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Template-Assisted Crystallization Behavior in Stirred Solutions of the Monoclonal Antibody Anti-CD20: Probability Distributions of Induction Times.

Charline J J GerardMaria L BriugliaNazer RajoubTeresa F MastropietroWenqian ChenJerry Y Y HengGianluca Di ProfioJoop H Ter Horst
Published in: Crystal growth & design (2022)
We present a method to determine the template crystallization behavior of proteins. This method is a statistical approach that accounts for the stochastic nature of nucleation. It makes use of batch-wise experiments under stirring conditions in volumes smaller than 0.3 mL to save material while mimicking larger-scale processes. To validate our method, it was applied to the crystallization of a monoclonal antibody of pharmaceutical interest, Anti-CD20. First, we determined the Anti-CD20 phase diagram in a PEG-400/Na 2 SO 4 /water system using the batch method, as, to date, no such data on Anti-CD20 solubility have been reported. Then, the probability distribution of induction times was determined experimentally, in the presence of various mesoporous silica template particles, and crystallization of Anti-CD20 in the absence of templates was compared to template-assisted crystallization. The probability distribution of induction times is shown to be a suitable method to determine the effect of template particles on protein crystallization. The induction time distribution allows for the determination of two key parameters of nucleation, the nucleation rate and the growth time. This study shows that the use of silica particles leads to faster crystallization and a higher nucleation rate. The template particle characteristics are shown to be critical parameters to efficiently promote protein crystallization.
Keyphrases
  • monoclonal antibody
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  • solid phase extraction
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  • deep learning
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