Literature DB >> 27050875

Mitigation of reversible self-association and viscosity in a human IgG1 monoclonal antibody by rational, structure-guided Fv engineering.

James C Geoghegan1, Ryan Fleming1, Melissa Damschroder1, Steven M Bishop2, Hasige A Sathish2, Reza Esfandiary2.   

Abstract

Undesired solution behaviors such as reversible self-association (RSA), high viscosity, and liquid-liquid phase separation can introduce substantial challenges during development of monoclonal antibody formulations. Although a global mechanistic understanding of RSA (i.e., native and reversible protein-protein interactions) is sufficient to develop robust formulation controls, its mitigation via protein engineering requires knowledge of the sites of protein-protein interactions. In the study reported here, we coupled our previous hydrogen-deuterium exchange mass spectrometry findings with structural modeling and in vitro screening to identify the residues responsible for RSA of a model IgG1 monoclonal antibody (mAb-C), and rationally engineered variants with improved solution properties (i.e., reduced RSA and viscosity). Our data show that mutation of either solvent-exposed aromatic residues within the heavy and light chain variable regions or buried residues within the heavy chain/light chain interface can significantly mitigate RSA and viscosity by reducing the IgG's surface hydrophobicity. The engineering strategy described here highlights the utility of integrating complementary experimental and in silico methods to identify mutations that can improve developability, in particular, high concentration solution properties, of candidate therapeutic antibodies.

Entities:  

Keywords:  AC-SINS; Antibody engineering; DLS; antibody developability; homology modeling; monoclonal antibody; reversible self-association; viscosity

Mesh:

Substances:

Year:  2016        PMID: 27050875      PMCID: PMC4968137          DOI: 10.1080/19420862.2016.1171444

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


  43 in total

Review 1.  Proximity energies: a framework for understanding concentrated solutions.

Authors:  Tom Laue
Journal:  J Mol Recognit       Date:  2012-03       Impact factor: 2.137

2.  Rheological and syringeability properties of highly concentrated human polyclonal immunoglobulin solutions.

Authors:  V Burckbuchler; G Mekhloufi; A Paillard Giteau; J L Grossiord; S Huille; F Agnely
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3.  Therapeutic Antibody Engineering To Improve Viscosity and Phase Separation Guided by Crystal Structure.

Authors:  Chi-Kin Chow; Barrett W Allan; Qing Chai; Shane Atwell; Jirong Lu
Journal:  Mol Pharm       Date:  2016-02-18       Impact factor: 4.939

Review 4.  Antibody structure, instability, and formulation.

Authors:  Wei Wang; Satish Singh; David L Zeng; Kevin King; Sandeep Nema
Journal:  J Pharm Sci       Date:  2007-01       Impact factor: 3.534

5.  High concentration formulations of recombinant human interleukin-1 receptor antagonist: II. Aggregation kinetics.

Authors:  John R Alford; Brent S Kendrick; John F Carpenter; Theodore W Randolph
Journal:  J Pharm Sci       Date:  2008-08       Impact factor: 3.534

6.  In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability.

Authors:  Vikas K Sharma; Thomas W Patapoff; Bruce Kabakoff; Satyan Pai; Eric Hilario; Boyan Zhang; Charlene Li; Oleg Borisov; Robert F Kelley; Ilya Chorny; Joe Z Zhou; Ken A Dill; Trevor E Swartz
Journal:  Proc Natl Acad Sci U S A       Date:  2014-12-15       Impact factor: 11.205

7.  A novel screening method to assess developability of antibody-like molecules.

Authors:  Neeraj Kohli; Nidhi Jain; Melissa L Geddie; Maja Razlog; Lihui Xu; Alexey A Lugovskoy
Journal:  MAbs       Date:  2015       Impact factor: 5.857

8.  Rapid analysis of antibody self-association in complex mixtures using immunogold conjugates.

Authors:  Shantanu V Sule; Craig D Dickinson; Jirong Lu; Chi-Kin Chow; Peter M Tessier
Journal:  Mol Pharm       Date:  2013-03-08       Impact factor: 4.939

9.  Structure-based engineering of a monoclonal antibody for improved solubility.

Authors:  Sheng-Jiun Wu; Jinquan Luo; Karyn T O'Neil; James Kang; Eilyn R Lacy; Gabriela Canziani; Audrey Baker; Maggie Huang; Qing Mike Tang; T Shantha Raju; Steven A Jacobs; Alexey Teplyakov; Gary L Gilliland; Yiqing Feng
Journal:  Protein Eng Des Sel       Date:  2010-06-11       Impact factor: 1.650

10.  IMGT/GENE-DB: a comprehensive database for human and mouse immunoglobulin and T cell receptor genes.

Authors:  Véronique Giudicelli; Denys Chaume; Marie-Paule Lefranc
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

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  21 in total

1.  Process optimization and protein engineering mitigated manufacturing challenges of a monoclonal antibody with liquid-liquid phase separation issue by disrupting inter-molecule electrostatic interactions.

Authors:  Qun Du; Melissa Damschroder; Timothy M Pabst; Alan K Hunter; William K Wang; Haibin Luo
Journal:  MAbs       Date:  2019-04-14       Impact factor: 5.857

2.  In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Authors:  Adriana-Michelle Wolf Pérez; Pietro Sormanni; Jonathan Sonne Andersen; Laila Ismail Sakhnini; Ileana Rodriguez-Leon; Jais Rose Bjelke; Annette Juhl Gajhede; Leonardo De Maria; Daniel E Otzen; Michele Vendruscolo; Nikolai Lorenzen
Journal:  MAbs       Date:  2019-01-18       Impact factor: 5.857

3.  Energetic Dissection of Mab-Specific Reversible Self-Association Reveals Unique Thermodynamic Signatures.

Authors:  Mandi M Hopkins; Arun Parupudi; Jared S Bee; David L Bain
Journal:  Pharm Res       Date:  2021-02-18       Impact factor: 4.200

4.  In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions.

Authors:  Dheeraj S Tomar; Li Li; Matthew P Broulidakis; Nicholas G Luksha; Christopher T Burns; Satish K Singh; Sandeep Kumar
Journal:  MAbs       Date:  2017-01-26       Impact factor: 5.857

5.  In Silico Prediction of Diffusion Interaction Parameter (kD), a Key Indicator of Antibody Solution Behaviors.

Authors:  Dheeraj S Tomar; Satish K Singh; Li Li; Matthew P Broulidakis; Sandeep Kumar
Journal:  Pharm Res       Date:  2018-08-20       Impact factor: 4.200

Review 6.  Factors affecting the physical stability (aggregation) of peptide therapeutics.

Authors:  Karolina L Zapadka; Frederik J Becher; A L Gomes Dos Santos; Sophie E Jackson
Journal:  Interface Focus       Date:  2017-10-20       Impact factor: 3.906

7.  Phase-Appropriate Application of Analytical Methods to Monitor Subvisible Particles Across the Biotherapeutic Drug Product Life Cycle.

Authors:  Roman Mathaes; Linda Narhi; Andrea Hawe; Anja Matter; Karoline Bechtold-Peters; Sophia Kenrick; Sambit Kar; Olga Laskina; John Carpenter; Richard Cavicchi; Ellen Koepf; E Neil Lewis; Rukman De Silva; Dean Ripple
Journal:  AAPS J       Date:  2019-10-30       Impact factor: 4.009

Review 8.  Strategies for Precise Engineering and Conjugation of Antibody Targeted-nanoparticles for Cancer Therapy.

Authors:  Yuan-Yuan Guo; Lu Huang; Zhi-Ping Zhang; De-Hao Fu
Journal:  Curr Med Sci       Date:  2020-07-17

9.  Charge Shielding Prevents Aggregation of Supercharged GFP Variants at High Protein Concentration.

Authors:  Joshua R Laber; Barton J Dear; Matheus L Martins; Devin E Jackson; Andrea DiVenere; Jimmy D Gollihar; Andrew D Ellington; Thomas M Truskett; Keith P Johnston; Jennifer A Maynard
Journal:  Mol Pharm       Date:  2017-09-18       Impact factor: 4.939

10.  Charge-mediated Fab-Fc interactions in an IgG1 antibody induce reversible self-association, cluster formation, and elevated viscosity.

Authors:  Jayant Arora; Yue Hu; Reza Esfandiary; Hasige A Sathish; Steven M Bishop; Sangeeta B Joshi; C Russell Middaugh; David B Volkin; David D Weis
Journal:  MAbs       Date:  2016-08-25       Impact factor: 5.857

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