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Biophysical and sequence-based methods for identifying monovalent and bivalent antibodies with high colloidal stability Magfur E Alam, Steven B. Geng, Christian Bender, Seth D Ludwig, Lars Linden, Rene Hoet, and Peter M Tessier Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/acs.molpharmaceut.7b00779 • Publication Date (Web): 20 Nov 2017 Downloaded from http://pubs.acs.org on November 22, 2017

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Molecular Pharmaceutics

Biophysical and sequence-based methods for identifying monovalent and bivalent antibodies with high colloidal stability Magfur E. Alam1,*, Steven B. Geng1,*, Christian Bender3, , Seth D. Ludwig1, Lars Linden4, Rene Hoet3 and Peter M. Tessier1,2,† 1 Isermann Department of Chemical & Biological Engineering, Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA 2 Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA 3 Bayer AG, ,Pharmaceuticals, Nattermannallee 1, Cologne, 50829, Germany 4 Bayer AG, Pharmaceuticals, Aprather Weg 18A Wuppertal, 42117, Germany

In vitro antibody discovery and/or affinity maturation are often performed using antibody fragments (Fabs), but most monovalent Fabs are reformatted as bivalent IgGs (monoclonal antibodies, mAbs) for therapeutic applications. One problem related to reformatting antibodies is that the bivalency of mAbs can lead to increased antibody self-association and poor biophysical properties (e.g., reduced antibody solubility and increased viscosity). Therefore, it is important to identify monovalent Fabs early in the discovery and/or optimization process that will display favorable biophysical properties when reformatted as bivalent mAbs. Here we demonstrate a facile approach for evaluating Fab self-association in a multivalent assay format that is capable of identifying antibodies with low self-association and favorable colloidal properties when reformatted as bivalent mAbs. Our approach (self-interaction nanoparticle spectroscopy, SINS) involves immobilizing Fabs on gold nanoparticles in a multivalent format (multiple Fabs per nanoparticle) and evaluating their selfassociation behavior via shifts in the plasmon wavelength or changes in the absorbance values. Importantly, we find that SINS measurements of Fab self-association are correlated with self-interaction measurements of bivalent mAbs and are useful for identifying antibodies with favorable biophysical properties. Moreover, the significant differences in the levels of self-association detected for Fabs and mAbs with similar frameworks can be largely explained by the physiochemical properties of the complementarity-determining regions (CDRs). Comparison of the properties of the CDRs in this study relative to approved therapeutic antibodies reveals several key factors (net charge, fraction of charged residues and presence of self-interaction motifs) that strongly influence antibody self-association behavior. Increased positive charge in the CDRs was observed to correlate with increased risk of high self-association for the mAbs in this study and as well as for clinicalstage antibodies in general. We expect that these findings will be useful for improving the development of therapeutic antibodies that are well suited for high concentration applications. Keywords: mAb, solubility, viscosity, formulation, AC-SINS, developability. Running title: Self-association analysis of antibodies and antibody fragments *

equal contribution

†

corresponding author ([email protected])

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INTRODUCTION Several attractive properties of antibodies – namely their high affinity, specificity, bifunctionality, stability and low toxicity – have led to widespread interest in using these molecules for therapeutic applications.1-3 Lead antibodies are typically identified as full-length monoclonal antibodies (mAbs) via immunization or as antigen-binding fragments (Fabs) via in vitro methods such as phage, yeast or ribosomal display.4-9 mAbs identified via either in vitro or in vivo methods commonly possess affinities that are not high enough for therapeutic applications, and these antibodies are typically further affinity matured in the Fab format using in vitro display methods.10, 11 The fact that these antibody discovery and affinity maturation methods require one or more reformatting steps between monovalent Fabs and bivalent mAbs raises questions about potential differences between their various properties. It is well established that avidity effects typically lead to higher apparent binding affinity for bivalent mAbs than monovalent Fabs.12 Antibody valency can also significantly impact the biophysical properties of antibodies (e.g., solubility and viscosity).13-17 The bivalent nature of mAbs has been suggested to facilitate the formation of extended mAb networks in concentrated antibody solutions and lead to abnormally high viscosity.13, 15 18-21 Indeed, the viscosity of associative antibodies can be significantly higher for mAbs than Fabs at equivalent mass concentrations.15 The effect of valency on antibody solubility can also be significant, as some associative antibodies are much more soluble as monovalent Fabs than as bivalent mAbs.14 These findings highlight that the biophysical properties of monovalent Fabs and bivalent mAbs can be significantly different, which emphasizes the need for methods that can evaluate the biophysical properties of Fabs in a manner that is indicative of their properties when reformatted as bivalent mAbs. Here we posit that a potential solution to this problem is to present multiple Fabs in close proximity via adsorption on gold nanoparticles and then to evaluate the colloidal interactions between the resulting Fab-gold conjugates. We reason that colloidal interactions between multivalent Fab-nanoparticle conjugates will be correlated with selfinteractions for the corresponding bivalent mAbs. This hypothesis is based in part on the fact that the Fab portions of mAbs – and the variable (VH and VL) regions in particular – are typically the most important determinants of antibody self-association.14-16, 22-26 To test this hypothesis, we have employed two closely related gold nanoparticle assays that we have reported previously, namely self-interaction nanoparticle spectroscopy (SINS)27, 28 and affinity-capture selfinteraction nanoparticle spectroscopy (AC-SINS).25, 26, 29-32 The SINS assay involves directly immobilizing antibodies on gold nanoparticles and evaluating colloidal interactions between the antibody-gold conjugates via red-shifted plasmon wavelengths (attractive self-interactions) or blue-shifted plasmon wavelengths (repulsive self-interactions). The AC-SINS method is similar to SINS except that the gold particles are first coated with polyclonal antibodies that capture human mAbs, and then these conjugates are used to immobilize 2 ACS Paragon Plus Environment

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target mAbs. Analysis of Fabs has not yet been reported using either method, and it is unclear which method is better suited for analyzing Fab self-association. Here we investigate the optimal manner for evaluating Fab self-association and evaluate whether such Fab measurements can be used to identify mAbs with low selfassociation and favorable biophysical properties.

RESULTS Multiple spectral properties of antibody-gold conjugates are sensitive to interparticle interactions As a first step toward our goal of developing an assay for relating the self-association behavior of monovalent Fabs and bivalent mAbs, we sought to determine the most sensitive approaches for quantifying colloidal interactions between antibody-gold conjugates based on measurements of the visible absorbance spectra (~450-650 nm). We typically measure colloidal interactions between antibody conjugates in terms of shifts of the plasmon wavelength, as red shifts typically correspond to attractive protein interactions and blue shifts typically correspond to repulsive interactions.25-31 However, shifts in the plasmon wavelength are accompanied by changes in absorbance that may be at least as sensitive (or even more sensitive) for detecting antibody colloidal interactions. Therefore, we compared the plasmon shifts and absorbance changes for gold particles (20 nm) coated with goat anti-human Fc antibodies that were used to capture human polyclonal antibody at different concentrations (Figs. 1A, 1B, S1 and S2). Subsaturating concentrations of human antibody (< ~10 µg/mL)31 led to cross-capture of the same human antibodies by multiple conjugates (similar to the general behavior that occurs in agglutination assays) and increased plasmon wavelengths, and the maximum plasmon wavelength occurred at ~2.5 µg/mL (Figs. 1A and 1B). Higher human antibody concentrations (20-80 µg/mL) led to reduced plasmon wavelengths and absorbance ratios because of the reduced probability of cross-capture of the same human antibodies by multiple anti-Fc conjugates (Figs. 1B and S2). Red-shifted absorbance spectra due to interactions between antibody-gold conjugates generally cause reductions in the absorbance values near the plasmon wavelength (e.g., ~535 nm for anti-Fc conjugates with immobilized human antibody) and increases in absorbance values at longer wavelengths (e.g., 650 nm; Fig. S1). Therefore, we evaluated whether the ratio of absorbance values at a long wavelength (650 nm) relative to the absorbance values at a shorter wavelength near the plasmon wavelength for control conjugates (535 nm) would be correlated with the plasmon wavelengths for the antibody-gold conjugates (Fig. 1B). In an attempt to eliminate variability in the absorbance measurements due to small differences in sample volume and particle concentration, we calculated the absorbance values at 650 and 535 nm relative to a common absorbance value (450 nm) to obtain the reported absorbance ratios ([A650 nm - A450 nm)/(A535 nm - A450 nm)]. Interestingly, the absorbance ratios display a generally similar dependence as a function of the human antibody concentration as the plasmon wavelengths (Fig. 1B), revealing that both measurements may provide similar information about

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colloidal interactions between the antibody conjugates. It should also be noted that the absorbance ratio values are much faster and simpler to measure than those for plasmon wavelengths. We also evaluated the sensitivity of the plasmon wavelengths and absorbance ratios to changes in the concentration of the antibody-gold conjugates (Figs. 1C, 1D and S1). Ideally, the colloidal measurements would depend weakly on the concentration or volume of the conjugates because it is difficult to control for small changes in either parameter. Therefore, we prepared anti-Fc conjugates and immobilized human polyclonal antibody at a relatively high antibody concentration (20 µg/mL) that largely suppresses interactions between conjugates (Figs. 1B and S2).31 Interestingly, up to threefold dilution of the gold conjugates with immobilized human antibody resulted in little change in the plasmon wavelength (0.3) displayed abnormally long elution times (increases in elution time of >20 min relative to the control antibody) or failed to elute. Conversely, the mAbs that displayed lower levels of self-association also showed lower elution times, and these elution times were correlated with the AC-SINS measurements (R2 values of 0.750.76). mAb O (trastuzumab) displayed favorable size-exclusion behavior (third most similar elution time to the control out of fifteen mAbs). The abnormal elution profiles of some of the mAbs at low arginine concentrations led us to also evaluate the fractional recovery of each mAb relative to a control mAb (mAb K; Figs. 5C and 5D). Most (~90%) of mAb K injected into the column was recovered at low arginine concentrations, and the other mAbs displayed a wide range of fractional recoveries relative to mAb K (~098%). Importantly, the fractional recoveries and AC-SINS measurements are highly correlated (R2 of 0.83-



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0.84). These findings collectively suggest that similar types of intermolecular interactions mediating mAb selfassociation also mediate non-specific interactions between mAbs and chromatographic matrices.30 Fab self-association measured in a multivalent assay format is correlated with mAb self-association The main goal of our study was to evaluate the feasibility of using antibody-gold conjugates to measure Fab self-association in a multivalent assay format to identify Fabs that will display low self-association when reformatted as mAbs. Therefore, we next sought to evaluate the self-association behavior of the corresponding human Fab fragments using AC-SINS. However, we found that immobilization of human Fabs on 20 nm gold particles coated with goat anti-human Fab antibody resulted in little impact on the spectral properties of the conjugates (data not shown). Given that the plasmon wavelength of gold nanoparticles increases with decreased interparticle separation distances, we reasoned that one way to increase the sensitivity for detecting Fab self-association was to eliminate the capture antibody and directly immobilize Fabs on gold nanoparticles (Fig. 6A). Based on our previous work using a similar methodology for mAbs (self-interaction nanoparticle spectroscopy, SINS),27 we used smaller (10 nm) gold particles for these studies relative to the larger (20 nm) gold particles used for ACSINS. We first evaluated the effect of human Fab concentration on the spectral properties of the conjugates, and found that intermediate-to-high Fab concentrations (20-80 µg/mL) resulted in similar plasmon wavelengths and absorbance ratios (Fig. S3). Therefore, we co-adsorbed mixtures of human monoclonal and polyclonal Fab at an intermediate human Fab concentration (constant value of 20 µg/mL), and evaluated the spectral properties of the conjugates (Figs. 6B and S4). Intermediate amounts of monoclonal Fab led to the strongest discrimination between the different antibodies in terms of plasmon wavelength shifts (60% Fab; Fig. 6B and S4) and absorbance ratio differences (50% Fab; Fig. S4). We also performed size-exclusion chromatography analysis for the panel of human Fabs (Fig. 6C). The Fabs displayed a wide range of elution times at low concentrations of arginine (20 mM), as observed for the corresponding mAbs (Fig. 4A). Comparison of the plasmon shifts with changes in elution time relative to a control antibody (human polyclonal Fab) revealed that Fabs with increased self-association displayed increased elution times (R2 of 0.65; Fig. 6D). Similar correlations were observed between changes in Fab elution time and absorbance ratio differences (R2 of 0.76; Fig. S5). The fact that both mAb and Fab selfassociation is correlated with abnormal size-exclusion chromatography behavior suggests that the Fab fragments of associative mAbs mediate non-specific interactions with the chromatographic matrix. These findings led us to test the main hypothesis of this work, namely that the self-association of monovalent Fabs measured in a multivalent format would be reflective of the self-association behavior of bivalent mAbs. Therefore, we directly compared the SINS measurements of Fab self-association (60% Fab) with the AC-SINS measurements of mAb self-association (100% mAb; Fig. 6E). Importantly, we found that


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the two measurements were correlated (R2 of 0.71). We also found similar correlations between absorbance ratio differences measured using SINS (50% Fab) and AC-SINS (50% mAb, R2 of 0.77; Fig. S5). These results both suggest that mAb self-association is primarily mediated via the Fab fragments14-16, 22-26 and that SINS measurements of Fab self-association are a useful indicator of the colloidal properties of bivalent mAbs after reformatting. Physiochemical properties of antibody CDRs are correlated with antibody self-association We finally sought to determine the molecular origins of the self-association behavior for the mAbs and Fabs investigated in this work. We first asked whether the isoelectric points of the mAbs were correlated with the AC-SINS measurements (Fig. S6). The two mAbs with the lowest theoretical isoelectric points (pIs) – which were mAbs K (pI of 7.1) and P (pI of 6.3) – displayed the lowest levels of self-association. However, most of the mAbs possess similar isoelectric points (7.7-8.5), and there was little overall correlation between the AC-SINS measurements and either the theoretical isoelectric point (R2 of 0.29-0.35; Fig. S6) or net charge at pH 7.4 (R2 of 0.15-0.20; Fig. S6). Given that the different mAbs have common VH and VL frameworks (except for trastuzumab, mAb O), we reasoned that the properties of the variable domains and especially the CDRs may be particularly important in mediating self-association. Therefore, we first investigated the impact of theoretical net charge of the variable regions (VH and VL) on mAb self-association (Figs. 7A and 7B). Interestingly, mAbs with increased positive charge in the variable regions displayed increased plasmon wavelength shifts (R2 of 0.54) and absorbance ratio differences (R2 of 0.59). These correlations were even stronger when only considering CDR charge (R2 values of 0.68-0.72; Figs. 7C and 7D). We also observed similar correlations for CDR charge and self-association for the corresponding Fabs (R2 of 0.68-0.80; Fig. S7). This suggests that increasing positive charge in the CDRs – at least at the pH and range of CDR charge tested in this study – increases self-association. To evaluate the generality of our findings that increased positive CDR charge leads to increased mAb selfassociation at physiological conditions (pH 7.4, PBS), we evaluated whether net CDR charge is correlated with published AC-SINS measurements for 137 clinical-stage antibodies.34 We found little direct correlation between the AC-SINS measurements and CDR charge (R2=0.05; data not shown). Nevertheless, we performed additional analysis to determine if mAbs with high self-association – as determined by plasmon shifts greater than the top 10% of values for 48 approved therapeutic antibodies (plasmon shifts of >11.8 nm) – were linked to increased positive CDR charge (Fig. 8). Notably, logistic regression analysis reveals that mAbs with increased positive CDR charge were more likely to display high self-association (p-value of 0.008 for the coefficient of the independent variable of the logistic function). Similar analysis revealed that the net charge of the variable regions – which is correlated with CDR charge (R2 of 0.59) – is also linked to increased probability of high mAb self-association (p-value of 0.012; Fig. S8).



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We also sought to identify other CDR features that may be correlated with antibody self-association (Fig. 9). Our general approach was to compare CDR sequences of the antibodies in this study with common frameworks (all mAbs except for mAb O) to those of 48 approved antibody drugs34 to identify if any of the CDR properties of our antibodies were unusual (Table II). First, we found that the net charge of some of the CDRs were unusual for a subset of the mAbs. For each CDR, we assigned mAbs a score of 0 if their CDR charge was between the lower 10% and upper 10% cutoffs for approved antibodies (Table II). Moreover, we assigned a score of +1 if the net CDR charge was more negative than the lower 10% cutoff because we find that increased negative charge is linked to reduced antibody self-association (at least in the range evaluated in this work). Conversely, we assigned a score of −1 to CDRs with net charges greater than the upper limit for the approved antibodies because we find that increased positive charge is linked to increased antibody selfassociation. We also applied the same scoring to the overall CDRs, which resulted in a maximum CDR charge score of +7 and a minimum score of -7. We also found that three other CDR properties were useful for identifying antibodies with high levels of self-association in our study. First, most approved antibodies34 have CDRs with moderate amounts of charged residues (Table II). We assigned unusual CDRs with fractions of charged residues greater than the upper 10% cutoff for the approved antibodies a score of -1 because of the increased risk of non-specific electrostatic interactions, while the other mAbs received a score of 0 (no penalty was assigned for having a low fraction of charged residues). Second, CDRs that contained three consecutive aromatic or histidine residues were given a score of -1, while CDRs without this motif were given a score of 0. Finally, CDRs that contained two consecutive arginines – an interactive motif that rarely occurs in approved antibodies (0.95) and setting the first derivative to zero. Preparation of Fabs using papain cleavage of IgGs Fabs were prepared via papain cleavage of IgGs using standard methods. Briefly, human mAbs (1-3 mg/mL) were mixed with papain agarose beads (20341, ThermoFisher Scientific) at a papain/substrate ratio of ~1/160. The cleavage reaction was performed at pH 7 (20 mM sodium phosphate, 10 mM EDTA and 20 mM cysteine HCl) and 37 °C for at least 5 h. The Fabs were purified using Protein A resin (17543801, MabSelect SuRe, GE Healthcare). SINS: Fab immobilization and analysis of nanoparticle conjugates Gold nanoparticles (10 nm, 1000 µL) were first added to 1.5 mL microcentrifuge tubes (1615-5500, USA Scientific) and sedimented at 21130 rcf for 30 min (nominal centrifuge temperature of 4 °C, samples warmed during centrifugation). Most (950 µL) of the resulting supernatant was then removed and discarded. The resulting pellet was resuspended to the original volume by adding 950 µL of Milli-Q water. The human polyclonal and monoclonal Fabs were buffer exchanged twice into 1.1 mM acetate (pH 4.0) using Zeba desalting columns. The Fab concentrations were next evaluated using UV absorbance measurements at 280 nm and an extinction coefficient of 1.4 mL/(mg⋅cm). The assumption of a constant Fab extinction coefficient (same value as used for mAbs) instead of calculating the extinction coefficients based on their amino acid sequences (~1.51-1.95 mL/mg-cm) resulted in Fab concentrations that were ~6-21% lower than the target concentrations. 13 ACS Paragon Plus Environment


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These differences are not expected to significantly impact the results because small changes in Fab concentration near the final target concentration (20 µg/mL) have little impact on the plasmon wavelengths and absorbance ratios. The Fab solutions were then diluted to a nominal concentration of 0.11 mg/mL, and the monoclonal and polyclonal Fab solutions were mixed at different ratios (0-100%). Two parts of the Fab mixtures were then combined with nine parts of the gold nanoparticles and allowed to incubate for 1 h at room temperature. Next, one part of PEG thiol (10 µM) in 0.2 mM acetate (pH 4.0) was mixed with 99 parts of the antibody-gold conjugates and allowed to incubate for 1 h at room temperature. Afterward, 10 µL of 5x PBS (pH 7.4) was added to the polystyrene 384-well plate, and then 40 µL of the gold conjugates were added. The solution was mixed by pipetting 40 µL up and down ten times. The mixtures were allowed to incubate for 1 h and then the absorbance spectra (450-650 nm) were measured using a Tecan Safire2 plate reader. The plasmon wavelengths were obtained by fitting 40 data points around the maximum value to a second order polynomial function and setting the first derivative to zero. Size-exclusion chromatography The mAb and Fab samples were analyzed via size-exclusion chromatography using a YMC Diol-200 column (0.78x30 cm, YMC) and a Waters 6000 HPLC. Prior to analysis of the antibody samples, human polyclonal IgG antibody was prepared at 1 mg/mL in PBS (pH 7.4) and 10 injections (200 µL per injection, injections separated by two column volumes at a flow rate of 0.5 mL/min) were performed to passivate the column. Next, the column was washed with six column volumes of PBS supplemented with 400 mM arginine (pH 7.4), and then equilibrated with PBS supplemented with either 20 or 400 mM (~6 column volumes). Finally, 100 µL of the monoclonal Fab (0.1 mg/mL) or IgG (0.1-0.4 mg/mL) samples were injected (in an order that was randomized between repeats), and their retention times and fractional recoveries were evaluated. Passivation of the column with polyclonal IgG was repeated after every 20 injections of antibodies performed with 20 mM arginine. The retention times were obtained from the Waters Empower Pro software for the mAbs and via manual curve fitting in Microsoft Excel (using a quadratic function to fit ~60 data points around the maximum value) for the Fabs. The fractional recoveries of injected mAbs were calculated by manually integrating the chromatograms in Microsoft Excel, dividing the areas by the extinction coefficients calculated based on the antibody amino acid sequences, and normalizing the peak areas to the value for mAb K. Charge calculations for antibodies and CDRs The theoretical net charges of the antibodies (including the CDRs) at pH 7.4 were calculated by summing the charges of Glu (-1), Asp (-1), Arg (+1), Lys (+1) and His (+0.1). The theoretical isoelectric point values of the antibodies were calculated using the ProtParam calculator.55 14 ACS Paragon Plus Environment

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Logistic regression analysis of AC-SINS measurements for clinical-stage antibodies The previously reported AC-SINS values for 137 clinical-stage antibodies (PBS, pH 7.4)34 were compared to the cutoff value that defines mAbs with the highest levels of self-association (top 10% for approved antibodies; AC-SINS plasmon shifts >11.8 nm). Next, the mAbs were binned based on their theoretical CDR or Fv net charges, and the percentages of mAbs in each bin exceeding the cutoff value (plasmon shifts >11.8 nm) were calculated. Finally, a logistic function [y=1/(1+exp(-A-Bx)] was fit to the data and the p-value of the independent coefficient (B) was calculated to evaluate statistical significance.

ACKNOWLEDGEMENTS We thank members of the Tessier lab for their assistance editing the manuscript. This work was supported by Bayer AG. We thank Dr. Heiner Apeler (Bayer AG, Analytical Development) for helping to initiate this collaborative research. CONFLICTS OF INTEREST P.M.T. has received consulting fees and/or honorariums for presentations of this and/or related research findings at MedImmune, Eli Lilly, Bristol-Myers Squibb, Janssen, Merck, Genentech, Amgen, Pfizer, Adimab, Abbvie, Abbott, DuPont, Schrödinger and Novo Nordisk.



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33. 34.

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Barthelemy, P. A.; Raab, H.; Appleton, B. A.; Bond, C. J.; Wu, P.; Wiesmann, C.; Sidhu, S. S. Comprehensive analysis of the factors contributing to the stability and solubility of autonomous human VH domains. J Biol Chem 2008, 283, 3639-54. Fennell, B. J.; McDonnell, B.; Tam, A. S.; Chang, L.; Steven, J.; Broadbent, I. D.; Gao, H.; Kieras, E.; Alley, J.; Luxenberg, D.; Edmonds, J.; Fitz, L. J.; Miao, W.; Whitters, M. J.; Medley, Q. G.; Guo, Y. J.; Darmanin-Sheehan, A.; Autin, B.; Shuilleabhain, D. N.; Cummins, E.; King, A.; Krebs, M. R.; Grace, C.; Hickling, T. P.; Boisvert, A.; Zhong, X.; McKenna, M.; Francis, C.; Olland, S.; Bloom, L.; Paulsen, J.; Somers, W.; Jensen, A.; Lin, L.; Finlay, W. J.; Cunningham, O. CDR-restricted engineering of native human scFvs creates highly stable and soluble bifunctional antibodies for subcutaneous delivery. MAbs 2013, 5, 882-95. Yadav, S.; Sreedhara, A.; Kanai, S.; Liu, J.; Lien, S.; Lowman, H.; Kalonia, D. S.; Shire, S. J. Establishing a link between amino acid sequences and self-associating and viscoelastic behavior of two closely related monoclonal antibodies. Pharm Res 2011, 28, 1750-64. Nilvebrant, J.; Tessier, P. M.; Sidhu, S. S. Engineered autonomous human variable domains. Curr Pharm Des 2016. Perchiacca, J. M.; Tessier, P. M. Engineering aggregation-resistant antibodies. Annu Rev Chem Biomol Eng 2012, 3, 263-86. Birtalan, S.; Zhang, Y.; Fellouse, F. A.; Shao, L.; Schaefer, G.; Sidhu, S. S. The intrinsic contributions of tyrosine, serine, glycine and arginine to the affinity and specificity of antibodies. J Mol Biol 2008, 377, 1518-28. Birtalan, S.; Fisher, R. D.; Sidhu, S. S. The functional capacity of the natural amino acids for molecular recognition. Mol Biosyst 2010, 6, 1186-94. Tiller, K. E.; Li, L.; Kumar, S.; Julian, M. C.; Garde, S.; Tessier, P. M. Arginine mutations in antibody complementarity-determining regions display context-dependent affinity/specificity trade-offs. J Biol Chem 2017. Valente, J. J.; Verma, K. S.; Manning, M. C.; Wilson, W. W.; Henry, C. S. Second virial coefficient studies of cosolvent-induced protein self-interaction. Biophys J 2005, 89, 4211-8. Scherer, T. M. Cosolute effects on the chemical potential and interactions of an IgG1 monoclonal antibody at high concentrations. J Phys Chem B 2013, 117, 2254-66. Kheddo, P.; Tracka, M.; Armer, J.; Dearman, R. J.; Uddin, S.; van der Walle, C. F.; Golovanov, A. P. The effect of arginine glutamate on the stability of monoclonal antibodies in solution. Int J Pharm 2014, 473, 126-33. Scherer, T. M. Role of Cosolute-Protein Interactions in the Dissociation of Monoclonal Antibody Clusters. J Phys Chem B 2015, 119, 13027-38. Wilkins, M. R.; Gasteiger, E.; Bairoch, A.; Sanchez, J. C.; Williams, K. L.; Appel, R. D.; Hochstrasser, D. F. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 1999, 112, 53152.


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Table I. Summary of the properties of the mAbs evaluated in this study. The theoretical isoelectric points were calculated using only their amino acid sequences and assuming full solvent exposure. The isoelectric points of Fv, Fab and IgG variants were determined by first calculating the isoelectric points for each chain (e.g., heavy or light chain), and then combining the values of the two chains using a weighted average based on the number of amino acids in each chain. The amino acid sequences of heavy chain CDR3 (HCDR3) are based on Kabat numbering (residues 95 to 102), and mAb O is trastuzumab. mAbs B, C, E and L are IgG1 antibodies with identical HCDR3s that contain limited sequence differences in light chain framework 1, heavy chain CDR1, light chain CDR3, and Fc regions. mAbs D, M and N are IgG2 antibodies with identical HCDR3s that contain limited differences in CDR1 of both the heavy and light chains. mAbs G and H are IgG1 antibodies with identical variable regions that contain limited differences in the Fc region. mAbs K and P have identical variable regions and differences in the CH1 and Fc regions due to their different isotypes. Theoretical isoelectric point mAb

Isotype

A B C D E G H I J K L M N O P

IgG1 IgG1 IgG1 IgG2 IgG1 IgG1 IgG1 IgG1 IgG1 IgG1 IgG1 IgG2 IgG2 IgG1 IgG2

Light chain lambda lambda lambda lambda lambda lambda lambda lambda lambda lambda lambda lambda lambda kappa lambda

VH

VL

Fv

Fab

IgG

HCDR3

8.6 6.5 6.5 8.7 6.5 8.0 8.0 6.8 8.1 5.0 6.5 8.7 8.7 8.0 5.0

9.1 6.8 6.8 8.0 6.8 8.7 8.7 6.3 5.1 5.0 6.8 8.0 8.0 9.0 5.0

8.8 6.6 6.6 8.4 6.6 8.3 8.3 6.6 6.7 5.0 6.6 8.4 8.4 8.5 5.0

8.8 7.9 7.9 8.1 7.9 8.6 8.6 7.8 7.6 7.1 7.9 8.1 8.1 8.3 6.0

8.5 7.8 7.8 7.9 7.9 8.4 8.4 7.8 7.7 7.1 7.8 7.9 7.9 8.3 6.3

DLPWRENPFNY EGLWAFDY EGLWAFDY GLDARRMDY EGLWAFDY GDYLVYSSYYFKS GDYLVYSSYYFKS ESPGYDFEY VRYNWNHGDWFDP EGETSFGLDV EGLWAFDY GLDARRMDY GLDARRMDY WGGDGFYAMDY EGETSFGLDV



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Table II. Summary of theoretical net charges and fraction of charged residues for the CDRs of approved antibodies. The fraction of charged residues (number of Glu, Asp, Arg and Lys residues relative to the total number of residues) and theoretical net charge of the CDRs were calculated for 48 approved antibody drugs.34 The lower and upper 10% values signify the percentile cutoffs for segregating the upper and lower 10% of the antibodies for each CDR property. The calculations were performed using CDR definitions that were defined by Kabat numbering.

CDR L1 L2 L3 H1 H2 H3 All

Net charge Average ± standard Lower deviation 10% 0.68 ± 1.25 -1.00 0.13 ± 0.94 -1.00 0.14 ± 0.77 -0.83 -0.18 ± 0.63 -1.00 0.07 ± 1.21 -1.30 -1.22 ± 0.92 -2.00 -0.38 ± 2.26 -3.30

Upper 10% 2.13 1.37 1.00 0.10 1.10 0 2.13

Fraction of charged residues Average ± standard Lower Upper deviation 10% 10% 0.137 ± 0.074 0.0833 0.267 0.149 ± 0.121 0 0.286 0.062 ± 0.082 0 0.222 0.076 ± 0.104 0 0.200 0.192 ± 0.088 0.0625 0.294 0.181 ± 0.108 0.0753 0.302 0.145 ± 0.035 0.0952 0.189


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FIGURE CAPTIONS Figure 1. Effect of gold nanoparticle and human antibody concentrations on plasmon wavelengths and absorbances for anti-Fc gold conjugates. (A) Absorbance spectra of anti-Fc gold conjugates after mixing with different concentrations of human polyclonal antibody. Gold nanoparticles (20 nm) coated with goat antihuman Fc antibody were used to immobilize human polyclonal antibody (0-20 µg/mL). (B) Plasmon wavelength and absorbance ratio values as a function of the concentration of human polyclonal antibody. (C) Absorbance spectra of antibody-gold conjugates at various dilutions. Gold nanoparticles coated with goat antihuman Fc antibody were used to immobilize human polyclonal antibody at a relatively high concentration (20 µg/mL), the conjugates were adjusted to a concentration of 8.4×1011 particles/mL (1x concentration), and diluted using different amounts of PBS. (D) Plasmon wavelength and absorbance ratio values as a function of the relative gold conjugate concentration. In (A) and (C), the absorbance spectra are representative examples, and the absorbance values were subtracted by the absorbance value at 450 nm. In (B) and (D), the absorbance ratios were calculated as (A650 nm – A450 nm) / (A535 nm – A450 nm), the data are averages from three independent experiments, and the error bars are standard errors (n=3). In (D), the values of the absorbance ratio are weakly impacted by conjugate concentration because increases in conjugate concentration cause relative decreases in absorbance values at 650 nm that are offset by relative increases in absorbance values at 535 nm. In (A-D), the experiments were performed in PBS (pH 7.4), and the spectra were obtained after 4 h of incubation. Figure 2. Affinity-capture self-interaction nanoparticle spectroscopy analysis of the self-association behavior for a diverse panel of human mAbs. (A) Schematic representation of affinity-capture selfinteraction nanoparticle spectroscopy (AC-SINS). Gold particles (20 nm) were first coated with goat antihuman Fc antibody, and then the conjugates were used to immobilize human antibodies. The total human antibody concentration during immobilization was constant (20 µg/mL), and the amount of human mAb relative to human polyclonal antibody was varied. (B) Plasmon shifts as a function of the percentage of human mAb mixed with the gold anti-Fc conjugates. The plasmon shifts were calculated as the plasmon wavelength for a sample with human mAb minus the plasmon wavelength for a sample with human polyclonal antibody. (C) Absorbance ratio differences as a function of the percentage of human mAb mixed with the gold anti-Fc conjugates. The absorbance ratio differences were calculated as (A650 nm, mAb – A450 nm, mAb) / (A535 nm, mAb – A450 nm, mAb) – (A650 nm, control – A450 nm, control) / (A535 nm, control – A450 nm, control), and the control was 0% mAb (100% human polyclonal antibody). In (B) and (C), the data are averages of three independent experiments and the error bars are standard errors. Figure 3. Comparison of plasmon shifts and absorbance ratio differences for human mAbs measured using AC-SINS. (A) Plasmon shifts measured for 50 and 100% mAb. (B) Absorbance ratio differences measured for 50% and 100% mAb. (C) Correlation between plasmon shifts (100% mAb) and absorbance ratio differences (50% mAb). The AC-SINS measurements and calculated values were obtained as described in Figure 2. The data are averages of three independent experiments and the error bars are standard errors. Figure 4. Size-exclusion chromatography analysis of human mAbs using different running buffers. mAbs were injected (0.1 mL) into the column (YMC Diol-200) equilibrated with a PBS running buffer (pH 7.4) that contained (A) 20 or (B) 400 mM arginine. The mAb samples were prepared at (A) 0.4 and (B) 0.1 mg/mL. The elution profiles are representative examples from two independent experiments (each of which contained two replicates). Figure 5. Comparison of AC-SINS and size-exclusion chromatography measurements for a panel of human mAbs. (A) Plasmon shifts (100% mAb) and (B) absorbance ratio differences (50% mAb) compared to changes in elution time for mAbs relative to the human polyclonal antibody using a PBS running buffer with 20 mM arginine. (C) Plasmon shifts (100% mAb) and (D) absorbance ratio differences (50% mAb) compared to the fractional recovery of each antibody from the size-exclusion column (relative to mAb K) using a PBS running buffer with 20 mM arginine. The size-exclusion chromatography data are averages of two replicates from a representative experiment, and the AC-SINS data are averages of three independent experiments. The error bars are standard errors.



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Figure 6. Self-interaction nanoparticle spectroscopy and size-exclusion chromatography analysis of human Fabs, and comparison to AC-SINS measurements for the corresponding mAbs. (A) Schematic representation of self-interaction nanoparticle spectroscopy (SINS). Gold particles (10 nm) were first coated with mixtures of human monoclonal and polyclonal Fab. The total human Fab concentration during immobilization was constant (20 µg/mL), and the amount of human monoclonal Fab relative to human polyclonal Fab was varied. Fab Q is a non-human (rat) antibody and was immobilized in same manner as the human Fabs via co-adsorption with human polyclonal Fab. (B) Plasmon shifts for human Fabs (60% monoclonal Fab) adsorbed directly on gold nanoparticles. (C) Size-exclusion analysis of human Fabs (0.1 mg/mL, 0.1 mL) injected into the column (YMC Diol-200) equilibrated with a PBS running buffer (pH 7.4, 20 mM arginine). The elution profiles are representative examples from two independent experiments (each of which contained two replicates). (D) Plasmon shifts (60% monoclonal Fab) as a function of change in elution time for human monoclonal Fabs relative to human polyclonal Fab. (E) Plasmon shifts for human Fabs (60% monoclonal Fab) measured using SINS versus plasmon shifts for the corresponding human mAbs measured using AC-SINS (100% monoclonal mAb). In (B), (D) and (E), the SINS and AC-SINS measurements were performed in PBS (pH 7.4), the SINS data are averages of two independent experiments, and the AC-SINS data are averages of three independent experiments. The error bars are standard errors. Figure 7. Comparison of AC-SINS measurements of human mAbs with their Fv and CDR theoretical net charges. Theoretical net charges for antibody (A, B) variable domains (Fv; VH and VL) and (C, D) CDRs calculated only using their amino acid sequences compared to AC-SINS measurements of plasmon shifts (100% mAb) or changes in absorbance ratios (50% mAb). The AC-SINS data are described in Figures 2 and 3. Figure 8. Comparison of CDR net charge and percentage of mAbs with high self-association for 137 clinical-stage antibodies. The AC-SINS measurements performed in PBS (pH 7.4) were reported previously,34 and the net charges of the CDRs were calculated using only their amino acid sequences. The percentage of clinical-stage antibodies with high self-association (AC-SINS plasmon shifts >11.8 nm) were calculated, binned (the number of mAbs in each bin are listed above each column), and analyzed via logistic regression. The p-value is specified for the coefficient of the independent variable of the logistic function. Figure 9. Comparison of AC-SINS measurements for human mAbs and sequence properties of the CDRs. CDR scores for human mAbs with common frameworks were computed based on similarity to approved antibody drugs and were compared to AC-SINS measurements of (A) plasmon shifts (100% mAb) and (B) changes in absorbance ratios (50% mAb). CDRs from the human mAbs in this study (except for mAb O) were analyzed in terms of their theoretical net charge, fraction of charged residues and whether they contained either of two self-interaction motifs (at least three consecutive aromatic or histidine residues or at least two consecutive arginine residues), and these properties were compared to the ranges observed for clinical-stage antibodies (Table II). Individual CDRs and the overall CDRs were each given a score +1 if their charge was equal to or below the lower 10% cutoff for approved antibodies, -1 if equal to or greater than the upper 10% cutoff or otherwise 0. Individual CDRs were given a score of -1 if their fraction of charged residues was equal to or greater than the upper 10% cutoff (otherwise they were scored as 0). CDRs that contained three consecutive aromatic or histidine residues were given a score of -1, while CDRs that lacked such a motif were given a score of 0. Likewise, CDRs that contained two consecutive arginines were given a score of -1, while CDRs without this motif were given a score of 0. The minimum possible CDR score of -25 suggests high risk for self-association, while the maximum possible score of +7 suggests low risk. The AC-SINS data are described in Figures 2 and 3.


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Figure 1. Effect of gold nanoparticle and human antibody concentrations on plasmon wavelengths and absorbances for anti-Fc gold conjugates. (A) Absorbance spectra of anti-Fc gold conjugates after mixing with different concentrations of human polyclonal antibody. Gold nanoparticles (20 nm) coated with goat anti-human Fc antibody were used to immobilize human polyclonal antibody (0-20 µg/mL). (B) Plasmon wavelength and absorbance ratio values as a function of the concentration of human polyclonal antibody. (C) Absorbance spectra of antibody-gold conjugates at various dilutions. Gold nanoparticles coated with goat anti-human Fc antibody were used to immobilize human polyclonal antibody at a relatively high concentration (20 µg/mL), the conjugates were adjusted to a concentration of 8.4×1011 particles/mL (1x concentration), and diluted using different amounts of PBS. (D) Plasmon wavelength and absorbance ratio values as a function of the relative gold conjugate concentration. In (A) and (C), the absorbance spectra are representative examples, and the absorbance values were subtracted by the absorbance value at 450 nm. In (B) and (D), the absorbance ratios were calculated as (A650 nm – A450 nm) / (A535 nm – A450 nm), the data are averages from three independent experiments, and the error bars are standard errors (n=3). In (D), the values of the absorbance ratio are weakly impacted by conjugate concentration because increases in conjugate concentration cause relative decreases in absorbance values at 650 nm that are offset by relative increases in absorbance values at 535 nm. In (A-D), the experiments were performed in PBS (pH 7.4), and the spectra were obtained after 4 h of incubation. 177x164mm (300 x 300 DPI)



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Figure 2. Affinity-capture self-interaction nanoparticle spectroscopy analysis of the selfassociation behavior for a diverse panel of human mAbs. (A) Schematic representation of affinitycapture self-interaction nanoparticle spectroscopy (AC-SINS). Gold particles (20 nm) were first coated with goat anti-human Fc antibody, and then the conjugates were used to immobilize human antibodies. The total human antibody concentration during immobilization was constant (20 µg/mL), and the amount of human mAb relative to human polyclonal antibody was varied. (B) Plasmon shifts as a function of the percentage of human mAb mixed with the gold anti-Fc conjugates. The plasmon shifts were calculated as the plasmon wavelength for a sample with human mAb minus the plasmon wavelength for a sample with human polyclonal antibody. (C) Absorbance ratio differences as a function of the percentage of human mAb mixed with the gold anti-Fc conjugates. The absorbance ratio differences were calculated as (A650 nm, mAb – A450 nm, mAb) / (A535 nm, mAb – A450 nm, mAb) – (A650 nm, control – A450 nm, control) / (A535 nm, control – A450 nm, control), and the control was 0% mAb (100% human polyclonal antibody). In (B) and (C), the data are averages of three independent experiments and the error bars are standard errors.


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82x215mm (300 x 300 DPI)



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Figure 3. Comparison of plasmon shifts and absorbance ratio differences for human mAbs measured using AC-SINS. (A) Plasmon shifts measured for 50 and 100% mAb. (B) Absorbance ratio differences measured for 50% and 100% mAb. (C) Correlation between plasmon shifts (100% mAb) and absorbance ratio differences (50% mAb). The AC-SINS measurements and calculated values were obtained as described in Figure 2. The data are averages of three independent experiments and the error bars are standard errors. 241x737mm (300 x 300 DPI)


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Figure 4. Size-exclusion chromatography analysis of human mAbs using different running buffers. mAbs were injected (0.1 mL) into the column (YMC Diol-200) equilibrated with a PBS running buffer (pH 7.4) that contained (A) 20 or (B) 400 mM arginine. The mAb samples were prepared at (A) 0.4 and (B) 0.1 mg/mL. The elution profiles are representative examples from two independent experiments (each of which contained two replicates). 153x283mm (300 x 300 DPI)



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Figure 5. Comparison of AC-SINS and size-exclusion chromatography measurements for a panel of human mAbs. (A) Plasmon shifts (100% mAb) and (B) absorbance ratio differences (50% mAb) compared to changes in elution time for mAbs relative to the human polyclonal antibody using a PBS running buffer with 20 mM arginine. (C) Plasmon shifts (100% mAb) and (D) absorbance ratio differences (50% mAb) compared to the fractional recovery of each antibody from the size-exclusion column (relative to mAb K) using a PBS running buffer with 20 mM arginine. The size-exclusion chromatography data are averages of two replicates from a representative experiment, and the AC-SINS data are averages of three independent experiments. The error bars are standard errors 178x178mm (300 x 300 DPI)


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Figure 6. Self-interaction nanoparticle spectroscopy and size-exclusion chromatography analysis of human Fabs, and comparison to AC-SINS measurements for the corresponding mAbs. (A) Schematic representation of self-interaction nanoparticle spectroscopy (SINS). Gold particles (10 nm) were first coated with mixtures of human monoclonal and polyclonal Fab. The total human Fab concentration during immobilization was constant (20 µg/mL), and the amount of human monoclonal Fab relative to human polyclonal Fab was varied. Fab Q is a non-human (rat) antibody and was immobilized in same manner as the human Fabs via co-adsorption with human polyclonal Fab. (B) Plasmon shifts for human Fabs (60% monoclonal Fab) adsorbed directly on gold nanoparticles. (C) Size-exclusion analysis of human Fabs (0.1 mg/mL, 0.1 mL) injected into the column (YMC Diol-200) equilibrated with a PBS running buffer (pH 7.4, 20 mM arginine). The elution profiles are representative examples from two independent experiments (each of which contained two replicates). (D) Plasmon shifts (60% monoclonal Fab) as a function of change in elution time for human monoclonal Fabs relative to human polyclonal Fab. (E) Plasmon shifts for human Fabs (60% monoclonal Fab) measured using SINS versus plasmon shifts for the corresponding human mAbs



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measured using AC-SINS (100% monoclonal mAb). In (B), (D) and (E), the SINS and AC-SINS measurements were performed in PBS (pH 7.4), the SINS data are averages of two independent experiments, and the AC-SINS data are averages of three independent experiments. The error bars are standard errors. 175x241mm (300 x 300 DPI)


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Figure 7. Comparison of AC-SINS measurements of human mAbs with their Fv and CDR theoretical net charges. Theoretical net charges for antibody (A, B) variable domains (Fv; VH and VL) and (C, D) CDRs calculated only using their amino acid sequences compared to AC-SINS measurements of plasmon shifts (100% mAb) or changes in absorbance ratios (50% mAb). The AC-SINS data are described in Figures 2 and 3. 176x174mm (300 x 300 DPI)



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Figure 8. Comparison of CDR net charge and percentage of mAbs with high self-association for 137 clinical-stage antibodies. The AC-SINS measurements performed in PBS (pH 7.4) were reported previously,34 and the net charges of the CDRs were calculated using only their amino acid sequences. The percentage of clinical-stage antibodies with high self-association (AC-SINS plasmon shifts >11.8 nm) were calculated, binned (the number of mAbs in each bin are listed above each column), and analyzed via logistic regression. The p-value is specified for the coefficient of the independent variable of the logistic function. 75x69mm (300 x 300 DPI)


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Figure 9. Comparison of AC-SINS measurements for human mAbs and sequence properties of the CDRs. CDR scores for human mAbs with common frameworks were computed based on similarity to approved antibody drugs and were compared to AC-SINS measurements of (A) plasmon shifts (100% mAb) and (B) changes in absorbance ratios (50% mAb). CDRs from the human mAbs in this study (except for mAb O) were analyzed in terms of their theoretical net charge, fraction of charged residues and whether they contained either of two self-interaction motifs (at least three consecutive aromatic or histidine residues or at least two consecutive arginine residues), and these properties were compared to the ranges observed for clinical-stage antibodies (Table II). Individual CDRs and the overall CDRs were each given a score +1 if their charge was equal to or below the lower 10% cutoff for approved antibodies, -1 if equal to or greater than the upper 10% cutoff or otherwise 0. Individual CDRs were given a score of -1 if their fraction of charged residues was equal to or greater than the upper 10% cutoff (otherwise they were scored as 0). CDRs that contained three consecutive aromatic or histidine residues were given a score of -1, while CDRs that lacked such a motif were given a score of 0. Likewise, CDRs that contained two consecutive arginines



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were given a score of -1, while CDRs without this motif were given a score of 0. The minimum possible CDR score of -25 suggests high risk for self-association, while the maximum possible score of +7 suggests low risk. The AC-SINS data are described in Figures 2 and 3. 159x308mm (300 x 300 DPI)


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Biophysical and Sequence-Based Methods for ... - ACS Publications

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