Cyanoacetohydrazide as a Novel Derivatization Agent for the Determination of UHPLC-HRMS Steroids in Urine.
Azamat TemerdashevMaria ZorinaBi-Feng YuanElina M GashimovaVictor V DotsenkoVitalij IoutsiSanka N AtapattuPublished in: Molecules (Basel, Switzerland) (2024)
The possibility of cyanoacetohydrazide usage as a novel derivatizing agent is demonstrated in the presented article, and a comparison with hydroxylamine as the most commonly used reagent is provided. Optimal conditions for steroid derivatization with cyanoacetohydrazide are provided. According to the collected data, the maximum yield of derivatives was observed at pH 2.8 within 70 min at 40 °C with 5 ng/mL limit of detection for all investigated analytes. It was shown that cyanoacetohydrazide derivatives produces both syn- and anti-forms as well as hydroxylamine, and their ratios were evaluated and shown in presented work. An efficiency enchantment from two to up to five times was achieved with a novel derivatization reagent. Its applicability for qualitative analysis of steroids in urine was presented at real samples. Additionally, the reproducible fragmentation of the derivatizing agent in collision-induced dissociation offers opportunities for simplified non-targeted steroidomic screening. Furthermore, cyanoacetohydrazide increases ionization efficiency in positive mode, which can eliminate the need for redundant high-resolution instrument runs required for both positive and negative mode analyses.
Keyphrases
- ms ms
- solid phase extraction
- tandem mass spectrometry
- gas chromatography
- high performance liquid chromatography
- simultaneous determination
- liquid chromatography tandem mass spectrometry
- high resolution mass spectrometry
- ultra high performance liquid chromatography
- liquid chromatography
- gas chromatography mass spectrometry
- high resolution
- mass spectrometry
- molecularly imprinted
- big data
- systematic review
- cancer therapy
- structure activity relationship
- diabetic rats
- label free
- atomic force microscopy
- drug induced
- deep learning
- endothelial cells
- machine learning
- sensitive detection