The importance of heat flow direction for reproducible and homogeneous freezing of bulk protein solutions

Abstract

Freezing is an important operation in biotherapeutics industry. However, water crystallization in solution, containing electrolytes, sugars and proteins, is difficult to control and usually leads to substantial spatial solute heterogeneity. Herein, we address the influence of the geometry of freezing direction (axial or radial) on the heterogeneity of the frozen matrix, in terms of local concentration of solutes and thermal history. Solutions of hemoglobin were frozen radially and axially using small-scale and pilot-scale freezing systems. Concentration of hemoglobin, sucrose and pH values were measured by ice-core sampling and temperature profiles were measured at several locations. The results showed that natural convection is the major source for the cryoconcentration heterogeneity of solutes over the geometry of the container. A significant improvement in this spatial heterogeneity was observed when the freezing geometry was nonconvective, i.e., the freezing front progression was unidirectional from bottom to top. Using this geometry, less than 10% variation in solutes concentration was obtained throughout the frozen solutions. This result was reproducible, even when the volume was increased by two orders of magnitude (from 30 mL to 3 L). The temperature profiles obtained for the nonconvective freezing geometry were predicted using a relatively simple computational fluid dynamics model. The reproducible solutes distribution, predictable temperature profiles, and scalability demonstrate that the bottom to top freezing geometry enables an extended control over the freezing process. This geometry has therefore shown the potential to contribute to a better understanding and control of the risks inherent to frozen storage.

Improving Heat Transfer at the Bottom of Vials for Consistent Freeze Drying with Unidirectional Structured Ice

“…However, instead of lowering the temperature with dry ice to favor nucleation at the bottom, we fill the curvature gap at the bottom of the vial to enhance its heat transfer on normal lyophilization shelves. Consistent nucleation has been achieved previously by suppressing natural convection in unidirectional freezing (bottom to top) (22). In this work, we evaluate how this freezing geometry can improve the structure of ice for enhancing lyophilization.…”

“…Both lower shelf temperature and lower volume of liquid contribute for faster cooling of the base. Under bottom to top freezing, natural convection is significantly suppressed and should have little influence on heat transfer (22); therefore, the liquid cooling is essentially determined by conduction across the vial's base and thermal diffusion from the liquid's bottom interface to the bulk. The critical aspect for reproducible nucleation at the bottom surface relies therefore on enabling a cooling rate that can substantially overcome heat thermal diffusion, otherwise most of the liquid goes below 0°C before the bottom layer reaches the nucleation temperature.…”

“…The governing differential equations were solved using a solver developed elsewhere (22), based on the open-source package OpenFOAM (version 2.3.0) (24). The transport equations were discretized by the finite volume method applied to arbitrary-shaped cells, using a segregated approach and collocated variables.…”

Application of Mechanistic Models for Process Design and Development of Biologic Drug Products

“…Additionally, the multiphase model demonstrated the importance of convective effects in freezing simulations due to the presence of buoyancy effects. Rodrigues et al [66] have developed a unidirectional freezing method, to address cryoconcentration due to density-gradient-driven convection from bottom to top. A small unidirectional freezing setup was assembled to study the scale-down of the Pilot-UFS, and a CFD model was developed for this freezing geometry.…”

Evaluating two process scale chromatography column header designs using CFD

“…Rathore et al employed CFD to establish the process design space for mixing in a 3 L bioreactor. Recently, CFD simulations were employed to simulate temperature profiles for a non‐convective freezing geometry in a bulk protein freezing application . Arlt and coworkers employed X‐ray Computed Tomography (CT) to measure axial variations in porosity, interstitial velocity and the axial dispersion coefficient in a 2.6 cm diameter column.…”