1H NMR-MS-based heterocovariance as a drug discovery tool for fishing bioactive compounds out of a complex mixture of structural analogues.
Ulrike GrienkePaul A FosterJulia ZwirchmayrAmmar TahirJudith Maria RollingerEmmanuel MikrosPublished in: Scientific reports (2019)
Chemometric methods and correlation of spectroscopic or spectrometric data with bioactivity results are known to improve dereplication in classical bio-guided isolation approaches. However, in drug discovery from natural sources the isolation of bioactive constituents from a crude extract containing close structural analogues remains a significant challenge. This study is a 1H NMR-MS workflow named ELINA (Eliciting Nature's Activities) which is based on statistical heterocovariance analysis (HetCA) of 1H NMR spectra detecting chemical features that are positively ("hot") or negatively ("cold") correlated with bioactivity prior to any isolation. ELINA is exemplified in the discovery of steroid sulfatase (STS) inhibiting lanostane triterpenes (LTTs) from a complex extract of the polypore fungus Fomitopsis pinicola.
Keyphrases
- drug discovery
- molecular docking
- magnetic resonance
- high resolution
- solid state
- mass spectrometry
- multiple sclerosis
- ms ms
- oxidative stress
- electronic health record
- small molecule
- liquid chromatography
- signaling pathway
- anti inflammatory
- high throughput
- high performance liquid chromatography
- gas chromatography
- drinking water
- density functional theory
- structure activity relationship
- machine learning
- artificial intelligence