Vanbillemont Brecht, Nicolaï Niels, Leys Laurens, De Beer Thomas
Laboratory of Pharmaceutical Process Analytical Technology (LPPAT), Department of Pharmaceutical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000 Ghent, Belgium.
BIOMATH, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Pharmaceutics. 2020 Feb 20;12(2):181. doi: 10.3390/pharmaceutics12020181.
The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature ( T i ) exceeds the the collapse temperature ( T c ) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of T i and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.
间歇式冷冻干燥机的标准操作是由协议驱动的。所有冷冻干燥阶段（即冷冻、一次干燥和二次干燥）都在固定时间点按顺序编程，并且在每个阶段，关键工艺参数（CPPs）通常保持恒定或在两个设定点之间进行线性插值。事实证明，这种操作间歇式冷冻干燥机的方式既耗时又低效。一种基于模型的优化和实时控制策略，包括模型输出不确定性，有助于在控制关键质量属性（CQAs）失败风险的同时加速一次干燥阶段。在实时控制策略的每次迭代中，计算一个设计空间以选择一组最佳的CPPs。控制策略的目标是避免产品结构损失，这种损失发生在升华界面温度（Ti）超过意外干扰期间常见的坍塌温度（Tc）时，同时防止导致压力控制丧失的阻塞流条件。当腔室压力和搁板流体系统故意受到适度的工艺干扰时，对所提出的方法进行了实验验证。此外，使用不确定性分析和比较压力测量技术预测了一次干燥阶段的结束。Ti的预测和一次干燥的结束均与实验数据一致。因此，证实了所提出的实时控制策略能够减轻间歇式冷冻干燥过程中适度干扰的影响。
