Table 1A. Parameter estimates for data presented in Figure 2.
Expert bias exists in the selection of initial factors to include in the model, but this bias is often constrained to a set
of actionable parameters, which can be addressed on the production floor to mitigate unexpected process behavior. The inclusion
of nonactionable factors in the model, such as maximum lactate production or initial glucose uptake rate, yields scientifically
relevant ideas for further investigation, but is not as useful for immediately improving production culture performance and
achieving production campaign targets. Actionable process parameters were studied from 25 batches of campaign 2 of this antibody
process and were used to build the model shown in Figure 2, Table 1A, and Table 1B.
Table 1B. Scaled estimates for data presented in Figure 2.
We know from our historical experience that models with an adjusted R2 of 0.76 and the root mean square error (RMSE)of 91 are capable of supporting data-based decisions. The linear equation was
solved to determine the magnitude and direction of parameter change necessary to maximize culture performance.
Figure 3. The model generates the direction and magnitude for each factor in order to maximize production culture performance
and minimize a product quality attribute.
We re-optimized the parameters to maximize culture productivity and minimize a product quality attribute (in this case with
a maximum but no minimum specification). The model achieved an optimum and prescribed specific targets for these parameters.
Figure 3 indicates the actions required to achieve this optimum:
Increase initial concentration of a production culture parameter
Decrease the time at which culture operation 1 occurs
Increase the time at which culture operation 2 occurs
Increase the time at which culture operation 3 occurs
Decrease the set point of operating parameter 1.
The parameter changes were implemented incrementally and thus resulted in a gradual improvement in culture productivity.
Figure 4. Performance of campaign 3 shows initial lower-than-expected productivity that was overcome by process parameter
changes. The variability of culture performance was 25% RSD.
Figure 4 demonstrates the gradual improvement in culture performance during campaign 3, which ultimately resulted in production
of 104% of the campaign goal. While campaign 3 of this antibody process met goals through incremental parameter changes,
the process variability was unacceptably large at 25% relative standard deviation (RSD). For the fourth campaign, the process
parameters were evaluated and targets were reset with capability in mind. Most parameters remain changed in the direction
prescribed by the MLR, but the magnitude decreased in order to maintain capability and minimize process variability.
Anurag S. Rathore, PhD, is a consultant, Biotech CMC Issues, and a member of the faculty in the department of chemical engineering at the Indian Institute of Technology. Rathore is also a member of BioPharm International's Editorial Advisory Board.
Articles by Anurag S. Rathore, PhD
