Literature DB >> 26736022

Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development.

Dheeraj S Tomar1, Sandeep Kumar1, Satish K Singh1, Sumit Goswami1, Li Li2.   

Abstract

Effective translation of breakthrough discoveries into innovative products in the clinic requires proactive mitigation or elimination of several drug development challenges. These challenges can vary depending upon the type of drug molecule. In the case of therapeutic antibody candidates, a commonly encountered challenge is high viscosity of the concentrated antibody solutions. Concentration-dependent viscosity behaviors of mAbs and other biologic entities may depend on pairwise and higher-order intermolecular interactions, non-native aggregation, and concentration-dependent fluctuations of various antibody regions. This article reviews our current understanding of molecular origins of viscosity behaviors of antibody solutions. We discuss general strategies and guidelines to select low viscosity candidates or optimize lead candidates for lower viscosity at early drug discovery stages. Moreover, strategies for formulation optimization and excipient design are also presented for candidates already in advanced product development stages. Potential future directions for research in this field are also explored.

Keywords:  Electrostatics; high concentration; hydrophobicity; intermolecular interactions; monoclonal antibody solution; networks; viscosity

Mesh:

Substances:

Year:  2016        PMID: 26736022      PMCID: PMC5074600          DOI: 10.1080/19420862.2015.1128606

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


  72 in total

1.  Construction, MD simulation, and hydrodynamic validation of an all-atom model of a monoclonal IgG antibody.

Authors:  J Paul Brandt; Thomas W Patapoff; Sergio R Aragon
Journal:  Biophys J       Date:  2010-08-04       Impact factor: 4.033

2.  Aggregate structure, morphology and the effect of aggregation mechanisms on viscosity at elevated protein concentrations.

Authors:  Gregory V Barnett; Wei Qi; Samiul Amin; E Neil Lewis; Christopher J Roberts
Journal:  Biophys Chem       Date:  2015-07-17       Impact factor: 2.352

3.  A recombinant human enzyme for enhanced interstitial transport of therapeutics.

Authors:  L H Bookbinder; A Hofer; M F Haller; M L Zepeda; G-A Keller; J E Lim; T S Edgington; H M Shepard; J S Patton; G I Frost
Journal:  J Control Release       Date:  2006-06-07       Impact factor: 9.776

Review 4.  Perspective on the Martini model.

Authors:  Siewert J Marrink; D Peter Tieleman
Journal:  Chem Soc Rev       Date:  2013-08-21       Impact factor: 54.564

5.  Behavior of monoclonal antibodies: relation between the second virial coefficient (B (2)) at low concentrations and aggregation propensity and viscosity at high concentrations.

Authors:  Shuntaro Saito; Jun Hasegawa; Naoki Kobayashi; Naoyuki Kishi; Susumu Uchiyama; Kiichi Fukui
Journal:  Pharm Res       Date:  2011-08-19       Impact factor: 4.200

6.  Molecular simulations of the pairwise interaction of monoclonal antibodies.

Authors:  Mauro Lapelosa; Thomas W Patapoff; Isidro E Zarraga
Journal:  J Phys Chem B       Date:  2014-11-10       Impact factor: 2.991

7.  Regulatory watch: Innovation in biologic new molecular entities: 1986-2014.

Authors:  Kathleen L Miller; Michael Lanthier
Journal:  Nat Rev Drug Discov       Date:  2015-02       Impact factor: 84.694

8.  Impact of deglycosylation and thermal stress on conformational stability of a full length murine IgG2a monoclonal antibody: observations from molecular dynamics simulations.

Authors:  Xiaoling Wang; Sandeep Kumar; Patrick M Buck; Satish K Singh
Journal:  Proteins       Date:  2012-11-12

9.  The concentration-dependence of macromolecular parameters.

Authors:  S E Harding; P Johnson
Journal:  Biochem J       Date:  1985-11-01       Impact factor: 3.857

10.  SDA 7: A modular and parallel implementation of the simulation of diffusional association software.

Authors:  Michael Martinez; Neil J Bruce; Julia Romanowska; Daria B Kokh; Musa Ozboyaci; Xiaofeng Yu; Mehmet Ali Öztürk; Stefan Richter; Rebecca C Wade
Journal:  J Comput Chem       Date:  2015-06-29       Impact factor: 3.376

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  33 in total

1.  Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Authors:  Derek M Mason; Simon Friedensohn; Cédric R Weber; Christian Jordi; Bastian Wagner; Simon M Meng; Roy A Ehling; Lucia Bonati; Jan Dahinden; Pablo Gainza; Bruno E Correia; Sai T Reddy
Journal:  Nat Biomed Eng       Date:  2021-04-15       Impact factor: 25.671

2.  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

3.  Challenges in Predicting Protein-Protein Interactions from Measurements of Molecular Diffusivity.

Authors:  Lea L Sorret; Madison A DeWinter; Daniel K Schwartz; Theodore W Randolph
Journal:  Biophys J       Date:  2016-11-01       Impact factor: 4.033

4.  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

5.  Preferential interactions of trehalose, L-arginine.HCl and sodium chloride with therapeutically relevant IgG1 monoclonal antibodies.

Authors:  Chaitanya Sudrik; Theresa Cloutier; Phuong Pham; Hardeep S Samra; Bernhardt L Trout
Journal:  MAbs       Date:  2017-07-31       Impact factor: 5.857

6.  Predicting Protein-Protein Interactions of Concentrated Antibody Solutions Using Dilute Solution Data and Coarse-Grained Molecular Models.

Authors:  Cesar Calero-Rubio; Ranendu Ghosh; Atul Saluja; Christopher J Roberts
Journal:  J Pharm Sci       Date:  2017-12-21       Impact factor: 3.534

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

Authors:  James C Geoghegan; Ryan Fleming; Melissa Damschroder; Steven M Bishop; Hasige A Sathish; Reza Esfandiary
Journal:  MAbs       Date:  2016-04-06       Impact factor: 5.857

8.  Weak IgG self- and hetero-association characterized by fluorescence analytical ultracentrifugation.

Authors:  Danlin Yang; John J Correia; Walter F Stafford Iii; Christopher J Roberts; Sanjaya Singh; David Hayes; Rachel Kroe-Barrett; Andrew Nixon; Thomas M Laue
Journal:  Protein Sci       Date:  2018-07       Impact factor: 6.725

9.  Modeling the depletion effect caused by an addition of polymer to monoclonal antibody solutions.

Authors:  Yu V Kalyuzhnyi; V Vlachy
Journal:  J Phys Condens Matter       Date:  2018-11-12       Impact factor: 2.333

10.  Assessment of Antibody Self-Interaction by Bio-Layer-Interferometry as a Tool for Early Stage Formulation Development.

Authors:  Martin Domnowski; Jan Jaehrling; Wolfgang Frieß
Journal:  Pharm Res       Date:  2020-01-08       Impact factor: 4.200

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