virus particle characterization

MSc Chemistry

Analytical Sciences

Literature Thesis

Virus Particle Characterization

Techniques to Quantify Virus Particle Aggregation and Integrity

by

Ewoud van Tricht

July 2013

Supervisor:

dr. Wim. Th. Kok

Literature study: Virus Particle Characterization

of 51 Ewoud van Tricht

Virus Particle Characterization

Techniques to Quantify Virus Particle Aggregation and Integrity

Literature thesis

Author : Ewoud van Tricht, Analytical Development, Crucell Holland B.V.

University : University of Amsterdam

Student number : 6129722

Study track : Analytical Chemistry

Supervisor : Dr. W. Th. Kok

Data : July 2013



ABSTRACT

Product characterization is of great importance in the bio-industry to ensure the safety,

efficacy and potency of the pharmaceutical product. Since viruses and virus-like particles

(e.g. virosomes), used in vaccines, can deviate from the expected integrity and/or size, the

choice of suitable analytical techniques is essential.

Beside mature virus particles, incomplete particles and empty particles are formed during

virus particle formation and production. The empty particles lack RNA or DNA but

contain proteins that, at high concentrations, can cause toxic or adverse effects.

Therefore, the amount of empty and incomplete particles should be kept at safe and low

levels [1]. Simultaneously, protein aggregation occurs during manufacturing and

bioprocessing due to pumping, stirring and shaking. Clinical studies have shown adverse

effects and safety issues due to protein aggregation [2, 3]. There is no established or

agreed-upon approach and no guidance in pharmacopoeia to ensure the quality of the

pharmaceutical product in terms of homogeneity of viral particles. However, the amount

of incomplete particles and aggregates should be monitored [4].

In this literature study, three different analytical techniques were assessed for virus

particle characterization. The described techniques are analytical ultra centrifuge (AUC),

field flow fractionation (FFF), and differential centrifugal sedimentation (DCS) and a

comparison was made based on their suitability, advantages, disadvantages, and

applications.



TABLE OF CONTENTS

Abstract .......................................................................................................................................... 3

Abbreviations ................................................................................................................................. 5

1. Introduction ............................................................................................................................ 6

2. Viruses and Virus-like Particles ........................................................................................... 8

2.1 Basic Structure and Background ........................................................................................... 8

3. Viral Particle Characteristics ............................................................................................. 10

3.1 Virus Particle Integrity ........................................................................................................ 10

3.2 Aggregation ......................................................................................................................... 10

4. Importance of Particle Characterization ........................................................................... 13

4.1 Guidelines and regulations .................................................................................................. 13

4.2 Impact of Incomplete Particles and Aggregation ................................................................ 13

5. Overview of Methods for Particle Analysis in Bio-industry ............................................ 15

5.1 Size Exclusion Chromatography and Gel Electrophoresis .................................................. 17

6. Evaluation of Three Techniques Used at Crucell ............................................................. 18

6.1 Analytical Ultra Centrifuge (AUC) ..................................................................................... 19

6.2 Differential Centrifugal Sedimentation (DCS) .................................................................... 28

6.3 Field Flow Fractionation (FFF) ........................................................................................... 38

7. Strengths and Weaknesses per Technique ........................................................................ 44

8. Discussion and Conclusions ................................................................................................ 47

9. References ............................................................................................................................. 49



ABBREVIATIONS

AF4 : Asymmetrical-flow Field Flow Fractionation

AUC : Analytical Ultra Centrifuge

CE : Capillary electrophoresis

DCS : Differential centrifugal sedimentation

DLS : Dynamic light scattering

DNA : Deoxyribonucleic acid

FACS : Fluorescence-activated cell sorting

FFF : Field Flow Fractionation

G : Gravitational force

HA : Haemagglutinin

kDa : Kilo Dalton

LOD : Limit of detection

MALS : Multi-angel light scattering

NA : Neuraminidase

QC : Quality control (department responsible for routine testing)

RI : Refractive index

RNA : Ribonucleic acid

RP-HPLC : Reversed Phase – High Performance Liquid Chromatography

Rpm : Rotations per minute

RSD : Relative standard deviation

SDS : Sodium dodecyl sulphate

SDS-PAGE : Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis

SE : Sedimentation Equilibrium

SEC : Size exclusion chromatography

SV : Sedimentation velocity

TCID50 : Median tissue culture infective dose

UV / VIS : Ultraviolet / Visible

VLP : Virus-like particle



1. INTRODUCTION

In the bio-industry it is of great importance to ensure the safety, efficacy and potency of

the product by characterization studies. Viruses and virus-like particles (e.g. virosomes),

used in vaccines, can deviate from the expected integrity and/or size, which makes the

choice of suitable analytical technique essential [4].

Ideally, only complete mature virus particles are formed during virus particles formation

and production. However, it is known that incomplete particles and empty particles are

introduced during manufacturing. Empty particles lack the ability to perform gene

delivery since they do not contain RNA or DNA. As a result the overall potency and

efficacy of the vaccine decreases. Simultaneously, protein aggregation can occur during

manufacturing and bioprocessing due to pumping, stirring and shaking. Many types of

aggregates are known and their size can differ from nanometers to the visible range [4-6].

Clinical studies have shown adverse effects and safety issues caused by protein

aggregation [2, 3]. Aggregates can for example cause anaphylactic (hypersensitivity)

reactions [7-9]. Moreover, empty particles lack RNA or DNA, which is not a safety issue

by itself, but still contain proteins that can cause toxic or adverse effect at high

concentrations. The amount of empty and incomplete particles should therefore be kept at

safe and low levels [1]. There is no established or agreed-upon approach and no

guidance in pharmacopoeia to ensure the quality of the pharmaceutical product in terms

of homogeneity of the viral particles. However, the amount of incomplete particles and

aggregates should be monitored [4].

Many analytical techniques have been described for the determination of protein

aggregation and incomplete particles in therapeutic protein formulations. Examples of

methods used in the past include HPLC, light scattering, turbidity, electron microcopy,

analytical ultra centrifuge (AUC) [10, 11], capillary electrophoresis [12-14], field flow

fractionation (FFF) and disk centrifugation (DCS) [4, 15]. All those techniques measure a

specific characteristic of the product but the ideal method, for viral particle analysis, has

not been reported yet [16].



This literature study discusses the suitability, advantages, disadvantages, and applications

of AUC, FFF and DCS for the analysis of virus and virus-like particles. The three

techniques are arbitrarily chosen based on the availability of the apparati within Crucell.

The suitability and applicability of the three techniques was established by comparing

analytical parameters like dynamic range, throughput, resolution, detection modes, a.o.



2. VIRUSES AND VIRUS-LIKE PARTICLES

A virus is defined as a nanoscale infectious agent that can only replicate and exist in

living cells of humans, animals, and plants [17]. Virus-like particles (VLP) have similar

characteristics compared to viruses; however, VLPs are non-infectious due to the lack of

viral genetic material.

2.1 BASIC STRUCTURE AND BACKGROUND

VIRUSES

The diameter of common viruses is approximately 20 to 400 nm and the majority has an

icosahedral (20 planes) or helical (spiral) symmetry [17]. Figure 1 shows that viruses

consist of either RNA or DNA, surrounded by a protective coating of proteins, called

capsid. Alternatively, some viruses are surrounded by a lipid envelope membrane and

possess receptor-binding glycoproteins (spikes) on the outside. The receptor-binding

glycoproteins connect to their host cell by a “lock-and-key” principle [17].

FIGURE 1: Basic structure of a virus from reference [17]. Viruses contain RNA or DNA, surrounded by a

protective coating of proteins (called capsid) and often a lipid layer. All viruses possess receptor-binding

glycoproteins (spike) for connecting to cells.



VIROSOMES

A virosome is a virus-like particle used as vaccine delivery system that can be used to

initiate the body’s immune system without using living or synthetically produced

microorganisms like viruses. Virosomes are approximately 150 to 250 nm in diameter

and are made of a phospholipid bilayer, in which glycoproteins and antigens are

embedded (Figure 2). Unlike virus material these virosomes do not contain genetic

material, which increases the safety of the product. [18].

FIGURE 2: Schematic overview of a virosome used for influenza at Crucell. A virosome consists of a

phospolipid bilayer with HA, NA and antigen bound on the outside.

VACCINES

A vaccine is a biological formulation containing an agent, e.g. viruses or virus-like

particles, which stimulates the body’s immune system. Upon vaccination the body will

recognize a potential disease-causing microorganism and quickly react by means of an

immune response. The agent used in the vaccine can be a death microorganism, synthetic

variant of the microorganism, or the surface proteins. A vaccine containing living virus

material is a high-risk pharmaceutical product. Therefore it is essential to analyze all

main components of the vaccine to guaranty the product is safe.



3. VIRAL PARTICLE CHARACTERISTICS

To ensure the safety and efficacy of a viral vaccine advanced analytical tools are required

to confirm amongst others the integrity, size, and aggregation state of the virus or virus-

like particles [19].

3.1 VIRUS PARTICLE INTEGRITY

Ideally, only complete mature virus particles are formed during virus particles formation

and production. However, it is known that incomplete particles and empty particles are

introduced during manufacturing. The empty particles are commonly named empty

capsids because they frequently lack either DNA or RNA and/or viral core proteins [20].

As a result empty capsids are not able to perform gene delivery resulting in an overall

drop in potency and efficacy of the vaccine. Since the empty capsids differ in density in

comparison to mature viral particles, centrifugation techniques are commonly used to

determine the particle hetereogeneity [20]. In addition to being empty, virus particles can

undergo chemical modification like oxidation, glycosydation, and deamidation, which

influences the integrity of the viral particle. The main contribute to chemical

modifications are, amongst others, temperature changes, absorption, and high protein

concentrations [5, 6].

3.2 AGGREGATION

Protein or virus aggregation is a biological phenomenon where proteins accumulate to

form multimers with different undefined characteristics [5-7, 21]. There is no defined

nomenclature for the different types of aggregates. However aggregates can be

categorized based on their specific properties. The categories that are commonly used in

industry are type of bonding, reversibility, solubility, and size [5, 6].

Aggregates can be formed by covalent or noncovalent binding. Covalently-linked

aggregates can consist of two or more monomers that are chemically bound by disulfide

bonds, while noncovalent aggregates are bound by weak forces like Van Der Waals

forces, hydrogen bonding, electrostatic interaction etc [6]. Their size can range from

dimers in the nanometer range to visible particles like “snow” or precipitation of several

micrometers (detectable by human eye) [5, 21]. Dependent on the characteristics either

soluble or insoluble aggregates are formed, where insoluble aggregates tend to

precipitate. In addition, aggregates can be either reversible or irreversible. The



reversibility (ability to dissolve again) of aggregates, depends on the equilibrium between

the monomer and possible multimers under specific conditions. Most often only small

aggregates are reversible, dependent on the type of bonding, temperature, pH and the

composition of the medium [5, 6].

WHAT CAUSES AGGREGATION

Little is known about the cause and extent of aggregation of proteinaceous particles in

vaccines. However, it is known that aggregation is stimulated by conformational changes

and chemical modification due to, for example, temperature, light, and high protein

concentrations. The modified or damaged form of the protein has a strong tendency to

aggregate or precipitate due to protein denaturation. Temperature increase can enhance

chemical modifications like oxidation and deamidation and might have effect on the

quaternary, tertiary or secondary structure of the protein. On the other hand, freezing and

thawing can cause container surface absorption and formation of particles due to pH

changes of the crystallized buffer [5-7]. Increase in protein concentrations has also been

reported to increase the amount of aggregates [6]. At high concentrations the solution

becomes occupied by macromolecules, called macromolecular crowding. This

phenomenon is reduced by diluting the high concentrated solution allowing weak

reversible interactions to dissociate. Moreover, the aggregation rate is strongly dependent

on the pH and ionic strength of the formulation buffer. Most often protein cleavage

occurs at acidic pH while oxidation and deamidation is enhanced at alkaline pH [5, 6].

Lastly, contaminants like host cell proteins, DNA, or even nonprotein materials

originating from packaging material or excipients, can influence the extend of

aggregation as well.

A typical manufacturing process of viral therapeutics includes all aggregation stimulating

factors as described above. The environment (pH, solvent, temperature etc.) should be

critically monitored during manufacturing to minimize the amount of aggregation [5].

Furthermore, the viruses undergo a significant amount of agitation stress due to pumping,

stirring and shaking during manufacturing and bioprocessing. Those operations stimulate

aggregation by thermal, shear, and cavitation effects [5-7].



The therapeutic proteins are not only stressed during the manufacturing process. Every

analytical technique introduces stress to the sample by dilution, change of pH, absorption

and filtration [21]. Since aggregates can be formed or dissociated/disrupted during the

sample preparation and analysis, it is very hard to measure the real content of a sample.



4. IMPORTANCE OF PARTICLE CHARACTERIZATION

It is important to analyze the amount of viral aggregation to ensure the safety, efficacy

and quality of the pharmaceutical product. There is no established or agreed-upon

approach, and no guidance in pharmacopoeia to ensure the quality of the pharmaceutical

product in terms of homogeneity of the viral particles. However, the amount of

incomplete particles and aggregates should be in control [4].

4.1 GUIDELINES AND REGULATIONS

Clear guidelines and criteria are available in pharmacopoeias [23-25] for visible and

subvisible particles present in pharmaceutical products. However, no guidance is given

for soluble aggregates like dimers and trimers of proteins or incomplete particles. The

limitations and criteria for these types of aggregates should be set case-by-case and

should be set to guaranty the safety and efficacy of the product [5, 22, 26]. Although the

pharmacopoeias do not require identification of soluble aggregates, it is of great

importance to determine soluble aggregates, since these aggregates can form visible and

sub-visible aggregates/particles. Furthermore, it is important to distinguish between

nonproteinaceous (particles from packaging etc.) and proteinaceous particles in case of

therapeutic proteins [22].

4.2 IMPACT OF INCOMPLETE PARTICLES AND AGGREGATION

Clinical studies have shown adverse effects and safety issues caused by protein

aggregation [2, 3]. Antibody aggregates can, for example, cause anaphylactic

(hypersensitivity) reactions [7-9]. The magnitude of the immune response or adverse

reaction is mostly based on the solubility and molecular weight of the protein. Large

multimers with a molecular weight above 100 kDa are known to be efficient immune

response inducers. Furthermore, aggregates and particles can also cause problems during

intravenous administration of the vaccine using a syringe [5] and irreversible aggregation

has shown to be a major problem in stability during long-term storage for protein

therapeutics [7]. On the other hand, studies on adenoviruses showed that there was no

correlation between aggregation levels and biological activity [4]. However, adenoviruses

vaccines have significant other problem, for example the empty capsids lack the ability to

perform gene delivery since they do not posses RNA or DNA, which impacts the overall

potency and efficacy of the vaccine. Furthermore, the empty capsids contain proteins that



can cause toxic or adverse effects at high concentrations. As a result, the amount of

empty and incomplete particles should be kept at safe and low levels [1].



5. OVERVIEW OF METHODS FOR PARTICLE ANALYSIS IN BIO-

INDUSTRY

Many analytical techniques have been described for the detection or characterization of

protein aggregation in therapeutic protein formulations. Examples of methods used in the

paste include HPLC, light scattering, turbidity, electron microcopy, analytical ultra

centrifuge [10, 11], capillary electrophoresis [12-14], field flow fractionation and disk

centrifugation, a.o [4, 15]. Although these methods measure a specific characteristic of

the product, the perfect technique has not been reported yet [16].

The life time of aggregates is an important factor that is less extensively discussed in

literature. However, the life time determines if aggregates can be analyzed by a certain

detection method, since the life time of protein multimers may vary from several seconds

to days [21]. In addition, interaction with column material and shear force might disrupt

or create aggregates. This means that most aggregates will never be detected by some of

the analytical methods due to their life time. Similarly, no single analytical technique will

be able to resolve and detect all aggregates in a therapeutic formulation, due to the life

time and the size range in which they occur [21, 22].

Figure 3 shows an overview of commonly used analytical techniques with their specific

molecular size range. In this study only the monomers and aggregates will be discussed in

the range of 1 nm to 10 µm.



FIGURE 3: Depiction of the suitability of analytical methods for size determination in specific size ranges

of subvisible and visible (protein) particles [22].



5.1 SIZE EXCLUSION CHROMATOGRAPHY AND GEL ELECTROPHORESIS

SEC is one of the most commonly used techniques to identify and quantify aggregation

of therapeutic proteins. The separation is based on the shape and size (hydrodynamic

radius) of the macromolecules in a molecular range of about 5 to 1000 kD. Large

aggregates will either be excluded (elute in void volume) or stuck to the inlet of the

column. Therefore, the SEC analysis is commonly performed by indirect estimation of

aggregation based on loss in area. Protein modification and differences in shape will

affect the accuracy of the measurement. Light scattering like MALS is typically used to

determine molecular weight of the monomer and aggregates. Shortcomings of SEC are

the sample preparation whereby the sample is diluted in mobile phase changing the

analyte properties and possibly causing dissociation of reversible aggregates. Another

issue is the nonspecific interaction with column material, especially for hydrophobic

species and aggregates. However, studies have shown that arginine can be used to

suppress protein absorption [6, 7, 22].

Gel electrophoresis or sodium dodecyl sulphate poly acrylamide gel electrophoresis

(SDS-PAGE) is another commonly used technique for estimating protein aggregation.

The separation is based on electric charge and hydrodynamic friction. The molecular

weight range is between 5 and 500 kDa, which is comparable to the range in SEC

analysis. The major drawback of this technique is that only covalently bound aggregates

can be measured since SDS denaturizes and disassociates all noncovalently bonds. At

last, the data obtained with SDS-PAGE is only qualitative. One tried to quantify the SDS-

PAGE results by using a densitometer and appropriate software. However these results

have a high risk to be inaccurate.



6. EVALUATION OF THREE TECHNIQUES USED AT CRUCELL

Although SEC has routinely been used to identify and quantify protein aggregates, a

major disadvantage is the loss of aggregates due to nonspecific binding to the column

material. To overcome this issue, column-free separation techniques, like analytical

ultracentrifugation (AUC) and field-flow fractionation (FFF), are required [27]. Crucell

has three analytical techniques in place that are matrix-free: AUC, FFF, and DCS

(Differential centrifugal sedimentation), which can be used to challenge SEC.



6.1 ANALYTICAL ULTRA CENTRIFUGE (AUC)

Analytical ultracentrifugation is an analytical technique that characterizes species based

on their sedimentation behavior in a centrifuge cell by real time optical detection [28].

The basic principle of sedimentation is described in paragraph 6.2, prior to the principles

of DCS. But for better understanding of AUC, a short summary is given in this

paragraph. The sedimentation process in AUC is influenced by three forces: the

gravitational force, the buoyant force (Archimedes’ principle) and hydrodynamic friction.

The first parameter, the gravitational force, is determined by the centrifugal speed and

radius of the centrifuge. The buoyant force is the second parameter influencing the

sedimentation process and is based on the partial specific volume (inverse of its density)

of the particle and the density of the surrounding solution. It is known that, amongst

others, detergents and glycoproteins can influence the buoyant force. Last of all, the

hydrodynamic friction is dependent on the shape and size of the molecule; larger particles

will experience more frictional drag or hydrodynamic friction. From the three forces

described above the Svedberg equation can be derived (Eq. 1) [27-31].

(Eq. 1)

Where:

Molecular mass Temperature (K)

Solvent density Sedimentation coefficient (1 S = 10-13

s

Gas constant Diffusion constant

Partial specific volume of particle

For a pure, single protein component, the Svedberg equation can provide information

about the molecular weight, the sedimentation coefficient and the diffusion coefficient.

Unfortunately this approach is not applicable to protein solutions containing

contamination like aggregates or incomplete particles without using complex algoritms

[27-30].

To explain the basic principle of AUC it is of importance to distinguish two modes of

operation: sedimentation velocity and sedimentation equilibrium. In sedimentation

velocity, also called “boundary” sedimentation, an ultracentrifuge cell is filled with

sample and real time optical detection takes place during sedimentation at high



centrifugal speed. In a few hours all particles including monomer and aggregates will

settle to the outside of the rotor dependent on their shape and molecular weight. The

sedimentation profiles obtained during sedimentation velocity show a moving boundary

between the lower and upper part of the centrifugal cell as shown in Figure 4.

FIGURE 4: Double-sector instrument. On sector is filled with sample and the other is filled with reference material to correct for

background absorbance [28]

The sedimentation behavior in sedimentation velocity is typically used to determine the

diffusion coefficient, sedimentation coefficient and homogeneity of the species. The

instrumentation and data interpretation is discussed in detail in the paragraphs below [27-

30].

For sedimentation equilibrium relatively low centrifugal speeds are used, where particles

in solution will be pushed towards the outside of the rotor, but will not settle like in

sedimentation velocity [28]. During the sedimentation equilibrium experiment the

concentration at the meniscus (inside of rotor) will gradually decrease, while the

concentration at the bottom of the cell will increase until equilibrium is reached. The

exponential-like sedimentation profile obtained in this experiment is used to determine

the molecular weight of the species. This experimental set-up is particularly used for

characterization of reversible aggregation [28]. The advantage of this approach, in

comparison to sedimentation velocity, is that the molecular weight determination is

independent of the solute shape since particles do not migrate during equilibrium. A

major drawback of sedimentation velocity is the low throughput; typical run times



published were between 20 and 24 hours per run. In this literature study the focus will be

on sedimentation velocity [28].

The AUC is able to rotate at speeds up to 60,000 rpm, which creates a typical centrifugal

force of up to 250,000 g [28]. This g-force is needed to sediment small particles, like

proteins. Sector-shaped centrifuge cells, covered by quartz or sapphire, are used, to make

optical detection possible. The sector-shaped compartment is required since parallel-

shaped cells would introduce convection. Double sector instruments are commonly used

where the second sector can be used as reference cell to correct for high absorbance

background of the solvent [28]. Figure 4 shows a double-sector including the absorbance

read-out.

A six channel sector is available to increase the sample throughput of the AUC. However,

this is sector is limited in use due to a speed limitation of around 40,000 rpm and is

therefore mainly used in sedimentation equilibrium experiments [31].

Optical detection is performed perpendicular to the plan of rotation and is typically based

on refractive index (Schlieren or Rayleigh interference) or absorbance [29] (Figure 5).

Schlieren optics measure the refractive index gradient and is commonly used for

molecules that do not have sufficient chromophores to use photoelectric absorbance.

Since the refractive index gradient is directly proportional to the concentration gradient,

the Schlieren readout can be used to determine the concentration of protein solutions [28,

31]. On the other hand, Rayleigh interference optics is based on the fact that the velocity

of light is dependent on the refractive index and is ten times more sensitive compared to

Schlieren. Rayleigh makes use of monochromatic light, often a laser, which retards in a

sample that has a higher refractive index compared to the reference wavelength measured

in the second sector. For both Schlieren and Rayleigh it holds true that the concentration

can only be measured relative to a known reference concentration. A great advantage of

the refractive index optics is that they are not disturbed by high absorbance background

of the solvent [28, 31].



FIGURE 5: Comparison of the data obtained from the (a) schlieren, (b) interference, (c) photoelectric absorbance optical systems

[28].

The advantage of absorbance optics over interference optics is the fact that the absolute

concentration is known at each point and should not have to be related to a reference

concentration. On the other hand, this detection method could suffer from aggregation

dependent absorption reduction due to specific surface reduction. Typical instruments use

a high-sensitive Xenon lamp that can measure wavelengths in a range of 190 to 800 nm.

New instruments even allow users to scan multiple wavelengths to obtain an absorbance

spectrum. At specific wavelengths absorbance optics can be up to 20 times more sensitive

compared to interference optics like Schlieren and Rayleigh. For all optics the

concentration read-out is a function of the centrifuge cell radius as shown in Figure 5

[28].

In most cases the sample can be purely introduced and measured in the sector cell.

However for ionic species such as proteins a supporting electrolyte, e.g. 100 to 200 mM

KCl or NaCl, should be added to suppress the charges. In general, the density of the

formulation should preferably be around the density of water [28].

A typical sedimentation profile obtained during sedimentation velocity experiment as

shown in Figure 6 displays the centrifuge cell radius on the x-axis and the absorbance on

the y-axis. Normally, a series of sedimentation profiles at different times is shown. Every

profile consists of a curve called the boundary and a flat region called the plateau. In

principle every single compound should give rise to a separated sedimentation boundary.



However two compounds with similar sedimentation coefficients will not be revolved (by

visual inspection) and will show as one sedimentation profile. The reduction in

absorbance of the plateau part of the profile in time is due to the sector shape of the

centrifugal cell. In theory the width or spread of the boundary is directly related to the

diffusion coefficient. Moreover, the marked spots on the curves are called the radial

position (midpoint) of the boundary, which can be used to calculate the sedimentation

coefficient by plotting the natural logarithm of the radial position versus time. The slope

of that curve gives the sedimentation coefficient times the gravitational force (sω2).

Unfortunately, this sedimentation coefficient is not the most precise estimate and a

significant amount of data is neglected to obtain this value [27-30].

FIGURE 6: Typical sedimentation profile obtained by absorbance optics [28].

If both the diffusion coefficient and sedimentation coefficient are known then the

molecular weight can be estimated by Svedberg’ equation (Eq. 1). However, this

approach only works if one can distinguish between boundary spread due to

heterogeneity from boundary spread due to diffusion. Advanced computer programs are

available to perform data analysis based on mathematical algorithms, which include:

LAMM, SEDANAL, SVEDBERG, SEDFIT and SEDPHAT. Most of these software

packages make use of Lamm’ equation (not shown) to determine both the sedimentation

and diffusion coefficient simultaneously.



In this literature study only SEDFIT will be discussed to show the abilities and inabilities

of this kind of mathematical data analyses programms. SEDFIT makes use of an adjusted

Lamm’s equation to derive a distribution of sedimentation coefficients (concentration

versus sedimentation coefficient) as shown in Figure 7. This is done by statistical criteria

of goodness-of-fit, involving least squares method. The profile obtained is similar to a

chromatogram and the relative concentration can be assessed by the area under the curve.

The inset in Figure 7 shows the molecular weight distribution based on Svedberg’s

equation, which only should be used for single molecules. An advantage of SEDFIT

compared to other software programs is the fact that the calculation does not require an

assumption about the number of species present like in SVEDBERG. A drawback of this

approach is the amount of minor aggregates that cannot be uniquely assigned since the

sedimentation coefficient depends on both the size and the shape [27-31].

FIGURE 7: Application of c(s) distribution to sedimentation profiles by SEDFIT software The inset shows the c(M) distribution [29].

Two main disadvantages of AUC the relatively high operational costs of the AUC

machine and the fact that very experienced operators are required to operate AUC.

Another drawback is the data processing that requires complex mathematical algorithms.

The selected program determines the result you will get and it is hard to check if that is

the real result. The great advantage of AUC is the ability to use both absorbance and

interference optics and is of the few methods that can separate the monomer, the

incomplete particles and aggregation in one run.

Applications of both sedimentation velocity and sedimentation equilibrium can be found

in literature. Examples of applications described in literature are polymer analysis [32],



self-association measurement of E. coli. [33], analysis of a viral envelope protein

aggregation [34], antibody analysis [27] etc.

Adenovirus application are described commonly [1, 16, 20, 30, 35] since AUC is able to

separate the monomer and both aggregation and incomplete particles in one run. Most of

the adenovirus applications make use of CsCl gradient to have optimal separation

between the complete (mature) and incomplete (empty capsids) particles based on density

differences. Yang et al. [20] describes a sedimentation equilibrium experiment in CsCl

using UV absorption at 260 (identification) and 320 nm (quantification of empty

particles). The experiment was run for 18 hours at 4°C and 40000 RPM. 400 µl of sample

was loaded and the equilibrium was established after 13 hours and showed 2 different

types of empty particles and 4 other virus forms. Identification was performed by gel

electrophoresis of DNA, infectivity by FACS, SDS-PAGE and RP-HPLC. In all cases the

data analysis was performed by SEDFIT to obtain sedimentation coefficient plots. The

percentage of empty capsid was determined based on the 320 nm signal (260 nm gave

40% underestimation) and was compared to RP-HPLC data. A linear regression was

found between RP-HPLC and AUC but this was only the case after adding an amount of

non-ionic surfactant (0.5% octyl β-D-glucopyranoside) to the sample, to reduce surface

interaction. The results of the samples with detergent showed a difference of 15%, in

comparison to samples without detergent. Finally, the stability indicating ability of this

method was evaluated by measuring samples stored at different days and temperatures.

Clear differences in sedimentation profile were shown for temperature stressed material.

The empty particle concentration in adenovirus reference material (ARM) was calculated

to be 1.3 or 1.5 x 1010

vp / ml, without and with detergent, respectively. This value was

10 times higher compared to RP-HPLC and possibly explained by the fact that RP-HPLC

measurement is based on one single specific protein in empty capsids, while some empty

capsids posses only a very little amount of that protein. RP-HPLC is often used for

adenoviruses to quantify the amount of incomplete particles based on specific protein

structures in the virus. Acetonitrile, from the mobile phase, causes the viruses to denature

to show up as fingerprints of specific proteins in the chromatogram. Vellekamp [54] and

Takahashi [1] both described a RP-HPLC that quantified the amount of incomplete

particles in adenoviruses based on specific proteins that only occurred in incomplete

particles.



On the other hand, Berkowitz et al. [16] claims that no linear behavior was found

between the virus peak and loaded amount in his sedimentation equilibrium due to so

called “hyper-sharp” bands, which caused significant refraction effects. Berkowitz does

not explain in details what the cause is of the hyper sharp bands and the main part of the

paper is about sedimentation velocity. The sedimentation velocity experiment was as

well performed in a CsCl gradient to prevent convection. The run time of this experiment

was only a few hours compared to 18 to 24 hours for sedimentation equilibrium and as

expected, band broadening due to diffusion occurred, resulting in a linear UV response

versus the virus load. The LOD at 230 nm was estimated to be 1 x 1011

adenovirus

particles per ml but unfortunately the quantitative results obtained were biased due to

differences in extinction coefficient between the empty capsids and the mature viral

particles. The discrepancy in extinction coefficient is probably caused by differences in

DNA/protein ratio and most likely also because of light scattering differences [16]. To

overcome the differences in refractive index Berkowitz performed a regular

sedimentation velocity experiment by using a Rayleigh interferometer (RI) instead. The

main advantage of RI is the fact that the response factor of the different species in an

adenovirus is similar, while the difference in response factor at 260 nm and 280 nm can

differ 20 times between proteins and DNA. Berkowitz claims that the uncertainty in the

RI response factor is around 5%. In addition, NaCl was added to the samples to raise the

ionic strength of the solutions and to test for electrostatic effects. The data showed that

charge effects due to low ionic strength of the buffer were not significant. The method

showed to be unsuitable in determining aggregation since a relative standard deviation of

76.1% was found between 10 different runs (values between 3.0 and 23.0% aggregation).

Moreover the sedimentation velocity experiment showed that 70% of the material was

aggregated (dimer, trimer and tetramer), while the same sample measured with band

sedimentation velocity in CsCl gave only 3% of aggregation. This proves that the buffer

composition is influencing the amount of aggregation measured, which makes the choice

of buffer composition critical. The total virus particle concentration obtained by AUC in

VP / ml was 2.2 x 1012

and this value was in line with values obtained by OD260

measurement in SDS. The great advantage of AUC is that the total virus particle

concentration can be corrected by the percentage of empty capsids. The percentage of

empty particles was calculated to be 8.6 ± 0.5 (RSD% 6.1, n=10) resulting in a corrected

virus particle concentration of around 2.0 x 1012

VP/ml. A disadvantage of RI is the 4



times lower sensitivity in comparison to UV detection, resulting in a limit of detection

(LOD) that was around 2 times lower.

In conclusion, the analytical ultra centrifuge (AUC) showed the ability to identify

incomplete particles and aggregation in one run. Both Berkowitz et al. [16] and Yang et

al. [20] described an AUC method for the quantification of incomplete adenovirus

particles. However, the quantification is not straight forward by any means.

Disadvantages of AUC are the low throughput and operational costs and unfortunately

complicated mathematical data analysis is required. The advantage of AUC is the fact

that both UV as refractive index detection can be performed simultaneously. Other

advantages of AUC are the ability to obtain the molecular weight independent of the

shape in the sedimentation equilibrium mode.



6.2 DIFFERENTIAL CENTRIFUGAL SEDIMENTATION (DCS)

Prior the principle of differential centrifugal sedimentation, the sedimentation theory is

described (sedimentation and Stokes’ law).

SEDIMENTATION AND STOKES’ LAW

Sedimentation of particles in a liquid can be used to characterize particle size

distributions. The sedimentation experiment can either be based on gravitational or

centrifugal forces. Gravitational sedimentation is performed at 1 g-force, which limits the

applicability of this mode to large particles only. Small particles will settle at such slow

rates that Brownian motion, random diffusion of particles during sedimentation, will be

significantly high. Therefore, the amount of time it takes for a small particle to sediment

will be too long to give acceptable analysis times. Due to these two disadvantages

centrifugal sedimentation is commonly used instead of gravitational sedimentation. Small

particles can be accurately measured by centrifugal sedimentation since the sedimentation

velocities are much higher than the Brownian diffusion [29, 36, 37].



The sedimentation velocity of particles depends on the density difference of the particle

and the surrounding liquid, and the hydrodynamic radius of the particle. Stokes’ law [38]

can be used to determine the size of the particles based on a fluid of known density and

viscosity, and the time required of a particle to settle a known distance (Eq. 2). This

equation is based on gravitational forces and must be modified when using a centrifugal

sedimentation, since the centrifugal force is dependent on the distance from the center of

rotation. Equation 3 shows the modified Stokes’ law [29, 36, 37].

(Eq. 2)

(Eq. 3)

Where:

Sedimentation velocity (m/s) Final radius of rotation (m)

Mass density of the particles

(kg/m3)

Initial radius of rotation (m)

Mass density of the fluid (kg/m3) Particle diameter (m)

gravitational force (m/s2) Rotational velocity (radians/s)

Radius of particle (m) Sedimentation time (s)

Viscosity of fluid (N s/m2)



INTEGRAL AND DIFFERENTIAL SEDIMENTATION

Two different types of sedimentation methods are commonly used: integral and

differential. Where integral sedimentation is the oldest of the two methods.

In integral sedimentation, also called homogeneous sedimentation, a light beam or X-ray

beam is used to measure particle concentration through a liquid at known distance. The

initial intensity that reaches the detector is at minimum since the particles in the fluid

block the light, called obscuration. During sedimentation particles will settle and the light

intensity will proportionally increase (Figure 8, left). The result of this analysis is a

particle size distribution based on the integral or sum, after mathematical differentiation,

of all particles smaller than a specific hydrodynamic size [29, 36, 37].

FIGURE 8: Integral (homogeneous mode) sedimentation versus differential (line-start mode) sedimentation

Two significant disadvantages of integral sedimentation are reported. First of all, it is

nearly impossible to accurately define the initial conditions after injection into the

centrifuge due to mixing of the sample. Secondly, the sedimentation cell or chamber must

be emptied and cleaned after each run, which limits the throughput of this operational

mode.

In contrast to integral sedimentation where the total sedimentation chamber is filled with

sample, in differential sedimentation the sample is injected on top of the sedimentation

cell (Figure 8, right). Therefore the initial light intensity is at maximum. After injection

the particles settle based on Stokes’ law, like in integral sedimentation mode. Every time

particles of a certain hydrodynamic size pass the detector, the light intensity is reduced.

The result of this experiment is based on a small part of the distribution, a differential. In

this case mathematical integration is performed to obtain a differential particle size



distribution. Figure 9 shows an overlay of both types of distribution. The differential

distribution is typically used and reported. The great advantage of differential

sedimentation compared to integral sedimentation is that no cleaning of the sedimentation

chamber is required, which makes it possible to continuously analyze samples without

stopping the centrifuge.

FIGURE 9: Differential versus integral distribution

Differential sedimentation only works well when an extra compound is added to the fluid

to increase the density of the surroundings, e.g. methanol, ethanol or sucrose etc.

Otherwise all particles will settle as bulk instead of individual particles. This bulk

sedimentation is called streaming and has a great negative effect on the resolution. By

adding a compound like ethanol, methanol or sucrose, a density gradient is formed in the

aqueous fluid. Many methods have been described to reduce streaming but sucrose is

most commonly used to create a slight density gradient. The required slope of the

gradient depends on the density of the samples to be analyzed. Another advantage of a

density gradient is the ability to exclude heat convection caused by friction. One

disadvantage is the fact that the gradient has a limited shelf life due to molecular

diffusion. Typical life times of the gradient lie between a few hours to 3 days [29, 37].

Common DCS instruments use an optically clear disk, with a diameter of around 12.5 to

15 cm, as centrifuge. Moreover, typical instruments can operate at speeds up to 24.000

RPM, which is approximately 40.000 to 48.000 g, dependent on the radius of the disk.

Samples are typically diluted in a fluid that has a slightly lower density than the fluid

used to sediment in. This difference in density between sample and disk fluid ensures that

the solutions are not mixed after injection in the disk center. Injection is performed by



syringe and after that the sample will form a thin film close to the fluid density gradient.

Sedimentation will occur according to Stokes’ law and detection takes place in the end of

the gradient. A schematic overview of a disk centrifuge is given in Figure 10 [29, 37].

FIGURE 10: Overview of disk centrifuge. On left side: top view and on right side: side view.

Detection takes place by monochromatic light of wavelengths around 400 to 500 nm but

some instruments use lower energy wavelengths of 650 nm. For particles below 100 nm it

is advised to use short wavelengths to obtain maximum detector sensitivity.

The light scattering properties do not only depend on the particle shape but also on the

polarizability and the refractive index of the specific particles and their surroundings. For

many commonly analyzed materials calculated refractive indexes, based on Mie light

scattering theory, are implemented into the software to give an estimate of the particle

concentration. For complex samples, like virus or virus like particles, it is hard to correct

with a known or calculated refractive index [29, 37]. Mie scattering described by the Mie

theory is mainly independent of the wavelength and should only be applied to particles

with a much larger size than the wavelength of detection. On the other hand, the

scattering of small particles is predominantly described by Rayleigh scattering theory.

The difference between Rayleigh and Mie scattering is depicted in Figure 11. It is shown

that small particles scatter light in all direction (Rayleigh), while large particles mainly

scattering light in the forward direction (Mie). The amount of light scattered in the

forward direction is directly proportional to the size of the particle [39, 40].



FIGURE 11: Difference between Mie scattering and Rayleigh scattering [41].

It is important to note that only the shape of the distribution (y-axis) is affected by these

optical properties and not the size axis (x-axis). Therefore the size measurement remains

based on the sedimentation process described by Stokes’ law.

X-ray detection is used to overcome refractive index corrections, since absorbed x-rays

are directly proportional to the particle concentration. However, a disadvantage of x-ray

detection is its low sensitivity, which makes it only suitable for homogeneous mode or

integral mode of DCS. X-ray detection is commonly used for clay and inorganic

materials but cannot be used for organic particles [29, 37].

The average measurement range of DCS is about 7.5 nm to 50 µm, though it is possible

to extend the range for specific types of materials [29]. The dynamic size range is in most

cases limited by the instrument. BI-DCP disk centrifuge particle size analyser, for

example, has a range of 10 nm to 30 µm [42], while the CPS DC24000 disk centrifuge

can accurately determine the size from 10 nm to 50 µm [43].

The theoretical resolution obtained by DCS can easily be estimated and depends on

multiple factors: the injection plug, the sedimentation distance and the detector split

width. Equation 4 can be used to calculate the theoretical resolution.

Where:

Theoretical resolution Experimental resolution

Injection plug (mm) Diameter of larger particles (µm)

Sedimentation distance (mm) Diameter of smaller particles µm)

Detector split width (mm)

(Eq. 4)

(Eq. 5)



Equation 4 shows that typical conditions (detector width: 0.5 mm, injection plug: 0.1 ml

= 0.066 mm and sedimentation distance: 10 mm) give a theoretical resolution of around

3%. This resolution can be increased by either increasing the sedimentation distance or

reducing the injection plug and/or detector window. Moreover, it should be noted that the

higher the resolution in percentage, the more difficult it is to separate two particles of

different sizes. In general, the resolution is decreased by two major experimental factors

called streaming and Brownian motion. The random diffusion caused by Brownian

motion has great impact on small particles (below 0.3 µm or particles with sedimentation

time greater than 12 minutes) resulting in band broadening. Other parameters that can

affect the resolution are deviations from Stokes’ law due to wall effects, for example,

absorption and adhesion [29, 37].

Equation 5 can be used to calculate experimental resolution in percentage. A resolution of

5% means that two perfectly narrow size distributions can be separated if they differ 5%

in diameter. In practice such resolutions are never obtained since size distributions are

never perfectly narrow. This is shown by typical values described in literature. For

example, a mixture of 9 different polystyrene particles in a range of 0.2 to 10 µm

(apparent hydrodynamic sizes) were separated with a average resolution of 46%, see

Figure 12 [37]. Another example is the separation between an adenovirus monomer of

±75 nm and a dimer of around 95 nm (apparent hydrodynamic size) with a percentage

resolution of 23% , see Figure 12 [4]. The dynamic range, mostly given as ratio of the

largest to the smallest particles in the distribution, used for adenoviruses is around 10

(equals 50 to 500 nm) and the range used for the polystyrene particles is 120 (0.1 - 12

µm) [29, 37].



FIGURE 12: Left: Example of 9 polystyrene particles in a range from 0.2 to 10.0 micron

Right: Example of Adenovirus incomplete and aggregate separation from 50 to 500 mm

The analysis time in DCS depends on the measurement range. The run time is typically

between 10 and 45 minutes in a size range of 25. Doubling the range to 50, means

approximately a four times increase in analysis time. In general the analysis time should

be kept below 45 minutes to obtain the best results [29, 37].

Internal standards are used to increase repeatability and accuracy. Values of around

0.25% RSD are reported in replicate analysis of unknowns [29, 37].

Typical DCS applications found in literature are particle size distribution measurements

of polymers [29, 37], latexes, SiO2 dispersions, pigment, UV-absorbers, virus particles

[4, 15], bacteria [44], protein clusters, oil emulsions, metallic powders [36, 37], and clay

[36]. The two virus applications are described in detail below.

Bondoc et al [15] describes the use of DCS for the size distribution analysis of

adenoviruses with the purpose to obtain an accurate size determination (x-axis) of

adenovirus aggregates. Prior to DCS analysis the diameter of the adenovirus was

determined 73 nm by electron microscopy. To ensure the accuracy of the measured size a

PVC calibration standard was used before every injection and for data-analysis. The

particle density and refractive index were estimated 1.33 g/ml and 1.45, respectively.

Samples containing different amounts of aggregated analyte were analyzed at 10000

RPM in a sucrose gradient resulting in an average apparent hydrodynamic diameter of 63



nm. The difference between the values obtained by electron microscopy and DCS were

explained by the fact that density of the particle was not estimated properly and due to

calculations of particles (assuming spherical particles). At least 3 types of aggregates

were observed in the disc analysis with apparent hydrodynamic diameters of 76, 84 and

94 nm and all values were confirmed by calculations. The developed method showed that

different types of adenovirus aggregates can be identified. However, the size distribution

is still dependent on a lot of estimated parameters like the particle density and shape, a.o.

Lastly, the results obtained were compared to OD260 and 280 measurements, which are

only indicative.

Shih et al [4] used a comparable set-up and the same instrument as Bondoc et al but in

this case the sample parameters were 1.24 g/ml for the particle density and 1.52 for the

refractive index. Analysis was performed in a sucrose gradient and took around 18

minutes. Prior each injection the system was calibrated by a latex bead analysis.

Moreover, the samples were treated by temperature stress and human sera to induce

forced aggregation. Different amounts of aggregates were mixed to obtain ratios of 20:80,

40:60, 60:40, and 80:20 and analyzed by the DCS instrument. An average apparent

hydrodynamic diameter of 63 nm was observed for the monomeric adenovirus, which is

10 nm higher compared to the result obtained by Bondoc et al The results of this study

showed repeatability of 1.4% RSD, recovery of 95 ± 8% (n=24) and intermediate

precision between 5 and 11 % RSD. The data was compared to AUC analysis and

showed a linear relationship between the amount of aggregation measured by AUC and

DCS. However, slightly higher amounts were measured by AUC, possibly due to

reversible aggregates that dissolved again by passage through the sucrose gradient. The

validated method was not suitable to quantify the amount of sub-particles (e.g.

incomplete particles) since it was claimed that the smaller particles reflect significantly

less light resulting in low accuracy.

In conclusion, DCS can be applied to a broad range of products. High resolution is

obtained if streaming and Brownian motion are under control. Complete “baseline”

separation is obtained if particle distributions differ more than 5% in size. In typical

analysis the dynamic range is around 75, with fixed disk speed. Ramped disk speed can

be used to extend the dynamic range to around 1000. Internal standards, like polymer

beats, can be used to calibrate the size axis. The drawback of this technique is the



inability to extend the dynamic range (> 75) without using ramped disk speed.

Furthermore, non-spherical particles will sediment slower than expected and will be

reported as smaller in size. Moreover, the analysis time for particles smaller than 50 nm is

significant long compared to larger particles. [4, 15, 29, 37, 44].



6.3 FIELD FLOW FRACTIONATION (FFF)

Field flow fractionation makes use of a laminar flow of solvent, which is pumped through

a narrow channel (70-300 µm) from the inlet (injection) to the outlet (detection). Due to

the parabolic flow profile the velocities vary from zero near to wall to a maximum in the

centre of the channel. An extra force (physical or chemical) is applied perpendicular to

the flow of the solvent to concentrate the sample near to semi permeable ultra filtration

membrane, called accumulation wall (Figure 13). The pore size and properties of the

membrane determines the size of molecules that are retained. When the perpendicular

field is generated by a cross-flow, like in flow FFF, the separation is only based on the

diffusion coefficient of the analytical species (Eq. 6).

FIGURE 13: LEFT: Schematic overview of Field flow fractionation separation channel.

RIGHT: Schematic description of the FFF separation mechanism

Since larger species have a slower Brownian motion, they will diffuse slower when

compared to smaller species. Therefore, larger species will have their steady-state closer

to the accumulation wall than smaller species. The velocity of the different species in the

longitudinal direction is dependent on their distance to the accumulation wall and

therefore of the velocity of the parabolic flow. Since the velocity of the parabolic flow

increases at increased distance from the channel walls, small molecules will reach the

detector faster than larger molecular [45-48].



Equation 6 shows that the retention factor in flow-FFF depends on parameters like the

channel volume, cross-flow rate and the diffusion coefficient. Where the cross-flow rate

is typically used to optimize retention times since this parameter is easily adaptable from

run to run.

(Eq. 6)

Where:

Retention factor (dimensionless) Cross-flow rate

Volume of the channel Diffusion coefficient

Channel thickness

Beside flow-FFF, many other cross-fields can be applied, like e.g. electric field, magnetic

field, and temperature, resulting in at least 10 different types of FFF. In this literature

study only flow-FFF will be discussed since this method is the most versatile of all FFF

techniques.

Two major types of flow-FFF are known; symmetrical and asymmetrical. In symmetrical

flow-FFF both channel walls are permeable while in asymmetrical flow-FFF (AF4) only

one channel wall is permeable. In AF4 a tapered shaped channel was introduced to

correct for the continuous loss of carrier liquid trough the channel. In this way the flow

rate remains stable between the inlet and outlet (Figure 14).

The basic operation principle of AF4 is based on 3 different steps: equilibration,

focusing/injection and elution. In the first step a constant flow rate is established from the

inlet to outlet with a cross-flow through the membrane. As soon as the flow rate is stable

the outlet flow rate is reversed to obtain a focusing point near to the injection port. At that

moment the channel flow rate is zero. Secondly, the sample is injected via the injection

port and will be focused at the focusing point created by the reversed outlet flow. In the

last step the sample is eluted by applying the same settings as used during the first step

(Figure 14). The instrumental conditions that can be varied during method optimization

are: injection flow rate, cross-flow rate, channel flow rate, ionic strength of carrier liquid,

membrane type, spacer height, channel width, and the focus flow rate and time [45].



Figure 14: Schematic overview of AF4 separation channel [45]

In AF4 an open channel is used and is therefore described as matrix-free separation

technique. In comparison to e.g. HPLC the open channel reduces shear effects and

adsorption [45].

Detection is commonly performed by UV, refractive index and/or light scattering. The

refractive index detector is designed to measure difference in refractive index, which is

proportional to the solute concentration. The major drawback of this detection technique

is the dependence of the refractive index on pressure and temperature. However, with

current instruments both pressure and temperature are easily controlled.

Detection techniques based on light scattering, like MALS, had become of great

importance in the biotechnology industry because they give absolute values of the molar

mass and size. For better understanding of MALS, a short introduction in light scattering

theory is described below.

When a laser beam interacts with protein, an osculating dipole is created due to the

electric field of the laser light itself. As a result the dipole will scatter or radiate a specific

amount of light, dependent on the polarizability of the protein. The intensity of the

scattered light does not only depend on the polarizability of the protein but also on the

amount of protein molecules. Thus 2 times more molecules will scatter twice as much,

compared to solutions that contain half of the amount of molecules. Moreover, dimers

scatter twice as much light in comparison to monomers, which means that the total

intensity of scattered light is directly proportional to the product of the molecular mass

and the molecular concentration.

Light scattering from macromolecules, like proteins, are dependent of the scattering

angle. Every part of the molecule will scatter light in different intensities directions. The



angular dependence of the scattered light can be measured to determine the size, which is

known as the root mean square (rms) radius or radius of gyration. In MALS, the light

scattering intensity and angular dependence is measured in up to 18 angles to obtain the

size and molecular weight of biomolecules.

A drawback of FFF is the difficulty to select a proper membrane since proteins can stick

to the membrane resulting in bad recovery. The advantage, on the other hand, is the

ability to measure in a very wide range. The dynamic range of AF4 is from around 2 nm

to up to 50 µm, were the lower limit is mainly dependent of the pore size of the ultra

filtration membrane [47]. In addition, MALS is an exact measurement and does not

require any calibration standards to determine size of the analyte.

Many applications found in literature described the use of AF4-MALS to quantify virus

particles or assess viral aggregation. Yohannes et al. [46], for example, wrote a review

about the use of AF4 in the bio industry. This review shows that for most viruses

regenerated cellulose is used as membrane. On the other hand, cellulose membranes are

used for liposome analysis. In all cases a combination of UV and MALS and/or refractive

index was used and typical buffers used are TRIS and phosphate buffers around pH 7 and

8. Viruses that were measured or quantified by AF4 include influenza virions [49],

murine polyomavirus, and virus-like particles.

A virus-like particles application is described by Lang et al. [50], which developed an

AF4 method for the determination of aggregation of virus-like particles (viral origin not

mentioned). In the first part of the development three different types of membrane were

assessed: regenerated cellulose, cellulose triacetate, and polyethersulfone. Regenerated

cellulose was chosen to be the optimal membrane since a recovery of 96 percent was

found by UV detection. Recoveries of 79 and 30 percent were found for cellulose

triacetate, and polyethersulfone, respectively. The method was optimized to resolve the

monomer virus-like particle including dimers and trimers within 35 minutes. Figure 15

shows the AF4-MALS data with on the primary y-axis the measured UV absorbance and

on the secondary y-axis the molar mass obtained by light intensity measurement by

MALS.



Figure 15: AF4 for virus-like particles by Lang et al. [50]

Quantification and determination of molecular weight was performed by UV and MALS

detection, respectively. Moreover, it was shown that the cross-flow rate and focusing time

didn’t cause induced aggregation. In the end of the study the developed AF4 method was

compared to DLS and SEC with AF4 exceeded the separation capacity of both

techniques. The results obtained by AF4 were confirmed by TEM pictures. Citkowicz et

al. [51] published a comparable AF4-MALS method were a protein aggregate (2500 kDa)

was separated from a virus-like particle (14000 kDa, 20 nm). This method was

successfully implemented as fast screening method for aggregation analysis.

Wei et al [49] successfully developed an AF4-MALS method for particle counting of

influenza virions. The method was developed by optimizing the cross-flow, focus time

and the cross-flow gradient and it was shown that those parameters are critical and need

to be optimized per type of sample. The data obtained in this study was compared to

SEC, electron microscopy, atomic spectroscopy and biological techniques like TCID50

(median tissue culture dose) and FFA (fluorescent focus assay), and the particle count

showed to be comparable. Table 1 illustrates the comparison between SEC-MALS and

AF4-MALS and it is shown that the virus particle count and standard deviation are in line

with each other. On the other hand, the percentage of monomer and aggregates is much

lower for SEC-MALS and AF4-MALS showed very bad precision. Based on the results it

can be concluded that AF4-MALS should only be used for virus counting and not for

determination of the monomers and aggregates. Therefore the added value of AF4

compared to SEC, in this specific case, is questionable.



TABLE 1: Comparison between SEC-MALS and AF4-MALS for influenza virions analysis [49]

Log10 VP / ml % monomer % aggregate

SEC-MALS 9.9 ± 0.8 81 ± 1.0 19 ± 4.4

AF4-MALS 10.4 ± 1.8 75 ± 8.2 25 ± 24.5

In addition, McEvoy et al [52] utilized the same AF4-MALS method as Wei et al and

searched for manners to improve the particle counting. In his study he made use of

defined size of adenovirus and polystyrene beads to come up with a mathematical model

for influenza virus particle counting. He was able to modify the spherical particle

counting equation to increase the recovery from 63.4 to 97%.

Asymmetrical-flow-filed flow fractionation (AF4) showed to be mainly suitable for

aggregate determination.. AF4 is not suitable for incomplete particle determination since

the separation mechanism is based on diffusion coefficient and complete and incomplete

particles have similar diffusion coefficients. This was confirmed by the fact that no

publications were found about incomplete particle determination of viral samples. In

conclusion. the main advantage of AF4 is the wide dynamic range (2 nm to 100 µm), the

huge versatility, the fact that it is an exact measurement (no standards required), and the

lack of stationary phase, which reduces non-specific interactions. Drawbacks of AF4 are

the importance of ultra membrane selection and dilution effects that occur during the run.



7. STRENGTHS AND WEAKNESSES PER TECHNIQUE

The methods described above all have their advantages and disadvantages and the added

value of the techniques should be evaluated case-by-case. No single technique is able to

straightforwardly quantify incomplete particles and aggregates in one run.

All strengths, weaknesses, and applications of the three different techniques are

summarized in Table 2.

Differential centrifugal sedimentation (DCS) showed the ability to identify the

incompletes and aggregation in one run. However, DCS will not be able to quantify the

incompletes straight forwardly due to fact that the refractive index of the particle and its

shape should be known to do so. Moreover, for an accurate determination of the size

distribution, a third parameter is required: the particle density. Those three parameters

can only be estimated, which makes it hard to accurately quantify the incomplete

particles. In addition, small particles scatter significant lower amount of light than large

particles, which makes it even harder to accurately quantify incomplete particles.

Although Shih et al [4] was able to validate a DCS method for the quantification of

adenovirus aggregates, it should be noted that the three parameters (density, refractive

index and shape) were estimated resulting in an unknown bias. In conclusion, the main

advantage of DCS is its broad measurement range, fast analysis and the limited amount

of knowledge required to run this method. Drawbacks of this technique are the limited

detection mode resulting only in a relative particle size determination and the amount of

parameters that require estimation to get quantitative data as shown in Table 2.

The analytical ultra centrifuge (AUC), like DCS, showed the ability to identify

incomplete particles and aggregation in one run. Both Berkowitz et al. [16] and Yang et

al. [20] described an AUC method for the quantification of incomplete adenovirus

particles. However, the quantification is not straight forward by any means; Yang had to

add, for example, an amount of non-ionic surfactant to reduced surface interaction and to

get results which were in line with RP-HPLC data. The advantage of AUC over DCS is

the fact that both UV as refractive index detection can be performed simultaneously.

Berkowitz also quantified the amount of aggregation but unfortunately they found a RSD

of 76.1% by ten-fold analysis of the same material making this method practically

useless for the quantification of aggregates. It was shown that the buffer composition was



influencing the amount of aggregation measured, which made the choice of buffer

composition critical. In conclusion, the advantages of AUC are the ability to obtain the

molecular weight independent of the shape in the sedimentation equilibrium mode.

Furthermore, AUC is the only technique, described in this literature study, able to both

indentify and quantify incompletes and aggregation in one run. Disadvantages of AUC

are the low throughput and operational costs and unfortunately complicated mathematical

data analysis is required.

Asymmetrical-flow-filed flow fractionation (AF4), on the other hand, showed to be

mainly suitable for aggregate determination. Lang et al described an AF4-MALS method

for the quantification of aggregates including dimers and trimers. This method exceeded

the separation capacities of SEC and DLS and the results obtained were confirmed by

TEM pictures. Furthermore, Wei et al published an AF4-MALS method development for

influenza virion particle counting. The results obtained were compared to a set of

analytical and biological techniques and the particle count showed to be comparable. In

this study the percentage of aggregation was also determined and was shown to be in line

with the results obtained by SEC. However, the relative standard deviation obtained with

AF4 was more than 5 times larger compared to SEC. In this specific case the added

value of AF4-MALS compared to AF4-SEC is questionable. AF4 is not suitable for

incomplete particle determination since the separation mechanism is based on diffusion

coefficient and complete and incomplete particles have similar diffusion coefficients.

This was confirmed by the fact that no publications were found about incomplete particle

determination of viral samples. In conclusion. the main advantage of AF4 is the wide

dynamic range (2 nm to 100 µm), the huge versatility, the fact that it is an exact

measurement (no standards required), and the lack of stationary phase, which reduces

non-specific interactions. Drawbacks of AF4 are the importance of ultra membrane

selection and dilution effects that occur during the run.



TABLE 2: Strengths and weaknesses of AUC, AF4 and DCS

AUC

Sedimentation velocity | Sedimentation equilibrium AF4 DCS

Separation

Medium Column-free chamber Column-free channel Column-free disk

Matrix Sample matrix or CsCl Mobile phase Sucrose-gradient

Principle Sedimentation coefficient Diffusion coefficient Sedimentation coefficient

Resolution Medium High Medium Medium

Dynamic range 1 to 200 nm 1 nm to 100 µm 7.5 nm to 50 µm

Suitability

Empty capsids Identity: Yes, Quantity: Yes Identity: No, Quantity: No Identity: Yes, Quantity: No

Aggregates Identity: Yes*, Quantity: Yes* Identity: Yes, Quantity: Yes Identity: Yes, Quantity: Yes

Throughput Medium Low High High

Run time 1-4 hours up to 24 hours < 1 hour < 1 hour

Technical

knowledge

required

High Medium Low

Detection modes UV, refractive index

UV, refractive index, light scattering Monochromatic light obscuration,

x-ray (integral mode only)

Result Hydrodynamic

size

Molecular

weight

Geometric radius

with MALS

Molecular

weight Hydrodynamic size

Advantages - Fast analysis in SV mode - Lack of stationary phase/shear force - Fast analysis

- Ability to identify and quantify

incompletes and aggregates in one run - MALS detection mode

- Ability to identify incompletes and

aggregates in one run

Disadvantages - Low throughput and high operational

costs - Critical selection of ultra membrane

- Estimates of particle shape, density

and refractive index required

- Complicated mathematical data

analysis

- Dilution and concentration effects

during run

- No absolute particle size

determination

Transferability

to QC

Low: difficult due to costs, throughput

and skills required Medium: only limitation are the costs

Medium: not possible to qualify

software for GMP regime

Virus

applications

Adenovirus [20],

Influenza virus [46],

virus-like particle [49],

adenovirus [52]

Adenovirus [4, 15],

* Buffer composition selection is critical



8. DISCUSSION AND CONCLUSIONS

Many analytical methods are available for the analysis and characterization of virus

particle aggregation and integrity. These methods include amongst others SDS-PAGE,

DLS, electron microscopy, SEC [53], AUC [10, 20, 30], AF4 [46, 51], DCS, HPLC [20,

54], and CE [55, 56]. All of those methods have added value in a specific range or for a

specific purpose. However, no single technique is suitable to characterize virus particle

aggregation and integrity in one single measurement [16].

For all three techniques many virus application were found and most of the application

were about adenovirus [4, 15, 16, 20, 35, 54]. In addition, for AF4, more virus

applications were described: influenza [49] and virus-like particles [19, 50, 51].

The main issue in aggregation analysis is the fact that aggregates poses different life-

times. As a result most aggregates will never be detected by some of the analytical

techniques. Furthermore, it is known that aggregates are introduced and dissolved during

analysis. This means that it is hardly possible to measure the real aggregation state of the

product. Many publications have shown that the choice of buffer or mobile phase is

critical and influences the amount of aggregation analyzed by a specific technique. These

issues and the fact no single method is suitable to cover the whole range and type of

aggregates, means that more than one technique should be used to determine and quantify

the amount of aggregation [21, 22].

Based on this literature study it became clear that all three techniques are suitable for

aggregation analysis, however, all techniques have some drawbacks. AUC, for example,

has limitations due to its low throughput, level of skills required and its expensiveness.

AF4 is expensive to purchase and DCS is a fast and relatively cheap but has software that

cannot be qualified for GMP (good manufacturing practice) regime, which makes it

difficult to implement on quality control laboratories. Without this latter drawback DSC

would, without a doubt be the technique of choice to be implemented on a quality control

laboratory for aggregation analysis. The proposal is to use AF4 and DCS as

characterization and QC techniques. AUC in sedimentation velocity mode can be used as

characterization technique to support the data obtained with AF4 and DCS. In all cases

SEC and SDS-PAGE should as well be considered as fast and easy tools to measure

aggregation.



In contrast to protein aggregation, the composition and amount of incomplete and/or

empty particles seem to be more consistent, which makes the determination more straight

forward. However, in some cases it was shown that the amount of incomplete particles

was affected by the selected technique [22].

The majority of the publications, which described virus particle integrity (incomplete and

complete particles) quantification, were based on AUC and RP-HPLC. Since RP-HPLC

is more easily transferred to quality control departments, this method should be

considered next to more expensive methods like AUC for adenovirus characterization.

For this reason RP-HPLC was added to the list of proposed techniques. For incomplete

particles measurement of viruses other than adenoviruses the techniques available for QC

analysis are rather limited. This means that other techniques should be explored or the

DCS software should be made suitable (work around) for working under GMP regime.

The proposed techniques for the identification and quantification of virus particle

aggregation and integrity are summarized in Table 3. It is advised to use more than one

technique per viral particle characteristic.

TABLE 3: Proposed techniques for virus particle characterization

Particle

characteristics

Proposal

Technique Suitability Type of method

Aggregation AF4

DCS

AUC SV mode

Identity + quantity

Identity + quantity

Identity + quantity

Characterization + QC

Characterization + QC*

Characterization

Integrity (incompletes,

empties)

AUC SV mode

RP-HPLC for adenoviruses

DSC

Identity + quantity

Identity + quantity

Identity

Characterization

Characterization + QC

Characterization + QC*

* DCS is very suitable for routine QC analysis; however, software cannot be qualified for GMP regime.

It can be concluded that none of the three techniques discussed in this literature essay is

the perfect technique for the characterization of virus particle aggregation and integrity.

In all cases more than one technique is required to come to best and valuable result.

Knowledge about the strengths and weaknesses per technique will help us in

characterizing a very complicated product, the viral vaccine.



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