Novel Method Detects Microbial Contamination in Cell Cultures, Accelerating Cell Therapy Manufacturing
Researchers at the Singapore-MIT Alliance for Research and Technology (SMART), specifically within the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group, in collaboration with MIT, A*STAR Skin Research Labs, and the National University of Singapore, have unveiled a groundbreaking method for the rapid detection of microbial contamination in cell therapy products (CTPs). This innovative approach leverages ultraviolet light absorbance of cell culture fluids coupled with machine learning to identify patterns indicative of contamination early in the manufacturing process.
Cell therapy is emerging as a transformative approach in medicine, offering potential treatments for cancers, inflammatory diseases, and chronic degenerative disorders. A significant hurdle in CTP manufacturing is the need for swift and reliable contamination detection to ensure patient safety.
Current sterility testing methods can take up to 14 days, potentially delaying critical treatments. While rapid microbiological methods (RMMs) can reduce this timeframe to seven days, they require complex procedures and skilled personnel. The newly developed method addresses this need by providing results in under half an hour without the need for cell staining or extraction.
The findings are detailed in a paper titled “Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products,” published in Scientific Reports. The method uses UV absorbance spectroscopy combined with machine learning for real-time, noninvasive detection of cell contamination during the early stages of manufacturing.
This new method’s advantages include eliminating cell staining, avoiding cell extraction, and delivering rapid results, providing a simple “yes/no” contamination assessment, and facilitating automation of cell culture sampling, which ultimately could reduce the cost of cell therapy manufacturing.
Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first author of the paper, highlights that this method serves as a preliminary safety check, enabling early detection and timely corrective actions. This approach can save costs, optimize resource allocation, and accelerate the manufacturing timeline.
Rajeev Ram, the Clarence J. LeBel Professor at MIT and a principal investigator at SMART CAMP, emphasizes the role of automation and machine learning in streamlining cell therapy manufacturing and reducing contamination risks. The method supports continuous monitoring of cell cultures and early detection of contamination.
Future research will focus on expanding the method’s applicability to a broader range of microbial contaminants and testing its robustness across various cell types. The method may also be useful for microbial quality control in the food and beverage industry.
