Retention modeling of oligonucleotides on an amide-based HILIC column: A descriptor-driven approach

Hydrophilic interaction chromatography (HILIC) has recently gained attention as a powerful tool for the analysis of nucleic acid-based therapeutics, offering high resolving power and excellent compatibility with mass spectrometry. However, the successful development of HILIC methods requires a clear understanding of the underlying retention mechanisms. Despite numerous studies, the respective contributions of hydrophilic partitioning, polar interactions, and ionic interactions to oligonucleotide retention remain poorly understood. In this work, the role of hydrogen bonding, dipole-dipole interactions, solvophobic effects, and ionic interactions in governing oligonucleotide retention have been systematically investigated. A series of targeted design-of-experiments studies, performed on a representative panel of samples to investigate the impact of oligonucleotide structure on retention, provided a refined framework for interpreting HILIC separations of oligonucleotides. All experiments were performed on an amide-bonded stationary phase, and the proposed descriptor-based retention model applies specifically to this surface chemistry. Our results demonstrate that solvophobic effects and hydrogen bonding are the dominant drivers of retention, whereas hydrophilic partitioning contributes negligibly under practical conditions. This mechanistic insight has important consequences for method development: introducing protic solvents into the mobile phase, conditions that would disrupt the water-rich layer, substantially increases selectivity and enables the resolution of closely related oligonucleotide species. Altogether, these findings shift the conceptual basis of HILIC for nucleic acids from a hydrophilic partitioning/interaction model toward a retention mechanism best described as a solvophobic-interaction‑dominated HILIC regime, in which protic solvents assist selectivity through H‑bond competition. This new perspective provides a more accurate foundation for designing, optimizing, and interpreting HILIC methods for oligonucleotide analysis.