Literature DB >> 21497188

Predicting solution aggregation rates for therapeutic proteins: approaches and challenges.

Christopher J Roberts1, Tapan K Das, Erinc Sahin.   

Abstract

Non-native aggregation is a common concern during therapeutic protein product development and manufacturing, particularly for liquid dosage forms. Because aggregates are often net irreversible under the conditions that they form, controlling aggregate levels requires control of aggregation rates across a range of solution conditions. Rational design of product formulation(s) would therefore benefit greatly from methods to accurately predict aggregation rates. This article focuses on the principles underlying current rate-prediction approaches for non-native aggregation, the limitations and strengths of different approaches, and illustrative examples from the authors' laboratories. The analysis highlights a number of reasons why accurate prediction of aggregation rates remains an outstanding challenge, and suggests some of the important areas for research to ultimately enable improved predictive capabilities in the future.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21497188     DOI: 10.1016/j.ijpharm.2011.03.064

Source DB:  PubMed          Journal:  Int J Pharm        ISSN: 0378-5173            Impact factor:   5.875


  26 in total

Review 1.  Protein particulate detection issues in biotherapeutics development--current status.

Authors:  Tapan K Das
Journal:  AAPS PharmSciTech       Date:  2012-05-08       Impact factor: 3.246

Review 2.  High-throughput biophysical analysis of protein therapeutics to examine interrelationships between aggregate formation and conformational stability.

Authors:  Rajoshi Chaudhuri; Yuan Cheng; C Russell Middaugh; David B Volkin
Journal:  AAPS J       Date:  2013-10-31       Impact factor: 4.009

3.  Probing structurally altered and aggregated states of therapeutically relevant proteins using GroEL coupled to bio-layer interferometry.

Authors:  Subhashchandra Naik; Ozan S Kumru; Melissa Cullom; Srivalli N Telikepalli; Elizabeth Lindboe; Taylor L Roop; Sangeeta B Joshi; Divya Amin; Phillip Gao; C Russell Middaugh; David B Volkin; Mark T Fisher
Journal:  Protein Sci       Date:  2014-07-28       Impact factor: 6.725

4.  High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy.

Authors:  Yuqi Liu; Isabelle Caffry; Jiemin Wu; Steven B Geng; Tushar Jain; Tingwan Sun; Felicia Reid; Yuan Cao; Patricia Estep; Yao Yu; Maximiliano Vásquez; Peter M Tessier; Yingda Xu
Journal:  MAbs       Date:  2013-12-06       Impact factor: 5.857

5.  A comparison of biophysical characterization techniques in predicting monoclonal antibody stability.

Authors:  Geetha Thiagarajan; Andrew Semple; Jose K James; Jason K Cheung; Mohammed Shameem
Journal:  MAbs       Date:  2016-05-21       Impact factor: 5.857

6.  Predicting unfolding thermodynamics and stable intermediates for alanine-rich helical peptides with the aid of coarse-grained molecular simulation.

Authors:  Cesar Calero-Rubio; Bradford Paik; Xinqiao Jia; Kristi L Kiick; Christopher J Roberts
Journal:  Biophys Chem       Date:  2016-07-22       Impact factor: 2.352

7.  Thermodynamic Unfolding and Aggregation Fingerprints of Monoclonal Antibodies Using Thermal Profiling.

Authors:  Richard Melien; Patrick Garidel; Dariush Hinderberger; Michaela Blech
Journal:  Pharm Res       Date:  2020-04-01       Impact factor: 4.200

8.  Parallel chromatography and in situ scattering to interrogate competing protein aggregation pathways.

Authors:  Diana Gomes; Rebecca K Kalman; Rebecca K Pagels; Miguel A Rodrigues; Christopher J Roberts
Journal:  Protein Sci       Date:  2018-06-13       Impact factor: 6.725

9.  Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation.

Authors:  Marco A Blanco; Erinc Sahin; Anne S Robinson; Christopher J Roberts
Journal:  J Phys Chem B       Date:  2013-12-10       Impact factor: 2.991

Review 10.  Protein aggregation and its impact on product quality.

Authors:  Christopher J Roberts
Journal:  Curr Opin Biotechnol       Date:  2014-08-28       Impact factor: 9.740

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