AbstractIndustrial processing of hemp (Cannabis sativa L.) produces side-streams, such as CBD-depleted “mother liquor” (CBD-ML), that are rich in structurally diverse but underexplored minor cannabinoids. To accelerate the discovery of bioactive cannabinoids within this complex matrix, we applied a computational pipeline integrating loop-injection high-resolution time-of-flight mass spectrometry (HR-TOF MS) chemical profiling with anti-inflammatory bioassay data. Using an ensemble of random forest models, we correlated mass spectrometric features with the inhibition of nitric oxide (NO) production in LPS-stimulated RAW264.7 macrophages. This predictive model identified an ion at m/z 357.2071 as a top predictor of bioactivity. Targeted isolation and structural elucidation by HRMS and NMR identified the bioactive compound as cannabidiolic acid ethyl ester (CBDA-EE), which likely arises as a byproduct during industrial processing of hemp. Validation studies confirmed that CBDA-EE modulates oxylipin production in stimulated macrophage cells, significantly reducing proinflammatory prostaglandins, including prostaglandin E2, while maintaining levels of prostaglandin D2 (PGD2) and its downstream metabolite 15-deoxy-PGD2. These findings demonstrate the utility of machine learning in streamlining the discovery of unstudied bioactive natural products from complex botanical mixtures.

AbstractIndustrial biocatalysis increasingly requires strategies that convert enzyme discovery into robust catalyst systems for process implementation. Her