the initial state of the protein (native, degraded or unfolded state) [4]. The major Debbie van der Burg 2

10 pathways are aggregation through unfolding intermediates and unfolded states, aggregation through protein self-association or chemical linkage, and aggregation through chemical degradation [10]. Usually, native proteins in solution are in equilibrium with a small amount of unfolding intermediates and completely unfolded or denatured proteins. These small amounts of unfolding intermediates are suggested to be precursors for the aggregation process. This could be explained by the higher number of hydrophobic residues directed toward the outside and the larger flexibility of the unfolding intermediates. Interaction between these intermediates leads to the formation of protein aggregates. Completely folded or unfolded proteins, in contrast, do not aggregate easily since the hydrophobic side chains are either mostly buried out of contact with water or randomly scattered. Proteins can also directly associate into protein aggregates from the native state. This self-association can be caused by just electrostatic or both electrostatic and hydrophobic interactions depending on the experimental conditions. Other weak forces such as van der Waals forces could also initiate self-association. Self-association often leads to reversible aggregates, which can be precursors of irreversible aggregates. Protein aggregation could also be initiated by chemical degradations which directly crosslink protein chains. The most common cross-linking reaction the disulfide bond/exchange. Other chemical degradation pathways can also induce protein aggregation. These pathways include oxidation, dimerization, deamidation, hydrolysis, and glycation. These chemical degradations often cause changes to the physical properties of the protein, such as protein hydrophobicity or association tendency, secondary/tertiary structures, and the thermodynamic/kinetic barrier to protein unfolding [10]. Figure 1: Schematic model of protein aggregation [2]. Initially formed aggregates are small, but they gradually become larger. Proteins associate to form dimers or oligomers. The formation of oligomers could lead to the formation of larger aggregates. Linear aggregates form when proteins associate uniformly, whereas amorphous Debbie van der Burg 3

11 aggregates form by the association of proteins in a disordered manner. Eventually, the aggregates become insoluble, visible aggregates [2], [10]. 2.2 Classification Protein aggregates can be formed via a variety of pathways. This results in a wide size range and a large diversity of protein aggregates, which makes classification difficult. Protein aggregates can be categorized into several types according to their characteristics [2], [6]. At the moment, there is no unified system to define aggregates. The imprecise terms to describe aggregates leads to confusion and is a bottleneck in the comparison of results across labs and organizations. Therefore, one system to classify protein aggregates should be used. Several publications classified protein aggregates on conformation, linkage, reversibility and size [4], [11] [13]. In the category size, aggregates are often classified as soluble aggregates, oligomers, subvisible aggregates and visible aggregates. The interpretation of this classification could lead to confusion, and therefore the quantitative classification of Narhi et al. [12] is recommended. In this quantitative classification aggregates are classified as nanometer (< 100 nm), submicron ( nm), micron (1-100 µm), and visible (> 100 µm) aggregates. Aggregates could be classified as reversible or irreversible. Reversible aggregates reverse to the lower molecular weight state when the solution condition that initiated aggregation (e.g., protein concentration, ph, temperature, or presence of a co-solute) is removed. The proteins in reversible aggregates retain their native structure except for the part contributing to the bonds. Irreversible aggregates do not reverse to the lower molecular weight state when the condition that initiated aggregation is removed [6], [12]. Aggregates that do not reverse to the monomeric state upon returning to the original solution condition, while they do return to the monomeric state upon manipulation of the solution conditions in other ways, can be classified as dissociable aggregates [12]. The conformation of the proteins in the aggregate can be classified as native or non-native. In native aggregates, the higher-order structure of the monomeric unit is largely retained. Non-native aggregates could be divided into the subcategories partially unfolded, misfolded, inherently disordered, unfolded, or amyloid. Aggregates can either be covalently or non-covalently linked. Non-covalent aggregates are formed via weak forces such as hydrophobic, electrostatic, van der Waals or hydrogen-bond mediated interactions whereas covalent aggregates are formed as a result of chemical bonds such as disulfide linkage [4], [6]. Narhi et al. [12] also added morphology as a fifth category. This category includes characteristics such as aspect ratio, surface roughness, how regular or amorphous the structure appears and whether it is a fiber or a sphere. The most common forms of aggregates observed in biopharmaceuticals are Debbie van der Burg 4

12 nanometer or small submicron, irreversible aggregates. The linkage could either be covalent or non-covalent, and they could have native or non-native conformation [11]. 2.3 Influencing factors and analysis This classification of proteins aggregates is not as black and white as it is stated. The reversibility of protein aggregates, for example, is actually a continuum of states between reversible and irreversible. The reversibility can be affected by solvent components, such as salts, sugars, organic modifiers, and other excipients, ph, temperature and time. In addition, reversible aggregates have a broad spectrum of lifetimes. For many techniques, especially for separation techniques, the time taken for measurement may be greater than the lifetime of a reversible aggregate. Therefore often only the longer-lived aggregates are detected [13]. When rapidly reversible self-association proteins are analyzed with a separation technique, there is a constant battle between separation and re-equilibration. The results then often depend on the rates of the association-dissociation reactions and the equilibrium constants, making analysis very complex. When separating protein aggregates with slower associationdissociation rates than the physical separation rate, resolved peaks often represent a dynamic mixture of multiple aggregate species instead of a pure, individual aggregate species. Separating protein aggregates with very slow association-dissociation reactions compared to the time scale of the separation results in resolved individual aggregate peaks [13]. The wide size range, the broad diversity and the changes to protein aggregates make analysis difficult. These factors impact the measurement approach and the proper interpretation of the data. It is impossible to have one analytical technique or approach that will provide complete answers, and that will work in all situation and products [13]. Many factors play a role in affecting protein aggregation, including temperature, ph, ionic strength, surface adsorption, shearing, shaking, the presence of metal ions, organic solvents and additives, protein concentration, purity and morphism, pressure, freezing and drying [14]. This means sample preparation and handling should be considered carefully. Also, analysis techniques, especially separation techniques, involve factors that can disrupt the distribution of aggregate species. These factors include dilution, change of solvent conditions, adsorption to surfaces, physical filtration or disruption, concentration on surfaces and shear [13]. The difficulty in using separation techniques is that the time taken for measurement may be greater than the lifetime of a reversible aggregate, or the measurement techniques themselves Debbie van der Burg 5

13 may destroy or create further aggregates. The latter is one of the most critical problems in analyzing protein aggregates. Therefore, it is important to use multiple, orthogonal techniques. 3 Non-separation techniques Several non-separation techniques are frequently used for the detection of protein aggregation. These techniques often do not induce conformational changes of protein aggregates and are therefore well suited for the qualitative analysis. However, these techniques measure an average of all species in solution and therefore provide little information about samples containing both small and large protein aggregates. For completeness, a short overview of these techniques is given below. 3.1 Turbidity Turbidity is a measure of light transmitted through a sample solution [15]. Large particles, such as protein aggregates, scatter light more than smaller particles. This reduces the amount of transmitted light. Therefore, the formation of protein aggregates or particles in solution leads to an increase in turbidity. The turbidity is either quantified by determination of the loss of light of the transmitted beam or by the intensity of the scattered light at a given angle. Better sensitivity can be obtained with light scattering since only a few particles are required to detect scattered light, while a large number of particles is needed to achieve a significant reduction in the transmitted light [9]. A UV-Vis spectrometer in the wavelength range of nm can be used to measure the transmitted light [15]. Turbidity measurements are nonspecific and do not provide information about size, concentration, or nature of protein aggregates. Nonetheless, it is highly useful for relative comparison of samples. Because of its high sensitivity for small aggregates, turbidity is capable of detecting the formation of particles in solution early during stability studies [15]. Figure 2: Schematic illustration of a turbidity measurement setup Debbie van der Burg 6

14 3.2 Light scattering Light scattering techniques are used for the sensitive detection of aggregates in solution and an estimation of the size of the protein aggregates. Light scattering can be categorized into two techniques: static light scattering (SLS) and dynamic light scattering (DLS). These two techniques provide very different characteristics of aggregates in solution [6]. Multi-angle light scattering The most used form of SLS is multi-angle light scattering (MALS). In MALS, the intensity of the scattered light of molecules in solution is used to determine the size. When a macromolecule is irradiated with laser light, the oscillating electric field of the light induces an oscillating dipole within the molecule. This oscillating dipole will re-radiate light. The intensity of the scattered light and the molecular weight of the molecule are directly related through the Rayleigh equation. This equation could be simplified when the intensity of the scattered light is measured at a 0 angle to the incident light beam. However, at this angle the scattered light and the incident beam are indistinguishable. In MALS, the light intensity is measured at multiple angles around the molecule to build a model of the scattered light as a function of angle. This model could be used to extrapolate to a value for the scattered light intensity at 0. The number of angles typically varies between 2 and 20 angles, where the intensity is detected simultaneously at each angle [16], [17]. Measuring at multiple angles leads to more precise estimates of molecular mass, size, conformation, and shape. Figure 3: Schematic illustration of a MALS measurement setup [18] Debbie van der Burg 7

15 Dynamic light scattering Dynamic light scattering (DLS) is a non-invasive technique which uses light scattering for the analysis of the particle size. In solution, particles undergo Brownian motion. When a solution is illuminated with a laser beam, light scatters from the moving molecules. The Brownian motion imparts a randomness to the phase of the scattered light. Addition of the scattered light from multiple particles creates a changing destructive or constructive interference. This results in time-dependent fluctuations in the intensity of the scattered light [19, 20]. These time-dependent fluctuations are directly related to the rate of diffusion of the molecules, which can be determined by a correlation function. As the particles move in the solvent, the intensity graph changes and the correlation between these graphs decays. The time at which the correlation starts to decay, indicates the diffusion coefficient, which is related to the particle s hydrodynamic radius by the Stokes-Einstein equation [19], [20]. The diffusion, however, is affected by water molecules, which may be encapsulated around the protein. Furthermore, the hydrodynamic radius depends on their shape and molecular mass. Therefore, the hydrodynamic radius may differ significantly from their true physical size and is not a reliable measure of the molecular mass. Figure 4: Left: Intensity graph at time point zero compared with graphs at later time points. Right: Correlation curve. An advantage of DLS is the minimal sample preparation, typically, samples can be placed in a clear glass tube or cuvette, and direct measurements can be made to assess the size distribution. The distribution is intensity weighted but can be converted to a weight weighted Debbie van der Burg 8

16 distribution or other types of distributions. In this conversion, assumptions are built in. Therefore, data should be interpreted with caution. Since the measurement is done directly on the sample, DLS is capable of following aggregation kinetics as the reaction proceeds. Nowadays, instruments are available that can perform high throughput measurements with the aid of 96- well plates. Originally scattered light is collected at a 90 angle. However, instruments that allow analysis at higher angles, or in back-scattering mode are now available, allowing analysis of relatively high concentrations or turbid samples [6]. DLS carried out in batch mode suffers from limitations as interference from dust particles, air bubbles, and other larger impurities. The data is intensity weighted, thus larger particles tend to affect the size distribution. This could be avoided using sample filtration, however, there is a risk of also removing larger aggregates. The capability to resolve various size distributions depends on the size range of interest. DLS might not be able to resolve dimer and trimers from the monomer. Turbid samples or samples with high concentrations of protein may cause a loss of intensity due to the inner filter effect [6]. Both MALS and DLS provide the average molecular mass for all molecules in solution. 3.3 Light obscuration Light obscuration (LO) is a technique used for the analysis of micron aggregates [21], [22] and is the primary method described in the current pharmacopeias Ph. Eur and US pharmacopeia <788>. In an LO measurement, a dilute sample is drawn into the system through a syringe. The sample passes between a laser and a detector. As particles pass the laser beam, they block a part of the light, producing a shadow on a light-sensitive detector. Using a calibration curve, the area of the produced shadow can be converted into the equivalent circular diameter of the particle [21] [23]. Figure 5: Particles passing through the laser beam and the detector in a light obscuration device. Debbie van der Burg 9

17 3.4 Particle counters Aggregation could eventually lead to the formation of insoluble particles. Detection of these particles could be conducted by particle counters. Particle counters use electrical-sensing zone analysis to detect the number of particles present in a solution. This measures changes in electrical impedance of an electrolyte produced as non-conductive particles pass through a small opening between two electrodes. Each particle produces a pulse of voltage, the size of which is considered to be dependent on particle volumetric size. However, this takes no account of shape or changes in resistivity between different particles. This technique has been used extensively to count particles ( µm) in the pharmaceutical industry, especially to monitor the quality of intravenous solutions. The technique is limited by the smallest available opening between the electrodes. This sets the lower limit in the size of particles that can be counted and leads to difficulty counting particles smaller than 2 µm [9]. 3.5 Microscopic techniques For the detection of particles several microscopic techniques could be utilized. Optical microscopy (OM) can be used to magnify visible particles up to a level where regular structures can be observed. It is a quick and easy technique for comparing samples by observing gross changes between them and by counting the number of particles seen in a specific area [9]. Electron microscopy (EM) is used to observe objects down to a few angstroms by using the shorter wavelength of the electron. Sample preparation is difficult in EM, and the conditions required are extreme. Samples are often frozen in liquid nitrogen, coated with metal vapor and observed in a vacuum. This could cause artefactual results [9]. Atomic force microscopy (AFM) utilizes a very fine stylus (5-60 nm tip radius) mounted on a short cantilever, typically µm in length, to systematically scan across a surface of interest. As the tip moves, it interacts with any molecular features on the surface and is deflected accordingly. These signals are amplified by corresponding deflections of a laser beam reflected off the surface of the probe and are converted into height signals by photoelectric circuitry. These signals are then processed to create a digital height map of the surface, which can be presented in a number of different formats. The low spring constant (<1N/m) of the cantilever minimizes the force acted upon the surface, but this may still be too much for a biological surface, leading to damage and a distorted image of the surface. However, this can Debbie van der Burg 10

18 be overcome using an AFM in tapping mode, where the probe is not in continuous contact with the surface but instead gently oscillates up and down, as it scans. AFM can scan fields from less than 20 nm up to 150 µm, with a spatial resolution of approximately 1 nm and a height resolution of 0.1 nm [9] 3.6 Spectroscopic techniques Structural analysis is carried out with several spectroscopic techniques. Aggregation of proteins often involve a change in the secondary structure. Characterizing the secondary structure could provide information about the mechanism of aggregation and the conformation of the protein aggregates [24]. Techniques that provide information on the secondary structure are nuclear magnetic resonance, circular dichroism, Raman spectroscopy, and Fourier transform infrared spectroscopy [11], [24]. These spectroscopic techniques make use of electromagnetic radiation to measure a quantity as a function of wavelength. The results are usually in the form of a spectrum of the response of the quantity at varying wavelengths and show an average of the entire molecular population [24]. Nuclear magnetic resonance (NMR) can provide detailed structural information about macromolecules at atomic resolution and helps to elucidate the compound s structure. It makes use of the atoms property to absorb electromagnetic fields and radiate the energy back out at frequencies specific for the strength of the field and the atom. With NMR, the magnetic properties of atomic nuclei, such as hydrogen, are analyzed to determine different local environments [24]. The chemical shifts observed with NMR can be used to identify regions of the secondary structure of the protein [25]. Circular Dichroism Circular Dichroism (CD) measures the difference in absorption of right-handed and left-handed circularly polarized light by chiral molecules. CD may arise from inherently asymmetric groups, such as the chiral a-carbon of the peptide bond. In the far UV spectrum ( nm), the contribution of peptide bonds to CD is dominant. As the CD band position and intensity of peptide bonds depend on their conformation, far UV CD spectroscopy allows for the assessment of the secondary structure of proteins. In the near UV CD region ( nm), only the aromatic residues and cystine absorb light. The near UV part of the spectrum provides information about the tertiary structure of a protein. The characterization of the secondary and tertiary structure allows the study of the conformational stability of protein [24] [27]. Debbie van der Burg 11

19 Raman spectroscopy is used to observe vibrational modes and is based on inelastic Raman scattering. Molecules illuminated by a laser, absorb a photon and transit to a virtual state between the ground state and the electronic excited state. The molecule and photon exchanged energy and the molecule emits a photon of lower energy than the absorbed photon, called Stokes-Raman scattering. When the molecule is in a vibrational state upon excitation, the photon can gain energy from the molecule, resulting in an emitted photon of higher energy. This is called anti-stokes Raman scattering, however, the intensity of this signal is much weaker than that of the Stokes Raman since a much smaller population of the molecules is in a vibrational state. Molecular groups rich in π-electrons, such as C=C, S-S, C-S, and S-H groups, give Raman signal. Raman spectroscopy can determine the secondary structure characteristics. Information about the secondary structure could be obtained from Raman shifts in specific spectral bands. These specific bands are the Amide I, Amide III, and the skeletal stretching mode. The Amide I band from ~1600 cm -1 to 1700 cm-1 is mainly caused by the C=O stretch vibrations combined with a small amount of C-N stretch vibrations. The Amide III band from ~1200 cm-1 to 1340 cm-1, is a result of the coupling of the C-N stretch and the N-H bend vibrations. The shapes of these bands change when the secondary structure changes. The N-Cα-C skeletal stretching mode, which has a band from ~930 cm -1 to 950 cm -1, is indicative of α-helix content. Table 1 summarizes these characteristic bands. Table 1: Correlation between Raman shift and protein secondary structure [28]. Figure 6 shows the Raman spectra of bovine serum albumin (BSA) before and after thermal stress. The characteristic bands for the skeletal stretching mode, Amide I, and Amide III are highlighted in this figure. The spectra show a clear transition from an α-helix rich structure into a β-sheet abundant structure. Debbie van der Burg 12

20 Figure 6: The Raman spectra of BSA (50 mg/ml at ph 7.4, PBS buffer), collected at 20 C and 90 C, respectively. Spectra are normalized to the phenylalanine intensity at 1004 cm-1 [28]. Raman spectroscopy can provide information about the secondary structure and provide structural information on aromatics, chromophores, and other side chains in proteins reflecting the tertiary structure. Therefore, Raman spectroscopy is well suited to monitor the transformation of native protein to protein aggregate by analyzing changes in the secondary structure or hydrophilic exposure of any side-change. It also has the ability to identify reversible and non-reversible changes by determining the presence of more disordered structures common in unfolded proteins [24], [26], [28], [29]. Fourier transform infrared spectroscopy (FT-IR) is another technique to observe vibrational modes and provides complementary information to Raman spectroscopy. Infrared spectroscopy measures absorption of light due to vibrations of the molecule in the in the range of cm -1 to 10 cm -1, the most commonly used range for the analysis of protein secondary structure is 4000 to 400 cm -1, which is called middle IR spectroscopy. Vibrations of functional groups such as amide groups are observed in this region [26]. In FT-IR, the absorption is measured at all wavelengths simultaneously instead of one by one. Infrared light is converted to an interferogram by an interferometer. This is usually a beam splitter which splits the infrared light in two beams. One part of the light goes to a fixed mirror, whereas the other part goes to a moving mirror. When the two beams meet at the beam splitter again, the two beams interfere with each other, resulting in the interferogram. This interferogram passes through the sample, which absorbs all different wavelengths characteristic for the proteins in it. These absorptions are subtracted from the interferogram and a variation in energy Debbie van der Burg 13

21 versus time is reported for all wavelengths simultaneously. This could be converted to a intensity versus frequency spectrum following the mathematical Fourier transform function [30]. FT-IR can also determine the secondary structure using vibrations. Figure 7 shows FT-IR spectra of salmon calcitonin (sct) and of sct in the presence of the metal ions Zn 2+ and Al 3+. The spectrum of the native sct shows a strong Amide I band at 1654cm -1, which is characteristic of an α-helix conformation. The spectra of the sct in the presence of the metal ions exhibit a broad band from 1650 cm cm -1, corresponding to random coil and β-sheet formation. This conformational change indicates aggregation [31]. Figure 7: FT-IR spectra of native sct and sct aggregates in the presence of Zn 2+ and Al 3+ ions [31]. FT-IR yields information on the secondary structure and conformational changes of proteins as well as intermolecular interactions. FT-IR can be applied on both liquid and solid samples. Analysis of highly aggregated samples is also possible with FT-IR [26]. Debbie van der Burg 14

22 4 Separation techniques Analyzing protein aggregates is challenging because of the unknown nature of the formed aggregates, the wide size range, and the concentration range [3], [26]. The choice of the technique is commonly governed by the size and the characteristics of the aggregates. Without prior knowledge about the size and characteristics of the aggregates, several techniques with different principles should be combined to obtain as much information as possible [6]. For a lot of separation techniques, it is impossible to quantify and characterize intact protein aggregates, without disrupting the higher order structure during analysis. Factors associated with separation techniques, such as shear stress, dilution, and incompatible mobile phases, change the aggregate conformation. In reversed phase liquid chromatography, for example, the mobile phase will denature the protein. Hence, only the primary structure can be analyzed. Techniques that could be utilized for the determination of intact protein aggregates and their advantages and limitations are discussed in this chapter. 4.1 Size exclusion chromatography Size exclusion chromatography (SEC) is a widely used analytical technique for the detection and quantification of most types of nanometer (<100 nm) and small submicron ( nm) aggregates. SEC separates protein aggregates on basis of their hydrodynamic radius. The column is filled with spherical porous particles with a controlled pore size. The column acts as a sieve where smaller molecules penetrate deep into the pores and therefore take a longer path through the column, whereas larger molecules are unable to enter the pores and therefore move more quickly. Consequently, small molecules will elute later than large molecules. The analytes are separated on the basis of molecular size differences, rather than by their chemical properties. The separation is ideally without any adsorption. A calibration curve, based on proteins or polymers of known molecular weight, could be used to determine the molecular weight of an unknown analyte [6], [26], [32], [33]. The concentration of the eluting protein is often monitored using a spectrophotometric, refractive index, or a light scattering detector [6], [26]. Typical column dimensions are 30 cm column length and mm inner diameter. Various particles of 3-20 µm with different pore sizes are commonly used. The analysis time when using this commonly used columns is min [33]. Debbie van der Burg 15

23 Figure 8: Illustration of a SEC separation. Left: small molecules can penetrate further into the pores of the porous bead than the larger molecules, and therefore take a longer path. Right: SEC chromatogram with the protein aggregate eluting before the larger monomer peak. SEC analyses are often used in routine analysis where high throughput is desired. In order obtain high throughput analysis, short analysis times are required. Since there is no retention, all analytes elute before the total void volume. Decreasing the analysis time in SEC could, therefore, be achieved by reducing the column size and increasing the flow rate. Decreasing the column length, however, proportionally reduces the number of theoretical plates. The main difficulty in achieving both high speed and high resolution in SEC is the slow mass transfer of the analytes between the interstitial space and the pore space. This mass transfer could be increased by increasing the temperature. The column performance in SEC could be described according to the following relationship: H = 3.5 d p (1+k)D M u k d 2 p u (1+k) 2 (Eq 1) D M where H is the plate height, d p is the particle diameter, k is the retention factor, D M is the molecular diffusion coefficient and u is the mobile phase linear velocity. Resolution between the aggregates and the native protein could be enhanced by reducing the particle size. Figure 9 shows an H-u plot of the estimated impact of the particle size and the mobile phase temperature on the column performance [33]. Debbie van der Burg 16

24 Figure 9: Theoretically expected impact of the particle size and mobile phase temperature on column performance. (For the calculations, a 50 kda protein was assumed) [33] The packing material for SEC columns can either be silica, with or without surface modification, or cross-linked polymeric packings with an hydrophobic, hydrophilic, or ionic character. For the analysis of protein aggregates, modified silica packing is mostly used. In order to prevent nonspecific interactions, silica is often modified with diol functional groups to neutralize the acidic surface of the silanol groups. Several hydrophilic cross-linked packings have also been developed for the separation of biopolymers. Most of these packings are proprietary hydroxylated derivatives of cross-linked polymethacrylates [33]. The appropriate pore size can be selected with the size of the protein and its aggregates to be separated. All particles larger than the largest pores in the stationary phase elute first with the interstitial volume. Smaller molecules elute in order of decreasing size. An example of pore sizes appropriate for different molecular weight ranges is given by Agilent in Table 2. If the proteins elute near the intestinal volume, a smaller pore size should be considered, whereas a larger pore size should be considered if the protein elutes near the total void volume. Debbie van der Burg 17

25 Table 2: Pore sizes appropriate for molecular weight ranges [34] Since separation in SEC is not based on retention, large pore volumes are required to obtain appropriate selectivity. Therefore generally, columns with 30 cm column length and 6 10 mm inner diameter are employed. Recently narrow bore column with 4.6 mm inner diameter and 15 cm column length are available. These columns offer similar separation power as standard columns while shortening the analysis time by a factor 3-4. For complex samples, high resolution separation is required. The resolution of a SEC column is directly proportional to the square root of the column length. For complex samples, long columns are required which can be obtained by joining multiple columns in series [33]. Although it is a straightforward technology, SEC analysis has a number of limitations. This limitation must be addressed in order to draw valid conclusions. The column can act as a filter, larger protein aggregates either elute with the void or accumulate on top of the column or precolumn. SEC involves significant dilution of the sample, potentially dissociating reversible aggerates. The range of molecular mass that can be separated, the dynamic range, is limited. Choosing a pore size for good separation of monomer and dimer often means that all species larger than trimer or tetramer are unresolved. This is inversely related to resolution. A less important limitation is that SEC cannot reliably measure the true molecular mass. Calibration curves of retention time versus molecular weight are constructed using globular molecules. Therefore, the elution position does not only depend on the molecular weight of the analyte, but also on its shape. Since the shape of protein and aggregates could vary (e.g., globular, rod-like or flexible chains), their hydrodynamic radius, rather than their molecular weight is determined [35]. Therefore, a calibration method that relates the hydrodynamic radius to its elution volume is required. An example of such a calibration method uses the linear relationship between ln(r H) and ln(1-k D), where R H is the hydrodynamic radius of the analyte and K D the partition coefficient, a function of the elution volume [35]. Figure 10 shows a molecular weight and a hydrodynamic Debbie van der Burg 18

26 radius based calibration curve. In the molecular weight based calibration curve, the dimer and trimer do not fall on the curve, suggesting they are not spherical. However, both dimer and trimer fall on the calibration curve when hydrodynamic radii are measured. Figure 10: SEC calibration curves generated with small proteins ranging from ribonuclease A to thyroglobulin (unfilled symbols). A: Molecular weight based calibration curve. B: Hydrodynamic radius based calibration curve. Filled symbols represent antibody dimer and trimer [35]. Another possibility for the determination of the molecular weight is the use of a multi angle light scattering detector. This allows for the accurate determination of the average molecular weight of the eluting aggregates without the use of calibration standards [6]. A final limitation is the nonspecific protein binding to the column stationary phase, this often causes an underestimation of aggregate quantity [33], [35], [36], modification of the aggregate conformation [33] and distribution [35], abnormal elution positions [33], [36], [37], and undesirable changes in peak shape and chromatographic resolution [32], [33]. Proteins interact with the stationary phase through electrostatic and hydrophobic interactions. If the protein aggregate and the stationary phase are of the same charge, repulsion could prevent the aggregate from entering the pores and decrease elution time. If the protein and the stationary phase are of opposite charge, adsorption could increase elution time. Hydrophobic interactions also lead to increased elution times [33]. A new column has a greater tendency to bind proteins. Also, low protein concentrations often have a low recovery [32], [36]. An ideal and robust method should lead to identical result regardless of how long the column has been used, and the protein recovery should be independent of the amount of protein loaded [32], [36]. Protein adsorption can be avoided by using the right column and mobile phase composition, for Debbie van der Burg 19

27 example, the addition of various co-solvents to the mobile phase can suppress the electrostatic and hydrophobic interactions [32], [33], [36]. To reduce hydrophobic interactions, some organic modifiers could be used [33]. However, the addition of organic modifiers could alter the protein aggregate distribution. Electrostatic interactions are commonly reduced by increasing the ionic strength or salt concentration of the mobile phase, increasing the concentration of a counter ion, adjusting the ph of the mobile phase to a value close to the isoelectric point of the protein, or addition of additives in the mobile phase. It should be considered that high concentrations of counter ions in the mobile phase can lead to increased hydrophobic interactions. It is good practice to use buffered mobile phases with an ionic strength of mm. A commonly used additive is arginine, which reduces the possible interactions with the stationary phase by binding to the protein, leading to an improvement in protein aggregates quantitation and peak shape. Figure 11 shows the SEC chromatographic profiles of recombinant human basic fibroblast growth factor in the presence and absence of arginine. Although the addition of arginine could improve the separation, it should be noted that arginine shows significant UV absorbance at wavelengths below 220 nm, and could, therefore, reduce the sensitivity [33]. Figure 11: SEC chromatographic profiles of recombinant human basic fibroblast growth factor (bfgf) in 0.2 M NaCl (A) and 0.2 M arginine (B). There is little resolution in the separation of bfgf (arrow) from the salt in when no arginine is added to the mobile phase, while baseline resolution is observed after addition of arginine [33]. Ryosuke Yumioka et al. [36] tested the effect of the use of arginine in the mobile phase. Two new identical columns were used for this study. All experimental conditions, except for the buffer, were equal. Mouse monoclonal antibody containing 4-5% of soluble aggregates was analyzed Debbie van der Burg 20

28 with both buffers. Figure 12 shows the results for the NaCl and the arginine buffer. The measured aggregate content was significantly higher for the arginine buffer than for the NaCl buffer. For both buffers, the first injections underestimated the aggregate content of ~4%, in the later injections, the arginine buffer resulted in a more reliable value. The arginine buffer also showed a better resolution between the monomer and the dimer [36]. Figure 12: Effects of repeated injection on the recovery of aggregates with NaCl and arginine buffer. The aggregate content of ten subsequent injections of mouse monoclonal antibody is plotted against the run number [36]. Ryosuke Yumioka et al. also tested the effect of the loaded amount of sample by injecting 20- fold different amount of the mouse monoclonal antibody. Injections of 1 and 20 mg were compared. When using the NaCl buffer, the method gave an aggregate content of 4.4% for the 20 mg injection and only 1.1% for the 1 mg injection. When using the arginine buffer, the method gave an aggregate content of 5.7% for the 20 mg injection and 4.1% for the 1 mg injection [36]. This is a great improvement. This experiment showed the importance of the buffer; an arginine buffer gives a more reliable and consistent result than a NaCl buffer, especially for low loading amounts. Although SEC is mostly used to analyze irreversible nanometer and small submicron aggregates, it can also be used to characterize the reversibility of aggregates. The behavior of reversible aggregates depends on the equilibrium and the interaction with the column, since this is affecting the profile of the eluting peaks, SEC can be used to characterize protein selfassociation. Protein aggregates can associate and dissociate in the column, the interconversion between the two species can be slow, intermediate or fast. The peak profile can determine the rate of interconversion. For slow interconversion, the column separates the associated species on the time scale of the experiment, meaning the species elute as distinct peaks. For rapid Debbie van der Burg 21

29 interconversion, the species rapidly re-equilibrate and the peaks will not be resolved. For the intermediate case, some separation, as well as asymmetry in the eluting peak, is expected. This three cases could be distinguished by injecting solutions of different initial protein concentration at various flow rates. For example, flow rates do not affect the distribution of the species for rapid interconversion, whereas for intermediate or slow interconversion, the flow rate will have an effect [6]. Supplementary information about the protein structure of monomers and aggregate species could be obtained simultaneously with conventional SEC analysis by additional fluorescence detection and the post column addition of a fluorescent dye. The dye interacts with hydrophobic residues, usually directed to the inside of the protein. After protein unfolding, hydrophobic surfaces become exposed and fluoresce intensity increases, enabling the detection of conformation changes [38]. The main advantage of SEC is the mild elution conditions that allow for the characterization of the protein with minimal impact on the conformational structure and the local environment [33]. Additional advantages of SEC are its ease of use, relatively high throughput, the equipment and columns are readily available, and it is often relatively simple to validate with high resolution, precision, and accuracy [6], [32]. Furthermore, the mobile phase can be varied to characterize and monitor reversibility of aggregates. SEC allows for both sizing and quantification of protein aggregates. Separation and detection in the range of 5 to 10 kda can be achieved. In addition, the method requires little sample preparation, often the samples can be injected directly without any modification, except for occasional dilution [6]. 4.2 Asymmetric flow field-flow fractionation Asymmetric flow field-flow fractionation (AF4) is a size-based separation technique. AF4 is a one-phase chromatography technique. Separation is achieved within a thin channel, formed by an impermeable upper plate and a permeable bottom plate separated by a spacer with a typical thickness of µm. The flow in the AF4 is split in two parts with flow regulators or an extra pump. One part is the carrier flow that moves in axial direction towards the outlet and detector. The other part is the cross flow, which is perpendicular to the carrier flow. The sample is injected into the channel. The analytes are moving under the influence of these two flows. The laminar carrier flow is driving the analytes towards the outlet of the channel, whereas the perpendicular cross-flow is forcing the analytes to accumulate at the semipermeable bottom plate. An ultra-filtration membrane with a typical size barrier of 10 kda covers the bottom plate Debbie van der Burg 22

30 to prevent the sample from penetrating the channel. Diffusion of the molecules creates a counteraction motion: analytes diffuse back to the center of the channel. The laminar carrier flow has a parabolic flow profile, the stream moves faster in the center of the channel flow than is does closer to the edges. Depending on their diffusion coefficients, analytes reach a certain height in the channel. Smaller particles have higher diffusion rates than larger particles and reach an equilibrium position higher up in the channel. At a higher position, the laminar carrier flow is faster, therefore smaller particles are being more rapidly transported along the channel than larger particles. Consequently, small particles elute before the larger ones [3], [39], [40]. Figure 13: Schematic illustration of an AF4 channel [39]. An AF4 experiment could basically be divided into three steps: sample injection, sample focusing, and elution. The analytes are separated in the elution step. During sample injection and focusing, the in-going flow enters the channel from both the inlet and the back and of the channel. This is concentration the analytes in a narrow banc near the entrance of the channel. After injection, a set focusing time will further concentrate the analytes. After the focusing step, the elution starts. The in-going flow now only enters the channel from the inlet and is split in the carrier and the cross flow. During elution, the cross flow continuously forces the particles against the accumulation wall, and diffusion causes the analytes to move back to the center of the channel. The further the analytes diffuse to the center, the faster they will elute [41], [42]. The focusing time should be optimized for every application. When the focusing time is too short, the sample band is wider than necessary, compromising resolution. When the focusing time is too long, smaller particles might be lost. Due to the focusing step, limitations regarding overloading are minimized [42]. Debbie van der Burg 23

31 Figure 14: Illustration of an AF4 separation and elution process. A) injection of molecules onto the AF4 channel. B) focusing (concentration) of analytes prior to analysis. C) elution, starting with the ending of the focusing flow and the particles moving down the channel. D) separation of small and large particles over time as they move down the channel [41]. The retention in AF4 only relies on the flows and the diffusion coefficient, which makes it easy to determine the molecular weight based on the retention. With some approximations, the elution time can be calculated as: t R = w2 6D i h (1 + F C F OUT ) (Eq 2) where w is the height of the channel (the thickness of the spacer), Di the diffusion coefficient of the component, Fc the cross flow and Fout the carrier flow. The size can be determined form the diffusion coefficient with the Stokes-Einstein equation [42]. Elution times depend on the flow ratio (Fc/Fout), and the spacer height. Elution times do not depend on the length or width of the channel. Of course, also the peak width is of importance. With a simplified equation, the standard deviation σt of a peak of a monodisperse compound can be calculated as: σ t = 0.82 w u C {ln (1 + F C F OUT )} 1/2 (Eq 3) where uc is the cross flow velocity. This formula only contains instrumental parameters, implying that all analytes elute as peaks with the same width [42]. Debbie van der Burg 24

32 The elution time depends on the flow ratio, and the peak width depends on both the flow ratio and the cross flow velocity. To obtain efficient separations, the system should run at flow rates as high as possible. In Figure 15 the flow ratio is kept constant, while the flow rates are varied. Due to the constant flow rates, the elution times are equal. With higher flow rates, the separation efficiency increased. It should be considered that there are limitations related to a high cross flow, such as the high pressure, the possibility of the channel to leak, possible loss of smaller analytes through the membrane, and possible adsorption when analytes are forced into the pores [42]. Figure 15: Effect of the flow rates on the separation efficiency in AF4. Separation model proteins with a flow ratio of 2.5, with (a) Fc = 1.5, Fout = 0.6 ml/min; (b) Fc = 2.5, Fout = 1.0 ml/min [42]. Figure 16 shows the correlation between the cross flow intensity and resolution of a separation of human serum albumin (HSA) containing ~10% of dimer and larger aggregates. When no cross flow is applied, monomer and aggregates are not separated. With higher cross flows, the analytes are forced against the accumulation wall, sample elution is prolonged and analyte fractionation is performed [43]. Figure 16: Fractionation of HSA at different AF4 separation conditions. Note the correlation between increasing cross flow strength and prolonged elution time/resolution power [43]. Debbie van der Burg 25

33 The high cross flow rate could potentially immobilize higher molecular weight aggregates on the ultra-centrifugation membrane. This higher molecular weight aggregates could be eluted by decreasing the cross flow after elution of the separated smaller aggregates. Figure x shows the analysis of the human serum albumin sample with 0% and 75% cross flow intensity. The 75% cross flow is reduced to 0% after 20 minutes; this enables the detection of the higher molecular weight aggregates. Now monomer (66.9 kda), dimer (133.8 kda), trimer (204 kda), and aggregates >10 6 Da can be analyzed. Flow programming enables a broad dynamic size range to be separated [43]. Figure 17: Fractogram of HSA using 0% and 75% cross flow intensities. Reducing the cross flow from 75% to 0% after 20 minutes [43]. AF4 separates protein aggregates ranging from a few nanometers to a few micrometers diameter [3], [26], [44], [45]. The open channel without stationary phase reduces shear and mechanical stress on the proteins, making it a gentle technique. The broad dynamic separation range, together with the open channel without stationary phase or packing material make this technique very suitable for the analysis of protein aggregates. Another great advantage is the wide choice of carrier liquid, allowing the sample to be analyzed in the formulation buffer [44]. This reduces changes to the aggregate structure due to the matrix. A few examples of the successful analysis of aggregates with AF4 are the analysis of BSA aggregates [46], submicron IgG aggregates [45], [47], oat globulin aggregates [48], and the analysis of aggregates in egg yolk [44]. Although AF4 is a gentle technique, several steps in AF4 could potentially affect delicate or weakly bound protein aggregates. Bria et al. [40] tested the effects of carrier fluid, syringe shear Debbie van der Burg 26

34 stress, focusing and dilution on aggregate stability. When the sample is loaded into the injection valve, shear can be experienced which influences the protein aggregate distribution. After injection, the sample is focused at the beginning of the channel by two opposing flows, prior to fractionation. This focusing step concentrates the sample, the concentration at the accumulation wall could be increased by fold. This increase in concentration can lead to aggregation and/or increased membrane interactions if excessively long focusing times are used. When the focusing flow is turned off, the sample components are transported along the length of the channel. During separation, analytes can experience shear and dilution which is inherent to separation techniques in general. This can cause dissociation of aggregates. Compared to SEC, shear rates are orders of magnitude lower in AF4. Because shear stress is less predominant in AF4, loosely bound aggregates stay intact during analysis when no focusing and cross-flow are used. Sample dilution is dependent on separation conditions and channel dimensions, in contrast to SEC, sample dilution does not necessarily increase along the length of the channel. Most dilution occurs when the analytes at the accumulation wall leave the channel through the channel outlet. All parts during separation are associated with either dilution, concentration or shear stress and may affect non-covalent protein aggregates. The carrier fluid seemed to have a significant impact on aggregate stability, almost complete and partial dissociation was observed in two different buffers. Although shear stress did not affect the AF4 results, altered aggregate size distributions were observed in samples exposed to shear stress analyzed by DLS. However, increased focusing times did not change the aggregate distribution. Aggregates partially dissociate to smaller aggregates species during separation. Low molecular weight aggregates are more likely to dissociate in the buffer and during separation than high molecular weight aggregates [40]. To reduce sample loss, the membrane has to be chosen carefully. The cut-off range and the membrane-protein interaction significantly affect sample loss and recovery. Membrane-protein interactions are most prominent at high cross-flow conditions, selecting low absorption membranes such as regenerated cellulose, often minimizes this problem. For better separation, the membrane must be thin, smooth, flat, and free of creases [49]. In order to ensure a gentle separation as well as a high resolution, a programmed cross-flow could be useful [44], [45]. AF4 can be combined with similar detectors as for SEC, such as UV, refractive index, fluorescence and light scattering detectors [3]. Advantages of AF4 are the lack of stationary phase, which reduces shear and mechanical stress and interaction with the sample, the little sample preparation required, the wide choice of carrier liquid, allowing the use of the formulation Debbie van der Burg 27

35 buffer, and the large separation range from a few nanometers to a few micrometers diameter. Disadvantages are the dilution and concentration effects during the measurement which could influence the separation or alter the aggregates. Also, solution viscosity and potential interactions of the analyte with the membrane can influence the separation [26]. 4.3 Centrifugation Another separation technique to analyze protein aggregates is centrifugation. Centrifugation techniques separate analytes in suspension by their particle size or density using sedimentation. The hydrodynamic radius and density can be calculated using Stokes law. Two types of centrifugation are commonly used for the separation of proteins: analytical ultracentrifugation and disk centrifugation [26] Analytical ultracentrifugation Analytical ultracentrifugation (AUC) is a separation technique based on mass, size and shape. It is one of the most important methods to study interactions of macromolecules under physiological conditions and is able to study both weak and strong interactions [50]. In AUC a sample is centrifuged, separating analytes from the liquid. Analytes are separated from the liquid at different times, according to their density. Larger analytes are separated more quickly than smaller analytes. This provides an indication of the molecular weight and the diameter of the analyte [51]. The analytes are monitored in real time by optical absorbance or interference systems. This enables precise observations of the behavior of analytes undergoing sedimentation [52]. With AUC, little to no sample preparation is necessary and nearly any type of analyte can be investigated over a wide range of concentrations and in a diverse variety of solvents. This enables the characterization of aggregates in relevant solutions such as their formulation buffer [3], [4], [52]. At high protein concentrations, dilution might be required to prevent unreliable molecular weight determinations due to non-ideality [4]. The impact of dilution on reversible aggregates needs to be considered carefully. Results obtained by AUC are not dependent on comparison standards and do not rely on assumptions concerning shape [9], [24], [26]. AUC is able to analyze aggregates over wide size range from 1 kda to over 2 GDa [24]. AUC is suitable for small protein aggregates up to 2000 kda, the technique seems to be unsuitable for particles larger than 100 nm due to scattering effects and rapid sedimentation of large particles which will hinder detection. Approaches which use reduced centrifugation speed to analyze protein particles are being developed [26]. Debbie van der Burg 28

36 A major advantage of AUC is the possibility to quantify protein aggregates, while formation or disruption of aggregates as a result of sample preparation, dilution or matrix effects is limited. Disadvantages are the often poor reproducibility and difficulty of assigning the limit of detection or quantification, the lengthy run time, and the requirement of highly specialized operators [3], [24]. Regular calibration and intensive maintenance of the system are required. Still, AUC has some strong advantages over SEC or AF4 such as the absence of interactions with columns or membranes, therefore AUC can be used as a qualitative orthogonal method for SEC or AF4. It can, for example, be used to verify that no aggregates potentially present in a sample are missed by SEC or AF4 [3]. There are several methods for characterization of heterologous protein-protein interactions including sedimentation velocity, sedimentation equilibrium, tracer sedimentation equilibrium and analytical band sedimentation. They all provide different information. Mostly used are sedimentation velocity (SV), which provides information about the size and shape of the molecule and sedimentation equilibrium (SE) which provides information regarding the molar mass, association constant, stoichiometry, and solution nonideality [4], [52]. Figure 18: Diagram of sediment velocity and sediment equilibrium AUC [24]. Sedimentation equilibrium Dynamic associations which are reversible on the time scale of the experiment cannot be physically separated. These interactions are in an equilibrium that depends on the total protein concentration. The method of choice for these dynamic interactions is sedimentation equilibrium AUC [50]. In SE-AUC the sample is centrifuged at moderate speed to deplete all proteins from the region close to the center of the rotor. The centrifugal force produces a concentration gradient across the cell. The sedimentation is counteracted by diffusion of the analytes, resulting in a thermodynamic equilibrium between diffusion and sedimentation. The analyses Debbie van der Burg 29

37 distribute in an exponential. The concentration distribution at equilibrium only depends on the molecular mass and is measured by absorbance or refractive index detection while the sample is spinning. Furthermore, the overall distribution of monomers and self-associating aggregates will also be in equilibrium and, therefore, reflects the higher molecular weight of the associated states and their proportion in the sample [50], [53]. The exponential distribution can be seen in Figure 19 which shows the data from a monomer dimer tetramer reversibly associating system. Figure 19 b shows the fit as a sum of three exponentials and Figure 19 a shows the distribution of residuals for the fit. Figure 19 c shows the distribution of species as a function of the total monomer concentration Figure 19: Equilibrium sedimentation data for a mutant VL domain of REI. a) distribution of residuals, b) fit of the data as a sum of three exponentials, c) derived distribution of species as a function of the total monomer concentration [50]. The information of the concentration profile significantly increases with larger column heights. However, the time taken to attain results is proportional to the square of the height of the solution column. A typical experiment requires between one and two days to perform [24]. Sedimentation velocity For the characterization of protein aggregate interactions of static nature, sedimentation velocity is the method of choice [50]. Sedimentation velocity is the most used mode in AUC. In a SV- AUC experiment, the sample is centrifuged at high speed (50,000-60,000 rpm). The centrifugal force is larger than in SE-AUC and rapidly forces the proteins away from the center of the rotor, Debbie van der Burg 30

38 until all proteins form a pellet at the outside of the cell. The analytes sediment at different rates, the resulting concentration profiles are measured as a function of time. The concentration profiles are often processed to sedimentation coefficient distributions c(s). A typical experiment requires 4-6 hours [54], [55]. The c(s) analysis implemented in the data-analysis package SEDFIT results in high resolution of individual components. This makes SV-AUC a powerful in resolving and quantitating low levels of protein aggregates [55]. SV-AUC offers significant improvement in the detection and resolution of aggregates ranging from 20 nm [56]. The wide range of solvent matrices that could Figure 20: Sedimentation velocity experiment. Left: concentration distribution analyzed in time. Right: Concentration profiles processed to sedimentation coefficient distribution c(s), the insert shows the corresponding molar mass distribution. be used, including the formulation buffer, the lack of sample preparation and the absence of column interactions cause minimal disturbance of protein aggregates [55] [57]. This allows more accurate quantitation of aggregate levels. Due to limitations of the optical detection system, dilution of the sample might still be necessary. The impact of such dilution on reversible association needs to be considered carefully. Disadvantages of SV-AUC are the low throughput, the need for specialized equipment and training, and the difficulty in validating AUC SV data analysis software. This makes SV-AUC unsuitable for routine release testing in the biopharmaceutical industry. Nonetheless, SV-AUC is a powerful orthogonal tool to SEC for the evaluation and optimization of SEC methods and to provide complementary supportive data about the drug product [55]. Debbie van der Burg 31

39 The analysis of protein aggregate levels is very sensitive to experimental variables. The precision of SV-AUC analysis could be improved by increasing the number of experiments, however, this is very time consuming while only slightly improving precision. Therefore, the precision should be improved by reducing the SV-AUC intrinsic variability. This includes laboratory practices, hardware components, and software modeling approaches. Gabrielson et al. [57] showed that the precision of protein aggregation measurements is affected by laboratory variations such as the state of the instrument, rotor, and cell housings used, analyst technique, cell alignment approach, centerpiece quality, sample characteristics. Data-analysis can also have an effect on the results [55], [58]. Over the last 20 years, data analysis has significantly improved due to the emergence of advanced data-analysis packages. This allows a wider use of AUC for a number of applications, including characterization of biopharmaceutical proteins and protein aggregates. The most versatile data-analysis package is SEDFIT [58]. SEDFIT results are commonly expressed in terms of sedimentation coefficient distribution, c(s). The specific rates of the analytes depend on their hydrodynamic properties are measured in Svedberg (S) units. Limitations are that c(s) is sensitive to the initial fitting parameters, such as the position of the meniscus, the type of noise, the resolution, the confidence level, and the integration range. Also, the use of regularization is known to result in small systemic errors in the sedimentation coefficients and relative abundance. This especially accounts for analytes at trace levels, which is often the case for protein aggregates in biopharmaceuticals. Wafer et al. [58] showed that these errors for detecting and quantifying protein aggregates in biopharmaceuticals could be reduced when using the Bayesian model in SEDFIT. In SEDFIT two types of regularization are employed: maximum entropy (ME) and Tikhonov- Phillips (TP). Both regularizations are well-established approaches to minimize oscillations in solutions to ill-conditioned problems, without significantly affecting the accuracy or precision. ME regularization biases the solutions toward the subset of solutions that contain the highest informational entropy (the least information) and is the default option in SEDFIT. ME has the assumption that all sedimentation coefficients are equally likely. The ME regularization is recommended for samples containing discrete species, which is often the case for pharmaceutical preparations of monoclonal antibodies. The TP regularization is based on other assumptions and biases the solution toward those that minimize the second derivative of the coefficient distribution (those with the least curvature). The TP regularization is recommended for samples that contain broad or heterogeneous distributions, such as solutions containing Debbie van der Burg 32

40 heat-stressed aggregates. Bayesian tools can be used in SEDFIT to address some of the limitations such as the bias and tendency to generate artificial peaks at large s values when using traditional regularization. The prior knowledge of the Bayesian tool is based on the normal c(s). An automatic Bayesian and a manual Bayesian could be used. The automatic Bayesian approach generates a second distribution, cp(s), based on the normal c(s), giving additional weight to the sediment coefficient of the detected analytes. Figure 21 shows sedimentation coefficient distributions for an IgG antibody drug conjugate sample using ME and TP regularizations a) without Bayesian approach and b) with Bayesian approach. Without Bayesian approach, the sedimentation coefficients of the monomeric species are similar for both regularization techniques, however, there are significant differences for the dimer and larger aggregates. When using the Bayesian approach, the two regularization techniques generate nearly identical distributions. The automatic Bayesian approach should only be applied to data from samples containing discrete species. For heterogeneous samples, or when additional control of the fitting is desired, a manual Bayesian analysis can be performed. With the manual Bayesian approach, the sensitivity of the results to different prior probabilities can be tested [58]. Figure 21: Overlay of sedimentation coefficient distributions for IgG antibody drug conjugate sample generated using ME (black) or TP (red) regularizations with the aggregated species in the main panel and the monomeric species in the inset a) using normal regularization and b) using the Bayesian approach [58]. SV-AUC and SE-AUC are the preferred methods for analyzing reversible self-association of proteins due to the limited sample dilution during the centrifuge run and the compatibility with many solvent conditions. The boundary formed during SV-AUC analysis becomes a reaction boundary in the presence of reversible self-association. Analysis of the amplitude and asymptotic shape of the reaction boundary can yield qualitative information on reversible Debbie van der Burg 33

41 interacting systems [59]. For SE-AUC, the association properties of protein could be investigated by analyzing the concentration gradient using appropriate mathematical models [59], [60] Disk centrifugation Disk centrifugation, also known as differential centrifugal sedimentation (DCS), measures size distributions using sedimentation theory. The sedimentation experiment can be based on either gravitational or centrifugal forces [61], [62]. Gravitational forces limit the application to large particles, therefore, centrifugal force is commonly applied [62]. The sedimentation velocity of particles is dependent on the density differences between the particles and the surrounding liquid, and the hydrodynamic radius of the particle. The size of the particles can be calculated by Stokes Law (Eq 4). The sedimentation velocity depends on the density difference between the analyte and the fluid (ρp ρf), the hydrodynamic radius of the analyte r, the viscosity of the fluid η, and the gravitational force g. [26], [62]. However, when a centrifuge is used, this equation must be modified (Eq 5) [62]. v = 2 9 (ρ p ρ f )gr 2 η (Eq 4) D = { (18η ln(r 0.5 f/r 0 )) } ((ρ p ρ f )w 2 t) (Eq 5) In the modified equation, D is the particle diameter, η the fluid viscosity, Rf the final radius of rotation, R0 the initial radius of rotation, ρp particle density, ρf the fluid density, ω the rotational velocity, and t the time required to sediment from R0 to Rf. DSC can be divided into two operation modes: the homogeneous start mode or integral sedimentation and the line start mode or differential sedimentation. In integral sedimentation, the sample is introduced in the form of a dilute dispersion. The particle concentration is detected by a light beam or X-ray beam at a known distance. As sedimentation progresses, the particles move outward and the measured concentration decreases [61], [62]. The result of this analysis is an integral representation of the particle size distribution by plotting the measured concentration of the particles against the calculated particle diameter. By applying mathematical Debbie van der Burg 34

42 differentiation with respect to diameter, a differential particle size distribution can be generated [62]. Figure 22: Integral sedimentation [62] The line start mode, or differential sedimentation, yields superior resolution to the homogeneous-start method [63]. In differential sedimentation, the sample is injected on top of the sedimentation cell. The particle concentration is detected by a light beam or X-ray beam at a known distance. The particles settle based on Stoke s law, larger particles settle faster than smaller particles. Every time particles of a certain hydrodynamic size pass the detector, the light intensity is reduced. Typically, the particle concentration is plotted against the particle diameter to obtain a differential distribution [62]. In disk centrifugation, the centrifuge is in the form of a disk, filled with a density gradient fluid, such as sucrose or glycerin solutions, orientated in a vertical direction. The density gradient avoids rapid sedimentation of the entire sample. DSC uses rotation speeds up to g. The sample is diluted in a fluid of a lower density and subsequently injected into the disk center. The sample fluid and disk fluid are not mixed, only the particles sediment from the disk center to the edge where they are detected [26]. After sample analysis, the instrument is ready for the next analysis. The centrifuge does not have to be emptied and cleaned. The limitation of continuous runs is that the density gradient slowly degrades due to molecular diffusion. After gradient degradation, the disk must be emptied and a new gradient should be formed. The typical lifetime of the gradient is between 2 and 72 hours, depending on the molecular weight and viscosity of the gradient [62]. Debbie van der Burg 35

43 Figure 23: Differential sedimentation [62] Since particle size determination by DCS is not absolute, calibration standards are required. The dynamic range of disk centrifugation is about 70 with a fixed centrifuge speed and about 1000 when using ramping speed during analysis. The time required for one analysis depends on the size range and the density of the particles. Generally, one analysis takes between 3 and 15 minutes [64]. Particles are detected by a light extinction or scattering detector, allowing concentration determination by Mie theory. Because of this detection method, a wider size range can be measured than with AUC where light absorption or interference is measured [26]. The size range depends on the density of the particles to be measured. For dense particles the maximum and minimum sizes are smaller (5 nm 10 µm for 6 g/cm 3 ) and for low density particles the maximum and minimum sizes are larger (20 nm to 75 µm for ~1 g/cm 3 ) [64]. Particle suspension in the density gradient could change the sample properties [26], potentially dissociating or forming aggregates. Little is described in the literature about DCS for the analysis of protein aggregates. DCS was used to analyze adenovirus aggregates [61], to determine the length distribution of amyloid fibrils [65], and to analyze size distributions of particles in a cytokine and human serum albumin solution [66]. However, DCS is mainly used for the characterization of nanoparticles, for example, interaction with protein [67] [69]. 4.4 Electrophoresis Sodium dodecyl sulfate polyacryl gel electrophoresis (SDS-PAGE) and capillary electrophoresis sodium dodecyl sulfate (CE-SDS), also known as capillary gel electrophoresis (CGE), separate proteins and protein aggregates based on their size. Debbie van der Burg 36

44 During electrophoresis, analytes migrate through an electrolyte solution under the influence of an electric field. When an electric field is applied to a solution containing charged molecules, these molecules start to move. Positively charged molecules move towards the negatively charged electrode, and negatively charged molecules move towards the positively charged electrode. Proteins and protein aggregates are charged due to the many charged groups on the side chains of their amino acids as well as their amino and carboxyl termini. This makes them accessible to electrophoresis. Figure 24: Schematic illustration of the principle of electrophoresis. In general electrophoresis techniques, the mobility depends on both mass and charge. Molecules possessing more charge are affected more by the electric field and, consequently, migrate faster. Larger molecules migrate slower through the viscous separation medium than smaller molecules. Because of the size to charged based separation, the molecular weight of analytes could not be determined. In both SDS-PAGE and CGE, the sample is treated with the anionic detergent SDS. SDS denatures the proteins and binds to most proteins with a uniform 1.4 grams of SDS per gram of protein. In the presence of SDS, all proteins and aggregates have lost their secondary, tertiary and quaternary structure and contain a uniform charge [4], [70]. The separation in the gel is now only dependent on the mass of the proteins and aggregates. The use of SDS disrupts non-covalent aggregates and, therefore, this method can only be applied for covalent and SDS non-dissociable aggregates [4] SDS-PAGE In SDS-PAGE, the mass separation is performed in a polyacrylamide gel. It is a very common, fairly robust method that is easy to perform and can supply information on approximate molecular weight and quantity [4]. By using appropriate standard proteins with known molecular masses, the molecular mass of the proteins and aggregates could be estimated [71]. To compare the migration distance of the proteins and aggregates to those of the known molecular Debbie van der Burg 37

45 weight protein ladder, visualization by a staining technique is required. Visualization could be achieved by several staining methods, such as Coomassie blue, silver, and fluorescence staining methods. Coomassie blue remains one of the most popular protein staining dyes due to its simplicity, mass spectrometry compatibility, and low-cost [72] [74]. As an example, Figure 25 shows a SDS-PAGE gel of the aggregation of the protein lactoperoxidase. Lactoperoxidase is oxidized with different concentrations of hypochlorous acid (HOCl). The bands in the gel show that the monomer concentration decreases with increasing HOCl concentration. The higher bands are aggregates, of which the concentration and size increase with increasing HOCl concentration. At the highest concentration, 300 µm, no aggregates were observed. This could indicate the formation of aggregates that were too large to enter the gel. Figure 25: Lactoperoxidase treated with different concentrations of hypochlorous acid (HOCl) [75]. Sample preparation usually includes a reduction and a temperature step to fully denature the protein. During heating at high temperature in SDS, protein aggregates could dissociate, or new aggregates could be formed. By comparing results obtained under reducing and nonreducing conditions, SDS-PAGE can differentiate between aggregates held together by disulfide bonds or by non-reducible bonds [4]. Under reducing conditions, disulfide bonds will be broken, whereas the disulfide bond will stay intact under non-reducing conditions. Peaks present under nonreducing conditions, but not under reducing conditions, are therefore disulfide linked covalent aggregates. SDS-PAGE is well suited for the determination of apparent molecular weight, size heterogeneity, purity, and manufacturing consistency [76]. The detection range is limited to protein aggregates Debbie van der Burg 38

46 between 5 and 500 kda. However, this weight range can be extended by various techniques such as gradient gels or particular buffer systems [4]. SDS-PAGE requires extensive skill in gel pouring, sampling, separation, and staining/destaining for the visualization and evaluation of separated bands [76] Capillary gel electrophoresis Capillary gel electrophoresis is the capillary based version of SDS-PAGE. In CGE, the mass separation is performed in a capillary filled with a gel. In capillary electrophoresis, often an electro osmotic flow (EOF) is present, which is the bulk flow of the solvent. The EOF transports all molecules in the same direction: when the EOF is towards the negative electrode, it speeds up positive ions, slows down or even changes the direction of negative ions and moves neutral molecules with the speed of the EOF. In CGE, this EOF is often suppressed by the gel, resulting in a migration of analytes only based on electrophoresis. The separation mechanism and obtained results are similar to SDS-PAGE, however, CGE shows many advantages over classical SDS-PAGE. In CGE, quantification is performed by on-column direct UV or fluorescence detection rather than by dye-binding, sample running can be automated, it has better resolving power, and it has the capability of accurate protein quantification and molecular weight determination [3], [77]. These advantages make that CGE is replacing SDS-PAGE. Improvements in sieving matrices have led to improved reproducibility and robustness [77]. Figure 26: Schematic illustration of a capillary gel electrophoresis system. When CGE is used both under reducing and non-reducing conditions, it has the ability to differentiate between covalent aggregates held together by disulfide bonds or by non-reducible bonds. This provides valuable information about the nature and mechanism of protein association. Immunotoxin can be constructed by cross-linking chemically deglycosylated ricin A- Debbie van der Burg 39

47 chain (dgrta) to monoclonal antibody. Figure 27 shows an electropherogram of dgrta under non-reducing and reducing conditions. Under non-reducing conditions, a high amount of covalently linked aggregates was detected. This aggregate peak significantly reduced under reducing conditions, indicating that most aggregates were linked by disulfide bonds. The small peak still observed contains aggregates held together by non-reducible bonds [78]. Figure 27: CGE analysis of dgrta aggregates a) under non-reducing conditions and b) under reducing conditions [78]. Sample preparation for SDS-PAGE and CGE is similar. The protein sample is treated with SDS at high temperature to ensure that the protein is fully denatured and covered by SDS. For reducing conditions, the samples are incubated at high temperature with a reducing agent such as dithiothreitol (DTT) or 2-mercaptoethanol to break the disulfide bonds. For SDS-PAGE, the proteins in the gel require visualization by a staining method. Sample preparation for SDS- PAGE and CGE should fully denature the proteins and dissolve any particles that could interfere with the analysis. When proteins are not fully denatured, they will not be fully saturated with SDS, and then the separation is not only dependent on mass. When particles are not completely dissolved, they will clog the gel [59], [79]. For both SDS-PAGE and CGE, low µg amounts of sample are needed for the analysis and throughput is medium to high [3]. Limitations of SDS-PAGE and CGE are that only covalent protein aggregates could be analyzed; that proteins containing a lot of hydrophobic residues, such as membrane proteins, bind to SDS more than twofold than globular proteins, resulting in abnormal electrophoretic mobility; and that the sample preparation could dissociate or form protein aggregates. In addition, SDS-PAGE and CGE are destructive techniques, the separated bands or peaks could not be collected for further characterization [11], [59]. Debbie van der Burg 40

48 5 Hyphenation separation - detection A detector is required to detect the information gained from the separation technique. For the detection of protein aggregates, several detection techniques could be utilized. Detection with a simple technique such as UV absorbance adds very little additional information and therefore often only provides information from the separation technique. When coupling the separation technique to an appropriate detection technique, additional information could be gained. Additional information on size could, for example, be obtained by light scattering detectors whereas mass spectrometers could assess supplementary information about mass, conformational integrity, and structural heterogeneity of protein therapeutics. This chapter will give an overview of detection techniques for the analysis of protein aggregates able to couple to the discussed separation techniques. 5.1 Light scattering Multi-angle light scattering and dynamic light scattering could be used as a detector to provide additional information. The use of a MALS or DLS detector allows for accurate molecular weight determination without the use of calibration standards. Light scattering is one of the few absolute methods available for the determination of molecular mass and structure. As a standalone technique, however, it has a number of limitations, including polydispersity of the sample, non-ideal thermodynamic behavior of analytes, unknown conformation of some analytes in solution, and possible interactions between analytes [80]. Also, light scattering measures a weight-average from all species present in solution and provides little information about aggregates in a solution containing both low- and high-molecular weight species. By combining a light scattering technique with a separation technique, some of the limitations can be overcome [80]. Coupling DLS or MALS as detection method to a separation technique allows the separation of aggregates followed by accurate and absolute determination of the molecular weight. In this setup, a UV or refractive index detector can monitor the concentration [6]. By combining SEC or AF4 with an inline DLS or MALS detector, qualitative and semi-quantitative product characterization in biopharmaceuticals becomes possible [81] Multi-angle light scattering As described in Chapter 3, multi-angle light scattering (MALS) uses the intensity of the scattered light of protein aggregates in multiple angles to determine the size. Debbie van der Burg 41

49 Figure 28: Schematic illustration of a MALS detector. MALS can measure molecular weights from 5 kda to several million Daltons. MALS is an effective, rapid, means to determine both the weight-average molecular weight (Mw) and radius of gyration (Rg) of soluble aggregates [6], [81]. Light scattering is one of the few absolute methods available for the determination of molecular mass and structure. SEC-MALS could be utilized to investigate the mechanisms of protein aggregation. It simultaneously provides information about the molecular weight and the concentration or mass fraction [81]. SEC-MALS has been utilized for the analysis of a broad range of nanometer and small submicron aggregates (from 20 nm to over 100 nm) and the semi-quantitative characterization of polydispersity and aggregate morphology [81]. Figure 29: Schematic illustration of AF4-UV-MALS experimental setup. In Figure 30 a SEC separation of a monomer and several aggregates is shown. The UV trace (dotted line) shows two peaks as typically observed by conventional SEC. The solid line shows the relative light scattering intensity measured by MALS. The symbols in the figure show the Mw values scaled by the monomer molecular weight. With SEC alone, calibration would be necessary for the size determination, and it would have been impossible to define the peaks as Debbie van der Burg 42

50 monodisperse or consisting of several size species. The combination with MALS shows that the peak at 8.5 min is the monodisperse monomer peak and that the peak at 6.5 min is the aggregate peak, consisting of a wide range of Mw values. Figure 30: SEC-MALS chromatogram and Mw profile for bovine α-chymotrypsinogen A with apprximatly 30% aggregate. Relative light scattering intensity (left vertical axis, only 90% scattering angle is shown), and UV absorbance at 280 nm (right vertical axis) are represented with a solid and dotted line, respectively. Mw values (scaled by the monomer mass) for each 1 s slice of the two peaks are given by the symbols (left vertical axis) [81]. The limitations of online MALS with SEC are similar to those of SEC itself. Also, the light scattering detector may not provide a reproducible and accurate mass determination at low concentrations of low-molecular weight species due to higher background scattering. The samples and the mobile phase should also be maintained dust and particulate free, requiring special sample preparation protocols [6]. Nevertheless, hyphenation of MALS detection and size-based separation techniques can enhance the accuracy of size analysis of complex samples Dynamic light scattering Although the literature provides limited examples, coupling a DLS instrument to separation techniques is possible. Chapter 3 describes the principle of DLS. DLS can measure aggregates in the size range of 1 nm to 10 µm [6]. Figure 31 shows an example of the AF4-DLS analysis of aggregated fullerene nanomaterial and silver nanoparticles. Debbie van der Burg 43

51 Figure 31: Left: AF4-DLS measurement of fullerene nanomaterial aggregates, the intensity of the scattered light (solid line) and the measured hydrodynamic radius (dots). Unretained analytes elute at the void at 5 min and retained analytes elute in order of increasing size from 7.5 to 18 min [82]. Right: AF4-DLS measurement of silver nanoparticles, UV intensity (solid line), Z-average (red dots) [83]. Although these examples illustrate the separation of nanoparticles, it shows the possibility of the use of a DLS as a detector for the analysis of protein aggregates. DLS as a detector allows for the direct determination of the hydrodynamic diameter without the use of calibration standards. 5.2 Electrospray Ionization Mass Spectrometry Comprehensive characterization of complex protein aggregate mixtures is challenging. To identify peaks observed with separation techniques, to resolve peaks that could not be separated by the separation technique, or to provide additional information about conformation and association/dissociation behavior, mass spectrometry could be coupled to the separation technique. Electrospray ionization mass spectrometry (ESI-MS) gained popularity for the analysis of complex protein mixtures. ESI applies an electrical field to a liquid to transfer ions to the gaseous phase. The electrical field induces a charge accumulation at the liquid surface. When the surface tension is broken, the shape of the drop changes to a Taylor cone and the spray appears. The charged droplets are subjected to a counter flow of drying gas. The solvent contained in the droplets evaporates, which causes them to shrink and their charge per unit volume to increase. Because of the accumulation of charge, the droplets become unstable and disintegrate into smaller droplets. This process is repeated a number of times. When the electric field on the surface becomes large enough, desorption of ions from the surface occurs. Eventually, the proteins are freed by evaporation of the solvent. Ions obtained from large Debbie van der Burg 44

52 molecules often carry a greater number of charges. Typically, a protein will carry one charge per thousand Daltons [84]. Compared to traditional methods for the investigation of protein conformation, such as circular dichroism, NMR, and X-ray, ESI-MS offers several advantages. For example, ESI-MS is sensitive, requiring fmol and amol amounts of protein samples. ESI analysis makes use of a protein solution, which is important since most separations take place in solution. In native ESI- MS no organic compounds are used as co-solvent. Analysis of intact proteins and protein complexes under near-physiological conditions is achieved by using neutral volatile buffer slats like ammonium acetate for protein sample preparation. Another advantage is that ESI generates multiply charged ions. This is a critical point since the charge state distribution observed in protein ESI mass spectra are affected by the conformations that the protein held in solution at the moment of its transfer into the gas phase. For a folded protein, typically a narrow charge state distribution in low charge states is observed, whereas for unfolded proteins the charge state distribution is broadened and shifted to high charge states. This is most likely caused by the greater capacity of unfolded proteins to accommodate charges on their surface [85]. Besides the advantages of ESI-MS, there are also limitations. MS measurements are carried out in a vacuum. Gas phase processes occurring prior to detection and characterization surely affect protein aggregates. The charge state distribution used to characterize dynamic processes in solution such as protein unfolding could be affected by gas phase processes. For example, the formation of metastable proteins in the electrosprayed droplets and their asymmetric dissociation may give rise to a population of highly charged ions [86]. Muneeruddin et al. characterized intact protein aggregates and conjugates using native ESI-MS as a detection technique for SEC [87] and ion-exchange chromatography (IXC) [88]. Native ESI- MS can provide information on the stoichiometry of non-covalent protein complexes in solution, evaluate the structural heterogeneity of protein therapeutics and assess the conformational integrity [87]. Unfortunately, the detection and quantification of non-covalent protein aggregates is not always straightforward due to the artifacts frequently produced during the ionization process. Detection with native ESI-MS can be off-line, by collecting fractions from the separation technique, however, the conformation of protein aggregates in the collected fractions can change prior to MS analysis. A better option is therefore online detection. Muneeruddin et al. [87] characterized BSA monomer, dimer, and trimer with SEC / native ESI- MS. The SEC chromatogram contained three peaks. With SEC alone it is not certain if the Debbie van der Burg 45

53 earlier eluting peaks are dimer and trimer or partially unfolded protein monomers. With native ESI-MS alone, it is unclear whether the observed dimers and trimers are originating from the sample solution, or if their presence is an artifact related to the forced association of proteins in the shrinking electrospray droplets. The combination of SEC and native ESI-MS resolves the data interpretation. The UV and TIC chromatograms are consistent, and MS has the ability to identify each TIC peak. The combination of SEC and ESI-MS allows for the identification of the SEC peaks and can distinguish between aggregates present in solution and those formed during the ESI process (artifacts of ESI MS). The charge state distribution analyzed with MS provides additional conformational information. For example, the dimer and trimer peak both contained high and low charge density ions, indicating that they undergo reversible and rapid loss of compactness. The charge state distribution of the BSA monomer was bimodal, indicating the presence of both compact and partially unfolded monomer. Another advantage of the use of a mass spectrometer is its ability to analyze co-eluting analytes with different masses. For example, the analysis of BSA (66 kda) and human serum transferrin (Tf, 80 kda), which could not be resolved by SEC alone, could be resolved with the combination of SEC and native ESI- MS as can be seen in Figure 32. Figure 32: Online SEC/MS characterization of an equimolar mixture of BSA and Tf: UV chromatogram (black); TIC (gray); extracted ion chromatograms of the BSA monomers (blue, +14 charge state), BSA dimers (charge state +20, magenta) and Tf monomers (charge state +16, red). Inset shows mass spectra averaged across the three SEC peaks (acquisition times are indicated on each trace) [87]. The combination of a separation technique like SEC and native ESI-MS allows complex protein mixtures to be analyzed. Not only the chromatographic information is obtained, but also the Debbie van der Burg 46

54 additional information from native ESI-MS, which provides information about mass and protein conformation integrity [87], [88]. Online detection with native ESI-MS also allows meaningful information for protein aggregates that undergo rapid dissociation or re-association on the chromatographic timescale [87]. 6 Technique evaluation 6.1 Discussion and comparison The wide size range and the diversity in types of protein aggregates requires the use of multiple, orthogonal techniques for the quantitative analysis and characterization of protein aggregates. The use of multiple, orthogonal techniques is especially important during method development [3], [11]. A comparison of analytical methods should be made in order to choose an optimal method for protein aggregate analysis. When comparing analytical methods for the quantitation and characterization of protein aggregates, size limitations, detection limits, sensitivity, resolution, and reproducibility should be considered carefully [11]. In addition, consideration must be given to how the technique may alter the aggregate distribution. The technique must accurately measure the aggregate concentration and the aggregate population should not be modified by association or dissociation of protein aggregates [35]. Table 3 shows the likelihood of dissociation or association of protein aggregates caused by several aspects related to the discussed separation techniques. Other properties of the discussed separation techniques are displayed in Table 4. Table 3: Likelihood of associating and dissociating aggregates by aspects related to separation techniques. The likelihood is rated with pluses and minus, where stands for not likely and pluses stand for increasing likelihood with + as minimal likelihood and +++ as very likely. Dissociation of aggregates SEC AF4 SV- AUC SE- AUC DSC SDS- PAGE Dilution Change of solvent conditions Adsorption to surfaces Physical filtration (e.g. column frit) Physical disruption (e.g. shear forces) Association of (new) aggregates Change of solvent conditions Surface or shear-induced denaturation Concentration on surface CGE Debbie van der Burg 47

55 Figure 33: Size range of aggregates to be analyzed by separation techniques. Table 3 shows that AUC is the softest technique for the analysis of protein aggregates. Due to the open cell and lack of sample preparation, stresses inducing protein aggregation are kept at a minimum. However, AUC has a low throughput and is therefore not suitable for routine analysis. Although it is not well suited for routine analysis, SV-AUC has become an important tool for quantitation of protein aggregates in biopharmaceuticals. It could be very useful for other applications, such as candidate section, formulation development, and product characterization. For this applications it offers several advantages over SEC due to its ability to analyze samples under various solution conditions, with minimal surface interactions, and with high resolving power [56]. Techniques that do have high throughput for rapid and efficient analysis of protein aggregates are SEC, AF4, and CGE [11]. In Table 3 SEC appears to be a quit harsh technique for protein aggregates, nonetheless, it is soft enough to analyze irreversible protein aggregates. Due to the high throughput, ease of use, robustness, and high resolution of SEC, it is often used in routine analysis. SEC is still considered the workhorse for the analysis of protein aggregates [4], [35]. AF4 is also often used for the analysis of protein aggregates. Due to the lack of stationary phase and little sample preparation, AF4 is a softer technique than SEC. AF4 can analyze protein aggregates over a wide size range and has the ability to use the formulation buffer as carrier liquid. However, AF4 is less mature than other Debbie van der Burg 48

56 Table 4: Properties of separation techniques for the analysis of protein aggregates SEC AF4 AUC SV l SE DCS SDS-PAGE CGE Separation Medium column filled with porous beads open channel open chamber disk filled with gradient solution slab gel capillary filled with gel Matrix mobile phase* carrier liquid** sample matrix sample matrix buffer buffer Principle molecular sieving diffusion sedimentation velocity sedimentation velocity separation by size in electric field separation by size in electric field Resolution high medium medium - high medium medium high Throughput high high medium low high medium high Run time < 1 hour < 1 hour 4-6 hours 1-2 days < 1 hour < 1 hour < 1 hour Technical knowledge required Detection modes low medium high low medium medium UV, refractive index, light scattering UV, refractive index, light scattering UV, refractive index monochromatic light obscuration, x-ray visual inspection after dye-binding UV, refractive index, light scattering Result hydrodynamic size molecular weight hydrodynamic size molecular weight hydrodynamic size molecular weight molecular weight Type of aggregates Size nanometer - submicron nanometer submicron micron nanometer nanometer submicron micron nanometer nanometer Linkage*** noncovalent - covalent noncovalent - covalent noncovalent - covalent noncovalent - covalent covalent covalent Reversibility*** irreversible reversible and irreversible reversible and irreversible reversible and irreversible irreversible irreversible *** different from formulation buffer, often contains high salt concentrations, could alter the protein aggregate distribution *** wide range of carrier liquids possible, including formulation buffer *** for the analysis of non-covalent and reversible aggregates, factors such as dilution, concentration, and shear should be carefully considered Debbie van der Burg 49 48

57 chromatography techniques such as SEC and often requires in-depth method development regarding channel dimensions, types of membranes, and flow rate to obtain acceptable separation and robustness. In addition, AF4 methods are difficult to validate. Therefore, AF4 applications in routine analysis of biopharmaceuticals are limited [3], [10]. CGE also has high throughput and is robust enough for reproducible quantification of protein aggregates. Therefore, it could also be used in routine analysis. CGE offers great separation efficiency, often superior to SEC. The sample preparation in CGE consist of denaturing and reducing the protein aggregates. Non-covalent bonds are broken and therefore it is only applicable for covalent aggregates [3], [89]. Although AF4 is more difficult to validate, it could be an alternative for SEC [42]. SEC, AF4 and CGE all suffer from limitations. Artifacts in SEC, such as adsorption of protein aggregates to the stationary phase, shear stress, dilution effects during injection, and mobile phase-induced changes in the aggregate distribution. In addition, the possible exclusion of large aggregates could underreport the aggregate concentration [56], [58]. AF4 is a softer technique than SEC, however, it only offers resolution over a limited size range, is less robust than SEC, and the sample is dynamically concentrated and diluted during the separation [58]. The sample preparation in CGE denatures and reduces protein aggregates. This sample preparation could potentially dissociate or form protein aggregates. In addition, only covalent protein aggregates could be analyzed [59]. Due to these limitations, an orthogonal technique is required. AF4, CGE, and SEC are orthogonal techniques, however, analytical ultracentrifugation is more often used for this purpose [58]. Due to the lack of stationary phase and sample preparation together with the in situ analysis of protein aggregates in their formulation buffer, AUC is not subjected to the limitations of SEC, AF4, and CGE and could therefore be used to minimize artifacts in the SEC, AF4, or CGE method [56], [58]. SDS- PAGE and CGE are also frequently used to verify SEC methods. Since CGE can be automated, has shorter analysis time, and the ability of online detection and quantitation, CGE is likely to replace SDS-PAGE as orthogonal technique to SEC [89]. Using an orthogonal technique during method development avoids redevelopment work and costly delays in product development [56]. Limited information or examples are found in the literature about the application of disk centrifugation for the analysis of protein aggregates. Since only AF4 and DCS could be applied for the analysis of larger protein aggregates in the submicron and micron aggregate range, the information about the application of DCS should be extended. The high throughput, limited sample preparation, and the matrix free environment make DSC a technique worth investigating. Limitations of DCS are the change in sample properties during suspension of the particles in the density gradient fluid and the required calibration standards. Debbie van der Burg 50

58 Additional information could be obtained when combining appropriate detection techniques to the separation techniques. For the assessment of the mass, structural heterogeneity and conformation of protein aggregates, native ESI-MS could be utilized as detection technique. The MS data could identify the peaks observed with the separation technique and the charge state distribution provides conformational information [87]. The use of light scattering detection techniques could provide valuable additional information about particle size and peak purity. The use of this detectors is therefore strongly recommended. 6.2 Case studies involving separation techniques Choosing the appropriate separation technique for the separation of protein aggregates is essential in order to obtain good results. The broad range in size and diversity in type of aggregates require several separation techniques to be applied. Not one technique is suitable for every type of aggregate. In this chapter, three case studies regarding the comparison of separation techniques are given Case study 1: Antibody analysis with SEC, AF4 and SV-AUC Gabrielson et al. [35] analyzed an unstressed and a stressed antibody sample with SEC, AF4, and SV-AUC together. Figure 34 shows the results from the unstressed sample. Table 5 lists the results from both the stressed and unstressed sample. This data shows a lack of agreement between the three techniques. Mainly the total aggregate concentration measured with SEC and SV-AUC provide different results, where SEC underreports the amount of aggregate, this difference is larger for the unstressed sample. In addition, SEC and SV-AUC detect different numbers of protein peaks. SEC detects only one aggregate peak for the unstressed sample and two aggregate peaks for the acidified sample, whereas SV-AUC detects three aggregate peaks for both samples. The AF4 results are intermediate to SEC and SV-AUC. AF4 did not achieve complete separation of high molecular weight soluble aggregates. The minor peaks in the AF4 fractogram are polydisperse aggregate populations. The MALS detector could detect high molecular weight aggregates undetected by SEC and SV-AUC. Debbie van der Burg 51

59 Figure 34: Unstressed antibody sample A) AUC continuous sedimentation coefficient distribution, B) Size exclusion chromatogram, C) AF4 fractogram [35]. Table 5: Monomer and Soluble Aggregate Concentrations as a Mass Percentage of Total Protein for Unstressed and Acidified Antibody Samples [35]. Antibody % by Mass of Total Protein Species Sample SV-AUC SEC AF4 Unstressed Monomer 95.8 ± ± ± 1.1 Total soluble aggregate 4.2 ± ± ± 1.1 Acidified Monomer 84.6 ± ± ± 2.8 Total soluble aggregate 15.4 ± ± ± 2.8 Gabrielson et al. collected the dimer peak from the SEC separation and re-analyzed this fraction with both SEC and SV-AUC. As can be seen in table 5, SEC analysis contains a loss of dimer and formation of monomer and trimer. The SV-AUC analysis retained more dimer than SEC. Table 6: Re-analyzed pure dimer with SEC and SV-AUC [35]. Technique % by Mass of Total Protein Monomer Dimer Trimer SEC 18.7 ± ± ± 5.0 SV-AUC 3.6 ± ± ± 3.2 This study shows that SEC underreports non-covalent soluble aggregate levels, and modifies the aggregate distribution. SEC induced protein losses and altered antibody association states. In SV-AUC experiments, the larger and smaller species sediment together, while in SEC, the larger species are pulled away from the smaller once, inducing system perturbation, altering the size distribution. AF4 seems promising for protein systems difficult to analyze with SEC, Debbie van der Burg 52

60 although the precision for aggregate quantitation is low compared to SEC and SV-AUC. Although the throughput of AUC is not large enough to use it as an everyday method, this study shows it can be employed as a primary calibration technique to quantify trace soluble aggregates accurately Case study 2: AF4 vs SEC for the analysis of submicron aggregates Hawe et al. [45] developed an AF4 method for the analysis of submicron aggregates. The results obtained by the developed AF4 method were compared with SEC. First, a stressed sample was analyzed by both SEC and AF4, showing poor reproducibility for SEC. Second, several stressed samples were analyzed by both SEC and AF4, where better separation was achieved for larger aggregates by AF4. Figure 35: a) SEC chromatograms of heat stressed IgG (n=10; #1, first injection; #10, 10 th injection), insert of non-stressed sample b) AF4 fractograms (n=5) of heat stressed IgG, insert of the non-stressed sample c) non-stressed, ph shifted, heated, and oxidized analyzed by SEC d) non-stressed, ph shifted, heated, and oxidized analyzed by AF4 [45]. Properties of separation techniques regarding size range, resolution, and accuracy should be carefully considered for each type of protein aggregate. This study clearly shows that for the analysis of submicron aggregates, AF4 is better suited than SEC. Debbie van der Burg 53

61 6.2.3 Case study 3: SDS-PAGE vs CGE for the analysis of stressed IgG samples Dong et al. [90] compared the results obtained by SDS-PAGE and CGE analysis of normal and heat stressed IgG samples. The two outer lanes in the SDS-PAGE gel are molecular weight markers, lanes 1-5 are normal IgG samples, and lanes 6-10 are heat stressed IgG samples. The normal IgG sample contained a major band at 150 kda and a minor band at 130 kda. The heat stressed sample contained a major band at 150 kda and four minor bands at 300, 130, 90, and 25 kda. The CGE data of the normal sample shows one major peak at 29 min and one small peak at 27.5 min. The CGE electropherogram of the heat stressed sample shows a major peak at 29 min and minor peaks at 27.5, 25.8, 19.2 and 16 min. Figure 36: CGE electropherogram and SDS-PAGE gel of normal and heat stressed IgG samples [90]. This study shows similar results for SDS-PAGE and CGE. However, CGE analysis achieved better resolution allows for easy quantitation. Debbie van der Burg 54