Developing a Mass Transfer Model for Lyophilization Primary Drying Kinetics

Microstructure CQAs
Original Publication Title

Understanding the Impact of Microstructures on Reconstitution and Drying Kinetics of Lyophilized Cake Using X-ray Microscopy and Image-Based Simulation

The drying time of lyophilization and resultant cake microstructure are dependent on each other as water and solvent leave a lyophilized cake. The drying rate affects the size, distribution, and tortuosity of the pores as these macropores evolve during the primary drying phase, which in return impact the further removal of water and solvent from the cake throughout the drying period. This interplay results in a microstructure that determines the reconstitution time for a given formulation. The current study employs advanced X-ray Microscopy (XRM) coupled with mathematical models to correlate the microstructure with the drying kinetics and the reconstitution time. The normalized diffusion coefficients, derived from the reconstructed 3D microstructure of the cake, correlate with the solid content of the pre-lyophilization solution and agree with the mass transfer coefficients from a semi-empirical drying model built with lyophilization process data. Specifically, a solution with less solid content leads to a lyophilized cake with larger pores, thinner walls, and a greater pore volume compared to a solution with more solid content. Consequently, models from the microstructure and drying experiments reveals faster mass transfer independently. While the mass transfer models from the cake structure and the lyophilization process data accurately represents the drying kinetics, both models are inadequate to describe the reconstitution process due to the significant impact from formulation ingredients that alter the mass transfer mechanism via solubility and wettability. In summary, X-ray microscopy imaging and mathematical models are powerful tools that provide insights into the lyophilization process from a new angle.

Yu (Elaine) Pu, Lisa Ma, Barton Dear, Aiden Zhu, Jianmin Li, Shawn Zhang, Weixian Shi

Published with BMS

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