(158e) Impact of Continuous Drug Product Processing on Formulation and Process Development Workflow
- Conference: AIChE Annual Meeting
- Year: 2014
- Proceeding: 2014 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
Monday, November 17, 2014 - 2:10pm-2:35pm
Continuous drug product processing can significantly reduce the cycle time from powder dispensing to product formation. However, the output of process development is not product but rather knowledge. The manufacture of product is a relatively small percentage of the knowledge generation cycle time. Non-production related activities include: planning experiments, defining safety measures, collecting samples, analyzing samples, integrating multiple data streams, and analyzing data. This must then be combined with experience and science and compared with experimental objectives. Typically new insights are gained and the next cycle of experiments is planned and executed.
It was observed that the production cycle time sets the tempo for the other aspects of the work. When experiments are measured in weeks (as in batch), the preceding and following steps are also measured in weeks. When it is days or hours (as in continuous), the preceding and following steps are also measured in days or hours. Continuous processing also greatly improves the reliability of the system at the critical interface between sample generation and sample analysis. Improved reliability in delivering the samples to the lab at the promised time improves efficiency in the lab. Taken to its extreme, this becomes real time analytical. This creates the ability to make decisions during the run rather than after the run. This causes decision makers to spend more time in and around the processing suite / lab. Additionally, because of the short time to perform an experiment the impact of a failed experiment is reduced. This means higher risk / higher return experiments can be performed. These experiments can lead to unique insights. Finally, rapid or real-time data enables adaptive design of experiments that can shrink or grow as the runs proceed. If managed well that can be a powerful technique. However, poorly managed adaptations can destroy a well designed experiment.