ABSTRACT
Randomized clinical trials substantiate cannabidiol (CBD) as a next-generation antipsychotic, effective in alleviating positive and negative symptoms associated with psychosis, while minimising the adverse effects seen with established treatments. Although the mechanisms remain debated, CBD is known to induce drug-responsive changes in lipid-based retrograde neurotransmitters. Lipid aberrations are also frequently observed with antipsychotics, which may contribute to their efficacy or increase the risk of undesirables, including metabolic dysfunction, obesity and dyslipidaemia. Our study investigated CBD's impact following lipid responses triggered by interaction with second-generation antipsychotics (SGA) in a randomized phase I safety study. Untargeted mass spectrometry assessed the lipidomic profiles of human sera, collected from 38 healthy volunteers. Serum samples were obtained prior to commencement of any medication (t = 0), 3 days after consecutive administration of one of the five, placebo-controlled, treatment arms designed to achieve steady-state concentrations of each SGA (amisulpride, 150 mg/day; quetiapine, 300 mg/day; olanzapine 10 mg/day; risperidone, 3 mg/day), and after six successive days of SGA treatment combined with CBD (800 mg/day). Receiver operating characteristics (ROC) refined 3712 features to a putative list of 15 lipids significantly altered (AUC > 0.7), classified into sphingolipids (53 %), glycerolipids (27 %) and glycerophospholipids (20 %). Targeted mass spectrometry confirmed reduced sphingomyelin and ceramide levels with antipsychotics, which mapped along their catabolic pathway and were restored by CBD. These sphingolipids inversely correlated with body weight after olanzapine, quetiapine, and risperidone treatment, where CBD appears to have arrested or attenuated these effects. Herein, we propose CBD may alleviate aberrant sphingolipid metabolism and that further investigation into sphingolipids as markers for monitoring side effects of SGAs and efficacy of CBD is warranted.
ABSTRACT
The analysis of ceramide (Cer) and sphingomyelin (SM) lipid species using liquid chromatography-tandem mass spectrometry (LC-MS/MS) continues to present challenges as their precursor mass and fragmentation can correspond to multiple molecular arrangements. To address this constraint, we developed ReTimeML, a freeware that automates the expected retention times (RTs) for Cer and SM lipid profiles from complex chromatograms. ReTimeML works on the principle that LC-MS/MS experiments have pre-determined RTs from internal standards, calibrators or quality controls used throughout the analysis. Employed as reference RTs, ReTimeML subsequently extrapolates the RTs of unknowns using its machine-learned regression library of mass-to-charge (m/z) versus RT profiles, which does not require model retraining for adaptability on different LC-MS/MS pipelines. We validated ReTimeML RT estimations for various Cer and SM structures across different biologicals, tissues and LC-MS/MS setups, exhibiting a mean variance between 0.23 and 2.43% compared to user annotations. ReTimeML also aided the disambiguation of SM identities from isobar distributions in paired serum-cerebrospinal fluid from healthy volunteers, allowing us to identify a series of non-canonical SMs associated between the two biofluids comprised of a polyunsaturated structure that confers increased stability against catabolic clearance.
