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

2024/06/21 by Elena Bandini, Rodrigo Castellano Ontiveros, Ardiana Kajtazi +2 · 1 voice
Chemistry · Biochemistry, Genetics and Molecular Biology · Computer Science · #Analytical Chemistry and Chromatography #Metabolomics and Mass Spectrometry Studies #Computational Drug Discovery Methods

paper · pdf · doi:10.1186/s13321-024-00873-6

openalex publication_date 2024/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

Abstract 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 ^∘ \hbox C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>45</mml:mn> <mml:mmultiscripts> <mml:mspace/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> , logP predominantly influenced retention, akin to reversed-phase columns, while at 5^∘ \hbox C <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>5</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> , 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.

Citations

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• The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
• A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction

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