On the use of model-based tools to optimize in-line a pharmaceuticals freeze-drying process

ABSTRACT

This article is focused on the use of model-based tools to design and optimize in-line a pharmaceutical freeze-drying process. Two control systems have been compared, a predictive one that uses the pressure rise test to monitor the state of the system and to estimate in-line the values of model parameters, named LyoDriver in the previous literature, and a controller where a soft sensor uses the temperature measurement obtained by a thermocouple to get the same information and to calculate on-line the design space of the process. In both cases, the goal of the controller is to maintain product temperature as close as possible to a limit value, without trespassing it, throughout the primary drying stage. An extended experimental campaign has been performed, where various products, with different characteristics, have been processed, namely, aqueous solutions containing sucrose, mannitol, or polyvinylpyrrolidone. Results evidence that both systems are effective in optimizing in-line the freeze-drying process, but shorter cycles can be obtained using the soft sensor. This is due to the fact that the soft sensor is not responsible for any product overheating and, thus, product temperature can be maintained very close to the limit value, while when using the pressure rise test as monitoring tool, a safety margin has to be used, because of the temperature increase during the pressure rise test. Besides, when using the soft sensor no least-square optimization problem is solved to estimate model parameters, and this can improve the robustness of the system. The main drawback is represented by the fact that this system requires thermocouples to measure product temperature, and this can be difficult in industrial-scale freeze-dryers, used to process large batches of vials in sterile conditions, but it can be performed quite easily in lab-scale units used for process design.

KEYWORDS:

• Freeze-drying
• LyoDriver
• modeling
• optimization
• pressure rise test
• soft sensor