Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products
Abstract We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse the absorbance spectra of cell cultures a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83114-y |
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| author | Shruthi Pandi Chelvam Alice Jie Ying Ng Jiayi Huang Elizabeth Lee Maciej Baranski Derrick Yong Rohan B. H. Williams Stacy L. Springs Rajeev J. Ram |
| author_facet | Shruthi Pandi Chelvam Alice Jie Ying Ng Jiayi Huang Elizabeth Lee Maciej Baranski Derrick Yong Rohan B. H. Williams Stacy L. Springs Rajeev J. Ram |
| author_sort | Shruthi Pandi Chelvam |
| collection | DOAJ |
| description | Abstract We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse the absorbance spectra of cell cultures and predict if a sample is sterile or contaminated. This label-free technique provides a rapid output (< 30 minutes) with minimal sample preparation and volume (< 1 mL). Spiking of 7 microbial organisms into mesenchymal stromal cells supernatant aliquots from 6 commercial donors showed that contamination events could be detected at low inoculums of 10 CFUs with mean true positive and negative rates of 92.7% and 77.7% respectively. The true negative rate further improved to 92% after excluding samples from a single donor with anomalously high nicotinic acid. In cells spiked with 10 CFUs of E. coli, contamination was detected at the 21-hour timepoint, demonstrating comparable sensitivity to compendial USP < 71 > test (~ 24 hours). We hypothesize that spectral differences between nicotinic acid and nicotinamide in the UV region are the underlying mechanisms for contamination detection. This approach can be deployed as a preliminary test during different CTP manufacturing stages, for real-time, continuous culture monitoring enabling early detection of microbial contamination, assuring safety of CTP. |
| format | Article |
| id | doaj-art-0863d25fd03040bf883a13e650f67a87 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0863d25fd03040bf883a13e650f67a872025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-024-83114-yMachine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy productsShruthi Pandi Chelvam0Alice Jie Ying Ng1Jiayi Huang2Elizabeth Lee3Maciej Baranski4Derrick Yong5Rohan B. H. Williams6Stacy L. Springs7Rajeev J. Ram8Critical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreCritical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology CentreAbstract We demonstrate the feasibility of machine-learning aided UV absorbance spectroscopy for in-process microbial contamination detection during cell therapy product (CTP) manufacturing. This method leverages a one-class support vector machine to analyse the absorbance spectra of cell cultures and predict if a sample is sterile or contaminated. This label-free technique provides a rapid output (< 30 minutes) with minimal sample preparation and volume (< 1 mL). Spiking of 7 microbial organisms into mesenchymal stromal cells supernatant aliquots from 6 commercial donors showed that contamination events could be detected at low inoculums of 10 CFUs with mean true positive and negative rates of 92.7% and 77.7% respectively. The true negative rate further improved to 92% after excluding samples from a single donor with anomalously high nicotinic acid. In cells spiked with 10 CFUs of E. coli, contamination was detected at the 21-hour timepoint, demonstrating comparable sensitivity to compendial USP < 71 > test (~ 24 hours). We hypothesize that spectral differences between nicotinic acid and nicotinamide in the UV region are the underlying mechanisms for contamination detection. This approach can be deployed as a preliminary test during different CTP manufacturing stages, for real-time, continuous culture monitoring enabling early detection of microbial contamination, assuring safety of CTP.https://doi.org/10.1038/s41598-024-83114-y |
| spellingShingle | Shruthi Pandi Chelvam Alice Jie Ying Ng Jiayi Huang Elizabeth Lee Maciej Baranski Derrick Yong Rohan B. H. Williams Stacy L. Springs Rajeev J. Ram Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products Scientific Reports |
| title | Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products |
| title_full | Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products |
| title_fullStr | Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products |
| title_full_unstemmed | Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products |
| title_short | Machine learning aided UV absorbance spectroscopy for microbial contamination in cell therapy products |
| title_sort | machine learning aided uv absorbance spectroscopy for microbial contamination in cell therapy products |
| url | https://doi.org/10.1038/s41598-024-83114-y |
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