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

Thermal imaging as a noncontact inline process analytical tool for product temperature monitoring during continuous freeze-drying of unit doses

Freeze-drying is a well-established technique to improve the stability of biopharmaceuticals which are unstable in aqueous solution. To obtain an elegant dried product appearance, the temperature at the moving sublimation interface T-i should be kept below the critical product temperature T-i,T-crit during primary drying. The static temperature sensors applied in batch freeze-drying provide unreliable Ti data due to their invasive character. In addition, these sensors are incompatible with the continuous freeze-drying concept based on spinning of the vials during freezing, leading to a thin product layer spread over the entire inner vial wall. During continuous freeze-drying, the sublimation front moves from the inner side of the vial toward the glass wall, offering the unique opportunity to monitor T-i via noncontact inline thermal imaging. Via Fourier's law of thermal conduction, the temperature gradient over the vial wall and ice layer was quantified, which allowed the exact measurement of T-i during the entire primary drying step. On the basis of the obtained thermal images, the infrared (IR) energy transfer was computed via the Stefan-Boltzmann law and the dried product mass transfer resistance (R-p) profile was obtained. This procedure allows the determination of the optimal dynamic IR heater temperature profile for the continuous freeze-drying of any product. In addition, the end point of primary drying was detected via thermal imaging and confirmed by inline near-infrared (NIR) spectroscopy. Both applications show that thermal imaging is a suitable and promising process analytical tool for noninvasive temperature measurements during continuous freeze-drying, with the potential for inline process monitoring and control

Quantitative risk assessment via uncertainty analysis in combination with error propagation for the determination of the dynamic Design Space of the primary drying step during freeze-drying

Traditional pharmaceutical freeze-drying is an inefficient batch process often applied to improve the stability of biopharmaceutical drug products. The freeze-drying process is regulated by the (dynamic) settings of the adaptable process parameters shelf temperature 7; and chamber pressure Pc. Mechanistic modelling of the primary drying step allows the computation of the optimal combination of Ts and Pc in function of the primary drying time. In this study, an uncertainty analysis was performed on the mechanistic primary drying model to construct the dynamic Design Space for the primary drying step of a freeze-drying process, allowing to quantitatively estimate and control the risk of cake collapse (i.e., the Risk of Failure (RoF)). The propagation of the error on the estimation of the thickness of the dried layer L-dried as function of primary drying time was included in the uncertainty analysis. The constructed dynamic Design Space and the predicted primary drying endpoint were experimentally verified for different RoF acceptance levels (1%, 25%, 50% and 99% RoF), defined as the chance of macroscopic cake collapse in one or more vials. An acceptable cake structure was only obtained for the verification runs with a preset RoF of 1% and 25%. The run with the nominal values for the input variables (RoF of 50%) led to collapse in a significant number of vials. For each RoF acceptance level, the experimentally determined primary drying endpoint was situated below the computed endpoint, with a certainty of 99%, ensuring sublimation was finished before secondary drying was started. The uncertainty on the model input parameters demonstrates the need of the uncertainty analysis for the determination of the dynamic Design Space to quantitatively estimate the risk of batch rejection due to cake collapse

Global Sensitivity Analysis as Good Modelling Practices tool for the identification of the most influential process parameters of the primary drying step during freeze-drying

Pharmaceutical batch freeze-drying is commonly used to improve the stability of biological therapeutics. The primary drying step is regulated by the dynamic settings of the adaptable process variables, shelf temperature T-s and chamber pressure P-c. Mechanistic modelling of the primary drying step leads to the optimal dynamic combination of these adaptable process variables in function of time. According to Good Modelling Practices, a Global Sensitivity Analysis (GSA) is essential for appropriate model building. In this study, both a regression based and variance-based GSA were conducted on a validated mechanistic primary drying model to estimate the impact of several model input parameters on two output variables, the product temperature at the sublimation front T-i and the sublimation rate m(sub). T-s; was identified as most influential parameter on both T-i and m(sub), followed by P-c and the dried product mass transfer resistance alpha(Rp) for 71 and m(sub), respectively. The GSA findings were experimentally validated for m(sub) via a Design of Experiments (DoE) approach. The results indicated that GSA is a very useful tool for the evaluation of the impact of different process variables on the model outcome, leading to essential process knowledge, without the need for time-consuming experiments (e.g., DoE)