Bioprocesses are complex, and the pressure on them is rising. A process has to be robust, it has to deliver product of the required quality, and it increasingly has to do both at pace and at lower cost. Following Quality by Design principles, the structured route to that outcome is process understanding: linking critical quality attributes and key performance indicators to critical process parameters and raw material attributes. That is a multidimensional task, and sound data science approaches are required to address it. Data science is no longer a nice addition to bioprocess development. It is the method.

What has changed is that the tools have caught up with the ambition. Alongside classical multivariate models such as ordinary least squares regression, digital twins now offer a powerful means of capturing process knowledge, and then of putting that knowledge to work. Regulatory expectations have moved in the same direction, towards lifecycle thinking and enhanced, science based development, which rewards teams that can show where their numbers come from. At the same time, commercial pressure on biopharma has made speed to clinic, right first time transfer and reliable manufacture a board level concern rather than a technical one.

This masterclass introduces the data science methodologies that make this practical. It covers three things in sequence: how to increase data quality through preprocessing, how to regress complex data sets to achieve genuine process understanding, and how to capture that understanding in mechanistic or hybrid digital twins. It then turns to deployment, with real world case studies showing how those twins help deploy knowledge for optimised bioprocessing, from process prediction through to multivariate closed loop control. Because none of it works without a foundation, the course also addresses the need for proper data governance, so that process data is available in a structured manner.

The programme is deliberately tool agnostic. You will not be sold a software platform. The focus is on methods, decision points and good modelling practice that transfer to whatever stack your organisation runs, illustrated throughout with real world case studies.

‍Key Takeaways & Outcome

BY THE END OF THIS MASTERCLASS, YOU WILL BE ABLE TO:

• Build the business and regulatory case: explain the business and regulatory needs behind a robust bioprocess control strategy, and what robustness is worth to your programme.
• Turn messy process data into usable signal: apply data preprocessing and feature engineering to complex, multi source bioprocess data so that analysis rests on real process signal.
• Design study plans that answer the question: set up study plans along QbD principles, and recognise the pitfalls in DoE and regression analysis that quietly invalidate a result.
• Link the variables that matter: establish defensible relationships between critical quality attributes, key performance indicators, critical process parameters and raw material attributes.
• Choose the right digital twin: judge when a static twin is sufficient and when a dynamic twin is justified, and understand where mechanistic and hybrid models fit.
• Build twins to good modelling practice: set up digital twins along good modelling practice, so that the models you rely on are properly constructed and documented.
• Deploy models where they pay back: work through the options for deploying digital twins for bioprocess optimisation, from process prediction to multivariate closed loop control.
• Set a holistic control strategy: use end to end digital twins and integrated process models to control the process as a whole rather than step by step.
• Put the data foundations in place: identify the data governance and digital transformation steps that make everything above possible.