The ability to gather process data on the production line is reshaping biopharmaceutical manufacturing, allowing drug companies to make more consistent, higher-quality medicines more efficiently and at lower cost. The challenge now is managing—and using—the masses of information generated during each production run according to the authors of , who say companies lacking effective data infrastructure risk falling behind. Sophia Bongard, PhD, a bioprocess research scientist at Roche Diagnostics in Germany, told “The biopharmaceutical industry needs to rethink data acquisition and analytics due to the increasing complexity and volume of data generated in bioprocessing,” adding that advances like process intensification and continuous processing are driving the need for a rethink in data acquisition and analytics in bioprocessing.
“These approaches lead to an increase in the complexity and volume of data generated, as modern bioprocesses can produce millions of data points from online sensors, offline analytics, and calculated dimensions,” she continues. “The rise of automated processes and analytical capabilities, such as continuous process analytical technologies (PAT), necessitates real-time monitoring and automated control of bioreactors.” Current IT systems can struggle to store process data effectively, resulting in the creation of information silos.
And this lack of interconnectivity can mean that valuable insights are missed, Bongard says. “The need to c.
