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In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

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Smart process development: Application of machine-learning and integrated process modeling for inclusion body

Cornelia Walther¹, Martin Voigtmann¹, Elena Bruna²

¹Process Science, Boehringer-Ingelheim RCV GmbH & CoKG, Wien, Austria.

Biotechnology Progress

|March 5, 2022

Summary

This study introduces integrated process models using machine learning and genetic algorithms to optimize biopharmaceutical production. This smart approach significantly boosts productivity compared to traditional methods.

Keywords:

downstream process genetic algorithm inclusion body integrated process model machine learning

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Area of Science:

Biopharmaceutical Manufacturing
Process Development
Machine Learning Applications

Background:

Sequential optimization of biopharmaceutical unit operations is time-consuming and overlooks inter-step interactions.
Conventional development strategies may not achieve optimal overall process performance due to a lack of integrated analysis.

Purpose of the Study:

To develop and utilize integrated process models for biopharmaceutical production using machine learning and genetic algorithms.
To evaluate the efficiency gains of data-driven integrated models compared to conventional development approaches.

Main Methods:

Developed data-driven integrated process models using gradient boosting machines and Gaussian processes.
Employed a genetic algorithm as a recommendation engine for process optimization.
Applied models to downstream unit operations including solubilization, refolding, and capture chromatography.

Main Results:

A two-step integrated model predicted a twofold increase in productivity for solubilization and refolding.
Extending to a three-step model (including capture chromatography) yielded a 50%-100% increase in productivity.
Demonstrated successful application of machine learning and optimization algorithms in downstream process development.

Conclusions:

Integrated process models are crucial for optimizing the entire biopharmaceutical production chain.
Machine learning and genetic algorithms offer a powerful approach to enhance efficiency and productivity in bioprocess development.
Considering all unit operations holistically leads to superior process performance.