机器学习辅助的紫外线吸收光谱检测细胞治疗产品中的微生物污染
Shruthi Pandi Chelvam1, Alice Jie Ying Ng1, Jiayi Huang1
1Critical Analytics for Manufacturing Personalized Medicine (CAMP), Singapore-MIT Alliance for Research and Technology Centre, Singapore, Singapore.
Scientific reports
|March 4, 2025
概括
机器学习增强的紫外线光谱技术快速检测细胞治疗产品 (CTP) 中的微生物污染. 这种无标签的方法分析吸收光谱,以便在CTP制造过程中早期确保安全.
科学领域:
- 生物技术是生物技术.
- 分析化学 分析化学
- 机器学习 机器学习
背景情况:
- 细胞疗法产品 (CTP) 的制造需要严格的微生物污染监测.
- 目前的方法可能耗时,延迟产品发布和安全评估.
研究的目的:
- 为了证明机器学习辅助的紫外线吸收光谱的可行性,以快速检测在CTP中的微生物污染.
- 建立一种无标签,快速,低容量方法,用于早期污染识别.
主要方法:
- 使用一级支持矢量机 (SVM) 分析细胞培养的紫外线吸收光谱.
- 开发了一种快速输出 (<30分钟) 的方法,采用最小的样本准备和体积 (<1毫升).
- 测试检测极限通过将各种微生物入多个捐赠者的中细胞层细胞中.
主要成果:
- 在较低的注射量 (10个殖民地形成单位 (CFU)) 上成功检测到微生物污染.
- 实现的平均真实阳性和阴性率分别为92.7%和77.7%.
- 已证明的污染检测可与综合USP <71>测试相比,在21小时检测出大肠杆菌.
结论:
- 机器学习辅助的紫外线光谱是一种可行的方法,用于在CTP制造过程中检测微生物污染.
- 这种技术提供了快速,无标签和敏感的监控,以提高CTP的安全性.
- 在各种CTP制造阶段进行实时,连续的培养监测的潜在应用.
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