Cell therapy represents a promising recent frontier in medicine, especially in treating diseases corresponding to cancers, inflammatory diseases, and chronic degenerative disorders by manipulating or replacing cells to revive function or fight disease. Nevertheless, a significant challenge in CTP manufacturing is quickly and effectively ensuring that cells are free from contamination before being administered to patients.
Existing sterility testing methods, based on microbiological methods, are labor-intensive and require as much as 14 days to detect contamination, which could adversely affect critically ailing patients who need immediate treatment. While advanced techniques corresponding to rapid microbiological methods (RMMs) can reduce the testing period to seven days, they still require complex processes corresponding to cell extraction and growth enrichment mediums, and so they are highly depending on expert employees for procedures corresponding to sample extraction, measurement, and evaluation. This creates an urgent need for brand new methods that supply quicker outcomes without compromising the standard of CTPs, meet the patient-use timeline, and use a straightforward workflow that doesn’t require additional preparation.
This method offers significant benefits over each traditional sterility tests and RMMs, because it eliminates the necessity for staining of cells to discover labelled organisms, avoids the invasive strategy of cell extraction, and delivers ends in under half-an-hour. It provides an intuitive, rapid “yes/no” contamination assessment, facilitating automation of cell culture sampling with a straightforward workflow. Moreover, the developed method doesn’t require specialized equipment, leading to lower costs.
“This rapid, label-free method is designed to be a preliminary step within the CTP manufacturing process as a type of continuous safety testing, which allows users to detect contamination early and implement timely corrective actions, including using RMMs only when possible contamination is detected. This approach saves costs, optimizes resource allocation, and ultimately accelerates the general manufacturing timeline,” says Shruthi Pandi Chelvam, senior research engineer at SMART CAMP and first writer of the paper.
“Traditionally, cell therapy manufacturing is labor-intensive and subject to operator variability. By introducing automation and machine learning, we hope to streamline cell therapy manufacturing and reduce the danger of contamination. Specifically, our method supports automated cell culture sampling at designated intervals to ascertain for contamination, which reduces manual tasks corresponding to sample extraction, measurement, and evaluation. This allows cell cultures to be monitored constantly and contamination to be detected at early stages,” says Rajeev Ram, the Clarence J. LeBel Professor in Electrical Engineering and Computer Science at MIT, a principal investigator at SMART CAMP, and the corresponding writer of the paper.
Moving forward, future research will concentrate on broadening the applying of the strategy to encompass a wider range of microbial contaminants, specifically those representative of current good manufacturing practices environments and previously identified CTP contaminants. Moreover, the model’s robustness will be tested across more cell types aside from MSCs. Beyond cell therapy manufacturing, this method may also be applied to the food and beverage industry as a part of microbial quality control testing to make sure food products meet safety standards.
