Mapping the unknown: an anchor-based molecular networking workflow for comprehensive identification of new...

The rapid emergence and structural diversity of New Psychoactive Substances (NPS) challenge toxicological screening, which usually relies on targeted detection of known compounds. To address this limitation, we developed a novel, database-independent analytical workflow capable of anticipating unknown or emerging NPS in complex biological matrices. A comprehensive workflow combining sample preparation, orthogonal liquid chromatography, and high-resolution tandem mass spectrometry (LC-HRMS/MS) was developed for the identification of new structures and their metabolites. Three extraction protocols were benchmarked in both reversed-phase (RP-LC) and hydrophilic interaction (HILIC) chromatographic modes to maximize analyte coverage. The method was optimized using 77 drug standards and extended to a mixture of 122 compounds (Mix122) to build an Anchor-Based Molecular Network (ABMN). Biological samples from presumed NPS consumers were integrated into the reference network to connect patient-derived features with anchor compounds. Data were processed with MZmine for feature extraction, MetGem for molecular networking, and SIRIUS for in silico structure prediction. Protocol P1 (protein precipitation and analyte concentration) provided the best extraction, recovering 90% of analytes with high chromatographic quality. P1 achieved the lowest limit of identification, enabling MS/MS acquisition for 100% of compounds at 50 ng/mL, 97% at 5 ng/mL, and 42% at 0.5 ng/mL. RP-LC and HILIC proved complementary, improving analyte coverage. The Mix122 dataset yielded chemically coherent clusters, supporting integration of clinical samples. Several structurally related analogues such as bromazolam, fluoromethamphetamine, or MDPHP were identified. The ABMN-based LC-HRMS/MS strategy provides a robust and transferable analytical platform for comprehensive and sensitive screening of unknown NPS, even at trace levels down to 1 ng/mL.