SMART CAMP’s Novel Method Offers Rapid Microbial Contamination Detection in Cell Cultures
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 pioneered a novel method for the rapid and automated detection of microbial contamination in cell therapy products (CTPs). This innovative approach, detailed in a paper published in Scientific Reports, leverages ultraviolet (UV) light absorbance measurements of cell culture fluids combined with machine learning algorithms to identify contamination patterns early in the manufacturing process.
Cell therapy is revolutionizing medicine, offering promising treatments for cancers, inflammatory diseases, and chronic degenerative disorders. However, ensuring the sterility of cell products before administration is a significant challenge. Traditional sterility testing methods can take up to 14 days, while rapid microbiological methods (RMMs) still require about seven days, involving complex procedures and skilled labor. These delays can be detrimental, especially for critically ill patients needing immediate treatment.
The new method developed by SMART CAMP researchers drastically reduces testing time to under half an hour. By using UV absorbance spectroscopy and machine learning, the method offers a label-free, noninvasive, and real-time assessment of cell contamination. It eliminates the need for cell staining or extraction, providing a simple “yes/no” result that can be easily integrated into automated cell culture sampling workflows. This approach not only accelerates the detection process but also lowers costs by removing the need for specialized equipment.
Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first author of the paper, emphasizes that this method serves as a preliminary safety check in CTP manufacturing. It allows for early detection of contamination, enabling timely corrective actions and optimizing resource allocation. The use of RMMs can then be reserved for situations where potential contamination is indicated, further streamlining the manufacturing timeline.
Rajeev Ram, the Clarence J. LeBel Professor at MIT and a principal investigator at SMART CAMP, highlights the importance of automation and machine learning in reducing labor intensity and operator variability in cell therapy manufacturing. The automated sampling and continuous monitoring capabilities of this method ensure that contamination is detected at the earliest possible stages, minimizing risks and improving overall efficiency.
The researchers plan to expand the method’s application to detect a broader range of microbial contaminants relevant to manufacturing environments and previously identified CTP contaminants. They also aim to test its robustness across various cell types beyond mesenchymal stem cells (MSCs). Furthermore, this technology has potential applications beyond cell therapy, including microbial quality control in the food and beverage industry to ensure product safety.
This innovative approach promises to significantly enhance the safety and efficiency of cell therapy manufacturing, bringing life-saving treatments to patients faster and more reliably.
