Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes (Part 2) - Understanding the relationship between the process and CQAs. - BioPharm

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Quality by Design: Industrial Case Studies on Defining and Implementing Design Space for Pharmaceutical Processes (Part 2)
Understanding the relationship between the process and CQAs.


BioPharm International
Volume 22, Issue 1

Case Study 2: Cell Culture Scale-Up


Figure 2
Another case study illustrating the role of process monitoring in demonstrating process performance within a design space was recently published.5 Multivariate data analysis (MVDA) and modeling were performed using representative data from small-scale mammalian cell culture batches (2-L) and large-scale batches (2,000-L) manufactured with a cell-culture process. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (purity, viable cell density, viability, and osmolality) were evaluated in this analysis. An MVDA model was created using the 14 representative small-scale batches (2 L), and then used to predict performance at the 2,000-L scale. Results presented in Figure 2 show that the process at 2,000 L is indeed very representative of the 2-L process. All of the 11 large-scale batches lie within the control limits calculated from the 2-L scale data with the exception of two batches that deviated slightly from the control limits between time values 12 and 13. Further investigation indicated that one of these two lots had the highest viable cell density (VCD) and the other had the highest lactate levels compared to all of the runs. Thus, these two batches were somewhat "atypical," illustrating the usefulness of the batch control charts in fault diagnosis during manufacturing to diagnose events such as equipment failures and raw material issues.

As highlighted in the case studies above, if the manufacturing process creeps outside the design space, process changes may be made and may require process characterization, validation, and filing of the changes to the approved design space.

PAT and QbD

Process Analytical Technology (PAT) has been defined as "a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes, with the goal of ensuring final product quality." As such, the goal of PAT, as explained in the FDA's PAT guidance document,2 is to "enhance understanding and control the manufacturing process," which is consistent with the idea that quality cannot be tested into products, but rather should be built-in by design. Thus, PAT is an enabler of the concept of Quality by Design. The design space is defined by the critical process parameters, possibly including material attributes identified from process characterization studies and their acceptable ranges. These parameters and attributes are the primary focus of online, inline, or at-line PAT applications. Situationally dependent, "real time" PAT assessments could provide the basis for continuous feedback and result in improved process robustness. Yu et al. have reviewed the PAT concept and discussed its application to a crystallization process.13 They presented a variety of in situ analytical methods that could be combined with chemometric tools to analyze multivariate process information and provide a basis for future improvements in the modeling, simulation, and control of crystallization processes. The following case studies illustrate the integration between PAT and QbD.


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