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Excerpt from:

Machine Learning for Bioprocess Sensor Innovation

By Gareth John Macdonald - January 31, 2024

Machine learning (ML) could allow drug firms to create predictive process models that optimize development, production, and quality control. But, before embracing ML on the factory floor, manufacturers will need data to “train” the computer algorithms that drive the approach. And this means having process sensors sophisticated enough to track multiple parameters in real-time in highly complex cell cultures according to an industry expert.

Machine learning is a specialized form of artificial intelligence in which computer programs learn to solve tasks or understand the dynamics of complex systems with minimal or no direction. The process is iterative, and the solutions improve over time as more data is introduced.

This need for training data is driving innovation in process sensors, says Govind Rao, PhD, who is director of the Center for Advanced Sensor Technology at the University of Maryland, Baltimore County.

“At the end of the day, AI/ML tools will allow for process monitoring to be simplified once data are generated at scale to relate process conditions to critical quality attributes. The need to run QC tests on quarantined bulk drug substance will be greatly reduced,” he explains. “However, to get there will require high-density process monitoring to allow ML/AI algorithms to relate process conditions to off-line measurements such as glycosylation, aggregation, etc.”