Mechanistic Complexity of Subvisible Particle Formation: Links to Protein Aggregation are Highly Specific

RESEARCH ARTICLE Mechanistic Complexity of Subvisible Particle Formation: Links to Protein Aggregation are Highly Specific B. ROBERT SIMLER,1 GUODONG HUI,2 JENNIFER E. DAHL,1 BERNARDO PEREZ-RAMIREZ1 1

BioFormulations Development, Genzyme Corporation, Framingham, Massachusetts 01701

2

Stability and Statistics, Genzyme Corporation, Framingham, Massachusetts 01701

Received 16 February 2012; revised 10 May 2012; accepted 2 August 2012 Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23299 ABSTRACT: There is little knowledge available on the mechanistic features of the protein aggregation pathway, which lead to subvisible particles (SVPs) (0.1–100 :m in size). Additionally, the relationship between soluble aggregates (SAs) (those that are less than 0.1 :m in size) and SVP formation is largely unknown. To better understand these relationships and the mechanism of SVP formation, we conducted agitation experiments on three different classes of proteins; two antibodies [an immunoglobulin G (IgG) 1 and an IgG4] and a glycoprotein. A quantification of SVPs, using the Brightwell Microfluidics Instrument, and levels of SAs by sizeexclusion chromatography were determined as a function of agitation time. Not surprisingly, the propensity to aggregate and particulate was different for each protein. However, integrated mass analysis in these studies showed that the relationship between SA and SVP formation is also protein and formulation dependent, and can vary greatly between molecules. Morphological and statistical analysis of SVPs in agitated and nonagitated samples revealed that changes in both the shape and the size distribution of the SVPs population are also protein dependent and highly defined. Collectively, these results suggest/illustrate the complexity of elucidating an aggregation mechanism that encompasses both SAs and SVPs. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci Keywords: protein aggregation; stability; formulation; subvisible particles; glycoprotein; antibody; mechanism; agitation

INTRODUCTION The development of a protein for biotherapeutic use requires the identification, monitoring, and control of many different degradation products. Among these possible degradations, the formation of protein aggregates is of a primary concern.1 Aggregation can occur throughout the entire manufacturing process, from purification and formulation to fill/finish and storage. There are several potential consequences of protein aggregation including a loss of enzymatic activity,2 and the induction of both neutralizing and nonneu-

Abbreviations used: ECD, equivalent circular diameter; GP, glycoprotein; HPLC, high-performance liquid chromatography; IgG, immunoglobulin G; MFI, microfluidic imaging; SEC, sizeexclusion chromatography; SVPs, subvisible particles. VP, visible particles Correspondence to: Bernardo Perez-Ramirez (Telephone: +508-270-2117; Fax +508-820-7664; E-mail: bernardo.perez@ genzyme.com) Journal of Pharmaceutical Sciences © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association

tralizing immunogenic reactions,3–5 which can compromise the safety profile.6,7 Although the definition of a protein “aggregate” has generally encompassed any higher order assembly of protein molecules,8 the majority of the investigational studies that have been conducted to further analyze this phenomenon have focused on what is now categorized as “soluble aggregates” (SAs). These aggregates include the smallest possible multimeric species from dimers and trimers to protein complexes, which measure up to 0.1 :m in size. Mechanistic insights into how these aggregates are assembled were first proposed in the Lumry–Eyring9 model, which postulated the existence of an aggregate-prone state. This state features an altered conformation that is reversibly populated from the native state of the protein. Aggregates are then formed irreversibly as the aggregateprone state combined with other such molecules to propagate a larger complex. Considerable work has been carried out since this model was presented to identify some of the characteristics of the aggregateprone state and the driving forces of aggregation. JOURNAL OF PHARMACEUTICAL SCIENCES

1

2

SIMLER ET AL.

Studies have highlighted the role of solution energetic and conformational influences upon the pathway for the formation of these SAs.10,11 Other studies suggest that the building blocks for SAs need not be a partially unfolded protein,11,12 but that native13,14 and completely unfolded forms15 of various proteins may form the framework for these species and are dictated by charge–charge interactions. The effect of a multitude of manufacturing variables on the formation of SAs is well known. For instance, formulation variables such as pH, ionic strength, the presence of a surfactant, and protein concentration can all result in changes to the rate of SA formation. Exposure to the air–water interface or the container surface within a storage vessel can promote aggregation.11,16,17 Freeze–thaw,18–20 various agitation stresses, such as shaking and stirring11,21–23 as well as environmental stresses, such as the exposure to light24 or elevated temperatures,25 can adversely affect the ability of a protein to remain in its native state. Recently, insight has been obtained into the profound effect that the presence of micro- or nanoparticles can have in acting as a nucleation site for protein aggregation.26,27 The extent of knowledge concerning the mechanistic pathway for the formation of subvisible protein particles (SVPs), those between 0.1 and 100 :m in size, is extremely limited when compared with that for SAs. One reason for this is that analytic and biophysical methods to measure and characterize these particles are still extremely limited and rather new when compared with those available to analyze SAs.8,28 Although size-exclusion chromatography (SEC) has been the high-throughput workhorse to quantify and study SAs, a battery of other techniques, such as analytical ultracentrifugation,29,30 field flow fractionation,31 and light scattering,32,33 have also been employed. Historically, light obscuration has been the only viable technique to study protein particles in this size range. This “gap” in available analytical capabilities to measure SVP has recently been highlighted6 as a potential area of concern in assessing the quality and safety of biotherapeutic protein drugs. Inherent physical features of SVP, specifically the size and repeating $-sheet motif thought to be present in many SVPs, have been postulated to make this class of aggregate a candidate to induce a neutralizing immunological response.34–38 As such, new technologies, such as the Flowcam Imaging System, and new applications with existing technology, such as the Coulter Counter, are being evaluated as to their potential to quantify and characterize SVP.28,39,40 Additionally, the presence of SVP is being evaluated with the same methodologies that have long been used to study SAs. For instance, SVP levels have been observed to increase during the storage of a drug product during stability studies.41 JOURNAL OF PHARMACEUTICAL SCIENCES

Many of the manufacturing variables that are complicit in the formation of SAs have been shown to also generate SVPs. Freeze–thaw,18 agitation,15,21,42 and temperature stresses15,25 all have shown the potential to result in increased amounts of SVP. Despite this current increased focus on SVP, little is known about the mechanism of their formation and their relationship to SA formation. A proposed pathway depicting the formation of SVP is shown in Scheme 1 where a native protein (N) undergoes the aforementioned conformational change proposed by the Lumry–Eyring model to populate an aggregateprone state (A∗ ). These converge to form a SA and as SAs combine, they eventually become large enough to be classified as “subvisible”. Although this model is certainly sufficient to act as a general illustration of the pathway to form SVP, the details that govern the transition from SA to SVP are still largely unknown. For instance, some studies indicate that the formation of SVP can be partially reversible.15 Others indicate that different stresses on the same protein result in the generation of different types of SVP.15,43 These findings imply that the formation of SVP from SAs is significantly more complex than Scheme 1 depicts it to be. This manuscript summarizes the results of an agitation study investigating the relationship between SA and SVP formation on three classes of proteins, two monoclonal antibodies [an immunoglobulin G (IgG) 1 and an IgG4] and a glycoprotein (GP) of approximately 100 kDa. It should be noted that the classification of aggregate species continues to cause confusion within the field as terminology is somewhat inconsistent. An attempt to harmonize this terminology was recently presented by Narhi et al.44 by categorizing aggregates by size with the terms submicron aggregates (<100 nm), nanometer aggregates (0.1–1 :m), micron aggregates (1–100 :m), and aggregates greater than 100 :m. However, a recent study by Scherer et al.45 classified “submicron” aggregates as less than 1 :m in size, whereas another study by Filipe et al.46 classified “submicron” aggregates as 0.1–1 :m in size. As such, for the purposes of this manuscript we have used more traditional designations to be as consistent as possible with recent studies that are similar to the analysis that is presented here though the terminology presented by Narhi et al.44 is also noted where appropriate. As such, the term “SAs” refers to aggregates <0.1 :m

Scheme 1. Proposed mechanistic pathway for the formation of subvisible (SVP) and visible particles (VP). DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

3

in size, whereas “SVPs” refers to the size range from 0.1 to 100 :m. The specific size ranges studied within these designations are included in the sections Results and Discussion. To analyze SVP, the Brightwell Microfluidic Imaging (MFI) system was used. This instrument has become crucial to the analysis of SVPs in both pharmaceutical and academic settings.43,26,27 Not only does it obtain more accurate counts than light obscuration methods for proteins43–45 , it provides digital imaging analysis to obtain pictures of the SVP present in a sample, and thus, allow for the determination of morphological parameters to describe those particles. This permits a more robust analysis of SVP than can be obtained from light obscuration, which only provides details on size. Barnard et al.18 eloquently showed in a recent study that the MFI instrument provides a much more sensitive measure of the amount of protein that is contained in SVP for a protein in a given formulation than SEC gives for the levels of SAs.18 Thus, this instrument was used in this study to quantify and characterize SVP in agitated protein samples to further investigate the mechanism of their formation.

the control samples to any temperature fluctuations within the room experienced by the test samples.

MATERIALS AND METHODS

SVP Measurements

Protein Preparation and Agitation

A Brightwell 4200 MFI (ProteinSimple, Ottawa, Ontario, Canada) system was used for particle quantification and characterization. Sizing accuracy of the instrument was verified using 2, 5, and 10 :m polystyrene bead size standards (Thermo Scientific, Waltham, Massachusetts). Counting accuracy was verified using 5 :m polystyrene bead 2000 particles/ mL concentration standards (Thermo Scientific). This instrument allows for particles between 1 and 70 :m to be analyzed, and particle concentrations in bin increments of 0.125 :m were calculated. Samples were introduced to the MFI via a 1 mL pipette tip. Samples were run in triplicate for each protein at every time point. Illumination was optimized before every sample was run. 0.20 mL of sample was used to purge the instrument, and 0.70 mL was passed through the flow cell for analysis. The MFI images approximately 87% of the sample running through it, so a volume of 0.6106 mL is used by the instrument when determining particle concentration levels. MVSS v1.0 software (ProteinSimple) was used to operate the instrument.

The three proteins investigated in this study were formulated into the buffers in which previous studies had determined they were most stable. The configurations were: (1) IgG1: 5 mg/mL protein, 50 mM histidine, 10 mM methionine, 3% sucrose, 0.01% polysorbate-80, pH 6.0. (2) IgG4: 10 mg/mL protein, 20 mM histidine, 10 mM methionine, 135 mM sodium chloride, 0.01% polysorbate-80, pH 6.0. (3) GP: 10 mg/mL protein, 10 mM histidine, 2% glycine, 2% mannitol, 0.01% polysorbate-80, pH 6.3. Thirty milliliters of each of protein was placed in a 50 mL polyethylene terephtalate glycol (PETG) bottle and placed on an orbital shaker within a box to prevent exposure to light. Samples were shaken at 400 rpm for 48 h. At time 0, 6, 24, and 48 h, samples were taken and analyzed for either SA or SVP. These experiments were carried out twice and the results from the two sets of experiments were averaged during data analysis. Control samples were created by adding 30 mL of each protein to a 50 mL PETG bottle. These control samples were placed in boxes to prevent exposure to light and were stored alongside the orbital shakers during the agitation experiments to subject DOI 10.1002/jps

Size-Exclusion Chromatography An Agilent 1200 HPLC system was used for all SEC analysis. Injections (50 :g) were loaded onto the column for each run and each sample was run in triplicate for each time point. The absorbance at 280 nm was monitored. Monomer and aggregate amounts were calculated as a percentage of the total areas for all the protein peaks in the chromatogram. Total peak areas were also monitored to assess whether there was a loss of protein upon analysis. For both the IgG1 and IgG4, the high-performance liquid chromatography was equipped with a TSK SWXL column (Tosohaus)0 preceded by a SWXL guard column. Samples were run at a flow rate of 0.45 mL/ min for a total of 35 min in a mobile phase consisting of 40 mM sodium phosphate, 150 mM NaCl, and pH 6.2. The GP was also run using a TSH SWXL size-exclusion column equipped with a SWXL guard column. These samples were run at a flow rate of 0.50 mL/min for 35 min. The mobile phase consisted of 20 mM sodium phosphate, 200 mM NaCl, and pH 6.2.

Data Fitting Data from each run were exported from MVSS software and subsequently imported into MVAS v1.1 software (ProteinSimple). MVAS was then used to create a comma-separated-value file containing all of the particle morphological data. These data were further analyzed within both Microsoft Excel and Sigmaplot 11.0 (Sysat, Chicago, Illinois). JOURNAL OF PHARMACEUTICAL SCIENCES

SIMLER ET AL.

To estimate the protein mass of the particles, the method described by Barnard et al.18 was used. Briefly, the equivalent circular diameter (ECD) for each particle was used to determine a spherical volume for that particle. Because protein aggregates consist of both protein and water, an arbitrary estimation was made by Barnard et al.18 to ascribe 75% of the particle volume to protein. This estimation is used here to provide a consistent method for mass calculation across these studies. A density of 1.43 ± 0.03 g/mL was then used to calculate the weight of each particle,47 and thus, the total protein mass per size bin can be estimated by Eq. 1 as follows: Protein mass per size bin = (0.75) × (Volume) ×(1.43g/mL) × (Number of particles)

(1)

Statistical analysis assessing changes in the size and shape distributions of the three proteins over 48 h of agitation was performed by paired Student t-tests. Comparisons between data sets resulting in p-values less than or equal to 0.05 were deemed to be statistically different, whereas values above 0.05 were classified as statistically indistinguishable.

RESULTS Effects of Agitation on SVP Count and Mass Three proteins, an IgG1, IgG4, and a GP, were subjected to agitation for 48 h. SVP counts were obtained as a function of agitation time for each of these proteins. The total SVP counts for each protein over the course of the agitation experiment are shown in Figure 1. The MFI instrument can count and characterize SVP from 1 to 70 :m, within the range of what would be categorized as “micron aggregates” using the terminology proposed by Narhi et al.44 In an effort to remain consistent with existing literature, the reported parameter for SVP size is the ECD. Very few particles were detected for any of the samples that were greater than 60 :m. Visual inspection of the images of these larger particles revealed than many did not appear to be proteinaceous in nature and were perhaps dust fibers. The presence of these foreign particles can certainly affect aggregation rates of protein.48,49 Nevertheless, to best ensure that only protein SVP are being considered, the analysis in Figure 1 (and throughout the rest of this manuscript) is performed on particles in the 1–60 :m size range. No larger visible particles were detected by visual inspection of the samples during the agitation. The data in Figure 1 show that as agitation time increases, the raw number of SVP in the two IgG samples also increases. However, the SVP counts for the GP remain fairly constant and within the error of each other over the entire course of 48 h of rigorJOURNAL OF PHARMACEUTICAL SCIENCES

,

,

/ L

4

,

,

,

,

Figure 1. Total subvisible particle concentrations (1–60 :m) as a function of agitation time for an IgG1 (white), IgG4 (red), and glycoprotein (blue).

ous shaking. Control samples for each protein, which were not shaken, did not result in increases in SVP counts (data not shown). Although the total number of SVP has been used in other studies as a potential probe of product quality, a more complete analysis of the SVP is obtained by examining the entire distribution of particle concentrations as a function of particle size. This analysis is shown in Figure 2, where the total particle concentration of increasing particle size bins (each 0.125 :m) is plotted. It should be noted that particle concentration (y axis) is plotted on a log scale in Figure 2 to better observe the low concentrations of larger SVP that are formed as a result of agitation as opposed to the linear scale used in Figure 1 to report particle concentration. These data show that the vast majority of the particles in these samples are very small (<5 :m). For both of the IgG molecules (Figs. 1a and 1b),1 the number of particles for almost every size bin increases as agitation time increases. Additionally, there are very few particles present before agitation in these two proteins that are larger than 15 :m. As agitation time increases, both IgG molecules, but in particular the IgG4, show a notable increase in the number of particles that are greater than 15 :m in size. In contrast, the GP (Fig. 1c) shows very few differences in SVP concentration in any size range as a function of agitation time. Although this is not unexpected given the data shown for the GP in Figure 1, one might expect to see an increase in the number of particles in the larger size range that is masked in the total SVP counts by the extremely large counts for particles less than 5 :m. This does not appear to be the case as there appears to be a small number of particles in the greater than 15 :m size range for samples analyzed at all agitation times. Although an enumeration of the total number of SVP can provide a useful comparative parameter DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

5

a

/ L

b

c

Figure 2. Total particle concentrations per size of particle measured from 1 to 60 :m after 0 (gray), 6 (red), 24 (blue), and 48 (green) h of agitation for an IgG1 (a), IgG4 (b), and glycoprotein (c).

between samples, a more descriptive method to determine the actual amount of protein included in the SVP is to estimate the mass of protein that is present within them. Figure 3 shows the integrated mass of the protein in the SVP as a function of SVP size. The integrated mass refers to the total mass of protein in SVP of a particular size or smaller.18 Thus, the final point on the graph at a SVP size of 60 :m is the total amount of protein contained in all the SVP in a particular sample. As has been noted in other studies,18,41 despite the extraordinarily large number of particles in the 1–5 :m size range, these sizes account for an exceedingly small percentage of the acDOI 10.1002/jps

tual protein mass present in the SVP population. For instance, at 48 h of agitation of IgG1, SVP in the size range of 1–5 :m make up approximately 96% of the total number of particles in the sample, but account for only 9% of the protein mass in the SVP (Figs. 2 and 3). The dependence of SVP protein mass as a function of agitation time resembles those obtained for total particle counts. Both the IgG molecules show an increase in SVP protein mass as agitation time increases, whereas the GP shows relatively little change over 48 h. This finding for the GP confirms the qualitative assessment of the binned particle counts as a JOURNAL OF PHARMACEUTICAL SCIENCES

6

SIMLER ET AL.

2.5

a 2.0

1.5

1.0

0.5

Integrated mass (µg mL–1)

0.0

b

4.0

3.0

2.0

1.0

0.0

c

2.5

2.0

1.5

1.0

0.5

0.0 0

10

20

30

40

50

ECD ( µm) Figure 3. Integrated mass of subvisible particles (1–60 :m) after 0 (black), 6 (red), 24 (blue), and 48 (green) h of agitation for an IgG1 (a), IgG4 (b), and glycoprotein (c).

JOURNAL OF PHARMACEUTICAL SCIENCES

DOI 10.1002/jps

Soluble aggregates (%)

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

7

6

4

2

0 0

10

20

30

40

50

Time (h) Figure 4. Percentage of soluble aggregate as a function of agitation time for an IgG1 (white), IgG4 (red), and a glycoprotein (blue). When not seen, error bars are smaller than the symbols.

function time (Fig. 2c), which found that not only is there no real change in total particle counts, there is also no shift in the distribution of particle sizes to larger particles as a result of agitation. Another interesting observation when comparing the dependence of total particle counts and changes in SVP protein mass can be found in the analysis of IgG4 data. Although both total number of particles and total SVP protein mass increase from 0 to 48 h of agitation for this molecule, the manner in which they change is different. Total SVP counts for the IgG4 (Fig. 1) increase from 0 to 6 h of agitation, remain constant from 6 to 24 h, and then increase further between 24 and 48 h. Conversely, SVP protein mass (Fig. 3b) is rather constant from 0 to 6 h and 6 to 24 h, and experiences the largest change from 6 to 24 h when the SVP counts are remaining constant. These data illustrate the value of assessing SVP formation by both mass and concentration. Most of the SVP generated in the first 6 h and the last 24 h of agitation are of very small size and represent very little of the actual protein mass becoming SVP. However, between 6 and 24 h, the major change that occurs is that the SVP are growing into larger particles, so although the total number does not increase appreciably, the total amount of protein transitioning to the SVP state is growing and would not be detected by only measuring SVP concentration. Relationships Between SA and SVP Formation In addition to measuring the extent of SVP formation as a result of 48 h of agitation, the levels of SA were also determined by SEC. Typically, aggregates greater than 0.1 :m in hydrodynamic radius will be filtered out by SEC. These SAs that are less than 0.1 :m in DOI 10.1002/jps

size are classified as “submicron aggregates” by Narhi et al.44 One concern when performing SEC experiments on degraded protein samples is that larger aggregates are filtered out by the column and are never detected during the analysis, though in some cases larger, flexible aggregates can pass through. To ensure that this was not occurring, the total protein peak area was monitored over the time course of agitation. No changes were seen in the total protein peak area for any of the proteins during agitation. Thus, within the sensitivity limits of SEC, there was no protein loss during agitation (data not shown). SAs consisted of dimers and larger oligomers for all three proteins. Typically, SA levels are reported in a mass percentage of the total protein mass. These results are shown in Figure 4. However, to provide a direct comparison to the SVP formation, these percentages are converted to a mass concentration of protein by simply multiplying the percentage of aggregate by the concentration of the protein. These results (Fig. 5) show that all three proteins display an increase in SA as a result of agitation. The two IgG molecules show a steady increase over 48 h, although the IgG4 is much more susceptible to aggregation than the IgG1. The GP shows a significant jump in aggregation within the first 6 h of agitation and a smaller, but steady increase through 48 h. The total mass of SVP as a function of agitation time is also plotted in Figure 5 and reflects previously discussed results where the two IgG molecules increase in SVP mass during agitation, whereas the GP does not show any tendency to produce SVP. As the mechanism in Scheme 1 describing the formation of SVP is highlighted by SAs eventually accumulating enough protein that they are big enough to JOURNAL OF PHARMACEUTICAL SCIENCES

8

SIMLER ET AL.

b

Mass of subvisible particles (µg mL–1 )

Mass of soluble aggregates (µg mL –1 )

a

c

Time (h) Figure 5. Total amount of protein in soluble aggregate (left y axis, circles) and subvisible particles from 1 to 60 :m (right y axis, squares) as a function of agitation time for an IgG1 (a, white), IgG4 (b, red), and a glycoprotein (c, blue).

be classified as “SVPs”, the relationship between the rate of SA and SVP formations were compared. To do this, a term, , was defined by Eq. 2:

=

SVP MassSA t /Masst SVP MassSA 0 /Mass0

JOURNAL OF PHARMACEUTICAL SCIENCES

(2)

where mass refers to the mass of either the SAs or the SVPs at either a given time (t) or at the initiation of the experiment (0). In this equation, the denominator establishes a baseline level for the ratio of the mass of protein in SAs and SVP. This ratio, after a certain agitation time, is then compared with this initial baseline level. Thus, at the initiation of the experiment,  will be equal to one because t = 0. Following DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

9

Log (theta)

10

1

0

10

20

30

40

50

Time (h) Figure 6. Theta analysis as a function of agitation time for an IgG1 (white), IgG4 (red), and a glycoprotein (blue).

agitation, if the protein is forming SAs faster than it is forming SVP, relative to the starting amounts of each aggregate species, this ratio will increase, whereas a decrease would indicate the SVP mass is increasing faster than SA. When subjected to this  analysis, the three proteins in this study showed very different responses (Fig. 6). The IgG1 displayed a negative slope indicative of the rate of SVP particle formation being faster than that for SAs. By 48 h, the increase in protein mass in SVP was nearly double the increase in mass of SAs. The GP showed an opposite response with the mass of protein in SAs doubling with respect to the mass of protein in SVP. This change is driven strictly by the changes in SA as the GP proved to be resistant to SVP formation as a result of agitation. The IgG4 molecule showed the most drastic change during agitation. Although protein mass in SVP increased over 48 h of agitation, the mass of protein being converted to SA was 10-fold greater than the mass being converted to SVP. These data illustrate that there is a wide range of possibilities as to how proteins subsequently populate SVP after formation of SA and that this conversion could be dependent on many factors. Morphological Assessment of SVP One of the advantages of the MFI instrument as a tool to study SVP is that not only can it count and size particles, but because each individual particle is imaged, other morphological parameters can be identified. The ECD, or the diameter of a circle that would occupy the same number of pixels of the digitized parDOI 10.1002/jps

ticle image, is used here to measure the size of the particle. Another useful morphological parameter to describe the particles is the circularity, a value that describes how closely a particle resembles a circle that ranges from zero (noncircular) to one (for a perfect circle). It is determined by dividing the circumference of an equivalent area circle by the perimeter of the particle and provides a good assessment of the shape of the particle. As a particle becomes more fibrous, the circularity will decrease. To analyze the effect of agitation on the shape distribution and to further analyze the effect on the size distribution, scatter plots were constructed showing the shape and size of every particle that has an ECD of 5 :m or greater. Because the digitization of particles smaller than 5 :m largely results in images with only a few pixels and thus are considered to be near spherical, they were not evaluated in further analysis. The scatter plots for the starting material and the samples that had been agitated for 48 h are shown in Figure 7. A qualitative assessment of these figures shows two things. First, it appears that there are more of the larger SVP (those that are 15 :m and larger) being formed as a result of agitation, as has been noted in the previous section. However, what is not clear is whether the only reason that more of these particles are observed after agitation is solely because there is an increase in the number of all particles, regardless of size, or if the actual distribution of particle sizes has been altered by agitation. Second, the shape analysis reveals that there is an apparent boundary that each protein adheres to when defining the morphology of the SVP that can be formed. For instance, although agitation resulted in a large number JOURNAL OF PHARMACEUTICAL SCIENCES

10

SIMLER ET AL.

a

b

c

Figure 7. Scatter-plot depicting the size (x axis) and shape (y axis) distributions of subvisible particles (1–60 :m) at time 0 (black) and after 48 h of agitation (pink) for an IgG1 (a), IgG4 (b), and a glycoprotein (c).

JOURNAL OF PHARMACEUTICAL SCIENCES

DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

Table 1.

Significance of Size/Shape Changes as a Function of Time for Each Molecule

Molecule

Variable

IgG1

Size

Shape

IgG4

Size

Shape

GP

Size

Shape

Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max)

of SVP being formed for the IgG1 molecule, nearly all of those SVP have a circularity greater than 0.4. Likewise, the IgG4 has only a limited number of particles that have a circularity less than 0.3. The GP seems to have a greater range of available SVP shapes in that there are many SVP with a circularity as low as 0.2 and even a handful that have a circularity lower than this. To obtain a better understanding of these data, a statistical analysis was conducted to compare the two variables and statistically determine how similar/different they are. Because there is little difference between the mean values for the size and shape parameters in some instances, a Student t-test is utilized to analyze the data. This analysis accounts for the variability in the distribution of data to assess whether the distributions of two sets of data are, from a statistical point of view, significantly different from each other. Table 1 shows the results comparing the size and shape distributions of the SVP within a class of protein at time 0 and after 48 h of agitation. The IgG1 showed a statistically significant change in the size distribution of the particles with a shift to an increased percentage of larger particles as a result of agitation. The IgG4 and the GP showed no change in size distribution profile. This result is not unexpected for the GP given that there were no changes in the particle counts over the course of the agitation experiment. However, this analysis indicates that although there is clearly an increase in the number of the larger SVP as a result of agitation for the IgG4, that increase is proportional to the increase in smaller particles, and there is no change to the size distribution for the IgG4 as a result of agitation. Statistical analysis revealed that all three proteins showed a change in their shape distribution profile as a result of agitation. This is not surprising for the DOI 10.1002/jps

11

Time = 0

Time = 48 h

p Value

Significant

8.24 (4.44) 6.63 5.13–36.38 0.82 (0.08) 0.84 0.49–0.94 8.85 (5.68) 6.88 5.13–54.13 0.77 (0.12) 0.80 0.37–0.93 9.21 (5.99) 7.13 5.13–57.88 0.76 (0.15) 0.82 0.30–0.94

9.11 (6.04) 7.13 5.13–58.88 0.81 (0.08) 0.83 0.38–0.93 8.71 (5.20) 6.88 5.13–46.88 0.73 (0.17) 0.81 0.21–0.93 8.71 (5.50) 6.88 5.13–52.63 0.69 (0.16) 0.70 0.09–0.99

0.005

Yes

0.045

Yes

0.629

No

<0.001

Yes

0.059

No

<0.001

Yes

IgG1 and IgG4 molecules where one would predict that the stress-induced particle formation could populate more fibrous particles and thus lead to a shift in the shape distribution toward lower circularity values. This finding is unexpected though for the GP as no changes in the SVP population had been detected up to this point. Thus, despite the fact that there are no increases in SVP concentration or in SVP size, the morphology of the SVP is altered to more fibrous particles as a result of agitation. Table 2 summarizes the shape and size distribution comparisons between the three molecules after 48 h of agitation. The size distribution profiles are statistically insignificant between any of the three molecules indicating that there may be some commonality between the classes of proteins as to what size distributions may be sampled as a result of stress-induced particle formation. However, the shape analysis indicates that the shape distribution profiles after 48 h of agitation are statistically different between the three proteins. This confirms the qualitative assessment of the scatter plots presented in Figure 7, which seems to show a defined boundary for circularity that is variable dependent on the protein. It also shows that stress-induced SVP morphology is governed by, as yet, undetermined rules.

DISCUSSION The results shown here display the large variation in response to agitation stress that can be seen in both the formation of SAs and SVP for a series of proteins. It should be noted that the agitation conditions used in these studies, with shaking speeds of 400 rpm, represent significantly more severe agitation stress than what is usually encountered in a pharmaceutical setting. However, because the goal of this study JOURNAL OF PHARMACEUTICAL SCIENCES

12

SIMLER ET AL.

Table 2.

Comparison of Size/Shape of SVPs Between Molecules after 48 h of Agitation

Variable Size

Shape

Size

Shape

Size

Shape

Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max)

Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max)

Mean (SD) Median Range (min–max) Mean (SD) Median Range (min–max)

IgG1

IgG4

p Value

Significant

9.11 (6.04) 7.13 5.13–58.88 0.81 (0.08) 0.83 0.38–0.93

8.71 (5.20) 6.88 5.13–46.88 0.73 (0.17) 0.81 0.21–0.93

0.065

No

<0.001

Yes

IgG1

GP

9.11 (6.04) 7.13 5.13–58.88 0.81 (0.08) 0.83 0.38–0.93

8.71 (5.50) 6.88 5.13–52.63 0.69 (0.16) 0.70 0.09–0.99

0.080

No

<0.001

Yes

IgG4

GP

8.71 (5.20) 6.88 5.13–46.88 0.73 (0.17) 0.81 0.21–0.93

8.71 (5.50) 6.88 5.13–52.63 0.69 (0.16) 0.70 0.09–0.99

0.986

No

<0.001

Yes

was to investigate the potential relationship between the formation of SAs and SVPs, this increased level of stress was needed to induce the significant change in these degradation products required to gain insight into this relationship. All three proteins show an increase in the levels of SAs as a result of the same agitation stress. It should also be noted that the values for SVP concentration at the initiation of the study were greater than zero for all samples and particularly high for the GP as the material used in this study were aged proteins. Although the SVP could be filtered out at the beginning of the study, there was no available method to reduce the amount of SA. Given that the goal of this study was to investigate the relationship of SVP to SA, it did not appear appropriate to remove the SVP from the samples without the ability to also remove the SA. Hence, all samples start with a baseline level of protein SVP. The formulations used for the three proteins in this study have been determined in previous studies to be those conditions where the proteins are most stable. Unfortunately, this precludes an unequivocal identification of the root cause of any differences seen in the tendencies to form SAs or SVPs. However, it should be noted that most of the major formulation parameters are consistent between the three proteins. All are formulated in a histidine buffer with a pH of 6.0–6.3 and all include the addition of 0.01% polysorbate-80. Agitation experiments were also carried out in identical bottles with identical fill volumes to ensure any influences from interactions between the air/water interface and container surfaces were consistent. The fact that many of the major components of the formulation are the same between the molecules increases JOURNAL OF PHARMACEUTICAL SCIENCES

the likelihood that any differences seen are specific to the protein and not to the formulation. However, it is impossible to determine whether other formulation components, such as salt, or the reduced concentration in the IgG1 are the root causes for any differences seen in the aggregation and SVP behavior between the three proteins. This study demonstrates the importance of the integrated mass analysis that was presented by Barnard et al.18 Given the variability that is seen in the enumeration of SVP by not only the MFI instrument, but other SVP counting techniques, there remains a question as to what constitutes a meaningful change in the number of SVP for a particular sample. The integrated mass analysis used both here and by Barnard et al.18 gives a much clearer picture into the overall particulation behavior of a protein. As seen here and in other studies,18,41 the raw particle counts are dominated by the smallest SVP that can be detected. However, the total mass of protein contained in the SVP population is predominantly found in the larger particles. Thus, if total particle counts are being monitored, a significant change in protein can be masked as the small number of larger SVP being formed is overwhelmed by the large counts of the very small SVP. The three proteins presented in this study, in their respective formulations, have very different tendencies to form SAs as a result of agitation stress. This is of course not surprising as it is well documented that the determinants that dictate this behavior, such as transient structural stability and surface charge distributions, are numerous and highly specific to an individual protein and are influenced by the DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

formulation they are stored in.8,50 Therefore, it should not be unexpected to see that the propensity to form SVP is also highly protein and formulation specific as many of the same factors that dictate SA formation likely play a role in the generation of SVP. However, the specificity of the relationship between SA and SVP formation is of note. In none of the three cases studied here is there a simple one to one relationship between them (i.e., a twofold increase in the amount of SA is detected along with a twofold increase in the amount of protein mass in SVP). In this study, each protein has a different relationship between the rate of SA and SVP formation as noted by the  analysis. Although kinetic factors cannot be ruled out in explaining these differences, it appears that the relationship between the formations of these two higher order species is specific to molecule and formulation. Additionally, the propensity of a protein to form SAs does not appear to be directly linked to its tendency to form SVP. This is noteworthy in that it implies that while the aforementioned determinants that dictate the soluble aggregation behavior of a protein may also influence the SVP formation, the specific causes of soluble aggregation and SVP formation for a given protein may be different. Hypothetically, agitation may induce a partial unfolding of the GP, which leads to the formation of SA. However, another factor, such as charge–charge interactions or steric hindrance, may prevent those SAs from ever agglomerating to a species that is big enough to be categorized as “subvisible.” Conversely, although agitation may not readily promote the formation of SAs in a molecule such as the IgG1 seen here, once those SAs are formed, perhaps they are particularly susceptible to agglomerate as a result of greater exposure to the air/water interface inherent in agitation studies. The evaluation of the circularity parameter provides additional evidence that the underlying root causes that dictate SVP formation are extraordinarily complex. Although qualitative differences in the appearance of particulate matter in a protein sample have been made, classifying it as either fibrous or amorphous, this is to our knowledge the first statistical evaluation of a morphological parameter between different proteins. This study shows that the distribution of shapes seen for the SVP population of a given protein can have defined limits. Although the reasons for this are unknown and the possible explanations numerous, there is clearly some inherent characteristic of this IgG1 molecule, be it within the protein itself or with its interaction to its environment, that prevents it from forming long, fibrillar particles. No such restrictions pertain to the GP where circularities as low as 0.2, representative of elongated structures, are observed even for particles with an ECD as small as 5 :m.

DOI 10.1002/jps

13

These results also highlight that extrinsic stress can result in changes to the SVP population outside of merely altering their number. One might expect that in the absence of changes in SVP concentrations, there would be no other changes to the SVP population. However, the statistical analysis of the GP shows that although the raw number of SVP does not change because of agitation, their morphology does, as the particles become more fibrous in nature. Agitation has been postulated to induce aggregation by many different mechanisms including local thermal events,51 cavitation52,53 and promotion of increased interactions between the protein and the air/water interface, and container surface.21,54,55 It is possible that any of these features promote a structural rearrangement of a certain population of SVP from an amorphous form to a more elongated fibrous form in the GP. Collectively, these results highlight the complexities in the mechanism that defines SVP formation. Although an extended Lumry–Eyring or nucleationpropagation mechanism presented in Scheme 1 provides a high-level illustration of the generation of SVP, it also greatly simplifies what is undoubtedly a much more intricate process. It is important to remember that the classifications of higher order species as “SAs,” “SVPs,” and “visible particles” are largely driven by analytical capabilities as each individual piece of instrumentation can detect an aggregate species of a specific size range. It is convenient to ascribe nomenclature to defined size ranges of aggregated species to help delineate the boundaries of these analytic capabilities. In reality, the protein aggregation pathway is a continuum of higher order species ranging from the smallest possible “aggregates” in dimers to visible flocculent species made up of an extraordinarily large number of protein molecules. The aggregate population of a protein will occupy discrete species in this continuum and there are no obligate steps outside of seeding the initial aggregate presented in the Lumry–Eying model. For instance, a protein can form tetramers from dimer without going through the trimeric state, just as a protein can be prone to form aggregates that are characterized as “SAs” without ever having those aggregates agglomerate to a large enough species to be categorized as “subvisible”. This work also reinforces that size is not the only aggregate feature that is important to characterize when describing a protein’s aggregation population. The morphology of those aggregates is also highly dependent on, but also highly specific too, each protein and its formulation. The ability of flow microscopy techniques opens the door for a more quantitative analysis of these morphological parameters. Consideration of these shape features highlights how

JOURNAL OF PHARMACEUTICAL SCIENCES

14

SIMLER ET AL.

complex the challenge of characterizing the aggregate population of a protein will be. But given that it is clear that proteins will populate a discrete set of sizes as evidenced by the boundaries presented in Figure 7, it is evident that to obtain a complete understanding of protein aggregation, morphological assessments will have to be employed as technologies improve, to more clearly monitor them.

CONCLUSIONS The work presented here illustrates the increase in challenges for elucidating protein aggregation mechanisms when SVPs are taken into account. The formation of SAs and SVPs, as well as the relationship between the two aggregate species, is highly protein and formulation specific. In addition, the morphology of SVPs, which can now be monitored by the MFI instrument, indicates that there are some governing rules as to how protein molecules are arranged in SVPs, specifically whether they will form amorphous, spherical particles or elongated, fibrous particles. As such, it is clear that to eventually produce an accurate assessment of the population of aggregates a protein is susceptible to form, additional considerations other than just size will have to be considered in the future as technology provides more robust methods to analyze such properties.

REFERENCES 1. Chi EY, Krishnan S, Randolph TW, Carpenter JF. 2003. Physical stability of proteins in aqueous solution: Mechanism and driving forces in nonnative protein aggregation. Pharm Res 20(9):1325–1336. 2. Porter S. 2001. Human immune response to recombinant human proteins. J Pharm Sci 90(1):1–11. 3. Rosenberg AS. 2006. Effects of protein aggregates: An immunologic perspective. AAPS J 8(3):E501–E507. 4. Schellekens H. 2003. The immunogenicity of biopharmaceuticals. Neurology 61(9 Suppl 5):S11–S12. 5. Wang W, Kelner DN. 2003. Correlation of rFVIII inactivation with aggregation in solution. Pharm Res 20(4):693–700. 6. Carpenter JF, Randolph TW, Jiskoot W, Crommelin DJ, Middaugh CR, Winter G, Fan YX, Kirshner S, Verthelyi D, Kozlowski S, Clouse KA, Swann PG, Rosenberg A, Cherney B. 2009. Overlooking subvisible particles in therapeutic protein products: Gaps that may compromise product quality. J Pharm Sci 98(4):1201–1205. 7. Patten PA, Schellekens H. 2003. The immunogenicity of biopharmaceuticals. Lessons learned and consequences for protein drug development. Dev Biol (Basel) 112:81–97. 8. Mahler HC, Friess W, Grauschopf U, Kiese S. 2009. Protein aggregation: Pathways, induction factors and analysis. J Pharm Sci 98(9):2909–2934. 9. Lumry R, Eyring H. 1954. Conformation changes of proteins. J Phys Chem 58:110–120. 10. Chi EY, Krishnan S, Kendrick BS, Chang BS, Carpenter JF, Randolph TW. 2003. Roles of conformational stability and colloidal stability in the aggregation of recombinant human granulocyte colony-stimulating factor. Protein Sci 12(5):903–913. JOURNAL OF PHARMACEUTICAL SCIENCES

11. Mahler HC, Muller R, Friess W, Delille A, Matheus S. 2005. Induction and analysis of aggregates in a liquid IgG1-antibody formulation. Eur J Pharm Biopharm 59(3):407–417. 12. Uversky VN. 2005. A GLYmmer of insight into fibril formation. Structure 13(8):1090–1092. 13. Cleland JL, Powell MF, Shire SJ. 1993. The development of stable protein formulations: A close look at protein aggregation, deamidation, and oxidation. Crit Rev Ther Drug Carrier Syst 10(4):307–377. 14. Ionescu RM, Vlasak J, Price C, Kirchmeier M. 2008. Contribution of variable domains to the stability of humanized IgG1 monoclonal antibodies. J Pharm Sci 97(4):1414–1426. 15. Kiese S, Pappenberger A, Friess W, Mahler HC. 2010. Equilibrium studies of protein aggregates and homogeneous nucleation in protein formulation. J Pharm Sci 99(2):632– 644. 16. Bam NB, Cleland JL, Yang J, Manning MC, Carpenter JF, Kelley RF, Randolph TW. 1998. Tween protects recombinant human growth hormone against agitation-induced damage via hydrophobic interactions. J Pharm Sci 87(12):1554–1559. 17. Chang BS, Kendrick BS, Carpenter JF. 1996. Surface-induced denaturation of proteins during freezing and its inhibition by surfactants. J Pharm Sci 85(12):1325–1330. 18. Barnard JG, Singh S, Randolph TW, Carpenter JF. 2011. Subvisible particle counting provides a sensitive method of detecting and quantifying aggregation of monoclonal antibody caused by freeze-thawing: Insights into the roles of particles in the protein aggregation pathway. J Pharm Sci 100(2):492–503. 19. Carpenter JF, Chang BS. 2004. Physical damage to proteins during freezing, drying and rehydration. 1st ed. New York City, New York: Springer. 20. Pikal-Cleland KA, Cleland JL, Anchordoquy TJ, Carpenter JF. 2002. Effect of glycine on pH changes and protein stability during freeze-thawing in phosphate buffer systems. J Pharm Sci 91(9):1969–1979. 21. Maa YF, Hsu CC. 1996. Effect of high shear on proteins. Biotechnol Bioeng 51(4):458–465. 22. Rathore N, Rajan RS. 2008. Current perspectives on stability of protein drug products during formulation, fill and finish operations. Biotechnol Prog 24(3):504–514. 23. Sluzky V, Tamada JA, Klibanov AM, Langer R. 1991. Kinetics of insulin aggregation in aqueous solutions upon agitation in the presence of hydrophobic surfaces. Proc Natl Acad Sci U S A 88(21):9377–9381. 24. Qi P, Volkin DB, Zhao H, Nedved ML, Hughes R, Bass R, Yi SC, Panek ME, Wang D, Dalmonte P, Bond MD. 2009. Characterization of the photodegradation of a human IgG1 monoclonal antibody formulated as a high-concentration liquid dosage form. J Pharm Sci 98(9):3117–3130. 25. Wang W. 1999. Instability, stabilization, and formulation of liquid protein pharmaceuticals. Int J Pharm 185(2):129–188. 26. Huang CT, Sharma D, Oma P, Krishnamurthy R. 2009. Quantitation of protein particles in parenteral solutions using micro-flow imaging. J Pharm Sci 98(9):3058–3071. 27. Sharma DK, King D, Oma P, Merchant C. 2010. Micro-flow imaging: Flow microscopy applied to sub-visible particulate analysis in protein formulations. AAPS J 12(3):455–464. 28. Narhi LO, Jiang Y, Cao S, Benedek K, Shnek D. 2009. A critical review of analytical methods for subvisible and visible particles. Curr Pharm Biotechnol 10(4):373–381. 29. Howlett GJ, Minton AP, Rivas G. 2006. Analytical ultracentrifugation for the study of protein association and assembly. Curr Opin Chem Biol 10(5):430–436. 30. Schuck P. 2003. On the analysis of protein self-association by sedimentation velocity analytical ultracentrifugation. Anal Biochem 320(1):104–124. 31. Qureshi RN, Kok WT. 2011. Application of flow field-flow fractionation for the characterization of macromolecules of DOI 10.1002/jps

MECHANISTIC COMPLEXITY OF SUBVISIBLE PARTICLE FORMATION

32. 33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

biological interest: A review. Anal Bioanal Chem 399(4):1401–1411. Philo JS. 2006. Is any measurement method optimal for all aggregate sizes and types? AAPS J 8(3):E564–E571. Philo JS. 2009. A critical review of methods for size characterization of non-particulate protein aggregates. Curr Pharm Biotechnol 10(4):359–372. Fradkin AH, Carpenter JF, Randolph TW. 2009. Immunogenicity of aggregates of recombinant human growth hormone in mouse models. J Pharm Sci 98(9):3247–3264. Hermeling S, Aranha L, Damen JM, Slijper M, Schellekens H, Crommelin DJ, Jiskoot W. 2005. Structural characterization and immunogenicity in wild-type and immune tolerant mice of degraded recombinant human interferon alpha2b. Pharm Res 22(12):1997–2006. Lebron JA, Wolf JJ, Kaplanski CV, Ledwith BJ. 2007. Nonclinical safety assessment of vaccines and the evaluation of novel adjuvants and delivery systems. New York City, New York: Wiley. Spohn G, Bachmann MF. 2008. Exploiting viral properties for the rational design of modern vaccines. Expert Rev Vaccines 7(1):43–54. Xiang SD, Scholzen A, Minigo G, David C, Apostolopoulos V, Mottram PL, Plebanski M. 2006. Pathogen recognition and development of particulate vaccines: Does size matter? Methods 40(1):1–9. Demeule B, Messick S, Shire SJ, Liu J. 2010. Characterization of particles in protein solutions: Reaching the limits of current technologies. AAPS J 12(4):708–715. Singh SK, Afonina N, Awwad M, Bechtold-Peters K, Blue JT, Chou D, Cromwell M, Krause HJ, Mahler HC, Meyer BK, Narhi L, Nesta DP, Spitznagel T. 2010. An industry perspective on the monitoring of subvisible particles as a quality attribute for protein therapeutics. J Pharm Sci 99(8):3302– 3321. Wuchner K, Buchler J, Spycher R, Dalmonte P, Volkin DB. 2010. Development of a microflow digital imaging assay to characterize protein particulates during storage of a high concentration IgG1 monoclonal antibody formulation. J Pharm Sci 99(8):3343–3361. Kiese S, Papppenberger A, Friess W, Mahler HC. 2008. Shaken, not stirred: Mechanical stress testing of an IgG1 antibody. J Pharm Sci 97(10):4347–4366.

DOI 10.1002/jps

15

43. Sharma DK, Oma P, Pollo MJ, Sukumar M. 2010. Quantification and characterization of subvisible proteinaceous particles in opalescent mAb formulations using micro-flow imaging. J Pharm Sci 99(6):2628–2642. 44. Narhi L, Schmit J, Bechtold-Peters K, Sharma D. 2012. Classification of protein aggregates. J Pharm Sci 101(2):493–498. 45. Scherer TM, Leung S, Owyang L, Shire SJ. 2012. Issues and challenges of subvisible and submicron particulate analysis in protein solutions. AAPS J 14(2):236–243. 46. Filipe V, Poole R, Oladunjoye O, Braeckmans K, Jiskoot W. 2012. Detection and characterization of subvisible aggregates of monoclonal IgG in serum. Pharm Res 29:2202–2212. 47. Quillin ML, Matthews BW. 2000. Accurate calculation of the density of proteins. Acta Crystallogr D Biol Crystallogr 56 (Pt 7):791–794. 48. Chi EY, Weickmann J, Carpenter JF, Manning MC, Randolph TW. 2005. Heterogeneous nucleation-controlled particulate formation of recombinant human platelet-activating factor acetylhydrolase in pharmaceutical formulation. J Pharm Sci 94(2):256–274. 49. Tyagi AK, Randolph TW, Dong A, Maloney KM, Hitscherich Jr. C, Carpenter JF. 2009. IgG particle formation during filling pump operation: A case study of heterogeneous nucleation on stainless steel nanoparticles. J Pharm Sci 98(1):94–104. 50. Roberts CJ. 2007. Non-native protein aggregation kinetics. Biotechnol Bioeng 98(5):927–938. 51. Santos O, Nylander T, Paulsson M, Tragardh C. 2006. Whey protein adsorption onto steel surfaces—Effect of temperature, flow rate, residence time and aggregation. J Food Eng 74:468–483. 52. Bernstein LS, Zakin MR, Flint EB, Suslick KS. 1996. Cavitation thermometry using molecular and continuum sonoluminescence. J Phys Chem 100:6612–6619. 53. Gulseren I, Guzey D, Bruce BD, Weiss J. 2007. Structural and functional changes in ultrasonicated bovine serum albumin solutions. Ultrason Sonochem 14(2):173–183. 54. Charman SA, Mason KL, Charman WN. 1993. Techniques for assessing the effects of pharmaceutical excipients on the aggregation of porcine growth hormone. Pharm Res 10(7):954–962. 55. Henson AF, Mitchell JR, Musselwhite PR. 1970. The surface coagulation of proteins during shaking. J Colloid Interface Sci 32(1):162–165.

JOURNAL OF PHARMACEUTICAL SCIENCES