Tyteca Eva, Desfontaine Vincent, Desmet Gert, Guillarme Davy
Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium.
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Switzerland.
J Chromatogr A. 2015 Feb 13;1381:219-28. doi: 10.1016/j.chroma.2014.12.077. Epub 2015 Jan 7.
The multi-modal retention mechanism in supercritical fluid chromatography (SFC) results in a non-linear dependency of log(k) on the fraction of organic solvent φ and log(φ). In the present study, the possibility of retention modeling for method development purposes in SFC was investigated, considering several non-linear isocratic relationships. Therefore, both isocratic and gradient runs were performed, involving different column chemistries and analytes possessing diverse physico-chemical properties. The isocratic retention data of these compounds could be described accurately using the non-linear retention models typically used in HILIC and reversed-phase LC. The interconversion between isocratic and gradient retention data was found to be less straightforward than in RPLC and HILIC because of pressure effects. The possibility of gradient predictions using gradient scouting runs to estimate the retention parameters was investigated as well, showing that predictions for other gradients with the same starting conditions were acceptable (always below 5%), whereas prediction errors for gradients with a different starting condition were found to be highly dependent on the compound. The second part of the study consisted of the gradient optimization of two pharmaceutical mixtures (one involving atorvastatin and four related impurities, and one involving a 16 components mixture including eight drugs and their main phase I metabolites). This could be done via individual retention modeling based on gradient scouting runs. The best linear gradient was found via a grid search and the best multi-segment gradient via the previously published one-segment-per-component search. The latter improved the resolution between the critical pairs for both mixtures, while still giving accurate prediction errors (using the same starting concentrations as the gradient scouting runs used to build the model). The optimized separations were found in less than 3 h and 8 h of analysis time (including equilibration times), respectively.
超临界流体色谱法(SFC)中的多模式保留机制导致log(k)对有机溶剂分数φ和log(φ)呈非线性依赖关系。在本研究中,考虑了几种非线性等度关系,研究了用于SFC方法开发的保留建模的可能性。因此,进行了等度和梯度洗脱,涉及不同的柱化学性质以及具有不同物理化学性质的分析物。这些化合物的等度保留数据可以使用HILIC和反相液相色谱中常用的非线性保留模型准确描述。由于压力效应,发现等度和梯度保留数据之间的相互转换比在反相液相色谱和HILIC中更不直接。还研究了使用梯度探索运行来估计保留参数进行梯度预测的可能性,结果表明,对于具有相同起始条件的其他梯度的预测是可以接受的(始终低于5%),而对于具有不同起始条件的梯度,预测误差高度依赖于化合物。研究的第二部分包括对两种药物混合物进行梯度优化(一种涉及阿托伐他汀和四种相关杂质,另一种涉及包含八种药物及其主要I相代谢物的16组分混合物)。这可以通过基于梯度探索运行的个体保留建模来完成。通过网格搜索找到最佳线性梯度,通过先前发表的每组分一段搜索找到最佳多段梯度。后者提高了两种混合物中关键对之间的分辨率,同时仍给出准确的预测误差(使用与用于建立模型的梯度探索运行相同的起始浓度)。分别在不到3小时和8小时的分析时间(包括平衡时间)内实现了优化分离。