Identification of antigonorrhoeal phytochemical lead compounds using a metabolomics-guided approach

Neisseria gonorrhoeae remains a high-priority pathogen according to the World Health Organisation Bacterial Priority Pathogens List. In South Africa, increasing antimicrobial resistance to tetracycline, ciprofloxacin, and penicillin is limiting available treatment options for N. gonorrhoeae infections. This challenge necessitates the exploration of alternative therapeutics, with natural products representing a promising source of novel scaffolds. This study employed a metabolomics-guided approach to tentatively identify antigonorrhoeal compounds from South African medicinal plants. Sixteen crude extracts prepared by ultrasonic extraction, and 112 solid-phase extraction (SPE) fractions, were screened against N. gonorrhoeae ATCC 49981 using broth microdilution. Leaves from Helichrysum odoratissimum (L.) Sweet and leaves and twigs from Terminalia phanerophlebia Engl. & Diels yielded the most active samples, with minimum inhibitory concentrations (MICs) as low as 6.25 µg/mL. SPE fractions from both plants were analysed by Ultra-Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UPLC-HRMS). Data were processed using the Waters UNIFI^® platform and analysed by Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) in MetaboAnalyst 6.0. Putative compounds were annotated using in silico tool, including SIRIUS, complemented by MassLynx, and a literature search, enabling tentative annotation of 23 compounds. Of these, 12 were prioritised by the Neisseria Bayesian model (score ≥ 0.5), including kaempferol and carvacrol in H. odoratissimum, and vitexin and isoscopoletin in T. phanerophlebia. Tanimoto similarity analysis revealed low structural similarity to ciprofloxacin (< 0.30), indicating novelty relative to fluoroquinolone scaffolds. These findings provide candidate compounds for further validation and demonstrate that integrating metabolomics with computational prediction can accelerate antigonorrhoeal discovery.