Reports from Bristol-Myers Squibb Company Add New Data to Findings in Biopharmaceuticals (Rapid Total Sialic Acid Monitoring During Cell Culture Process Using a Machine Learning Model Based Soft Sensor).

A recent study conducted by Bristol-Myers Squibb Company in Devens, Massachusetts has developed a machine learning model-based technology to rapidly predict the total sialic acid content (TSA) in biotherapeutic proteins during cell culture. Traditional methods of quantifying TSA typically take several hours or longer, but this new technology allows for instant prediction of TSA values based on measured process parameters. The study found that the Random Forest model was the most promising for predicting TSA, although challenges remain in forecasting values at the edges of the calibration range. This research highlights the transformative power of machine learning in bioprocessing and introduces a rapid and efficient tool for sialic acid prediction. Future endeavors may focus on enhancing model precision and exploring process control capabilities. [Extracted from the article]