Patrick Sagmeister from Exputec presents best-practices for bioprocess data analytics/ bioprocess characterization. Interested? Check out his talk in Newcastle April 26th or retrieve the slides from Slideshare.
Abstract:
Identifying process variability using data science based process characterization
Process validation is an essential step in the commercialization of a new (biological-) drug. For drug product commercialization, manufacturers must validate the drug’s manufacturing process. This ensures, that the manufacuring process delivers consistently a quality product and that the patient is not at risk.
Recently, US and European regulators have issued new process validation guidelines. The new guidelines now emphasize:
- the demonstration of process understanding;
- risk-based identification of critical process parameters;
- implementation of well-validated control strategies.
As a result of the new guidelines, it is now state of the art that drug manufacturers thoroughly investigate and “characterize” the manufacturing processes (stage 1 process validation) and thoroughly monitor the manufacturing process to demonstrate a state of control (Continued Process Verification, Stage 3 of the process validation).
To put the new guidelines into industrial practice, data management and (statistical-) data analytics now play a key role.
This talk explores data management and data analytics workflows for process characterization and continued process verification, in particular:
- Data management and data analytics workflows as prerequisite for process validation
- Statistical data analytics for process validation
- Scale down model qualification
- Experimental design
- Calculation of normal operating ranges/ proven acceptable ranges
- Monitoring/ Continuous process verification
- Concept study how to leverage data from process characterization to develop a continuous process verification plan.
