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A model-based approach for the rational design of the freeze-thawing of a protein-based formulation.

Andrea ArsiccioLivio MarencoRoberto Pisano
Published in: Pharmaceutical development and technology (2020)
Proteins are unstable molecules that may be severely injured by stresses encountered during freeze-thawing. Despite this, the selection of freeze-thaw conditions is currently empirical, and this results in reduced process control. Here we propose a mathematical model that takes into account the leading causes of protein instability during freeze-thawing, i.e. cold denaturation and surface-induced unfolding, and may guide the selection of optimal operating conditions. It is observed that a high cooling rate is beneficial for molecules that are extremely sensitive to cold denaturation, while the opposite is true when ice-induced unfolding is dominant. In all cases, a fast thawing rate is observed to be beneficial. The simulation outputs are confirmed by experimental data for myoglobin and lactate dehydrogenase, suggesting that the proposed modeling approach can reproduce the main features of protein behavior during freeze-thawing. This approach can therefore guide the selection of optimal conditions for protein-based formulations that are stored in a frozen or freeze-dried state.
Keyphrases
  • protein protein
  • amino acid
  • binding protein
  • high glucose
  • diabetic rats
  • oxidative stress
  • machine learning
  • room temperature
  • deep learning
  • stress induced