Mapping phytochemicals and neuroprotective mechanisms in five medicinal plants: a knowledgebase-driven approach

Neurophytochemistry is a growing research area that studies bioactive metabolites and signaling mediators in plants for potential neurotherapeutic applications. Phytochemicals can modulate the CNS homeostasis by modulating the activity of neurotransmitter receptors systems, such as γ-aminobutyric acid (GABA), serotonin, and dopamine. They can also promote neuronal survival by enhancing brain-derived neurotrophic factor (BDNF) expression, inhibiting nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-mediated neuroinflammation, and activating the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway to enhance cellular antioxidant defenses, thereby attenuating oxidative damage, mitochondrial impairment, deficits in neurotrophic signaling, apoptotic pathways, and protein misfolding and aggregation. Advancing phytomedicine applications hinges on a deep understanding of these hallmark pathological processes.

An integrated database of phytochemicals constituents was developed for the following five medicinal plants with reported neuroprotective and cognitive potential (Melissa officinalis, Salvia officinalis, Rubus ulmifolius, Hypericum perforatum, and Papaver rhoeas). Data on phytochemical occurrence, chemotaxonomy, molecular descriptors, mechanistic evidence, blood-brain barrier (BBB) permeability, gastrointestinal stability, bioaccessibility, microbial biotransformation, and level of experimental evidence were obtained through systematic literature mining and manual expert curation.

The database currently includes 46 species-specific metabolites from the selected taxa. The metabolites are primarily flavonoids, phenolic acids, terpenoids, prenylated phloroglucinols, naphthodianthrones, and alkaloids. The majority are associated with antioxidant activity, while some are associated with anti-inflammatory molecular mechanisms. Evidence of BBB permeability, however, is limited and data on microbial biotransformation requires further refinement. This database links phytochemical occurrence with molecular descriptors, providing a framework that will support candidate prioritization as it grows. Overall, the database will aid in feature annotation metabolite prioritization detected by untargeted LC-QTOF-MS/MS, enhancing the interpretability of future metabolomics studies.