A recent phase 1 trial of the fatty acid amide hydrolase (FAAH) inhibitor BIA 10-2474 led to the death of one volunteer and produced mild-to-severe neurological symptoms in four others. Although the cause of the clinical neurotoxicity is unknown, it has been postulated, given the clinical safety profile of other tested FAAH inhibitors, that off-target activities of BIA 10-2474 may have played a role. Here, we use activity-based proteomic methods to determine the protein interaction landscape of BIA 10-2474 in human cells and tissues. This analysis revealed that the drug inhibits several lipases that are not targeted by PF04457845, a highly selective and clinically tested FAAH inhibitor. BIA 10-2474, but not PF04457845, produced substantial alterations in lipid networks in human cortical neurons, suggesting that promiscuous lipase inhibitors have the potential to cause metabolic dysregulation in the nervous system.

Diacylglycerol lipase (DAGL)-α and -β are enzymes responsible for the biosynthesis of the endocannabinoid 2-arachidonoylglycerol (2-AG). Selective and reversible inhibitors are required to study the function of DAGLs in neuronal cells in an acute and temporal fashion, but they are currently lacking. Here, we describe the identification of a highly selective DAGL inhibitor using structure-guided and a chemoproteomics strategy to characterize the selectivity of the inhibitor in complex proteomes. Key to the success of this approach is the use of comparative and competitive activity-based proteome profiling (ABPP), in which broad-spectrum and tailor-made activity-based probes are combined to report on the inhibition of a protein family in its native environment. Competitive ABPP with broad-spectrum fluorophosphonate-based probes and specific β-lactone-based probes led to the discovery of α-ketoheterocycle LEI105 as a potent, highly selective and reversible dual DAGL-α/DAGL-β inhibitor. LEI105 did not affect other enzymes involved in endocannabinoid metabolism including abhydrolase domain-containing protein 6, abhydrolase domain-containing protein 12, monoacylglycerol lipase and fatty acid amide hydrolase and did not display affinity for the cannabinoid CB1 receptor. Targeted lipidomics revealed that LEI105 concentration-dependently reduced 2-AG levels, but not anandamide levels, in Neuro2A cells. We show that cannabinoid CB1-receptor-mediated short-term synaptic plasticity in a mouse hippocampal slice model can be reduced by LEI105. Thus, we have developed a highly selective DAGL inhibitor and provide new pharmacological evidence to support the hypothesis that ‘on demand biosynthesis’ of 2-AG is responsible for retrograde signaling.

In recent years, lipids have come to the foreground as signaling mediators in the central nervous system (CNS) 1,2 . While classical neurotransmitters are stored in synaptic vesicles and released on fusion with the plasma membrane of neurons, due to their lipophilic nature, lipids readily diffuse through membranes and are not stored in vesicles. It is, therefore, generally accepted that signaling lipids are produced 'on demand' and are rapidly metabolized to terminate their biological action 3 . In particular, NAEs, including N-palmitoylethanolamine (PEA), N-oleoylethanolamine (OEA) and the endocannabinoid anandamide (N-arachidonoylethanolamine, AEA) have emerged as key lipid signaling molecules. Genetic deletion or pharmacological inhibition of the main NAE hydrolytic enzyme, fatty acid amide hydrolase (FAAH), revealed elevated anandamide, PEA and OEA levels in brain and implicated these molecules in the modulation of various physiological processes such as pain, stress, anxiety, appetite, cardiovascular function and inflammation [4][5][6][7] . The physiological effects resulting from perturbation of the production of anandamide and other NAEs in living systems are, however, poorly studied, partly because of a lack of pharmacological tools to modulate their biosynthetic enzymes 8 . NAPE-PLD is generally considered a principal NAE biosynthetic enzyme 9,10 . Biochemical and structural studies have demonstrated that NAPE-PLD is a membrane-associated, constitutively active zinc hydrolase with a metallo-β-lactamase fold 11 . The enzyme generates a broad range of NAEs by hydrolysis of the phosphodiester bond between the phosphoglyceride and the NAE in N-acylphosphatidylethanolamines (NAPEs) 12 . Knockout (KO) studies have shown that the Ca 2+ -dependent conversion of NAPE to NAEs bearing both saturated and polyunsaturated fatty acyl groups are fivefold reduced in brain lysates from mice that genetically lack Napepld 13 . In accordance, reduced levels of saturated and mono-unsaturated NAEs were observed in the brains of NAPE-PLD KO mice [13][14][15] . Anandamide levels were not reduced in the transgenic model reported by Leung et al., which suggested the presence of compensatory mechanisms 13 . Indeed, multiple alternative biosynthetic pathways for anandamide have been discovered since 10 .

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients’ (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.