Digital Twin Process Could Slash Microbial Protein Costs

A consortium of companies has developed what they call a digital twin of a microbial process to produce protein A.

Novasign, based in Vienna, hopes its participation through the ECOnti consortium will help manufacturers slash the costs of microbial proteins by improving experimental design.

According to the company, the digital twin can reduce the number of experiments needed to understand process behavior by 70% compared to Design of Experiments (DoE).

“Generally, the biggest problem in the industry right now is it’s not very efficient,” explains Mark Duerkop, PhD, CEO of Novasign.

“We need methods to learn more efficiently from experiments, design better experiments, and adapt process trajectories if something goes wrong.”

According to Duerkop, Novasign began developing an end-to-end digital twin of the full processing chain for microbial protein production as part of the ECOnti consortium three years ago.

Novasign says it develops digital twins spanning an entire process—from upstream to downstream—with the goal of improving both process development and manufacturing efficiency.

“The digital twin supports process development by systematically recommending the next set of experiments based on model-informed insights,” he says.

“During manufacturing, it can detect deviations from the intended process trajectory and support corrective actions.”

For example, if the digital twin is used to recover a process following disturbances, such as pH shifts or feed pump failure, manufacturers could significantly reduce product losses.

However, this remains for the future, he says, as the U.S. Food and Drug Administration (FDA) requires extensive validation before approving self-optimizing or autonomous manufacturing processes.

At the recent Bioprocessing Summit Europe, Duerkop presented a showcase on using the Novasign Studio software for full process control for 30 consecutive days.

He also showed how the software can use small-scale experimental data to inform scale-up and, in biosimilar development and viral vector manufacturing, can reduce experimental effort by up to 64%.

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