Literature DB >> 32938318

Molecular computations of preferential interactions of proline, arginine.HCl, and NaCl with IgG1 antibodies and their impact on aggregation and viscosity.

Theresa K Cloutier1, Chaitanya Sudrik1, Neil Mody2, Sathish A Hasige2, Bernhardt L Trout1.   

Abstract

Preferential interactions of excipients with the antibody surface govern their effect on the stability of antibodies in solution. We probed the preferential interactions of proline, arginine.HCl (Arg.HCl), and NaCl with three therapeutically relevant IgG1 antibodies via experiment and simulation. With simulations, we examined how excipients interacted with different types of surface patches in the variable region (Fv). For example, proline interacted most strongly with aromatic surfaces, Arg.HCl was included near negative residues, and NaCl was excluded from negative residues and certain hydrophobic regions. The differences in interaction of different excipients with the same surface patch on an antibody may be responsible for variations in the antibody's aggregation, viscosity, and self-association behaviors in each excipient. Proline reduced self-association for all three antibodies and reduced aggregation for the antibody with an association-limited aggregation mechanism. The effects of Arg.HCl and NaCl on aggregation and viscosity were highly dependent on the surface charge distribution and the extent of exclusion from highly hydrophobic patches. At pH 5.5, both tended to increase the aggregation of an antibody with a strongly positive charge on the Fv, while only NaCl reduced the aggregation of the antibody with a large negative charge patch on the Fv. Arg.HCl reduced the viscosities of antibodies with either a hydrophobicity-driven mechanism or a charge-driven mechanism. Analysis of this data presents a framework for understanding how amino acid and ionic excipients interact with different protein surfaces, and how these interactions translate to the observed stability behavior.

Entities:  

Keywords:  Nacl; Preferential interaction coefficients; aggregation; arginine.HCl; formulation; mAbs; proline; viscosity

Year:  2020        PMID: 32938318      PMCID: PMC7531574          DOI: 10.1080/19420862.2020.1816312

Source DB:  PubMed          Journal:  MAbs        ISSN: 1942-0862            Impact factor:   5.857


  43 in total

1.  Interaction of arginine with proteins and the mechanism by which it inhibits aggregation.

Authors:  Diwakar Shukla; Bernhardt L Trout
Journal:  J Phys Chem B       Date:  2010-10-28       Impact factor: 2.991

2.  Solubilization of aromatic and hydrophobic moieties by arginine in aqueous solutions.

Authors:  Jianguo Li; Manju Garg; Dhawal Shah; Raj Rajagopalan
Journal:  J Chem Phys       Date:  2010-08-07       Impact factor: 3.488

3.  Preferential interaction coefficients of proteins in aqueous arginine solutions and their molecular origins.

Authors:  Diwakar Shukla; Bernhardt L Trout
Journal:  J Phys Chem B       Date:  2010-12-27       Impact factor: 2.991

Review 4.  Formulation and delivery issues for monoclonal antibody therapeutics.

Authors:  Ann L Daugherty; Randall J Mrsny
Journal:  Adv Drug Deliv Rev       Date:  2006-05-22       Impact factor: 15.470

5.  Design of therapeutic proteins with enhanced stability.

Authors:  Naresh Chennamsetty; Vladimir Voynov; Veysel Kayser; Bernhard Helk; Bernhardt L Trout
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-01       Impact factor: 11.205

6.  Molecular computations of preferential interaction coefficients of proteins.

Authors:  Diwakar Shukla; Chetan Shinde; Bernhardt L Trout
Journal:  J Phys Chem B       Date:  2009-09-17       Impact factor: 2.991

7.  Contrasting the Influence of Cationic Amino Acids on the Viscosity and Stability of a Highly Concentrated Monoclonal Antibody.

Authors:  Barton J Dear; Jessica J Hung; Thomas M Truskett; Keith P Johnston
Journal:  Pharm Res       Date:  2016-11-11       Impact factor: 4.200

8.  Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties.

Authors:  Li Li; Sandeep Kumar; Patrick M Buck; Christopher Burns; Janelle Lavoie; Satish K Singh; Nicholas W Warne; Pilarin Nichols; Nicholas Luksha; Davin Boardman
Journal:  Pharm Res       Date:  2014-06-07       Impact factor: 4.200

Review 9.  Anatomy of the antibody molecule.

Authors:  E A Padlan
Journal:  Mol Immunol       Date:  1994-02       Impact factor: 4.407

10.  Improving Viscosity and Stability of a Highly Concentrated Monoclonal Antibody Solution with Concentrated Proline.

Authors:  Jessica J Hung; Barton J Dear; Aileen K Dinin; Ameya U Borwankar; Sumarth K Mehta; Thomas T Truskett; Keith P Johnston
Journal:  Pharm Res       Date:  2018-04-30       Impact factor: 4.200

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  4 in total

Review 1.  Computational models for studying physical instabilities in high concentration biotherapeutic formulations.

Authors:  Marco A Blanco
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

2.  Ionic Liquid-Based Strategy for Predicting Protein Aggregation Propensity and Thermodynamic Stability.

Authors:  Talia A Shmool; Laura K Martin; Richard P Matthews; Jason P Hallett
Journal:  JACS Au       Date:  2022-09-09

3.  The impact of forced degradation conditions on mAb dimer formation and subsequent influence on aggregation propensity.

Authors:  Michael J Knight; Léontine Floret; Nisha Patel; John O'Hara; Elizabeth Rodriguez
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 6.440

4.  An experimental approach probing the conformational transitions and energy landscape of antibodies: a glimmer of hope for reviving lost therapeutic candidates using ionic liquid.

Authors:  Talia A Shmool; Laura K Martin; Liem Bui-Le; Ignacio Moya-Ramirez; Pavlos Kotidis; Richard P Matthews; Gerhard A Venter; Cleo Kontoravdi; Karen M Polizzi; Jason P Hallett
Journal:  Chem Sci       Date:  2021-06-22       Impact factor: 9.825

  4 in total

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