Is a 10% yield improvement possible within your regulatory filing?
- An inefficient crystallization step was identified for a cost saving initiative but the proven acceptable ranges within the filing were tight and options for improvement were limited
- A data-rich interrogation of a scaled-down process using PAT and automation platforms identified multiple areas for improvement all within the proven acceptable ranges
- In just 12 weeks a process was scaled-down, and then back up, resulting in a 10% yield improvement all within a tight regulatory filing.
Legacy processes designed before the advent of data rich methods can be inefficient and worthy of improvement as part of a raft of cost-saving initiatives being deployed across the pharmaceutical industry. The challenge with these processes is that the proven acceptable ranges within the filing often limit the redesign and were originally documented in the absence of data rich process interrogations.
With this challenge in mind, APC scientists developed a scale-down / scale-up platform that can take inefficient processes from plant to lab, and back again, in record time. The platform combines computational fluid dynamics (to model the various mixing conditions available within the filing) with data rich experiments (that provide time resolved kinetic information about each crystallization and reaction step). By combining virtual and lab-based experiments scientists can build rapid process understanding and find the breakthrough needed to improve a critical quality attribute like product yield, all within the regulatory filing.
This breakthrough enabled a medium-sized pharmaceutical company to increase yield by 10% for a crystallization step that had been identified for a cost saving initiative. In just 12 weeks a scale down model was established in the lab and key yield losses were traced to the mother liquor. By optimizing particle size and shape through minor adjustments to key process parameters the filtration properties were drastically improved taking overall crystallization yield, post filtration from 80% to 90%.
Using data-rich experimentation and mixing models using CFD a right first scale up of the new process was achieved within the yield improvements translating directly back to the plant.