A metabolomics multivariate statistical approach for obtaining data-driven information in neuropharmacological research

Abstract

Brain metabolism is exquisitely responsive to activation or inhibition of brain activity, including activation or inhibition of receptors. By multivariate statistical analysis of the metabolic labelling patterns produced following one hour incubation with [3-^13C]pyruvate in the presence of receptor ligands of known activity, we have developed a metabolic “footprint”  of the GABAergic system. Using this experimental paradigm, any compounds potentially acting on the GABAergic system can then be compared against the footprint and their mode of action as well as the preferred receptor sites identified and characterized.  It seems obvious that such an approach would be most valuable for drugs of uncertain pharmacological profiles acting on multiple targets. This approach has already proven useful for ?-hydroxybutyrate (GHB) and was recently applied to ethanol ^[1].  We showed that the effects of ethanol on reducing glucose metabolism are not via substitution of ethanol for other substrates, or by production of acetate but likely occur via action at GABA receptors, specifically ?4?3? receptors.