A new method has been developed to ensure that cell therapy products like CAR-T therapy are safe for use in patients.
The current standard processes of testing for bacteria and fungi can take one to two weeks to give a result, which may delay treatment for patients.
To speed up the sterility testing process, Dr Stacy Springs and colleagues at Singapore-MIT Alliance for Research and Technology in Singapore, set out to create “a rapid untargeted approach for the sensitive detection of microbial contaminants”, they explain in the journal Microbiology Spectrum.
Their new sequencing methodology uses a machine learning algorithm called XGBoost to see if a sample is contaminated with a range of microbial species. It can then correctly classify the contaminant, all within 24 hours.
This could be used to test T-cell therapy to treat blood cancers, the team reports, benefiting patients by providing treatments to patients more quickly.
Lead author Dr James Strutt said: “The practical application of this discovery is vast; it offers faster product validation for biopharmaceutical manufacturers, reducing downtime and potentially accelerating product-to-market timelines.
“These advancements hold significant promise for the biopharmaceutical industry, as they not only enhance quality control but also improve overall efficiency and cost-effectiveness, ultimately benefiting patients by ensuring the safety and reliability of cell therapy products.”
Principle investigator Dr Stacy Springs added: “Our rapid method offers a more efficient way to not only detect microbial contamination, but identify the contaminating species.
“We demonstrated that this method can deliver a high-sensitivity microbial sterility assessment within just 24 hours, providing a valuable tool for researchers and hopefully practitioners in the near future.”
Source:
Strutt JPB, Natarajan M, Lee E, Teo DBL, Sin WX, Cheung KW, Chew M, Thazin K, Barone PW, Wolfrum JM, Williams RBH, Rice SA, Springs SL. (2023) “Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing.” Microbiology Spectrum, doi: 10.1128/spectrum.01350-23
Link: https://journals.asm.org/doi/abs/10.1128/spectrum.01350-23
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