State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry & Chemical Engineering and Center of Materials Analysis, Nanjing University, Nanjing 210023, China.
Taizhou Medical High-Tech Industrial Zone Public Platform Service Center, Taizhou 225300, China.
Molecules. 2023 Feb 28;28(5):2270. doi: 10.3390/molecules28052270.
The -octanol-water partition coefficient (log) is an important physicochemical parameter which describes the behavior of organic compounds. In this work, the apparent -octanol/water partition coefficients (log) of basic compounds were determined using ion-suppression reversed-phase liquid chromatography (IS-RPLC) on a silica-based C18 column. The quantitative structure-retention relationship (QSRR) models between log and log (logarithm of retention factor corresponding to 100% aqueous fraction of mobile phase) were established at pH 7.0-10.0. It was found that log had a poor linear correlation with log at pH 7.0 and pH 8.0 when strongly ionized compounds were included in the model compounds. However, the linearity of the QSRR model was significantly improved, especially at pH 7.0, when molecular structure parameters such as electrostatic charge and hydrogen bonding parameters A and B were introduced. External validation experiments further confirmed that the multi-parameter models could accurately predict the log value of basic compounds not only under strong alkaline conditions, but also under weak alkaline and even neutral conditions. The log values of basic sample compounds were predicted based on the multi-parameter QSRR models. Compared with previous work, the findings of this study extended the pH range for the determination of the log values of basic compounds, providing an optional mild pH for IS-RPLC experiments.
-辛醇-水分配系数(log)是描述有机化合物行为的重要物理化学参数。在这项工作中,使用基于硅胶的 C18 柱在离子抑制反相液相色谱(IS-RPLC)上测定了碱性化合物的表观-辛醇/水分配系数(log)。在 pH 7.0-10.0 下建立了 log 与 log(对应于 100%水相流动相的保留因子的对数)之间的定量结构-保留关系(QSRR)模型。当将强电离化合物包括在模型化合物中时,发现 log 在 pH 7.0 和 pH 8.0 时与 log 呈较差的线性相关。然而,当引入静电电荷和氢键参数 A 和 B 等分子结构参数时,QSRR 模型的线性度显著提高,特别是在 pH 7.0 时。外部验证实验进一步证实,多参数模型不仅可以在强碱性条件下,而且可以在弱碱性甚至中性条件下准确预测碱性化合物的 log 值。根据多参数 QSRR 模型预测了碱性样品化合物的 log 值。与以前的工作相比,本研究的结果扩展了碱性化合物 log 值测定的 pH 范围,为 IS-RPLC 实验提供了可选的温和 pH。