Viscosities and Protein Interactions of Bispecific Antibodies and

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Viscosities and Protein Interactions of Bispecific Antibodies and Their Monospecific Mixtures Mahlet Asfaw Woldeyes, Lilian L. Josephson, Danielle L. Leiske, William J. Galush, Christopher J. Roberts, and Eric M. Furst Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.8b00706 • Publication Date (Web): 29 Aug 2018 Downloaded from http://pubs.acs.org on August 31, 2018

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Molecular Pharmaceutics

Viscosities and protein interactions of bispecific antibodies and their monospecific mixtures Mahlet A. Woldeyes1‡, Lilian L. Josephson1†‡, Danielle L. Leiske 2†, William J. Galush2, Christopher J. Roberts1*, Eric M. Furst1* 1

Department of Chemical & Biomolecular Engineering, University of Delaware, Newark, DE

2

Early Stage Pharmaceutical Development, Genentech Inc., A Member of the Roche Group,

South San Francisco, CA.

KEYWORDS: Microrheology, light scattering, monoclonal antibody, bispecific antibody, protein interactions, protein mixtures.

ACS Paragon Plus Environment

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ABSTRACT Solution viscosities (η) and protein-protein interactions (PPI) of three monoclonal antibodies (mAb-A, mAb-B, mAb-C), two bispecific antibodies (BsAb-A/B, BsAb-A/C), and two 1:1 binary mixtures (mAb-A + mAb-B and mAb-A + mAb-C) were measured. mAb-A and mAb-C have similar isoelectric point (pI) values, but significantly different η versus protein concentration (c2) profiles. The viscosity of the mAb-A + mAb-C mixture followed an Arrhenius mixing rule and was identical to viscosity of the bispecific BsAb-A/C. In contrast, mAb-A and mAb-B had similar η versus c2 profiles, but the Arrhenius mixing rule failed to predict the higher viscosities of their mixtures. The viscosity of the bispecific BsAb-A/B followed the 1:1 mAb-A + mAb-B mixture at all concentrations. The nature of the interactions for mAb-A, mAb-B, the BsAb-A/B bispecific and the 1:1 mAb-A + mAb-B mixture were characterized by static and dynamic light scattering (SLS and DLS). MAb-A and mAb-B exhibited net attractive and repulsive electrostatic interactions, respectively. The bispecific antibody (BsAb-A/B) had shortranged attractive interactions, suggesting that the increase in viscosity for this molecule and the mAb-A + mAb-B mixture was due to cross-interactions between Fab regions. At high and low ionic strength and protein concentrations, the Rayleigh scattering profile, the collective diffusion coefficient and viscosity for the mixture closely followed that for the bispecific antibody. These results highlight the possible anomalous viscosity increases of bispecific antibodies constructed from relatively low-viscosity mAbs, but demonstrates a potentially fruitful approach of using mAb mixtures to predict the viscosity of candidate bispecific constructs.


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INTRODUCTION Improvements in genetic engineering, development, processing and manufacturing of biotherapeutics have made monoclonal antibodies (mAbs) a leading source of new pharmaceuticals

1–5

. The next generation of protein based therapeutics aims to further enhance

efficacy and targeting to antigens by several approaches, including dual targeting strategies with bispecific antibodies (BsAbs)6. BsAbs have two distinct antigen binding (Fab) regions, and advances in genetic engineering have resulted in over 60 different antibody formats7–9. BsAbs have been developed with several new capabilities including the ability to: (1) redirect immune cells to tumor cells to enhance tumor killing; (2) block two mediators or pathways simultaneously; and (3) interact with two different surface antigens to increase binding specificity10. Currently, there are more than 30 BsAbs in clinical trials for autoimmune, oncology, or chronic inflammatory indications9. Previous studies of BsAbs have focused primarily on antibody engineering, overcoming synthesis challenges, purification, and production11,12,21,13–20. Due to their recent introduction, in-depth biophysical characterization studies of BsAbs have been more limited than mAbs. In the case of mAbs, studies of protein-protein interactions22–25, solution microstructure26–28, protein-solvent interactions29, and interfacial behavior (adsorption to solid/liquid30,31 or air/liquid32,33 interface) have provided deeper molecular insights and improved approaches for development strategies. As BsAbs approach late-stage development, many issues may arise that are unique to these new antibody formats. Biophysical characterization studies can be greatly time and material consuming, and are typically reserved for molecules that are further in development. In contrast, screening studies are more often used during early development


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phases and must satisfy time and material constraints when transitioning to late stage development. Solution rheology is one key physical property that is often screened during early development and late stage discovery, since downstream processing and manufacturing steps (such as filtration, pumping and filling), as well as final product administration, are substantially affected by solution viscosity34. In early stages of development, protein material is typically scarce, and thus small-scale rheology techniques are advantageous. Unlike the growing literature on mAb viscosity and its molecular basis23,25–28,35–46, to date there is only one study dedicated to characterizing viscosity of a bispecific antibody (DVD-Ig format)47. The BsAb molecule used in that study had a larger molecular weight (~200 kDa) than common monospecific IgG mAbs (~150 kDa), and therefore a larger size and intrinsic viscosity; the effect of molecule size on the viscosity was found to be significant at high concentration47. The present report focuses on bispecific antibodies in comparison with mAbs constructed with the same isotype and molecular weight. Protein interactions are another biophysical characteristic that are important to understand during early stage development. In solutions, proteins can exhibit short and long-ranged attractive and repulsive interactions. Such protein-protein interactions (PPI) influence processes and properties such as crystallization48, liquid-liquid phase separation49, opalescence50, aggregation22,51,52 , and high solutions viscosities38,53. Prior studies focused on the effects of PPI measured at low protein concentration, on the aggregation kinetics54 and viscosity47 of a DVD-Ig using static and dynamic light scattering (SLS and DLS). The results suggested that the dominant contributor to the protein interactions changes based on the antibody concentration. However, these observations may not be applicable to other BsAb molecules as the DVD-Ig has a


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significantly larger molecular weight than the mAb it was compared to, where the effect of molecular size was convoluted with impact that PPI has on the solution viscosity. The present work focuses on the relationship between antibody solution viscosity and molecular interactions of similar mono- and bispecific molecules. Viscosity and protein interactions were measured using multiple particle tracking (MPT) microrheology and light scattering (LS), respectively. MPT has been used to measure the rheological properties of biologics, biological fluids and biomaterials under different conditions, including concentrated genomic DNA solutions55,56, bovine serum albumin and its viscoelastic response with the addition of urea57, protein-layer and network formation of amyloid fibrils,58–60 and highthroughput characterization of mAb solution viscosities57,61,62. The measured viscosity profiles of three monospecific mAbs, two bispecific antibodies BsAbs, and two mAb mixtures are discussed. The viscosity profiles of the mAb mixtures are compared to predictions from the Arrhenius mixture rule, and this is used to infer the cross-interactions of the antibodies in mixture. The two mAbs, and corresponding BsAbs and mAb mixtures that demonstrated strong cross-interactions (viscosity of mixture that deviated the most from Arrhenius mixture) were further studied using SLS and DLS to better understand the contributions from PPI. The resulting Rayleigh scattering profiles, second virial coefficient (B22), diffusion coefficient, and protein interaction parameter (kD) were analyzed in conjunction with the viscosity at a common pH/buffer with low versus high-ionic strength conditions. Interestingly, in all conditions examined in this work, the Rayleigh scattering, the collective diffusion coefficient and viscosity versus concentration profiles for the mAb mixture closely followed the profile of the BsAb, suggesting an approach to using mixtures as predictor for solution behavior of bispecific antibodies during therapeutic antibody development.


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EXPERIMENTAL SECTION Sample Preparation. Three humanized monospecific monoclonal antibodies (mAb-A, mAb-B, mAb-C) and two bispecific antibodies (BsAb-A/B, and BsAb-A/C, produced through the knobinto-hole approach and the co-culture of bacterial strains63) were supplied by Genentech Inc. (South San Francisco, CA) and stored at 4°C. Key protein properties are reported in Table 1. Stock 20 mM histidine formulation buffer was prepared and adjusted to pH 6.0 by combining Lhistidine free-base and L-histidine-HCl (Sigma-Aldrich, St. Louis, MO) and filtered using a bottle-top filter unit prior to use (Nalgene rapid-flow sterile disposable filter units, surfactant-free cellulose acetate membrane, 0.2 µm pore size, Thermo Fisher Scientific, Waltham, MA). High ionic strength buffer (for light scattering experiments) was prepared by gravimetric addition of 150 mM NaCl (Fisher Scientific, Fair Lawn, NJ) to the 20 mM histidine buffer at pH 6.0. The proteins were first buffer-exchanged into the formulation buffers using Slide-A-Lyzer dialysis cassettes (10 kDa molecular weight cut off, Thermo Fisher Scientific). The dialyzed antibody solutions were then concentrated (> 150 mg/ml) using 10 kDa MWCO Amicon-Ultra centrifugal tubes (Millipore) through membrane centrifugation at 3200 relative centrifugal force (RCF). The concentrations of stock solutions were determined from the average of 5 measurements in a Nanodrop 2000c (Thermo Fisher Scientific) at 1:10 dilution factor. The extinction coefficients at 280 nm (A280) used for the Nanodrop measurements are reported in Table 1. The concentrated protein solutions were then diluted volumetrically or gravimetrically with the formulation buffers to achieve concentrations ranging from 1-150 mg/ml.


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Particle tracking microrheology and viscosity analysis. Multiple particle tracking measurements on the protein solutions were taken from each equilibrated sample in capillary tubes (inner dimensions = 2.0 × 0.2 × 50 mm, glass thickness = 0.14 mm, Vitrocom, Mountain Lakes, NJ) sealed with a fast-curing thiolene resin (NOA81, Norland Products, Cranbury, NJ) onto a microscope slide. Fluorescence video microscopy was performed on a Nikon Ti-E perfect focus inverted microscope equipped with a motorized X,Y stage and a high-speed camera (Clara interline CCD camera, Andor, Oxford Instruments, 30 frames per second, σ = 10 ms); the Brownian motion of the probe particles was captured at 40× magnification (ELWD Plan Fluor objective, NA 0.6, Nikon) and at 23 oC. The camera settings are selected so that the static and dynamic error of particle tracking experiments were minimized64 and the time step was constant at the desired frames per second. As each movie was saved, the microscope stage was moved to the next sample and refocused. The image stacks were analyzed in MATLAB (Mathworks Inc., Natick, MA); the probe positions locations were tracked by a brightness-weighted centroid algorithm first developed by Crocker and Grier65, and trajectories were formed by linking probe positions in consecutive video frames. The rheology of the material of interest was extracted from the dynamics of the embedded probe particles. The one-dimensional projection of the ensemble-averaged mean square displacement (MSD), 〈∆ 〉, was calculated from the probe trajectories at each lag time (τ). Plots of the MSD are shown in the Supporting Information (SI). Their linearity confirms that the samples are Newtonian fluids, as expected from the Stokes-Einstein-Sutherland equation, which relates the MSD (projected in one dimension) to the viscosity (η) by

= 3〈∆ 〉

(1)


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where a is the probe radius and kT is the thermal energy. The sample viscosity was calculated by fitting the corresponding one-dimensional probe particle van Hove self-correlation function

− , = 2〈Δ 〉/ exp 2〈Δ 〉

(2)

at lag times between 0.08-0.78 sec. The optimal lag time of each fit was selected using a method that maximizes the number of the displacement measurements while minimizing the particle tracking error66. A detailed description of the particle tracking trajectories and statistics using the van Hove self-correlation function and excess kurtosis and its Z-statistic parameter, are described in previous publications62,66. PEGylation of Tracer Particles. Fluorescently labeled tracer particles were obtained from Molecular Probes (Eugene, OR); the surface of the carboxylate-modified polystyrene (PS-CML) (2a = 1.0 ± 0.025 µm, 2% solids) particles were chemically grafted with poly(ethylene glycol) (PEG) through a carbodiimide reaction67. The PS-CML particles (500 µL) were first washed with Milli-Q water (resistivity 18.2 MΩ.cm, Millipore, Billerica, MA) three times, and then dispersed in 50 mM MES with 0.05% Proclin 300 at pH 5.2 (Polylink coupling buffer, Polysciences, Inc, Warrington, PA). A 100 µL solution of 200 mg/mL of 1-ethyl-3-(-3dimethylaminopropyl) carbodiimide (EDAC, Polysciences) was made in the coupling buffer; 50 µL of this activation solution was added to the particle suspension immediately and the mixture was incubated at room temperature under rotation for 15 minutes. The supernatant in the probeEDAC mixture was discarded, and the probe particles were immediately re-suspended in the amine-modified PEG solution, which was made by dissolving 100 mg of methyl-PEG-amine (MW=5000 or 10000, Creative PEGWorks, Winston-Salem, NC) in 500 µL of 200 mM PBS buffer at pH 7.0. The reaction proceeded for at least 8 hours at room temperature, under rotation


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and shielded from light. Excess EDAC, isourea by-products, and unreacted methyl-PEG-amine were removed by washing the reacted probes in solution at least 3 times in Milli-Q water. The resulting PEGylated polystyrene (PS-PEG) particles were sonicated for 20 minutes and observed under a microscope to verify their colloidal stability in water and the formulation buffer. The stock solutions of the probes were concentrated to approximately 10% w/v, so only a small volume (0.2 µL) of probes was added to each 20 µL sample, resulting in a final particle concentration of approximately 0.1% w/v. Microrheology requires the probe particles to be well dispersed in the samples. Through microscopy imaging, MPT provides immediate visual confirmation of the particle stability and dispersion. The PS-PEG probe particles used in this study were well dispersed and stable in all five antibodies and conditions examined. Static light scattering. Static light scattering (SLS) experiments were conducted using a Wyatt DynaPro NanoStar (Wyatt, Santa Barbara, CA) instrument with laser wavelength of 658 nm and temperature maintained at 25°C to quantify protein-protein interactions (PPI). In SLS, the average scattered intensity at a given angle can be determined and used to calculate the excess Rayleigh scattering, "#! , as previously reported68,69, by

where

/0 ,-.

1

"#! = &',()) * − 2& + * %

(3)

is excess Rayleigh scattering at 90o, Mw,app and M2 are the apparent and true molecular

weight of the protein, * is concentration of protein, and B22 is the osmotic second virial coefficient. At high protein concentrations, the zero-q limit structure factor (Sq=0) can be calculated using

234! = 1 + * =

"#! %& *

(4)


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The protein-protein Kirkwood-Buff integral is related to B22, G22 = -2B22, in the limit of dilute concentrations or sufficiently weak PPI68. B22 and Sq=0 quantify deviations from ideality caused by PPI in dilute and concentrated protein conditions, respectively. In that case, a positive value of B22 or Sq=0 < 1 (negative value of B22 or Sq=0 > 1) corresponds to net repulsive (attractive) interactions. Dynamic Light Scattering. Dynamic light scattering (DLS) experiments were conducted simultaneously with SLS at a 90 ͦ scattering angle. The intensity autocorrelation function, 7 8, was used to calculate the collective diffusion coefficient (DC) and polydispersity using the methods of cumulants70. All samples measured had polydispersity below 0.2, consistent with no measurable aggregate formation. Molecular diffusion is impacted by contributions from hydrodynamic as well as thermodynamic interactions69,71. This relation is shown in Eq.5 where, D0 is the infinite-dilution diffusion coefficient and Hq=0 is the hydrodynamic factor.

9: =

9! ;34! 234!

(5)

By combining results from SLS and DLS, Hq=0 can be inferred as a function of c2. The collective diffusion coefficient is related to the protein interaction (kD) parameter via a series expansion of Eq.5 as a function of the protein concentrations in the dilute regime. At dilute conditions25,

9: = 9< 1 + = * .

(6)

RESULTS Solution Viscosities of Antibodies. Multiple-particle tracking microrheology (MPT) was used to measure the viscosity of the three monospecific mAbs (A, B, C) and two bispecific antibodies


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(BsAb-A/B, BsAb-A/C) and 1:1 binary mixtures (mAb-A + mAb-B and mAb-A + mAb-C) in 20 mM histidine chloride at pH 6. In Fig. 1A the viscosity is plotted as a function of the concentration of mAb-A, mAb-C, BsAb-A/C, as well as 1:1 mixtures of (mAb-A + mAb-C). The corresponding mean squared displacements (MSD) are shown in Fig. S1. Of the three protein molecules, mAb-C has the highest viscosity (23.9 ± 2.1 mPa-s) at 150 mg/mL, as well as the sharpest rise in viscosity as concentration increases. It requires only 100 mg/mL of mAb-C to reach 5 mPa-s in viscosity, whereas mAb-A requires a concentration of ~130 mg/mL to reach the same viscosity. This suggests a fundamental difference in protein solution behavior for mAb-A and mAb-C. MAb-C and mAb-A have similar isoelectric points (pI = 6.95 and 6.25, respectively), yet they exhibit notably different viscosity behavior as a function of concentration. For an ideal mixture, the viscosity of the antibody mixtures can be calculated using Arrhenius's mixture model, which states that the logarithm of the viscosity of a mixture (ηmix) varies linearly in proportion to its components72,73:

ln AB# = C B ln B

(7a)

Thus, for a 1:1 binary mixture between component A and B,

ln AB# = D ln D + E ln E

(7b)

with xA = xB = 0.5, essentially giving a mixture viscosity that is the geometric mean of the individual mAb viscosities. In the present case, eq.7b was used to predict the mAb mixture viscosity from the measured viscosity of the individual mAbs and compared to the viscosity of the mixtures measured using MPT. Although mAb-A and mAb-C apparently have different underlying interactions that lead to different viscosity behavior for the mono-protein solutions, the Arrhenius mixture relation


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describes their mixture viscosity well (see Fig. 1A). There is no appreciable difference between the measured viscosity of the 1:1 mixture of mAb-A + mAb-C and the expected viscosity of the mixture calculated by eq.7 (shown with solid line in Fig. 1A). The viscosity of the bispecific antibody (BsAb-A/C) also tracks closely with the mixture. These observations suggest that mAbA and mAb-C do not exhibit cross-interactions that are significantly different from their own (like-like) intermolecular protein-protein interactions. The viscosity for the second set of proteins (mAb-A, mAb-B, and BsAb-A/B) are shown in Fig. 1B. The corresponding mean squared displacements (MSD) are shown in Fig. S2. Although mAb-A and mAb-B have significantly different isoelectric points (pI = 6.25 and 9.25, respectively), the two molecules exhibit nearly identical viscosity profiles as a function of concentration. The Arrhenius mixing rule (Eq.7) predicts a quantitatively similar viscosity profile for the mixture (1:1 mAb-A + mAb-B) (solid line in Fig. 1B) as compared to mAb-A and mAb-B, but the microrheology results show that the viscosity of the physical mixture is significantly greater than that of the single species solutions for all concentrations above ~100mg/ml. The bispecific (BsAb-A/B) also shows a similarly higher viscosity profile for all concentrations above 100 mg/ml. These observations suggest that mAb-A and mAb-B have stronger cross-interactions, which leads to higher viscosity of the mixture. Additionally, by combining mAb-A and mAb-B into a single bispecific antibody, the anisotropic charge distribution on the two arms of the antibody due the difference in pI of these mAbs presumably cause similar cross interactions between molecules, resulting in a significant increase in viscosity of BsAb-A/B compared to the single species. In an attempt to better understand the nature of the protein interactions that


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resulted in strong cross-interaction between these two mAbs in the binary mixture as well as BsAb, light scattering experiments were conducted.

Protein-Protein Interactions of Strongly Cross-interacting Antibodies. Static and dynamic light scattering (SLS and DLS) were used to determine the excess Rayleigh scattering and collective diffusion coefficient, respectively, as a function of protein concentration for mAb-A, mAb-B, BsAb-A/B, and mAb-A + mAb-B mixtures in 20 mM histidine chloride at pH 6.0 for low (0 mM added NaCl) and high (150 mM added NaCl) ionic strength. The higher NaCl concentration was included in the LS analysis to examine the potential role of electrostatic interactions in the net PPI of these antibodies.

Protein-protein interactions at low protein concentrations. The excess Rayleigh scattering at 90° scattering angle

/0 ,-.

1

was determined as a function of low concentration for the two

monospecific, one bispecific antibody, and the mAb-A + mAb-B mixtures; the data is plotted in Fig. 2. Experimental data are shown with symbols and lines corresponding to the model fits to Eq.3. At low ionic strength (Fig. 2A) the antibodies have different Rayleigh scattering profiles at the same pH. For instance, the upward curvature for

/0 ,-.

1

versus concentration of mAb-A and

BsAb-A/B indicate net-attractive interactions, while mAb-B exhibits a slight downward curvature, indicating net-repulsive PPI. At high ionic strength (Fig. 2B), the excess Rayleigh scattering profile collapses to similar values for mAb-A and mAb-B, demonstrating the charge screening effect when the ionic strength is increased. At both high and low ionic strength, the Rayleigh scattering profile for the mixture closely follows that of the bispecific antibody. The collective diffusion coefficient (DC) determined from the DLS data (see Supporting Information)


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have similar trends for the three antibodies and the mixture. At low ionic strength, DC increases with increasing concentration for mAb-B, and suggests net-repulsive protein-protein interactions. DC decreases with increasing concentration for mAb-A, BsAb-A/B and, the 1:1 mixture, consistent with net-attractive interactions. At high ionic strength, DC decreases with increasing protein concentration for all cases. Additionally, the collective diffusion coefficient for the 1:1 mAb-A + mAb-B mixture tracks closely to that of the BsAb-A/B, suggesting that the mixture and bispecific exhibit similar net interactions. The protein interactions at low protein concentration were quantified using Eq.3 and Eq.6 for SLS and DLS, respectively, and the results are shown in Fig. 3. The protein-protein interactions (PPI) parameter values from Eq.3 (the osmotic second virial coefficient, B22) for the three antibodies at low and high ionic strength are shown in Fig. 3A, while the protein interactions parameter (kD) values from DLS are shown in Fig. 3B. The apparent molecular weight and the infinite dilution diffusion coefficient parameter were also obtained from the fit to Eq.3 and Eq.6, respectively, and are shown in Fig. S4. At low ionic strength, the results in Fig. 3A demonstrate that mAb-B results in a positive B22 value that is similar, within statistical significance, to the B22-steric value attained from Monte Carlo molecular simulations74 (dashed line in Fig. 3A). This suggests net repulsive proteinprotein interactions that are not significantly different from steric repulsions. On the other hand, both mAb-A and BsAb-A/B have moderately attractive PPI relative to steric repulsions. The arrows in Fig. 3A indicate the change upon the increase in ionic strength, illustrating a change in the magnitude and sign of the net PPI for each case. No arrow is shown for BsAb-A/B because the low and high ionic strength values are not statistically different. At high ionic strength, all three antibodies have attractive PPI relative to steric repulsion. It is interesting to note that mAb-


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B goes from repulsive to attractive PPI, and mAb-A has a decrease in the magnitude of attractive PPI. However, BsAb-A/B showed no significant change in PPI with increase in ionic strength, but the magnitude of attraction between molecules is larger (more negative B22) than either mAbA or mAb-B at low or high ionic strength. The analogous data from DLS is shown in Fig. 3B, the results of which support the conclusions from SLS (Fig. 3A), where net-repulsive interactions are found at low ionic strength for mAb-B (positive kD), and the other two antibodies show negative kD values. Similarly, negative kD values were found in all cases for the high ionic strength conditions, and kD was independent of ionic strength for BsAb-A/B. Protein-protein interactions at high protein concentrations. At high concentration, PPI is dominated by short-ranged and multi-body intermolecular interactions, which have been hypothesized to play a role in determining protein solution viscosities75,76. Therefore, LS experiments were also conducted at high protein concentrations spanning the conditions in Fig. 2. The results are shown in Fig. 4 and Fig. 5 for SLS and DLS, respectively. The excess Rayleigh scattering profiles for the three antibodies and the mixture, along with a reference for the Rayleigh scattering profile of a steric-only coarse-grained mAb model74 are shown in Fig. 4. Similar to what was observed at low concentrations, at low ionic strength mAb-B displays netrepulsive PPI that are comparable to steric repulsions. mAb-A, BsAb-A/B, and mAb-A + mAb-B 1:1 mixture each display net-attractive PPI, relative to steric-only behavior. However, at a concentration ~50 mg/mL a crossover between mAb-A and the bispecific (and mixture) is observed. That is, at concentrations above 50 mg/ml mAb-A scattered more light than BsAb-A/B as seen by the higher

FGH -. I

value of mAb-A. This corresponds to having greater attractions

between mAb-A molecules compared to attractions between BsAb-A/B molecules.


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At high ionic strength (Fig. 4B), the Rayleigh scattering profile for mAb-A was lower than that observed at low ionic strength (Fig. 4A), which implies that screening of attractive electrostatic interactions occurs at both low and high protein concentrations. In contrast, mAb-B exhibited an increased Rayleigh scattering profile as ionic strength increased, suggesting the presence of repulsive electrostatic interactions. Rayleigh scattering profiles for BsAb-A/B were not significantly impacted by the change in ionic strength (see Supporting Information), suggesting that short-ranged non-electrostatic attractive interactions play a significant role in the net PPI of BsAb-A/B. For each antibody species, the excess Rayleigh scattering had a highly non-linear dependence with protein concentration; there is a maximum in the excess Rayleigh ratio versus concentrations (cf. Fig. 4). This maximum is at least partially attributed to an increase in repulsive interactions at elevated protein concentrations from molecular crowding or excluded volume effects77. The corresponding zero-q limit structure factor (Sq=0) results were calculated from the excess Rayleigh scattering using eq. 4 and are shown in the Supporting Information as they do not lead to different conclusions from the Rex versus c2 results. The DLS results in Fig. 5 show the collective diffusion coefficient (DC) as a function of high protein concentrations. Fig. 5A shows DC increases with increasing concentration initially for mAb-B, suggesting net-repulsive protein-interactions, then decreases after a maximum value at ~75 mg/ml. This turnover is due to the competing effects of thermodynamic and hydrodynamic interactions as shown by the relation in eq. 576. DC decreases with increasing concentration for mAb-A, BsAb-A/B and the binary mixture suggest net-attractive protein-interactions in each case. At high ionic strength (Fig. 5B), all antibodies and the mixture show a decrease in DC with increasing concentrations indicating net-attractive protein interactions. The cross-over at ~50 mg/ml between mAb-A and BsAb-A/B (and 1:1 mAb-A/mAb-B mixture) at low ionic strength,


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is again observed with the DLS results. The origin of this crossover (or switch) is not yet well understood. As mentioned in the Methods section, the results from SLS and DLS can be combined to calculate the zero-q limit hydrodynamic factor (Hq=0) as a function of concentration using eq. 5. The Hq=0 versus concentration profiles for all antibodies at low and high ionic strength are shown in the Supporting Information. The hydrodynamic factor decreased with increasing concentration at low ionic strength for mAb-A and mAb-B and all proteins at high ionic strength (Fig. S6 CD). The initial increase in Hq=0 with concentration for BsAb-A/B (and mixture) at low ionic strength illustrates the balance between hydrodynamic and thermodynamic interactions under attractive solution conditions.78–80 This qualitative behavior was previously observed in βlactoglobulin and was attributed to the formation of short-lived transient cluster/aggregates formed under attractive conditions78.

DISCUSSION Reviewing the experiments discussed in the previous section, there are three principal issues to consider: (1) the viscosities of the monospecific mAbs A, B, and C; (2) the viscosities of the mAb binary mixtures (1:1 of mAb-A + mAb-B and 1:1 of mAb-A + mAb-C) and their corresponding bispecifics (BsAb-A/B, BsAb-A/C); and (3) the possible underlying proteinprotein interactions that could account for similarities and differences between the mixtures and bispecific molecules. With respect to the solution viscosities of the three monospecific molecules (cf. Fig. 1), the pI values (alternatively protein net-charge) is not a good predictor of the viscosity increase. MAb-A and mAb-B have a distinctly different pI values (6.25 and 9.25, respectively), but the viscosity


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change with protein concentration for these mAbs was not significantly different. On the other hand, mAb-A and mAb-C, with similar pI values of 6.25 and 6.95, respectively, had significantly different concentration dependence of the solution viscosity. The measured viscosities of the 1:1 mixtures of the monospecific mAbs were equivalent to the BsAb viscosity for both BsAb-A/B and BsAb-A/C. Additionally, the Arrhenius mixing rule was used to predict the viscosity of the mixtures as a surrogate for predicting the viscosity of BsAbs, using the measured viscosity of the mAbs. Previous work72 tested this simple mixing relation on three different mAbs, and found that the Arrhenius rule predicted the viscosity of the antibody mixtures across different concentrations and compositions. Despite their own complex viscosity behavior (especially at high concentrations), combinations of those proteins agreed well with eq. 7. It may be that the IgG1 antibody molecules used in the prior study do not experience crossinteractions that are fundamentally different from their own protein-protein interactions, similar to the mAb-A and mAb-C mixture in this study. In the case of the mAb-A + mAb-B mixture, significantly different intramolecular interactions could give rise to a mixture viscosity that deviates from the predictions of the Arrhenius mixing relation (eq. 7). Such deviations have been observed in mixtures of organic solvents81,82. Further analysis of the viscosity of the mAb-A + mAb-B mixture supports the hypothesis that strong cross-interactions occur between the molecules. While the Arrhenius mixing equation could predict the measured viscosity of the 1:1 mAb-A and mAb-C mixture based on measurements of the single-component solutions, it failed in the case of the 1:1 mAb-A and mAb-B mixture. We find that by including a term that accounts for cross-interactions in eq. 7, leading to a modified form of the Grunberg-Nissan model83, ln AB# = D ln D + E ln E + D E * J

(8)


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the mixture viscosity can be predicted with reasonable agreement from measurements of the single component solutions. (See the supporting information for a discussion of the modified Grunberg-Nissan equation that was used to empirically model the mixture viscosity of these cross-interacting mAbs.) The protein-protein interactions of the mAb-A and mAb-B antibodies and their mixture were characterized using SLS and DLS to better understand the cross-interactions between that lead to the increase in viscosity of BsAb-A/B. The osmotic virial coefficient (B22) and the protein interaction parameter (kD) shown in Fig. 3 are consistent with intermolecular interactions for mAb-B that are dominated by repulsive electrostatic interactions at low ionic strength and are screened by the addition of salt. The decrease in attractive interactions observed with increased ionic strength for mAb-A suggest an anisotropic charge distribution (long ranged attractive electrostatic interactions) plays a role in the overall PPI. However, despite the difference in netcharge between mAb-A and mAb-B, and the sensitivity to salt for the single component solution PPIs, BsAb-A/B interactions did not change significantly with ionic strength, suggesting that non-electrostatic attractions play a predominant role. Based on the low-concentration light scattering experiments, the correlation between viscosity and interactions is not clear. The unusual trends in the viscosity profiles of mAb-A + mAb-B mixture and BsAb-A/B (Fig. 1B) compared to the monospecific antibodies, are not explained by the trends of the B22 or kD interaction parameters (Fig. 3). At low ionic strength, both mAb-A and BsAb-A/B have moderately attractive PPI, but only BsAb-A/B solutions ultimately produce a much larger change in viscosity with increasing concentration. In contrast, mAb-A and mAb-B have similar viscosity profiles, but significantly different protein-protein interactions.


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At high concentrations, intermolecular interactions are more greatly influenced by shortranged pairwise and multi-body interactions. The net-PPI measured at low concentrations qualitatively predicted the protein interactions at higher concentrations. However, an interesting change in the scattering behavior was observed between mAb-A and BsAb-A/B, where the scattering intensity was significantly higher for mAb-A above 50 mg/ml (Fig. 4A). Although, the reason for the crossover is not fully understood, it can possibly indicate intermolecular interactions that result in the higher viscosity observed for BsAb-A/B at high concentrations. Additionally, the increase of the hydrodynamic factor below 25 mg/ml for BsAb-A/B (Fig. S7) can potentially be attributed to the presence of short-lived transient clusters present under attractive conditions80. Transient clusters have been interpreted to have a radius of gyration slightly greater than the monomer, Rg ≈ 7 nm75. For example, Godfrin et al.27 observed a strong correlation between the zero-shear viscosity (i.e. the viscosity measured in microrheology) and the characteristic length scale L of the microstructures that were inferred from q-dependent scattering, η ~ L3. This scaling is consistent with that for semi-dilute polymer solutions28 and particulate suspensions84. At the highest concentrations of BsAb-A/B, a characteristic length scale of L ≈ 21 nm is estimated using this scaling relationship. The time scale (~10 ns) and length scale (~10 nm) of those transient structures27,75 are outside the capabilities of the techniques employed to study the antibodies in this work.

CONCLUSIONS In this work, we reported solution viscosities (η) and protein-protein interactions (PPI) of three monoclonal antibodies (mAb-A, mAb-B, mAb-C), two bispecific antibodies (BsAb-A/B, BsAbA/C), and two 1:1 binary mixtures (mAb-A + mAb-B and mAb-A + mAb-C). Our experiments


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showed that mAb mixtures can exhibit non-Arrhenius mixing behavior. While the results of this study are inconclusive with respect to the mechanism for the anomalous viscosity increase of BsAb-A/B and mixtures of mAb-A + mAb-B, our work does suggest at least an empirical strategy for determining if a given bispecific will have an unusual change in viscosity, by measuring their monospecific mixtures. This can be done before engineering a bispecific antibody. In-depth studies might not be suitable for a protein molecule with a rapid development timeline, but the capability to screen for and discover unusual viscosity behavior using small sample rheology measurements is important in early development efforts. In this respect, the microrheology experiments used in this study require small volumes (on the order of 1 µL), short acquisition times (on the order of seconds), and can probe multiple frequencies simultaneously. These capabilities permit high-throughput characterization of scarce materials57,61,62, which fills a need for small volume rheology in early pharmaceutical development stages. ASSOCIATED CONTENT Supporting Information. The supporting information includes: mean-squared displacement figures for mAb-A, B, C and BsAb-A/C and BsAb-A/B; comparison of viscosity, Rayleigh scattering and diffusion coefficient of BsAb-A/B at low and high ionic strength; diffusion coefficients at low concentration; low concentration fit parameters from SLS (apparent molecular weight) and DLS (infinite dilution diffusion coefficient) for mAb-A, B and BsAbA/B; zero-q limit structure and hydrodynamic factors for mAb-A, B and BsAb-A/B; viscosity model for cross-interacting mAb mixtures. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION


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Corresponding Author * EMF: Tel 302-831-0102, fax 302-831-1048, e-mail [email protected]; CJR: Tel 302-831-0838, fax 302-831-1048, e-mail [email protected] Present Addresses †LLJ: Ecolab, Eagan, MN 55121; DLL: Seattle Genetics, Bothell, WA 98021 Author Contributions ‡These authors contributed equally. ACKNOWLEDGMENT Genentech is acknowledged for donating material and supporting this work. Support from the Biomolecular Interaction Technologies Center (BITC) and the National Science Foundation (NSF GRF to M.A. Woldeyes) is gratefully acknowledged. Wyatt Technologies is also acknowledged for loaning equipment used in this work. REFERENCES (1)

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Mewis, J.; Wagner, N. J. Colloidal Suspension Rheology; Cambridge University Press: Cambridge, 2011.

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Ross, P. D.; Minton, A. P. Hard Quasispherical Model for the Viscosity of Hemoglobin Solutions. Biochem. Biophys. Res. Commun. 1977, 76 (4), 971–976.

Table of Contents Graphic

Table 1. Properties of antibodies and a simplified schematic of the structures of mAb-A, mAb-B, as well as an illustrative diagram of the synthesis and final structure of BsAb-A/B based on the knob-in-hole bispecific format. Molecule

MW

Ext. Coefficient

Theoretical pI

(kDa)

(cm2/mg)

mAb-A

145.0

1.430

6.25

mAb-B

145.2

1.540

9.25

mAb-C

144.9

1.353

6.95

BsAb-A/B

145.2

1.528

7.40

BsAb-A/C

144.9

1.430

6.45


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Figure 1. Viscosity as a function of protein concentration for (A) mAb-A (black squares), mAbC (purple circles), mAb-A + mAb-C mixture (blue downward triangles) and BsAb-A/C (green upward triangles) (B) mAb-A, mAb-B (red circles), mAb-A + mAb-B mixture (orange diamonds) and BsAb-A/B (blue triangle) formulated in 20 mM histidine chloride, pH 6.0, at 23°C. The lines between data points are fit to the modified Mooney equation73,85 with the values of fit parameters shown in Table S1. The solid purple lines indicate the predicted binary mixture viscosity by the Arrhenius mixing rule (Eq.7).


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Figure 2. Low protein concentrations excess Rayleigh scattering as a function of protein concentrations with solid lines corresponding to fit to Eq.3 for mAb-A (black squares), mAb-B (red circles), BsAb-A/B (blue triangles), and mAb-A + mAb-B mixture (orange diamonds) formulate in 20 mM histidine chloride at pH 6.0 with (A) 0 mM NaCl and (B) 150 mM NaCl.


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Figure 3. Protein interaction parameters from SLS and DLS for mAb-A, mAb-B, and BsAb-A/B formulate in 20 mM histidine chloride at pH 6.0 with 0 mM (blue circles) and 150 mM NaCl (red rectangles). (A) Osmotic second virial coefficient (B22) with black dashed line corresponding B22-steric value attained from Monte Carlo molecular simulations. (B) Protein interaction parameter (kD). Error bars correspond to 95% confidence levels for fitted parameters.


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Figure 4. Excess Rayleigh scattering as a function of protein concentration for mAb-A (black squares), mAb-B (red circles), BsAb-A/B (blue triangles), and mAb-A + mAb-B mixture (orange diamonds) formulate in 20 mM histidine chloride at pH 6.0 with (A) 0 mM NaCl and (B) 150 mM NaCl. Purple dashed line corresponds to excess Rayleigh scattering for steric-only coarsegrained mAb model74.


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Figure 5. Collective diffusion coefficient as a function of protein concentration for mAb-A (black squares), mAb-B (red circles), BsAb-A/B (blue triangles), and mAb-A + mAb-B mixture (orange diamonds) formulate in 20 mM histidine chloride at pH 6.0 with (A) 0 mM NaCl and (B) 150 mM NaCl.


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Figure 1A 272x208mm (300 x 300 DPI)



Figure 1B 272x208mm (300 x 300 DPI)


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Table of Content Graphics 88x35mm (300 x 300 DPI)


Viscosities and Protein Interactions of Bispecific Antibodies and

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