Genetic Engineering & Biotechnology News

AUG 2018

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

Issue link: https://gen.epubxp.com/i/1007345

Contents of this Issue

Navigation

Page 19 of 33

18 | AUGUST 2018 | Genetic Engineering & Biotechnology News | GENengnews.com See Bioreactors on page 20 number of bioprocess experts to explain how bioreactor technology and process design can satisfy the biopharma industry's multiple and potentially conflicting desires, in- cluding greater cell densities, increased titers, larger yields, faster process transfers, and higher quality. Taken togeth- er, the biomanufacturing specialists' comments emphasize the importance of striking the right balance, of knowing how to orchestrate bioreactor and process variables so that they reinforce each other and generate surging, puls- ing, or continuous upstream flows, whatever sort of per- formance may be needed. Process Intensification The aim of upstream optimization is to develop processes that increase product titers, says Stefan R. Schmidt, Ph.D., head of production operations at BioAtrium, part of a joint venture launched by Lonza and Sanofi. According to Lonza, the joint venture will encompass a large-scale biologics pro- duction facility, which is being built in Visp, Switzerland. "When we talk about process intensification," says Dr. Schmidt, "there are two angles: decreasing processing times, and increasing cell densities." In upstream processes, these angles converge on the ultimate goal of higher titers and yields. "The aim," he insists, "is to make more product from each batch." Process intensification, with its emphasis on yield, can guide the earliest stages of process development. For exam- ple, yield considerations can influence the development and selection of cell lines. Process intensification can also be ap- plied a little later. For example, at BioAtrium, Dr. Schmidt is growing higher density cell cultures while ensuring that nutrient levels are always optimized. "Cell cultures are often overfed with glucose," he notes. "Unfortunately, excess glucose can lessen the quality of the resulting product. In theory, establishing a feedback loop al- lows glucose levels to be optimized in real time. Doing so, however, requires online analytical technologies. As it hap- pens, these technologies are being developed, particularly in the field of spectroscopic analysis." Process intensification is a complex task. Doing it effec- tively can involve rethinking the bioreactors in which cultur- ing takes place. "Growing cells generate heat whether they are of mam- malian, insect, or bacterial origin," Dr. Schmidt points out. "This heat needs to be managed and removed to ensure that the optimal conditions for the culture are maintained. "Heat generated by cells is a problem for single-use, dis- posable bioreactors, which usually have an insulating layer. One approach to this issue is to use smaller volumes. At pres- ent, 2000-L reactors are standard, but 1000-L reactors could be of significant help when it comes to dissipating heat." "For steel bioreactors," Dr. Schmidt says, "heat dissipa- tion is more straightforward—for obvious reasons." When one is trying to increase cell densities, one must also consider aeration, which can be impeded by the particulate matter, which is abundant in cultures grown at higher densi- ties. "To address this issue," Dr. Schmidt asserts, "it is nec- essary to find a way of mixing the culture in a manner that does not disrupt optimal growth." Fortunately, technology developers are keeping pace with efforts to intensify upstream processes. "Bioreactors and as- sociated technologies are continually being improved," Dr. Schmidt observes. "And suppliers are normally very respon- sive to end-user demands." "Pall is a good example," he continues. "The company has been very proactive when it comes to acquiring technol- ogies that facilitate continuous processing, which is a fast- emerging area of demand for the biopharmaceutical industry. "Repligen, too, is very good at facilitating process intensi- fication. Although the company does not offer a wide range of bioprocessing technologies, it has been very active at im- plementing measures that facilitate short run times." A Learning Process Monitoring the many variables that impact conditions in bioreactors generates data—a lot of data. This information is key to ensuring that proteins and monoclonal antibodies generated by upstream processes are of the desired quantity and quality. Data from bioreactors can also be used for optimization, suggests Wei-Chien Hung, Ph.D., a process development scientist at Alexion. According to Dr. Hung, a data analysis method called machine learning (ML) can be used to tweak production processes. ML, a subdiscipline of artificial intelligence, refines auto- mated decision-making processes by enabling them to opti- mize themselves through the statistical analysis of data. With ML, processes effectively train themselves. "We use ML to identify the parameters that have the big- gest impact on product quality and to help reduce 'noise,'" Dr. Hung says. "ML helps us to create more accurate predic- tive models." Such models, Dr. Hung continues, indicate that parameters such as ammonia, glucose, and glutamic acid have the greatest impact on product attributes such as titer and sialic acid content. The first stage of the ML process is to collect data for all the parameters that characterize the operations of a bio- reactor. Once sufficient information has been collected, it is fed into various ML algorithms, which include decision tree, random forest, and naive Bayes algorithms. Then the "learn- ing"—the analytical model building—begins. "These algorithms are kind of like a black box—you in- put the data and wait for the results," Dr. Hung remarks. "Over time, we found that the decision tree was the most effective of the algorithms in terms of the accuracy of the model produced." The more data, the better the learning process, Dr. Hung and his colleagues learned. "We decided to apply ML to help us build better models of our upstream processes because the product in question had a low titer," he recalls. "However, what was initially a disadvantage became an advantage be- cause it required that we collect data from multiple produc- tion runs, which ultimately helped us to produce more ac- curate predictive models." The main application of these models is to optimize con- ditions within the bioreactor. These models, Dr. Hung adds, can also be applied to other parts of the production process. "One of the ways we use the models upstream is to stream- line the use of raw materials," Dr. Hung points out. "When we have established the most important parameters, we no longer need to prescreen raw materials. "Previously, when we still had to prescreen raw materials, we used upstream culturing at a small scale. But with our ML models, we can be confident that we have already identi- fied the most important parameters in terms of the quality of Bioprocessing In addition to developing therapeutic antibodies and participating in gene sequencing efforts, Regeneron Pharmaceuticals is an innovator of industrial operations. For example, the company uses analytical tools such as computational fluid dynamics to develop bioprocessing models. According to the company, such models can obviate the need for process modifications or investigations at scale. Bioreactors Pull Out Some of the Stops Continued from page 1

Articles in this issue

Links on this page

Archives of this issue

view archives of Genetic Engineering & Biotechnology News - AUG 2018