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The Use of Flow Cytometry for the Detection of Subvisible Particles in Therapeutic Protein Formulations
The Use of Flow Cytometry for the Detection of Subvisible Particles in Therapeutic Protein Formulations HENRYK MACH, AKHILESH BHAMBHANI, BRIAN K. MEYER, STEVEN BUREK, HARRISON DAVIS, JEFFREY T. BLUE, ROBERT K. EVANS Merck Research Laboratories, Bioprocess Analytical and Formulation Sciences, West Point, Pennsylvania 19486 Received 10 September 2010; revised 2 November 2010; accepted 2 November 2010 Published online 13 December 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.22414 ABSTRACT: The amount, identity, and size distribution of particles in parenteral therapeutic protein formulations are of immense interest due to potential safety and efficacy-related implications. In this communication, we describe the use of a flow cytometer equipped with forwardand side-scattering as well as fluorescence detectors, to determine the number of subvisible particles in monoclonal antibody formulations. The method appears to detect particles of size 1 : and larger, requiring relatively small sample volumes to estimate subvisible particle counts. Additionally, it facilitates differentiation of proteinaceous particles after staining with a fluorescent hydrophobic dye. The method is expected to be particularly well suited for pharmaceutical development, because it provides increased throughput due to the use of a 96-well autosampler. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:1671–1678, 2011 Keywords: analytical biochemistry; high-throughput technologies; imaging methods; light scattering; particle size; protein aggregation; protein formulation; stability
INTRODUCTION Although there are a large number of therapeutic monoclonal antibodies (mAbs) under development, significant gaps in analytical capabilities still exist. For example, none of the state-of-the-art analytical methods can alone satisfactorily monitor subvisible particles that appear during production, filling, and storage,1,2 with particles sized below 2 : being particularly challenging to characterize.3 Moreover, with multiple projects usually developed in parallel, high-throughput methods are preferred to manage the volume of analytical work.4 In order to meet these demands, plate-based dynamic light scattering (DLS) instruments have been developed.5 However, DLS relies on the deconvolution of an exponential function derived from observed fluctuations in Brownian motion,6 providing an intensitybased average hydrodynamic radii rather than particle counts. Newer light-scattering technologies, such as nanoparticle tracking analysis, rely on imaging of laser-illuminated volumes, but again, they suffer
Correspondence to: Henryk Mach (Telephone: 215-652-4689; Fax: 215-993-1750; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 100, 1671–1678 (2011) © 2010 Wiley-Liss, Inc. and the American Pharmacists Association
from problems related to small sampling volume and size limitations.7,8 On the contrary, the conventional particle counting methods, based on electrosensing or light obscuration, that are widely used for official batch release, generally measure only particles larger than 1.5–2 :, require relatively large measurement volumes, and typically have low throughput due to operator-assisted sequential sample analysis.9,10 Newer flow imaging systems in practice are limited to detecting particles of 2 : or greater, although they demonstrate increased sensitivity in detection of translucent particles in comparison with conventional light obscuration methods.11–13 In contrast, particles as small as 500 nm can be detected by flow cytometry (FC).14 Additionally, the total volume required to obtain a statistically significant result, although orders of magnitude larger in comparison with DLS and microscopy approaches, is still relatively small (100–200 :L) in relation to the volume typically required for other flow-based imaging or light obscuration systems (0.5–2 mL).15 Besides cell counting and sorting, flow cytometers have been used to detect protein particle and/ or cell association phenomena. For example, baculovirus particles and their aggregates have been previously detected, platelet aggregation events have been monitored with this technique and amyloid
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fibrils have been characterized.16–19 In this work, we use SYPRO Orange dye (Invitrogen, Carlsbad, California) capable of binding to hydrophobic surfaces of partially destabilized proteins,20 with concomitant intensity enhancement, to preferentially stain protein-containing particles, and measure their numbers in therapeutic protein preparations. Another aspect that is important from the utility point of view is that the FC method is used in a highthroughput manner-–employing a 96-well autosampler. High throughput is critical in bioprocess and/ or formulation development, where screening of conditions and/or excipients requires analysis of large numbers of samples from both real-time and accelerated stability testing. This approach is distinct from the typical use of flow cytometers in which the study of the nature of the interactions between selected macromolecules, or cell sorting, is the primary goal.
MATERIALS AND METHODS Materials Human recombinant mAb, referred to as mAb1, was produced by recombinant methods within Merck and Co. mAb1 was supplied in surfactant-free, histidinecontaining buffer, at pH 6.0 and concentrations of 5–70 mg/mL. Ultraviolet multicomponent analysis methods were used to measure concentrations, as reported previously.21,22 mAb1 stored for 3 years at 2◦ C–8◦ C (dye titration experiment) and additionally stressed for several minutes at 70◦ C–75◦ C until turbidity appeared (serial dilution experiment) was used to represent samples containing significant amount of subvisible particles. Additionally, mAb1 was subjected to pumping through an EDM3295 piston pump (Bausch–Strobel Machine Company, Clinton, Connecticut) to represent sample containing processinduced particulates. To generate silicone oil particles, approximately 10 mL of phosphate-buffered saline (pH 7.4) was shaken by hand for about 1 min in a 30 mL Luer-Lok Tip plastic syringe (BectonDickinson, Franklin Lakes, New Jersey). Flow Cytometry A Beckman-Coulter FC-500 flow cytometer (BeckmanCoulter, Brea, California) equipped with a 488 nm argon laser and a system of emission filters capable of measuring the fluorescence of up to five different dyes in the range between 500 and 800 nm, and forward- and side-scattering, was utilized. For each detector, sensitivity and gain were optimized to maximize the detection of existing particles. Typically, the gain settings were 20 and 10 for forward- and side-scattering, respectively, and the sensitivity settings were 400 for all detectors. The mid-500 nm range (FL3) was used to assess JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
the fluorescence emission from SYPRO Orange. The acquisition time was set to 30 s, with flow rate set at “high,” unless otherwise indicated. SYPRO Orange solution was introduced by mixing 5× stock with the sample at 1:1 volume ratio prior to the measurement. Aqueous SYPRO Orange stocks were used the same day. The FlowCheck standard solution (Beckman-Coulter) containing 106 particles/mL was used to determine the effective scanning volume, which was approximately 35 :L using the above settings. Therefore, each number obtained from runs of actual samples under the same conditions was multiplied by 106 /(Flowcheck count) to obtain particle numbers per 1 mL. In effect, each detected event translated to about 30 particles when expressed in number per milliliter units. Microflow Digital Imaging A microflow digital imaging system DPA 4100 from Brightwell Inc. (Ottawa, ON, Canada), at the setpoint 1 (2–300 :m), was used to visualize and count subvisible particles in the mAb1 solutions. A peristaltic pump (Masterflex L/S, Cole-Parmer, Vernon Hills, IL) was used to introduce sample to the flow cell for digital imaging under continuous flow. A 400 : SP1 flow cell was used at a flow rate of 0.22 mL/min. Total volume was 0.75 mL, and initial 0.25 mL was not used in the analysis. The capability of the system to count and measure subvisible particles was verified using a 5 :, 3000 particles/mL, Count-Cal particle size standard (Thermo Scientific, Fremont, California).
RESULTS Detection and Quantitation of Protein Aggregates In a typical flow cytometer, an aqueous transparent sheath fluid flows through the detection cell, and the sample is introduced in a continuous manner into its center at a slower flow rate, resulting in dynamic spreading of the introduced particles to permit precise (“single-file”) detection at the center of the flow cell. The central section of the flow cell is illuminated by one or more lasers, and typically two light-scattering detectors are employed, positioned at forward and side angles, for the detection of the presence of particles and potential categorization. A system of filters is in the place that permits collection of fluorescence signals at various spectral regions, allowing use of complex dye combinations. The FC systems are designed primarily to study biological phenomena within living cells. In some systems, the cells can be physically separated (sorted) based on the dynamic signals detected. The detectors can be adjusted for sensitivity that determines the threshold over the background noise, and the gain that determines the multiplication factor for the Y-scale. DOI 10.1002/jps
THE USE OF FLOW CYTOMETRY FOR THE DETECTION OF SUBVISIBLE PARTICLES
Typical fluorescence versus forward scatter plots of undiluted, aggregate-containing mAb1 solutions are shown in Figure 1a. As expected, very low (baseline) fluorescence signals were recorded, because proteins do not have any fluorescent chromophores absorbing at 488 nm. Figure 1b shows the fluorescence signal of SYPRO Orange-containing samples. As expected, SYPRO Orange dye bound to protein particulates, result in about 100-fold increase of the fluorescence signal. The plot also reveals that small fraction of particles did not stain, presumably due to their nonprotein origin. To test the specificity of the dye toward protein aggregates, silicone oil microdroplets extracted from a commercially available plastic syringe, were measured in the absence (Fig. 1c) and in the presence of SYPRO Orange (Fig. 1d). Although increased sidescattering signal in the dye-containing sample suggests that some interactions with the particles took place, the fluorescence signal was low in the range comparable to the control that did not contain the dye. In order to test the specificity, range, and linearity of the FC detection, measurements of serially diluted nonfiltered solutions of aggregated mAb1 were
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performed. Prefiltered, particle-free solutions of the same antibody were used as a diluent. A filter with nominal pore size of 0.1 : was used to remove any subvisible particles in the diluent. A control experiment with filtered buffer failed to detect subvisible particles in the filtrate. All the samples contained the same amount of protein (20 mg/mL). The first one was the filtered sample that served as a control that did not contain proteinaceous subvisible particles. The subsequent samples had an increasing proportion of nonfiltered material, resulting in progressively higher subvisible particle counts. As the content of aggregates increased in the series, the number of counted particles proportionally increased (Fig. 2a). Because the sample analysis by FC is typically performed in an ambient laboratory environment, and the plates are not covered to allow sample access by the loading needle, the experiments are susceptible to contamination with airborne particulates. The randomness of the appearance of such particles, presumably nonproteinaceous, would compromise the sensitivity of the assay, unless the protein aggregate particles could be differentiated from contaminating “dust.” Indeed, although the difference between
Figure 1. Fluorescence- versus side-scattering intensity plots of mAb1 solution and a buffer containing particles from a syringe. (a). mAb1 only. The total particle count was 2427 ± 89 (71,400 ± 2600 per 1 mL). (b) mAb1 in the presence of 2.5× SYPRO Orange. The total particle count was 2733 ± 62 (79,900 ± 1800 per 1 mL). (c) Buffer solution shaken by hand for 1 min in a plastic syringe. The total particle count was 3116 ± 102 (91,200 ± 3000 per 1 mL). (d) The same buffer in the presence of 2.5× SYPRO Orange. The total particle count was 3169 ± 65 (92,700 ± 1900 per 1 mL). Phosphate-buffered saline, pH 7.4, was used in all cases. DOI 10.1002/jps
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Figure 3. The effect of SYPRO Orange concentration of the fluorescence of subvisible particles. SYPRO Orange stock (50×) was serially diluted and mixed 1:1 with mAb1 solutions. Left axis, average fluorescence of the detected particles (filled triangles). Right axis, the corresponding particle counts; total (filled circles), fluorescent (open circles). Figure 2. Flow cytometry of serially diluted mAb1 solution (20 mg/mL) containing heat-generated subvisible particles, diluted in 0.1 :-filtered control solution of the same antibody. The total count is denoted by diamonds and fluorescent count is denoted by squares. The results of four measurements were averaged. The gain settings for forwardand side-scattering were 5 and 2, respectively. The sensitivity was 400, and the measurement time was 300 s (most counts were detected within 200 s). The lines denote leastsquares fits to the data. (a) 0–20 mg/mL range. (b) 0–0.3 mg/mL range.
to about 8× dye concentration, after which a sharp increase is seen. We have not investigated the potential root cause of the observed increase. One can speculate that noncovalent proteinaceous particles could have been broken up with the excess of dye, or the dye might have reached its solubility limit. On the basis of these results, however, the final concentration of SYPRO Orange between 1× and 4× was considered to be optimal and robust. Use of Flow Cytometry for Evaluation of Stressed Samples
fluorescent and total particle count was minimal at high protein aggregate levels, it became very significant at low particle counts. The use of the fluorescent dye allowed selective detection of protein aggregates at the level of approximately 500 particles/mL (Fig. 2, panel B). Effect of SYPRO Orange on the Measured Particle Binding of dye molecules to hydrophobic surfaces on protein aggregates may potentially promote their dissociation and increase particle counts, or cause local unfolding and further aggregation. To explore such scenarios, an mAb1 preparation was mixed 1:1 with a serially diluted SYPRO Orange stock (50×). It should be noted that the manufacturer does not disclose the structure of this dye and thus the concentration is provided in units of “×”-–it is supplied as 5000× solution in dimethyl sulfoxide (DMSO). The results (Fig. 3) show the expected increase of the fluorescence signal from the particles as the amount of available dye is increasing, up to about 2× dye concentration, followed by a decline that is presumably caused by selfquenching and/or inner filter effects. The total and fluorescent counts observed appear to be constant up JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
We have examined a set of samples that was subjected to the stress associated with pumping through a Bausch–Strobel piston pump (Fig. 4). As expected, the total particle numbers detected with FC (first bar in each group) were only slightly higher than the fluorescent particle numbers (second bar in each group), consistent with the expectation that the vast majority of particles contained proteins. The particle counts determined by microflow imaging (MFI; third bar in each group) were lower in all cases, ranging from about 15% to 100% of the value observed for FC. To assess whether the size distribution had an effect on the observed particle ratios between these two methods, we have generated the fluorescence- versus side-scattering plots for the “1-pass” (MFI:FC ratio of ∼0.15) and “10-pass” samples (MFI:FC ratio of ∼1.0; inset of Fig. 4, left panels). For reference, we have included plots obtained from 1 and 5 : nonfluorescent standard polystyrene particles (inset of Fig. 4, right panels). It can be seen that 1 : particle data points fall on the leftmost region of the plot, suggesting that they are at the detection limit. Indeed, 0.5 : standard particles were not detected under the settings that were used in this study (data not shown). DOI 10.1002/jps
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Figure 4. The comparison of flow cytometry (FC) and microflow imaging (MFI) based on the analysis of mAb1 samples after piston pump treatment. The protein (∼5 mg/mL) was subjected to 1-, 3-, 5-, and 10-passes through a piston pump. In each group of bars, the first bar (white) denotes total FC counts, the second bar (grey) denotes fluorescently stained particles detected by FC (FC-Fluor), and the third bar (black) denotes the particle counts (>2 :m) detected by MFI. Error bars denote standard deviations of three measurements. Inset: SYPRO Orange fluorescence- versus side-scattering intensity FC plots for 1-pass (top left) and 10-pass (bottom left) samples. 1 : (top right) and 5 : (bottom right) polystyrene standard data is shown for reference. See Materials and Methods for experimental details.
Five micron standard particles, on the other hand, fell near the right edge of the plot, suggesting that larger particles would be detected but not resolved with the arbitrary settings applied in this study (see Materials and Methods for details). Such a narrow range is a result of the use of linear scales for the light-scattering intensity signals in this particular instrument. Logarithmic scales are used for fluorescence, and that results in the capture of essentially all particles within vertical boundaries. Comparing “1-pass” sample distribution to standard particle plots, it can be seen that most of the particles resided in the vicinity of 1 : standard particle side-scattering region. These observations are consistent with previous reports, in which the majority (∼99%) of piston pump-generated particles in buffer were in the range of 0.25–0.95 :m, as measured by static light scattering.10 On the contrary, more heavily stressed samples (e.g., “10-pass”) have generally higher side light-scattering intensities, consistent with a more efficient MFI detection, indicating that they are larger than 2 :.
DISCUSSION With the apparent deficiencies in the analysis of the subvisible particles (especially those smaller than 10 :) in therapeutic protein formulations,1,3,23 new methods that would permit efficient monitoring are needed. These preliminary results encourage further DOI 10.1002/jps
evaluation of FC as a practical tool that may facilitate development of safer and more efficacious therapeutics. However, there are large numbers of outstanding issues that will need to be addressed before the potential of this approach is fully realized. One of the questions that arise is the relative limit of detection for FC as compared with MFI and light obscuration methods. Although the observed ratios of total particle counts appear to be in line with the observed detection limits (∼0.5–1 : for Beckman FC500 flow cytometer and ∼2 : for Brightwell Technologies DPA4100 imaging instrument), the exact detection limits may depend on the data detection settings. In the case of FC, it is the matter of the threshold voltage. In fact, with the settings used in this study, optimized for low background noise, 1 :, but not 0.5 :, standard polysterene beads were quantitatively detected with FC. The counts obtained for 1 : particles accounted for 81% and 84% of theoretically predicted counts (based on the nominal concentration and size provided by the manufacturer) for the sample with and without SYPRO Orange, respectively (data not shown). None of these samples showed significant fluorescence, further suggesting the selectivity of the method for proteinaceous particles. For comparison, light obscuration instruments, which use beam attenuation rather than the intensity of scattered light to detect particles moving one-by-one through the focus zone, can in principle be used for sizes 0.5 : and JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
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larger, but precision or reproducibility are low at that size range,24 and typically 2 : limit is adopted in therapeutic protein development practice.2 In the case of MFI, the set threshold of the pixel intensity determines whether the particle is detected. The latter is affected not only by the inherent optical density of the medium, but also the depth of the field. With practical settings used to characterize subvisible particle size distributions (setpoint #1), 1 : particles were not detected by MFI (not illustrated). The lower detection limit is critical, because often the majority of subvisible particles appear to be in the range of approximately 1 : in diameter. For example, 10 mM potassium phosphate, 150 mM NaCl, and pH 7.0 buffer, when passed through a piston pump had particles averaging approximately 0.5 : with 99% of population between 0.25 and 0.95 :m as measured by static light scattering.10 Samples of therapeutic cytokine formulated with human serum albumin, with 40,000 total particles, had 90% of population in the 1–2mm size range, as determined by averaging a number of light obscuration measurements using a PAMAS system (Bad Salzflen, Germany).25 Both sensitivity and throughput of FC approach brings a promise of improving the practically achievable detection limit. These light-based particle detection techniques have unrealized potential for improved resolution. Using light-scattering detection, single particles can be imaged at the size range below 100 nm, and similarly sized objects can also be detected with microscopes.7 However, the chosen optical magnification is a compromise between the resolution and the total volume that can be observed and scanned in a reasonable amount of time. Additionally, there are mechanical and software engineering issues to be resolved to achieve adequate scanning and subsequent data processing. The situation is similar to the problems encountered in astronomy, in which the analysis of a large number of objects in the space requires tremendous technical and computational resources. Microflow imaging by the virtue of analyzing every single object detected, especially larger than few microns, provides not only the size distribution, but also morphological information that may be helpful in identifying the source of the particles detected. Light obscuration systems will also return reasonably precise size distribution as the length of time of light beam attenuation for any single particle is related to its dimensions. Light-scattering signals collected by FC, in principle, are also dependent on particle size and shape. However, the relationship is very complex, and the information that can be gained from the plots of forward scatter versus side scatter can be relative at best.26 For example, the sample that passed through the piston pump only once (Fig. 4) appears to have lower ratio of MFI counts /FC counts, when compared with other more stressed piston-pumped JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
samples, a result of large portion of particles being below MFI, but above FC detection limits. The relative size distributions obtained with FC can only be qualitatively judged by comparison with the plots obtained with standard polystyrene spherical standards (inset of Fig. 4). Another situation in which useful information about size distribution can be gained from FC is when the particle population is very homogenous, as in typical applications with living cells, in which distinct regions of fluorescence versus scatter plots can be defined to further characterize or digitally separate for quantitative purposes. In principle, one of the major advantages of this approach lies in its throughput that permits unattended analysis of a large number of samples. However, streamlining of the relative size distribution analysis can be difficult due to theoretically complex and ill-defined intensity-based data, reducing day-to-day data utilization to total and fluorescent count numbers. Another aspect that may be difficult to study with this method will be the concentration- and temperaturedependent opalescence,27 due to the reversible nature of the association. The FC method appears to strike an optimal balance between the total sample volume required (100–200 :L), the volume actually scanned (30–100 :L), and the time required for one scan (∼1–2 min). The total amount of sample required in the current application, although prohibitive at very early stages (e.g., during protein engineering) of the development, would be considered typical for preformulation and formulation activities. In addition, relatively short data acquisition time allows a scan of a complete 96-well plate before significant evaporation occurs. For longer scans, potential volume loss due to evaporation can be minimized by controlling the humidity of the chamber containing the autosampler. The total volume scanned, after accounting for the fact that typically more particles are detected in comparison with MFI, makes these two methods very similar in terms of statistical power. Larger data sets would be needed for more precise comparisons of the relative error levels in these two methods. Judging from error bars in Figures 2–4, however, and available literature data, one may expect that the FC methods will be at least as precise as the MFI methods.11,12 The flow-through methods require adequate procedures to assure that the flow cells and the preceding tubing are free of particles from the previous run. In theory, no particles should be present when the washing buffer is being run through the flow cell. In practice, it is generally possible only after prolonged purging of the system, often using cleaning agents (e.g., bleach and surfactants), which is not feasible with large sample sets. As a result, some carryover often occurs. Obviously, the level of background noise will affect the limit of detection of subvisible particles. DOI 10.1002/jps
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The ability to selectively stain proteinaceous particles with a fluorescent dye helps with this respect (see Fig. 2b). However, the utility of FC approach will diminish when the prevailing subvisible counts are diminished to the point at which they will be comparable to perhaps 10 times the inverse of the sampling volume, that is, 10 × 1/0.030 mL or about 300. In such a case, a few background particles from the previous sample that were transiently bound to the tubing will have a potential to introduce significant artifacts. The morphological data acquired by MFI permits differentiation of particle identity, for example, between round silicone oil droplets and protein fibrillike assemblies.9 The algorithms that would automate this recognition process in tens of thousands of images in every sample are still being developed. In contrast, FC offers a simpler approach-–staining with a fluorescent dye. Generally, protein aggregates are expected to bind such dyes,28–30 and this property is expected to allow differentiation between drug-containing and extraneous particles. Protein drug-containing particles may be of different origin—formed during the purification process (e.g., in protein A step), filling (e.g., pump-shed particles coated with the protein), and storage. At the same time, extraneous particles can also contain proteins of human or animal origin, although these can be minimized through aseptic procedures. The hydrophobicity-sensitive dye used in this study, SYPRO Orange, was chosen for its convenience (supplied in DMSO and directly diluted to aqueous buffers), low cost, and compatibility with laser and detection options of the particular instrument. However, SYPRO Orange binds to surfactant ensembles, and therefore its signal is greatly reduced in the presence of surfactant micelles.31 If that precludes meaningful data analysis, a recently introduced dye that does not bind to surfactants and has similar selectivity for protein aggregates is available.32 Attenuation of the fluorescence of the detected proteinaceous particles can also be a result of the presence of a specific excipient that may compete for hydrophobic sites present on the aggregates or the effect on the dye itself (quenching). When such a situation is observed, MFI data should also be acquired, and off-line experiments be performed to assess the effect of the buffer on the fluorescence of the dye. The ability to selectively detect fluorescent particles also brings opportunities for more sophisticated applications, such as use of fluorescently tagged antibodies directed to the epitopes that are expected to be present on subvisible particles, or use of dyes that are selective for specific nonproteinaceous particles. Despite a range of possible complications in terms of dye binding, its fluorescence, and data interpretation, the method still may be found useful in protein process development and formulation practice. One reason is that often only a relative ranking of the alternatives is sought to efDOI 10.1002/jps
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fectively drive the development process. More importantly, however, a unique opportunity to differentiate between proteinaceous particles and other contaminants can be helpful in identifying their origin.
CONCLUSIONS In summary, flow cytometry appears to be a promising method for therapeutic protein formulation development with respect to efficient monitoring of the appearance of subvisible particles. In addition to measuring total particle counts, it appears to specifically detect protein aggregates that may be present in these particles, because random dust particles presumably are not stained with SYPRO Orange. Because of the relatively large sampling volume, representative particle counts can usually be obtained. Most importantly, the method uses a microtiter plate autosampler and thus it provides increased throughput that allows routine monitoring of the subvisible particle content of a large number of stability samples with very little effort. The high throughput can potentially be useful in elucidation of the effects of various nonprotein particulates (e.g., steel microparticles and silicone oil droplets) on the particle formation of given therapeutic protein under a number of conditions. Sufficient precision, selectivity, modest sample requirement, and high throughput may potentially make this method a preferred one for formulation screening, process development, and extensive stability studies in which large data sets are being generated
ACKNOWLEDGMENTS The authors thank Harrison Davis for help with setting up the MFI instrument.
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6. Koppel DE. 1972. Analysis of macromolecular polydispersity in intensity correlation spectroscopy: The method of cumulants. J Chem Phys 57(11):4814–4820. 7. Griffiths D, Hole P, Smith J, Carr B. 2009. Characterization of nanoparticle dispersions by size and scattering intensity simultaneously. Nanotech Conf Expo 1:414–416. 8. Filipe V, Hawe A, Jiskoot W. 2010. Critical evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm Res 27(5):796–810. 9. 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 10:373–381. 10. Tyagi AK, Randolph TW, Dong A, Maloney KM, Hitscherich C Jr., 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. 11. 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. 12. Wuchner K, Buechler 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. 13. 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 99:2628–2642. 14. Cytomics FC-500 FLow Cytometry System. Beckman-Coulter online documentation, DS-9812A., Accessed June 7, 2010, at: www.coulterflow.com/bciflow/instrumentsus.php!. 15. Demeule B, Gurny R, Arvinte T. 2007. Detection and characterization of protein aggregates by fluorescence microscopy. Int J Pharm 329(1–2):37–45. 16. Jorio H, Tran R, Meghrous J, Bourget L, Kamen A. 2006. Analysis of baculovirus aggregates using flow cytometry. J Virol Methods 134(1–2):8–14. 17. Ginsberg MH, Frelinger AL, Lam SCT, Forsyth J, McMillan R, Plow EF, Shattil SJ. 1990. Analysis of platelet aggregation disorders based on flow cytometric analysis of membrane glycoprotein IIb-IIIa with conformation-specific monoclonal antibodies. Blood 76(10):2017–2023. 18. Harding SA, Din JN, Sarma J, Jessop A, Weatherall M, Fox KAA, Newby DE. 2007. Flow cytometric analysis of circulating platelet-monocyte aggregates in whole blood: Methodological considerations. Thromb Haemost 98(2):451– 456.
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19. Wall J, Solomon A. 1999. Flow cytometric characterization of amyloid fibrils. Methods Enzymol 309:460–466. 20. Lo MC, Aulabaugh A, Jin G, Cowling R, Bard J, Malamas M, Ellestad G. 2004. Evaluation of fluorescence-based thermal shift assays for hit identification in drug discovery. Anal Biochem 332(1):153–159. 21. Mach H, Middaugh CR, Lewis RV. 1992. Statistical determination of the average values of the extinction coefficients of tryptophan and tyrosine in native proteins. Anal Biochem 200(1):74–80. 22. Mach H, Sanyal G, Volkin DB, Middaugh CR. 1997. Applications of ultraviolet absorption spectroscopy to the analysis of biopharmaceuticals. In Therapeutic protein and peptide formulation and delivery; Shahrokh Z, Sluzky V, Cleland JL, Shire SJ, Randolph TW, Eds. Washington, District of Columbia: American Chemical Society Symposium Series 675:186–205. 23. Mahler HC, Friess W, Grauschopf U, Kiese S. 2009. Protein aggregation: Pathways, induction factors and analysis. J Pharm Sci 98(9):2909–2934. 24. Etzler F, Sanderson M. 1995. Particle size analysis: A comparative study of various methods. Part Part Syst Charact 12:217–224. 25. Hawe A, Friess W. 2007. Stabilization of a hydrophobic recombinant cytokine by human serum albumin. J Pharm Sci 96:2987–2999. 26. Mie G. 1908. Contributions to the optics of turbid media, especially colloidal metal solutions. Ann Phys 25:377–445. 27. Cromwell MEM, Carpenter JF, Scherer T, Randolph TW. 2008. Opalescence in antibody formulations is a solution critical phenomenon. In Abstracts of papers, 236th ACS National Meeting, Philadelphia, Pennsylvania: BIOT-029. 28. Demeule B, Palais C, Machaidze G, Gurny R, Arvinte T. 2009. New methods allowing the detection of protein aggregates: A case study on trastuzumab. MAbs 1(2):142–150. 29. Hawe A, Friess W, Sutter M, Jiskoot W. 2008. Online fluorescent dye detection method for the characterization of immunoglobulin G aggregation by size exclusion chromatography and asymmetrical flow field flow fractionation. Anal Biochem 378(2):115–122. 30. Hawe A, Sutter M, Jiskoot W. 2008. Extrinsic fluorescent dyes as tools for protein characterization. Pharm Res 25(7):1487–1499. 31. Steinberg TH, Haugland RP, Singer VL. 1996. Application of SYPRO orange and SYPRO red protein gel stains. Anal Biochem 239(2):238–245. 32. 2010. ProteostatTM thermal shift stability assay. Publication ENZ-51027-K400. PLymouth Meeting, Pennsylvania: Enzo Life Sciences.
DOI 10.1002/jps
INTRODUCTION Although there are a large number of therapeutic monoclonal antibodies (mAbs) under development, significant gaps in analytical capabilities still exist. For example, none of the state-of-the-art analytical methods can alone satisfactorily monitor subvisible particles that appear during production, filling, and storage,1,2 with particles sized below 2 : being particularly challenging to characterize.3 Moreover, with multiple projects usually developed in parallel, high-throughput methods are preferred to manage the volume of analytical work.4 In order to meet these demands, plate-based dynamic light scattering (DLS) instruments have been developed.5 However, DLS relies on the deconvolution of an exponential function derived from observed fluctuations in Brownian motion,6 providing an intensitybased average hydrodynamic radii rather than particle counts. Newer light-scattering technologies, such as nanoparticle tracking analysis, rely on imaging of laser-illuminated volumes, but again, they suffer
Correspondence to: Henryk Mach (Telephone: 215-652-4689; Fax: 215-993-1750; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 100, 1671–1678 (2011) © 2010 Wiley-Liss, Inc. and the American Pharmacists Association
from problems related to small sampling volume and size limitations.7,8 On the contrary, the conventional particle counting methods, based on electrosensing or light obscuration, that are widely used for official batch release, generally measure only particles larger than 1.5–2 :, require relatively large measurement volumes, and typically have low throughput due to operator-assisted sequential sample analysis.9,10 Newer flow imaging systems in practice are limited to detecting particles of 2 : or greater, although they demonstrate increased sensitivity in detection of translucent particles in comparison with conventional light obscuration methods.11–13 In contrast, particles as small as 500 nm can be detected by flow cytometry (FC).14 Additionally, the total volume required to obtain a statistically significant result, although orders of magnitude larger in comparison with DLS and microscopy approaches, is still relatively small (100–200 :L) in relation to the volume typically required for other flow-based imaging or light obscuration systems (0.5–2 mL).15 Besides cell counting and sorting, flow cytometers have been used to detect protein particle and/ or cell association phenomena. For example, baculovirus particles and their aggregates have been previously detected, platelet aggregation events have been monitored with this technique and amyloid
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fibrils have been characterized.16–19 In this work, we use SYPRO Orange dye (Invitrogen, Carlsbad, California) capable of binding to hydrophobic surfaces of partially destabilized proteins,20 with concomitant intensity enhancement, to preferentially stain protein-containing particles, and measure their numbers in therapeutic protein preparations. Another aspect that is important from the utility point of view is that the FC method is used in a highthroughput manner-–employing a 96-well autosampler. High throughput is critical in bioprocess and/ or formulation development, where screening of conditions and/or excipients requires analysis of large numbers of samples from both real-time and accelerated stability testing. This approach is distinct from the typical use of flow cytometers in which the study of the nature of the interactions between selected macromolecules, or cell sorting, is the primary goal.
MATERIALS AND METHODS Materials Human recombinant mAb, referred to as mAb1, was produced by recombinant methods within Merck and Co. mAb1 was supplied in surfactant-free, histidinecontaining buffer, at pH 6.0 and concentrations of 5–70 mg/mL. Ultraviolet multicomponent analysis methods were used to measure concentrations, as reported previously.21,22 mAb1 stored for 3 years at 2◦ C–8◦ C (dye titration experiment) and additionally stressed for several minutes at 70◦ C–75◦ C until turbidity appeared (serial dilution experiment) was used to represent samples containing significant amount of subvisible particles. Additionally, mAb1 was subjected to pumping through an EDM3295 piston pump (Bausch–Strobel Machine Company, Clinton, Connecticut) to represent sample containing processinduced particulates. To generate silicone oil particles, approximately 10 mL of phosphate-buffered saline (pH 7.4) was shaken by hand for about 1 min in a 30 mL Luer-Lok Tip plastic syringe (BectonDickinson, Franklin Lakes, New Jersey). Flow Cytometry A Beckman-Coulter FC-500 flow cytometer (BeckmanCoulter, Brea, California) equipped with a 488 nm argon laser and a system of emission filters capable of measuring the fluorescence of up to five different dyes in the range between 500 and 800 nm, and forward- and side-scattering, was utilized. For each detector, sensitivity and gain were optimized to maximize the detection of existing particles. Typically, the gain settings were 20 and 10 for forward- and side-scattering, respectively, and the sensitivity settings were 400 for all detectors. The mid-500 nm range (FL3) was used to assess JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
the fluorescence emission from SYPRO Orange. The acquisition time was set to 30 s, with flow rate set at “high,” unless otherwise indicated. SYPRO Orange solution was introduced by mixing 5× stock with the sample at 1:1 volume ratio prior to the measurement. Aqueous SYPRO Orange stocks were used the same day. The FlowCheck standard solution (Beckman-Coulter) containing 106 particles/mL was used to determine the effective scanning volume, which was approximately 35 :L using the above settings. Therefore, each number obtained from runs of actual samples under the same conditions was multiplied by 106 /(Flowcheck count) to obtain particle numbers per 1 mL. In effect, each detected event translated to about 30 particles when expressed in number per milliliter units. Microflow Digital Imaging A microflow digital imaging system DPA 4100 from Brightwell Inc. (Ottawa, ON, Canada), at the setpoint 1 (2–300 :m), was used to visualize and count subvisible particles in the mAb1 solutions. A peristaltic pump (Masterflex L/S, Cole-Parmer, Vernon Hills, IL) was used to introduce sample to the flow cell for digital imaging under continuous flow. A 400 : SP1 flow cell was used at a flow rate of 0.22 mL/min. Total volume was 0.75 mL, and initial 0.25 mL was not used in the analysis. The capability of the system to count and measure subvisible particles was verified using a 5 :, 3000 particles/mL, Count-Cal particle size standard (Thermo Scientific, Fremont, California).
RESULTS Detection and Quantitation of Protein Aggregates In a typical flow cytometer, an aqueous transparent sheath fluid flows through the detection cell, and the sample is introduced in a continuous manner into its center at a slower flow rate, resulting in dynamic spreading of the introduced particles to permit precise (“single-file”) detection at the center of the flow cell. The central section of the flow cell is illuminated by one or more lasers, and typically two light-scattering detectors are employed, positioned at forward and side angles, for the detection of the presence of particles and potential categorization. A system of filters is in the place that permits collection of fluorescence signals at various spectral regions, allowing use of complex dye combinations. The FC systems are designed primarily to study biological phenomena within living cells. In some systems, the cells can be physically separated (sorted) based on the dynamic signals detected. The detectors can be adjusted for sensitivity that determines the threshold over the background noise, and the gain that determines the multiplication factor for the Y-scale. DOI 10.1002/jps
THE USE OF FLOW CYTOMETRY FOR THE DETECTION OF SUBVISIBLE PARTICLES
Typical fluorescence versus forward scatter plots of undiluted, aggregate-containing mAb1 solutions are shown in Figure 1a. As expected, very low (baseline) fluorescence signals were recorded, because proteins do not have any fluorescent chromophores absorbing at 488 nm. Figure 1b shows the fluorescence signal of SYPRO Orange-containing samples. As expected, SYPRO Orange dye bound to protein particulates, result in about 100-fold increase of the fluorescence signal. The plot also reveals that small fraction of particles did not stain, presumably due to their nonprotein origin. To test the specificity of the dye toward protein aggregates, silicone oil microdroplets extracted from a commercially available plastic syringe, were measured in the absence (Fig. 1c) and in the presence of SYPRO Orange (Fig. 1d). Although increased sidescattering signal in the dye-containing sample suggests that some interactions with the particles took place, the fluorescence signal was low in the range comparable to the control that did not contain the dye. In order to test the specificity, range, and linearity of the FC detection, measurements of serially diluted nonfiltered solutions of aggregated mAb1 were
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performed. Prefiltered, particle-free solutions of the same antibody were used as a diluent. A filter with nominal pore size of 0.1 : was used to remove any subvisible particles in the diluent. A control experiment with filtered buffer failed to detect subvisible particles in the filtrate. All the samples contained the same amount of protein (20 mg/mL). The first one was the filtered sample that served as a control that did not contain proteinaceous subvisible particles. The subsequent samples had an increasing proportion of nonfiltered material, resulting in progressively higher subvisible particle counts. As the content of aggregates increased in the series, the number of counted particles proportionally increased (Fig. 2a). Because the sample analysis by FC is typically performed in an ambient laboratory environment, and the plates are not covered to allow sample access by the loading needle, the experiments are susceptible to contamination with airborne particulates. The randomness of the appearance of such particles, presumably nonproteinaceous, would compromise the sensitivity of the assay, unless the protein aggregate particles could be differentiated from contaminating “dust.” Indeed, although the difference between
Figure 1. Fluorescence- versus side-scattering intensity plots of mAb1 solution and a buffer containing particles from a syringe. (a). mAb1 only. The total particle count was 2427 ± 89 (71,400 ± 2600 per 1 mL). (b) mAb1 in the presence of 2.5× SYPRO Orange. The total particle count was 2733 ± 62 (79,900 ± 1800 per 1 mL). (c) Buffer solution shaken by hand for 1 min in a plastic syringe. The total particle count was 3116 ± 102 (91,200 ± 3000 per 1 mL). (d) The same buffer in the presence of 2.5× SYPRO Orange. The total particle count was 3169 ± 65 (92,700 ± 1900 per 1 mL). Phosphate-buffered saline, pH 7.4, was used in all cases. DOI 10.1002/jps
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Figure 3. The effect of SYPRO Orange concentration of the fluorescence of subvisible particles. SYPRO Orange stock (50×) was serially diluted and mixed 1:1 with mAb1 solutions. Left axis, average fluorescence of the detected particles (filled triangles). Right axis, the corresponding particle counts; total (filled circles), fluorescent (open circles). Figure 2. Flow cytometry of serially diluted mAb1 solution (20 mg/mL) containing heat-generated subvisible particles, diluted in 0.1 :-filtered control solution of the same antibody. The total count is denoted by diamonds and fluorescent count is denoted by squares. The results of four measurements were averaged. The gain settings for forwardand side-scattering were 5 and 2, respectively. The sensitivity was 400, and the measurement time was 300 s (most counts were detected within 200 s). The lines denote leastsquares fits to the data. (a) 0–20 mg/mL range. (b) 0–0.3 mg/mL range.
to about 8× dye concentration, after which a sharp increase is seen. We have not investigated the potential root cause of the observed increase. One can speculate that noncovalent proteinaceous particles could have been broken up with the excess of dye, or the dye might have reached its solubility limit. On the basis of these results, however, the final concentration of SYPRO Orange between 1× and 4× was considered to be optimal and robust. Use of Flow Cytometry for Evaluation of Stressed Samples
fluorescent and total particle count was minimal at high protein aggregate levels, it became very significant at low particle counts. The use of the fluorescent dye allowed selective detection of protein aggregates at the level of approximately 500 particles/mL (Fig. 2, panel B). Effect of SYPRO Orange on the Measured Particle Binding of dye molecules to hydrophobic surfaces on protein aggregates may potentially promote their dissociation and increase particle counts, or cause local unfolding and further aggregation. To explore such scenarios, an mAb1 preparation was mixed 1:1 with a serially diluted SYPRO Orange stock (50×). It should be noted that the manufacturer does not disclose the structure of this dye and thus the concentration is provided in units of “×”-–it is supplied as 5000× solution in dimethyl sulfoxide (DMSO). The results (Fig. 3) show the expected increase of the fluorescence signal from the particles as the amount of available dye is increasing, up to about 2× dye concentration, followed by a decline that is presumably caused by selfquenching and/or inner filter effects. The total and fluorescent counts observed appear to be constant up JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
We have examined a set of samples that was subjected to the stress associated with pumping through a Bausch–Strobel piston pump (Fig. 4). As expected, the total particle numbers detected with FC (first bar in each group) were only slightly higher than the fluorescent particle numbers (second bar in each group), consistent with the expectation that the vast majority of particles contained proteins. The particle counts determined by microflow imaging (MFI; third bar in each group) were lower in all cases, ranging from about 15% to 100% of the value observed for FC. To assess whether the size distribution had an effect on the observed particle ratios between these two methods, we have generated the fluorescence- versus side-scattering plots for the “1-pass” (MFI:FC ratio of ∼0.15) and “10-pass” samples (MFI:FC ratio of ∼1.0; inset of Fig. 4, left panels). For reference, we have included plots obtained from 1 and 5 : nonfluorescent standard polystyrene particles (inset of Fig. 4, right panels). It can be seen that 1 : particle data points fall on the leftmost region of the plot, suggesting that they are at the detection limit. Indeed, 0.5 : standard particles were not detected under the settings that were used in this study (data not shown). DOI 10.1002/jps
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Figure 4. The comparison of flow cytometry (FC) and microflow imaging (MFI) based on the analysis of mAb1 samples after piston pump treatment. The protein (∼5 mg/mL) was subjected to 1-, 3-, 5-, and 10-passes through a piston pump. In each group of bars, the first bar (white) denotes total FC counts, the second bar (grey) denotes fluorescently stained particles detected by FC (FC-Fluor), and the third bar (black) denotes the particle counts (>2 :m) detected by MFI. Error bars denote standard deviations of three measurements. Inset: SYPRO Orange fluorescence- versus side-scattering intensity FC plots for 1-pass (top left) and 10-pass (bottom left) samples. 1 : (top right) and 5 : (bottom right) polystyrene standard data is shown for reference. See Materials and Methods for experimental details.
Five micron standard particles, on the other hand, fell near the right edge of the plot, suggesting that larger particles would be detected but not resolved with the arbitrary settings applied in this study (see Materials and Methods for details). Such a narrow range is a result of the use of linear scales for the light-scattering intensity signals in this particular instrument. Logarithmic scales are used for fluorescence, and that results in the capture of essentially all particles within vertical boundaries. Comparing “1-pass” sample distribution to standard particle plots, it can be seen that most of the particles resided in the vicinity of 1 : standard particle side-scattering region. These observations are consistent with previous reports, in which the majority (∼99%) of piston pump-generated particles in buffer were in the range of 0.25–0.95 :m, as measured by static light scattering.10 On the contrary, more heavily stressed samples (e.g., “10-pass”) have generally higher side light-scattering intensities, consistent with a more efficient MFI detection, indicating that they are larger than 2 :.
DISCUSSION With the apparent deficiencies in the analysis of the subvisible particles (especially those smaller than 10 :) in therapeutic protein formulations,1,3,23 new methods that would permit efficient monitoring are needed. These preliminary results encourage further DOI 10.1002/jps
evaluation of FC as a practical tool that may facilitate development of safer and more efficacious therapeutics. However, there are large numbers of outstanding issues that will need to be addressed before the potential of this approach is fully realized. One of the questions that arise is the relative limit of detection for FC as compared with MFI and light obscuration methods. Although the observed ratios of total particle counts appear to be in line with the observed detection limits (∼0.5–1 : for Beckman FC500 flow cytometer and ∼2 : for Brightwell Technologies DPA4100 imaging instrument), the exact detection limits may depend on the data detection settings. In the case of FC, it is the matter of the threshold voltage. In fact, with the settings used in this study, optimized for low background noise, 1 :, but not 0.5 :, standard polysterene beads were quantitatively detected with FC. The counts obtained for 1 : particles accounted for 81% and 84% of theoretically predicted counts (based on the nominal concentration and size provided by the manufacturer) for the sample with and without SYPRO Orange, respectively (data not shown). None of these samples showed significant fluorescence, further suggesting the selectivity of the method for proteinaceous particles. For comparison, light obscuration instruments, which use beam attenuation rather than the intensity of scattered light to detect particles moving one-by-one through the focus zone, can in principle be used for sizes 0.5 : and JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
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larger, but precision or reproducibility are low at that size range,24 and typically 2 : limit is adopted in therapeutic protein development practice.2 In the case of MFI, the set threshold of the pixel intensity determines whether the particle is detected. The latter is affected not only by the inherent optical density of the medium, but also the depth of the field. With practical settings used to characterize subvisible particle size distributions (setpoint #1), 1 : particles were not detected by MFI (not illustrated). The lower detection limit is critical, because often the majority of subvisible particles appear to be in the range of approximately 1 : in diameter. For example, 10 mM potassium phosphate, 150 mM NaCl, and pH 7.0 buffer, when passed through a piston pump had particles averaging approximately 0.5 : with 99% of population between 0.25 and 0.95 :m as measured by static light scattering.10 Samples of therapeutic cytokine formulated with human serum albumin, with 40,000 total particles, had 90% of population in the 1–2mm size range, as determined by averaging a number of light obscuration measurements using a PAMAS system (Bad Salzflen, Germany).25 Both sensitivity and throughput of FC approach brings a promise of improving the practically achievable detection limit. These light-based particle detection techniques have unrealized potential for improved resolution. Using light-scattering detection, single particles can be imaged at the size range below 100 nm, and similarly sized objects can also be detected with microscopes.7 However, the chosen optical magnification is a compromise between the resolution and the total volume that can be observed and scanned in a reasonable amount of time. Additionally, there are mechanical and software engineering issues to be resolved to achieve adequate scanning and subsequent data processing. The situation is similar to the problems encountered in astronomy, in which the analysis of a large number of objects in the space requires tremendous technical and computational resources. Microflow imaging by the virtue of analyzing every single object detected, especially larger than few microns, provides not only the size distribution, but also morphological information that may be helpful in identifying the source of the particles detected. Light obscuration systems will also return reasonably precise size distribution as the length of time of light beam attenuation for any single particle is related to its dimensions. Light-scattering signals collected by FC, in principle, are also dependent on particle size and shape. However, the relationship is very complex, and the information that can be gained from the plots of forward scatter versus side scatter can be relative at best.26 For example, the sample that passed through the piston pump only once (Fig. 4) appears to have lower ratio of MFI counts /FC counts, when compared with other more stressed piston-pumped JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 100, NO. 5, MAY 2011
samples, a result of large portion of particles being below MFI, but above FC detection limits. The relative size distributions obtained with FC can only be qualitatively judged by comparison with the plots obtained with standard polystyrene spherical standards (inset of Fig. 4). Another situation in which useful information about size distribution can be gained from FC is when the particle population is very homogenous, as in typical applications with living cells, in which distinct regions of fluorescence versus scatter plots can be defined to further characterize or digitally separate for quantitative purposes. In principle, one of the major advantages of this approach lies in its throughput that permits unattended analysis of a large number of samples. However, streamlining of the relative size distribution analysis can be difficult due to theoretically complex and ill-defined intensity-based data, reducing day-to-day data utilization to total and fluorescent count numbers. Another aspect that may be difficult to study with this method will be the concentration- and temperaturedependent opalescence,27 due to the reversible nature of the association. The FC method appears to strike an optimal balance between the total sample volume required (100–200 :L), the volume actually scanned (30–100 :L), and the time required for one scan (∼1–2 min). The total amount of sample required in the current application, although prohibitive at very early stages (e.g., during protein engineering) of the development, would be considered typical for preformulation and formulation activities. In addition, relatively short data acquisition time allows a scan of a complete 96-well plate before significant evaporation occurs. For longer scans, potential volume loss due to evaporation can be minimized by controlling the humidity of the chamber containing the autosampler. The total volume scanned, after accounting for the fact that typically more particles are detected in comparison with MFI, makes these two methods very similar in terms of statistical power. Larger data sets would be needed for more precise comparisons of the relative error levels in these two methods. Judging from error bars in Figures 2–4, however, and available literature data, one may expect that the FC methods will be at least as precise as the MFI methods.11,12 The flow-through methods require adequate procedures to assure that the flow cells and the preceding tubing are free of particles from the previous run. In theory, no particles should be present when the washing buffer is being run through the flow cell. In practice, it is generally possible only after prolonged purging of the system, often using cleaning agents (e.g., bleach and surfactants), which is not feasible with large sample sets. As a result, some carryover often occurs. Obviously, the level of background noise will affect the limit of detection of subvisible particles. DOI 10.1002/jps
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The ability to selectively stain proteinaceous particles with a fluorescent dye helps with this respect (see Fig. 2b). However, the utility of FC approach will diminish when the prevailing subvisible counts are diminished to the point at which they will be comparable to perhaps 10 times the inverse of the sampling volume, that is, 10 × 1/0.030 mL or about 300. In such a case, a few background particles from the previous sample that were transiently bound to the tubing will have a potential to introduce significant artifacts. The morphological data acquired by MFI permits differentiation of particle identity, for example, between round silicone oil droplets and protein fibrillike assemblies.9 The algorithms that would automate this recognition process in tens of thousands of images in every sample are still being developed. In contrast, FC offers a simpler approach-–staining with a fluorescent dye. Generally, protein aggregates are expected to bind such dyes,28–30 and this property is expected to allow differentiation between drug-containing and extraneous particles. Protein drug-containing particles may be of different origin—formed during the purification process (e.g., in protein A step), filling (e.g., pump-shed particles coated with the protein), and storage. At the same time, extraneous particles can also contain proteins of human or animal origin, although these can be minimized through aseptic procedures. The hydrophobicity-sensitive dye used in this study, SYPRO Orange, was chosen for its convenience (supplied in DMSO and directly diluted to aqueous buffers), low cost, and compatibility with laser and detection options of the particular instrument. However, SYPRO Orange binds to surfactant ensembles, and therefore its signal is greatly reduced in the presence of surfactant micelles.31 If that precludes meaningful data analysis, a recently introduced dye that does not bind to surfactants and has similar selectivity for protein aggregates is available.32 Attenuation of the fluorescence of the detected proteinaceous particles can also be a result of the presence of a specific excipient that may compete for hydrophobic sites present on the aggregates or the effect on the dye itself (quenching). When such a situation is observed, MFI data should also be acquired, and off-line experiments be performed to assess the effect of the buffer on the fluorescence of the dye. The ability to selectively detect fluorescent particles also brings opportunities for more sophisticated applications, such as use of fluorescently tagged antibodies directed to the epitopes that are expected to be present on subvisible particles, or use of dyes that are selective for specific nonproteinaceous particles. Despite a range of possible complications in terms of dye binding, its fluorescence, and data interpretation, the method still may be found useful in protein process development and formulation practice. One reason is that often only a relative ranking of the alternatives is sought to efDOI 10.1002/jps
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fectively drive the development process. More importantly, however, a unique opportunity to differentiate between proteinaceous particles and other contaminants can be helpful in identifying their origin.
CONCLUSIONS In summary, flow cytometry appears to be a promising method for therapeutic protein formulation development with respect to efficient monitoring of the appearance of subvisible particles. In addition to measuring total particle counts, it appears to specifically detect protein aggregates that may be present in these particles, because random dust particles presumably are not stained with SYPRO Orange. Because of the relatively large sampling volume, representative particle counts can usually be obtained. Most importantly, the method uses a microtiter plate autosampler and thus it provides increased throughput that allows routine monitoring of the subvisible particle content of a large number of stability samples with very little effort. The high throughput can potentially be useful in elucidation of the effects of various nonprotein particulates (e.g., steel microparticles and silicone oil droplets) on the particle formation of given therapeutic protein under a number of conditions. Sufficient precision, selectivity, modest sample requirement, and high throughput may potentially make this method a preferred one for formulation screening, process development, and extensive stability studies in which large data sets are being generated
ACKNOWLEDGMENTS The authors thank Harrison Davis for help with setting up the MFI instrument.
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DOI 10.1002/jps