25 research outputs found
A model predictive control strategy of the dynamic primary drying settings for a batch freeze-drying process
A model predictive control strategy of the dynamic primary drying settings for a batch freeze drying process
Model-based optimisation and control strategy for the primary drying phase of a lyophilisation process
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 (Ti) exceeds the the collapse temperature (Tc) 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 Ti 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
The impact of formulation composition and process settings of traditional batch versus continuous freeze-drying on protein aggregation
In-situ X-ray imaging of sublimating spin-frozen solutions
Spin-freeze-drying is a promising technique to enable long-term storage of pharmaceutical unit doses of aqueous drug solutions. To investigate the sublimation of the ice during the primary phase of freeze-drying, X-ray imaging can yield crucial temporally resolved information on the local dynamics. In this paper, we describe a methodology to investigate the sublimation front during single unit-dose freeze-drying using 4D in-situ X-ray imaging. Three spin-frozen samples of different solutions were imaged using this methodology and the process characteristics were analysed and reduced to two-dimensional feature maps
4D micro-computed X-ray tomography as a tool to determine critical process and product information of spin freeze-dried unit doses
Maintaining chemical and physical stability of the product during freeze-drying is important but challenging. In addition, freeze-drying is typically associated with long process times. Therefore, mechanistic models have been developed to maximize drying efficiency without altering the chemical or physical stability of the product. Dried product mass transfer resistance ( Rp ) is a critical input for these mechanistic models. Currently available techniques to determine Rp only provide an estimation of the mean Rp and do not allow measuring and determining essential local (i.e., intra-vial) Rp differences. In this study, we present an analytical method, based on four-dimensional micro-computed tomography (4D- μ CT), which enables the possibility to determine intra-vial Rp differences. Subsequently, these obtained Rp values are used in a mechanistic model to predict the drying time distribution of a spin-frozen vial. Finally, this predicted primary drying time distribution is experimentally verified via thermal imaging during drying. It was further found during this study that 4D- μ CT uniquely allows measuring and determining other essential freeze-drying process parameters such as the moving direction(s) of the sublimation front and frozen product layer thickness, which allows gaining accurate process knowledge. To conclude, the study reveals that the variation in the end of primary drying time of a single vial could be predicted accurately using 4D- μ CT as similar results were found during the verification using thermal imaging
Modelling the primary drying step for the determination of the optimal dynamic heating pad temperature in a continuous pharmaceutical freeze-drying process for unit doses
In the pharmaceutical industry, traditional freeze-drying of unit doses is a batch-wise process associated with many disadvantages. To overcome these disadvantages and to guarantee a uniform product quality and high process efficiency, a continuous freeze-drying process is developed and evaluated. The main differences between the proposed continuous freeze-drying process and traditional freeze-drying can be found firstly in the freezing step during which the vials are rotated around their longitudinal axis (spin freezing), and secondly in the drying step during which the energy for sublimation and desorption is provided through the vial wall by conduction via an electrical heating pad. To obtain a more efficient drying process, the energy transfer has to be optimised without exceeding the product and process limits (e.g. cake collapse, choked flow). Therefore, a mechanistic model describing primary drying during continuous lyophilisation of unit doses based on conduction via heating pads was developed allowing the prediction of the optimal dynamic power input and temperature output of the electric heating pads. The model was verified by experimentally testing the optimal dynamic primary drying conditions calculated for a model formulation. The primary drying endpoint of the model formulation was determined via in-line NIR spectroscopy. This endpoint was then compared with the predicted model based endpoint. The mean ratio between the experimental and model based predicted drying time for six verification runs was 1.05 +/- 0.07, indicating a good accordance between the model and the experimental data