Digitally enabled approaches for the scale up of mammalian cell bioreactors

Abstract

With recent advances in digitisation and big data analytics, more pharmaceutical firms are adopting digital tools to achieve modernisation. The biological phenomena within bioreactors are a key target for such digital approaches, as these processes are often complicated and difficult to scale. Historically, rules of thumb have been used to match performance indicators across bioreactor scales. Although such methods are well-established and frequently employed by industry, no universal solution has been developed to overcome the many challenges faced in process development and scale-up. Several computer-based methodologies can potentially be applied to bioreactor scale-up, including knowledge driven and data-driven techniques. This review assesses the state of the art in digital advances in scaling bioreactors and the advantages and limitations of scaling techniques. Traditional approaches and their constraints are outlined. The application of knowledge-based techniques is then considered and compared to data-driven models. The ability to transfer processes across bioreactor scales, to compare data and predict process indicators across scales are then examined. Finally, the role of hybrid modelling and digital twins and their potential in bioprocess development are explored.
Keywords
Machine learning
Mechanistic modelling
Biomanufacturing
Bioreactor

Description