Hi,
developing a process for drug substance production is done in several steps within BUs, e.g. there are different purification steps
Picture: each color is a BU where a DOE has been run and a model has been found which explains the process. Inputs are different in each BU, the experiments have been conducted independently, so output material in BU1 is not the same used in BU2 for further processing.
This is my customer a challenge: They want to combine all that data/models to get a final model for the whole process.
As the several models are from independent DOEs each, and the inputs are as well not necessarily the same in each unit operation, I wonder if you have a good way on how to deal with such a situation.
- Just combining will lead to several missing values,
- then it is not a continuous process that the material created in one BU will be used in the experiment for the next BU, it has new material which should act like the other material but not for sure.
If that would be a continuous process I even thought about SEM to set-up the process and embed variables and knowledge gained from the several steps into one big model.
The other idea I'd have is simulation and using validation techniques with null factors and assuming we have a continuous setup and see the results and then test with confirmation runs.
Any ideas or experiences?
Thx,
Martin
