# PLANTA Protocol for the Direct Detection and Identification of Bioactive Compounds in Complex Mixtures via Combined NMR-HPTLC-Based Heterocovariance

## TL;DR

The PLANTA protocol combines NMR and HPTLC to detect and identify bioactive compounds in complex natural product mixtures before isolation.

## Contribution

The PLANTA protocol introduces novel methods for resolving overlapping NMR signals and linking NMR peaks to HPTLC spots to improve bioactive compound identification.

## Key findings

- The PLANTA protocol achieved an 89.5% detection rate of active metabolites in an artificial extract.
- It correctly identified 73.7% of the detected bioactive compounds.
- The integration of NMR-HetCA with HPTLC enhances dereplication confidence and addresses challenges in complex mixture analysis.

## 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.