AI & Digital Bioprocessing

Insights

Anil Sebastian, PhD

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Digital bioprocessing

Bioprocess development is increasingly becoming data-rich, multidimensional and predictive. The opportunity is not to replace experiments, but to make each one count.

Perspective

From "running more experiments" to designing smarter ones

Every upstream programme generates far more data than is typically used: growth and metabolite profiles, process parameters, online sensor and spectroscopic data, and product-quality results. Combined with biological and process understanding, multivariate analysis and machine learning can extract more knowledge from existing data, reveal critical process relationships and guide where the next experiments should go.

The result is better development decisions made earlier – reducing unnecessary experimentation, development time and technical risk.

Toolkit

Where digital tools fit in

  • DoE to generate information-rich data by design
  • MVDA to understand how parameters, metabolism and outcomes interact
  • PAT, including Raman spectroscopy, for richer process monitoring
  • Mechanistic and hybrid AI/ML models for prediction and scale effects
  • Explainable approaches, so models support scientific judgement

Research highlight

Explaining clone instability

A recent study used explainable AI-driven flux balance analysis to explore CHO cell stability during prolonged passaging, where peak antibody titre fell by around 35% – illustrating how AI and metabolic modelling can help explain, and ultimately anticipate, performance drift.1

Explore further

Sources

  1. Exploring CHO cell stability during prolonged passaging via explainable AI-driven flux balance analysis. PMC12992790.
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Let's talk about your programme

Whether you are designing a CLD programme, stuck on an upstream process, preparing for technology transfer, or assessing a biologics asset before you commit.

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+44 7723 059341