Next-generation hyphenated analytical technologies in natural product research: From high-resolution separation and structural elucidation to dereplication, metabolomics and drug discovery.

Abstract

Natural products occupy a unique position at the interface of chemistry, biology and medicine and continue to provide structurally diverse molecules for drug discovery, chemical biology, nutrition and therapeutic development. Their investigation, however, is complicated by the enormous chemical complexity of natural-product extracts, the presence of structurally related metabolites, isomeric and isobaric compounds, wide concentration ranges, matrix effects and the frequent occurrence of previously uncharacterized constituents. Over the past decade, advances in chromatographic separation, high-resolution mass spectrometry (HRMS), tandem mass spectrometry (MS/MS), ion mobility spectrometry (IMS), nuclear magnetic resonance (NMR) spectroscopy and computational analysis have fundamentally changed how these challenges are addressed. High performance liquid chromatography-mass spectrometry (HPLC-MS), HPLC-MS/MS, HPLC-NMR, LC-solid-phase extraction (SPE)-NMR and capillary electrophoresis-mass spectrometry (CE-MS) have evolved from individual hyphenated techniques into integrated analytical workflows capable of linking chemical separation with molecular characterization, dereplication and biological prioritization. In parallel, molecular networking, spectral databases, in silico fragmentation, machine learning and metabolomics platforms have transformed the interpretation of increasingly complex datasets. This review critically examines these developments, emphasizing the complementary information generated by chromatographic, MS and NMR measurements and their integration with computational approaches. Particular attention is given to HRMS/MS, data-independent acquisition, ion mobility, LC-SPE-NMR, molecular networking, spectral-library searching, metabolite annotation confidence and emerging artificial-intelligence-assisted workflows. The review argues that the principal transformation in natural-product analysis is not simply improved instrumental sensitivity, but the emergence of multidimensional and computationally integrated workflows that connect extraction, separation, detection, structural annotation, dereplication, metabolomics and bioactivity-guided discovery. Remaining challenges including incomplete databases, isomer discrimination, annotation uncertainty, reproducibility and the need for orthogonal structure confirmation are discussed, together with future opportunities for automated, multidimensional and FAIR-data-enabled natural-product discovery.

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