Literature DB >> 32679214

Evaluation of Super Refined™ Polysorbate 20 With Respect to Polysorbate Degradation, Particle Formation and Protein Stability.

Nidhi Doshi1, Raphael Fish1, Karina Padilla1, Sandeep Yadav2.   

Abstract

Super Refined™ and Tween™ 20 HP polysorbate 20 (PS20) are two commercially available compendial grades of PS20 frequently used in biopharmaceutical formulations as protein stabilizing surfactants. PS20 degradation has been a major concern recently for potentially limiting drug product shelf life due to free fatty acid particle formation. This work is a side-by-side comparison of SR and HP PS20 in terms of PS20 degradation, particle formation and protein stability. The use of SR grade PS20 results in higher levels of oxidative PS20 degradation, protein oxidation, peroxides and protein aggregation, and therefore requires addition of methionine as an antioxidant to mitigate these issues. No clear root cause was identified as to why SR PS20 is more prone to oxidative degradation. This work also suggests that SR PS20 is less prone to particle formation than HP PS20 when there is preferential degradation of mono-esters of PS20, while more susceptible to particle formation when there is preferential degradation of higher order esters of PS20. Overall, this publication summarizes the potential risks and benefits of SR PS20 compared to HP PS20 to enable a formulator to make an informed decision when choosing between the two surfactant grades in their drug product formulations.
Copyright © 2020. Published by Elsevier Inc.

Entities:  

Keywords:  Excipients; Formulation; Oxidation; Particles; Polysorbate 20; Polysorbate degradation; Super Refined™; Surfactants; Tween™ 20; mAb

Mesh:

Substances:

Year:  2020        PMID: 32679214     DOI: 10.1016/j.xphs.2020.06.030

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  2 in total

1.  Evaluating a Modified High Purity Polysorbate 20 Designed to Reduce the Risk of Free Fatty Acid Particle Formation.

Authors:  Nidhi Doshi; Kyle Ritchie; Tamanna Shobha; Jamie Giddings; Kathrin Gregoritza; Rosalynn Taing; Stephen Rumbelow; Jeff Chu; Anthony Tomlinson; Aadithya Kannan; Miguel Saggu; Si Kai Cai; Victor Nicoulin; Wenqiang Liu; Steve Russell; Lin Luis; Sandeep Yadav
Journal:  Pharm Res       Date:  2021-09-08       Impact factor: 4.200

2.  Combining Machine Learning and Backgrounded Membrane Imaging: A Case Study in Comparing and Classifying Different Types of Biopharmaceutically Relevant Particles.

Authors:  Christopher P Calderon; Ana Krhač Levačić; Constanze Helbig; Klaus Wuchner; Tim Menzen
Journal:  J Pharm Sci       Date:  2022-06-01       Impact factor: 3.784

  2 in total

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