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 variability.
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.
DOWNSTREAM PROCESSING
BioPharm: In implementing QbD, what would you identify as the CQAs in a typical downstream bioprocess in which the product was produced using cell culture?
Weber (CMC Biologics): This is probably the most difficult question to answer. We have found the CQAs for downstream are very product dependent. Glycosylation and sialylation are definitely two outputs that require product and product quality understanding and are the most common across different cell-culture processes. After that, variation in the product profile begins to manifest itself too much, preventing universal CQAs to be established.
McKnight (Genentech): Some CQAs are generated in, or substantially changed by, certain downstream unit operations. Size variants or charge variants, for example, may be generated during hold times, and product quality attributes such as host-cell proteins and size variants are typically reduced through the purification process and controlled to an acceptable level by consistent purification process performance.
Rathore (IIT Delhi): The downstream process attributes would be similar to those mentioned earlier for upstream processing. These include process-related impurities (e.g., antifoam, additives added during the processing, Protein A leachate), host-cell impurities (e.g., host-cell proteins), and product-related impurities (e.g., aggregate, basic variants, acidic variants, glycoxylation pattern). The big difference is that the ease of measurement of these attributes improves significantly in downstream processing as the samples are relatively cleaner.
BioPharm: In implementing QbD, what would you identify as the CPPs in a typical downstream bioprocess in which the product was produced using cell culture? What are the most difficult parameters to control and why?
McKnight (Genentech): Chromato-graphy unit operations are typically most sensitive to the pH and ionic strength of wash and elution conditions. Control of these critical buffers is managed through batch preparation and release prior to use based on acceptable pH and conductivity ranges. Process capability is sufficient to reproducibly prepare buffers within narrow pH and conductivity ranges, such that only a subset of the most sensitive buffers are typically determined to be CPPs.
Rathore (IIT Delhi): Typical CPPs in downstream processing would include parameters such as pH, conductivity, temperature, and gradient for chromatography steps; temperature, agitation rate, and sparge rate for refolding steps; and pH and hold time for the viral inactivation step. The challenge in downstream processing mainly comes from the fact that the steps are relatively short in duration. For example, a typical elution in chromatography column may be 30–60 minutes. Our ability to measure and then take action, therefore, is quite challenged if the assay is not a real-time assay (such as HPLC).
Johanning (QAtor): CPPs in a typical downstream process involving cell culture include holding time, pH control, temperature control, and UV control. UV control/cutting is the most difficult parameter to control because UV cutting techniques/equipment are still an area for further development. They relatively often challenge batch release (out of specification).
Vanden Boom (Hospira): Originator and biosimilar products have similar downstream CPPs. For chromatography steps, these may include protein load, pH (load or elution depending on step), temperature, flow rate, and conductivity for ion-exchange steps. Viral inactivation steps may have temperature, pH, or detergent concentration as a CPP depending on the modality of inactivation. Pressure and filter volume represent CPPs for viral filtration steps. Again, if engineering controls permit tight control of these parameters, some may be downgraded to a lower parameter designation.
Weber (CMC Biologics): Using a QbD approach, downstream CPPs would be proposed only through using risk assessments of the possible impact to pre-identified CQAs. Downstream CPPs will vary greatly depending on the nature of the molecule, the purification strategy, and the order and timing of unit operations. Nevertheless, there are certain potential CPPs common to many downstream processes. The following list of operations/parameters is potentially responsible for affecting product quality/CQAs, particularly toward the end of a purification process.
Not all of these would necessarily end up as CPPs:
• Viral inactivation: pH of inactivation, time of inactivation, and concentration of inactivation solution
• Viral filtration: filter load density (mg/mL membrane) and filtration pressure
• Chromatography operations: load density, pH and/or conductivity of buffers, residence time, volumes, and eluent concentration
• Filtration operations: load density, transmembrane pressure, crossflow rate, and diafiltration diavolumes.
The most difficult parameters to control are those with narrow allowed ranges. Using QbD, well-designed DOEs are intended to grant maximum flexibility in defining allowed ranges.
BioPharm: What tools are typically used to measure CPPs and the resultant process inputs and outputs in a downstream process, including PAT tools?
McKnight (Genentech): CPPs for chromatography operations frequently include pH and conductivity of the most sensitive wash or elution buffers. Control of these critical buffers is managed through batch preparation and release prior to use based on acceptable pH and conductivity ranges. This practice predates the PAT initiative, but serves the intended purpose of PAT.
Rathore (IIT Delhi): The same aforementioned tools would be applicable here as well as we are measuring the same attributes. As mentioned previously, the difference lies in the fact that the samples are much cleaner and hence easier to analyze. However, the time for decision is significantly less, thus making PAT implementation more of a challenge.
Weber (CMC Biologics): Put simply, the right downstream equipment for chromatography and filtration should have built-in monitoring of all potential CPP parameters and should cover a wide range of operational values. This can either transmit out for a PAT approach, or can allow for well-designed process monitoring.