Modeling of Biopharmaceutical Processes—Part 1: Microbial and Mammalian Unit Operations - Process-modeling tools can ensure smooth technology transfer of microbial and mammalian processes from b

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Modeling of Biopharmaceutical Processes—Part 1: Microbial and Mammalian Unit Operations
Process-modeling tools can ensure smooth technology transfer of microbial and mammalian processes from bench to commercial scale.


BioPharm International
Volume 21, Issue 6


Figure 2. Impact of agitation speed on product titer in a scale-down production bioreactor model
When the length scale for turbulent eddies approaches the length scale of the cells in culture, cell damage may occur. This theory has been applied to microcarrier cultures, in which cell death can be correlated to eddy lengths that are smaller than the microcarrier diameter, and to suspension cell cultures, where cell aggregate sizes were found to correlate with eddy length.12,13 It should be noted that energy does not dissipate uniformly over the entire bioreactor volume, and that local energy dissipation rates may differ significantly compared to the mean energy dissipation rate. Despite this caveat, the mean energy dissipation rate is most often used in agitation scaling calculations.

Case Study: Scale-Up of a Mammalian Cell-Culture Unit Operation


Figure 3. Cellular specific productivity at manufacturing scale compared to a scale-down model operated with tip-speed agitation scaling (500 rpm) and energy dissipation scaling (275 rpm). Statistical analysis by Dunnett's method shows that scaling by energy dissipation more accurately reflects the manufacturing scale performance versus scaling by tip speed.
In a case study involving technology transfer of a biotech process, we evaluated the impact of agitation speed on cell culture performance. Agitation speed was varied in a small-scale model bioreactor over a range that encompassed both the tip-speed and energy dissipation rate calculated for the full-scale bioreactor. As shown in Figure 2, we found that product titer correlated with agitation speed across this range. The cause for the titer correlation was found to be specific productivity, rather than cell growth. When the small scale model data are compared to manufacturing scale data as shown in Figure 3, it is evident that the specific productivity matched across scales for the agitation scaled by energy dissipation (275 rpm), whereas the specific productivity was lower for the constant tip speed condition (500 rpm).


Figure 4. The impact of agitation speed on harvest day cell aggregation. Cell clumping (middle figure) was more consistent with the manufacturing scale (left figure) when agitation was scaled by constant mean energy dissipation than when agitation was scaled by tip speed (right figure).
Further, on visual observations of microscopic cell count images (Figure 4), cell clumping observed when agitation was scaled by constant mean energy dissipation (275 rpm) was more consistent with the manufacturing scale than when agitation was scaled by tip speed (500 rpm). These results are consistent with observations reported in the literature that the size of cell clumps is related to the Kolmogorov eddy length.15 The turbulent eddies serve to disrupt cell aggregates that are comparable in size to the turbulent length scale. Because the eddy length scale is a function of energy dissipation rate as described above, cell clumping is expected to correlate with energy dissipation rate. A possible explanation for the increased cellular specific productivity is that the cells in moderate sized clumps are more productive than individual cells. Another possibility is that cell counts are less accurate for the highly clumped cultures, and the apparent changes in cell-specific productivity could be related to changes in cell density. Regardless, it is clear from these results that the best match to the manufacturing scale data was achieved with a constant energy dissipation rate scaling strategy, rather than constant tip speed scaling.


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