Characterization of the monoclonal antibody aggregation process and identification of stability factors that could be used as indicators of aggregation propensity with an emphasis on a large number of samples and low protein material consumption.
Differential scanning calorimetry, dynamic light scattering and size exclusion chromatography were used as the main methodological approaches. Conformational stability, colloidal stability and aggregation kinetics were assessed for two different IgG monoclonal antibody (mAbs) subclasses. Aggregation was induced by exposing the mAbs to 55°C for 3 weeks. mAb samples were prepared in different formulations and concentrations from 1 mg/mL to 50 mg/mL.
High temperature stress of mAb samples revealed that monoclonal antibodies followed first order aggregation kinetics, which suggests that the rate-limiting step of monomer loss was unimolecular. Conformational stability of mAbs was estimated with denaturation temperature measurements. Colloidal stability was assessed with dynamic interaction parameter k D . The correlation between aggregation kinetics and colloidal and conformational stability factors was evaluated and the dynamic interaction parameter was found to be a promising predictor of aggregation propensity of monoclonal antibodies. The meaning of using an intermolecular interaction parameter for prediction of what is essentially a unimolecular process is also discussed.
This work estimates the significance of different predictors of aggregation propensity at high concentrations as a part of a high throughput, low resource screening method and is a contribution towards determining protein aggregation phenomena in actual systems used for the development and production of biopharmaceuticals.
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Dynamic light scattering
Differential scanning calorimetry
Size exclusion chromatography
Chi EY, Krishnan S, Randolph TW, Carpenter JF. Physical stability of proteins in aqueous solution: mechanism and driving forces in nonnative protein aggregation. Pharm Res. 2003;20(9):1325–34.
Cohen SIA, Vendruscolo M, Dobson CM, Knowles TPJ. From macroscopic measurements to microscopic mechanisms of protein aggregation. J Mol Biol. 2012;421:160–71.
Roberts D, Keeling R, Tracka M, Walle CF v d, Uddin S, Warwicker J, et al. The role of electrostatics in protein-protein interactions of a monoclonal antibody. Mol Pharm. 2014;11:2475–89.
Quigley A, Williams DR. The second virial coefficient as a predictor of protein aggregation propensity: a self-interaction chromatography study. Eur J Pharm Biopharm. 2015;96:282–90.
Mossallatpour S, Ghahghaei A. The effect of Hoffmeister salts on the chaperoning action of β-casein in preventing aggregation of reduced α-Lactalbumin. Biom J. 2015;1:58–68.
Arzensek D, Kuzman D, Podgornik R. Hofmeister effects in monoclonal antibody solution interactions. J Phys Chem B. 2015;119(33):10375–89.
Sanchez-Ruiz JM. Theoretical analysis of Lumry-Eyring models in differential scanning calorimetry. Biophys J. 1992;61:921–35.
George A, Wilson WW. Predicting protein crystallization from a dilute solution property. Acta Cryst. 1994;50:361–5.
G. W. H. Hochne, W. F. Hemminger and F. H. J., Differential Scanning Calorimetry, Berlin: Springer, 2003.
H. C. Hulst, Dynamic light scattering: with applications to chemistry, biology, and Physics, Dover Publications Inc., 1981.
J. B. Berne and R. Pecora, Light scattering by small particles, Dover Publications Inc, 2000.
Striegel AM, Yau WW, Kirkland JJ, Bly DD. Modern size-exclusion liquid chromatography. New Jersey: Wiley; 2009.
Sun T, Chance RR, Graessley WW, Lohse DJ. A study of the separation principle in size exclusion chromatography. Macromolecules. 2004;37:4304–12.
Bajaj H, Sharma VK, Badkar A, Zeng D, Nema S, Kalonia DS. Protein structural conformation and not second virial coefficient relates to long-term irreversible aggregation of a monoclonal antibody and ovalbumin in solution. Pharm Res. 2006;23:1382–94.
Saluja A, Fesinmeyer M, Hogan S, Brems DN, Gokam YR. Diffusion and sedimentation interaction parameters for measuring the second virial coefficient and their utility as predictors of protein aggregation. Biophys J. 2010;99:2657–65.
Wilson WW, DeLucas LJ. Applications of the second virial coefficient: protein crystallization and solubility. Acta Cryst. 2014;F70:543–54.
Petsev DN, Denkov ND. Diffusion of charged colloidal particles at low volume fraction: theoretical model and light scattering experiments. J Colloid Interface Sci. 1992;149:329.
Li S, Xing D, Li J. Dynamic light scattering application to study protein interactions in electrolyte solutions. J Biol Phys. 2004;30:313–24.
Stranz M, Kastango ES. A review of pH and Osmolarity. Int J Pharm Compd. 2013;6(3):216–20.
Lehermayr C, Mahler HC, Mäder K, Fischer S. Assesment of net charge and protein-protein interactions of different monoclonal antibodies. J Pharm Sci. 2011;100(7):2551–62.
Buck PM, Kumar S, S. S. K. Insights into the potential aggregation liabilities of the bI2 fab fragment via elevated temperature molecular dynamics. Protein Eng Des Sel. 2013;26(3):1–11.
Skamris T, Xinsheng T, Thorolfsson M, Karkov HS, Rassmusen HB, Langkilde AE, et al. Monoclonal antibodies follow distinct aggregation pathways during production - relevant acidic incubation and neutralization. Pharm Res. 2015;33:716–28.
Wang X, Das KD, Singh SK, Kumar S. Potential aggregation prone regions in biotherapeutics. MAbs. 2009;1(3):254–67.
Tartaglia GG, Vendruscolo M. The Zyggregator method for predicting aggregation propensities. Chem Soc Rev. 2008;37:1395–401.
Shire SJ, Shahrokh Z, Liu J. Challenges in the development of high protein concentration formulation. J Pharm Sci. 2004;93(6):1390–402.
Treuheit MJ, Andrew AK, Brems DN. Inverse relationship of protein concentration and aggregation. Pharm Res. 2002;19(4):511–6.
Saluja A, Sadineni V, Mungikar A, Nashine V, Kroetsch A, Dahlheim C, et al. Significance of unfolding thermodynamics for predicting aggregation kinetics: a case study on high concentration solutions of a multi-domain protein. Pharm Res. 2014;31:1575–87.
Mukaka MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69–71.
Vazquez-Rey M, Lang DA. Aggregates in monoclonal antibody manufacturing processes. Biotechnol Bioeng. 2011;108:1494–508.
Carter PJ. Potent antibody therapeutics by design. Nat Rev Immunol. 2006;6:343–57.
B. J. Dear, J. J. Hung, T. M. Truskett and K. P. Johnston, Contrasting the influence of cationic amino acids on the viscosity and stability of a highly concentrated monoclonal antibody, Pharm Res, p. [Epub ahead of print], 2016.
Wang W. Advanced protein formulations. Protein Sci. 2015;24(7):1031–9.
Pindrus M, Shire SJ, Kelley RF, Demeule B, Wong R, Xu Y, et al. Solubility Challenges in high concentration monoclonal antibody formulations: relationship with amino acid sequence and intermolecular interactions. Mol Pharm. 2015;12(11):3896–907.
Kopito RR. Unfolding the secrets of protein aggregation. Trends Cell Biol. 2016;26(8):559–60.
R. Y. C. Huang, R. E. Iacob, S. R. Krystek, M. Jin, H. Wei, L. Tao, T. K. Das, A. A. Tymiak, J. R. Engen and G. Chen, Characterization of aggregation propensity of a human fc-fusion protein therapeutic by hidrogen/deuterium exchange mass spectrometry, J Am Soc Mass Spectrom, vol. Published online 15 August, 2016.
Barnett GV, Drenski M, Razinkov V, Reed WF, Roberts CJ. Identifying protein aggregation mechanisms and quantifying aggregation rates from combined monomer depletion and continuous scattering. Anal Biochem. 2016;511:80–91.
Schön A, Clarkson BR, Siles R, Ross R, Brown RK, Freire E. Denatured state aggregation parameters derived from concentration dependence of protein stability. Anal Biochem. 2015;488:45–50.
Sathish JG, Sethu S, Bielsky MC, de Haan L, French NS, Govindappa K, et al. Challenges and approaches for the development of safer immunomodulatory biologics. Nat Rev Drug Discov. 2013;12(4):306–24.
Welfle K, Misselwitz R, Hausdorf G, Höhne W, Welfle H. Conformation, pH-induced conformational changes, and thermal unfolding of anti-p24 (HIV-1) monoclonal antibody CB4-1 and its fab and fc fragments. Biochim Biophys Acta. 1999;1431:120–31.
Arzenšek D, Kuzman D, Podgornik R. Colloidal interactions between monoclonal antibodies in aqueous solutions. J Colloid Interface Sci. 2012; doi:10.1016/j.jcis.2012.06.055.
Zhang X, Zhang L, Tong H, Peng B, Rames M, Zhang S, et al. 3D structural fluctuation of IgG1 antibody revealed by individual particle electron tomography. Sci Rep. 2015;5:9830.
Yadav S, Liu J, Shire S, Kalonia D. Specific interactions in high concentration antibody solutions resulting in high viscosity. J Pharm Sci. 2010;99(3):1152–68.
Sorret L, DeWinter M, Schwartz D, Randolph T. Challenges in predicting protein-protein interactions from measurements of molecular diffusivity. Biophys J. 2016;111(9):1831–42.
Ben-Yaakov D, Andelman D, Podgornik R. Beyond standard Poisson-Boltzman theory: ion-specific interactions in aqueous solutions. J Phys Condens Matter. 2009;21(42):424106.
Bauer K, Gobel M, Schwab M, Schermeyer M, Hubbuch J. Concentration-dependent changes in apparent diffusion coefficients as indicator for colloidal stability of protein solutions. Int J Pharm. 2016;511(1):276–87.
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Zidar, M., Šušterič, A., Ravnik, M. et al. High Throughput Prediction Approach for Monoclonal Antibody Aggregation at High Concentration. Pharm Res 34, 1831–1839 (2017). https://doi.org/10.1007/s11095-017-2191-6
- aggregation modelling
- aggregation predictors
- intermolecular interactions
- protein aggregation