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.
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.