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Concentration Dependent Viscosity of Monoclonal Antibody Solutions: Explaining Experimental Behavior in Terms of Molecular Properties

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Abstract

Purpose

Early identification of monoclonal antibody candidates whose development, as high concentration (≥100 mg/mL) drug products, could prove challenging, due to high viscosity, can help define strategies for candidate engineering and selection.

Methods

Concentration dependent viscosities of 11 proprietary mAbs were measured. Sequence and structural features of the variable (Fv) regions were analyzed to understand viscosity behavior of the mAbs. Coarse-grained molecular simulations of two problematic mAbs were compared with that of a well behaved mAb.

Results

Net charge, ξ-potential and pI of Fv regions were found to correlate with viscosities of highly concentrated antibody solutions. Negative net charges on the Fv regions of two mAbs with poor viscosity behaviors facilitate attractive self-associations, causing them to diffuse slower than a well-behaved mAb with positive net charge on its Fv region. An empirically derived equation that connects aggregation propensity and pI of the Fv region with high concentration viscosity of the whole mAb was developed.

Conclusions

An Fv region-based qualitative screening profile was devised to flag mAb candidates whose development, as high concentration drug products, could prove challenging. This screen can facilitate developability risk assessment and mitigation strategies for antibody based therapeutics via rapid high throughput material-free screening.

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Abbreviations

<x 2>:

Average mean squared displacement

CDR:

Complementarity determining region

CH1:

First domain in constant portion of a heavy chain

CH2:

Second domain in constant portion of a heavy chain

CH3:

Third domain in constant portion of a heavy chain

cIEF:

Capillary Iso-Electric Focusing

cP:

Centi Poise

D:

Diffusion coefficient

DFv :

Dipole moment for Fv portion of a mAb

Dwhole mAb :

Dipole moment of a full length mAb

Fab:

Fragment antigen binding

Fc:

Fragment crystallize-able

Fv:

Fragment variable

GB/VI:

Generalized Born/Volume Integral

IgG:

Immunoglobulin G

kB :

Boltzmann constant

KD hydromomFv :

Kyte-Doolittle hydrophobicity moment for Fv portion of a mAb

LAMMPS:

Large-scale Atomic/Molecular Massively Parallel Simulator

mAb:

Monoclonal antibody

MOE:

Molecular Operations Environment

NormASAhphob:

Normalized hydrophobic surface area

NresFv :

Number of residues in the Fv region

PaggTANGOFv :

Normalized aggregation propensity of an Fv region computed by using TANGO

PaggWaltzFv :

Normalized aggregation propensity of an Fv region computed by using Waltz

pIFv :

Isoelectric point for Fv portion of a mAb

pIwhole mAb :

Isoelectric point of a full length mAb

RMSE:

Root Mean Square Error

RMSG:

Root Mean Square Gradient

rpm:

Revolutions per minute

UF/DF:

Ultra-Filtration/Dia-Filtration

UV–vis:

Ultraviolet–visible

VH :

Variable domain of a heavy chain

VL :

Variable domain of a light chain

Zapp whole mAb :

Apparent charge on a full length mAb

ZappFv :

Apparent charge on Fv portion

Zconstant regions :

Net charge on the constant regions of a mAb

ZFv :

Net charge on Fv portion of a mAb

Zwhole mAb :

Net charge on a full length mAb

η:

Measured viscosity of an antibody solution

ηrel :

Relative viscosity of an antibody

ηsp :

Specific viscosity of an antibody

μ:

Particle mobility

ξconstant regions :

Zeta potential of constant regions of a mAb

ξFv :

Zeta potential of Fv portion of a mAb

ξ-potential:

Zeta potential

ξwhole mAb :

Zeta potential of a full length mAb

References

  1. Liu J, Nguyen MD, Andya JD, Shire SJ. Reversible self-association increases the viscosity of a concentrated monoclonal antibody in aqueous solution. J Pharm Sci. 2005;94:1928–40.

    Article  PubMed  CAS  Google Scholar 

  2. Johnsonand HR, Lenhoff AM. Characterization and Suitability of Therapeutic Antibody Dense Phases for Subcutaneous Delivery. Mol Pharm 2013.

  3. Galush WJ, Le LN, Moore JM. Viscosity behavior of high-concentration protein mixtures. J Pharm Sci. 2012;101:1012–20.

    Article  PubMed  CAS  Google Scholar 

  4. Narasimhan C, Mach H, Shameem M. High-dose monoclonal antibodies via the subcutaneous route: challenges and technical solutions, an industry perspective. Ther Deliv. 2012;3:889–900.

    Article  PubMed  CAS  Google Scholar 

  5. Jezek J, Rides M, Derham B, Moore J, Cerasoli E, Simler R, et al. Viscosity of concentrated therapeutic protein compositions. Adv Drug Deliv Rev. 2011;63:1107–17.

    Article  PubMed  CAS  Google Scholar 

  6. Cromwell ME, Hilario E, Jacobson F. Protein aggregation and bioprocessing. AAPS J. 2006;8:E572–9.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Lilyestrom WG, Yadav S, Shire SJ, Scherer TM. Monoclonal antibody self-association, cluster formation, and rheology at high concentrations. J Phys Chem B. 2013;117:6373–84.

    Article  PubMed  CAS  Google Scholar 

  8. Guo Z, Chen A, Nassar RA, Helk B, Mueller C, Tang Y, et al. Structure-activity relationship for hydrophobic salts as viscosity-lowering excipients for concentrated solutions of monoclonal antibodies. Pharm Res. 2012;29:3102–9.

    Article  PubMed  CAS  Google Scholar 

  9. Kamerzell TJ, Pace AL, Li M, Danilenko DM, McDowell M, Gokarn YR, et al. Polar solvents decrease the viscosity of high concentration IgG1 solutions through hydrophobic solvation and interaction: formulation and biocompatibility considerations. J Pharm Sci. 2013;102:1182–93.

    Article  PubMed  CAS  Google Scholar 

  10. Srinivasan C, Weight AK, Bussemer T, Klibanov AM. Non-aqueous suspensions of antibodies are much less viscous than equally concentrated aqueous solutions. Pharm Res. 2013;30:1749–57.

    Article  PubMed  CAS  Google Scholar 

  11. He F, Becker GW, Litowski JR, Narhi LO, Brems DN, Razinkov VI. High-throughput dynamic light scattering method for measuring viscosity of concentrated protein solutions. Anal Biochem. 2010;399:141–3.

    Article  PubMed  CAS  Google Scholar 

  12. Connolly BD, Petry C, Yadav S, Demeule B, Ciaccio N, Moore JM, et al. Weak interactions govern the viscosity of concentrated antibody solutions: high-throughput analysis using the diffusion interaction parameter. Biophys J. 2012;103:69–78.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. He F, Woods CE, Trilisky E, Bower KM, Litowski JR, Kerwin BA, Becker GW, Narhi LO, Razinkov VI. Screening of monoclonal antibody formulations based on high-throughput thermostability and viscosity measurements: Design of experiment and statistical analysis. J Pharm Sci 2010.

  14. Agrawal NJ, Kumar S, Wang X, Helk B, Singh SK, Trout BL. Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions. J Pharm Sci. 2011;100:5081–95.

    Article  PubMed  CAS  Google Scholar 

  15. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci U S A. 2009;106:11937–42.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. Wang X, Das TK, Singh SK, Kumar S. Potential aggregation prone regions in biotherapeutics: a survey of commercial monoclonal antibodies. MAbs. 2009;1:254–67.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang X, Singh SK, Kumar S. Potential aggregation-prone regions in complementarity-determining regions of antibodies and their contribution towards antigen recognition: a computational analysis. Pharm Res. 2010;27:1512–29.

    Article  PubMed  CAS  Google Scholar 

  18. Wang X, Kumar S, Buck PM, Singh SK. Impact of deglycosylation and thermal stress on conformational stability of a full length murine IgG2a monoclonal antibody: Observations from molecular dynamics simulations. Proteins. 2013;81:443–60.

    Article  PubMed  CAS  Google Scholar 

  19. Buck PM, Kumar S, Wang X, Agrawal NJ, Trout BL, Singh SK. Computational methods to predict therapeutic protein aggregation. Methods Mol Biol. 2012;899:425–51.

    Article  PubMed  CAS  Google Scholar 

  20. Buck PM, Kumar S, Singh SK. Insights into the potential aggregation liabilities of the b12 Fab fragment via elevated temperature molecular dynamics. Protein Eng Des Sel. 2013;26:195–206.

    Article  PubMed  CAS  Google Scholar 

  21. Buck PM, Kumar S, Singh SK On the role of aggregation prone regions in protein evolution, stability, and enzymatic catalysis: insights from diverse analyses. PLoS Comp Biol. In press: 2013.

  22. Yadav S, Laue TM, Kalonia DS, Singh SN, Shire SJ. The influence of charge distribution on self-association and viscosity behavior of monoclonal antibody solutions. Mol Pharm. 2012;9:791–802.

    Article  PubMed  CAS  Google Scholar 

  23. Yadav S, Sreedhara A, Kanai S, Liu J, Lien S, Lowman H, et al. 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.

    Article  PubMed  CAS  Google Scholar 

  24. Harn N, Allan C, Oliver C, Middaugh CR. Highly concentrated monoclonal antibody solutions: direct analysis of physical structure and thermal stability. J Pharm Sci. 2007;96:532–46.

    Article  PubMed  CAS  Google Scholar 

  25. Kamerzell TJ, Kanai S, Liu J, Shire SJ, Wang YJ. Increasing IgG concentration modulates the conformational heterogeneity and bonding network that influence solution properties. J Phys Chem B. 2009;113:6109–18.

    Article  PubMed  CAS  Google Scholar 

  26. Neergaard MS, Kalonia DS, Parshad H, Nielsen AD, Moller EH, van de Weert M. Viscosity of high concentration protein formulations of monoclonal antibodies of the IgG1 and IgG4 subclass - prediction of viscosity through protein-protein interaction measurements. Eur J Pharm Sci. 2013;49:400–10.

    Article  PubMed  CAS  Google Scholar 

  27. Chaudhri A, Zarraga IE, Kamerzell TJ, Brandt JP, Patapoff TW, Shire SJ, et al. Coarse-grained modeling of the self-association of therapeutic monoclonal antibodies. J Phys Chem B. 2012;116:8045–57.

    Article  PubMed  CAS  Google Scholar 

  28. Chaudhri A, Zarraga IE, Yadav S, Patapoff TW, Shire SJ, Voth GA. The role of amino acid sequence in the self-association of therapeutic monoclonal antibodies: insights from coarse-grained modeling. J Phys Chem B. 2013;117:1269–79.

    Article  PubMed  CAS  Google Scholar 

  29. Yadav S, Shire SJ, Kalonia DS. Viscosity behavior of high-concentration monoclonal antibody solutions: correlation with interaction parameter and electroviscous effects. J Pharm Sci. 2012;101:998–1011.

    Article  PubMed  CAS  Google Scholar 

  30. Laue T. Proximity energies: a framework for understanding concentrated solutions. J Mol Recognit. 2012;25:165–73.

    Article  PubMed  CAS  Google Scholar 

  31. Yadav S, Shire SJ, Kalonia DS. Factors affecting the viscosity in high concentration solutions of different monoclonal antibodies. J Pharm Sci. 2010;99:4812–29.

    Article  PubMed  CAS  Google Scholar 

  32. Yadav S, Liu J, Shire SJ, Kalonia DS. Specific interactions in high concentration antibody solutions resulting in high viscosity. J Pharm Sci. 2010;99:1152–68.

    Article  PubMed  CAS  Google Scholar 

  33. Saito S, Hasegawa J, Kobayashi N, Kishi N, Uchiyama S, Fukui K. Behavior of monoclonal antibodies: relation between the second virial coefficient (B (2)) at low concentrations and aggregation propensity and viscosity at high concentrations. Pharm Res. 2012;29:397–410.

    Article  PubMed  CAS  Google Scholar 

  34. Warne NW. Development of high concentration protein biopharmaceuticals: the use of platform approaches in formulation development. Eur J Pharm Biopharm. 2011;78:208–12.

    Article  PubMed  CAS  Google Scholar 

  35. Bolton GR, Boesch AW, Basha J, Lacasse DP, Kelley BD, Acharya H. Effect of protein and solution properties on the Donnan effect during the ultrafiltration of proteins. Biotechnol Prog. 2011;27:140–52.

    Article  PubMed  CAS  Google Scholar 

  36. Creighton TE The physical and chemical basis of molecular biology, Helvetian Press, 2010.

  37. Saphire EO, Stanfield RL, Crispin MD, Morris G, Zwick MB, Pantophlet RA, et al. Crystal structure of an intact human IgG: antibody asymmetry, flexibility, and a guide for HIV-1 vaccine design. Adv Exp Med Biol. 2003;535:55–66.

    Article  PubMed  CAS  Google Scholar 

  38. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res. 2000;28:235–42.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  39. Fernandez-Escamilla AM, Rousseau F, Schymkowitz J, Serrano L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol. 2004;22:1302–6.

    Article  PubMed  CAS  Google Scholar 

  40. Maurer-Stroh S, Debulpaep M, Kuemmerer N, Lopez de la Paz M, Martins IC, Reumers J, et al. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods. 2010;7:237–42.

    Article  PubMed  CAS  Google Scholar 

  41. Mant CT, Kovacs JM, Kim HM, Pollock DD, Hodges RS. Intrinsic amino acid side-chain hydrophilicity/hydrophobicity coefficients determined by reversed-phase high-performance liquid chromatography of model peptides: comparison with other hydrophilicity/hydrophobicity scales. Biopolymers. 2009;92:573–95.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  42. Plimpton SJ. Fast parallel algorithms for short-range molecular dynamics. J Comp Physiol. 1995;117:1–19.

    Article  CAS  Google Scholar 

  43. Medhi J. Statistical methods: An introductory text. New Delhi: Wiley Eastern Limited; 1992.

    Google Scholar 

  44. Minton AP. Influence of macromolecular crowding upon the stability and state of association of proteins: predictions and observations. J Pharm Sci. 2005;94:1668–75.

    Article  PubMed  CAS  Google Scholar 

  45. Mooney M. The viscosity of a concentrated suspension of spherical particles. J Colloid Sci. 1951;6:162–70.

    Article  CAS  Google Scholar 

  46. Teplyakov A, Zhao Y, Malia TJ, Obmolova G, Gilliland GL. IgG2 Fc structure and the dynamic features of the IgG CH2-CH3 interface. Mol Immunol. 2013;56:131–9.

    Article  PubMed  CAS  Google Scholar 

  47. Davies AM, Rispens T, Ooijevaar-de Heer P, Gould HJ, Jefferis R, Aalberse RC, et al. Structural determinants of unique properties of human IgG4-Fc. J Mol Biol. 2014;426:630–44.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  48. Ely KR, Herron JN, Harker M, Edmundson AB. Three-dimensional structure of a light chain dimer crystallized in water. Conformational flexibility of a molecule in two crystal forms. J Mol Biol. 1989;210:601–15.

    Article  PubMed  CAS  Google Scholar 

  49. Vincentand KJ, Zurini M. Current strategies in antibody engineering: Fc engineering and pH-dependent antigen binding, bispecific antibodies and antibody drug conjugates. Biotechnol J. 2012;7:1444–50.

    Article  Google Scholar 

  50. Vafa O, Gilliland GL, Brezski RJ, Strake B, Wilkinson T, Lacy ER, et al. An engineered Fc variant of an IgG eliminates all immune effector functions via structural perturbations. Methods. 2014;65:114–26.

    Article  PubMed  CAS  Google Scholar 

  51. Saltzman WM, Radomsky ML, Whaley KJ, Cone RA. Antibody diffusion in human cervical mucus. Biophys J. 1994;66:508–15.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  52. Miao L, Qin H, Koehl P, Song J. Selective and specific ion binding on proteins at physiologically-relevant concentrations. FEBS Lett. 2011;585:3126–32.

    Article  PubMed  CAS  Google Scholar 

  53. Gokarn YR, Fesinmeyer RM, Saluja A, Razinkov V, Chase SF, Laue TM, et al. Effective charge measurements reveal selective and preferential accumulation of anions, but not cations, at the protein surface in dilute salt solutions. Protein Sci. 2011;20:580–7.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  54. Lehermayr C, Mahler HC, Mader K, Fischer S. Assessment of net charge and protein-protein interactions of different monoclonal antibodies. J Pharm Sci. 2011;100:2551–62.

    Article  PubMed  CAS  Google Scholar 

  55. Laue T. How solvent and excipient properties impact aggregation and viscosity. Palm Springs: CHI PepTalk; 2013.

    Google Scholar 

  56. Bas DC, Rogers DM, Jensen JH. Very fast prediction and rationalization of pKa values for protein-ligand complexes. Proteins. 2008;73:765–83.

    Article  PubMed  CAS  Google Scholar 

  57. Rostkowski M, Olsson MH, Sondergaard CR, Jensen JH. Graphical analysis of pH-dependent properties of proteins predicted using PROPKA. BMC Struct Biol. 2011;11:6.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  58. Kieseritzkyand G, Knapp EW. Optimizing pKa computation in proteins with pH adapted conformations. Proteins. 2008;71:1335–48.

    Article  Google Scholar 

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Acknowledgments and Disclosures

Pfizer Business Technology is thanked for computational facilities. A postdoctoral fellowship to P.M.B. by Pfizer Inc. is gratefully acknowledged. Drs. Donna Luisi, David Sek, Norman MacDougall and Robert Walters are acknowledged for several constructive discussions and help with experiments. S.K. acknowledges his discussions with Joseph McLaughlin on viscosity measurements and data manipulations. All authors are employees of Pfizer Inc.

LL, CB, PN, NL, DB and JL performed the experiments. SK performed data analyses and PMB performed Coarse-grained simulations. LL and SK wrote most of the manuscript. SK and SKS conceived the concept of using molecular modeling to understand viscosity. All authors read and contributed towards improving the manuscript draft.

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Authors and Affiliations

  1. Biotherapeutics Pharmaceutical Sciences Research and Development Pfizer Inc., 1 Burtt Road, Andover, Massachusetts, USA

    Li Li, Christopher Burns, Janelle Lavoie, Nicholas W. Warne, Pilarin Nichols, Nicholas Luksha & Davin Boardman

  2. Biotherapeutics Pharmaceutical Sciences Research and Development Pfizer Inc., 700 Chesterfield Parkway West, Chesterfield, Missouri, 63017, USA

    Sandeep Kumar, Patrick M. Buck & Satish K. Singh

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  1. Li Li
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  2. Sandeep Kumar
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  3. Patrick M. Buck
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  6. Satish K. Singh
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  7. Nicholas W. Warne
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Correspondence to Sandeep Kumar.

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Li, L., Kumar, S., Buck, P.M. et al. Concentration Dependent Viscosity of Monoclonal Antibody Solutions: Explaining Experimental Behavior in Terms of Molecular Properties. Pharm Res 31, 3161–3178 (2014). https://doi.org/10.1007/s11095-014-1409-0

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