PLANTA Protocol for the Direct Detection and Identification of Bioactive Compounds in Complex Mixtures via Combined NMR-HPTLC-Based Heterocovariance
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 (<b>P</b>hytochemica<b>L A</b>nalysis for <b>N</b>a<b>T</b>ural bio<b>A</b>ctives)" protocol, for the detection and identification of bioactive compounds in complex natural extracts prior to isolation combining the <b>NMR</b>-<b>Het</b>ero<b>C</b>ovariance <b>A</b>pproach (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 (<b>S</b>tatistical <b>H</b>eterocovariance - <b>S</b>pectro<b>C</b>hromatograph<b>Y</b>), a new cross-correlation method linking orthogonal datasets by identifying the corresponding HPTLC spot from a single NMR peak and reconstructing of the <sup>1</sup>H 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.
Authors
- Nektarios Aligiannis (ORCID: https://orcid.org/0000-0002-3335-8135)
- Evagelos Gikas (ORCID: https://orcid.org/0000-0002-0107-9991)
- Vaios Amountzias
Institutions
- National and Kapodistrian University of Athens (GR)
Publication Details
- Journal
- Analytical Chemistry
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1021/acs.analchem.5c02192
- Primary Topic
- Analytical Chemistry and Chromatography
- Type
- article
- Field-Weighted Citation Impact
- 0.00