The secret of reversed-phase/weak cation exchange retention mechanisms in mixed-mode liquid chromatography applied for small drug molecule analysis

Resolving complex sample mixtures by liquid chromatography in a single run is challenging. The so-called mixed-mode liquid chromatography (MMLC) which combines several retention mechanisms within a sin-gle column, can provide resource-efficient separation of solutes of diverse nature. The Acclaim Mixed-Mode WCX-1 column, encompassing hydrophobic and weak cation exchange interactions, was employed for the analysis of small drug molecules. The stationary phase's interaction abilities were assessed by analysing molecules of different ionisation potentials. Mixed Quantitative Structure-Retention Relation-ship (QSRR) models were developed for revealing significant experimental parameters (EPs) and molec-ular features governing molecular retention. According to the plan of Face-Centred Central Composite Design, EPs (column temperature, acetonitrile content, pH and buffer concentration of aqueous mobile phase) variations were included in QSRR modelling. QSRRs were developed upon the whole data set (global model) and upon discrete parts, related to similarly ionized analytes (local models) by applying gradient boosted trees as a regression tool. Root mean squared errors of prediction for global and lo-cal QSRR models for cations, anions and neutrals were respectively 0.131; 0.105; 0.102 and 0.042 with the coefficient of determination 0.947; 0.872; 0.954 and 0.996, indicating satisfactory performances of all models, with slightly better accuracy of local ones. The research showed that influences of EPs were de-pendant on the molecule's ionisation potential. The molecular descriptors highlighted by models pointed out that electrostatic and hydrophobic interactions and hydrogen bonds participate in the retention pro-cess. The molecule's conformation significance was evaluated along with the topological relationship be-tween the interaction centres, explicitly determined for each molecular species through local models. All models showed good molecular retention predictability thus showing potential for facilitating the method development. (c) 2023 Elsevier B.V. All rights reserved.