Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms.

Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At 45 ∘ C , logP predominantly influenced retention, akin to reversed-phase columns, while at 5 ∘ C , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction.

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

High-throughput and reliable assessments of the ionization constant of monoprotic organic acids through an arginine based mixed mode HPLC.

The charge state of a molecule is the single most prominent attribute ruling out its interactions with the surrounding environment. In a previous study, the retention of acids on the new Celeris™ Arginine (ARG) column was found to be predominantly driven by electrostatics and, specifically, their charge state. Therefore, we analysed 41 compounds in liquid chromatography with ultraviolet detection to study possible relationships between the analytical retention on this phase and the pKa of the acidic solutes. Highly significant relationships were observed indicating either a linear (r2 = 0.86) or a quadratic (r2= 0.89) trend. To improve the throughput of the method, this was transferred to LC mass spectrometry, allowing the analysis of a molecule every 3 mins. The developed method was found to be fast, reliable, accurate, easily automatable and simple to set up. Finally, the analytical column's being industrially manufactured and commercially available offers broad applicability.

Pharmaceutical impurity analysis by comprehensive two-dimensional temperature responsive × reversed phase liquid chromatography.

In this study, the possibilities of temperature responsive × reversed phase liquid chromatography (TRLC × RPLC) are assessed in terms of pharmaceutical impurity analysis. Due to the increased peak capacity per unit time they offer, two-dimensional LC approaches are gaining relevance for the analysis of complex drug formulations. Because the latter depicts a larger predisposition for the occurrence of an increased number of impurities, current 1D-HPLC approaches often prove insufficient. Since many LC × LC methods are limited by modulation, solvent compatibility, orthogonality, and sensitivity issues, the combination of TRLC × RPLC is explored in this work for pharmaceutical impurity analysis. As this combination of a purely aqueous separation with RPLC allows for systematic and optimization-free refocusing in the second dimension, it opens possibilities for generic LC × LC requiring minimal to no method development, in this way overcoming a major perceived contemporary hurdle of LC × LC. The approach is demonstrated with a representative mixture of 17 solutes comprising 11 corticosteroids and 6 progestogens. Orthogonality and peak capacities were assessed on three RP core-shell column selectivities (Poroshell EC-C18, phenyl-hexyl and PFP). Although the TRLC × EC-C18 combination offered somewhat better orthogonality, the combination with the PFP column proved the best for the separation at hand. Depending on the composition of the mixture, the use of full, shifted, or segmented gradients allowed facile optimization of the separation. The developed platform allowed detection of the impurities at the 0.05% level compared to a selected main compound, while also opening up possibilities for analysis of formulations comprising two active ingredients.