Scale-Down Considerations
The applicability of the process understanding obtained from small-scale experiments depends on the validity of the small-scale
model.7 Before executing laboratory-scale experiments, a scale-down strategy must be established for all relevant unit operations
in the process. As an example, the scale-down strategy for the production bioreactor has been provided, which is one of the
most challenging unit operations in terms of establishing a scale-down model.
The strategy encompasses two approaches: one for scale-independent parameters and the other for scale-dependent parameters.
For scale-independent parameters, such as temperature, pH, dissolved oxygen, seeding density, and nutrient feed rate, the
small-scale model operates at the same set points and uses the same or comparable online or offline control strategies as
the commercial-scale bioreactors. This ensures similarity between small-scale and commercial-scale operations. For scale-dependent
parameters, such as agitation and gas sparging, scale-up effects should be minimized between the small-scale model and commercial-scale
bioreactors. This is achieved through scale-up/scale-down studies that facilitate the determination of appropriate operating
conditions.
Figure 4
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Of all scale-dependent parameters, four high-risk parameters—agitation, pressure, surface:volume ratio (S:V), and air sparging—were
identified through a risk analysis. Their major scale-up effects and the characterization strategy are illustrated in Figure
4. The five major scale-up effects were identified based on process development and scale-up experience. In the multidimensional
space, the farther away from the central point, the more pronounced the scale-up effect. For each scale-up effect, an acceptable
range was established, within which comparable process performance was observed using corresponding scale-down models. The
combined acceptable ranges formed the acceptable space.
Figure 5
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When both the commercial-scale and small-scale bioreactors are operated within the same acceptable space, significant scale-up
or scale-down effects can be avoided. As a result, the design space established for scale-independent parameters in the laboratory-scale
model can be applied to the commercial-scale operation.
Figure 6
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As an example, the establishment of the acceptable range for hydrodynamic stress for an NS0 process is summarized below. Two
scale-down models were used: a 3-L small-scale bioreactor with a wide range of agitation rates, and a recirculation "torture
chamber" model in which cells circulated between a high-shear microfluidic device and a 2-L small-scale bioreactor.8 Both models demonstrated that the NS0 cell line used in the MAb process can withstand an intensive energy dissipation rate
(shear) without significant effects on cell growth, productivity (Figures 5 and 6), or product quality (not shown). Though
slightly higher in the torture chamber model, cell growth and product titer were within regular batch-to-batch variation.
Establishing the Design Space
An experimental strategy was developed based on several factors, including parameter risk score, operational considerations,
and overall process understanding. For example, although chromatography column lifetime may be an important parameter, it
is infeasible to study it in a multivariate study along with other parameters because of complexity and cost considerations.
Design of Experiments (DOE) and multivariate analyses were our preferred methods to study parameters with potential interactions,
but other approaches, such as one factor at a time (OFAT) and challenge studies, also were used. One DOE study for the production
bioreactor focus area is summarized below as an example of the experimental strategy and the definition of the design space.
Seven parameters with potential interactions in the fed-batch production process were studied in one set of DOE studies:
- seeding cell density
- cell density at nutrient feed initiation
- nutrient feed supplementation rate
- process duration
- bioreactor temperature
- bioreactor pH
- bioreactor dissolved oxygen.
It would have been ideal to study all seven parameters together in a single design, but it was not practical because of resource
(bioreactor) limitations. Therefore, we used a two-round DOE approach. The first round was a screening study that included
only the first four parameters in the list. The parameters of significant impact were then combined with the remaining three
parameters in the second round DOE study.
Figure 7
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First Round DOE. A two-level (high and low) full factorial design including center points was used. The study showed that antibody acidic
species level was the most sensitive attribute. The two significant parameters that affect the acidic species level were seeding
cell density and culture duration, as shown in Figure 7. These two parameters influence acidic species levels cumulatively,
without interaction.
Figure 8
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Second Round DOE. Seeding cell density and culture duration were incorporated into the second round DOE study with culture pH, temperature,
and DO. A central composite design was used to study four of these five parameters. Culture duration was not included in the
central composite design. Instead, it was studied by analyzing samples on different days of all batches in the second round
DOE study. The second round DOE study revealed again that antibody acidic species level was the most sensitive quality attribute.
As indicated in Figure 8 below, three parameters—temperature, seeding density and culture duration—affect acidic species level
cumulatively.
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