Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (t_R) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R² of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.

在没有保留数据的情况下准确预测一组目标分析物的分离条件是常规分析检测的基础，但仍然是一项非常具有挑战性的任务。在本文中，介绍了一种质量源于设计 (QbD) 优化工作流程，该工作流程能够发现在亲水相互作用液相色谱 (HILIC) 中分离新化合物的最佳色谱条件。该工作流程的特点是将定量结构保留关系 (QSRR) 方法与实验设计 (DoE) 原则相结合，用于对两性离子、酰胺、胺和裸硅胶 HILIC 固定相，流动相包含不同的乙腈含量、流动相 pH、和盐浓度。一种同时考虑保留时间 (t _R ) 和结构相似性用于确定最佳分析物集以训练 QSRR，以最大限度地提高预测准确性。高度预测保留模型（平均R ²0.98) 并且 QSRR 模型预测性能的统计分析表明它们能够仅根据新化合物的分子结构预测其保留时间，预测的均方根误差范围为 7.6-11.0% . 此外，获得的药物测试化合物的保留数据用于计算它们的分离选择性，将其用作 DoE 优化器的输入，以选择最佳分离条件。在选定的最佳工作条件下进行的实验分离与理论预测非常吻合。据我们所知，这是首次对 QbD 优化工作流程进行研究，该工作流程辅以基于双过滤的保留建模，以促进 HILIC 中的方法开发过程。