With the worldwide appetite for biologics soaring—the U.S. alone accounts for roughly $100 billion annually, and that’s growing at 11% CAGR—efforts to optimize bioprocessing technology remain vigorous and varied.
Faster assays for cell-line screening, small-scale modeling to predict large-scale production effects, and new microfluidics able to finely control microenvironments are just a few of the many technologies on the horizon.
One of the most pressing concerns for all companies manufacturing biologics is preventing viral contamination; raw materials constitute a considerable portion of that risk. Switching to different raw materials to mitigate the risk is often done, particularly the migration away from materials of animal origin. The problem, however, is difficulty predicting the effects of these changes on legacy processes. Sofie Goetschalckx, Genzyme’s manufacturing cell culture science lead, technology division, discusses her firm’s practices.
“Reducing viral risk is our primary goal in changing materials at the moment, although sometimes we assess a second supplier so if one company goes out of business or can no longer provide the product, we will still have a supplier who can produce the material,” says Goetschalckx.
“We’ve developed qualified small-scale models. So we have a 10-liter cell culture model that is performing similar to our ‘at-scale’ 4,000 liter bioreactor, and we have a downstream model that performs similar to our overall product quality generated at scale,” she adds. “We take three different lots from the supplier so we have some variability in the raw material and run the small-scale models and see what the impact is.”
Building these small-scale models takes two to three years, according to Goetschalckx. One of the most problematic issues is controlling PCO2. “At small scale, PCO2 is completely different than at scale and has a huge impact on cell growth and recovered product quality. Our cells tend to grow a little better at scale than at small scale.” Genzyme adds more CO2 to the small-scale model process to compensate.
Sampling size also has an effect. At small scale the effect is much larger and more significant than at scale. “It turns out there are some differences that are challenging that you can solve but some you cannot solve,” she notes. Depending upon the results and the risk assessment on the criticality of the material, “we determine if we need additional data at scale in a kind of engineering run. Ultimately, this package of data will be reviewed by laboratory quality and also regulatory authorities to assess if you need to resubmit. Resubmission is very rare.”