Literature DB >> 26399600

Computational tool for the early screening of monoclonal antibodies for their viscosities.

Neeraj J Agrawal1,2, Bernhard Helk3, Sandeep Kumar4, Neil Mody5, Hasige A Sathish5, Hardeep S Samra5, Patrick M Buck4, Li Li6, Bernhardt L Trout1.   

Abstract

Highly concentrated antibody solutions often exhibit high viscosities, which present a number of challenges for antibody-drug development, manufacturing and administration. The antibody sequence is a key determinant for high viscosity of highly concentrated solutions; therefore, a sequence- or structure-based tool that can identify highly viscous antibodies from their sequence would be effective in ensuring that only antibodies with low viscosity progress to the development phase. Here, we present a spatial charge map (SCM) tool that can accurately identify highly viscous antibodies from their sequence alone (using homology modeling to determine the 3-dimensional structures). The SCM tool has been extensively validated at 3 different organizations, and has proved successful in correctly identifying highly viscous antibodies. As a quantitative tool, SCM is amenable to high-throughput automated analysis, and can be effectively implemented during the antibody screening or engineering phase for the selection of low-viscosity antibodies.

Keywords:  antibodies; biotherapeutics; computer simulation; spatial charge map; viscosity

Mesh:

Substances:

Year:  2015        PMID: 26399600      PMCID: PMC4966561          DOI: 10.1080/19420862.2015.1099773

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


  19 in total

1.  WAM: an improved algorithm for modelling antibodies on the WEB.

Authors:  N R Whitelegg; A R Rees
Journal:  Protein Eng       Date:  2000-12

2.  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

3.  Viscosity analysis of high concentration bovine serum albumin aqueous solutions.

Authors:  Sandeep Yadav; Steven J Shire; Devendra S Kalonia
Journal:  Pharm Res       Date:  2011-04-14       Impact factor: 4.200

4.  Developability index: a rapid in silico tool for the screening of antibody aggregation propensity.

Authors:  Timothy M Lauer; Neeraj J Agrawal; Naresh Chennamsetty; Kamal Egodage; Bernhard Helk; Bernhardt L Trout
Journal:  J Pharm Sci       Date:  2011-09-20       Impact factor: 3.534

5.  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

6.  Highly viscous antibody solutions are a consequence of network formation caused by domain-domain electrostatic complementarities: insights from coarse-grained simulations.

Authors:  Patrick M Buck; Anuj Chaudhri; Sandeep Kumar; Satish K Singh
Journal:  Mol Pharm       Date:  2014-11-20       Impact factor: 4.939

7.  Rational design of viscosity reducing mutants of a monoclonal antibody: hydrophobic versus electrostatic inter-molecular interactions.

Authors:  Pilarin Nichols; Li Li; Sandeep Kumar; Patrick M Buck; Satish K Singh; Sumit Goswami; Bryan Balthazor; Tami R Conley; David Sek; Martin J Allen
Journal:  MAbs       Date:  2015       Impact factor: 5.857

8.  The role of amino acid sequence in the self-association of therapeutic monoclonal antibodies: insights from coarse-grained modeling.

Authors:  Anuj Chaudhri; Isidro E Zarraga; Sandeep Yadav; Thomas W Patapoff; Steven J Shire; Gregory A Voth
Journal:  J Phys Chem B       Date:  2013-01-25       Impact factor: 2.991

9.  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

10.  Understanding and modulating opalescence and viscosity in a monoclonal antibody formulation.

Authors:  Branden A Salinas; Hasige A Sathish; Steven M Bishop; Nick Harn; John F Carpenter; Theodore W Randolph
Journal:  J Pharm Sci       Date:  2010-01       Impact factor: 3.534

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  35 in total

Review 1.  Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development.

Authors:  Dheeraj S Tomar; Sandeep Kumar; Satish K Singh; Sumit Goswami; Li Li
Journal:  MAbs       Date:  2016-01-06       Impact factor: 5.857

Review 2.  Structure, heterogeneity and developability assessment of therapeutic antibodies.

Authors:  Yingda Xu; Dongdong Wang; Bruce Mason; Tony Rossomando; Ning Li; Dingjiang Liu; Jason K Cheung; Wei Xu; Smita Raghava; Amit Katiyar; Christine Nowak; Tao Xiang; Diane D Dong; Joanne Sun; Alain Beck; Hongcheng Liu
Journal:  MAbs       Date:  2018-12-17       Impact factor: 5.857

3.  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

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.  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

7.  Understanding the Role of Preferential Exclusion of Sugars and Polyols from Native State IgG1 Monoclonal Antibodies and its Effect on Aggregation and Reversible Self-Association.

Authors:  Chaitanya M Sudrik; Theresa Cloutier; Neil Mody; Hasige A Sathish; Bernhardt L Trout
Journal:  Pharm Res       Date:  2019-05-24       Impact factor: 4.200

8.  High-throughput developability assays enable library-scale identification of producible protein scaffold variants.

Authors:  Alexander W Golinski; Katelynn M Mischler; Sidharth Laxminarayan; Nicole L Neurock; Matthew Fossing; Hannah Pichman; Stefano Martiniani; Benjamin J Hackel
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-08       Impact factor: 11.205

9.  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

10.  Multiscale Coarse-Grained Approach to Investigate Self-Association of Antibodies.

Authors:  Saeed Izadi; Thomas W Patapoff; Benjamin T Walters
Journal:  Biophys J       Date:  2020-04-29       Impact factor: 4.033

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