非线性预测建模使复杂固定相分析物相互作用的色谱方法在硅优化

I. H. Ahmad, G. L. Losacco, E. Regalado
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引用次数: 0

摘要

开发用于分离和分析复杂多组分混合物的可靠分析方法往往具有挑战性,这反映出新药和疫苗工艺的复杂性日益增加。在整个制药行业,小分子硅液相色谱(LC)方法的发展策略已经达到成熟阶段。然而,由于构象变化通常会影响色谱保留,因此大分子的直接方法仍然难以捉摸。尽管如此,通过部署正确的回归留存模型(ln k vs. %B和ln k vs. 1/T),实验留存时间和预测留存时间之间的良好相关性是可能的(ΔtR < 0.1%)。通过使用不同的朝乱相和变性流动相的硅色谱方法开发大分子产生了优异的结果。使用现成的软件部署线性和非线性(多项式回归)保留模型,作为多种蛋白质(12-670 kDa)和肽的几种色谱参数(梯度斜率和柱温)的函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear Predictive Modelling Enables In Silico Optimization of Chromatographic Methods for Complex Stationary Phase‑Analyte Interactions
The development of robust analytical assays for separation and analysis of complex multicomponent mixtures can often be challenging, reflecting the increased complexity of new medicine and vaccine processes. In silico liquid chromatography (LC) method development strategies for small molecules have reached a mature stage across the pharmaceutical industry. However, a straightforward approach for large molecules remains elusive because of conformational changes that can often influence chromatographic retention. Nonetheless, an excellent correlation between experimental and predicted retention time is possible by deploying the correct regression retention models in terms of ln k vs. %B and ln k vs. 1/T (ΔtR < 0.1%). Excellent outcomes generated through in silico chromatographic method development of large molecules using different chaotropic and denaturing mobile phases are illustrated. Linear and nonlinear (polynomial regression) retention models using readily available software were deployed as a function of several chromatographic parameters (gradient slope and column temperature) for a variety of proteins (12–670 kDa) and peptides.
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