Recommended Best Practices for Process Monitoring Instrumentation in Pharmaceutical Freeze Drying—2017

Nail, Steven L.; Tchessalov, Serguei; Shalaev, Evgenyi; Ganguly, Arnab; Renzi, Ernesto; Dimarco, Frank; Wegiel, Lindsay A.; Ferris, Steven J.; Kessler, William J.; Pikal, Michael J.; Sacha, Greg; Alexeenko, Alina; Thompson, T. N.; Reiter, Cindy; Searles, James A.; Coiteux, Paul

doi:10.1208/s12249-017-0733-1

Cited by 77 publications

(63 citation statements)

References 10 publications

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“…Each individual vial was weighed before and after primary drying. The value of K v changes in function of P c , therefore, this cycle was repeated at six different pressure levels: 5, 10, 15, 20, 25 and 30 Pa. Type-K thermocouples (Conrad Electronic, Hirschau, Germany) were placed inside four different vials, which were randomly distributed at the border and the center of the batch, against the bottom in the center of the vial, to monitor the ice temperature during primary drying [24]. K v was calculated at each pressure level according to equation 5 [12]:…”

Section: Determination Of the Vial Heat Transfer Coefficientmentioning

confidence: 99%

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

Bockstal

Mortier

Corver

et al. 2017

European Journal of Pharmaceutics and Biopharmaceutics

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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 T and chamber pressure P. Mechanistic modelling of the primary drying step allows the computation of the optimal combination of T and P 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 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.

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Section: Determination Of the Vial Heat Transfer Coefficientmentioning

confidence: 99%

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

Bockstal

Mortier

Corver

et al. 2017

European Journal of Pharmaceutics and Biopharmaceutics

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“…To measure the product temperature directly three additional thin gauge type-K thermocouples (Conrad Electronic, Hirschau, Germany) were available in the drying chamber. To measure chamber pressure both a Pirani and Capacitance pressure gauge are present, yielding the opportunity to apply a comparative pressure measurement methodology [16]. Due to the difference of the measurement principle of the two pressure gauges, it is possible to monitor the gas composition in the freeze-drying chamber.…”

Section: Supervisory Control Of the Freeze-dryermentioning

confidence: 99%

Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process

et al. 2020

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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.

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“…Corner vials were defined as vials with fewer neighbors than center vials, which were arranged in a hexagonal neighbor packaging. T P was measured with thermocouples or wireless sensors placed at the bottom center of the vials according to literature [11].…”

Section: Freeze Drying Proceduresmentioning

confidence: 99%

Energy Transfer in Vials Nested in a Rack System During Lyophilization

2020

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Small batch sizes are a consequence of more personalized medicine and reflect a trend in the biopharmaceutical industry. Freeze drying of vials nested in a rack system is a tool used in new flexible pilot scale processing lines. Understanding of heat transfer mechanisms in the rack loaded with vials not in direct contact with each other is necessary to ensure high quality. Lyophilization in the rack vial system enables a homogeneous drying with a reduced edge-vial-effect and shielding against radiation from surrounding components, e.g., the chamber wall. Due to the separation effect of the rack, direct shelf contact contributes approx. 40% to the overall energy transfer to the product during primary drying. Hence overall the rack is a flexible, robust tool for small batch production, which ensures a controlled heat transfer resulting in a uniform product.

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Recommended Best Practices for Process Monitoring Instrumentation in Pharmaceutical Freeze Drying—2017

Cited by 77 publications

References 10 publications

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

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

Model-Based Optimisation and Control Strategy for the Primary Drying Phase of a Lyophilisation Process

Energy Transfer in Vials Nested in a Rack System During Lyophilization

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