The Manufacturing Sciences laboratory and data groups interact like this:
Favorable special cause signals at large-scale give us opportunities for finding significant factors and interactions that produced these special causes. With a significant correlation adjusted (for cell culture: R2> 0.65 and p < 0.05), we are able to justify expending lab resources to test our hypothesis.
Significant actionable factors from the multivariate analysis of large-scale data become the basis for a DOE. Once the experiment design is vetted, documents can be drafted and experiment prepped to test those conditions.
There are a lot of reasons we go to the lab first. Here are a few:
Should your small-scale experiments confirm your hypothesis, your post-experiment memo becomes the justification for plant trials. Depending on how your organization views setpoint changes within the acceptable limits or license limits, you will run into varying degrees of justification to "fix what isn't broken." Usually, the summary of findings attached to the change order is sufficient for with-license changes to process setpoints. If your outside-of-license-limitsfindings can produce significant (20 to 50%) increase in yields (or improvements in product quality), you may have to go to the big guns (Process Sciences) to get more N and involve the nice folks in Regulatory Affairs.
From a plant trial perspective, I've seen large-scale process changes run under QA-approved planned deviation for big changes. I've seen on-the-floor production-supervision-approved changes for within acceptable range changes. I've seen managers so panicked by a potentially failing campaign that they shoot first and ask questions later (i.e. intiate the QA discrepancies, address the cGMP concerns later).
Whatever the case. The flow of hypothesis from the plant to the lab is how companies gain process knowledge and process understanding. The flow of plant trials from the lab back to the plant is how we realize continuous improvement.
More reading:
Credit goes to Jesse Bergevin for inculcating this model under adverse conditions.
Favorable special cause signals at large-scale give us opportunities for finding significant factors and interactions that produced these special causes. With a significant correlation adjusted (for cell culture: R2> 0.65 and p < 0.05), we are able to justify expending lab resources to test our hypothesis.
Significant actionable factors from the multivariate analysis of large-scale data become the basis for a DOE. Once the experiment design is vetted, documents can be drafted and experiment prepped to test those conditions.
There are a lot of reasons we go to the lab first. Here are a few:
- You have more N (data samples)
- You can test beyond the licensed limits
- You get to isolate variables
- You get the scientific basis for changing your process.
Should your small-scale experiments confirm your hypothesis, your post-experiment memo becomes the justification for plant trials. Depending on how your organization views setpoint changes within the acceptable limits or license limits, you will run into varying degrees of justification to "fix what isn't broken." Usually, the summary of findings attached to the change order is sufficient for with-license changes to process setpoints. If your outside-of-license-limitsfindings can produce significant (20 to 50%) increase in yields (or improvements in product quality), you may have to go to the big guns (Process Sciences) to get more N and involve the nice folks in Regulatory Affairs.
From a plant trial perspective, I've seen large-scale process changes run under QA-approved planned deviation for big changes. I've seen on-the-floor production-supervision-approved changes for within acceptable range changes. I've seen managers so panicked by a potentially failing campaign that they shoot first and ask questions later (i.e. intiate the QA discrepancies, address the cGMP concerns later).
Whatever the case. The flow of hypothesis from the plant to the lab is how companies gain process knowledge and process understanding. The flow of plant trials from the lab back to the plant is how we realize continuous improvement.
More reading:
- Biologics Manufacturing Sciences Lab
- Biologics Manufacturing Science Data
- Multivariate Analysis in Biologics Manufacturing
Credit goes to Jesse Bergevin for inculcating this model under adverse conditions.
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