RESEARCH ARTICLE – Pharmaceutical Biotechnology
Label-Free Flow Cytometry Analysis of Subvisible Aggregates in Liquid IgG1 Antibody Formulations 2 ¨ 1 ROBERT FURST, ¨ HIROTAKA NISHI,1 ROMAN MATHAS, GERHARD WINTER1 1
Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilian University Munich, Munich 81377, Germany 2 Department of Pharmacy, Pharmaceutical Biology, Ludwig Maximilian University Munich, Munich 81377, Germany Received 2 August 2013; revised 8 October 2013; accepted 18 October 2013 Published online 11 November 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23782
ABSTRACT: The objective of this study was to characterize and quantify label-free subvisible antibody particles in different formulations based on their size and physical properties by flow cytometry. Protein subvisible particles were prepared under various stress conditions and analyzed by applying different analytical techniques [light obscuration (LO), microflow imaging (MFI), and flow cytometry (FACS)] for the detection of aggregates. The capability of the FACS method to detect and count subvisible particles was evaluated and benchmarked against conventional techniques. FACS can analyze particles down to 500 nm reducing the gap between size-exclusion chromatography and LO. The applied methods of FACS, LO, and MFI displayed a proportional correlation between the total particle counts, however, FACS C 2013 Wiley Periodicals, Inc. and the can provide additional information on the structural characteristics of such aggregated particles. American Pharmacists Association J Pharm Sci 103:90–99, 2014 Keywords: monoclonal antibody; protein formulation; protein aggregation; subvisible particle; particle sizing; flow cytometry; light obscuration; microflow imaging; refractive index; analytical biochemistry
INTRODUCTION Humanized monoclonal antibodies have become major pharmaceutical products in the treatment of many diseases such as cancer, allergies, and autoimmune diseases.1 With the development of these products, improving the limited stability, especially in preventing protein aggregates during manufacturing, shipping, storage, and delivery, is one of the key challenges because protein aggregates can be easily induced under stressed conditions such as temperature fluctuation, agitation, freeze–thawing, light, or exposure to interfaces.2,3 The protein aggregates, which have a non-native conformation, have been widely recognized to have an impact on the product quality but also on the biological activity and immunogenicity.4,5 Protein aggregates are heterogeneous species that have many variations in size and conformation, and are generally classified into soluble and insoluble aggregates based on size. Soluble aggregates can be measured by size-exclusion chromatography (SEC) (e.g., dimer, trimer, or oligomer, ≤0.1 :m).6 Insoluble aggregates are further classified into subvisible particles (0.1–100 :m) and visible particles (≥100 :m).6 This wide range of protein aggregate sizes provides a significant challenge in developing analytical methods and requires complementary methods because a single analytical method cannot cover the full size range.7 In the pharmaceutical industry, protein soluble aggregates and visible particles with diameters of below 0.1 :m and above 100 :m, respectively, are routinely monitored for the release of products mainly by SEC and visual inspection, respectively. Analytical ultracentrifugation8,9 and light scattering techniques10
Correspondence to: Gerhard Winter (Telephone: +49-89-2180-77933; Fax: +49-89-2180-77058; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 103, 90–99 (2014)
C 2013 Wiley Periodicals, Inc. and the American Pharmacists Association
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are also used in analyzing the size, hydrodynamic diameter, or the average molecular weight of protein soluble aggregates. On the other hand, subvisible particles with diameters from 1 to 100 :m are analyzed by light obscuration (LO), which is one of the standard techniques listed in the pharmacopeia. Because of the fact that there are no regulatory guidelines on protein particles below 10 :m, many authors, for example, Carpenter et al.,6 point out a gap between current scientific understandings and the guidelines for the protein aggregates below 10 :m, and raise the concern about their risks in product safety (immunogenicity) as well as in efficacy. The immunogenicity of the protein particles above 0.1 :m may be enhanced by their increased uptake into dendritic cells, and following facilitated maturation, activation or proliferation, and enhanced stimulation of B and T lymphocytes.11 Although conventional analytical methods such as LO and microflow imaging (MFI) can measure subvisible particles as small as 1.00 and 0.75 :m, respectively, it is not clear if the measured particle sizes and numbers are sufficiently accurate or not because 1 :m is the lower detection and quantification limit of these methods. Therefore, further analytical technologies, which can quantify and characterize subvisible particles with a diameter of below 10 :m, are still needed. Recently, several emerging technologies have been developed to count and/or characterize subvisible particles to close “submicron size gap.” Specifically, resonant mass measurement (RMM, Archimedes system) is employed to analyze submicron and micron particles.12,13 Here, the sample solutions pass through a microchannel inside a resonating cantilever. Particles are detected and sized by a frequency change of the cantilever, which is proportional to the buoyant mass of particles. In addition, RMM clearly distinguishes silicon oil droplets and protein particles separately by negative and positive buoyant masses. However, heterogeneous particles of silicon oil and protein will lead to underestimated particle size.
RESEARCH ARTICLE – Pharmaceutical Biotechnology
Moreover, the narrow capillaries are fragile to clogging by bigger particles. Coulter counter and nanoparticle tracking analysis have also been applied to count and/or characterize subvisible particles.14,15 However, coulter counter requires sufficient conductivity, which is not granted in formulation screenings. Nanoparticle tracking analysis allows an analysis in nanometer size range, whereas it is less accurate in counting. Flow cytometry (FACS) is a laser-based biophysical technique, which can detect light scattering and light emission from nonstained or stained particles, and has been mainly employed in the field of cell biology.16 Flow cytometry is one of the few methods available in counting and sorting thousands of individual particles every second in real time even in a relatively small total sample volume (100–200 :L). Recently, the potential of this technique for analyzing the number and size of subvisible particles in protein formulations began to be explored using fluorescently labeled protein.14,17–19 The identification of optimal formulation conditions such as pH, ionic strength, and excipient content is a key element in successful protein formulation development. A high-throughput approach is desired and usually applied for screening protein monomer content by SEC. However, most of the particle analytics do not figure an auto sampler. Flow cytometry allows high-throughput screening using 96- or 384-well plates.20 Therefore, it can be a strong tool for determining optimal protein formulations. In recent studies, which were reported from other groups, detection of subvisible particles as small as 200 nm was successfully carried out using a fluorescence detector.19 However, fluorescent labeling of protein may affect the aggregation behavior of the protein, and moreover, fluorescently labeled proteins cannot be routinely used in formulation development, nor, of course, in production control of protein drugs in clinical use. In this report, the successful application of flow cytometry for characterizing and quantifying protein subvisible particles without fluorescent labeling is described. First, the effect of the instrument parameters such as detector voltage and flow rate on the detection of particles, the linearity of particle count versus sample dilution is investigated, and the general application of flow cytometry for quantifying such unlabeled subvisible particles is described. Next, protein particles prepared under heating stress are analyzed using flow cytometry and the number of particles with diameters of above 1 :m are compared with those measured by LO and MFI. The protein particles are also analyzed after mixing them with silica microparticles to show the ability of flow cytometry to differentiate particles not only by size but also by their structure and morphology. Finally, flow cytometry is applied to characterize and quantify monoclonal antibody subvisible particles in formulations after various stresses have been applied.
MATERIALS AND METHODS Materials The humanized monoclonal antibody A (IgG1 subclass) used in this investigation was produced and purified at Daiichi Sankyo Company, Ltd., Tokyo, Japan and was also studied in Refs. 21 and22 . The theoretical isoelectric point of the antibody is 6.5. Silica microparticles with diameters of 0.2, 0.5, 1.0, 1.5, 3.0, 5.0, and 10 :m were purchased from Kisker Biotech GmbH & Company KG (Steinfurt, Germany) and were sonicated before DOI 10.1002/jps.23782
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use with a SONOPULS UW3200 probe type sonicator (Bandelin Electronic GmbH & Company KG, Berlin, Germany) to dissociate a small amount of aggregates. Sodium phosphate dibasic dihydrate (AppliChem GmbH, Gatersleben, Germany) and sodium phosphate monobasic dihydrate (Sigma–Aldrich Company LLC, St. Louis, Missouri) were used as the buffering agent for the formulations. Sucrose and sodium chloride were purchased from Sigma–Aldrich Company LLC and VWR International, LLC (Radnor, Pennsylvania), respectively. 1.5 mL of monoclonal antibody A formulation was filled in a 2 mL vial (Schott AG, Mainz, Germany) and stoppered with a rubber stopper (West Pharmaceutical Services, Lionville, Pennsylvania). The protein concentration was determined from the UV absorbance at 280 nm using a Nano Drop (Thermo Fisher Scientific Inc., Rockford, Illinois). The extinction coefficient at 280 nm of the monoclonal antibody A is 1.54 mL/(mg cm). All buffers were filtrated through a 0.22 :m cellulose acetate filter before use (VWR International, LLC). Silica Microparticle Measurement Silica microparticle suspensions with diameters of 0.5 and 10 :m were diluted with 150 mM sodium phosphate buffer at the ratio of 1:20,000 and 1:200, respectively, and analyzed using flow cytometry to optimize the instrument parameters. Silica microparticle suspensions with diameters of 0.5 and 5 :m were diluted with 150 mM sodium phosphate buffer at the ratio of 1:20,000 and 1:2,000, respectively, and were further diluted with the same buffer at the concentration of 100%, 50%, 20%, 10%, 5%, 2%, and 1%. The series of diluted silica microparticle suspensions were analyzed using flow cytometry to evaluate the linearity of the particle counts versus the sample dilution. In addition, the particle counts of 5 :m silica microparticle measured by flow cytometry were compared with those measured by LO. The effect of detector voltage on particle count was studied using silica microparticles with a diameter of 1.5 :m after dilution. The dilution ratio was 1:15,000. All experimental samples were analyzed twice. Preparation of Protein Subvisible Particles Monoclonal antibody A solution at 2 mg/mL in 10 mM sodium phosphate buffer containing 140 mM sodium chloride, pH 6.0 was heated in a thermostatic chamber for 3 h at 65◦ C to generate protein subvisible particles. After heating, the monoclonal antibody A solutions were cooled back to ambient temperature and the aliquots were dispersed in carrier or core fluid at the ratio of 1:20. Carrier or core fluids were 10, 20, 50, 100, and 150 mM sodium phosphate buffer at pH 5.0, 6.5, and 8.0, with or without sucrose. Sucrose was contained in 10, 20, 50, and 100 mM sodium phosphate buffers at final concentrations of 2.0%, 1.8%, 1.4%, and 0.7% (w/v), respectively. Silica microparticle suspensions with diameters of 1.5, 3.0, and 5.0 :m were mixed with the thermally stressed monoclonal antibody A formulation in 150 mM sodium phosphate buffer at the final ratio of 18,000,000:1, 3,600,000:1, and 1,125,000:1, respectively, to compare their forward scattering (FSC)– side scattering (SSC) dot plot profiles. Flow Cytometry A BD FACSCantoTM II flow cytometer (Becton, Dickinson and Company, San Jose, California) equipped with a 488 nm blue laser (air-cooled, 20 mW solid state) and a 633 nm red laser Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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(17 mW HeNe) was used to analyze protein subvisible particles. Hundred microliters of each sample in FACS tubes (Becton, Dickinson and Company) was analyzed. Low-angle FSC and 90◦ SSC were detected by the flow cytometer to characterize the protein subvisible particles. All flow cytometry data sets were collected using BD FACSF low sheath fluid with the low flow rate of 10 :L/min. The measurements were carried out at the FSC and SSC detector voltages of 258 and 272 (“microsetting”) to detect particles with diameters of up to 10 :m, and those of 429 and 423 (“nano-setting”) to detect particles with diameters of as small as 0.5 :m, and data were collected for 2 min to compare the number of events in each measurement. LO Measurement A SVSS-C system with a HCB-LD-25/25 sensor (PAMAS, Partikelmess- und Analysesystem GmbH, Rutesheim, Germany) was used. Three 0.3 mL aliquots were analyzed for each silica microparticle and stressed monoclonal antibody A sample with a prerun volume of 0.3 mL. The samples were drawn and analyzed at a fixed rate of 10 mL/min. Before each measurement, particle-free, highly purified water was flushed through the system until particle counts of less than 50 particles/mL ≥1 :m and less than 5 particles/mL ≥10 :m were reached. The average of the last three measurements was calculated and the results were reported as the number of particle counts per mL. MFI Analysis Subvisible particles were visualized and counted on a microflow digital imaging system DPA 4100 (ProteinSimple, Inc., Santa Clara, California) with a “high-mag” cell. The sample solution was introduced to the flow cell at a flow rate of 0.1 mL/min using a peristaltic pump (Masterflex L/S, Cole-Parmer, Vernon Hills, Illinois). Stress Testing of monoclonal antibody A Five monoclonal antibody A solutions at protein concentrations of 2 mg/mL in 120 mM sodium phosphate buffer, pH 5.0, 6.5, and 8.0, in 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, and in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5 were stressed under four stress conditions to prepare 20 different stressed monoclonal antibody A formulations. Heating, shaking, and light exposure were carried out at 50◦ C for 7 days in heating oven (Binder GmbH, Tuttlingen, Germany), at 700 rpm for 24 h (Thermomixer, Eppendorf, Hamburg, Germany) and at 50 W/m2 for 6 days (SUNTEST CPS, Atlas Material Testing Technology LLC, Chicago, Illinois), respectively. Freeze– thawing cycles were repeated three times between −80◦ C and ambient temperature. The samples were frozen at −80◦ C for 3 h, then moved to a refrigerator at −20◦ C and incubated for 7 days, and finally thawed at room temperature for 3 h in one cycle. Generated protein subvisible particles were analyzed using flow cytometry, LO, and MFI without dilution unless mentioned otherwise.
RESULTS Silica microparticles with diameters from 0.2 to 10 :m were analyzed using flow cytometry to optimize detector voltage. Silica microparticles with a diameter of 0.2 :m could not be detected Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
with the light scattering detector even if the detector voltage was increased. However, silica microparticles with diameters of above 0.5 :m were clearly detected (Fig. 1). Detector voltage for particle sizing was optimized based on the FSC signal from the particles because FSC is the most accurate indicator of particle size.23 To achieve optimal detection on the two settings, a “nano-setting” with FSC and SSC detector voltages of 429 and 423, respectively, was optimized for particles from 0.5 to 3 :m, and a “micro-setting” with FSC and SSC detector voltages of 258 and 272, respectively, for particles from 1 to 10 :m. Notice should be taken that protein subvisible particles prepared under heating stress (65◦ C, 3 h) could be evaluated with the “nano-setting” without exceeding the upper limit of FSC intensity, suggesting that almost all protein particles in this formulation had diameters of below 3 :m. Figure 2 shows particle counts for dilution of silica microparticles with diameters of 0.5 and 5 :m analyzed at the two optimal detector voltage settings. Silica microparticles with a diameter of 0.5 and 5 :m were analyzed with the “nano-setting” and “micro-setting,” respectively. As shown in Figure 2, particle counts correlate with the dilution ratio at both detector voltage settings (r2 > 0.99). Compared with the upper counting limit of the LO instrument of 200,000 particles/mL, the flow cytometry could quantify up to 750,000 particles/mL of 0.5 :m and 1,500,000 particles/mL of 5 :m particles. The inset of Figure 2b shows a comparison of particle counts measured by flow cytometry and LO indicating good agreement between both methods. Next, silica microparticles with a diameter of 1.5 :m were evaluated at the two detector voltage settings and the particle counts were compared with one another to confirm if flow cytometry could really quantify particle number at different detector voltage settings. Silica microparticles with a diameter of 1.5 :m were chosen because this size could be evaluated at both voltage settings. The total particle number counted with the “nano-setting” and “micro-setting” were 615,000 ± 36,000 and 572,000 ± 15,000, respectively, and they showed good agreement. It should be noted that FSC intensity threshold was 200 in all measurements, and furthermore the SSC intensity threshold was set at 200 to reduce noise when particles were measured with the “nano-setting.” To summarize the results described above briefly, flow cytometry could detect and quantify silica nano and microparticles with diameters in a wide size range from 0.5 to 10 :m with excellent linearity when the two detector voltage settings were combined. Now, subvisible protein particles of monoclonal antibody A were prepared under heating stress (65◦ C, 3 h) and measured by flow cytometry after dilution with 150 mM sodium phosphate buffer, pH 6.5. Figure 3 shows a FSC–SSC dot plot obtained for the stressed monoclonal antibody A. Each dot represents a counted particle and those in the marked area shown in Figure 3 represent particles with diameters of above 1 :m. As will be described later, 1.5 :m silica microparticles was chosen as a standard for 1 :m because LO showed that more than 90% of these particles were in a size range from 1.00 to 1.42 :m. A detection threshold of 1 :m at an FSC scattering intensity of 2300 was determined based on an FSC scattering intensity of silica microparticles with a declared diameter of 1.5 :m (Fig. 5b). According to the result shown in Figure 3, it can be assumed that most of the subvisible particles in stressed monoclonal antibody A formulation have diameters of below 1 :m, which can be detected by neither LO nor MFI. This fact illustrates that flow DOI 10.1002/jps.23782
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Figure 1. FSC–SSC dot plots of silica microparticles with diameters of (a) 0.5, (b) 1, (c) 1.5, (d) 3, and (e) 5 :m measured in 150 mM sodium phosphate buffer, pH 6.5. FSC and SSC detector voltages in flow cytometric analysis were 429 and 423, respectively.
cytometry can be one of the few methods available for detecting and counting subvisible protein particles with diameters of below 1 :m. Figure 4 shows the total number of subvisible particles of monoclonal antibody A with diameters of above 1 :m determined in phosphate buffers (pH 6.5) at buffer concentrations of 10, 20, 50, 100, and 150 mM using flow cytometry, LO, and MFI. LO and MFI detected particles with diameters larger than 1.00 and 0.75 :m, respectively, and approximately 75% of the total particles existed in the smallest size fraction of 1.00– 1.42 :m in LO and 0.75–1.75 :m in MFI. Notice should be taken that sucrose was added to buffers (10–100 mM sodium phosphate buffer) to adjust the refractive index of core fluids when the samples were analyzed by flow cytometry. No effect of the sucrose on particle counts could be found by LO (data not shown). As shown in Figure 4, flow cytometry analysis showed the same trend for buffer concentration dependence of the particle counts as LO and MFI, but the retained particle counts were highest for FACS and lowest for LO. Silica microparticles with diameters of 1.5, 3, and 5 :m were mixed with the thermally stressed monoclonal antibody A formulation in 150 mM sodium phosphate buffer (pH 6.5) and the particles were analyzed using flow cytometry and LO. Figures 5a and 5b show the size distribution obtained by LO and the FSC–SSC dot plot obtained by flow cytometry, respectively, of thermally stressed monoclonal antibody A, silica microparticles and a mixture of them. Figure 6a shows size distribution of particles up to 25 :m because LO has detected no particles above 25 :m in the thermally stressed monoclonal antibody A formulation. As shown in Figure 5a, more than 90% of the particles in the stressed monoclonal antibody A formulation DOI 10.1002/jps.23782
existed in a size range from 1.00 to 2.89 :m. Likewise, most of the silica microparticles with diameters of 1.5, 3, and 5 :m were shown to exist in size ranges from 1.00 to 1.42 :m, from 1.42 to 2.03 :m, and from 2.89 to 5.85 :m, respectively. After mixing silica microparticles with monoclonal antibody A subvisible particles, none of the size distributions was changed and they showed combined size distributions in the case where the mixture was analyzed. Because the size distribution of silica microparticles and monoclonal antibody A subvisible particle are overlapping, two populations of the particles are hardly differentiated by LO. On the other hand, as shown in Figure 5b, silica microparticles and monoclonal antibody A subvisible particles can be easily distinguished on the FSC–SSC dot plots obtained by flow cytometry. This is due to the different SSC intensities of silica microparticles and monoclonal antibody A subvisible particles, which result from the relative difference in their particle structures. This result suggests that flow cytometry can separate particles based on their physical properties such as density/structure as a second parameter in addition to size. Monoclonal antibody A formulations with different buffer compositions were now stressed under various conditions. Figure 6 shows the results of subvisible particle measurement using flow cytometry, LO, and MFI. The numbers of particles analyzed by flow cytometry represent the particle counts for those with diameters of above 1 :m, whereas the number of particles measured by LO and MFI shows the total number of subvisible particles detected by these methods. As shown in Figure 6, flow cytometry, LO, and MFI show good correlation in the trend among all five formulations (F1–F5) after stressing. Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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Figure 2. Particle counts for dilution of silica microparticles with diameters of (a) 0.5 :m and (b) 5 :m measured by flow cytometry at FSC and SSC detector voltages of (a) 429 and 423 and (b) 258 and 272. The measurement was carried out in 150 mM sodium phosphate buffer, pH 6.5. The inset is the comparison of particle counts for dilution between flow cytometry (dashed line) and LO (solid line).
As described above, flow cytometry may separate particles based not only on their sizes but also on their physical properties such as density or morphology. If particles have the same physical properties, they should have the same SSC intensity when they are compared with one another at the same FSC intensity. In other words, they should have the same slope of a log–log plot of SSC intensity versus FSC intensity. Figures 7a–7d show FSC–SSC dot plots of stressed monoclonal antibody A formulation in 20 mM sodium phosphate containing 1.2% (w/v) sucrose, pH 6.5 after heating, freeze–thawing, shaking, and light exposure, respectively. Although all formulations except for F4 after shaking showed the same slopes after stressing, FSC–SSC dot plots of monoclonal antibody A formulation after shaking showed different FSC–SSC dot plot profiles and slopes as shown in Figure 7. This result suggests that subvisible particles of monoclonal antibody A after shaking Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
in this formulation have different density or morphology. When the images obtained by MFI were compared, subvisible particles after heating and shaking looked different in their shapes as shown in the inset of Figures 7a and 7c. Subvisible particles after heating stress were nearly spherical, whereas those after shaking looked to be rod-like aggregates. In addition, monoclonal antibody A formulation in 120 mM sodium phosphate containing 0.01% (w/v) polysorbate 80, pH 6.5 showed two populations after light exposure (Fig. 7d). The two populations might be caused by the different physical properties of the particles. These results suggest that flow cytometry provides additional information about particles other than size, which cannot be obtained by LO and can only be derived from MFI when a detailed analysis of the images is carried out. Of course, further investigation will be necessary to validate this potential. DOI 10.1002/jps.23782
RESEARCH ARTICLE – Pharmaceutical Biotechnology
Figure 3. FSC–SSC dot plot of thermally stressed monoclonal antibody A (65◦ C, 3 h) diluted with 150 mM sodium phosphate, pH 6.5. The dots in the square represent particles with diameters of above 1 :m. Threshold of 1 :m was 2300 of the FSC intensity.
All light scattering methods as well as LO are sensitive to refractive index changes of the formulation buffer. We assessed that process for flow cytometry using thermally stressed monoclonal antibody A (65◦ , 3 h) in different phosphate buffer con-
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Figure 4. Subvisible particles counts of monoclonal antibody A measured by LO and MFI in phosphate buffers at the buffer concentration of 10, 20, 50, 100, and 150 mM, and by flow cytometry in phosphate buffers at the buffer concentration of 10, 20, 50, 100, and 150 mM containing sucrose. FSC and SSC detector voltages in flow cytometric analysis were 258 and 272, respectively.
centrations from 10 to 150 mM (Fig. 8). The refractive index of the phosphate buffer increased from 1.332 to 1.335 as buffer concentration increased from 10 to 150 mM. As shown in Figure 8, FSC–SSC dot plot profiles changed as refractive indices of the core fluids changed. SSC intensity increased but the total number of events decreased as phosphate buffer concentration
Figure 5. Silica particles with diameters of 1.5, 3, and 5 :m were mixed with thermally stressed monoclonal antibody A formulation, and analyzed by (a) LO and (b) flow cytometry. FSC and SSC detector voltages in flow cytometric analysis were 258 and 272, respectively. DOI 10.1002/jps.23782
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Figure 6. Comparison of subvisible particle counts of monoclonal antibody A in five formulations after heating, freeze–thawing, shaking, and light exposure measured between LO, MFI, and flow cytometry. Buffer composition of the formulation buffers were as follows; F1: 120 mM sodium phosphate buffer, pH 5.0, F2: 120 mM sodium phosphate buffer, pH 6.5, F3: 120 mM sodium phosphate buffer, pH 8.0, F4: 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, F5: 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5
decreased. Considering that the refractive index of BD FACSFlow sheath fluid was 1.335, which was close to that of 150 mM phosphate buffer, it can be concluded that the refractive index mismatch between core fluid and sheath fluid at lower buffer concentration interfered with light scattering detection from particles. Flow cytometry experiments were carried out after adjusting the refractive indices of core fluids to 1.335 by adding sucrose to the phosphate buffers. Figure 8 shows FSC–SSC dot plots of the stressed monoclonal antibody A in phosphate buffers containing sucrose. No changes in the characteristic dot plot were observed.
DISCUSSION Our study was related to protein subvisible particle measurement using flow cytometry without fluorescent labeling. Although some groups have reported on the application of flow cytometry to subvisible particle measurement in protein formulations, labeled proteins have been used in their studies Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
and this may prevent the extended application of this method because fluorescent labeled proteins cannot be routinely used in formulation development studies nor would they ever be present in commercial products in case such products were to be tested by such a method. Our results showed that silica microparticles could be detected and counted in a wide size range from 0.5 to 10 :m using flow cytometry with a light scattering detector. Two laser settings should be applied to allow optimal counting for the size range from 0.5 to 10 :m. The applied setting is also applied for MFI measurement when one magnification from two possible magnifications is chosen. As shown in Figure 2, the lowest particle concentration was about 15,000 and 3500 particles/mL for 0.5 and 5 :m silica microparticles, respectively. In our study, the background noise of the “nano-setting” had a very good signal to noise ratio with only 10,000 particles/mL for submicron particles. There is practically no noise for micrometer particles. Subvisible particles of a model monoclonal antibody prepared under heating stress (65◦ C, 3 h) could be analyzed DOI 10.1002/jps.23782
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Figure 7. FSC–SSC dot plots of monoclonal antibody A after (a) heating, (b) freeze–thawing, (c) shaking, and (d) light exposure in 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, and (e) after light exposure in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5. Flow cytometry was carried out at FSC and SSC detector voltages of 258 and 272 for sample (a)–(d) and at FSC and SSC detector voltages of 429 and 423 for sample (e).
at a high detector voltage setting without fluorescent labeling. When the value of FSC intensity from the subvisible particles of monoclonal antibody A was compared with that from 1.5 :m silica microparticles, most of the particles of monoclonal antibody A were of submicrometer range. Because LO and MFI cannot detect particles with diameters of below 1 and 0.75 :m, respectively, flow cytometer is one of the few methods that can analyze subvisible particles with diameters of below 1 :m. As shown in Figure 4, particle counts for aggregates of monoclonal antibody A decreased when the particles were diluted with increasingly concentrated phosphate buffers. Interestingly, this buffer concentration dependence of particle formation or dissolution may be related to the specific feature of monoclonal antibody A which induces liquid–liquid phase separation under a low ionic strength condition.21 Although the exact mechanism of how liquid-liquid phase separation is induced is still unknown, reversible self-association driven by electrostatic interaction is suggested to be a major cause, because pH and ionic strength affect the phase behavior of monoclonal antibody A. Flow cytometry is also an extremely powerful technology that allows the physical separation of particles of interest from a heterogeneous population.24 If a population can be identified in an analytical cytometer, it can be retrieved using a flow sorter. As shown in Figure 5b, silica microparticles and protein subvisible particles had different SSC intensity when they were compared at same value of FSC intensity. This might reflect the difference in their physical properties such as density. Different FSC–SSC dot plot profiles were also observed when protein subvisible particles from different stress exposures were analyzed (Fig. 7). This may also be related to different physical DOI 10.1002/jps.23782
properties of subvisible particles formed under different stress conditions. In four out of five cases, the particles showed only one population even though they had different FSC–SSC dot plot profiles. However, in one case, interestingly, monoclonal antibody A formulation in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5 showed two populations after light exposure (Fig. 7d). This result suggests that light may induce at least two totally different types of subvisible particles with different physical properties. van Beers et al.25,26 studied immunogenicity of aggregates of rhIFN$ induced by oxidation and described that the combination of aggregation, oxidation, and change in conformation determined the immunogenicity of the aggregates. This indicates that size analysis of protein aggregates is not enough to understand the relationship between protein aggregates and their immunogenicity. As described above, flow cytometry might evaluate physical properties as well as size of particles and could be used to retrieve and separate particles of interest from a heterogeneous population for further studies, for example, for immunogenicity research. Finally, difficulties and limitations when applying flow cytometric analysis to subvisible particle measurement shall be discussed. As already reported, LO and MFI may be impacted by the difference of the refractive index between protein particles and carrier fluids. When the difference of a refractive index between protein particles and carrier fluids is small, in other words, when the particles are transparent, the obscuration signal becomes weak and LO underestimates the particles.27 MFI may be less affected by these effects because MFI observes the particles visually, and even in cases where the contrast of the images is not sufficient, these images can be reanalyzed using Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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Figure 8. The FSC–SSC dot plots of stressed formulation of monoclonal antibody A observed in (a) 10 mM phosphate buffer with or without 2.0% (w/v) sucrose, (b) 20 mM phosphate buffer with or without 1.8% (w/v) sucrose, (c) 50 mM phosphate buffer with or without 1.4% (w/v) sucrose, (d) 100 mM phosphate buffer with or without 0.7% (w/v) sucrose, (e) 150 mM phosphate buffers without sucrose. The sucrose concentration was determined to adjust the refractive index of the buffers to 1.335. Notice should be taken that the unstressed formulations gave almost no event in flow cytometric analysis. FSC and SSC detector voltages were 258 and 272, respectively.
the saved images. However, refractive index matching between particles and carrier fluids is also important in MFI.27 This should be the case with flow cytometric analysis as well because this technique is also an optical measurement. Of course, the refractive index mismatch is usually not a problem in analyzing relatively large particles in the micrometer range or cells, but may be one for small particles in the submicrometer range. This may cause a problem in analyzing protein formulations because solvent condition such as pH, ionic strength, or concentration of excipients is optimized for each protein drug for stability and not for refractive index match. The difference in refractive index between core (sample) fluids and sheath fluids may also impact the light scattering detection from particles in flow cytometric analysis because core fluid containing target particles is hydrodynamically focused and flows in the center of sheath fluids.28 We have solved the problem with refractive index mismatch by adding sucrose to the phosphate buffers to adjust the refractive indices of core fluids. However, sugars as typified by sucrose are known to stabilize proteins by preferential interaction29 and widely added in protein formulations. In this case, we confirmed that adding sucrose to the formulation buffers did not affect particle size distribution using LO.
CONCLUSIONS Flow cytometry can quantify and characterize protein subvisible particles with diameters of below 10 :m in protein formulations without fluorescent labeling and therefore can be added to the list of methods available for such protein subvisible particles. Flow cytometric analysis has a great advantage in Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
analyzing such protein particles because flow cytometry cannot only quantify particles with diameters of below 1 :m but also characterize particles based on their physical properties such as density or morphology. However, it is necessary to recognize that flow cytometry is sensitive to refractive index mismatch between core (sample) fluid and sheath fluid.
REFERENCES 1. Brekke OH, Sandlie I. 2003. Therapeutic antibody for human diseases at the dawn of the twenty-first century. Nat Rev Drug Discov 2:52–62. ´ 2. Vazquez-Rey M, Lang DA. 2011. Aggregates in monoclonal antibody manufacturing processes. Biotechnol Bioeng 108:1494–1508. 3. Luo Q, Joubert MK, Stevenson R, Ketchem RR, Narhi LO, Wypych J. 2011. Chemical modifications in therapeutic protein aggregates generated under different stress conditions. J Biol Chem 286:25134– 25144. 4. Braun A, Kwee L, Labow MA, Alsenz J. 1997. Protein aggregates seem to play a key role among the parameters influencing the antigenicity of interferon alpha (INF-alpha) in normal and transgenic mice. Pharm Res 14:1472–1478. 5. Hermeling S, Crommelin DJ, Schellekens H, Jiskoot W. 2004. Structure–immunogenicity relationships of therapeutic proteins. Pharm Res 21:897–903. 6. Carpenter JF, Randolph TR, Jiskoot W, Crommelin DJA, Middaugh CR, Winter G, Fan Y, 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 98:1201–1205. 7. Z¨olls S, Tantipolphan R, Wiggenhorn M, Winter G, Jiskoot W, Friess W, Hawe A. 2012. Particles in therapeutic protein formulations, part 1: Overview of analytical methods. J Pharm Sci 101:914–935. DOI 10.1002/jps.23782
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8. Gabrielson JP, Brader ML, Pekar AH, Mathis KB, Winter G, Carpenter JF, Randolph TW. 2007. Quantitation of aggregate levels in a recombinant humanized monoclonal antibody formulation by sizeexclusion chromatography, asymmetrical flow field flow fractionation, and sedimentation velocity. J Pharm Sci 96:268–279. 9. Krayukhina E, Uchiyama S, Nojima K, Okada Y, Hamaguchi I, Fukui K. 2013. Aggregation analysis of pharmaceutical human immunoglobulin preparations using size-exclusion chromatography and analytical ultracentrifugation sedimentation velocity. J Biosci Bioeng 115:104–110. ¨ 10. 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 59:407–417. 11. Wang W, Singh SK, Li Ning, Toler MR, King KR, Nema S. 2012. Immunogenicity of protein aggregates—Concerns and realities. Int J Pharm 431:1–11. 12. Patel AR, Lau D, Liu J. 2012. Quantification and characterization of micrometer and submicrometer subvisible particles in protein therapeutics by use of a suspended microchannel resonator. Anal Chem 84:6833–6840. 13. Weinbuch D, Z¨olls S, Wiggenhorn M, Friess W, Winter G, Jiskoot W, Hawe A. 2013. Micro–Flow Imaging and Resonant Mass Measurement (Archimedes) – Complementary Methods to Quantitatively Differentiate Protein Particles and Silicone Oil Droplets. J Pharm Sci 102:2152– 2165. 14. Ludwig DB, Trotter JT, Gabrielson JP, Carpenter JF, Randolph TW. 2011. Flow cytometry: A promising technique for the study of silicon oil-induced particulate formation in protein formulations. Anal Biochem 410:191–199. 15. Barnard JG, Rhyner MN, Carpenter JF. 2012. Critical evaluation and guidance for using the coulter method for counting subvisible particles in protein formulation. J Pharm Sci 101:140–153. 16. Lindstr¨om S. 2012. Flow cytometry and microscopy as means of studying single cells: A short introductional overview. Methods Mol Biol 853:13–15. 17. Mach H, Bhambhani A, Meyer BK, Burek S, Davis H, Blue JT, Evans RK. 2011. The use of flow cytometry for the detection of sub-
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visible particles in therapeutic protein formulations. J Pharm Sci 100:1671–1678. 18. Mach H, Arvinte T. 2011. Addressing new analytical challenges in protein formulation development. Eur J Pharm Biopharm 78:196– 207. 19. 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. 20. Krutzik PO, Nolan GP. 2006. Fluorescent cell barcording in flow cytometry allows high-throughput drug screening and signaling profiling. Nat Methods 3:361–368. 21. Nishi H, Miyajima M, Nakagami H, Noda M, Uchiyama S, Fukui K. 2010. Phase separation of an IgG1 antibody solution under a low ionic strength condition. Pharm Res 27:1348–1360. 22. Nishi H, Miyajima M, Kubota K, Hasegawa J, Uchiyama S, Fukui K. 2011. Fc domain mediated self-association of an IgG1 monoclonal antibody under a low ionic strength condition. J Biosci Bioeng 112:326– 332. 23. Shapiro HM. 2003. Practical flow cytometry. New Jersey: Wiley, pp 273–410. 24. Marion GM. 2007. Flow cytometry: Principles and applications. New Jersey: Humana Press, pp 257–276. 25. van Beers MMC, Sauerborn M, Gilli F, Brinks V, Schellekens H, Jiskoot W. 2010. Aggregated recombinant human interferon beta induces antibodies but no memory in immune-tolerant transgenic mice. Pharm Res 27:1812–1824. 26. van Beers MMC, Sauerborn M, Gilli F, Brinks V, Schellekens H, Jiskoot W. 2011. Oxidized and aggregated recombinant human interferon beta in human interferon beta transgenic mice. Pharm Res 28:2394–2402. 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 3:455–464. 28. Shapiro HM. 2003. Practical flow cytometry. New Jersey: Wiley, pp 101–223. 29. Arakawa T, Timasheff SN. 1982. Stabilization of protein structure by sugars. Biochemistry 21:6536–6544.
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Label-Free Flow Cytometry Analysis of Subvisible Aggregates in Liquid IgG1 Antibody Formulations 2 ¨ 1 ROBERT FURST, ¨ HIROTAKA NISHI,1 ROMAN MATHAS, GERHARD WINTER1 1
Department of Pharmacy, Pharmaceutical Technology and Biopharmaceutics, Ludwig Maximilian University Munich, Munich 81377, Germany 2 Department of Pharmacy, Pharmaceutical Biology, Ludwig Maximilian University Munich, Munich 81377, Germany Received 2 August 2013; revised 8 October 2013; accepted 18 October 2013 Published online 11 November 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23782
ABSTRACT: The objective of this study was to characterize and quantify label-free subvisible antibody particles in different formulations based on their size and physical properties by flow cytometry. Protein subvisible particles were prepared under various stress conditions and analyzed by applying different analytical techniques [light obscuration (LO), microflow imaging (MFI), and flow cytometry (FACS)] for the detection of aggregates. The capability of the FACS method to detect and count subvisible particles was evaluated and benchmarked against conventional techniques. FACS can analyze particles down to 500 nm reducing the gap between size-exclusion chromatography and LO. The applied methods of FACS, LO, and MFI displayed a proportional correlation between the total particle counts, however, FACS C 2013 Wiley Periodicals, Inc. and the can provide additional information on the structural characteristics of such aggregated particles. American Pharmacists Association J Pharm Sci 103:90–99, 2014 Keywords: monoclonal antibody; protein formulation; protein aggregation; subvisible particle; particle sizing; flow cytometry; light obscuration; microflow imaging; refractive index; analytical biochemistry
INTRODUCTION Humanized monoclonal antibodies have become major pharmaceutical products in the treatment of many diseases such as cancer, allergies, and autoimmune diseases.1 With the development of these products, improving the limited stability, especially in preventing protein aggregates during manufacturing, shipping, storage, and delivery, is one of the key challenges because protein aggregates can be easily induced under stressed conditions such as temperature fluctuation, agitation, freeze–thawing, light, or exposure to interfaces.2,3 The protein aggregates, which have a non-native conformation, have been widely recognized to have an impact on the product quality but also on the biological activity and immunogenicity.4,5 Protein aggregates are heterogeneous species that have many variations in size and conformation, and are generally classified into soluble and insoluble aggregates based on size. Soluble aggregates can be measured by size-exclusion chromatography (SEC) (e.g., dimer, trimer, or oligomer, ≤0.1 :m).6 Insoluble aggregates are further classified into subvisible particles (0.1–100 :m) and visible particles (≥100 :m).6 This wide range of protein aggregate sizes provides a significant challenge in developing analytical methods and requires complementary methods because a single analytical method cannot cover the full size range.7 In the pharmaceutical industry, protein soluble aggregates and visible particles with diameters of below 0.1 :m and above 100 :m, respectively, are routinely monitored for the release of products mainly by SEC and visual inspection, respectively. Analytical ultracentrifugation8,9 and light scattering techniques10
Correspondence to: Gerhard Winter (Telephone: +49-89-2180-77933; Fax: +49-89-2180-77058; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 103, 90–99 (2014)
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are also used in analyzing the size, hydrodynamic diameter, or the average molecular weight of protein soluble aggregates. On the other hand, subvisible particles with diameters from 1 to 100 :m are analyzed by light obscuration (LO), which is one of the standard techniques listed in the pharmacopeia. Because of the fact that there are no regulatory guidelines on protein particles below 10 :m, many authors, for example, Carpenter et al.,6 point out a gap between current scientific understandings and the guidelines for the protein aggregates below 10 :m, and raise the concern about their risks in product safety (immunogenicity) as well as in efficacy. The immunogenicity of the protein particles above 0.1 :m may be enhanced by their increased uptake into dendritic cells, and following facilitated maturation, activation or proliferation, and enhanced stimulation of B and T lymphocytes.11 Although conventional analytical methods such as LO and microflow imaging (MFI) can measure subvisible particles as small as 1.00 and 0.75 :m, respectively, it is not clear if the measured particle sizes and numbers are sufficiently accurate or not because 1 :m is the lower detection and quantification limit of these methods. Therefore, further analytical technologies, which can quantify and characterize subvisible particles with a diameter of below 10 :m, are still needed. Recently, several emerging technologies have been developed to count and/or characterize subvisible particles to close “submicron size gap.” Specifically, resonant mass measurement (RMM, Archimedes system) is employed to analyze submicron and micron particles.12,13 Here, the sample solutions pass through a microchannel inside a resonating cantilever. Particles are detected and sized by a frequency change of the cantilever, which is proportional to the buoyant mass of particles. In addition, RMM clearly distinguishes silicon oil droplets and protein particles separately by negative and positive buoyant masses. However, heterogeneous particles of silicon oil and protein will lead to underestimated particle size.
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Moreover, the narrow capillaries are fragile to clogging by bigger particles. Coulter counter and nanoparticle tracking analysis have also been applied to count and/or characterize subvisible particles.14,15 However, coulter counter requires sufficient conductivity, which is not granted in formulation screenings. Nanoparticle tracking analysis allows an analysis in nanometer size range, whereas it is less accurate in counting. Flow cytometry (FACS) is a laser-based biophysical technique, which can detect light scattering and light emission from nonstained or stained particles, and has been mainly employed in the field of cell biology.16 Flow cytometry is one of the few methods available in counting and sorting thousands of individual particles every second in real time even in a relatively small total sample volume (100–200 :L). Recently, the potential of this technique for analyzing the number and size of subvisible particles in protein formulations began to be explored using fluorescently labeled protein.14,17–19 The identification of optimal formulation conditions such as pH, ionic strength, and excipient content is a key element in successful protein formulation development. A high-throughput approach is desired and usually applied for screening protein monomer content by SEC. However, most of the particle analytics do not figure an auto sampler. Flow cytometry allows high-throughput screening using 96- or 384-well plates.20 Therefore, it can be a strong tool for determining optimal protein formulations. In recent studies, which were reported from other groups, detection of subvisible particles as small as 200 nm was successfully carried out using a fluorescence detector.19 However, fluorescent labeling of protein may affect the aggregation behavior of the protein, and moreover, fluorescently labeled proteins cannot be routinely used in formulation development, nor, of course, in production control of protein drugs in clinical use. In this report, the successful application of flow cytometry for characterizing and quantifying protein subvisible particles without fluorescent labeling is described. First, the effect of the instrument parameters such as detector voltage and flow rate on the detection of particles, the linearity of particle count versus sample dilution is investigated, and the general application of flow cytometry for quantifying such unlabeled subvisible particles is described. Next, protein particles prepared under heating stress are analyzed using flow cytometry and the number of particles with diameters of above 1 :m are compared with those measured by LO and MFI. The protein particles are also analyzed after mixing them with silica microparticles to show the ability of flow cytometry to differentiate particles not only by size but also by their structure and morphology. Finally, flow cytometry is applied to characterize and quantify monoclonal antibody subvisible particles in formulations after various stresses have been applied.
MATERIALS AND METHODS Materials The humanized monoclonal antibody A (IgG1 subclass) used in this investigation was produced and purified at Daiichi Sankyo Company, Ltd., Tokyo, Japan and was also studied in Refs. 21 and22 . The theoretical isoelectric point of the antibody is 6.5. Silica microparticles with diameters of 0.2, 0.5, 1.0, 1.5, 3.0, 5.0, and 10 :m were purchased from Kisker Biotech GmbH & Company KG (Steinfurt, Germany) and were sonicated before DOI 10.1002/jps.23782
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use with a SONOPULS UW3200 probe type sonicator (Bandelin Electronic GmbH & Company KG, Berlin, Germany) to dissociate a small amount of aggregates. Sodium phosphate dibasic dihydrate (AppliChem GmbH, Gatersleben, Germany) and sodium phosphate monobasic dihydrate (Sigma–Aldrich Company LLC, St. Louis, Missouri) were used as the buffering agent for the formulations. Sucrose and sodium chloride were purchased from Sigma–Aldrich Company LLC and VWR International, LLC (Radnor, Pennsylvania), respectively. 1.5 mL of monoclonal antibody A formulation was filled in a 2 mL vial (Schott AG, Mainz, Germany) and stoppered with a rubber stopper (West Pharmaceutical Services, Lionville, Pennsylvania). The protein concentration was determined from the UV absorbance at 280 nm using a Nano Drop (Thermo Fisher Scientific Inc., Rockford, Illinois). The extinction coefficient at 280 nm of the monoclonal antibody A is 1.54 mL/(mg cm). All buffers were filtrated through a 0.22 :m cellulose acetate filter before use (VWR International, LLC). Silica Microparticle Measurement Silica microparticle suspensions with diameters of 0.5 and 10 :m were diluted with 150 mM sodium phosphate buffer at the ratio of 1:20,000 and 1:200, respectively, and analyzed using flow cytometry to optimize the instrument parameters. Silica microparticle suspensions with diameters of 0.5 and 5 :m were diluted with 150 mM sodium phosphate buffer at the ratio of 1:20,000 and 1:2,000, respectively, and were further diluted with the same buffer at the concentration of 100%, 50%, 20%, 10%, 5%, 2%, and 1%. The series of diluted silica microparticle suspensions were analyzed using flow cytometry to evaluate the linearity of the particle counts versus the sample dilution. In addition, the particle counts of 5 :m silica microparticle measured by flow cytometry were compared with those measured by LO. The effect of detector voltage on particle count was studied using silica microparticles with a diameter of 1.5 :m after dilution. The dilution ratio was 1:15,000. All experimental samples were analyzed twice. Preparation of Protein Subvisible Particles Monoclonal antibody A solution at 2 mg/mL in 10 mM sodium phosphate buffer containing 140 mM sodium chloride, pH 6.0 was heated in a thermostatic chamber for 3 h at 65◦ C to generate protein subvisible particles. After heating, the monoclonal antibody A solutions were cooled back to ambient temperature and the aliquots were dispersed in carrier or core fluid at the ratio of 1:20. Carrier or core fluids were 10, 20, 50, 100, and 150 mM sodium phosphate buffer at pH 5.0, 6.5, and 8.0, with or without sucrose. Sucrose was contained in 10, 20, 50, and 100 mM sodium phosphate buffers at final concentrations of 2.0%, 1.8%, 1.4%, and 0.7% (w/v), respectively. Silica microparticle suspensions with diameters of 1.5, 3.0, and 5.0 :m were mixed with the thermally stressed monoclonal antibody A formulation in 150 mM sodium phosphate buffer at the final ratio of 18,000,000:1, 3,600,000:1, and 1,125,000:1, respectively, to compare their forward scattering (FSC)– side scattering (SSC) dot plot profiles. Flow Cytometry A BD FACSCantoTM II flow cytometer (Becton, Dickinson and Company, San Jose, California) equipped with a 488 nm blue laser (air-cooled, 20 mW solid state) and a 633 nm red laser Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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(17 mW HeNe) was used to analyze protein subvisible particles. Hundred microliters of each sample in FACS tubes (Becton, Dickinson and Company) was analyzed. Low-angle FSC and 90◦ SSC were detected by the flow cytometer to characterize the protein subvisible particles. All flow cytometry data sets were collected using BD FACSF low sheath fluid with the low flow rate of 10 :L/min. The measurements were carried out at the FSC and SSC detector voltages of 258 and 272 (“microsetting”) to detect particles with diameters of up to 10 :m, and those of 429 and 423 (“nano-setting”) to detect particles with diameters of as small as 0.5 :m, and data were collected for 2 min to compare the number of events in each measurement. LO Measurement A SVSS-C system with a HCB-LD-25/25 sensor (PAMAS, Partikelmess- und Analysesystem GmbH, Rutesheim, Germany) was used. Three 0.3 mL aliquots were analyzed for each silica microparticle and stressed monoclonal antibody A sample with a prerun volume of 0.3 mL. The samples were drawn and analyzed at a fixed rate of 10 mL/min. Before each measurement, particle-free, highly purified water was flushed through the system until particle counts of less than 50 particles/mL ≥1 :m and less than 5 particles/mL ≥10 :m were reached. The average of the last three measurements was calculated and the results were reported as the number of particle counts per mL. MFI Analysis Subvisible particles were visualized and counted on a microflow digital imaging system DPA 4100 (ProteinSimple, Inc., Santa Clara, California) with a “high-mag” cell. The sample solution was introduced to the flow cell at a flow rate of 0.1 mL/min using a peristaltic pump (Masterflex L/S, Cole-Parmer, Vernon Hills, Illinois). Stress Testing of monoclonal antibody A Five monoclonal antibody A solutions at protein concentrations of 2 mg/mL in 120 mM sodium phosphate buffer, pH 5.0, 6.5, and 8.0, in 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, and in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5 were stressed under four stress conditions to prepare 20 different stressed monoclonal antibody A formulations. Heating, shaking, and light exposure were carried out at 50◦ C for 7 days in heating oven (Binder GmbH, Tuttlingen, Germany), at 700 rpm for 24 h (Thermomixer, Eppendorf, Hamburg, Germany) and at 50 W/m2 for 6 days (SUNTEST CPS, Atlas Material Testing Technology LLC, Chicago, Illinois), respectively. Freeze– thawing cycles were repeated three times between −80◦ C and ambient temperature. The samples were frozen at −80◦ C for 3 h, then moved to a refrigerator at −20◦ C and incubated for 7 days, and finally thawed at room temperature for 3 h in one cycle. Generated protein subvisible particles were analyzed using flow cytometry, LO, and MFI without dilution unless mentioned otherwise.
RESULTS Silica microparticles with diameters from 0.2 to 10 :m were analyzed using flow cytometry to optimize detector voltage. Silica microparticles with a diameter of 0.2 :m could not be detected Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
with the light scattering detector even if the detector voltage was increased. However, silica microparticles with diameters of above 0.5 :m were clearly detected (Fig. 1). Detector voltage for particle sizing was optimized based on the FSC signal from the particles because FSC is the most accurate indicator of particle size.23 To achieve optimal detection on the two settings, a “nano-setting” with FSC and SSC detector voltages of 429 and 423, respectively, was optimized for particles from 0.5 to 3 :m, and a “micro-setting” with FSC and SSC detector voltages of 258 and 272, respectively, for particles from 1 to 10 :m. Notice should be taken that protein subvisible particles prepared under heating stress (65◦ C, 3 h) could be evaluated with the “nano-setting” without exceeding the upper limit of FSC intensity, suggesting that almost all protein particles in this formulation had diameters of below 3 :m. Figure 2 shows particle counts for dilution of silica microparticles with diameters of 0.5 and 5 :m analyzed at the two optimal detector voltage settings. Silica microparticles with a diameter of 0.5 and 5 :m were analyzed with the “nano-setting” and “micro-setting,” respectively. As shown in Figure 2, particle counts correlate with the dilution ratio at both detector voltage settings (r2 > 0.99). Compared with the upper counting limit of the LO instrument of 200,000 particles/mL, the flow cytometry could quantify up to 750,000 particles/mL of 0.5 :m and 1,500,000 particles/mL of 5 :m particles. The inset of Figure 2b shows a comparison of particle counts measured by flow cytometry and LO indicating good agreement between both methods. Next, silica microparticles with a diameter of 1.5 :m were evaluated at the two detector voltage settings and the particle counts were compared with one another to confirm if flow cytometry could really quantify particle number at different detector voltage settings. Silica microparticles with a diameter of 1.5 :m were chosen because this size could be evaluated at both voltage settings. The total particle number counted with the “nano-setting” and “micro-setting” were 615,000 ± 36,000 and 572,000 ± 15,000, respectively, and they showed good agreement. It should be noted that FSC intensity threshold was 200 in all measurements, and furthermore the SSC intensity threshold was set at 200 to reduce noise when particles were measured with the “nano-setting.” To summarize the results described above briefly, flow cytometry could detect and quantify silica nano and microparticles with diameters in a wide size range from 0.5 to 10 :m with excellent linearity when the two detector voltage settings were combined. Now, subvisible protein particles of monoclonal antibody A were prepared under heating stress (65◦ C, 3 h) and measured by flow cytometry after dilution with 150 mM sodium phosphate buffer, pH 6.5. Figure 3 shows a FSC–SSC dot plot obtained for the stressed monoclonal antibody A. Each dot represents a counted particle and those in the marked area shown in Figure 3 represent particles with diameters of above 1 :m. As will be described later, 1.5 :m silica microparticles was chosen as a standard for 1 :m because LO showed that more than 90% of these particles were in a size range from 1.00 to 1.42 :m. A detection threshold of 1 :m at an FSC scattering intensity of 2300 was determined based on an FSC scattering intensity of silica microparticles with a declared diameter of 1.5 :m (Fig. 5b). According to the result shown in Figure 3, it can be assumed that most of the subvisible particles in stressed monoclonal antibody A formulation have diameters of below 1 :m, which can be detected by neither LO nor MFI. This fact illustrates that flow DOI 10.1002/jps.23782
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Figure 1. FSC–SSC dot plots of silica microparticles with diameters of (a) 0.5, (b) 1, (c) 1.5, (d) 3, and (e) 5 :m measured in 150 mM sodium phosphate buffer, pH 6.5. FSC and SSC detector voltages in flow cytometric analysis were 429 and 423, respectively.
cytometry can be one of the few methods available for detecting and counting subvisible protein particles with diameters of below 1 :m. Figure 4 shows the total number of subvisible particles of monoclonal antibody A with diameters of above 1 :m determined in phosphate buffers (pH 6.5) at buffer concentrations of 10, 20, 50, 100, and 150 mM using flow cytometry, LO, and MFI. LO and MFI detected particles with diameters larger than 1.00 and 0.75 :m, respectively, and approximately 75% of the total particles existed in the smallest size fraction of 1.00– 1.42 :m in LO and 0.75–1.75 :m in MFI. Notice should be taken that sucrose was added to buffers (10–100 mM sodium phosphate buffer) to adjust the refractive index of core fluids when the samples were analyzed by flow cytometry. No effect of the sucrose on particle counts could be found by LO (data not shown). As shown in Figure 4, flow cytometry analysis showed the same trend for buffer concentration dependence of the particle counts as LO and MFI, but the retained particle counts were highest for FACS and lowest for LO. Silica microparticles with diameters of 1.5, 3, and 5 :m were mixed with the thermally stressed monoclonal antibody A formulation in 150 mM sodium phosphate buffer (pH 6.5) and the particles were analyzed using flow cytometry and LO. Figures 5a and 5b show the size distribution obtained by LO and the FSC–SSC dot plot obtained by flow cytometry, respectively, of thermally stressed monoclonal antibody A, silica microparticles and a mixture of them. Figure 6a shows size distribution of particles up to 25 :m because LO has detected no particles above 25 :m in the thermally stressed monoclonal antibody A formulation. As shown in Figure 5a, more than 90% of the particles in the stressed monoclonal antibody A formulation DOI 10.1002/jps.23782
existed in a size range from 1.00 to 2.89 :m. Likewise, most of the silica microparticles with diameters of 1.5, 3, and 5 :m were shown to exist in size ranges from 1.00 to 1.42 :m, from 1.42 to 2.03 :m, and from 2.89 to 5.85 :m, respectively. After mixing silica microparticles with monoclonal antibody A subvisible particles, none of the size distributions was changed and they showed combined size distributions in the case where the mixture was analyzed. Because the size distribution of silica microparticles and monoclonal antibody A subvisible particle are overlapping, two populations of the particles are hardly differentiated by LO. On the other hand, as shown in Figure 5b, silica microparticles and monoclonal antibody A subvisible particles can be easily distinguished on the FSC–SSC dot plots obtained by flow cytometry. This is due to the different SSC intensities of silica microparticles and monoclonal antibody A subvisible particles, which result from the relative difference in their particle structures. This result suggests that flow cytometry can separate particles based on their physical properties such as density/structure as a second parameter in addition to size. Monoclonal antibody A formulations with different buffer compositions were now stressed under various conditions. Figure 6 shows the results of subvisible particle measurement using flow cytometry, LO, and MFI. The numbers of particles analyzed by flow cytometry represent the particle counts for those with diameters of above 1 :m, whereas the number of particles measured by LO and MFI shows the total number of subvisible particles detected by these methods. As shown in Figure 6, flow cytometry, LO, and MFI show good correlation in the trend among all five formulations (F1–F5) after stressing. Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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Figure 2. Particle counts for dilution of silica microparticles with diameters of (a) 0.5 :m and (b) 5 :m measured by flow cytometry at FSC and SSC detector voltages of (a) 429 and 423 and (b) 258 and 272. The measurement was carried out in 150 mM sodium phosphate buffer, pH 6.5. The inset is the comparison of particle counts for dilution between flow cytometry (dashed line) and LO (solid line).
As described above, flow cytometry may separate particles based not only on their sizes but also on their physical properties such as density or morphology. If particles have the same physical properties, they should have the same SSC intensity when they are compared with one another at the same FSC intensity. In other words, they should have the same slope of a log–log plot of SSC intensity versus FSC intensity. Figures 7a–7d show FSC–SSC dot plots of stressed monoclonal antibody A formulation in 20 mM sodium phosphate containing 1.2% (w/v) sucrose, pH 6.5 after heating, freeze–thawing, shaking, and light exposure, respectively. Although all formulations except for F4 after shaking showed the same slopes after stressing, FSC–SSC dot plots of monoclonal antibody A formulation after shaking showed different FSC–SSC dot plot profiles and slopes as shown in Figure 7. This result suggests that subvisible particles of monoclonal antibody A after shaking Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
in this formulation have different density or morphology. When the images obtained by MFI were compared, subvisible particles after heating and shaking looked different in their shapes as shown in the inset of Figures 7a and 7c. Subvisible particles after heating stress were nearly spherical, whereas those after shaking looked to be rod-like aggregates. In addition, monoclonal antibody A formulation in 120 mM sodium phosphate containing 0.01% (w/v) polysorbate 80, pH 6.5 showed two populations after light exposure (Fig. 7d). The two populations might be caused by the different physical properties of the particles. These results suggest that flow cytometry provides additional information about particles other than size, which cannot be obtained by LO and can only be derived from MFI when a detailed analysis of the images is carried out. Of course, further investigation will be necessary to validate this potential. DOI 10.1002/jps.23782
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Figure 3. FSC–SSC dot plot of thermally stressed monoclonal antibody A (65◦ C, 3 h) diluted with 150 mM sodium phosphate, pH 6.5. The dots in the square represent particles with diameters of above 1 :m. Threshold of 1 :m was 2300 of the FSC intensity.
All light scattering methods as well as LO are sensitive to refractive index changes of the formulation buffer. We assessed that process for flow cytometry using thermally stressed monoclonal antibody A (65◦ , 3 h) in different phosphate buffer con-
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Figure 4. Subvisible particles counts of monoclonal antibody A measured by LO and MFI in phosphate buffers at the buffer concentration of 10, 20, 50, 100, and 150 mM, and by flow cytometry in phosphate buffers at the buffer concentration of 10, 20, 50, 100, and 150 mM containing sucrose. FSC and SSC detector voltages in flow cytometric analysis were 258 and 272, respectively.
centrations from 10 to 150 mM (Fig. 8). The refractive index of the phosphate buffer increased from 1.332 to 1.335 as buffer concentration increased from 10 to 150 mM. As shown in Figure 8, FSC–SSC dot plot profiles changed as refractive indices of the core fluids changed. SSC intensity increased but the total number of events decreased as phosphate buffer concentration
Figure 5. Silica particles with diameters of 1.5, 3, and 5 :m were mixed with thermally stressed monoclonal antibody A formulation, and analyzed by (a) LO and (b) flow cytometry. FSC and SSC detector voltages in flow cytometric analysis were 258 and 272, respectively. DOI 10.1002/jps.23782
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Figure 6. Comparison of subvisible particle counts of monoclonal antibody A in five formulations after heating, freeze–thawing, shaking, and light exposure measured between LO, MFI, and flow cytometry. Buffer composition of the formulation buffers were as follows; F1: 120 mM sodium phosphate buffer, pH 5.0, F2: 120 mM sodium phosphate buffer, pH 6.5, F3: 120 mM sodium phosphate buffer, pH 8.0, F4: 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, F5: 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5
decreased. Considering that the refractive index of BD FACSFlow sheath fluid was 1.335, which was close to that of 150 mM phosphate buffer, it can be concluded that the refractive index mismatch between core fluid and sheath fluid at lower buffer concentration interfered with light scattering detection from particles. Flow cytometry experiments were carried out after adjusting the refractive indices of core fluids to 1.335 by adding sucrose to the phosphate buffers. Figure 8 shows FSC–SSC dot plots of the stressed monoclonal antibody A in phosphate buffers containing sucrose. No changes in the characteristic dot plot were observed.
DISCUSSION Our study was related to protein subvisible particle measurement using flow cytometry without fluorescent labeling. Although some groups have reported on the application of flow cytometry to subvisible particle measurement in protein formulations, labeled proteins have been used in their studies Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
and this may prevent the extended application of this method because fluorescent labeled proteins cannot be routinely used in formulation development studies nor would they ever be present in commercial products in case such products were to be tested by such a method. Our results showed that silica microparticles could be detected and counted in a wide size range from 0.5 to 10 :m using flow cytometry with a light scattering detector. Two laser settings should be applied to allow optimal counting for the size range from 0.5 to 10 :m. The applied setting is also applied for MFI measurement when one magnification from two possible magnifications is chosen. As shown in Figure 2, the lowest particle concentration was about 15,000 and 3500 particles/mL for 0.5 and 5 :m silica microparticles, respectively. In our study, the background noise of the “nano-setting” had a very good signal to noise ratio with only 10,000 particles/mL for submicron particles. There is practically no noise for micrometer particles. Subvisible particles of a model monoclonal antibody prepared under heating stress (65◦ C, 3 h) could be analyzed DOI 10.1002/jps.23782
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Figure 7. FSC–SSC dot plots of monoclonal antibody A after (a) heating, (b) freeze–thawing, (c) shaking, and (d) light exposure in 20 mM sodium phosphate buffer containing 1.2% (w/v) sucrose, pH 6.5, and (e) after light exposure in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5. Flow cytometry was carried out at FSC and SSC detector voltages of 258 and 272 for sample (a)–(d) and at FSC and SSC detector voltages of 429 and 423 for sample (e).
at a high detector voltage setting without fluorescent labeling. When the value of FSC intensity from the subvisible particles of monoclonal antibody A was compared with that from 1.5 :m silica microparticles, most of the particles of monoclonal antibody A were of submicrometer range. Because LO and MFI cannot detect particles with diameters of below 1 and 0.75 :m, respectively, flow cytometer is one of the few methods that can analyze subvisible particles with diameters of below 1 :m. As shown in Figure 4, particle counts for aggregates of monoclonal antibody A decreased when the particles were diluted with increasingly concentrated phosphate buffers. Interestingly, this buffer concentration dependence of particle formation or dissolution may be related to the specific feature of monoclonal antibody A which induces liquid–liquid phase separation under a low ionic strength condition.21 Although the exact mechanism of how liquid-liquid phase separation is induced is still unknown, reversible self-association driven by electrostatic interaction is suggested to be a major cause, because pH and ionic strength affect the phase behavior of monoclonal antibody A. Flow cytometry is also an extremely powerful technology that allows the physical separation of particles of interest from a heterogeneous population.24 If a population can be identified in an analytical cytometer, it can be retrieved using a flow sorter. As shown in Figure 5b, silica microparticles and protein subvisible particles had different SSC intensity when they were compared at same value of FSC intensity. This might reflect the difference in their physical properties such as density. Different FSC–SSC dot plot profiles were also observed when protein subvisible particles from different stress exposures were analyzed (Fig. 7). This may also be related to different physical DOI 10.1002/jps.23782
properties of subvisible particles formed under different stress conditions. In four out of five cases, the particles showed only one population even though they had different FSC–SSC dot plot profiles. However, in one case, interestingly, monoclonal antibody A formulation in 120 mM sodium phosphate buffer containing 0.01% (w/v) polysorbate 80, pH 6.5 showed two populations after light exposure (Fig. 7d). This result suggests that light may induce at least two totally different types of subvisible particles with different physical properties. van Beers et al.25,26 studied immunogenicity of aggregates of rhIFN$ induced by oxidation and described that the combination of aggregation, oxidation, and change in conformation determined the immunogenicity of the aggregates. This indicates that size analysis of protein aggregates is not enough to understand the relationship between protein aggregates and their immunogenicity. As described above, flow cytometry might evaluate physical properties as well as size of particles and could be used to retrieve and separate particles of interest from a heterogeneous population for further studies, for example, for immunogenicity research. Finally, difficulties and limitations when applying flow cytometric analysis to subvisible particle measurement shall be discussed. As already reported, LO and MFI may be impacted by the difference of the refractive index between protein particles and carrier fluids. When the difference of a refractive index between protein particles and carrier fluids is small, in other words, when the particles are transparent, the obscuration signal becomes weak and LO underestimates the particles.27 MFI may be less affected by these effects because MFI observes the particles visually, and even in cases where the contrast of the images is not sufficient, these images can be reanalyzed using Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
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Figure 8. The FSC–SSC dot plots of stressed formulation of monoclonal antibody A observed in (a) 10 mM phosphate buffer with or without 2.0% (w/v) sucrose, (b) 20 mM phosphate buffer with or without 1.8% (w/v) sucrose, (c) 50 mM phosphate buffer with or without 1.4% (w/v) sucrose, (d) 100 mM phosphate buffer with or without 0.7% (w/v) sucrose, (e) 150 mM phosphate buffers without sucrose. The sucrose concentration was determined to adjust the refractive index of the buffers to 1.335. Notice should be taken that the unstressed formulations gave almost no event in flow cytometric analysis. FSC and SSC detector voltages were 258 and 272, respectively.
the saved images. However, refractive index matching between particles and carrier fluids is also important in MFI.27 This should be the case with flow cytometric analysis as well because this technique is also an optical measurement. Of course, the refractive index mismatch is usually not a problem in analyzing relatively large particles in the micrometer range or cells, but may be one for small particles in the submicrometer range. This may cause a problem in analyzing protein formulations because solvent condition such as pH, ionic strength, or concentration of excipients is optimized for each protein drug for stability and not for refractive index match. The difference in refractive index between core (sample) fluids and sheath fluids may also impact the light scattering detection from particles in flow cytometric analysis because core fluid containing target particles is hydrodynamically focused and flows in the center of sheath fluids.28 We have solved the problem with refractive index mismatch by adding sucrose to the phosphate buffers to adjust the refractive indices of core fluids. However, sugars as typified by sucrose are known to stabilize proteins by preferential interaction29 and widely added in protein formulations. In this case, we confirmed that adding sucrose to the formulation buffers did not affect particle size distribution using LO.
CONCLUSIONS Flow cytometry can quantify and characterize protein subvisible particles with diameters of below 10 :m in protein formulations without fluorescent labeling and therefore can be added to the list of methods available for such protein subvisible particles. Flow cytometric analysis has a great advantage in Nishi et al., JOURNAL OF PHARMACEUTICAL SCIENCES 103:90–99, 2014
analyzing such protein particles because flow cytometry cannot only quantify particles with diameters of below 1 :m but also characterize particles based on their physical properties such as density or morphology. However, it is necessary to recognize that flow cytometry is sensitive to refractive index mismatch between core (sample) fluid and sheath fluid.
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