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Water Proton NMR—A Tool for Protein Aggregation Characterization Marc B Taraban, Roberto A. DePaz, Brian Lobo, and Yihua Bruce Yu Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 25 Apr 2017 Downloaded from http://pubs.acs.org on April 26, 2017

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Analytical Chemistry

Water Proton NMR—A Tool for Protein Aggregation Characterization

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Marc B. Taraban,1 Roberto A. DePaz,2 Brian Lobo,2 Y. Bruce Yu1*

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Maryland 21201

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Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore,

Formulation Sciences, MedImmune, One MedImmune Way, Gaithersburg, Maryland 20878

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*Corresponding author: Prof. Y. Bruce Yu (Telephone: +410-706-7514; Fax: +410-706-5017).

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E-mail address: [email protected]

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The Supporting Information is available free of charge on the ACS Publications website at DOI:

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10.1021/acs.analchem.xxxxxxx

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Flow-chart of experimental design; micro-flow imaging (MFI) particle counts; descriptions of

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NMR relaxation measurements, and raw data on water relaxation rates; comparative table for

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different analytical techniques

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ABSTRACT

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Formulation stability is a critical attribute of any protein-based biopharmaceutical drug due to a

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protein’s inherent tendency to aggregate.

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characterization of protein aggregates are prone to a number of limitations, and usually require

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additional manipulations with the sample, such as dilution, separation, labeling and use of special

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cuvettes.

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aggregates with a novel approach that employs the water proton transverse relaxation rate R2(1H2O).

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We explored differences in the sensitivity of conventional techniques—size-exclusion chromatography

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(SEC), micro-flow imaging (MFI), and dynamic light scattering (DLS)—and water NMR (wNMR)

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towards the presence of monoclonal antibody aggregates generated by different stresses.

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demonstrate that wNMR outperformed SEC, DLS, and MFI in that it was most consistently sensitive to

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increases in both soluble and insoluble aggregates, including subvisible particles. The simplicity of

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wNMR, its sensitivity, and possibility of noninvasive measurements are unique advantages that would

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permit its application for more efficient and higher throughput optimization of protein formulations.

Advanced analytical techniques currently used for

In the present work, we compared conventional techniques for the analysis of protein

We

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Discovery and development of any novel drug involves a combination of many advanced research and

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analytical techniques. The complexity of technologies employed grows significantly for protein-based

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pharmaceuticals, also known as biologics. Many biological and physico-chemical methods are used to

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characterize and study the toxicity, safety, and efficacy of biologics. These techniques can include, but

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are not limited to, size based methods (SEC, MFI, HIAC, AUC, DLS, LC-MS), charge based methods

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(isoelectric focusing, cation/anion exchange chromatographies, higher-order structure measurements

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(circular dichroism, fluorescence, and UV-vis spectroscopies), functional potency/activity assays and

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glycan analysis. Safety and efficacy of biologics are assessed using suitable animal models as part of

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toxiciology/pharmacokinetic studies. Among the most important aspects is also the stability of protein-

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based formulations. Inherent instability of proteins in biopharmaceutical formulations is a serious

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obstacle for the development of biologics. This challenge is particularly pressing when biologics are

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formulated at high protein concentration, in an effort to reduce the injection volume for better patient

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compliance and product market expansion.1 Proteins in aqueous solutions can be degraded due to

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chemical reactions such as deamidation, oxidation, hydrolysis—all of these are mediated by the protein-

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water interactions. Protein aggregation is also of concern.2 This propensity of proteins to aggregate is

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one of the most widely recognized challenges in the development and formulation of protein-based

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therapeutics,3 and therefore, the stability of protein formulations against aggregation is a field of active

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research.4

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immunogenicity,6,7 allergies,8 etc., stability studies of protein formulations are important to understand

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in what way a formulation composition provides the best long-term stability under different handling,

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transportation, and storage conditions.

Due to serious and potentially life-threatening risk of altered pharmacokinetics,5

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Among biopharmaceuticals, monoclonal antibodies (mAbs) are particularly vulnerable to

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aggregation due to their relatively large size (often more than one thousand amino acids per molecule)

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and structural complexity such as multiple glycosylation variants in one product.9,10 The large size and

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complexity of mAbs press the need to refine/modernize existing techniques as well as to develop new

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techniques to study the stability of their formulations.11

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conformational stability and particle size to observe unfolding and aggregation using differential

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scanning fluorimetry and light scattering techniques.12 Recently, the characterization of subvisible

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particles of protein aggregates has been extensively probed using nanoparticle tracking analysis

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(NTA).13 Instrumental sophistications of NTA equipment and high skill levels necessary for data

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processing and analysis put restrictions on its wider use. Dilution of the sample, limits on protein

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concentration as well as dependence on the optimization quality of the parameters favor the applicability

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of NTA only as a complementary method to other analytical techniques. Resonant mass measurement

Such techniques involve exploration of


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(RMM) is another advanced technique capable of detecting protein aggregates in the subvisible particle

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size range.14 Although the size range of RMM is somewhat narrower as compared to dynamic light

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scattering (DLS),15 RMM is very useful to distinguish between aggregates/particles of different nature

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(proteinaceous vs. non-proteinaceous).

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chromatography (SEC), dynamic light scattering (DLS), and micro-flow imaging (MFI) remain among

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the most frequently employed techniques for protein aggregate analysis when studying the stability of

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biopharmaceutical formulations.16

More straightforward methods, such as size-exclusion

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SEC is by far the most widely used method of protein aggregates detection, quantitation and sizing.16

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However, this method by its nature is capable of detecting only soluble aggregates, and due to in-line

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filters, column filters, and the size of column packing particles, the detectable size of aggregates is

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commonly thought to be < 0.1 µm.17 Moreover, the separation of high-molecular weight (larger size)

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aggregates for analytical purposes is also limited by the molecular weight cutoff of the column.

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Aggregates of higher weights often coalesce in a single elution peak in the void volume of the column.

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Caution should also be considered for possible dissociation of the aggregates during sample dilution,

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and/or dissociation or adsorption of the aggregates on the HPLC column.18 DLS provides fast and

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reliable data on the hydrodynamic radii of the particulates under conditions approximating Brownian

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motion. However, due to the above restriction of free-diffusive particles, DLS has certain limits for the

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sample concentration, viscosity, and particle size range. Moreover, DLS does not allow quantification

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of different aggregate populations, because the scattering intensity of large particles dominates even

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when present at minute amounts.19 MFI, a microscopy-based method, counts particles and separates

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them into several bin sizes. MFI has a size limit similar to any other optical microscopy technique—

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reliable particle size detection range is ≥ 1 µm.20

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techniques that characterize aggregate size distributions (DLS and MFI) is the existence of so-called

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size gap for subvisible particles17 where these two techniques are prone to unreliable results (the reliable

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size ranges are: < 0.1 µm, for SEC; < 1 µm for DLS; and > 1 µm for MFI),21 especially at higher

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protein concentrations.

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preparation procedures; at the very least, the drug solution needs to be drawn out of the vial and

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transferred to another container prior to instrumental analysis.

Of special importance with regard to optical

Additionally, these widely used techniques also require invasive sample

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There is clearly a need to implement a new simple technique that permits a formulation scientist to

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make quick assessment of a formulation condition against stress-induced protein aggregation or a

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protein analytical scientist to determine the extent of protein aggregation in a sample. Preferably, such a

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technique would be sensitive to all types of common stresses, aggregates of different sizes, and changes

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in protein structure/conformation, and could be used during testing for batch release and during stability 3 ACS Paragon Plus Environment


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studies. Ideally it should not require any manipulation/perturbation of the sample such as dilution, chromatographic separation, labeling/dye addition, transferring to a cuvette, physical contact with a probe, etc. In fact, a noninvasive technique which is responsive to the formation of protein aggregates in a wide range of particle sizes would be a much desired tool for development and stability studies of biopharmaceutical formulations and characterization of the aggregation state of a protein sample in general. As a powerful analytical technique, NMR has been extensively used to study protein aggregation.16 The capability of NMR to provide an important data on the self-association of therapeutic proteins based on the dynamic properties (relaxation, diffusion, etc.) of excipients/additives has been recently demonstrated.22

However, traditional NMR approaches are overwhelmingly aimed at the solute

properties such as structure/mobility of a therapeutic protein and/or excipient, putting the solvent outside the scope of research. Moreover, such structural studies often require expensive high-resolution highfield (hundreds of MHz) advanced NMR instruments as well as invasive sample manipulations such as D2O addition. Whereas due to solvent-solute interaction, the solvent is capable to provide valuable information on the solute properties, and this is particularly true in the case of water which in very high concentrations (> 90%) is present in biopharmaceutical formulations.

Importantly, due to high

intensity, monitoring of the properties of water NMR signal could be achieved using low-field benchtop NMR spectrometers. For aqueous biopharmaceuticals, the labile protons of proteins are inevitably in fast proton exchange with water. Hence one might expect that protein aggregation could have certain repercussion for the water proton NMR (wNMR) signal. Indeed, the formation of protein aggregates is known to affect the transverse relaxation rate of water proton NMR signal R2(1H2O) resulting in significant growth of the proton relaxation rate.23 R2(1H2O) could be easily measured noninvasively using inexpensive benchtop NMR spectrometers.24 Data acquisition and analysis with benchtop NMR spectrometers are straightforward, and do not require involvement of highly-skilled personnel. Advantageously, the bore size/sample diameter in benchtop NMR is not a limitation and is wide enough to accommodate the drug product vial without opening it for analysis. Indeed, we have previously shown that noninvasively, in situ, measured R2(1H2O) could be a sensitive indicator of protein aggregation induced by temperature25 or pH,26 and could correlate with a number of qualitative and quantitative parameters of the aggregates.

Moreover, such sensitivity of R2(1H2O) has been

demonstrated in different aqueous media in the presence of different additives, from common PBS (phosphate buffered saline) buffers25 at pH 7.4 to HEPES buffer26 containing EDTA and citric acid at pH 4.0-5.0. We demonstrated that in thermally stressed model protein systems (bovine serum albumin and human γ-globulin), R2(1H2O) increased with the percentage of soluble aggregates. In this case, the 4 ACS Paragon Plus Environment

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molecular weight/size of the submicron-sized aggregates linearly correlated with R2(1H2O), and due to different size and properties of the proteins and their aggregates (BSA, ~66 kDa; γ-globulin, ~150 kDa) such linear dependences have clearly distinguishable slopes (e.g., 0.033 s-1/nm vs. 0.011 s-1/nm, respectively).25 Yet, a completely different nonlinear trend of the R2(1H2O) vs. size dependence was observed in pH-induced aggregation.26 Our earlier results25,26 also provide general insight into the range of protein concentrations where R2(1H2O) begins to demonstratesensitivity to protein aggregation. Broadly speaking, the response of R2(1H2O) is more pronounced for higher protein concentrations. For example, in the aggregation process, the span of R2(1H2O) changes was noticeably larger for BSA and γglobulin solutions (both 15 mg/mL)25 than for insulin (0.4 mg/mL).26 However, even at 0.4 mg/mL,26 R2(1H2O) reliably responded to the formation of aggregates. Our earlier observations raised a question about the response and sensitivity of R2(1H2O) to soluble and/or insoluble aggregates generated by different stresses. As a point of reference, all seven new biologics approved in 2016 were mAbs, and had concentrations from 10 mg/mL to 150 mg/mL.27 In view of the high importance of mAb-based biologics, in this work we have explored a mAb formulation with a relatively intermediate protein concentration of 55 mg/mL. In the present work, we used wNMR to detect the aggregation of a mAb under different stresses— freeze-thaw cycling, thermal stress at elevated temperatures, and controlled agitation. We explored how R2(1H2O) responded to the presence of aggregates generated through different stresses, and whether R2(1H2O) was sensitive to mAb aggregates of different sizes, including subvisible particles.

We

compared wNMR with conventional techniques—SEC, MFI, DLS. Our findings suggest that for all studied stresses, R2(1H2O) was consistently sensitive to the presence of both soluble and insoluble mAb aggregates within a wide size range: < 0.45 µm, 0.45–5 µm, and ≥ 5 µm. This work demonstrates that wNMR could be used to monitor protein aggregation in biopharmaceutical formulations during stability studies.

EXPERIMENTAL SECTION Materials mAb, an IgG1 monoclonal antibody with a molecular weight of approximately 150 kDa, was produced and purified at MedImmune (Gaithersburg, Maryland) and prepared into 20 mM histidine, pH 6 at a concentration of 55 mg/mL. Prior to all experiments and initiating stressed conditions, all protein samples and the blank histidine buffer were filtered through a 0.22 µm Sterivex® filter (Millipore, Billerica, Massachusetts). All sample preparation procedures were carried out in a biosafety cabinet at ambient temperature. 5 ACS Paragon Plus Environment


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Generation of mAb Aggregates by Different Stresses Three different stresses were used in this study—freeze-thaw cycling, heating at 50 °C, and vigorous agitation. The filtered solution of mAb was aliquoted (5 mL) into 6R Type I borosilicate glass vials, closed with chlorobutyl rubber stoppers, and secured by Flip-Off® seals (all purchased from West Pharmaceutical Services, Inc., Exton, Pennsylvania). Both vials and stoppers had been sterilized using standard autoclaving procedures. Freeze-Thaw. Vials with mAb solution were placed into a methanol-filled freezing bath of an FTS Systems controlled rate freezer Bio-Cool BC-III-80A02 (SP Scientific, Inc., Warminster, Pennsylvania). Samples of mAb in the vials were then subjected to 16 freeze-thaw cycles. Each cycle consisted of sample equilibration at 5 °C for 2 hours, followed by a temperature ramp to -40 °C at a rate of 0.1 °C/min, followed by equilibration at -40 °C for 2 hours, and followed by a temperature ramp back to 5 °C at a rate of 0.1 °C/min. A non-frozen control sample was maintained at 5°C.

Heating at 50 °C.

Vials were placed in a VWR Mini Incubator (VWR International, Radnor,

Pennsylvania) which was previously equilibrated at 50 °C and were subjected to thermal stress at 50 °C for 36 h. A non-heated control sample was maintained at 5°C.

Agitation. To avoid imbalances, two vials with identical sample volume were symmetrically inserted into round polyurethane sample holder fixed to the top of a VWR Digital MiniVortexer 945303 (VWR International, Radnor, Pennsylvania). Agitation of the samples was performed continuously at ~1,600 rpm for 24 hours at ambient temperature. A non-agitated control sample was maintained at 5°C.

After completion of each stress procedure, the stressed mAb solution was split into three different portions for further analysis: (i) an aliquot of 1.6 mL of the sample without further manipulation (nonfiltered); (ii) the remaining sample volume was filtered through a 5 µm Millex® filter (Millipore, Billerica, Massachusetts), and 1.6 mL of the filtrate was aliquoted for analysis (5 µm filtered); and (iii) the residual solution, after 5 µm filtration, was additionally filtered through a 0.45 µm Millex® filter (Millipore, Billerica, Massachusetts), and 1.6 mL of the resulting filtrate was aliquoted for analysis (0.45 µm filtered).

A flow-chart of sample preparation procedures summarizing the general

experimental design is provided in Supporting Information (Scheme S1). Methods


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Stressed mAb solutions, including non-filtered, 5 µm filtered and 0.45 µm as well as non-stressed mAb solution (as control) were studied by SEC, MFI, DLS, and wNMR. SEC. SEC was performed with an Agilent HPLC 1200 system with UV detection at 280 nm.

The

separation column was 7.8 mm × 300 mm TSKgel G3000SWXL 5 µm and the guard column was 6.0 mm × 40 mm TSKgel Guard SWXL 7 µm (Tosoh Bioscience, King of Prussia, Pennsylvania). The inline filter pore size was 2 µm. The mobile phase was 0.1 M Na2HPO4, 0.1 M Na2SO4, 0.05% NaN3 at pH 6.8 which was filtered before use through a 0.22 µm filter. The elution was performed at a flow-rate of 1 mL/min at ambient temperature. Prior to injection, an aliquot of 20 µL of the mAb solution was diluted from 55 mg/mL to ∼10 mg/mL by adding 80 µL of phosphate-buffered saline, 1X PBS (Gibco Life Technologies Corporation, Grand Island, New York). Injection volume was 25 µL of the diluted sample, and the column was preand post-washed by single injections of 100 µL of ultrapure deionized water. All samples awaiting injection were kept in an autosampler tray at 5 °C. The percent fraction of the peaks in a sample corresponding to aggregates, monomer, and fragments was calculated relative to the total area of all known protein peaks in the chromatogram. MFI. Subvisible particle counts were obtained using an imaging-based particle counter MFI 5200 (Protein Simple, Santa Clara, California). Pre-filtered (0.22 µm) deionized water was used to optimize background illumination. Approximately 600 µL of the mAb solution was used for analysis, where the first 200 µL was used to flush the flow-cell prior to counting to remove Schlieren lines generated as a result of the mixing of the sample and water. All measurements were done at ambient temperature. Software of the instrument was set to record the particle counts for the particle size ranges ≥ 1 µm, ≥ 2 µm, ≥ 5 µm, ≥10 µm, and ≥ 25 µm with an aspect ratio less than 0.85. DLS.

DLS experiments were conducted using a multi-angle spectrometer (Photocor Instruments)

equipped with a He-Ne laser as the light source and a high-resolution photomultiplier detection system with automated goniometer for precise angle setting.28 An aliquot of 600 µL of each mAb solution was placed into a sterilized cylindrical glass vial (O.D. 6 mm). The measurements were conducted at 20 °C ± 0.1 °C. Data were accumulated for 60 sec, and the autocorrelation functions were obtained. DynaLS software (SoftScientific, Inc., Tirat Carmel, Israel) was used to analyze the autocorrelation functions from DLS. Processing of the DLS autocorrelation function results in the diffusion coefficients D of the particles present in the analyzed sample which is derived from29,30


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1 , τr = Dq 2

where

4πn sin(θ ) 2 q=

λ

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(1)

Here, τr is the time of decay of the random optical fluctuations of the refractive index due to diffusive Brownian motions of the particles, q is the wave number reflecting the difference in the wave vectors of the incident and scattered light, n is the refractive index of the solvent (n = 1.3328 for water at 20°C), λ is the wavelength of the incident light in vacuum (λ = 633 nm for a He-Ne laser), and θ is the scattering angle (90° in our experiments).

Hydrodynamic radii Rh of the particles were then derived from

diffusion coefficients D using the Stokes-Einstein equation29,31 Rh =

k BT 6πη D

(2)

where kB is the Boltzmann’s constant (1.381 × 10-23 J⋅K-1), T is the absolute temperature (293 K), and η is the viscosity of the solvent (9.998 × 10-4 Pa·s for water at 20°C). wNMR—Transverse Relaxation Rate Measurements. The water proton transverse relaxation time T2 was measured using a benchtop wide-bore (I.D. 18 mm) time-domain UNIQ PMR 20 at 0.47 T magnetic field, 19.5 MHz resonance frequency for 1H (Magnetic Resonance Resources, Inc., Fitchburg, Massachusetts). One mL of the mAb sample in a 2 mL sterile polypropylene vial was placed directly into an 18-mm NMR tube for the UNIQ PMR 20 instrument (see Supporting Information for photos of a sample in the NMR tube). Relaxation time measurements were conducted after a 30 minute temperature equilibration of the sample inside the permanent magnet cavity. For stable operation of the permanent magnet of UNIQ PMR 20 instrument, the temperature in the thermostated room was set to 18°C (±0.5°C), and the temperature inside the probe was set to 22°C (±0.1°C). Lower sensitivity of low-field time-domain NMR spectrometers and the capability to tune radiofrequency (RF) power in our spectrometer’s hardware prevented the effects of radiation damping. The CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence32 was used in transverse relaxation time T2 measurements. The relaxation delay was always larger than 5T1 (where T1 is the longitudinal relaxation time of water protons) and was set to 15 sec. The interval between the 180° pulses, the inter-pulse delay, τ was 200 µs in the CPMG sequence.

Eight transients were collected, and each transient was

followed by collection of 4000 echo signals with decreasing echo intensity over time.

Total

measurement time for one sample was about 2 min. The water proton T2 value is extracted from the decay of echo signal intensity over time t using the following equation: I(t) = I0 × exp(-t/T2)

(4)


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where I(t) is the echo signal intensity at time t; I0 is the initial echo intensity at t = 0. Single-exponential fit of the echo signal decay data was performed using a built-in proprietary Allegro–FX software (Magnetic Resonance Resources, Inc., Fitchburg, Massachusetts). The resulting values of T2 were converted into the water transverse relaxation rate R2(1H2O) in s-1 (R2(1H2O) = 1/T2).

R2(1H2O)

measurements for each mAb solution were repeated 5 times; the reported R2(1H2O) values are the arithmetic means of the five measurements with characteristic standard deviation in the range of ± 1×10-3–2×10-3 s-1. RESULTS AND DISCUSSION In order to understand the sensitivity of R2(1H2O) to protein aggregates induced by different stresses, we subjected the mAb solution separately to three different stresses—freeze-thaw cycling, heating at 50 °C, and vigorous agitation. To evaluate the sensitivity of R2(1H2O) to protein aggregates of different sizes, an aliquot of each stressed solution was filtered through a 5 µm filter, and an aliquot of the resulting filtrate was sequentially filtered through a 0.45 µm filter. We then measured R2(1H2O) of unstressed, nonfiltered and filtered stressed samples followed by analyses by conventional techniques used to study protein aggregation—SEC, MFI, and DLS.

These conventional techniques were used here for

comparison to wNMR to understand the ability of wNMR to detect various sizes of mAb aggregates. As seen from Fig. 1, R2(1H2O) detected the formation of mAb aggregates induced by all three types of stresses. Compared to unstressed controls, R2(1H2O) increased significantly in each stressed solution. Five µm filtration of the stressed solution led to a decrease of R2(1H2O) in all cases. This suggests that R2(1H2O) is sensitive to the presence of mAb aggregates ≥ 5 µm. Further filtration using a 0.45 µm filter also led to a decrease of R2(1H2O), though to a lesser extent, but for the agitated solution this change was negligible. Nevertheless, after two sequential filtrations, R2(1H2O) of all stressed solutions was significantly larger than the unstressed control, suggesting the sensitivity of wNMR to the presence of mAb aggregates smaller than 0.45 µm.



Freeze-Thaw

1.25

Heating

R2(1H2O), s-1

Agitation

1.20

1.15

1.10

No n 0. 5 µ m filte 45 r µ m filt ed e fil red te Co red n No trol 5 n 0. µ m filte 45 r µ m filt ed er fil ed te Co red n No tro 5 nfil l 0. µ m te 45 r µ m filt ed er fil ed te Co red nt ro l

1.05

Figure 1. Response of the transverse relaxation rate of water R2(1H2O) to the formation of mAb aggregates produced by different stresses and the effect of sequential 5 µm and 0.45 µm filtrations on R2(1H2O). Error bars show standard deviation of the mean calculated by five consecutive measurements of R2(1H2O) for the same solution.

Different size distributions of soluble mAb aggregates that originated from different stresses were evident from the differences in the relative amounts of smaller-sized (SSA) and larger-sized (LSA) soluble aggregates as determined by SEC (Fig. 2 and Table 1). Here, we designated aggregates as SSA and LSA based on their retention times in SEC—SSA 7-8 min, and LSA ~ 6 min (Fig. 2c). The total percent of soluble aggregates were similar for all three stresses (Table 1). The percent SSA aggregates, however, were highest after freeze-thaw cycling, while heating and agitation produced relatively more LSA aggregates than SSA aggregates. a

b

1000

c 150

1000 Freeze-Thaw Agitation Heating, 50°C

Control

800

600

600

400

Freeze-Thaw Agitation Heating, 50°C

100

mAU

800

mAU

mAU

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400

50

200

200

0

0

4

6

8

10

Time, min

12

14

0 4

6

8

10

12

Time (min)

14

4

5

6

7

8

Time (min)

Figure 2. Size-exclusion chromatograms reflecting the difference in soluble mAb aggregates produced by different stresses: (a) unstressed control solution; (b) chromatogram of stressed solutions; and (c) enlarged view of the section from (b) inside the black square brackets.

Sequential filtrations with 5 µm and 0.45 µm filters had essentially no effect on the total percentage of soluble aggregates measured by SEC, shown in Table 1. A small reduction in the total percentage of 10 ACS Paragon Plus Environment

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soluble aggregates was observed after filtration in the case of agitation, and for the other two stresses the total percentage of soluble aggregates remained unchanged (Table 1). Also, as seen from Table 1, filtration did not alter the ratio of SSA and LSA for each stress type.



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310 Table 1. Percentage of (%SSA and %LSA) soluble mAb aggregates, and total percentage of soluble aggregates (Total %Aggr) before and after filtration by size-exclusion chromatography (SEC).a

Heating 50°C

FreezeThaw

Aggregates

Agitation

1 2 311 3 312 4 313 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 314 30 315 31 316 32 317 33 318 34 35 319 36 37 320 38 321 39 40 322 41 42 323 43 44 324 45 325 46 47 326 48 49 327 50 51 328 52 53 329 54 55 330 56 57 58 59 60

Unstressed control

Stressed Nonfiltered

5 µm filtered

0.45 µm filtered

%SSA

0.8

7.4

7.3

7.3

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2.0

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9.3

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9.3

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7.8

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3.2

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5.9

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0.6

10.1

9.2

9.3

a

Calculated relative to the total area of all peaks in a chromatogram. Except for the agitation-stressed solution, no significant loss of material was seen during sequential filtration, the total AUC (area under the curve) before and after filtration remained unchanged, for all stresses. Designation of SSA and LSA aggregates was based on their retention times; SSA is > 7 min, and LSA ~ 6 min.

Of note, the absence of significant changes in total protein, observed both prior to and after filtration as determined by the total area under the curve by SEC is consistent with earlier exploration of large protein aggregates produced by freeze-thaw cycling stress.33 This observation could be explained by a relatively low total mass of insoluble larger aggregates compared to the total mass of soluble smaller aggregates and the total protein mass. Analysis of subvisible (between 1 µm and 100 µm)17 particle counts by MFI revealed similar particle concentrations for freeze-thaw and heating stresses within the particle size range from ≥ 1 µm to ≥ 25 µm, whereas agitation produced almost an order of magnitude greater particle counts within the above range (Fig. 3).


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a

b

c Nonfiltered

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≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

1

10

≥ 1 µm ≥ 2 µm ≥ 5 µm ≥ 10 µ m ≥ 25 µ m

Particle Concentration (#/mL)

331

1 2 3 332 4 5 333 6 7 8 334 9 10 335 11 12 13 336 14 15 337 16 338 17 339 18 340 19 341 20 21 342 22 23 343 24 25 344 26 345 27 28 346 29 30 347 31 32 348 33 349 34 35 350 36 37 351 38 39 352 40 353 41 42 354 43 44 355 45 46 356 47 357 48 49 358 50 51 359 52 53 360 54 361 55 56 362 57 58 59 60

Figure 3. Concentrations of subvisible particles from MFI data for nonfiltered and filtered stressed solutions with the unstressed solution as the control: (a) freeze-thaw cycling; (b) heating, 50 °C; and (c) agitation.

As expected, and seen in Fig. 3, sequential filtrations had a noticeable effect on the concentrations of subvisible particles after all three stresses. After 5 µm filtration, particle counts dropped significantly within the entire size range from ≥ 1 µm to ≥ 25 µm, and not only for particles ≥ 5 µm. This suggests that the 5 µm filter also removed particles smaller than 5 µm. This is not surprising considering that 5 µm is the average pore size of the filter and pores with sizes smaller than 5 µm are inevitable. Sequential filtration using a 0.45 µm filter essentially removed most of the subvisible particles, and the subvisible particle counts for all three stressed solutions became very close to those of the unstressed controls (Fig. 3); this result is consistent with the fact that MFI cannot detect particles < 1 µm. In the DLS experiments, we observed very distinctive patterns of particle size distributions (PSDs) in the fully stressed solutions (Fig. 4). After freeze-thaw cycling, the mAb monomer peak, with a hydrodynamic radius centered at 5-6 nm, displayed slight broadening (Fig. 4, left). Additionally, very small intensity peaks at much larger particle sizes were detected by DLS after freeze-thaw cycling. Heating at 50 °C resulted in essentially a bimodal PSD (Fig. 4, middle). Agitation led to a broad range of particle sizes, from 5-6 nm to ∼10 µm, (Fig. 4, right). These clear distinctions favor the conclusion that very different types of mAb aggregates were generated under different stresses. Because the PSDs after freeze-thaw cycling and heating at 50 °C stresses did not show the presence of large mAb aggregates (≥ 1 µm), sequential filtrations using 5 µm and 0.45 µm filters had little or no effect on the resulting PSDs (Fig. 4, left and middle). However, because PSDs after agitation stress are heavily populated by large particulates (~ 10 µm), sequential filtrations significantly affected the PSD profile; the first filtration cut off the particles ≥ 5 µm, and the second filtration removed all particles ≥ 0.45 µm (Fig. 4, right), as expected. 13 ACS Paragon Plus Environment


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363 1.0 0.8

1.0

0.4

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0.0

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Nonfiltered 5 µm filtered 0.45 µm filtered

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 364 33 365 34 366 35 367 36 37 368 38 369 39 40 370 41 42 371 43 372 44 45 373 46 47 374 48 49 375 50 376 51 52 377 53 54 378 55 56 379 57 58 59 60

0.0

1

10

100

1000 10000

Rh, nm

Agitation

Figure 4. DLS analysis of the particle size distributions (PSD) of the mAb aggregates produced by different stresses before and after filtration. Insets show the unstressed control. Data are normalized by the intensity of the highest peak in each PSD.

Although of very valuable qualitative information content, DLS does not provide quantitative description of the mAb aggregates formed under different stresses. Comparative quantitative analysis of DLS results is complicated due to the high polydispersity of the observed PSDs. Despite the accurate identification of the average hydrodynamic radius of a given particle population (or particle populations), the relative intensities of the peaks cannot be used for reliable quantification of relative percentage of different aggregate populations.

This is especially the case for agitation, where

calculations of the average hydrodynamic radii Rh of aggregates could be hampered by inter-particle interference. Larger particles and particles strongly interacting with each other and/or with the media are often not observed due to the above mentioned Brownian diffusion limitation, which depending on solution viscosity and concentration could span from ≥ 1 µm to ~ 10 µm and above.31 Moreover, the intensity of the size distribution peaks is not in any way related to the population percentage of mAb aggregates with a given Rh. As previously described, larger aggregates, even if present in a very small 14 ACS Paragon Plus Environment

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fraction, have much greater scattering intensity as compared to smaller particulates.19 There is no

380

rigorous analytical expression describing the dependence of the scattering intensity on the size of the scatterer. Such expression would depend on many sometimes unknown factors, for example, the relationship between the particle size and the wavelength of the incident light,34 particle shape, scattering volume, optical contrast, particle refractive index, etc.35 The implications of the above limitations are indeed evident from the fact that MFI data showed the presence of larger particles after all three stresses, while DLS detected only higher counts after agitation (cf. Fig. 3 and Fig. 4). In the case of freeze-thaw and heating stresses, this could be a result of smaller concentrations of subvisible particles compared to agitation (agitation produced an order of magnitude higher particle counts), as well as their non-Brownian motion due to some specific interparticle interactions. For the sole purpose of comparing the sensitivity of the different analytical techniques, the raw data from SEC, MFI and wNMR were normalized to the same scale. Here, the corresponding sensitivity responses XSEC (for total percent fraction of soluble aggregates from SEC), XwNMR (for water proton relaxation rate from wNMR), or XMFI (for particle concentration ≥ 1 µm from MFI) were calculated as XSEC, XwNMR, or XMFI = (Xi - Xc)/(Xs - Xc),

(5)

where Xs is a quantitative characteristic measured by a given technique for the fully stressed solution before filtration, Xi is a quantitative characteristic measured by a given technique for any stressed solution (nonfiltered, 5 µm filtered, or 0.45 µm filtered, respectively), and Xc is a quantitative characteristic measured by a given technique for the unstressed control. In this approach, for all sensitivity responses, fully stressed solution before filtration corresponds to 1, and unstressed control corresponds to 0, while filtered solutions fell between 1 and 0. This analysis was not performed for DLS measurements due to its qualitative nature. wNMR

SEC Freeze-Thaw

Heating

Freeze-Thaw

Agitation

Heating

MFI Agitation

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0.8

0.8

0.8

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Heating

Agitation

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0.0 o 5 nfi µ l 0. m, ter 45 fi ed µ m lter , f ed ilt er ed No n fil 5 t µ 0. m, ere 45 fil d µ m te , f r ed ilt er ed N on fil 5 te µ 0. m, red 45 fil t µ m er , f ed ilt er ed

N

o 5 nf il µ 0. m, ter 45 fi ed µ m lter , f ed ilt er ed N on 5 fil µ t 0. m, ere 45 fil d µ m te , f red ilt er ed N on fil 5 te µ 0. m, red 45 fil t µ m er , f ed ilt er ed

N

5 µ fil 0. m, ter 45 f i ed l µ m ter , f ed ilt er ed N on fil 5 µ t 0. m, ere 45 fil d µ m te , f r ed ilt er ed N on fil 5 te µ re 0. m, 45 fil d µ m ter , f ed ilt er ed

0.0

N on

1 2 381 3 4 382 5 383 6 7 384 8 9 385 10 11 386 12 387 13 14 388 15 16 389 17 18 390 19 391 20 21 392 22 23 393 24 25 394 26 27 28 395 29 30 396 31 397 32 33 398 34 35 399 36 400 37 38 401 39 40 402 41 42 403 43 404 44 45 405 46 47 406 48 49 407 50 408 51 52 409 53 54 410 55 411 56 412 57 58 59 60


Figure 5. Sensitivity responses resulting from sequential filtrations of stressed mAb solutions using 5 µm and 0.45 µm filters.



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413

1 2 414 3 4 415 5 416 6 7 417 8 9 418 10 11 419 12 420 13 14 421 15 16 422 17 18 423 19 424 20 21 425 22 23 426 24 25 427 26 428 27 28 429 29 30 31 430 32 431 33 34 432 35 36 433 37 434 38 39 435 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Of all of the techniques, SEC demonstrated minimal sensitivity towards sequential solution filtration, because it detects only soluble aggregates, and most of these aggregates were smaller than 0.45 µm (Fig. 5, left). The use of a 5 µm filter resulted in a considerable drop in particle concentrations observed by MFI. However, sequential filtration using a 0.45 µm filter had a smaller effect on MFI’s sensitivity response XMFI (Fig. 5, right). Moreover, after two sequential filtration steps the value XMFI approached 0, very close to the value for the unstressed control. Apparently, this was due to the aforementioned fact that 5 µm filtration also removed significant amounts of particles ≤ 5 µm (Fig. 3), and second filtration step using 0.45 µm made the solutions essentially free of subvisible particles. In the case of wNMR, 5 µm filtration lowered R2(1H2O) significantly in all stressed solutions while 0.45 µm filtration had less notable effect.

However, even after removal of subvisible and submicron

particles, R2(1H2O) was still well above the unstressed control, suggesting a high sensitivity of this method toward the presence of small soluble aggregates. The comparison shown in Fig. 5 reveals that wNMR was sensitive to mAb aggregates of very different sizes—from smaller soluble aggregates detected by SEC to micron-sized particulates observed by MFI. Aggregates, both soluble and insoluble, within three size ranges, ≤ 0.45 µm, from ≥ 0.45 µm to ≤ 5 µm, and ≥ 5 µm, contribute to the observed R2(1H2O). SUMMARY To summarize, it would be most illustrative to consolidate the results as a flow-chart representing the sequence of experimental steps as well as our main observations (Fig. 6). Additional information on the sensitivity of the analytical techniques observed at different steps of the experimental sequence could be found in Supporting Information (Table S5).


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Figure 6. Flow-chart of the experimental design and the sensitivity of different analytical techniques towards mAb aggregates induced by different stresses and in different size ranges. References to Figures and Table in red refer to the experimental data supporting each conclusion. F/T: freeze-thaw cycling.

Our main goal was to explore the response of wNMR to mAb aggregates induced by different stresses and of different sizes and to compare wNMR with more established techniques, including SEC, MFI and DLS. To this end, we induced mAb aggregation using three types of stresses and then performed sequential filtrations of particles and aggregates, analyzing the sensitivity of each technique to different aggregate sizes after each filtration step. The responses of the various techniques to stresses and filtrations were evaluated in a systematic fashion. As seen from Fig. 6, all four techniques, SEC, DLS, MFI and wNMR, can detect aggregates induced by all three types of stresses. However, each of the conventional techniques display size limits. SEC detected small soluble aggregates but not large micron-sized aggregates. MFI detected large micron-sized aggregates, but not small soluble aggregates. DLS reliably detected smaller aggregates, but displayed uneven performance in detecting large micron17 ACS Paragon Plus Environment


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sized aggregates. In contrast, wNMR consistently and reliably detected aggregates, both small and large, suggesting aggregates of very different sizes contribute to the observed value of R2(1H2O). Moreover, after the removal of all subvisible particles by two sequential filtrations, the values of R2(1H2O) observed for three stresses differed from each other (Fig. 1) with the amount of soluble aggregates as detected by SEC being similar (Table 1), one might infer that the response of R2(1H2O) to smaller/soluble aggregates also depends on their structure and/or size. Possible mechanisms underlying the sensitivity of R2(1H2O) toward solute association in aqueous solutions were extensively discussed in our previous papers.25,26 Such sensitivity mainly comes from changes in the protein-water proton exchange capacity as a result of protein aggregation.25 It has been shown recently that due to ultrafast proton transfer between water molecules within the network of hydrogen bonds in bulk water36 such differences could easily propagate over a large population of water molecules, thus affecting the observed water proton transverse relaxation rate R2(1H2O). Another mechanism could involve the formation of water-filled compartments inside the protein aggregates. The diffusion exchange effects between water molecules in such compartments and bulk water result in local magnetic field gradients and an increase of R2(1H2O).37 A more detailed insight into this mechanism will be a subject of our further investigations. CONCLUSION The water transverse relaxation rate R2(1H2O) can serve as a sensitive tool to detect the presence of protein aggregates induced by common physical stresses. It can consistently detect both small and large aggregates and subvisible particles. Further, wNMR measurements with a desktop spectrometer involve no sample handling or sample consumption. These features, absent in all other existing analytical techniques, suggest wNMR could be a powerful tool for analyzing protein aggregates in biologics. ACKNOWLEDGEMENTS Dynamic light scattering experiments were conducted at Light Scattering Center, University of Maryland (College Park). The financial support of MedImmune within the framework of collaborative program between MedImmune and University of Maryland Baltimore is gratefully acknowledged. SUPPORTING INFORMATION. Raw data on particle counts in aggregated monoclonal antibody from micro-flow imaging experiments within the range from 1 µm to 25 µm, brief description of water proton transverse relaxation measurements using low-field benchtop NMR instrument, as well as the raw results of the measurements. File name: SI_Yu.docx.


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REFERENCES (1)

Shire, S. J.; Shahrokh, Z.; Liu, J. J. Pharm. Sci. 2004, 93,1390–1402.

(2)

Manning, M. C.; Chou, D. K.; Murphy, B. M.; Payne, R. W.; Katayama, D. S. Pharm. Res. 2010, 27, 544–575.

(3)

Rizzo, J. M.; Shi, S.; Li, Y.; Semple, A.; Esposito, J. J.; Yu, S.; Richardson, D.; Antochshuk, V.; Shameem, M. J. Pharm. Sci. 2015, 104, 1632–1640.

(4)

Kim, J. H.; Joshi, S. B.; Esfandiary, R.; Iyer, V.; Bishop, S. M.; Volkin, D. B.; Middaugh, C. R. J. Pharm. Sci. 2016, 105, 1028–1035.

(5)

Terada, T.; Takahashi, T.; Arikawa, H.; Era, S. Biosci. Biotechnol. Biochem. 2016, 80, 1356–1361.

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Torosantucci, R.; Schöneich, C.; Jiskoot, W. Pharm. Res. 2014, 31, 541–553.

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Fathallah, A. M.; Chiang, M.; Mishra, A.; Kumar, S.; Xue, L.; Middaugh, C. R. J. Pharm. Sci. 2015, 104, 3691–3702.

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Neergaard, M. S.; Nielsen, A. D.; Parshad, H.; van de Weert, M. J. Pharm Sci. 2014, 103, 115– 127.

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Kayser, V.; Chennamsetty, N.; Voynov, V.; Forrer, K.; Helk, B.; Trout, B. L. Biotechnol. J. 2011, 6, 38–44.

(10) Saito, S.; Hasegawa, J.; Kobayashi, N.; Tomitsuka, T.; Fukui, K. Pharm. Res. 2013, 30, 1263– 1280. (11) D’Addio, S. M.; Bothe, J. R.; Neri, C.; Walsh, P. L.; Zhang, J.; Pierson, E.; Mao, Y.; Gindy, M.; Leone, A.; Templeton, A. C. J. Pharm. Sci. 2016, 105, 2989–3006. (12) Goldberg, D.; Bishop, S. M.; Shah, A. U.; Sathich, H. A. J. Pharm. Sci. 2011, 100, 1306–1315. (13) Tian, X.; Nejadnik, M. R.; Baunsgaard, D.; Henriksen, A.; Rischel, C.; Jiskoot, W. J. Pharm. Sci. 2016, 105, 3366–3375. (14) Snell, J. R.; Zhou, C., Carpenter, J. F.; Randolph, T. W. J. Pharm. Sci. 2016, 105, 3057–3063. (15) Panchal, J.; Kotarek, J.; Marszal, E.; Topp, E. M. AAPS J. 2014, 16, 440–451. (16) Wang, W.; Roberts, C. J. (eds.) Aggregation of Protein Therapeutics; Wiley: Hoboken, 2010. (17)

Carpenter, J. F.; Randolph, T. W.; Jiskoot, W.; Crommelin, D. J. A.; Middaugh, C. R.; Winter, G.; Fan, Y. –X.; Kirshner, S.; Verthelyi, D.; Kozlowski, S.; Clouse, K. A.; Swann, P. G.; Rosenberg, A.; Cherney, B. J. Pharm. Sci. 2009, 98, 1201–1205.

(18) Carpenter, J. F.; Randolph, T. W.; Jiskoot, W.; Crommelin, D. J. A.; Middaugh, C. R.; Winter, G. J. Pharm. Sci. 2010, 99, 2200–2208.


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(19) Arakawa, T.; Philo, J. S.; Ejima, D.; Tsumoto, K.; Arisaka, F. BioProcess Int. 2007, 5, 36–47. (20) US Pharmacopeia. USP/NF general chapter . Measurement of Subvisible Particulate Matter in Therapeutic Protein Injections; United States Pharmacopeial Convention: Rockville, Maryland, 2014. (21) Hamrang, Z.; Hussain, M.; Tingey, K.; Tracka, M.; Casas-Finet, J. R.; Uddin, S., van der Walle, C. F.; Pluen, A. J. Pharm. Sci. 2015, 104, 2473–2481. (22) Kheddo, P.; Cliff, M. J.; Uddin, S.; van der Walle, C. F.; Golovanov, A. P. mAbs 2016, 8, 1245– 1258. (23) Hills, B. P.; Takacs, S. F.; Belton, P. S. Mol. Phys. 1989, 67, 919–937. (24) Metz, H.; Mäder, K. Int. J. Phar. 2008, 364, 170–175. (25) Feng, Y.; Taraban, M. B.; Yu, Y. B. Chem. Commun. 2015, 51, 6804–6807. (26) Taraban, M. B.; Truong, H. C.; Feng, Y.; Jouravleva, E. V.; Anisimov, M. A.; Yu, Y. B. J. Pharm. Sci. 2015, 104, 4132-4141. (27)

Mullard, A. Nature Rev. Drug Discov. 2017, 16, 73-76.

(28) Subramanian, D.; Ivanov, D. A.; Yudin, I. K.; Anisimov, M. A.; Sengers, J. V. J. Chem. Eng. Data 2011, 56, 1238–1248. (29) Berne, B. J.; Pecora, R. Dynamic Light Scattering: With Applications to Chemistry, Biology, and Physics; Dover Publications: Mineola, 2000. (30) Provencher, S. W.; Stepanek, P. Part. Part. Syst. Charact. 1996, 13, 291–294. (31) Chu, B. Laser Light Scattering: Basic Principles and Practice, Academic Press: Boston, 1996. (32) Meiboom, S.; Gill, D. Rev. Sci. Instrum. 1958, 29, 688-691. (33) Barnard, J. G.; Singh, S.; Randolph, T. W.; Carpenter, J. F. J. Pharm. Sci. 2011, 100, 492–503. (34) Khlebtsov, N. G. Colloid J. 2002, 65, 652–655. (35) Fabelinskii, I. L. Molecular Scattering of Light, Plenum Press: New York, 1968. (36) Wolke, C. T.; Fournier, J. A.; Dzugan, L. C.; Fagiani, M. R.; Odbadrakh, T. T.; Knorke, H.; Jordan, K. D.; McCoy, A. B.; Asmis, K. R.; Johnson, M. A. Science 2016, 354, 1131–1135. (37) Hills, B. P.; Wright, K. M.; Belton, P. S. Mol. Phys. 1989, 67, 193–208.


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For TOC Only Freeze-Thaw

-1

1.25

Heating

Agitation

1.20 1.15

1

R2( H2O), s

1.10 1.05

0. 5 µ Stre 45 m s µ m f il s t f er C ilte on r tr ol S 5 0. µ tre 45 m s µ m fil s t f er C ilte on r tr ol 0. 5 µ Stre 45 m s µ m fil s t f er C ilte on r tro l

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Figure 1. Response of the transverse relaxation rate of water R2(1H2O) to the formation of mAb aggregates produced by different stresses and the effect of sequential 5 µm and 0.45 µm filtrations on R2(1H2O). Error bars show standard deviation of the mean calculated by five consecutive measurements of R2(1H2O) for the same solution. 155x131mm (110 x 110 DPI)

ACS Paragon Plus Environment

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Figure 2. Size-exclusion chromatograms reflecting the difference in soluble mAb aggregates produced by different stresses: (a) unstressed control solution; (b) chromatogram of stressed solutions; and (c) enlarged view of the section from (b) inside the black square brackets. 500x162mm (110 x 110 DPI)



Figure 3. Concentrations of subvisible particles from MFI data for nonfiltered and filtered stressed solutions with the unstressed solution as the control: (a) freeze-thaw cycling; (b) heating, 50 °C; and (c) agitation. 523x189mm (110 x 110 DPI)


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Figure 4. DLS analysis of the particle size distributions (PSD) of the mAb aggregates produced by different stresses before and after filtration. Insets show the unstressed control. Data are normalized by the intensity of the highest peak in each PSD. 483x345mm (110 x 110 DPI)



Figure 5. Sensitivity responses resulting from sequential filtrations of stressed mAb solutions using 5 µm and 0.45 µm filters. 536x195mm (110 x 110 DPI)


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Figure 6. Flow-chart of the experimental design and the sensitivity of different analytical techniques towards mAb aggregates induced by different stresses and in different size ranges. References to Figures and Table in red refer to the experimental data supporting each conclusion. F/T: freeze-thaw cycling. 426x410mm (110 x 110 DPI)



TOC Graphic 191x165mm (110 x 110 DPI)


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Water Proton NMR: A Tool for Protein Aggregation ... - ACS Publications

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