The Bioprocess Model Maker

Industry is waking up to the fact that modeling can reduce time and costs in bioprocess development, while also helping to meet Quality by Design (QbD) initiatives. In recent years, data-driven models, in particular, are becoming a popular choice, but developing such models is not straightforward. It is crucial to remember that a model’s success hinges on the data used to create it. Jarka Glassey, Professor of Chemical Engineering Education at Newcastle University, UK, tells us more.

If we could measure not just quantity, but also the quality of a product at each step of the bioprocess, we could begin to look at how to modify process conditions to achieve desired quality attributes, which is exactly what the FDA wants for QbD. To do this, we need to be able to measure in-line – and in small concentrations compared with all the other components that may be present in the biopharma broth. It is a significant challenge – even more so when we consider doing it cost effectively. From my point of view, we either need remote sensing technology – and to this end we are actually working on disposable, printed sensors that can be used wherever needed (4) – or physical sensors that give immediate and reliable answers about product quality. We are not at this stage yet, but the rate of progress in the field of modeling and monitoring is accelerating. In five years’ time, things may be very different.

On the other side of the fence, there is a danger that experts in model development may not know enough about bioprocessing to understand the best data to introduce into the model – perhaps they will choose specific variables rather than derived variables, for example (which a biologist will often use naturally). Such a decision drastically limits the potential of the model – and I’ve seen many models written off without being given a proper chance. Data-based modeling techniques are only as good as the data used to develop the model. And one bad experience with a model can put a company off using models ever again...

You could also invest in a proprietary model, but fledgling companies tend not to have a solid understanding of their own process, let alone enough knowledge to explain those processes for the purpose of model development. Working with academia can be another effective option – but expect much longer timelines; a PhD student usually needs three years to complete a PhD, whereas a business may only have six months to make a crucial decision on whether to go ahead with a project or not. That said, taking the academic route does develop solid process understanding along the way. Importantly, academics have the freedom to use whatever tools are most appropriate; many companies that already have a specific approach to modeling tend to try to shoehorn everything into that current modeling approach, whereas academia tends to look more broadly at what will best suit each individual project. Overall, I think it’s really important for industry and academia to work together more. Academia can come up with fantastic ideas, but they are not always feasible in the real world because of cost. Academia can learn realism by collaborating with industry, whereas industry benefits from academia’s freedom of exploration, which often results in breakthrough ideas as opposed to incremental improvements.

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