“… Fig. 28 Resistance to water vapor as a function of product depth in the case of different freezing protocols for mannitol solutions: ( ) VISF [23] , ( ) suspended-vial freezing [22] , ( ) conventional freezing [21] , and in the case of water-TMDD particle-based products atomized at ( ) 24 kHz and ( ) 48 kHz [1] . …”
Section: Videomentioning
confidence: 99%
“…28 show a comparison of the resistance to vapor flow for particle-based material and bulk products. The mannitol-based bulk products were produced using the conventional freezing [21], using suspended-vial freezing [22] and vacuum-induced surface freezing (VISF) [23], [24]. For the particle-based material, the frozen microparticles consisted of a water mixture 35% w/w of TMDD atomized at 48 kHz and 24 kHz [1].Fig.…”
Section: Videomentioning
confidence: 99%
“…For the particle-based material, the frozen microparticles consisted of a water mixture 35% w/w of TMDD atomized at 48 kHz and 24 kHz [1].Fig. 28Resistance to water vapor as a function of product depth in the case of different freezing protocols for mannitol solutions: () VISF [23], () suspended-vial freezing [22], () conventional freezing [21], and in the case of water-TMDD particle-based products atomized at () 24 kHz and () 48 kHz [1].…”
A multi-scale approach can be used to simulate the drying behavior of microparticles in packed-bed. Data outcomes from discrete element method (DEM) and computational fluid dynamics (CFD) simulations can be used to estimate some relevant product characteristics, such as the porosity, tortuosity, voids in the bed and permeability which are required by the multi scale model. Data from DEM simulations are presented, with a particular focus on the influence of the model parameters, packing characteristics and inhomogeneities (wall effect and particles segregation); computational costs and scala bility are also considered. Data on the properties of packings as modeled at the macroscale are presented with regard to the thermal conductivity of gases in the Knudsen regime and effective properties of packed-beds modeled as a pseudo-homogeneous medium. A mathematical model of the freeze-drying of single microparticles and its outcomes are first presented. Data outcomes from the mathematical model at the macroscale concerning the drying behavior of microparticles in a tray and in a vial are then presented and can be used for process design. Some further data, with detailed interpretation and discussion of the presented data, can be found in the related research data article, “A multi-scale computational framework for modelling the freeze-drying of microparticles in packed-beds” (Capozzi et al., 2019).
“… Fig. 28 Resistance to water vapor as a function of product depth in the case of different freezing protocols for mannitol solutions: ( ) VISF [23] , ( ) suspended-vial freezing [22] , ( ) conventional freezing [21] , and in the case of water-TMDD particle-based products atomized at ( ) 24 kHz and ( ) 48 kHz [1] . …”
Section: Videomentioning
confidence: 99%
“…28 show a comparison of the resistance to vapor flow for particle-based material and bulk products. The mannitol-based bulk products were produced using the conventional freezing [21], using suspended-vial freezing [22] and vacuum-induced surface freezing (VISF) [23], [24]. For the particle-based material, the frozen microparticles consisted of a water mixture 35% w/w of TMDD atomized at 48 kHz and 24 kHz [1].Fig.…”
Section: Videomentioning
confidence: 99%
“…For the particle-based material, the frozen microparticles consisted of a water mixture 35% w/w of TMDD atomized at 48 kHz and 24 kHz [1].Fig. 28Resistance to water vapor as a function of product depth in the case of different freezing protocols for mannitol solutions: () VISF [23], () suspended-vial freezing [22], () conventional freezing [21], and in the case of water-TMDD particle-based products atomized at () 24 kHz and () 48 kHz [1].…”
A multi-scale approach can be used to simulate the drying behavior of microparticles in packed-bed. Data outcomes from discrete element method (DEM) and computational fluid dynamics (CFD) simulations can be used to estimate some relevant product characteristics, such as the porosity, tortuosity, voids in the bed and permeability which are required by the multi scale model. Data from DEM simulations are presented, with a particular focus on the influence of the model parameters, packing characteristics and inhomogeneities (wall effect and particles segregation); computational costs and scala bility are also considered. Data on the properties of packings as modeled at the macroscale are presented with regard to the thermal conductivity of gases in the Knudsen regime and effective properties of packed-beds modeled as a pseudo-homogeneous medium. A mathematical model of the freeze-drying of single microparticles and its outcomes are first presented. Data outcomes from the mathematical model at the macroscale concerning the drying behavior of microparticles in a tray and in a vial are then presented and can be used for process design. Some further data, with detailed interpretation and discussion of the presented data, can be found in the related research data article, “A multi-scale computational framework for modelling the freeze-drying of microparticles in packed-beds” (Capozzi et al., 2019).
“…The nucleation temperature determines the morphology of the final product, with a high nucleation temperature resulting in larger ice crystals (Searles et al, 2001). In turn, the ice crystal size corresponds to the pore size of the dried product, provided that no collapse occurs, and has therefore an impact on process efficiency (Capozzi and Pisano, 2018;Hottot et al, 2007;Kasper and Friess, 2011;Pikal et al, 2002). For instance, a large pore size speeds up the removal of water by sublimation, reducing the primary drying time, but results in a slow desorption rate during secondary drying (Oddone et al, 2016;Oddone et al, 2017).…”
During the freezing step of a typical freeze drying process, the temperature at which nucleation is induced is generally stochastically distributed, resulting in undesired within-batch heterogeneity. Controlled nucleation techniques have been developed to address this problem; these make it possible to trigger the formation of ice crystals at the same time and temperature in all the batch. Here, the controlled nucleation technique known as vacuum induced surface freezing is compared to spontaneous freezing for the freeze drying of human plasma, a highly concentrated system commonly stored in a dried state. The potency of Factor VIII (FVIII), a sensitive, labile protein present in plasma, and the reconstitution time of the dried cakes are evaluated immediately after freeze drying, and after 1, 3, 6 or 9 months storage at different degradation temperatures. We show that the application of controlled nucleation significantly reduces the reconstitution time and in addition helps to improve FVIII stability.
“…4,5 In general, the freezing process should be carefully controlled to ensure the quality of freeze-dried biopharmaceuticals. 6 By contrast, during drying, the main stress that could cause the protein denaturation is the removal of the shell of water surrounding the protein surface. 7 These side effects can result in protein unfolding and aggregation, which in any case lead to loss of the pharmaceutical activity.…”
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