Amountzias Vaios, Cheilari Antigoni, Vontzalidou Argyro, Benaki Dimitra, Gikas Evagelos, Aligiannis Nektarios
Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens 15771, Greece.
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens 15771, Greece.
Anal Chem. 2024 Dec 17;96(50):20090-20097. doi: 10.1021/acs.analchem.4c05080. Epub 2024 Dec 6.
Conventional isolation methods in natural products chemistry are time-consuming and costly and often result in the isolation of moderately active compounds or the detection of already known natural products (NPs). A fast and cost-effective way to identify bioactive metabolites in plant extracts prior to isolation has been developed based on the nuclear magnetic resonance (NMR)-heterocovariance approach (NMR-HetCA). In order to evaluate in depth the application of this chemometrics-based drug discovery methodology, simple mixtures of 10 standard NPs simulating a fast centrifugal partition chromatography (FCPC) fractionation (artificial fractions, ArtFrcts), as well as a more complex mixture of 59 natural standard substances simulating a crude plant extract (artificial extract, ArtExtr), were prepared. FCPC was employed for the fractionation of the ArtExtr, while the inhibitory activity of all fractions against DPPH was evaluated, and their chemical profile was recorded using NMR spectroscopy. Spectral information was processed in the MATLAB environment, and statistical approaches, including HetCA and statistical total correlation spectroscopy (STOCSY), were applied to identify bioactive compounds. Total heterocovariance plots (pseudospectra) facilitated the detection of highly correlated metabolites and led to the direct identification of 52.6% of the active compounds. The success in identifying the ArtExtr bioactive substances increased to 63.2% when spectral alignment was implemented. HetCA incorporates chromatographic (fractionation), spectroscopic (NMR profiling), and bioactivity results along with advanced chemometrics and could be established as a method of choice for the rapid and effective identification of bioactive NPs in plant extracts prior to isolation.
天然产物化学中的传统分离方法既耗时又昂贵，而且常常导致分离出活性中等的化合物，或者检测到已知的天然产物（NP）。基于核磁共振（NMR）-异协方差方法（NMR-HetCA），已开发出一种在分离之前鉴定植物提取物中生物活性代谢物的快速且经济高效的方法。为了深入评估这种基于化学计量学的药物发现方法的应用，制备了10种标准NP的简单混合物以模拟快速离心分配色谱法（FCPC）分级分离（人工馏分，ArtFrcts），以及59种天然标准物质的更复杂混合物以模拟粗植物提取物（人工提取物，ArtExtr）。使用FCPC对ArtExtr进行分级分离，同时评估所有馏分对DPPH的抑制活性，并使用核磁共振光谱记录其化学特征。在MATLAB环境中对光谱信息进行处理，并应用包括HetCA和统计全相关光谱法（STOCSY）在内的统计方法来鉴定生物活性化合物。总异协方差图（伪光谱）有助于检测高度相关的代谢物，并直接鉴定出52.6%的活性化合物。当实施光谱比对时，鉴定ArtExtr生物活性物质的成功率提高到63.2%。HetCA将色谱（分级分离）、光谱（NMR分析）和生物活性结果与先进的化学计量学相结合，可以作为一种在分离之前快速有效地鉴定植物提取物中生物活性NP的首选方法。
