3. PROCESS ANALYSIS AND IDENTIFICATION OF PROCESS
3.4 Analysis of major points for variability and control from historical process data
All processes where the end product is the cell enclose variability in output. The purpose of analysing the historical data is to compare both the mean output and the variability in the output, with the product specification119. This will provide a statistically informed assessment of process capability (i.e. the probability of achieving specification on any given run) and the required process improvement to achieve product specification with acceptable frequency118, 119.
Analysing all previous runs that have been performed with current parameter sets gives an indication of whether the problem are process parameter controls or process parameter
values (something is controlled or poorly controlled). Removing identified sources of variation that can be controlled is essential, even if they are not the basis of current problem.
A measurement of process capability is based on the standard deviation of the process output and therefore assumes a normal distribution of the process output without statistical outliers118. Before meaningful assessment of process capability it is therefore necessary to identify if these conditions exist and, if not, normalize the data and/or identify causes of outlying process runs. The manufacturing terminology for a normally distributed process output without statistical outliers is ‘in-control’ or subject to intrinsic variation119
. This type of process variation generally arises from a lack of precision in the control of process parameters (such as the dispensed volume from a pipette); this will usually lead to a statistically normally distributed variability in process output. Uncontrolled process variation is caused by ‘special events’ (examples in cell culture may include a missed media change, a defective reagent batch, an incorrect cell density or error in cytokine calculation etc.). Because special events are one-off occurrences, the effect is to generate a process output that is a statistical outlier. Uncontrolled processes cannot be assessed for process capability, are not easy to optimise or manage, and limit the tools that can be used to address process variation118, 119.
All critical process outputs should be assessed for distribution, control and capability. These will differ for different outputs (such as cell expansion, viability or markers) as each will respond differently to variability in process parameters and have different specification windows. The capability of the process must be assessed against the worst capability120 (i.e. the critical process output most likely to deviate from specification, in this case the number of cells extracted from each cord).
As mentioned in previous section of this chapter the analysis of historical data is an essential step for reducing process variation. It ensures the process problem is clearly defined and bench marked for future validation studies.
The critical quality measurement of the process output analysed from the cord bank’s historical data was the number of cells extracted from 48 (200-400mg) frozen and fresh slices, of 8 umbilical cords (as a measurement of tissue quality). A successful (within specification) output was considered to be a cell yield between 125000 – 106 cells. The chart
below (Fig. 2.1) illustrates the results achieved from the quality measurements of 48 slices of UCT.
Fig. 2.1 Chart shows the frequency with which different cell yields were achieved from 200- 400mg slices of UCT of 8 different cords. Best outcome was considered to be in the region 125000 – 106 cells from a T25 flask after 7 days in culture), therefore cell yield was within
specification only 6.25% of the time (3 slices out of 48).
The frequency chart above can be used to deduce a series of key statements:
The existing process framework has produced in specification product, and therefore is capable of doing so on a reproducible basis if appropriately controlled.
However, under current processing controls, product will be in specification only approximately 6.25% of the time (cell numbers above estimatedprocess specification lower limit). The process is therefore not capable.
The process mean produces a product with cell numbers in the region 102
cells, which represents a low cell density.
Considering all the above statements, it can be concluded that in order to reach process specification with acceptable frequency, the process requires two remedial actions:
Firstly, the intrinsic process variation needs to be reduced.
0 5 10 15 20 25 0 200000 400000 600000 800000 1000000 Freq u en cy Cell number
Secondly, the process mean needs to be shifted higher (i.e. as the current successful runs lie on the edge of the distribution, simply reducing variation around the current mean would deliver a more consistent but permanently outside specification product). Note: Process control is defined relative to intrinsic process variation; it is quite probable that reducing intrinsic process variation may cause statistically uncontrolled events to be identified. This will need to be identified via validation runs of process changes.
The next step after analysing the major points for variability and control from historical process data is to investigate what are the contributors to the process variation identified. This analysis should be carried out on any available intermediate measurements in the production process119, 120 (i.e. tissue viability at receipt, intermediate yields - primary cell count after isolation process/in process counts/harvest count after expansion) or expansion rates (would be estimated based on seeding/yield accuracy). The variation seen in output will be reflected in key variables or intermediate outputs earlier in the process. It would be particularly informative to understand if the data distribution characteristics are equivalent over the multiple sets of intermediate counts.