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This paper is focused on the methods based on the Pressure Rise Test (PRT) used to monitor the primary drying of a lyophilisation process. Details about the model-based algorithms proposed to interpret the PRT, namely the Manometric Temperature Measurement (MTM), the Pressure Rise Analysis (PRA), and the Dynamic Parameters Estimation (DPE) are briefly summarized and various features of the models used by these algorithms, in particular the role of the vial wall and of radiation on the thermal balance of the system, are investigated. The optimal selection of the sampling frequency and of the time interval between two tests is discussed, and the influence of the duration of the test on the results is investigated by means of mathematical simulation: results obtained from the PRT can be significantly improved by optimizing the duration of the test. Moreover, the problem of misleading results obtained at the end of the primary drying is investigated, taking into account the problem of illconditioning of the algorithms. An improved version of the DPE algorithm is proposed to cope with this problem: its effectiveness is demonstrated by means of mathematical simulations and experimental runs.

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The use of mathematical modeling is demonstrated to be very effective not only for cycle development, but also for solving problem of process transfer. This study showed that inter-vial variability remains significant when vials are loaded on plastic trays, and how inter-vial variability can be taken into account during process design.

Freezing is widely used during the manufacturing process of protein-based therapeutics, but it may result in undesired loss of biological activity. Many variables come into play during freezing that could adversely affect protein stability, creating a complex landscape of interrelated effects. The current approach to the selection of freezing conditions is however nonsystematic, resulting in poor process control. Here we show how mathematical models, and a design space approach, can guide the selection of the optimal freezing protocol, focusing on protein stability. Two opposite scenarios are identified, suggesting that the ice-water interface is the dominant cause of denaturation for proteins with high bulk stability, while the duration of the freezing process itself is the key parameter to be controlled for proteins that are susceptible to cold denaturation. Experimental data for lactate dehydrogenase and myoglobin as model proteins support the model results, with a slow freezing rate being optimal for lactate dehydrogenase and the opposite being true for myoglobin. A possible application of the calculated design space to the freezing and freeze-drying of biopharmaceuticals is finally described, and some considerations on process efficiency are discussed as well.

This paper deals with the monitoring and control of the freeze-drying of pharmaceuticals in vials taking into account batch heterogeneity. Firstly, the problem of non-uniformity of the batch is addressed: the vials in the chamber of the freeze-dryer can, in fact, exhibit different dynamics due not only to radiation from the wall of the chamber, but also to temperature gradients on the heating shelf, vapor fluid dynamics and non-uniform inert distribution, as it has been evidenced by means of Computational Fluid Dynamics simulations. Then, the effect of batch heterogeneity on the performance of the monitoring and control system is discussed, and a new tool is presented: it is based on an advanced algorithm, the Dynamic Parameters Estimation, that estimates the state of the system (product temperature and residual ice content) by using the results of the Pressure Rise Test, coupled with a controller (LyoDriver) that changes the shelf temperature in order to maintain product temperature below the maximum allowed value, thus minimizing the duration of primary drying.