Retention modelling in ternary solvent-strength gradient elution reversed-phase chromatography using 30mm columns

Perspective on the Future Approaches to Predict Retention in Liquid Chromatography

The demand for rapid column screening, computer-assisted method development and method transfer, and unambiguous compound identification by LC/MS analyses has pushed analysts to adopt experimental protocols and software for the accurate prediction of the retention time in liquid chromatography (LC). This Perspective discusses the classical approaches used to predict retention times in LC over the last three decades and proposes future requirements to increase their accuracy. First, inverse methods for retention prediction are essentially applied during screening and gradient method optimization: a minimum number of experiments or design of experiments (DoE) is run to train and calibrate a model (either purely statistical or based on the principles and fundamentals of liquid chromatography) by a mere fitting process. They do not require the accurate knowledge of the true column hold-up volume V₀, system dwell volume V_dwell (in gradient elution), and the retention behavior (k versus the content of strong solvent φ, temperature T, pH, and ionic strength I) of the analytes. Their relative accuracy is often excellent below a few percent. Statistical methods are expected to be the most attractive to handle very complex retention behavior such as in mixed-mode chromatography (MMC). Fundamentally correct retention models accounting for the simultaneous impact of φ, I, pH, and T in MMC are needed for method development based on chromatography principles. Second, direct methods for retention prediction are ideally suited for accurate method transfer from one column/system configuration to another: these quality by design (QbD) methods are based on the fundamentals and principles of solid-liquid adsorption and gradient chromatography. No model calibration is necessary; however, they require universal conventions for the accurate determination of true retention factors (for 1 < k < 30) as a function of the experimental variables (φ, T, pH, and I) and of the true column/system parameters (V₀, V_dwell, dispersion volume, σ, and relaxation volume, τ, of the programmed gradient profile at the column inlet and gradient distortion at the column outlet). Finally, when the molecular structure of the analytes is either known or assumed, retention prediction has essentially been made on the basis of statistical approaches such as the linear solvation energy relationships (LSERs) and the quantitative structure retention relationships (QSRRs): their ability to accurately predict the retention remains limited within 10-30%. They have been combined with molecular similarity approaches (where the retention model is calibrated with compounds having structures similar to that of the targeted analytes) and artificial intelligence algorithms to further improve their accuracy below 10%. In this Perspective, it is proposed to adopt a more rigorous and fundamental approach by considering the very details of the solid-liquid adsorption process: Monte Carlo (MC) or molecular dynamics (MD) simulations are promising tools to explain and interpret retention data that are too complex to be described by either empirical or statistical retention models.

Computer assisted liquid chromatographic method development for the separation of therapeutic proteins

This review summarizes the use of computer assisted liquid chromatographic method development for the analytical characterization of protein biopharmaceuticals.

Exploring better column selectivity choices in ultra-high performance liquid chromatography using quality by design principles

An older method for amlodipine was reworked with the goal to reduce the analysis time of 60min below 6min. To select the best column for short and robust analysis, 9 different UHPLC column chemistries were investigated using 3-dimensional resolution spaces based on 12 experiments using modelling software. The main variables used were gradient time (tG), temperature (T) and the pH of eluent A. The best critical resolution was calculated and located in a 3-dimensional space in an automated fashion and the corresponding best experiments were carried out. The work (9×12=108 runs) was finished with an UHPLC instrument in less than 24h. The comparison between predictions and real experiments showed an excellent correlation with differences typically less than 0.04min (<3s) in average, although the set points were located at quite different conditions on gradient times, pH's and temperatures for the individual columns. All columns could perform the required baseline separation at their individual best working points with satisfactory results.

A binary-like approach for the computer assisted method development of isocratic and programmed ternary solvent elutions in reversed-phase liquid chromatography

A specific tool to enable exploration of multisolvent isocratic and programmed elutions for the computer assisted method development of reversed-phase liquid chromatography separations is described. The tool is purposely identical to those used in the optimization of binary solvent systems, which are by far the most commonly used by chromatographers. Existing data from failed binary solvent optimization processes are reused to explore ternary solvent systems with a few additional isocratic and programmed runs. This allows the development of efficient retention models for ternary systems, although the work of the chromatographer remains identical to that for optimization of binary systems. The retention models are used to develop an unattended optimization process and finally, the chromatographer selects the most satisfactory solution for testing and implementing in routine analysis. The process is exemplified with a mixture of 12 compounds that cannot be separated satisfactorily in aqueous binary solvent systems with methanol and acetonitrile as modifiers.

Simple models for the effect of aliphatic alcohol additives on the retention in reversed-phase liquid chromatography

Four retention models for the effect of aliphatic alcohol additives on the retention of analytes in reversed-phase liquid chromatography have been developed following either a semi-thermodynamic treatment or an empirical approach. Their performance was tested using the experimental retention times of six non-polar analytes (alkylbenzenes) and ten o-phthalaldehyde derivatives of amino acids under different isocratic chromatographic runs when a small amount of ethanol, 1-propanol, 1-butanol, 1-pentanol, 1-hexanol or 1-heptanol was added to methanol/water mixtures containing a constant amount of methanol. It was shown that for the structurally simple alkylbenzenes all the models can be adopted for retention prediction with good results. In contrast, just one out of four models, that with the fewest approximations, predicts satisfactorily the retention properties of amino acids derivatives. However, the most interesting feature is that this model can predict the effect of an alcohol-additive on the retention properties of solutes, even if this additive was not used in chromatographic runs done for the fitting procedure, provided that it belongs to the same homologous series of alkanols. This feature is also observed in all models described the retention of alkylbenzenes.

Retention prediction in reversed-phase liquid chromatography systems with methanol/water mobile phases containing different alkanols as additives

In an effort to gain enhancement of selectivity in reversed-phase liquid chromatography, retention was tuned in this study by introducing short and medium straight-chained-length alkanol additives (methanol (MeOH), ethanol, 1-propanol, 1-butanol, 1-pentanol, 1-hexanol or 1-heptanol) at low concentrations in mobile phases containing MeOH as the main organic solvent. A six-parameter retention model considering simultaneously the contents of the main organic modifier and of the alcohol additive as well as of the number of alkyl chain of additive was developed by a direct combination of equations expressing separately a linear dependence of the retention upon each of these factors. The effectiveness of the above model was tested in the retention prediction of a mixture of six alkylbenzenes under isocratic conditions with mobile phases containing as an additive any member of the homologues series of alkanols (with 1-7 carbon atoms) at different low concentrations in a wide range of MeOH-water mixtures. The prediction was excellent in all cases even when the alkanol additives used in experiments for the fitting procedure are different than those used in chromatographic runs done for testing the prediction ability of the proposed model.