在液体染色学中量化化峰值的置信区间估计-通过多变量曲线分辨率-交替最小平方和贝叶斯推理技术的多变量曲线分辨率组合进行光二极管阵列检测
Yusuke Tamai1, Akira Noda1, Eiichi Yamamoto2
1Shimadzu Corporation, Technology Research Laboratory, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
Journal of chromatography. A
|June 12, 2023
概括
本研究引入了贝叶斯推理算法与多变量曲线分辨率交替最小平方 (MCR-ALS) 结合,以准确量化复杂药物杂质. 该方法为制药质量控制中的凝结物质提供可靠的置信区间.
科学领域:
- 分析化学 分析化学
- 制药科学 制药科学
- 计算化学的计算化学
背景情况:
- 越来越复杂的候选药物对质量控制构成重大挑战,原因是难以分离的类似物质.
- 传统的高性能液态染色法 (HPLC) 方法难以量化表现出类似结构和物理化学性质的杂质,导致凝结峰值.
- 现有的多变量曲线分辨率-交替最小平方 (MCR-ALS) 方法可以使用紫外线光谱来分离凝聚峰值,但对类似物质产生很大的量化误差.
研究的目的:
- 开发一种新的算法,以提高定量数据的可靠性,用于制药分析中结类似物质.
- 通过使用MCR-ALS和贝叶斯推理的组合,提高活性药物成分和配方中的杂质量计的准确性.
- 为难分离杂质的定量数据分配科学有效的置信区间.
主要方法:
- 将贝叶斯推论应用于MCR-ALS方法,以改进凝结峰的分离和量化.
- 开发一种算法,为类似物质的定量数据赋予置信区间.
- 使用模拟和真实高性能液态染色学-紫外线 (HPLC-UV) 数据集的验证,包括telmisartan类似物,具有不同的强度比和分辨率.
主要成果:
- 开发的算法成功地分配了预测置信区间,其中包括几乎所有测试场景中峰值区域的真实值,即使信号噪声比低.
- 该方法在不同的强度比和分辨率中在模拟和真实HPLC-UV数据中展示了强大的性能.
- 给峰值区域分配了可靠的置信区间,证实了定量数据的准确性和科学有效性.
结论:
- 贝叶斯推理与MCR-ALS的整合提供了一个强大的工具,用于解决和量化药品质量控制中具有挑战性的凝结杂质.
- 这种方法克服了传统HPLC-UV检测的局限性,提供了具有相关置信区间的科学有效的定量数据.
- 预计开发的算法将大大改善活性药物成分和相关配方中的杂质的评估.
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