Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Fissore, Davide; Pisano, Roberto

doi:10.3390/pr3020406

Cited by 38 publications

(35 citation statements)

References 39 publications

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“…Thus, to assess the effect of ice crystal size on t d and T max , a simple onedimensional model was used. 46 The primary drying model was implemented in MATLAB and solved using the finite differences approach, with a 60 s timestep.…”

Section: Simulation Approachmentioning

confidence: 99%

Considerations on Protein Stability During Freezing and Its Impact on the Freeze-Drying Cycle: A Design Space Approach

Arsiccio

Giorsello

Marenco

et al. 2020

Journal of Pharmaceutical Sciences

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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.

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Section: Simulation Approachmentioning

confidence: 99%

Considerations on Protein Stability During Freezing and Its Impact on the Freeze-Drying Cycle: A Design Space Approach

Arsiccio

Giorsello

Marenco

et al. 2020

Journal of Pharmaceutical Sciences

Self Cite

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“…As widely reported in literature [12,24,[30][31][32][33], the product resistance can be calculated as:…”

Section: Evaluation Of the Product Resistance From The Mass Flow Ratementioning

confidence: 99%

“…Both the parameters 0 and 1 exhibited greater values and also greater standard deviations for cycles performed with spontaneous nucleation (data set S5) than for cycles performed with controlled nucleation (data set S5cn 6) to calculate the value of the product resistance at a dried layer thickness of 5 mm, which was compared in Table 4 with relevant data reported in literature [24,26,32,40]. Regardless of the freezing protocol, the values of determined in this study appeared to be in good agreement with the previously published values, and in particular with the value evaluated by Bosca et al [24] under spontaneous nucleation and by Konstantinidis et al [26] under controlled nucleation.…”

Section: Determination Of the Product Resistance Variabilitymentioning

confidence: 99%

Determination of the dried product resistance variability and its influence on the product temperature in pharmaceutical freeze-drying

Scutellà

Trelea

Bourlés³

et al. 2018

European Journal of Pharmaceutics and Biopharmaceutics

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During the primary drying step of the freeze-drying process, mass transfer resistance strongly affects the product temperature, and consequently the final product quality. The main objective of this study was to evaluate the variability of the mass transfer resistance resulting from the dried product layer (R) in a manufacturing batch of vials, and its potential effect on the product temperature, from data obtained in a pilot scale freeze-dryer. Sublimation experiments were run at -25 °C and 10 Pa using two different freezing protocols: with spontaneous or controlled ice nucleation. Five repetitions of each condition were performed. Global (pressure rise test) and local (gravimetric) methods were applied as complementary approaches to estimate R. The global method allowed to assess variability of the evolution of R with the dried layer thickness between different experiments whereas the local method informed about R variability at a fixed time within the vial batch. A product temperature variability of approximately ±4.4 °C was defined for a product dried layer thickness of 5 mm. The present approach can be used to estimate the risk of failure of the process due to mass transfer variability when designing freeze-drying cycle.

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“…In an attempt to minimize the trial and error experiments, researchers have developed mathematical models for the determination of the optimum processing conditions based on the governing heat and mass transfer equations. 4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] In these mathematical models, 2 input parameters, namely the overall vial heat transfer coefficient and the resistance to mass transfer of the dried product, are experimentally determined. The overall vial heat transfer coefficient is typically determined from a water sublimation test, whereas the resistance to mass transfer of the dried product is determined using the drug formulation.…”

Section: Introductionmentioning

confidence: 99%

An Experimental-Based Approach to Construct the Process Design Space of a Freeze-Drying Process: An Effective Tool to Design an Optimum and Robust Freeze-Drying Process for Pharmaceuticals

Assegehegn

Fuente

Franco

et al. 2020

Journal of Pharmaceutical Sciences

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The application of quality by design (QbD) is becoming an integral part of the formulation and process development for pharmaceutical products. An essential feature of the QbD philosophy is the design space. In this sense, a new approach to construct a process design space (PDS) for the primary drying section of a freeze-drying process is addressed in this paper. An effective customized design of experiments (DoE) is developed for freeze-drying experiments. The results obtained from the DoE are then used to construct the product-based PDS. The proposed product-based PDS construction approach has several advantages, including (1) eliminating assumptions on the heat transfer coefficient and dried product resistance, as it is constructed from experimental results specifically obtained from a given formulation, yielding more realistic and reliable results and (2) PDS construction based on a narrow range of product temperatures and considering the variations in product temperature and sublimation rate of vials across a shelf. This guarantees the effectiveness and robustness of the process and facilitates the process scale-up and transfer. The PDS developed herein was experimentally verified. The PDS predicted parameters were in excellent agreement with the experimentally obtained parameters.

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Computer-Aided Framework for the Design of Freeze-Drying Cycles: Optimization of the Operating Conditions of the Primary Drying Stage

Cited by 38 publications

References 39 publications

Considerations on Protein Stability During Freezing and Its Impact on the Freeze-Drying Cycle: A Design Space Approach

Considerations on Protein Stability During Freezing and Its Impact on the Freeze-Drying Cycle: A Design Space Approach

Determination of the dried product resistance variability and its influence on the product temperature in pharmaceutical freeze-drying

An Experimental-Based Approach to Construct the Process Design Space of a Freeze-Drying Process: An Effective Tool to Design an Optimum and Robust Freeze-Drying Process for Pharmaceuticals

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