A fundamental study of the impact of pressure on the adsorption mechanism in reversed-phase liquid chromatography

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.

Characterization of Drug-Polymer Adsorption Isotherms in Body-on-a-Chip Systems by Inverse Liquid-Solid Chromatography

Body-on-a-chip and human-on-a-chip systems are currently being used to augment and could eventually replace animal models in drug discovery and basic biological research. However, hydrophobic molecules, especially therapeutic compounds, tend to adsorb to the polymer materials used to create these microfluidic platforms, which may distort the dose-response curves that feed into pharmacokinetic/pharmacodynamic (PK/PD) models, which translate preclinical data into predictions of clinical outcomes. Inverse liquid-solid chromatography paired with a numerical optimization based on the Langmuir model of adsorption was used to characterize the adsorption isotherm parameters of drugs to polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA), polymers commonly used in these platforms. The adsorption isotherms were then compared against concentration measurements of drugs recirculated in these platforms. This research further illustrates the point that by quantifying drug or drug candidate interactions before system dosing and including this data in the PK/PD models, then polymers used in these platforms need not be limited to "less-adsorbing" materials.

Multilayer adsorption in liquid chromatography - The surface heterogeneity below an adsorbed multilayer

A numerical method was introduced for the estimation of the surface heterogeneity below an adsorbed multilayer of the analyte. The calculation procedure is based on the raw adsorption isotherm data points obtained by frontal analysis experiments. To permit the mapping of the nature of the analyte-surface interaction, a numerical procedure was used to pre-estimate the adsorbate-adsorbate interactions occurring during the adsorption process. The surface heterogeneity estimation was carried out using the affinity-energy distribution calculations with assuming local BET isotherm. In the local BET isotherm the pre-estimated adsorbate-adsorbate interaction constant was used, and the surface heterogeneity was described. After the test of the numerical method with benchmark isotherms, the algorithm was tested on several experimental isotherms. The isotherms were measured using phenol as test molecule on reversed phase adsorbents, with different surface coverage of the octadecyl ligands. The surface of the non-end-capped stationary phases showed detectable heterogeneity, while the surface end-capped phases were found to be homogeneous.