School of Pharmaceutical Sciences, University of Geneva, CMU - Rue Michel Servet 1, 1211, Geneva 4, Switzerland.
Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, CMU - Rue Michel Servet 1, 1211, Geneva 4, Switzerland.
J Sep Sci. 2022 Sep;45(17):3276-3285. doi: 10.1002/jssc.202200161. Epub 2022 May 26.
This paper describes an approach to rapidly and easily calculate the linear solvent strength parameters, namely log k and S, under reversed-phase liquid chromatography conditions. This approach, which requires two preliminary gradient experiments to determine the retention parameters, was applied to various representative compounds including small molecules, peptides, and proteins. The retention time prediction errors were compared to the ones obtained with a commercial HPLC modeling software, and a good correlation was found between the values. However, two important constraints have to be accounted for to maintain good predictions with this new approach: i) the retention factor at the initial composition of the preliminary gradient series have to be large enough (i.e., log k above 2.1) and ii) the retention models have to be sufficiently linear. While these two conditions are not always met with small molecules or even peptides, the situation is different with large biomolecules. This is why our simple calculation method should be preferentially applied to calculate the linear solvent strength parameters of protein samples.
本文描述了一种在反相液相色谱条件下快速简便地计算线性溶剂强度参数（即 log k 和 S）的方法。该方法需要进行两次初步梯度实验来确定保留参数，应用于各种代表性化合物，包括小分子、肽和蛋白质。将保留时间预测误差与商业 HPLC 建模软件获得的结果进行比较，发现两者之间存在良好的相关性。然而，为了保持该新方法的良好预测，需要考虑两个重要限制条件：i）初步梯度系列初始组成时的保留因子必须足够大（即 log k 大于 2.1）；ii）保留模型必须足够线性。虽然这两个条件并不总是适用于小分子甚至肽，但对于大分子生物分子来说情况则不同。这就是为什么我们的简单计算方法应优先用于计算蛋白质样品的线性溶剂强度参数。
