Institute of Energy and Process Engineering, ETH Zurich, Sonneggstrasse 3, CH-8092 Zurich, Switzerland.
The Janssen Pharmaceutical Companies of Johnson & Johnson, Hochstrasse 201, CH-8200 Schaffhausen, Switzerland.
Int J Pharm. 2022 Sep 25;625:122051. doi: 10.1016/j.ijpharm.2022.122051. Epub 2022 Jul 28.
Biopharmaceuticals commonly require freezing to ensure the stability of the active pharmaceutical ingredients (APIs). At commercial scale, freezing is typically carried out over the course of days in pallets comprising tens of thousands of vials. The selected process conditions have to ensure both complete freezing in all vials and a satisfactory manufacturing throughput. Current process design, however, is mainly experimental, since no mechanistic understanding of pallet freezing and its underlying phenomena has been achieved so far. Within this work, we derive a mechanistic modeling framework and compare the model predictions with engineering run data from the Janssen COVID-19 vaccine. The model qualitatively reproduced all observed trends and reveals that stochastic ice nucleation governs both process duration and batch heterogeneity. Knowledge on the ice nucleation kinetics of the formulation to be frozen thus is required to identify suitable freezing process conditions. The findings of this work pave the way towards a more rational design of pallet freezing, from which a plethora of frozen drug products may benefit. For this reason, we provide open source access to the model in the form of a python package (Deck et al., 2021).
生物制药通常需要冷冻以确保活性药物成分 (API) 的稳定性。在商业规模下，冷冻通常在托盘上进行，托盘包含成千上万支小瓶，持续数天。所选的工艺条件必须确保所有小瓶完全冷冻，并达到令人满意的生产通量。然而，目前的工艺设计主要是实验性的，因为到目前为止，还没有对托盘冷冻及其潜在现象有机械理解。在这项工作中，我们推导出一个机械建模框架，并将模型预测与杨森 COVID-19 疫苗的工程运行数据进行比较。该模型定性地再现了所有观察到的趋势，并表明随机成核控制着工艺持续时间和批次异质性。因此，需要了解要冷冻的制剂的成核动力学，以确定合适的冷冻工艺条件。这项工作的结果为托盘冷冻的更合理设计铺平了道路，大量冷冻药物产品可能从中受益。为此，我们以 Python 包的形式（Deck 等人，2021 年）提供了模型的开源访问。
