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...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83114-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2045-2322 |