CASE STUDY: TRANSFERRING THE DRUG-SUBSTANCE PROCESS FOR A RECOMBINANT GLYCOPROTEIN PRODUCT
In the case study presented here, the drug substance (DS) manufacturing process for a recombinant glycoprotein product was
transferred to the receiving site. The product had been manufactured commercially at the sending site for over a decade. The
DS process consists of cell culture and recovery operations. Cell culture includes three types of operations:
- Cell source maintenance starting with a thaw of the working cell bank ampul(s) and serial scale-up in spinner flasks and seed-train
- Cell mass accumulation in inoculum-train bioreactors of increasing volumes
- Cell growth and product formation in the production bioreactor.
Recovery includes four types of operations:
- Primary recovery of the cell culture fluid via tangential flow microfiltration (TFF),
- Purification through several chromatography steps
- Viral inactivation and virus filtration
- The final ultrafiltration and diafiltration step to achieve the target protein concentration in the formulation buffer.
The formulated bulk is filtered through a sterilizing-grade filter and stored frozen in storage vessels until further processing
for fill and finish.
To illustrate how the quality risk-management concept can be applied to process transfers, four gaps identified as part of
this case study and their associated risk assessment and mitigation strategies are discussed below. Two of the gaps were from
the cell culture portion of the process, while the other two were from the recovery portion. Their risk classifications ranged
from medium to high, depending on their potential effect on the process and product as outlined in Table I.
Gap example # 1: production bioreactor gap
During the process walkthrough, certain aspects of the bioreactor design configurations were found to differ between the
sending and receiving sites, even though the production bioreactor designs at the sending and receiving sites were highly
similar, having identical aspect ratios and baffle configurations. Specifically, one of the two impellers in the production
bioreactor was different, and the maximum sparged air-flow rate for the production bioreactor at the receiving site was lower
than that at the sending site.
The typical bioreactor operating parameters can be categorized into volume-independent and volume-dependent parameters (3).
Volume-independent parameters include pH, dissolved oxygen (DO), and temperature-control set points, which remain constant
during a process transfer or scale change (i.e., scale up or scale down). Volume-dependent parameters include parameters that
can be scaled linearly, such as nutrient feed volumes, and parameters that can not be scaled linearly, such as agitation speed
and sparged gas-flow rate in bioreactors. The criterion applied here to select the production bioreactor agitation speed at
the receiving site was to maintain approximately the same energy dissipation rate as at the sending site, which was estimated
by using the empirical correlation between energy dissipation rate and agitation speed (4).
In a bioreactor, oxygen and carbon dioxide are typically sparged to control the culture DO level and pH, respectively. For
large-scale bioreactors (i.e., > 1,000 L), air is also sparged to reduce the CO2 concentration in the sparged gas and therefore increase the driving force for CO2 transfer from the culture liquid to the sparged gas (i.e., CO2 stripping). For large-scale bioreactors, mass transfer between the culture fluid and gas phase is mainly driven by sparging
and the contribution to mass transfer from the overlay gas flow is negligible (5). The sparging mass-transfer coefficient
can be empirically correlated with the energy-dissipation rate and the sparged gas-flow rate (6). Even though the energy-dissipation
rate was maintained constant between the sending and receiving sites, the difference in the maximum sparged air-flow rate
between the sending and receiving sites would likely result in different CO2 stripping effects and therefore different pCO2 levels. Differences in pCO2 levels could in turn cause differences in osmolality because the culture pH is controlled by based addition. Elevated pCO2 and osmolality levels have been shown by others to negatively affect cell growth and productivity in CHO cells (7, 8). Depending
on the cell line, product-quality attributes such as glycoform distributions may also be affected (9). Additionally, elevated
pCO2 and osmolality levels may contribute to poor cell culture process performance when coupled with other factors such as raw
material issues (10). Therefore, this gap in the production-bioreactor configuration was classified as a medium risk.
Figure 1a: Production culture pCO2 profiles of the engineering runs. The black lines represent the expected range based on historical data from the sending
The risk-mitigation approach taken here was to first compare the existing standard sparged gas-flow control strategies between
the sending and receiving sites. Based on the available process data from both sites (e.g. pH setpoint, bicarbonate concentration
of the cell culture media, peak oxygen uptake rate, etc.), a sparged air-flow rate was selected for the full-scale engineering
runs at the receiving site. The goal was to maintain the production culture pCO2 profile within the expected range that had been established based on the historical manufacturing data from the sending site.
Additionally, a response plan was put in place before the initiation of the engineering runs so that the sparged air-flow
rate could be adjusted if the real-time pCO2 profile of the first engineering run had deviated from the expected range. The pCO2 and osmolality profiles of the engineering runs are shown in Figures 1a and 1b, respectively. Both profiles were maintained
well within the expected ranges, thereby demonstrating that the selected air-flow rate at the receiving site was adequate
to achieve an equivalent pCO2 stripping effect to that of the sending site process. Therefore, the risk associated with the gap in the production bioreactor
configuration was successfully mitigated.
Figure 1b: Production culture osmolality profiles of the engineering runs. The black lines represent the expected range based
on historical data from the sending site.