Following the advanced examples of successful process digitalization and automation in the ‘older industries’ such as automotive and finance, several trends could be observed in the biopharmaceutical industry. Over the past decade, one witnessed a shift from yield maximization to quality optimization, the utilization of miniaturized and parallelized high throughput techniques, continuous bioprocessing and continuous data acquisition as well as the utilization of data- and knowledge-driven tools for process analysis, forecasting, monitoring and control. These trends face substantial challenges, as for instance, therapeutic proteins feature several dozen critical quality attributes (CQAs) including their glycosylation and charge variant profiles as well as their aggregated and low molecular weight forms, all which are highly important for the efficacy and safety of the drug. In order to eventually fulfill the standards and goals of the industry 4.0 era, the methodologies and technologies associated to previous trends must be further developed and extensively utilized in the biopharmaceutical process industry.
Throughout the past years, we elaborated several digital solutions based on advanced engineering statistics, machine learning and deterministic approaches for the analysis, modeling and interpretation of bioprocesses. Furthermore, we integrated them into the process development workflow in several collaboration projects with the biopharmaceutical industry. This presentation will show the possibilities to accelerate development and reduce risks in bioprocessing through digital engineering solutions, and will outline key technology and business drivers to master the digital transformation challenge in bioprocessing.