ReviewMolecules (Basel, Switzerland)2026
Capillary Electrophoretic Enantioseparation of Profens: Advances in Chiral Selectors, Separation Strategies, and Analytical Applications.
Review in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Profens are chiral nonsteroidal anti-inflammatory drugs (NSAIDs) whose enantiomers may differ in pharmacokinetics, pharmacological activity and pharmacotoxicological profile, making enantioselective analysis relevant for pharmaceutical quality control and bioanalytical applications. Capillary electrophoresis (CE) has been extensively investigated for their enantioseparation because of its high efficiency, low sample and reagent consumption, and the possibility of easily modifying the chiral recognition environment. This review traces the development of CE methods for profen enantioseparation from early exploratory studies focused on chiral selector (CS) screening to the most recent reports available up to 2026, highlighting the progressive shift toward quantitative, validated, and application-oriented methods. The influence of CS type and concentration, background electrolyte (BGE) composition and pH, organic modifiers, polarity, and capillary surface modification is discussed in relation to enantioselectivity and analytical performance. Although cyclodextrin (CD)-based systems remain predominant, the available literature shows that profen enantioseparation is strongly analyte-dependent and that no single CS or set of conditions is universally applicable. The review also examines the transition from proof-of-concept enantioseparation toward validated methods for enantiomeric impurity determination, configurational stability assessment, and the analysis of pharmaceutical, biological, and environmental samples, while highlighting the analytical performance and current limitations of CE-based approaches. Future developments should focus on improving sensitivity, robustness, and applicability to complex matrices while making greater use of rational CS selection, multivariate optimization, and complementary detection approaches.
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