Perspective on the Future Approaches to Predict Retention in Liquid Chromatography

Area of Science:

• Analytical Chemistry
• Chromatography Science

Background:

• Accurate prediction of retention time in liquid chromatography (LC) is essential for rapid column screening, computer-assisted method development, and reliable compound identification in LC/Mass Spectrometry (LC/MS) analyses.
• Classical and advanced approaches for retention time prediction have been developed over the past three decades to meet these demands.

Purpose of the Study:

• This perspective critically reviews existing methods for predicting retention times in LC.
• It also proposes future requirements and directions to enhance the accuracy of these predictions.

Main Methods:

• Discusses inverse methods (statistical or chromatography-based models) for screening and optimization, which require minimal experiments and do not necessitate precise knowledge of column parameters.
• Explains direct methods (Quality by Design - QbD) for accurate method transfer, relying on fundamental principles of adsorption and gradient chromatography without model calibration.
• Covers statistical approaches like Linear Solvation Energy Relationships (LSERs) and Quantitative Structure Retention Relationships (QSRRs) for structure-based prediction, often combined with AI for improved accuracy.

Main Results:

• Inverse methods offer excellent relative accuracy (below a few percent) and are suitable for complex behaviors like mixed-mode chromatography (MMC).
• Direct methods, while requiring accurate determination of parameters, are ideal for method transfer across different LC systems.
• Structure-based methods (LSERs, QSRRs) currently have limited accuracy (10-30%), but combined with AI and similarity approaches, accuracy can be improved below 10%.

Conclusions:

• There is a need for fundamentally correct retention models, especially for complex scenarios like MMC, that account for multiple experimental variables.
• Future advancements should focus on rigorous, fundamental approaches, including molecular simulations (Monte Carlo, Molecular Dynamics), to interpret complex retention data.
• Adopting these advanced techniques will significantly improve the accuracy and reliability of retention time predictions in LC/MS analyses.