检测两次LC-MS运行之间的罕见差异的方法
Zhongqi Zhang1, Jason Richardson1, Bhavana Shah1
1Process Development, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320, USA.
Analytical biochemistry
|June 11, 2023
概括
一种新的统计方法检测了生物制药样本之间罕见的差异,没有复制品. 这种方法通过减少假阳性和假阴性来改善多属性方法 (MAM) 的新峰值检测.
科学领域:
- 生物制药学分析分析
- 分析化学是一种分析化学.
- 质谱测量质量谱测量
背景情况:
- 基于LC-MS的多属性方法 (MAM) 对于监测生物制药质量属性至关重要.
- 检测新的或缺失的峰值对于MAM实施和比较分析至关重要.
- 由于信号强度依赖于MS的变化性,比较没有复制品的类似样本具有挑战性.
研究的目的:
- 开发一种统计方法,用于检测两个非常相似的样本之间的罕见差异.
- 为了使样品可靠地比较,而不需要重复分析.
- 提高生物制药质量控制中新峰值检测的准确性.
主要方法:
- 开发了一种新的统计方法,假设大多数组件在样本之间是同等的.
- 该方法考虑了信号强度相关的相对变化.
- 分析了单克隆抗体片映射数据集,以验证该方法.
主要成果:
- 该统计方法成功地检测出高度相似的样本之间的罕见差异.
- 该方法证明适合在MAM中检测新峰值.
- 在没有大幅增加虚假负面率的情况下,实现了虚假阳性率的显著降低.
结论:
- 开发的统计方法提供了一个强大的解决方案,用于识别复杂样本中的微妙变化.
- 这种技术提高了MAM在生物制药特征表征方面的可靠性.
- 该方法在检测各种科学研究中的罕见差异方面具有更广泛的适用性.
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