Let's apply what we know of cell culture productivity KPIs.
Below is a control chart of a process that produces a stable, albeit variable titer:
The titer is a very simple data point to collect. QC measures it following their procedures and they spit out this one number every time a sample gets submitted.
Time required at the production culture stage to achieve this 2g/L is 10 days give or take a few hours.
The culture duration is also relatively easy to determine since we know the timestamp of inoculation and we know the harvest time. An arithmetic subtraction is all that is required to find this number.
It stands to reason that control chart of culture volumetric productivity shows a stable, in-control KPI.
It turns out, there was a scheduled facility shutdown after Run 8. And starting with Run 9, there was a mis-specified parameter that determines the fermentor volume.
Our control chart shows special cause signals from Run 9 - 12 indicating that it took 4 batches before the QA Change Control system was able to push through the change.
If you look at it, the control chart for capacity-based VP doesn't look that different from the culture VP. Even with runs 9 - 12 at 83% capacity, there is still no obvious, control-limit-violating-special-cause signals.
The shutdown lasted seven days and you can see that even though the bioreactors were cleaned and sterilized, they were left fallow for several days before the plant went back into production. The turn time spikes
Let's have a look at plant-based volumetric productivity.
Again, we see here that runs 9-11 show a depressed plant-volumetric productivity, but still no obvious control chart violations. Plant-based volumetric productivity is a lot harder to compute since you're talking non-row data (in the SQL sense).
Typically, your manufacturing control system (MCS) is enumerating UnitProcedures and storing each UnitProcedure in their own row. To compute the turn time, you actually have to list out the previous several UnitProcedures and find the previous harvest and reliably getting this data is a pain in the butt.
Plant-based volumetric productivity violates Principle #2 of MSAT data:
Alls I'm saying is that you need not forge ahead and apply every KPI that you learn about. In some cases, getting the data may cost more than the data is worth.
Related articles:
Below is a control chart of a process that produces a stable, albeit variable titer:
The titer is a very simple data point to collect. QC measures it following their procedures and they spit out this one number every time a sample gets submitted.
Time required at the production culture stage to achieve this 2g/L is 10 days give or take a few hours.
The culture duration is also relatively easy to determine since we know the timestamp of inoculation and we know the harvest time. An arithmetic subtraction is all that is required to find this number.
Culture Volumetric Productivity
The culture volumetric productivity is computed by dividing titer by culture duration.
It stands to reason that control chart of culture volumetric productivity shows a stable, in-control KPI.
It turns out, there was a scheduled facility shutdown after Run 8. And starting with Run 9, there was a mis-specified parameter that determines the fermentor volume.
Our control chart shows special cause signals from Run 9 - 12 indicating that it took 4 batches before the QA Change Control system was able to push through the change.
Capacity-based Volumetric Productivity
By including bioreactor volume - which is determined by load cells or radar and known to the control system's process historian - we can compute capacity-based volumetric productivity:
If you look at it, the control chart for capacity-based VP doesn't look that different from the culture VP. Even with runs 9 - 12 at 83% capacity, there is still no obvious, control-limit-violating-special-cause signals.
The shutdown lasted seven days and you can see that even though the bioreactors were cleaned and sterilized, they were left fallow for several days before the plant went back into production. The turn time spikes
Let's have a look at plant-based volumetric productivity.
Plant-based Volumetric Productivity
Again, we see here that runs 9-11 show a depressed plant-volumetric productivity, but still no obvious control chart violations. Plant-based volumetric productivity is a lot harder to compute since you're talking non-row data (in the SQL sense).
Typically, your manufacturing control system (MCS) is enumerating UnitProcedures and storing each UnitProcedure in their own row. To compute the turn time, you actually have to list out the previous several UnitProcedures and find the previous harvest and reliably getting this data is a pain in the butt.
Plant-based volumetric productivity violates Principle #2 of MSAT data:
The benefits derived from collecting the data needs to out-weight the costs.In this case, for this operation where variability in other parameters are relatively high, all this extra work doesn't give you much that much benefit.
Conclusion
In the perfect world, data is easy to get and KPIs tell you a lot. In reality, it may tell you that you need to reduce your process variability before your KPIs are worth collecting.Alls I'm saying is that you need not forge ahead and apply every KPI that you learn about. In some cases, getting the data may cost more than the data is worth.
Related articles:
