top of page
Search

Computational simulation tools in Lyophilization

Building a designs pace for a freeze drying process has classically required extensive experimental runs, that can be long in time and expensive, more if it is required to use the final pharmaceutical product, in a final set up, in a classified area inside a pharmaceutical plant.


Computational simulation tools then are paramount for reducing time and costs.


In words of Sushas V. Patankar, in his book, Numerical Heat Transfer and Fluid Flow: “An optimal prediction effort should thus be a judicious combination of computation and experiment. The proportions of the two ingredients would depend on the nature of the problem, on the objectives of the prediction, and on the economic and other constrains of the situation”.


The most popular parametrization of the primary drying was detailed more than 40 years ago by Michael Pikal. Lyoptimus is taken over the challenge to support freeze-drying applications, considering the specifics of every project: technical, economical and quality. Adapted to the new times, computation and information technologies, and new developments in life science.


How do you include in your design space aspects as:


-Different heat transfer coefficient Kv: between the centered and perimetrical vials 

-Stochastic nucleation generating different product resistance Rp in the same batch

-Different nucleation temperatures between pilot scale and production

-Freeze-dryer maximum capacity

-Filling height for maximizing the bulk production


The design space curves are still valid and useful, but only 2D, two axis. Computation can provide multiple variable analysis, with fast and detailed information.



Comments


bottom of page