Development of Flow Imaging Analysis for Subvisible Particle Characterization in Glatiramer Acetate

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Development of Flow Imaging Analysis for Subvisible Particle Characterization in Glatiramer Acetate INNA LEVIN, SHIRI ZIGMAN, ARTHUER KOMLOSH, JUERGEN KETTENRING Teva Pharmaceutical Industries Ltd., Discovery and Product Development, Global R&D, CMC, Israel, Netanya Received 5 April 2015; revised 13 May 2015; accepted 28 May 2015 Published online 22 June 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.24550 ABSTRACT: Proteins, peptides, colloids, and polymers present a rapidly growing field of pharmaceutical industry. Bringing these products into market, however, is a huge regulatory challenge, especially because many of these therapeutics are intended for parenteral administration. Physicochemical properties of such products—size, shape, surface potential, and extent of particle–particle interaction—have to be well understood and monitored throughout manufacturing, release, and stability testing. First and foremost, size distribution of subvisible particles (SVP) in these products should be reliably measured. We present development of a flow imaging method to assess  SVP in the polypeptide injectable therapeutic product—glatiramer acetate (Copaxone ). Flow imaging comprises optical inspection of a flowing liquid and allows quantitation of particles in the range of 1–500 ␮m. The challenges of method development are discussed and C 2015 Wiley Periodicals, Inc. the method performance characteristics—accuracy, precision, linearity, and specificity—are demonstrated.  and the American Pharmacists Association J Pharm Sci 104:3977–3983, 2015 Keywords: protein aggregation; peptides; particle size; injectables; protein formulation R

INTRODUCTION Over the last years, the number of approvals of peptide and biopharmaceutical products has been steadily increasing.1,2 With their extremely complex structures, these products often provide a higher degree of specificity and potency than their small molecule counterparts, which explains their popularity. The growth in peptide and biopharmaceutical production is accompanied by a need to efficiently characterize injectable products for the presence of subvisible particles (SVP) in the 0.1–100-:m size range.3 This need is dictated by the potential risk of blood vessel occlusion4 and of immune response activation.5–8 Moreover, biological products have an increased tendency to form particles/aggregates via self-association. There are several reasons for this. First, peptides and proteins are often formulated at high concentrations (>10 mg/mL), some of them with the addition of different salts, which mask their net charge and promote colloidal instability.9 In addition, the complex native secondary, tertiary, and quaternary structures of peptides and proteins are usually fragile, and prone to change upon chemical degradation (oxidation, deamidation, etc.), pH and temperature variations, shear stress and exposure to air–liquid and solid– liquid interfaces. Such conformation instability often leads to aggregation and loss of activity.10–12 The combination of this inherent propensity for aggregation on one hand, and the ultimate need to control aggregation on the contrary, has, in the last decade, led to explosive development of analytical methods that characterize SVPs in peptide/protein products. To develop a manufacturing process with minimal and wellcontrolled SVP levels, it is important to minimize the propensity of products to aggregate, starting from the very first

development stages—that of product design and formulation development.13–15 The compendial method for SVP characterization in the 10–100-:m size range is light obscuration (LO) (Carver16 and USP monograph <788>). LO, however, has a major drawback—an inability to assess morphology and thus differentiate between particles of various natures, air bubbles, silicon oil droplets, nonprotein particles, and protein aggregates. This shortcoming prevents efficient troubleshooting during product development and adjustment of the manufacturing process. In addition, LO is highly sample-consuming, and has been reported to significantly underestimate particulate quantities.17–19 More precise and efficient methods for SVP characterization are thus crucial. Several complementary techniques to characterize SVP have evolved. These include light scattering-based nanoparticle tracking analysis,10 the resonant mass measurement based Archimedes system,20 and microscopy-based flow 18,21,22 All these tools provide SVP size distribution imaging. with various degrees of particle identification. In addition,  atomic force microscopy and G3-ID microscopy can provide partial chemical identification of particles using Raman spectroscopy.23,24 Of all these SVP characterization tools, flow imaging is best suited for high-throughput measurement of multiple samples. In flow imaging, a sample is passed through a flow cell, where it is illuminated by a visible light source and imaged, allowing for large sample volumes and precise quantitation. The flow imaging detection range is approximately 1–500 :m, and for all detected particles, images are automatically captured and classified, based on particle size, morphology, image intensity, and other image attributes. There are currently two classes  of flow imaging systems available on the market—MFI , by  Protein Simple (San Jose, California), and FlowCAM , by Fluid Imaging Technologies (Scarborough, Maine). Method development with MFI systems has been reported,25 and thorough comparisons of the two systems have been published.19,26 R

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Correspondence to: Inna Levin (Telephone: +972-54-5290069; Fax: +972-98639654; E-mail: [email protected]); Juergen Kettenring (E-mail: [email protected]) This article contains supplementary material available from the authors upon request or via the Internet at http://wileylibrary.com. Journal of Pharmaceutical Sciences, Vol. 104, 3977–3983 (2015)  C 2015 Wiley Periodicals, Inc. and the American Pharmacists Association

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Here, we present for the first time the development of flow imaging for SVP quantitation in injectable glatiramer acetate  (GA; Copaxone ). To the best of our knowledge, this is also the first time when flow imaging is developed to control quality of  a pharmaceutical peptide product. Copaxone is approved for treatment of patients with relapsing-remitting multiple sclerosis and clinically isolated syndrome. GA, the active substance  of Copaxone , is a complex mixture of polypeptides of varying sizes assembled from four naturally occurring amino acids—Lglutamic acid, L-alanine, L-lysine, and L-tyrosine—in a defined molar ratio. The molecular weight distribution of the GA components spans a wide range of about 2500–20,000 Da.27 The biggest challenge of flow imaging development is inherently nonhomogeneous SVP distribution, resulting from rapid sedimentation of particles in the 1–500-:m size range. Here, we discuss how sample homogeneity and method precision can be achieved. We demonstrate how flow imaging is further developed to achieve method accuracy. We then present method qualification in terms of linearity and specificity. Finally, high  image quality of the FlowCAM system at 10x magnification is demonstrated. R

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MATERIALS AND METHODS Materials Polystyrene microsphere standard (10 :m), CC10 from Thermo Scientific, Waltham, MA was used as a concentration and size standard. The concentration standard was at a nominal concentration of 3000 (±10%) part/mL and of nominal size of 10.00 ± 0.08 :m. Concentration standard was sonicated for 20 min prior to use. In addition, polystyrene microsphere standard of nominal size of 6.01 ± 0.04 :m, 4206A from Thermo Scientific, was used as a size standard. It was diluted 1:100 with water prior to use. Finally, 20 :m 3000 (±10%) part/mL polystyrene microsphere count standard (CC20 from Thermo Scientific) and 10, 20, and 50 :m polystyrene microsphere size standards (correspondingly, 4210, 4220, and 4250 from Thermo Scientific) were used for periodic routine instrument performance qualifications.  GHP Acrodisc (0.2 :m; hydrophilic polypropylene) syringe filters were purchased from Pall Corporation, Port Washington, NY. R

Flow Imaging Analysis Subvisible particles in a size range of 1–500 :m were ana lyzed by FlowCAM VS-IV system (Fluid Imaging Technologies), equipped with a LED visible light source (wavelength span of 400–700 nm); 10x magnification lens [numerical aperture (NA) = 0.3; Olympus, Center Valley, PA), FC80FV flow cell with the 537 × 715 :m2 field of view (height × width), digital camera with a 0.56-:m/pixel resolution (as projected into the object plane), and a 0.5-mL syringe pump that draws sample  vertically (top to bottom) through the flow cell. FlowCAM was  controlled by the VisualSpreadsheet software version 3.1.10. Excellent reviews on factors, limiting spatial, and pixel resolution were published by Gamble et al.28 and by Brown.29 In brief, theoretical instrument spatial resolution is limited by NA  and for FlowCAM is about 1.1 :m for the middle wavelength of 550 nm. Pixel resolution of 0.56 :m/pixel theoretically allows detection of 1.1 :m objects and larger and simple shape R

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characterization (round versus rod-like) for 2 :m objects and larger. Therefore, although the qualified size range of the instrument was 3–100 :m, all particles within the 1–500-:m range were imaged for information. Unless otherwise specified, five consecutive aliquots of 100 :L were analyzed for each sample. Samples were loaded at a flow of 0.5 mL/min, and analyzed at a flow rate of 40 :L/min, and at an imaging rate of 20 frames/s. Prior to each sample analysis, the flow cell was washed with 2-propanol and water and then with a 250-:L sample aliquot (the procedure was adapted from Wuchner et al.17 ). Dark pixels with a threshold value of 20 were selected for particle detection. Particle size was evaluated as equivalent spherical diameter (ESD)—the mean Feret diameter, based on 36 sample measurements conducted every 5°. Particle concentration (part/mL) in four size ranges— (1) ESD ࣙ 1 :m; (2) ESD ࣙ 5 :m; (3) ESD ࣙ 10 :m; and (4) ESD ࣙ 25 :m—was calculated as an average of all replicate measurements, along with standard deviations. Standards (10 and 6 :m) were evaluated after selecting particles with ESD within the 8–14 and 4–8 :m size ranges correspondingly, and with aspect ratio (AR; the ratio between minimal and maximal Feret diameters) above 0.7. This selection allowed exclusion from the counting of any contaminating particles and debris of degraded standard. Sample Preparation The test sample was a sample of GA, intentionally spiked with peptide aggregates, produced by stress heating. For initial screening of heat-stress conditions, GA samples were incubated overnight at either 40°C or 85°C, and then analyzed. For later routine test sample preparations, 800 mg of GA were diluted in 10 mL of aqueous mannitol (80 mg/mL), and then diluted to 20 mL with water to obtain a sample containing 40 mg/mL API and 40 mg/mL mannitol. This sample was then filtered through a 0.2-:m syringe filter and incubated overnight at 85°C. The sample was then sonicated for 10 min, centrifuged for 1 min at 2665g, the pellet discarded, and the supernatant decanted and used as a test sample. Although SVP levels varied between different test sample preparations, each individual preparation provided consistent SVP results within 3 h (the proven stability period) and was used for method development and qualification within these time limits. For the specificity study, a 40% (v/v) SO emulsion in water was added to test the sample at a ratio of 1:10. For the linearity study, 40 mg/mL mannitol formulation buffer was prepared, filtered through a 0.2-:m syringe filter, and used to prepare a series of dilutions of the test sample.

RESULTS AND DISCUSSION Choosing a Proper Test Sample for Method Development and Qualification Preparation of a proper test sample that would be used to determine method precision, specificity, and linearity is a prerequisite for any method development. The aim of flow imaging is to quantify nonprotein particles along with proteinaceous aggregates that might result from peptide or protein degradation. The latter often cannot be easily detected because refractive index (RI) and absorption coefficient of these particles and of formulation buffer often closely match over the entire wavelength DOI 10.1002/jps.24550

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Figure 1. Peptide particles observed in stressed (85°C, 24 h) GA sample (test sample). Image fragmentation events can be seen in the first row, images 6–8 from the left.

range employed by a flow microscope, resulting in extremely low-contrast images.30 An ideal test sample to challenge method detection capability should thus bear optical properties—RI and absorption coefficient—of the peptide/protein product for which the method is being designed. The test sample should be spiked with aggregates ideally bearing the optical properties of real aggregates expected in the product. Regular batches of the same product would not make a good test sample, as they normally contain very low levels of aggregates. To this end, we prepared a series of GA aliquots, stressheated for 24 h—an approach that was suggested by Z¨olls et al.26 and by Carpenter et al.31 SVP in each of the aliquots was measured in the size ranges of 1–500, 5–500, 10–500, and 25– 500 :m (Table S1 in Supplementary Material). The aliquot, incubated at 85°C, contained high levels of SVP in all size ranges, and was chosen as a test sample. Particles in this aliquot were  very well detected by the FlowCAM (Fig. 1), indicating excellent optical resolution of the instrument. Image fragmentation, or double detection of parts of the same particle because of poor boundary definition, reported by Z¨olls et al.,26 was occasionally observed (Fig. 1). Image fragmentation occurs when continuous series of pixels below threshold value are encountered in the particle, whereupon its image is split into multiple images. As seen from Figure 1, this was a relatively rare event.

although the AR = 0.7 cut-off is an easy and straightforward way to achieve specificity for protein aggregates, it inevitably comes at the expense of method accuracy for particles in the 1– 2-:m size range, where shape is not efficiently detected. This said, as long as the same filter is applied to all samples, comparison between samples can still be valid, even for the less accurate 1–2 :m size range. The ability of the flow imaging method to specifically count nonspherical SVP was then proved by spiking the test sample with 4% silicone oil (SO) and comparing the spiked and nonspiked particle counts. The SO level, chosen for evaluation of method specificity, exceeded by far any amount of SO expected to be present in syringed products.32 Specificity was demonstrated by comparison of SPV counts of nonspherical particles in spiked and nonspiked samples. SVP amounts were not affected by spiking (see Table S2 in Supplementary Material), and the differences in counts in the 5–500- and 10–500-:m size ranges were within 15%—the accepted method error assessed in method precision (see below). Counts in the 1–500-:m size range are also provided; however, they cannot be used as proof of specificity as they are strongly dominated by 1–2 :m particles, whose shape is not detected efficiently. We therefore conclude that the flow imaging method with an AR threshold of 0.7 is specific for counting nonspherical SVP in peptide/protein samples in the 5–500-:m size range.

Evaluation of Method Specificity

Method Development

As mentioned above, the aim of flow imaging is to quantify nonprotein particles and proteinaceous aggregates, whereas oil droplets and air bubbles, normally present in abundance in syringed drug products, should be either omitted or quantified separately. Because oil and air particles are normally of a regular spherical shape, this classification can be easily achieved in flow imaging based on the AR morphology parameter (the ratio between minimal and maximal Feret diameters). We observed that the AR > 0.7 threshold would adequately filter out all spherical particles in GA samples while omitting a minimal number of peptide aggregates (Fig. 2). It should be noted that

Method setup was dictated by the maximal operational image rate (20 frames/s). At given flow cell geometry and at a flow of 40 :L/min, 92% sampling efficiency could be achieved, that is, 92% of a flowing sample could be imaged. At these setup conditions, the aliquot volume of 100 :L was suggested to be optimal as it gave reasonable counting statistics for the largest particles in the test sample, while keeping a realistic measurement time of 2.5 min per aliquot or 12.5 min per sample. Method setup was also found robust toward changes of flow and image rates: particle counts in the 1–500, 5–500, and 10–

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Figure 2. Specificity of flow imaging method. (a) Images of silicone oil and of air bubbles, filtered out by AR > 0.7 statistical filter in a test sample, spiked with 4% silicone oil. (b) AR/size histogram of the same sample in the 5–500-:m size range, showing two distinctive groups of particles: air bubbles and oil drops with AR > 0.7, shown in red and nonspherical particles, shown in black.

500-:m size ranges changed by less than 4%, when image rate was halved and by less than 11% when flow rate was doubled— see Table S3 in Supplementary Material. Method setup was then optimized to achieve the highest experimental accuracy and precision. The counting accuracy was tested on a polystyrene microsphere standard, with a nominal concentration of 3000 part/mL ±10% and a nominal size of 10.00 ± 0.08 :m. In the very first stages of method development, the accuracy was 39% (Table 1), which was attributed Levin et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:3977–3983, 2015

to adsorption of polystyrene beads on the inner polypropy lene surfaces of the FlowCAM apparatus. Indeed, substitution of all polypropylene parts with their glass analogs restored method accuracy (Table 1). Size accuracy was also achieved (Table 1). In addition, size accuracy was verified routinely using polystyrene microsphere standard of nominal size of 6.01 ± 0.04 :m. The method setup was then optimized to assure the highest measurement precision. This optimization was performed R

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Table 1. Accuracy of Flow Imaging Method Count Standard (3000 part/mL, 10.00 :m) Observed Counts

STD—1 STD—2 STD—3 STD—4 STD—5 Average RSD (%) Accuracy (%)

Size Standard (6.01 :m) Observed Size

Observed Size

Initial Setup part/mL

Improved Setup part/mL

Mean ESD :m

Mean ESD :m

1373 673 1113 1631 1101 1178 30 39

3032 2608 3172 2799 3443 3011 11 100

10.35 10.43 10.55 10.40 10.40 10.40 0.7 104

6.43 6.51 6.33 6.33 N/A 6.40 1.4 107

Polystyrene microsphere count reference standard nominal concentration is 3000 part/mL ±10% and diameter 10 ± 0.08 :m. Polystyrene microsphere size reference standard nominal diameter is 6.01 ± 0.04 :m.

Table 2. Precision (Repeatability) of the Flow Imaging Method* Initial Setup

Improved Setup

Size Range

part/mL

RSD (%)

part/mL

RSD (%)

>1 mm >5 mm >10 mm >25 mm

157,889 19,869 8493 1820

7% 17% 17% 22%

257,512 22,660 4055 153

4% 5% 8% 30%

*Nonspherical particles (AR < 0.7) are quantified. Each value is an average of five consecutive sample measurements with RSD calculated over all measured replicates.

using the test sample (stress-heated GA). Sample homogeneity is a prerequisite of high precision of any method. Homogeneity of SVP cannot be easily achieved. For example, particles in the 1–500-:m size range having a specific volume of 0.74 cm3 /g, that of a protein,33 would normally sediment under gravity when suspended in aqueous solution. Thus, for a reliable SVP measurement, a sample has to be mixed extensively immediately prior to each replicate measurement.19 Extensive mixing, however, can lead to additional protein/peptide aggregation, and eventually to skewed results. To avoid additional aggregation, the test sample was mixed gently but thoroughly and then immediately divided into several 300 :L single-measurement aliquots. Each aliquot was additionally mixed gently immediately prior to the measurement. To further improve precision, field-of-view margins were made narrower than the flow-cell width. It was observed that nonround aggregates, flowing adjacent to flow-cell walls, tend to stick to the wall, which caused multiple imaging and resulted in impaired precision and accuracy. To overcome this setback, 33 :m field of view margins were set by software on both sides of the flow cell, reducing the field of view by 11%. Although this brought the sampling efficiency down from 92% to 83%, the change was accepted as it resulted in improved experimental precision (Table 2). We conclude that precision of the method is less than 15% for particles in the 1–500, 5–500, and 10– 500 :m size ranges. Linearity Linearity of SVP quantitation in the flow imaging method was demonstrated on the test sample (stress-heated GA), diluted DOI 10.1002/jps.24550

with formulation buffer to various concentrations in the 4– 40-mg/mL range. Good linearity results (coefficient of determination, R2 > 0.97) for the 1–500, 5–500, and 10–500-:m size ranges are demonstrated in Figure S1 in Supplementary Material. For the 25–500-:m size range, R2 = 0.79 was obtained (data not shown), which is explained by low abundance of this type of particle (224–1338 part/mL for various sample concentrations). Linearity of SVP quantitation depends primarily on two experimental factors: the concentration dependence of aggregation and linearity of the instrument response. The observed overall linearity of the method suggests that, under the experimental conditions used in this study, peptide aggregation in the heat-stressed GA sample is irreversible, that is, particles do not dissolve when the peptide product is diluted with buffer. However, this might not be the case for standard or nonheat-stressed GA samples, for which this flow imaging method has been developed. In some samples, aggregation might be reversible and aggregates would dissolve upon dilution. It is thus suggested that regardless of the linearity observed here, undiluted samples should be measured whenever experimental setup permits. In addition, the observed linearity suggests that under the experimental conditions used in this study, the observed particle concentrations lie within a linear response range of the instrument. It is conceivable that in flow imaging, quantitation saturation might occur at high particle concentrations; in fact, such saturation has previously been reported by Scherer et al.25 Saturation would occur when spatial resolution between pairs of particles becomes low, hampering separate imaging. In addition, quantitation saturation might occur at high-product concentrations because the increased RI of the solution would match that of aggregates more closely than in diluted solutions. It is therefore important to set the limits of particle concentrations that can be measured without saturation. The actual linearity limits are best demonstrated on protein/peptide aggregates than on spherical standard beads. It was demonstrated that for the SVP quantitation method presented here, linearity is observed up to 300,000 part/mL in the 1–500-:m size range, up to 35,000 part/mL for particles in the 5–500-:m size range, and up to 10,000 part/mL for particles in the 10–500-:m size range. A test sample spiked with a higher concentration of aggregates would be required to set the actual limits of response linearity of the method. Levin et al., JOURNAL OF PHARMACEUTICAL SCIENCES 104:3977–3983, 2015

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CONCLUSIONS Subvisible particles size distribution is an important quality attribute of any parenteral pharmaceutical product. Here, we demonstrated how a flow imaging method for accurate SVP quantitation in the 1–500-:m size range can be developed and qualified. Such a method can be used to quantify and control SVP during peptide manufacturing, production troubleshooting, and characterization of stressed samples. We have demonstrated how a GA test sample, intentionally spiked with peptide aggregates, was produced by stress heating. Along with a polystyrene standard, this test sample was used to develop experimental conditions and finally to qualify the method. The major development comprised: (1) introduction of fluidics with nonadsorbing inner surfaces, (2) field of view reduction, and (3) sample preparation, homogenic in terms of SVP. Counting accuracy of 100% and size accuracy of 104% were demonstrated. Precision of less than 15% was observed for particles in 1–500, 5–500, and 10–500 :m size ranges. Linearity of R2 < 0.97 was assessed for the same size ranges.  We conclude that the FlowCAM instrument combines excellent image quality, optical resolution, and accuracy with moderate, yet acceptable precision. Our approaches to method development may be useful to other groups using flow imaging analysis. R

ACKNOWLEDGMENTS The authors would like to thank Dr. Yousif Sahly, Dr. Vera Weinstein, Dr. Vladimir Ioffe, and Dr. Cheryl Balshayi for critical evaluation of the manuscript, Lew Brown from Fluid Imag ing for his helpful advice on FlowCAM operation principles, and Ariel Roytman and David Merkel Golan from Merkel Technologies Ltd. (Fluid Imaging Distributor in Israel) for their  helpful assistance with FlowCAM instrument. R

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