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PLANTA Protocol for the Direct Detection and Identification of Bioactive Compounds in Complex Mixtures via Combined
Vaios Amountzias1, Evagelos Gikas2, Nektarios Aligiannis1
1Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
Abstract:
The assignment of bioactivity to compounds within complex natural product (NPs) mixtures remains a significant challenge in NPs research. The present research introduces a comprehensive protocol, named "PLANTA (PhytochemicaL Analysis for NaTural bioActives)" protocol, for the detection and identification of bioactive compounds in complex natural extracts prior to isolation combining the NMR-HeteroCovariance Approach (NMR-HetCA), high-performance thin-layer chromatography (HPTLC), and chemometric techniques. This study emphasizes two novel components: STOCSY-guided targeted spectral depletion, adapted to resolve overlapping NMR signals in complex matrices, improve minor component detection, and facilitate identification through NMR databases, as well as a new SHY variant termed SH-SCY (Statistical Heterocovariance - SpectroChromatographY), a new cross-correlation method linking orthogonal datasets by identifying the corresponding HPTLC spot from a single NMR peak and reconstructing of the 1H NMR spectrum from a specific HPTLC spot, enhancing dereplication confidence. In this proof-of-concept study, an artificial extract (ArtExtr) composed of 59 standard compounds was evaluated for the detection of compounds active against the free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). Statistical approaches were applied to the spectral, chromatographic, and bioactivity data to identify the highly correlated bioactive compounds. The PLANTA protocol achieved an 89.5% detection rate of active metabolites and 73.7% correct identification of them. The integration of NMR and HPTLC with HetCA provides a robust and sensitive strategy for preisolation identification of bioactive constituents. This methodology addresses core challenges in metabolite profiling of complex mixtures and offers a streamlined, reproducible workflow for natural product dereplication and discovery.
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