Bioreactor control decisions based on two-hour-old analytical data cannot support continuous processing or closed-loop automation. Process Raman spectroscopy provides the real-time measurement infrastructure needed for model predictive control, but implementation requires robust chemometric models and integration into control architectures designed for manual intervention.
Most bioreactor control decisions rest on data that is already out of date by the time it reaches the operator.
It is the consequence of a process where critical parameters — viable cell density (VCD), glucose, lactate, key metabolites — are still monitored predominantly through manual sampling and offline or at-line assays. A sample pulled and analysed on a two-hour cycle informs a decision that arrives after the culture has moved on.
For years this was tolerable, because bioprocessing’s automation ambitions were modest. Hold within spec. Avoid excursions. Document deviations. A few hours of analytical lag did not materially compromise any of that.
Those ambitions have changed. Continuous processing, closed-loop feedback control, model predictive control of feeds and bleeds, perfusion approaching autonomy — these are now serious programmes in serious facilities. All of them rest on an assumption that the measurement infrastructure has not met: that the variables driving control are known in something close to real time.
A control loop cannot close faster than the slowest measurement in the chain. A feeding algorithm calibrated on glucose values two hours old is not a control algorithm. It is a scheduled intervention with optimisation hopes attached.
The consequences are paid in yield. Process engineers running on instruments that update slowly build safety margins into feed strategies, harvest decisions, and media addition profiles. Those margins translate directly into lower VCDs than the process could sustain, higher media consumption than it needs, and narrower product quality consistency than is achievable.
The useful question is not whether real-time insight would help, but what it takes to deliver it. In-line process Raman is one of the more tractable answers, measuring multiple critical process parameter (CPPs) continuously and non-destructively. The difficult problems are less often discussed: chemometric models that hold up under real cell culture dynamics, integration into control architectures not designed for high-frequency inputs, and establishing the robustness needed to hand over decisions that have historically sat with humans.
Those are the questions European Pharmaceutical Review is taking up in an upcoming webinar, ‘Optimising efficiency and yield through bioprocessing automation’. The session covers how process Raman enables closed-loop control in perfusion, the practical implementation considerations, and where measurable performance improvements actually appear.
Bioprocessing’s automation narrative has run ahead of its measurement infrastructure. Closing that gap is not a question of more data. It is a question of data arriving in time to matter.
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