Contactless conductivity detector as a tool for improving universality and sensitivity of capillary electrophoresis-frontal analysis: Proof of concept.
Taťána BržezickáHana MlčochováZdenĕk GlatzLenka KohútováPublished in: Journal of separation science (2024)
Drug binding to plasma proteins influences processes such as liberation, adsorption, disposition, metabolism, and elimination of drugs, which are thus one of the key steps of a new drug development. As a result, the characterization of drug-protein interactions is an essential part of these time- and money-consuming processes. It is important to determine not only the binding strength and the stoichiometry of interaction, but also the binding site of a drug on a protein molecule, because two drugs with the same binding site can mutually affect free drug concentration. Capillary electrophoresis-frontal analysis with mobility shift affinity capillary electrophoresis is one of the most used affinity capillary electrophoresis methods for the characterization of these interactions. In this study, a well-known sensitivity problem of most capillary electrophoresis-frontal analyses using ultraviolet detection is solved by its combination with contactless conductivity detection, which provided sixfold lower limits of quantitation and detection. Binding parameters of the human serum albumin-salicylic acid model affinity pair were evaluated by this newly developed approach and by the classical approach with ultraviolet detection primarily used for their mutual comparison. The results of both approaches agreed well and are also in agreement with literature data obtained using different techniques.
Keyphrases
- capillary electrophoresis
- mass spectrometry
- loop mediated isothermal amplification
- real time pcr
- label free
- liquid chromatography
- drug induced
- working memory
- functional connectivity
- systematic review
- binding protein
- magnetic resonance imaging
- magnetic resonance
- high performance liquid chromatography
- computed tomography
- machine learning
- dna binding
- big data
- protein protein
- deep learning