BioPharm: What measurement tools are typically used to measure CPPs and the resultant process inputs and outputs, including PAT tools,
in an upstream process?
McKnight (Genentech): CPPs are a subset of the environmental and batch-recipe settings (e.g., timing of feeds, culture duration) used to perform
the process. Temperature and pH, which are commonly CPPs, are measured using on-line probes (a technology that is officially
PAT, but far preceding the 'PAT initiative'). Timing of events and generation of basal and feed media are controlled with
traditional process controls and standard operating procedures. Online cell-density measurement is an area of active development
as is online nutrient measurement technology to enable advanced feeding or timing strategies.
Weber (CMC Biologics): Assuming this question is referring to the monitoring and control of CPPs and not the tools used to actually establish CPPs,
the majority of upstream CPPs are monitored online and offline. Online instruments such as CO2 probes are verified against offline instruments to ensure that the conditions within the bioreactor have not caused the probes
to 'drift.' Adjustments are made to the online instruments if drift is detected. Other outputs such as cell count and viability
are measured strictly offline on a routine basis. PAT tools can be effective, though not necessary, to monitor parameters
such as temperature and pH, but are not necessarily value-added for culture health outputs such as viable cell density, viability,
or doubling time.
Rathore (IIT Delhi): For upstream process, tools that researchers have used include:
- Surface plasmon resonance (to assess product concentration and affinity)
- High-performance liquid chromatography (HPLC) (to assess product concentration and structure)
- Capillary electrophoresis (to assess product concentration and structure)
- Dielectric spectroscopy (to determine biomass)
In-situ microscopy (to characterize cell population)
- Flow cytometry (to characterize cell population)
- Metal oxide field effect transistor (to sense biological contaminants)
- Infrared spectroscopy (to detect media components)
In-situ 2D fluorometry (to detect media components and metabolic end products)
- Raman spectroscopy (to detect media components and metabolic end products)
- UV spectroscopy (to measure homogenate components)
- Mass spectroscopy (to detect metabolic end products)
- HPLC (to detect media components and metabolic end products).
Not all of these are amenable for online applications, but together they capture various attributes of upstream processing.
BioPharm: Given the inherent variability in biologics manufacturing, how does QbD improve process understanding and control? What are
the limitations of QbD in upstream bioprocessing?
McKnight (Genentech): The inherent variability in biologics manufacturing, and the general inability to define mechanistic equations (i.e., mechanistic
process models versus empirical process models), limits the ability to precisely predict the outcome of specific runs at manufacturing
scale. Application of multivariate, statistically designed experiments, however, is still valuable for identifying CPPs, defining
parameter acceptable ranges, and understanding the variability that may be expected from the manufacturing-scale process.
Biological variability likely limits the ability to control even the best understood process solely through control of process
parameters—the need for some degree of product testing will be necessary to control for inherent variability.
Rathore (IIT Delhi): Implementation of QbD necessitates creation of information relating the process to the product and the product to the clinic.
It is this understanding that lays the foundation for appropriate process control. A major limitation that I see with respect
to implementation of QbD in upstream processing is the complexity of the sample medium due to the presence of the large variety
of process related, host-cell related, and product-related impurities. Another limitation is the fact that the fermentation
process is so complex. With the aforementioned, tools it is easy to monitor different process and product attributes. However,
many different alterations in operating conditions and raw-material attributes can lead to similar changes in the process
outputs and hence monitoring is merely the first and simpler step. The difficulty comes in diagnosing the root cause of variability
and effectively dealing with it in real time.
Girard (Spinnovation): Precisely because QbD is a scientific, risk-based, and proactive approach to biologics development, one can use it to define
the ideal characteristics of a product to achieve CQAs relating directly to its clinical performance. On the basis of this
information, product formulation and processes are designed within a specific framework to ensure the product meets these
attributes. Variability within this framework can be monitored allowing scrutiny of the process to assure consistent product
quality. However, it is important to consider the CQAs in a matrix since one knows that a biological system has the capability
to compensate or adjust its metabolic pathway.
Vanden Boom (Hospira): The enhanced design-space knowledge derived from the systematic risk assessments and design of experiment (DOE) work completed
in association with a QbD approach offers the potential to significantly improve the level of process control for mammalian
cell culture-derived products. A key factor to realize the full benefit of QbD is the establishment of robust small-scale
model(s) of the upstream manufacturing process. This may be more challenging for certain products resulting in limitations
to fully using QbD for upstream bioprocessing steps for these products. In the case of biosimilar products, the bioanalytical
characterization of the originator product provides another useful input to determine the significance of product and process
Weber (CMC Biologics): A QbD approach can lead to an early focus and understanding of what influences the CQAs. While scale-down models combined
with screening and design space DOEs can be used to understand cell expansion and the cell-production bioreactor processes,
full purification can be time consuming and costly for full characterization of the upstream process. In addition, as the
downstream process is characterized, optimizations/changes in the downstream process can lead to the need to repeat upstream
characterization efforts. Hence, the sooner a correlation between the main upstream outputs (such as viability) and primary
product quality attributes (such as glycosylation) can be established, bench-scale work can be minimized.