By Jeff Tremain and Ted Tharp
Lyophilization is a complex and important process commonly utilized for the preservation of biopharmaceutical therapies. Lyophilization can often be resource-intensive requiring many labor hours, time, and investment, especially when working with certain classes of biologic drug substances.
Until recently, attempts at “optimizing” the lyophilization process were based on trial and error. This costly approach was rationalized by most biopharma and CDMO/CMO partners as a cost of doing business. In fact, in many biopharmaceutical manufacturing environments, pilot-scale trial and error based on know how is still commonplace and accepted as a starting point for many lyophilization processes. But, even in environments where the drug substance itself is inexpensive, the trial approach can add up to significant costs during the development phase of a product. Committing headcount, production facilities, and analytical services in early-stage product development with potentially long-drying cycles, may lead to both a lost batch and productivity. This inevitably disrupts pipeline progression and precludes more profitable and efficient manufacturing.
Scale-up and technology transfer of lyophilization processes remain a challenge. But recent data-driven advances in steady-state computer modeling and subsequent bench-scale lyophilization practices are having a positive impact on the optimization of lyophilization processes. Computational fluid dynamics and analysis of specific product, process, and laboratory equipment attributes allow us to model the entire lyophilization process in both laboratory and manufacturing environments. Those attributes include a broad range of factors, including formulation, critical temperatures, vial dimensions and fill volume, pressure, heat transfer and sublimation rates.
Modeling also allows one to distinguish and compensate for variables in equipment performance and design, as well as the impact of those variables on specific formulations and primary packaging. This produces an educated baseline before beginning to place product in the lyophilization chamber.