Recent Development of Optimization of Lyophilization Process

Abstract

The objective of this review is to survey the development of the optimization of lyophilization. The optimization study of the lyophilizer has been roughly developing by the order of (i) trial-and-error approach, (ii) process modeling using mathematical models, (iii) scalability, and (iv) quality-by-design. From the conventional lyophilization studies based on the trial-and-error, the key parameters to optimize the operation of lyophilization were found out, i.e., critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs). The mathematical models using the key parameters mentioned above have been constructed from the viewpoints of the heat and mass transfer natures. In many cases, it is revealed that the control of the primary drying stage determines the outcome of the lyophilization of products, as compared with the freezing stage and the secondary drying stage. Thus, the understanding of the lyophilization process has proceeded. For the further improvement of the time and economical cost, the design space is a promising method to give the possible operation range for optimizing the lyophilization operation. This method is to search the optimized condition by reducing the number of key parameters of CMAs, CPPs, and CQAs. Alternatively, the transfer of lyophilization recipe among the lab-, pilot-, and production-scale lyophilizers (scale-up) has been examined. Notably, the scale-up of lyophilization requires the preservation of lyophilization dynamics between the two scales, i.e., the operation of lab- or pilot-scale lyophilizer under HEPA-filtrated airflow condition. The design space determined by focusing on the primary drying stage is large and involves the undesired variations in the quality of final products due to the heterogeneous size distribution of ice crystals. Accordingly, the control of the formation of the ice crystal with large size gave impact on the product quality and the productivity although the large water content in the final product should be improved. Therefore, the lyophilization should take into account the quality by design (QbD). The monitoring method of the quality of the product in lyophilization process is termed the “process analytical technology (PAT).” Recent PAT tools can reveal the lyophilization dynamics to some extent. A combination of PAT tools with a model/scale-up theory is expected to result in the QbD, i.e., a quality/risk management and an in situ optimization of lyophilization operation.

The influence of emulsion drying on the fatty acid composition, bioactive compounds content and oxidative stability of encapsulated bio-oils

“…). It is worth noting that the freezing temperatures in the first and second stages and the absence of air prevents product deterioration caused by oxidation or chemical modification (Anwar & Kunz, 2011;Kawasaki, Shimanouchi, & Kimura, 2019;Velasco, Holgado, Dobarganes, & Márquez-Ruiz, 2009).…”

Eubacterium rectale Attenuates HSV-1 Induced Systemic Inflammation in Mice by Inhibiting CD83

“…Lyophilization is a process for improving the stability of a product in the form of a dry powder without losing the characteristics of the product. The process involves removing moisture from the product in a vacuum at a sufficiently low temperature ( 77 , 78 ). Freeze-dried storage of bacterial strains provides ease of use.…”

Advanced Process Analytical Technology in Combination with Process Modeling for Endpoint and Model Parameter Determination in Lyophilization Process Design and Optimization

“…Processes 2021, 9, x FOR PEER REVIEW 2 of 22 approach using mathematical models and Process Analytical Technology in order to deepen process understanding and accelerate process development [10,11].…”

“…The demand for lyophilization technology will rise because of the increasing number of biological products that are in development [7][8][9]. This increased demand triggers a change in process development from trial-and-error to a quality-by-design-based approach using mathematical models and Process Analytical Technology in order to deepen process understanding and accelerate process development [10,11].…”