Evaluation of solid-phase extraction procedures for the quantitation of venlafaxine in human saliva by high-performance liquid chromatography.
Ewelina DziurkowskaMarek WesołowskiPublished in: Journal of separation science (2018)
In recent years, the use of human saliva for diagnostic purposes has evoked great interest. Thus, the aim of this study was to choose the optimal solid-phase extraction cartridges and extraction solvents for the quantitation of venlafaxine in saliva. Blank saliva samples spiked with venlafaxine concentrations between 25 and 750 ng/mL were analyzed using five solid-phase extraction columns (C18, C8, Strata-X, Strata-X-C, and Strata-X-AW), washing solvents (deionized water, phosphate buffer at pH 5.5, and their mixtures with methanol), and elution solvents (methanol, acetonitrile, and their mixtures with 25% ammonia). A high-performance liquid chromatography system was used to quantify venlafaxine in saliva. The results of this study revealed that nine of 25 procedures enabled quantitation of venlafaxine in the tested concentration range. The procedure that used a C18 cartridge, a mixture of methanol and deionized water as the washing solvent, and methanol as the elution solvent was the most effective and allowed quantitation of all venlafaxine concentrations with an acceptable recovery. In contrast, the Strata-X-C cartridge could not detect venlafaxine at the lowest concentration (25 ng/mL). The data acquired from the high-performance liquid chromatography system were confirmed by a multivariate data analysis.
Keyphrases
- high performance liquid chromatography
- solid phase extraction
- tandem mass spectrometry
- ionic liquid
- liquid chromatography tandem mass spectrometry
- liquid chromatography
- data analysis
- simultaneous determination
- molecularly imprinted
- mass spectrometry
- ultra high performance liquid chromatography
- gas chromatography mass spectrometry
- endothelial cells
- gas chromatography
- carbon dioxide
- high resolution mass spectrometry
- room temperature
- ms ms
- big data
- magnetic resonance imaging
- electronic health record
- high resolution
- machine learning