🔬 Manufacturers need high-density process sensors to train ML algorithms.
🚫 Sensors should be noninvasive and prevent contamination.
🧫 Researchers have developed a noninvasive CO2 sensor for cell culture monitoring.
💡 Machine learning can still be applied even with limited real-time data.
📚 A machine learning-based method for protein quality assessment using limited data has been developed.
💪 ML has the power to simplify process monitoring and improve bioprocess outcomes.
Introduction:
Machine learning (ML) has the potential to revolutionize the biopharmaceutical industry by allowing drug firms to create predictive process models that optimize development, production, and quality control. However, to implement ML, manufacturers need data to train the computer algorithms, which requires high-density process monitoring using sophisticated sensors. These sensors should be noninvasive to prevent contamination in sterile bioprocesses.
- In order to embrace machine learning in bioprocesses, manufacturers need data to train the algorithms, which can be obtained through high-density process monitoring using sophisticated sensors.
- Bioprocess sensors need to be noninvasive to prevent contamination in sterile bioprocesses.
- Researchers at the University of Maryland have developed a noninvasive sensor for monitoring CO2 in cell culture, which can provide real-time data for machine learning models.
- Even when real-time process data is limited, machine learning can still be applied by incorporating mechanistic models.
- A machine learning-based method has been developed that can generate effective models using limited amounts of data, demonstrating the power of AI/ML.
Conclusion:
The implementation of machine learning in bioprocesses requires the use of sophisticated sensors for high-density process monitoring. These sensors need to be noninvasive to prevent contamination. The University of Maryland researchers have developed a noninvasive CO2 sensor as an example. Machine learning can still be applied even with limited real-time data by incorporating mechanistic models. This has the potential to greatly simplify process monitoring and reduce the need for quality control tests. Overall, machine learning has the power to optimize bioprocesses in the biopharmaceutical industry and improve production and quality control.
