Identification of the designer steroid Androsta-3,5-diene-7,17-dione in a dietary supplement.
Lisa M LorenzValerie M ToomeyAdam C LanzarottaRick A FlurerTravis M FalconerPublished in: Drug testing and analysis (2019)
A liquid chromatography-mass spectrometry (LC-MS) screen for known anabolic-androgenic steroids in a dietary supplement product marketed for "performance enhancement" detected an unknown compound having steroid-like spectral characteristics. The compound was isolated using high performance liquid chromatography with ultraviolet detection (HPLC-UV) coupled with an analytical scale fraction collector. After the compound was isolated, it was then characterized using gas chromatography with simultaneous Fourier Transform infrared detection and mass spectrometry (GC-FT-IR-MS), liquid chromatography-high resolution accurate mass-mass spectrometry (LC-HRAM-MS) and nuclear magnetic resonance (NMR). The steroid had an accurate mass of m/z 285.1847 (error-0.57 ppm) for the protonated species [M + H]+ , corresponding to a molecular formula of C19 H24 O2 . Based on the GC-FT-IR-MS data, NMR data, and accurate mass, the compound was identified as androsta-3,5-diene-7,17-dione. Although this is not the first reported identification of this designer steroid in a dietary supplement, the data provided adds information for identification of this compound not previously reported. This compound was subsequently detected in another dietary supplement product, which contained three additional active ingredients.
Keyphrases
- mass spectrometry
- liquid chromatography
- gas chromatography
- high resolution
- high performance liquid chromatography
- tandem mass spectrometry
- high resolution mass spectrometry
- simultaneous determination
- magnetic resonance
- solid phase extraction
- capillary electrophoresis
- gas chromatography mass spectrometry
- loop mediated isothermal amplification
- multiple sclerosis
- ms ms
- big data
- electronic health record
- data analysis
- healthcare
- real time pcr
- high speed
- high throughput
- machine learning
- computed tomography
- label free
- social media
- health information
- solid state
- preterm infants
- bioinformatics analysis