SMART CAMP Develops Novel Method for Rapid Microbial Contamination Detection in Cell Cultures
Researchers at the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology (SMART), in collaboration with MIT, A*STAR Skin Research Labs, and the National University of Singapore, have unveiled a groundbreaking method for the swift and automated detection of microbial contamination in cell therapy products (CTPs). This innovative approach promises to significantly reduce the time required for sterility testing, expediting the delivery of crucial treatments to patients.
Cell therapy is at the forefront of medical innovation, offering potential treatments for a range of diseases, including cancers and chronic degenerative disorders. However, ensuring the sterility of cell products remains a critical challenge.
Current sterility testing methods are labor-intensive and can take up to 14 days to yield results. Rapid microbiological methods (RMMs) offer improvements, reducing the testing period to seven days, but still involve complex processes and skilled labor. This has created a demand for faster, simpler, and equally reliable methods.
Published in Scientific Reports, the research details how UV absorbance spectroscopy, combined with machine learning, enables real-time detection of cell contamination during the early stages of manufacturing. The method is label-free and noninvasive.
The new method offers several advantages. It eliminates the need for cell staining, avoids invasive cell extraction, and provides results in under 30 minutes. The method gives a clear “yes/no” contamination assessment, which is conducive to automation and a simpler workflow. Moreover, it requires no specialized equipment, reducing costs.
Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first author of the paper, explains that this method is designed as a preliminary step for continuous safety testing in CTP manufacturing. It allows users to detect contamination early and implement timely corrective actions, optimizing resource allocation and accelerating the manufacturing timeline.
Rajeev Ram, principal investigator at SMART CAMP, notes that the integration of automation and machine learning aims to streamline cell therapy manufacturing and reduce contamination risks. The method supports automated cell culture sampling at designated intervals, reducing manual tasks and enabling continuous monitoring.
Future research will focus on broadening the method’s application to a wider range of microbial contaminants and testing the model’s robustness across more cell types. The method could also be applied to microbial quality control testing in the food and beverage industry.
